return op.Output(0)
}
-// Returns the complex conjugate of a complex number.
-//
-// Given a tensor `input` of complex numbers, this operation returns a tensor of
-// complex numbers that are the complex conjugate of each element in `input`. The
-// complex numbers in `input` must be of the form \\(a + bj\\), where *a* is the
-// real part and *b* is the imaginary part.
-//
-// The complex conjugate returned by this operation is of the form \\(a - bj\\).
-//
-// For example:
+// Return the reduction indices for computing gradients of s0 op s1 with broadcast.
//
-// ```
-// # tensor 'input' is [-2.25 + 4.75j, 3.25 + 5.75j]
-// tf.conj(input) ==> [-2.25 - 4.75j, 3.25 - 5.75j]
-// ```
-func Conj(scope *Scope, input tf.Output) (output tf.Output) {
+// This is typically used by gradient computations for a broadcasting operation.
+func BroadcastGradientArgs(scope *Scope, s0 tf.Output, s1 tf.Output) (r0 tf.Output, r1 tf.Output) {
if scope.Err() != nil {
return
}
opspec := tf.OpSpec{
- Type: "Conj",
+ Type: "BroadcastGradientArgs",
Input: []tf.Input{
- input,
+ s0, s1,
},
}
op := scope.AddOperation(opspec)
- return op.Output(0)
-}
-
-// ResourceSparseApplyMomentumAttr is an optional argument to ResourceSparseApplyMomentum.
-type ResourceSparseApplyMomentumAttr func(optionalAttr)
-
-// ResourceSparseApplyMomentumUseLocking sets the optional use_locking attribute to value.
-//
-// value: If `True`, updating of the var and accum tensors will be protected
-// by a lock; otherwise the behavior is undefined, but may exhibit less
-// contention.
-// If not specified, defaults to false
-func ResourceSparseApplyMomentumUseLocking(value bool) ResourceSparseApplyMomentumAttr {
- return func(m optionalAttr) {
- m["use_locking"] = value
- }
-}
-
-// ResourceSparseApplyMomentumUseNesterov sets the optional use_nesterov attribute to value.
-//
-// value: If `True`, the tensor passed to compute grad will be
-// var - lr * momentum * accum, so in the end, the var you get is actually
-// var - lr * momentum * accum.
-// If not specified, defaults to false
-func ResourceSparseApplyMomentumUseNesterov(value bool) ResourceSparseApplyMomentumAttr {
- return func(m optionalAttr) {
- m["use_nesterov"] = value
- }
+ return op.Output(0), op.Output(1)
}
-// Update relevant entries in '*var' and '*accum' according to the momentum scheme.
-//
-// Set use_nesterov = True if you want to use Nesterov momentum.
-//
-// That is for rows we have grad for, we update var and accum as follows:
-//
-// accum = accum * momentum + grad
-// var -= lr * accum
+// Returns the gradient of `Tile`.
//
-// Arguments:
-// var_: Should be from a Variable().
-// accum: Should be from a Variable().
-// lr: Learning rate. Must be a scalar.
-// grad: The gradient.
-// indices: A vector of indices into the first dimension of var and accum.
-// momentum: Momentum. Must be a scalar.
+// DEPRECATED at GraphDef version 3: TileGrad has been replaced with reduce_sum
//
-// Returns the created operation.
-func ResourceSparseApplyMomentum(scope *Scope, var_ tf.Output, accum tf.Output, lr tf.Output, grad tf.Output, indices tf.Output, momentum tf.Output, optional ...ResourceSparseApplyMomentumAttr) (o *tf.Operation) {
+// Since `Tile` takes an input and repeats the input `multiples` times
+// along each dimension, `TileGrad` takes in `multiples` and aggregates
+// each repeated tile of `input` into `output`.
+func TileGrad(scope *Scope, input tf.Output, multiples tf.Output) (output tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{}
- for _, a := range optional {
- a(attrs)
- }
opspec := tf.OpSpec{
- Type: "ResourceSparseApplyMomentum",
+ Type: "TileGrad",
Input: []tf.Input{
- var_, accum, lr, grad, indices, momentum,
+ input, multiples,
},
- Attrs: attrs,
}
- return scope.AddOperation(opspec)
+ op := scope.AddOperation(opspec)
+ return op.Output(0)
}
-// Creates a sequence of numbers.
-//
-// This operation creates a sequence of numbers that begins at `start` and
-// extends by increments of `delta` up to but not including `limit`.
-//
-// For example:
+// Constructs a tensor by tiling a given tensor.
//
-// ```
-// # 'start' is 3
-// # 'limit' is 18
-// # 'delta' is 3
-// tf.range(start, limit, delta) ==> [3, 6, 9, 12, 15]
-// ```
+// This operation creates a new tensor by replicating `input` `multiples` times.
+// The output tensor's i'th dimension has `input.dims(i) * multiples[i]` elements,
+// and the values of `input` are replicated `multiples[i]` times along the 'i'th
+// dimension. For example, tiling `[a b c d]` by `[2]` produces
+// `[a b c d a b c d]`.
//
// Arguments:
-// start: 0-D (scalar). First entry in the sequence.
-// limit: 0-D (scalar). Upper limit of sequence, exclusive.
-// delta: 0-D (scalar). Optional. Default is 1. Number that increments `start`.
-//
-// Returns 1-D.
-func Range(scope *Scope, start tf.Output, limit tf.Output, delta tf.Output) (output tf.Output) {
+// input: 1-D or higher.
+// multiples: 1-D. Length must be the same as the number of dimensions in `input`
+func Tile(scope *Scope, input tf.Output, multiples tf.Output) (output tf.Output) {
if scope.Err() != nil {
return
}
opspec := tf.OpSpec{
- Type: "Range",
+ Type: "Tile",
Input: []tf.Input{
- start, limit, delta,
+ input, multiples,
},
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// Computes gradients for SparseSegmentSqrtN.
-//
-// Returns tensor "output" with same shape as grad, except for dimension 0 whose
-// value is output_dim0.
-//
-// Arguments:
-// grad: gradient propagated to the SparseSegmentSqrtN op.
-// indices: indices passed to the corresponding SparseSegmentSqrtN op.
-// segment_ids: segment_ids passed to the corresponding SparseSegmentSqrtN op.
-// output_dim0: dimension 0 of "data" passed to SparseSegmentSqrtN op.
-func SparseSegmentSqrtNGrad(scope *Scope, grad tf.Output, indices tf.Output, segment_ids tf.Output, output_dim0 tf.Output) (output tf.Output) {
- if scope.Err() != nil {
- return
+// StridedSliceGradAttr is an optional argument to StridedSliceGrad.
+type StridedSliceGradAttr func(optionalAttr)
+
+// StridedSliceGradBeginMask sets the optional begin_mask attribute to value.
+// If not specified, defaults to 0
+func StridedSliceGradBeginMask(value int64) StridedSliceGradAttr {
+ return func(m optionalAttr) {
+ m["begin_mask"] = value
}
- opspec := tf.OpSpec{
- Type: "SparseSegmentSqrtNGrad",
- Input: []tf.Input{
- grad, indices, segment_ids, output_dim0,
- },
+}
+
+// StridedSliceGradEndMask sets the optional end_mask attribute to value.
+// If not specified, defaults to 0
+func StridedSliceGradEndMask(value int64) StridedSliceGradAttr {
+ return func(m optionalAttr) {
+ m["end_mask"] = value
}
- op := scope.AddOperation(opspec)
- return op.Output(0)
}
-// Computes the mean along sparse segments of a tensor.
-//
-// Read @{$math_ops#segmentation$the section on segmentation} for an explanation of
-// segments.
-//
-// Like `SegmentMean`, but `segment_ids` can have rank less than `data`'s first
-// dimension, selecting a subset of dimension 0, specified by `indices`.
-//
-// Arguments:
-//
-// indices: A 1-D tensor. Has same rank as `segment_ids`.
-// segment_ids: A 1-D tensor. Values should be sorted and can be repeated.
-//
-// Returns Has same shape as data, except for dimension 0 which
-// has size `k`, the number of segments.
-func SparseSegmentMean(scope *Scope, data tf.Output, indices tf.Output, segment_ids tf.Output) (output tf.Output) {
- if scope.Err() != nil {
- return
+// StridedSliceGradEllipsisMask sets the optional ellipsis_mask attribute to value.
+// If not specified, defaults to 0
+func StridedSliceGradEllipsisMask(value int64) StridedSliceGradAttr {
+ return func(m optionalAttr) {
+ m["ellipsis_mask"] = value
}
- opspec := tf.OpSpec{
- Type: "SparseSegmentMean",
- Input: []tf.Input{
- data, indices, segment_ids,
- },
+}
+
+// StridedSliceGradNewAxisMask sets the optional new_axis_mask attribute to value.
+// If not specified, defaults to 0
+func StridedSliceGradNewAxisMask(value int64) StridedSliceGradAttr {
+ return func(m optionalAttr) {
+ m["new_axis_mask"] = value
}
- op := scope.AddOperation(opspec)
- return op.Output(0)
}
-// Pop the element at the top of the stack.
+// StridedSliceGradShrinkAxisMask sets the optional shrink_axis_mask attribute to value.
+// If not specified, defaults to 0
+func StridedSliceGradShrinkAxisMask(value int64) StridedSliceGradAttr {
+ return func(m optionalAttr) {
+ m["shrink_axis_mask"] = value
+ }
+}
+
+// Returns the gradient of `StridedSlice`.
//
-// Arguments:
-// handle: The handle to a stack.
-// elem_type: The type of the elem that is popped.
+// Since `StridedSlice` cuts out pieces of its `input` which is size
+// `shape`, its gradient will have the same shape (which is passed here
+// as `shape`). The gradient will be zero in any element that the slice
+// does not select.
//
-// Returns The tensor that is popped from the top of the stack.
-func StackPopV2(scope *Scope, handle tf.Output, elem_type tf.DataType) (elem tf.Output) {
+// Arguments are the same as StridedSliceGrad with the exception that
+// `dy` is the input gradient to be propagated and `shape` is the
+// shape of `StridedSlice`'s `input`.
+func StridedSliceGrad(scope *Scope, shape tf.Output, begin tf.Output, end tf.Output, strides tf.Output, dy tf.Output, optional ...StridedSliceGradAttr) (output tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{"elem_type": elem_type}
+ attrs := map[string]interface{}{}
+ for _, a := range optional {
+ a(attrs)
+ }
opspec := tf.OpSpec{
- Type: "StackPopV2",
+ Type: "StridedSliceGrad",
Input: []tf.Input{
- handle,
+ shape, begin, end, strides, dy,
},
Attrs: attrs,
}
return op.Output(0)
}
-// Computes the sum along sparse segments of a tensor.
-//
-// Like `SparseSegmentSum`, but allows missing ids in `segment_ids`. If an id is
-// misisng, the `output` tensor at that position will be zeroed.
-//
-// Read @{$math_ops#segmentation$the section on segmentation} for an explanation of
-// segments.
+// StridedSliceAttr is an optional argument to StridedSlice.
+type StridedSliceAttr func(optionalAttr)
+
+// StridedSliceBeginMask sets the optional begin_mask attribute to value.
//
-// For example:
+// value: a bitmask where a bit i being 1 means to ignore the begin
+// value and instead use the largest interval possible. At runtime
+// begin[i] will be replaced with `[0, n-1) if `stride[i] > 0` or
+// `[-1, n-1]` if `stride[i] < 0`
+// If not specified, defaults to 0
+func StridedSliceBeginMask(value int64) StridedSliceAttr {
+ return func(m optionalAttr) {
+ m["begin_mask"] = value
+ }
+}
+
+// StridedSliceEndMask sets the optional end_mask attribute to value.
//
-// ```python
-// c = tf.constant([[1,2,3,4], [-1,-2,-3,-4], [5,6,7,8]])
+// value: analogous to `begin_mask`
+// If not specified, defaults to 0
+func StridedSliceEndMask(value int64) StridedSliceAttr {
+ return func(m optionalAttr) {
+ m["end_mask"] = value
+ }
+}
+
+// StridedSliceEllipsisMask sets the optional ellipsis_mask attribute to value.
//
-// tf.sparse_segment_sum_with_num_segments(
-// c, tf.constant([0, 1]), tf.constant([0, 0]), num_segments=3)
-// # => [[0 0 0 0]
-// # [0 0 0 0]
-// # [0 0 0 0]]
-//
-// tf.sparse_segment_sum_with_num_segments(c,
-// tf.constant([0, 1]),
-// tf.constant([0, 2],
-// num_segments=4))
-// # => [[ 1 2 3 4]
-// # [ 0 0 0 0]
-// # [-1 -2 -3 -4]
-// # [ 0 0 0 0]]
-// ```
-//
-// Arguments:
-//
-// indices: A 1-D tensor. Has same rank as `segment_ids`.
-// segment_ids: A 1-D tensor. Values should be sorted and can be repeated.
-// num_segments: Should equal the number of distinct segment IDs.
-//
-// Returns Has same shape as data, except for dimension 0 which
-// has size `num_segments`.
-func SparseSegmentSumWithNumSegments(scope *Scope, data tf.Output, indices tf.Output, segment_ids tf.Output, num_segments tf.Output) (output tf.Output) {
- if scope.Err() != nil {
- return
- }
- opspec := tf.OpSpec{
- Type: "SparseSegmentSumWithNumSegments",
- Input: []tf.Input{
- data, indices, segment_ids, num_segments,
- },
+// value: a bitmask where bit `i` being 1 means the `i`th
+// position is actually an ellipsis. One bit at most can be 1.
+// If `ellipsis_mask == 0`, then an implicit ellipsis mask of `1 << (m+1)`
+// is provided. This means that `foo[3:5] == foo[3:5, ...]`. An ellipsis
+// implicitly creates as many range specifications as necessary to fully
+// specify the sliced range for every dimension. For example for a 4-dimensional
+// tensor `foo` the slice `foo[2, ..., 5:8]` implies `foo[2, :, :, 5:8]`.
+// If not specified, defaults to 0
+func StridedSliceEllipsisMask(value int64) StridedSliceAttr {
+ return func(m optionalAttr) {
+ m["ellipsis_mask"] = value
}
- op := scope.AddOperation(opspec)
- return op.Output(0)
}
-// SparseToDenseAttr is an optional argument to SparseToDense.
-type SparseToDenseAttr func(optionalAttr)
+// StridedSliceNewAxisMask sets the optional new_axis_mask attribute to value.
+//
+// value: a bitmask where bit `i` being 1 means the `i`th
+// specification creates a new shape 1 dimension. For example
+// `foo[:4, tf.newaxis, :2]` would produce a shape `(4, 1, 2)` tensor.
+// If not specified, defaults to 0
+func StridedSliceNewAxisMask(value int64) StridedSliceAttr {
+ return func(m optionalAttr) {
+ m["new_axis_mask"] = value
+ }
+}
-// SparseToDenseValidateIndices sets the optional validate_indices attribute to value.
+// StridedSliceShrinkAxisMask sets the optional shrink_axis_mask attribute to value.
//
-// value: If true, indices are checked to make sure they are sorted in
-// lexicographic order and that there are no repeats.
-// If not specified, defaults to true
-func SparseToDenseValidateIndices(value bool) SparseToDenseAttr {
+// value: a bitmask where bit `i` implies that the `i`th
+// specification should shrink the dimensionality. begin and end
+// must imply a slice of size 1 in the dimension. For example in
+// python one might do `foo[:, 3, :]` which would result in
+// `shrink_axis_mask` being 2.
+// If not specified, defaults to 0
+func StridedSliceShrinkAxisMask(value int64) StridedSliceAttr {
return func(m optionalAttr) {
- m["validate_indices"] = value
+ m["shrink_axis_mask"] = value
}
}
-// Converts a sparse representation into a dense tensor.
+// Return a strided slice from `input`.
//
-// Builds an array `dense` with shape `output_shape` such that
+// Note, most python users will want to use the Python `Tensor.__getitem__`
+// or `Variable.__getitem__` rather than this op directly.
//
-// ```
-// # If sparse_indices is scalar
-// dense[i] = (i == sparse_indices ? sparse_values : default_value)
+// The goal of this op is to produce a new tensor with a subset of
+// the elements from the `n` dimensional `input` tensor. The subset is chosen using
+// a sequence of `m` sparse range specifications encoded into the arguments
+// of this function. Note, in some cases
+// `m` could be equal to `n`, but this need not be the case. Each
+// range specification entry can be one of the following:
//
-// # If sparse_indices is a vector, then for each i
-// dense[sparse_indices[i]] = sparse_values[i]
+// - An ellipsis (...). Ellipses are used to imply zero or more
+// dimensions of full-dimension selection and are produced using
+// `ellipsis_mask`. For example, `foo[...]` is the identity slice.
//
-// # If sparse_indices is an n by d matrix, then for each i in [0, n)
-// dense[sparse_indices[i][0], ..., sparse_indices[i][d-1]] = sparse_values[i]
+// - A new axis. This is used to insert a new shape=1 dimension and is
+// produced using `new_axis_mask`. For example, `foo[:, ...]` where
+// `foo` is shape `(3, 4)` produces a `(1, 3, 4)` tensor.
+//
+//
+// - A range `begin:end:stride`. This is used to specify how much to choose from
+// a given dimension. `stride` can be any integer but 0. `begin` is an integer
+// which represents the index of the first value to select while `end` represents
+// the index of the last value to select. The number of values selected in each
+// dimension is `end - begin` if `stride > 0` and `begin - end` if `stride < 0`.
+// `begin` and `end` can be negative where `-1` is the last element, `-2` is
+// the second to last. `begin_mask` controls whether to replace the explicitly
+// given `begin` with an implicit effective value of `0` if `stride > 0` and
+// `-1` if `stride < 0`. `end_mask` is analogous but produces the number
+// required to create the largest open interval. For example, given a shape
+// `(3,)` tensor `foo[:]`, the effective `begin` and `end` are `0` and `3`. Do
+// not assume this is equivalent to `foo[0:-1]` which has an effective `begin`
+// and `end` of `0` and `2`. Another example is `foo[-2::-1]` which reverses the
+// first dimension of a tensor while dropping the last two (in the original
+// order elements). For example `foo = [1,2,3,4]; foo[-2::-1]` is `[4,3]`.
+//
+// - A single index. This is used to keep only elements that have a given
+// index. For example (`foo[2, :]` on a shape `(5,6)` tensor produces a
+// shape `(6,)` tensor. This is encoded in `begin` and `end` and
+// `shrink_axis_mask`.
+//
+// Each conceptual range specification is encoded in the op's argument. This
+// encoding is best understand by considering a non-trivial example. In
+// particular,
+// `foo[1, 2:4, None, ..., :-3:-1, :]` will be encoded as
+//
+// ```
+// begin = [1, 2, x, x, 0, x] # x denotes don't care (usually 0)
+// end = [2, 4, x, x, -3, x]
+// strides = [1, 1, x, x, -1, 1]
+// begin_mask = 1<<4 | 1 << 5 = 48
+// end_mask = 1<<5 = 32
+// ellipsis_mask = 1<<3 = 8
+// new_axis_mask = 1<<2 4
+// shrink_axis_mask = 1<<0
// ```
//
-// All other values in `dense` are set to `default_value`. If `sparse_values` is a
-// scalar, all sparse indices are set to this single value.
+// In this case if `foo.shape` is (5, 5, 5, 5, 5, 5) the final shape of
+// the slice becomes (2, 1, 5, 5, 2, 5).
+// Let us walk step by step through each argument specification.
//
-// Indices should be sorted in lexicographic order, and indices must not
-// contain any repeats. If `validate_indices` is true, these properties
-// are checked during execution.
+// 1. The first argument in the example slice is turned into `begin = 1` and
+// `end = begin + 1 = 2`. To disambiguate from the original spec `2:4` we
+// also set the appropriate bit in `shrink_axis_mask`.
+//
+// 2. `2:4` is contributes 2, 4, 1 to begin, end, and stride. All masks have
+// zero bits contributed.
+//
+// 3. None is a synonym for `tf.newaxis`. This means insert a dimension of size 1
+// dimension in the final shape. Dummy values are contributed to begin,
+// end and stride, while the new_axis_mask bit is set.
+//
+// 4. `...` grab the full ranges from as many dimensions as needed to
+// fully specify a slice for every dimension of the input shape.
+//
+// 5. `:-3:-1` shows the use of negative indices. A negative index `i` associated
+// with a dimension that has shape `s` is converted to a positive index
+// `s + i`. So `-1` becomes `s-1` (i.e. the last element). This conversion
+// is done internally so begin, end and strides receive x, -3, and -1.
+// The appropriate begin_mask bit is set to indicate the start range is the
+// full range (ignoring the x).
+//
+// 6. `:` indicates that the entire contents of the corresponding dimension
+// is selected. This is equivalent to `::` or `0::1`. begin, end, and strides
+// receive 0, 0, and 1, respectively. The appropriate bits in `begin_mask` and
+// `end_mask` are also set.
+//
+// *Requirements*:
+// `0 != strides[i] for i in [0, m)`
+// `ellipsis_mask must be a power of two (only one ellipsis)`
//
// Arguments:
-// sparse_indices: 0-D, 1-D, or 2-D. `sparse_indices[i]` contains the complete
-// index where `sparse_values[i]` will be placed.
-// output_shape: 1-D. Shape of the dense output tensor.
-// sparse_values: 1-D. Values corresponding to each row of `sparse_indices`,
-// or a scalar value to be used for all sparse indices.
-// default_value: Scalar value to set for indices not specified in
-// `sparse_indices`.
//
-// Returns Dense output tensor of shape `output_shape`.
-func SparseToDense(scope *Scope, sparse_indices tf.Output, output_shape tf.Output, sparse_values tf.Output, default_value tf.Output, optional ...SparseToDenseAttr) (dense tf.Output) {
+// begin: `begin[k]` specifies the offset into the `k`th range specification.
+// The exact dimension this corresponds to will be determined by context.
+// Out-of-bounds values will be silently clamped. If the `k`th bit of
+// `begin_mask` then `begin[k]` is ignored and the full range of the
+// appropriate dimension is used instead. Negative values causes indexing
+// to start from the highest element e.g. If `foo==[1,2,3]` then `foo[-1]==3`.
+// end: `end[i]` is like `begin` with the exception that `end_mask` is
+// used to determine full ranges.
+// strides: `strides[i]` specifies the increment in the `i`th specification
+// after extracting a given element. Negative indices will reverse
+// the original order. Out or range values are
+// clamped to `[0,dim[i]) if slice[i]>0` or `[-1,dim[i]-1] if slice[i] < 0`
+func StridedSlice(scope *Scope, input tf.Output, begin tf.Output, end tf.Output, strides tf.Output, optional ...StridedSliceAttr) (output tf.Output) {
if scope.Err() != nil {
return
}
a(attrs)
}
opspec := tf.OpSpec{
- Type: "SparseToDense",
+ Type: "StridedSlice",
Input: []tf.Input{
- sparse_indices, output_shape, sparse_values, default_value,
+ input, begin, end, strides,
},
Attrs: attrs,
}
return op.Output(0)
}
-// Counts the number of occurrences of each value in an integer array.
+// Return a slice from 'input'.
//
-// Outputs a vector with length `size` and the same dtype as `weights`. If
-// `weights` are empty, then index `i` stores the number of times the value `i` is
-// counted in `arr`. If `weights` are non-empty, then index `i` stores the sum of
-// the value in `weights` at each index where the corresponding value in `arr` is
-// `i`.
+// The output tensor is a tensor with dimensions described by 'size'
+// whose values are extracted from 'input' starting at the offsets in
+// 'begin'.
//
-// Values in `arr` outside of the range [0, size) are ignored.
+// *Requirements*:
+// 0 <= begin[i] <= begin[i] + size[i] <= Di for i in [0, n)
//
// Arguments:
-// arr: int32 `Tensor`.
-// size: non-negative int32 scalar `Tensor`.
-// weights: is an int32, int64, float32, or float64 `Tensor` with the same
-// shape as `arr`, or a length-0 `Tensor`, in which case it acts as all weights
-// equal to 1.
//
-// Returns 1D `Tensor` with length equal to `size`. The counts or summed weights for
-// each value in the range [0, size).
-func Bincount(scope *Scope, arr tf.Output, size tf.Output, weights tf.Output) (bins tf.Output) {
+// begin: begin[i] specifies the offset into the 'i'th dimension of
+// 'input' to slice from.
+// size: size[i] specifies the number of elements of the 'i'th dimension
+// of 'input' to slice. If size[i] is -1, all remaining elements in dimension
+// i are included in the slice (i.e. this is equivalent to setting
+// size[i] = input.dim_size(i) - begin[i]).
+func Slice(scope *Scope, input tf.Output, begin tf.Output, size tf.Output) (output tf.Output) {
if scope.Err() != nil {
return
}
opspec := tf.OpSpec{
- Type: "Bincount",
+ Type: "Slice",
Input: []tf.Input{
- arr, size, weights,
+ input, begin, size,
},
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// Computes the sum along sparse segments of a tensor.
-//
-// Read @{$math_ops#segmentation$the section on segmentation} for an explanation of
-// segments.
+// SizeAttr is an optional argument to Size.
+type SizeAttr func(optionalAttr)
+
+// SizeOutType sets the optional out_type attribute to value.
+// If not specified, defaults to DT_INT32
+func SizeOutType(value tf.DataType) SizeAttr {
+ return func(m optionalAttr) {
+ m["out_type"] = value
+ }
+}
+
+// Returns the size of a tensor.
//
-// Like `SegmentSum`, but `segment_ids` can have rank less than `data`'s first
-// dimension, selecting a subset of dimension 0, specified by `indices`.
+// This operation returns an integer representing the number of elements in
+// `input`.
//
// For example:
//
-// ```python
-// c = tf.constant([[1,2,3,4], [-1,-2,-3,-4], [5,6,7,8]])
-//
-// # Select two rows, one segment.
-// tf.sparse_segment_sum(c, tf.constant([0, 1]), tf.constant([0, 0]))
-// # => [[0 0 0 0]]
-//
-// # Select two rows, two segment.
-// tf.sparse_segment_sum(c, tf.constant([0, 1]), tf.constant([0, 1]))
-// # => [[ 1 2 3 4]
-// # [-1 -2 -3 -4]]
-//
-// # Select all rows, two segments.
-// tf.sparse_segment_sum(c, tf.constant([0, 1, 2]), tf.constant([0, 0, 1]))
-// # => [[0 0 0 0]
-// # [5 6 7 8]]
-//
-// # Which is equivalent to:
-// tf.segment_sum(c, tf.constant([0, 0, 1]))
// ```
-//
-// Arguments:
-//
-// indices: A 1-D tensor. Has same rank as `segment_ids`.
-// segment_ids: A 1-D tensor. Values should be sorted and can be repeated.
-//
-// Returns Has same shape as data, except for dimension 0 which
-// has size `k`, the number of segments.
-func SparseSegmentSum(scope *Scope, data tf.Output, indices tf.Output, segment_ids tf.Output) (output tf.Output) {
+// # 't' is [[[1, 1,, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]]]]
+// size(t) ==> 12
+// ```
+func Size(scope *Scope, input tf.Output, optional ...SizeAttr) (output tf.Output) {
if scope.Err() != nil {
return
}
+ attrs := map[string]interface{}{}
+ for _, a := range optional {
+ a(attrs)
+ }
opspec := tf.OpSpec{
- Type: "SparseSegmentSum",
+ Type: "Size",
Input: []tf.Input{
- data, indices, segment_ids,
+ input,
},
+ Attrs: attrs,
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// Computes hyperbolic sine of x element-wise.
-func Sinh(scope *Scope, x tf.Output) (y tf.Output) {
+// Returns the complex conjugate of a complex number.
+//
+// Given a tensor `input` of complex numbers, this operation returns a tensor of
+// complex numbers that are the complex conjugate of each element in `input`. The
+// complex numbers in `input` must be of the form \\(a + bj\\), where *a* is the
+// real part and *b* is the imaginary part.
+//
+// The complex conjugate returned by this operation is of the form \\(a - bj\\).
+//
+// For example:
+//
+// ```
+// # tensor 'input' is [-2.25 + 4.75j, 3.25 + 5.75j]
+// tf.conj(input) ==> [-2.25 - 4.75j, 3.25 - 5.75j]
+// ```
+func Conj(scope *Scope, input tf.Output) (output tf.Output) {
if scope.Err() != nil {
return
}
opspec := tf.OpSpec{
- Type: "Sinh",
+ Type: "Conj",
Input: []tf.Input{
- x,
+ input,
},
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// Computes rectified linear 6: `min(max(features, 0), 6)`.
-func Relu6(scope *Scope, features tf.Output) (activations tf.Output) {
+// ResourceSparseApplyMomentumAttr is an optional argument to ResourceSparseApplyMomentum.
+type ResourceSparseApplyMomentumAttr func(optionalAttr)
+
+// ResourceSparseApplyMomentumUseLocking sets the optional use_locking attribute to value.
+//
+// value: If `True`, updating of the var and accum tensors will be protected
+// by a lock; otherwise the behavior is undefined, but may exhibit less
+// contention.
+// If not specified, defaults to false
+func ResourceSparseApplyMomentumUseLocking(value bool) ResourceSparseApplyMomentumAttr {
+ return func(m optionalAttr) {
+ m["use_locking"] = value
+ }
+}
+
+// ResourceSparseApplyMomentumUseNesterov sets the optional use_nesterov attribute to value.
+//
+// value: If `True`, the tensor passed to compute grad will be
+// var - lr * momentum * accum, so in the end, the var you get is actually
+// var - lr * momentum * accum.
+// If not specified, defaults to false
+func ResourceSparseApplyMomentumUseNesterov(value bool) ResourceSparseApplyMomentumAttr {
+ return func(m optionalAttr) {
+ m["use_nesterov"] = value
+ }
+}
+
+// Update relevant entries in '*var' and '*accum' according to the momentum scheme.
+//
+// Set use_nesterov = True if you want to use Nesterov momentum.
+//
+// That is for rows we have grad for, we update var and accum as follows:
+//
+// accum = accum * momentum + grad
+// var -= lr * accum
+//
+// Arguments:
+// var_: Should be from a Variable().
+// accum: Should be from a Variable().
+// lr: Learning rate. Must be a scalar.
+// grad: The gradient.
+// indices: A vector of indices into the first dimension of var and accum.
+// momentum: Momentum. Must be a scalar.
+//
+// Returns the created operation.
+func ResourceSparseApplyMomentum(scope *Scope, var_ tf.Output, accum tf.Output, lr tf.Output, grad tf.Output, indices tf.Output, momentum tf.Output, optional ...ResourceSparseApplyMomentumAttr) (o *tf.Operation) {
if scope.Err() != nil {
return
}
+ attrs := map[string]interface{}{}
+ for _, a := range optional {
+ a(attrs)
+ }
opspec := tf.OpSpec{
- Type: "Relu6",
+ Type: "ResourceSparseApplyMomentum",
Input: []tf.Input{
- features,
+ var_, accum, lr, grad, indices, momentum,
},
+ Attrs: attrs,
}
- op := scope.AddOperation(opspec)
- return op.Output(0)
+ return scope.AddOperation(opspec)
}
-// Computes the sum along segments of a tensor.
-//
-// Read @{$math_ops#segmentation$the section on segmentation} for an explanation of
-// segments.
-//
-// Computes a tensor such that
-// `(output[i] = sum_{j...} data[j...]` where the sum is over tuples `j...` such
-// that `segment_ids[j...] == i`. Unlike `SegmentSum`, `segment_ids`
-// need not be sorted and need not cover all values in the full
-// range of valid values.
+// Creates a sequence of numbers.
//
-// If the sum is empty for a given segment ID `i`, `output[i] = 0`.
-// If the given segment ID `i` is negative, the value is dropped and will not be
-// added to the sum of the segment.
+// This operation creates a sequence of numbers that begins at `start` and
+// extends by increments of `delta` up to but not including `limit`.
//
-// `num_segments` should equal the number of distinct segment IDs.
+// For example:
//
-// <div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;">
-// <img style="width:100%" src="https://www.tensorflow.org/images/UnsortedSegmentSum.png" alt>
-// </div>
+// ```
+// # 'start' is 3
+// # 'limit' is 18
+// # 'delta' is 3
+// tf.range(start, limit, delta) ==> [3, 6, 9, 12, 15]
+// ```
//
// Arguments:
+// start: 0-D (scalar). First entry in the sequence.
+// limit: 0-D (scalar). Upper limit of sequence, exclusive.
+// delta: 0-D (scalar). Optional. Default is 1. Number that increments `start`.
//
-// segment_ids: A tensor whose shape is a prefix of `data.shape`.
-//
-//
-// Returns Has same shape as data, except for the first `segment_ids.rank`
-// dimensions, which are replaced with a single dimension which has size
-// `num_segments`.
-func UnsortedSegmentSum(scope *Scope, data tf.Output, segment_ids tf.Output, num_segments tf.Output) (output tf.Output) {
+// Returns 1-D.
+func Range(scope *Scope, start tf.Output, limit tf.Output, delta tf.Output) (output tf.Output) {
if scope.Err() != nil {
return
}
opspec := tf.OpSpec{
- Type: "UnsortedSegmentSum",
+ Type: "Range",
Input: []tf.Input{
- data, segment_ids, num_segments,
+ start, limit, delta,
},
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// Returns which elements of x are finite.
+// Computes gradients for SparseSegmentSqrtN.
//
-// @compatibility(numpy)
-// Equivalent to np.isfinite
-// @end_compatibility
-func IsFinite(scope *Scope, x tf.Output) (y tf.Output) {
+// Returns tensor "output" with same shape as grad, except for dimension 0 whose
+// value is output_dim0.
+//
+// Arguments:
+// grad: gradient propagated to the SparseSegmentSqrtN op.
+// indices: indices passed to the corresponding SparseSegmentSqrtN op.
+// segment_ids: segment_ids passed to the corresponding SparseSegmentSqrtN op.
+// output_dim0: dimension 0 of "data" passed to SparseSegmentSqrtN op.
+func SparseSegmentSqrtNGrad(scope *Scope, grad tf.Output, indices tf.Output, segment_ids tf.Output, output_dim0 tf.Output) (output tf.Output) {
if scope.Err() != nil {
return
}
opspec := tf.OpSpec{
- Type: "IsFinite",
+ Type: "SparseSegmentSqrtNGrad",
Input: []tf.Input{
- x,
+ grad, indices, segment_ids, output_dim0,
},
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// MatMulAttr is an optional argument to MatMul.
-type MatMulAttr func(optionalAttr)
-
-// MatMulTransposeA sets the optional transpose_a attribute to value.
+// Computes the mean along sparse segments of a tensor.
//
-// value: If true, "a" is transposed before multiplication.
-// If not specified, defaults to false
-func MatMulTransposeA(value bool) MatMulAttr {
- return func(m optionalAttr) {
- m["transpose_a"] = value
- }
-}
-
-// MatMulTransposeB sets the optional transpose_b attribute to value.
+// Read @{$math_ops#segmentation$the section on segmentation} for an explanation of
+// segments.
//
-// value: If true, "b" is transposed before multiplication.
-// If not specified, defaults to false
-func MatMulTransposeB(value bool) MatMulAttr {
- return func(m optionalAttr) {
- m["transpose_b"] = value
+// Like `SegmentMean`, but `segment_ids` can have rank less than `data`'s first
+// dimension, selecting a subset of dimension 0, specified by `indices`.
+//
+// Arguments:
+//
+// indices: A 1-D tensor. Has same rank as `segment_ids`.
+// segment_ids: A 1-D tensor. Values should be sorted and can be repeated.
+//
+// Returns Has same shape as data, except for dimension 0 which
+// has size `k`, the number of segments.
+func SparseSegmentMean(scope *Scope, data tf.Output, indices tf.Output, segment_ids tf.Output) (output tf.Output) {
+ if scope.Err() != nil {
+ return
+ }
+ opspec := tf.OpSpec{
+ Type: "SparseSegmentMean",
+ Input: []tf.Input{
+ data, indices, segment_ids,
+ },
}
+ op := scope.AddOperation(opspec)
+ return op.Output(0)
}
-// Multiply the matrix "a" by the matrix "b".
+// Pop the element at the top of the stack.
//
-// The inputs must be two-dimensional matrices and the inner dimension of
-// "a" (after being transposed if transpose_a is true) must match the
-// outer dimension of "b" (after being transposed if transposed_b is
-// true).
+// Arguments:
+// handle: The handle to a stack.
+// elem_type: The type of the elem that is popped.
//
-// *Note*: The default kernel implementation for MatMul on GPUs uses
-// cublas.
-func MatMul(scope *Scope, a tf.Output, b tf.Output, optional ...MatMulAttr) (product tf.Output) {
+// Returns The tensor that is popped from the top of the stack.
+func StackPopV2(scope *Scope, handle tf.Output, elem_type tf.DataType) (elem tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{}
- for _, a := range optional {
- a(attrs)
- }
+ attrs := map[string]interface{}{"elem_type": elem_type}
opspec := tf.OpSpec{
- Type: "MatMul",
+ Type: "StackPopV2",
Input: []tf.Input{
- a, b,
+ handle,
},
Attrs: attrs,
}
return op.Output(0)
}
-// Selects elements from `x` or `y`, depending on `condition`.
+// Computes the sum along sparse segments of a tensor.
//
-// The `x`, and `y` tensors must all have the same shape, and the
-// output will also have that shape.
+// Like `SparseSegmentSum`, but allows missing ids in `segment_ids`. If an id is
+// misisng, the `output` tensor at that position will be zeroed.
//
-// The `condition` tensor must be a scalar if `x` and `y` are scalars.
-// If `x` and `y` are vectors or higher rank, then `condition` must be either a
-// scalar, a vector with size matching the first dimension of `x`, or must have
-// the same shape as `x`.
-//
-// The `condition` tensor acts as a mask that chooses, based on the value at each
-// element, whether the corresponding element / row in the output should be
-// taken from `x` (if true) or `y` (if false).
-//
-// If `condition` is a vector and `x` and `y` are higher rank matrices, then
-// it chooses which row (outer dimension) to copy from `x` and `y`.
-// If `condition` has the same shape as `x` and `y`, then it chooses which
-// element to copy from `x` and `y`.
+// Read @{$math_ops#segmentation$the section on segmentation} for an explanation of
+// segments.
//
// For example:
//
// ```python
-// # 'condition' tensor is [[True, False]
-// # [False, True]]
-// # 't' is [[1, 2],
-// # [3, 4]]
-// # 'e' is [[5, 6],
-// # [7, 8]]
-// select(condition, t, e) # => [[1, 6], [7, 4]]
-//
+// c = tf.constant([[1,2,3,4], [-1,-2,-3,-4], [5,6,7,8]])
//
-// # 'condition' tensor is [True, False]
-// # 't' is [[1, 2],
-// # [3, 4]]
-// # 'e' is [[5, 6],
-// # [7, 8]]
-// select(condition, t, e) ==> [[1, 2],
-// [7, 8]]
+// tf.sparse_segment_sum_with_num_segments(
+// c, tf.constant([0, 1]), tf.constant([0, 0]), num_segments=3)
+// # => [[0 0 0 0]
+// # [0 0 0 0]
+// # [0 0 0 0]]
//
+// tf.sparse_segment_sum_with_num_segments(c,
+// tf.constant([0, 1]),
+// tf.constant([0, 2],
+// num_segments=4))
+// # => [[ 1 2 3 4]
+// # [ 0 0 0 0]
+// # [-1 -2 -3 -4]
+// # [ 0 0 0 0]]
// ```
//
// Arguments:
//
-// x: = A `Tensor` which may have the same shape as `condition`.
-// If `condition` is rank 1, `x` may have higher rank,
-// but its first dimension must match the size of `condition`.
-// y: = A `Tensor` with the same type and shape as `x`.
+// indices: A 1-D tensor. Has same rank as `segment_ids`.
+// segment_ids: A 1-D tensor. Values should be sorted and can be repeated.
+// num_segments: Should equal the number of distinct segment IDs.
//
-// Returns = A `Tensor` with the same type and shape as `x` and `y`.
-func Select(scope *Scope, condition tf.Output, x tf.Output, y tf.Output) (output tf.Output) {
+// Returns Has same shape as data, except for dimension 0 which
+// has size `num_segments`.
+func SparseSegmentSumWithNumSegments(scope *Scope, data tf.Output, indices tf.Output, segment_ids tf.Output, num_segments tf.Output) (output tf.Output) {
if scope.Err() != nil {
return
}
opspec := tf.OpSpec{
- Type: "Select",
+ Type: "SparseSegmentSumWithNumSegments",
Input: []tf.Input{
- condition, x, y,
+ data, indices, segment_ids, num_segments,
},
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// Returns the truth value of x OR y element-wise.
+// SparseToDenseAttr is an optional argument to SparseToDense.
+type SparseToDenseAttr func(optionalAttr)
+
+// SparseToDenseValidateIndices sets the optional validate_indices attribute to value.
//
-// *NOTE*: `LogicalOr` supports broadcasting. More about broadcasting
-// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
-func LogicalOr(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) {
+// value: If true, indices are checked to make sure they are sorted in
+// lexicographic order and that there are no repeats.
+// If not specified, defaults to true
+func SparseToDenseValidateIndices(value bool) SparseToDenseAttr {
+ return func(m optionalAttr) {
+ m["validate_indices"] = value
+ }
+}
+
+// Converts a sparse representation into a dense tensor.
+//
+// Builds an array `dense` with shape `output_shape` such that
+//
+// ```
+// # If sparse_indices is scalar
+// dense[i] = (i == sparse_indices ? sparse_values : default_value)
+//
+// # If sparse_indices is a vector, then for each i
+// dense[sparse_indices[i]] = sparse_values[i]
+//
+// # If sparse_indices is an n by d matrix, then for each i in [0, n)
+// dense[sparse_indices[i][0], ..., sparse_indices[i][d-1]] = sparse_values[i]
+// ```
+//
+// All other values in `dense` are set to `default_value`. If `sparse_values` is a
+// scalar, all sparse indices are set to this single value.
+//
+// Indices should be sorted in lexicographic order, and indices must not
+// contain any repeats. If `validate_indices` is true, these properties
+// are checked during execution.
+//
+// Arguments:
+// sparse_indices: 0-D, 1-D, or 2-D. `sparse_indices[i]` contains the complete
+// index where `sparse_values[i]` will be placed.
+// output_shape: 1-D. Shape of the dense output tensor.
+// sparse_values: 1-D. Values corresponding to each row of `sparse_indices`,
+// or a scalar value to be used for all sparse indices.
+// default_value: Scalar value to set for indices not specified in
+// `sparse_indices`.
+//
+// Returns Dense output tensor of shape `output_shape`.
+func SparseToDense(scope *Scope, sparse_indices tf.Output, output_shape tf.Output, sparse_values tf.Output, default_value tf.Output, optional ...SparseToDenseAttr) (dense tf.Output) {
if scope.Err() != nil {
return
}
+ attrs := map[string]interface{}{}
+ for _, a := range optional {
+ a(attrs)
+ }
opspec := tf.OpSpec{
- Type: "LogicalOr",
+ Type: "SparseToDense",
Input: []tf.Input{
- x, y,
+ sparse_indices, output_shape, sparse_values, default_value,
},
+ Attrs: attrs,
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// Compute the regularized incomplete beta integral \\(I_x(a, b)\\).
-//
-// The regularized incomplete beta integral is defined as:
-//
-//
-// \\(I_x(a, b) = \frac{B(x; a, b)}{B(a, b)}\\)
-//
-// where
+// Counts the number of occurrences of each value in an integer array.
//
+// Outputs a vector with length `size` and the same dtype as `weights`. If
+// `weights` are empty, then index `i` stores the number of times the value `i` is
+// counted in `arr`. If `weights` are non-empty, then index `i` stores the sum of
+// the value in `weights` at each index where the corresponding value in `arr` is
+// `i`.
//
-// \\(B(x; a, b) = \int_0^x t^{a-1} (1 - t)^{b-1} dt\\)
+// Values in `arr` outside of the range [0, size) are ignored.
//
+// Arguments:
+// arr: int32 `Tensor`.
+// size: non-negative int32 scalar `Tensor`.
+// weights: is an int32, int64, float32, or float64 `Tensor` with the same
+// shape as `arr`, or a length-0 `Tensor`, in which case it acts as all weights
+// equal to 1.
//
-// is the incomplete beta function and \\(B(a, b)\\) is the *complete*
-// beta function.
-func Betainc(scope *Scope, a tf.Output, b tf.Output, x tf.Output) (z tf.Output) {
+// Returns 1D `Tensor` with length equal to `size`. The counts or summed weights for
+// each value in the range [0, size).
+func Bincount(scope *Scope, arr tf.Output, size tf.Output, weights tf.Output) (bins tf.Output) {
if scope.Err() != nil {
return
}
opspec := tf.OpSpec{
- Type: "Betainc",
+ Type: "Bincount",
Input: []tf.Input{
- a, b, x,
+ arr, size, weights,
},
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// Computes the sum along sparse segments of a tensor divided by the sqrt of N.
-//
-// N is the size of the segment being reduced.
-//
-// Like `SparseSegmentSqrtN`, but allows missing ids in `segment_ids`. If an id is
-// misisng, the `output` tensor at that position will be zeroed.
+// Computes the sum along sparse segments of a tensor.
//
// Read @{$math_ops#segmentation$the section on segmentation} for an explanation of
// segments.
//
+// Like `SegmentSum`, but `segment_ids` can have rank less than `data`'s first
+// dimension, selecting a subset of dimension 0, specified by `indices`.
+//
+// For example:
+//
+// ```python
+// c = tf.constant([[1,2,3,4], [-1,-2,-3,-4], [5,6,7,8]])
+//
+// # Select two rows, one segment.
+// tf.sparse_segment_sum(c, tf.constant([0, 1]), tf.constant([0, 0]))
+// # => [[0 0 0 0]]
+//
+// # Select two rows, two segment.
+// tf.sparse_segment_sum(c, tf.constant([0, 1]), tf.constant([0, 1]))
+// # => [[ 1 2 3 4]
+// # [-1 -2 -3 -4]]
+//
+// # Select all rows, two segments.
+// tf.sparse_segment_sum(c, tf.constant([0, 1, 2]), tf.constant([0, 0, 1]))
+// # => [[0 0 0 0]
+// # [5 6 7 8]]
+//
+// # Which is equivalent to:
+// tf.segment_sum(c, tf.constant([0, 0, 1]))
+// ```
+//
// Arguments:
//
// indices: A 1-D tensor. Has same rank as `segment_ids`.
// segment_ids: A 1-D tensor. Values should be sorted and can be repeated.
-// num_segments: Should equal the number of distinct segment IDs.
//
// Returns Has same shape as data, except for dimension 0 which
// has size `k`, the number of segments.
-func SparseSegmentSqrtNWithNumSegments(scope *Scope, data tf.Output, indices tf.Output, segment_ids tf.Output, num_segments tf.Output) (output tf.Output) {
+func SparseSegmentSum(scope *Scope, data tf.Output, indices tf.Output, segment_ids tf.Output) (output tf.Output) {
if scope.Err() != nil {
return
}
opspec := tf.OpSpec{
- Type: "SparseSegmentSqrtNWithNumSegments",
+ Type: "SparseSegmentSum",
Input: []tf.Input{
- data, indices, segment_ids, num_segments,
+ data, indices, segment_ids,
},
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// Compute the upper regularized incomplete Gamma function `Q(a, x)`.
-//
-// The upper regularized incomplete Gamma function is defined as:
-//
-// \\(Q(a, x) = Gamma(a, x) / Gamma(a) = 1 - P(a, x)\\)
-//
-// where
-//
-// \\(Gamma(a, x) = int_{x}^{\infty} t^{a-1} exp(-t) dt\\)
-//
-// is the upper incomplete Gama function.
-//
-// Note, above `P(a, x)` (`Igamma`) is the lower regularized complete
-// Gamma function.
-func Igammac(scope *Scope, a tf.Output, x tf.Output) (z tf.Output) {
+// Computes hyperbolic sine of x element-wise.
+func Sinh(scope *Scope, x tf.Output) (y tf.Output) {
if scope.Err() != nil {
return
}
opspec := tf.OpSpec{
- Type: "Igammac",
+ Type: "Sinh",
Input: []tf.Input{
- a, x,
+ x,
},
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// LogUniformCandidateSamplerAttr is an optional argument to LogUniformCandidateSampler.
-type LogUniformCandidateSamplerAttr func(optionalAttr)
-
-// LogUniformCandidateSamplerSeed sets the optional seed attribute to value.
-//
-// value: If either seed or seed2 are set to be non-zero, the random number
-// generator is seeded by the given seed. Otherwise, it is seeded by a
-// random seed.
-// If not specified, defaults to 0
-func LogUniformCandidateSamplerSeed(value int64) LogUniformCandidateSamplerAttr {
- return func(m optionalAttr) {
- m["seed"] = value
+// Computes rectified linear 6: `min(max(features, 0), 6)`.
+func Relu6(scope *Scope, features tf.Output) (activations tf.Output) {
+ if scope.Err() != nil {
+ return
}
-}
-
-// LogUniformCandidateSamplerSeed2 sets the optional seed2 attribute to value.
-//
-// value: An second seed to avoid seed collision.
-// If not specified, defaults to 0
-func LogUniformCandidateSamplerSeed2(value int64) LogUniformCandidateSamplerAttr {
- return func(m optionalAttr) {
- m["seed2"] = value
+ opspec := tf.OpSpec{
+ Type: "Relu6",
+ Input: []tf.Input{
+ features,
+ },
}
+ op := scope.AddOperation(opspec)
+ return op.Output(0)
}
-// Generates labels for candidate sampling with a log-uniform distribution.
-//
-// See explanations of candidate sampling and the data formats at
-// go/candidate-sampling.
+// Computes the sum along segments of a tensor.
//
-// For each batch, this op picks a single set of sampled candidate labels.
+// Read @{$math_ops#segmentation$the section on segmentation} for an explanation of
+// segments.
//
-// The advantages of sampling candidates per-batch are simplicity and the
-// possibility of efficient dense matrix multiplication. The disadvantage is that
-// the sampled candidates must be chosen independently of the context and of the
-// true labels.
+// Computes a tensor such that
+// `(output[i] = sum_{j...} data[j...]` where the sum is over tuples `j...` such
+// that `segment_ids[j...] == i`. Unlike `SegmentSum`, `segment_ids`
+// need not be sorted and need not cover all values in the full
+// range of valid values.
//
-// Arguments:
-// true_classes: A batch_size * num_true matrix, in which each row contains the
-// IDs of the num_true target_classes in the corresponding original label.
-// num_true: Number of true labels per context.
-// num_sampled: Number of candidates to randomly sample.
-// unique: If unique is true, we sample with rejection, so that all sampled
-// candidates in a batch are unique. This requires some approximation to
-// estimate the post-rejection sampling probabilities.
-// range_max: The sampler will sample integers from the interval [0, range_max).
+// If the sum is empty for a given segment ID `i`, `output[i] = 0`.
+// If the given segment ID `i` is negative, the value is dropped and will not be
+// added to the sum of the segment.
//
-// Returns A vector of length num_sampled, in which each element is
-// the ID of a sampled candidate.A batch_size * num_true matrix, representing
-// the number of times each candidate is expected to occur in a batch
-// of sampled candidates. If unique=true, then this is a probability.A vector of length num_sampled, for each sampled
-// candidate representing the number of times the candidate is expected
-// to occur in a batch of sampled candidates. If unique=true, then this is a
-// probability.
-func LogUniformCandidateSampler(scope *Scope, true_classes tf.Output, num_true int64, num_sampled int64, unique bool, range_max int64, optional ...LogUniformCandidateSamplerAttr) (sampled_candidates tf.Output, true_expected_count tf.Output, sampled_expected_count tf.Output) {
+// `num_segments` should equal the number of distinct segment IDs.
+//
+// <div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;">
+// <img style="width:100%" src="https://www.tensorflow.org/images/UnsortedSegmentSum.png" alt>
+// </div>
+//
+// Arguments:
+//
+// segment_ids: A tensor whose shape is a prefix of `data.shape`.
+//
+//
+// Returns Has same shape as data, except for the first `segment_ids.rank`
+// dimensions, which are replaced with a single dimension which has size
+// `num_segments`.
+func UnsortedSegmentSum(scope *Scope, data tf.Output, segment_ids tf.Output, num_segments tf.Output) (output tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{"num_true": num_true, "num_sampled": num_sampled, "unique": unique, "range_max": range_max}
- for _, a := range optional {
- a(attrs)
- }
opspec := tf.OpSpec{
- Type: "LogUniformCandidateSampler",
+ Type: "UnsortedSegmentSum",
Input: []tf.Input{
- true_classes,
+ data, segment_ids, num_segments,
},
- Attrs: attrs,
}
op := scope.AddOperation(opspec)
- return op.Output(0), op.Output(1), op.Output(2)
+ return op.Output(0)
}
-// Returns (x - y)(x - y) element-wise.
-//
-// *NOTE*: `SquaredDifference` supports broadcasting. More about broadcasting
-// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
-func SquaredDifference(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) {
- if scope.Err() != nil {
- return
+// ResourceStridedSliceAssignAttr is an optional argument to ResourceStridedSliceAssign.
+type ResourceStridedSliceAssignAttr func(optionalAttr)
+
+// ResourceStridedSliceAssignBeginMask sets the optional begin_mask attribute to value.
+// If not specified, defaults to 0
+func ResourceStridedSliceAssignBeginMask(value int64) ResourceStridedSliceAssignAttr {
+ return func(m optionalAttr) {
+ m["begin_mask"] = value
}
- opspec := tf.OpSpec{
- Type: "SquaredDifference",
- Input: []tf.Input{
- x, y,
- },
+}
+
+// ResourceStridedSliceAssignEndMask sets the optional end_mask attribute to value.
+// If not specified, defaults to 0
+func ResourceStridedSliceAssignEndMask(value int64) ResourceStridedSliceAssignAttr {
+ return func(m optionalAttr) {
+ m["end_mask"] = value
}
- op := scope.AddOperation(opspec)
- return op.Output(0)
}
-// Forwards the input to the output.
+// ResourceStridedSliceAssignEllipsisMask sets the optional ellipsis_mask attribute to value.
+// If not specified, defaults to 0
+func ResourceStridedSliceAssignEllipsisMask(value int64) ResourceStridedSliceAssignAttr {
+ return func(m optionalAttr) {
+ m["ellipsis_mask"] = value
+ }
+}
+
+// ResourceStridedSliceAssignNewAxisMask sets the optional new_axis_mask attribute to value.
+// If not specified, defaults to 0
+func ResourceStridedSliceAssignNewAxisMask(value int64) ResourceStridedSliceAssignAttr {
+ return func(m optionalAttr) {
+ m["new_axis_mask"] = value
+ }
+}
+
+// ResourceStridedSliceAssignShrinkAxisMask sets the optional shrink_axis_mask attribute to value.
+// If not specified, defaults to 0
+func ResourceStridedSliceAssignShrinkAxisMask(value int64) ResourceStridedSliceAssignAttr {
+ return func(m optionalAttr) {
+ m["shrink_axis_mask"] = value
+ }
+}
+
+// Assign `value` to the sliced l-value reference of `ref`.
//
-// This operator represents the loop termination condition used by the
-// "pivot" switches of a loop.
+// The values of `value` are assigned to the positions in the variable
+// `ref` that are selected by the slice parameters. The slice parameters
+// `begin, `end`, `strides`, etc. work exactly as in `StridedSlice`.
//
-// Arguments:
-// input: A boolean scalar, representing the branch predicate of the Switch op.
+// NOTE this op currently does not support broadcasting and so `value`'s
+// shape must be exactly the shape produced by the slice of `ref`.
//
-// Returns The same tensor as `input`.
-func LoopCond(scope *Scope, input tf.Output) (output tf.Output) {
+// Returns the created operation.
+func ResourceStridedSliceAssign(scope *Scope, ref tf.Output, begin tf.Output, end tf.Output, strides tf.Output, value tf.Output, optional ...ResourceStridedSliceAssignAttr) (o *tf.Operation) {
if scope.Err() != nil {
return
}
+ attrs := map[string]interface{}{}
+ for _, a := range optional {
+ a(attrs)
+ }
opspec := tf.OpSpec{
- Type: "LoopCond",
+ Type: "ResourceStridedSliceAssign",
Input: []tf.Input{
- input,
+ ref, begin, end, strides, value,
},
+ Attrs: attrs,
}
- op := scope.AddOperation(opspec)
- return op.Output(0)
+ return scope.AddOperation(opspec)
}
-// ApproximateEqualAttr is an optional argument to ApproximateEqual.
-type ApproximateEqualAttr func(optionalAttr)
+// ArgMaxAttr is an optional argument to ArgMax.
+type ArgMaxAttr func(optionalAttr)
-// ApproximateEqualTolerance sets the optional tolerance attribute to value.
-// If not specified, defaults to 1e-05
-func ApproximateEqualTolerance(value float32) ApproximateEqualAttr {
+// ArgMaxOutputType sets the optional output_type attribute to value.
+// If not specified, defaults to DT_INT64
+func ArgMaxOutputType(value tf.DataType) ArgMaxAttr {
return func(m optionalAttr) {
- m["tolerance"] = value
+ m["output_type"] = value
}
}
-// Returns the truth value of abs(x-y) < tolerance element-wise.
-func ApproximateEqual(scope *Scope, x tf.Output, y tf.Output, optional ...ApproximateEqualAttr) (z tf.Output) {
+// Returns the index with the largest value across dimensions of a tensor.
+//
+// Note that in case of ties the identity of the return value is not guaranteed.
+//
+// Arguments:
+//
+// dimension: int32 or int64, must be in the range `[-rank(input), rank(input))`.
+// Describes which dimension of the input Tensor to reduce across. For vectors,
+// use dimension = 0.
+func ArgMax(scope *Scope, input tf.Output, dimension tf.Output, optional ...ArgMaxAttr) (output tf.Output) {
if scope.Err() != nil {
return
}
a(attrs)
}
opspec := tf.OpSpec{
- Type: "ApproximateEqual",
+ Type: "ArgMax",
Input: []tf.Input{
- x, y,
+ input, dimension,
},
Attrs: attrs,
}
return op.Output(0)
}
-// Returns x / y element-wise.
+// Returns which elements of x are finite.
//
-// *NOTE*: `Div` supports broadcasting. More about broadcasting
-// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
-func Div(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) {
+// @compatibility(numpy)
+// Equivalent to np.isfinite
+// @end_compatibility
+func IsFinite(scope *Scope, x tf.Output) (y tf.Output) {
if scope.Err() != nil {
return
}
opspec := tf.OpSpec{
- Type: "Div",
+ Type: "IsFinite",
Input: []tf.Input{
- x, y,
+ x,
},
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// Returns x * y element-wise.
+// MatMulAttr is an optional argument to MatMul.
+type MatMulAttr func(optionalAttr)
+
+// MatMulTransposeA sets the optional transpose_a attribute to value.
//
-// *NOTE*: `Multiply` supports broadcasting. More about broadcasting
-// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
-func Mul(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) {
- if scope.Err() != nil {
- return
- }
- opspec := tf.OpSpec{
- Type: "Mul",
- Input: []tf.Input{
- x, y,
- },
+// value: If true, "a" is transposed before multiplication.
+// If not specified, defaults to false
+func MatMulTransposeA(value bool) MatMulAttr {
+ return func(m optionalAttr) {
+ m["transpose_a"] = value
}
- op := scope.AddOperation(opspec)
- return op.Output(0)
}
-// BiasAddAttr is an optional argument to BiasAdd.
-type BiasAddAttr func(optionalAttr)
-
-// BiasAddDataFormat sets the optional data_format attribute to value.
+// MatMulTransposeB sets the optional transpose_b attribute to value.
//
-// value: Specify the data format of the input and output data. With the
-// default format "NHWC", the bias tensor will be added to the last dimension
-// of the value tensor.
-// Alternatively, the format could be "NCHW", the data storage order of:
-// [batch, in_channels, in_height, in_width].
-// The tensor will be added to "in_channels", the third-to-the-last
-// dimension.
-// If not specified, defaults to "NHWC"
-func BiasAddDataFormat(value string) BiasAddAttr {
+// value: If true, "b" is transposed before multiplication.
+// If not specified, defaults to false
+func MatMulTransposeB(value bool) MatMulAttr {
return func(m optionalAttr) {
- m["data_format"] = value
+ m["transpose_b"] = value
}
}
-// Adds `bias` to `value`.
-//
-// This is a special case of `tf.add` where `bias` is restricted to be 1-D.
-// Broadcasting is supported, so `value` may have any number of dimensions.
+// Multiply the matrix "a" by the matrix "b".
//
-// Arguments:
-// value: Any number of dimensions.
-// bias: 1-D with size the last dimension of `value`.
+// The inputs must be two-dimensional matrices and the inner dimension of
+// "a" (after being transposed if transpose_a is true) must match the
+// outer dimension of "b" (after being transposed if transposed_b is
+// true).
//
-// Returns Broadcasted sum of `value` and `bias`.
-func BiasAdd(scope *Scope, value tf.Output, bias tf.Output, optional ...BiasAddAttr) (output tf.Output) {
+// *Note*: The default kernel implementation for MatMul on GPUs uses
+// cublas.
+func MatMul(scope *Scope, a tf.Output, b tf.Output, optional ...MatMulAttr) (product tf.Output) {
if scope.Err() != nil {
return
}
a(attrs)
}
opspec := tf.OpSpec{
- Type: "BiasAdd",
+ Type: "MatMul",
Input: []tf.Input{
- value, bias,
+ a, b,
},
Attrs: attrs,
}
return op.Output(0)
}
-// SparseReduceSumSparseAttr is an optional argument to SparseReduceSumSparse.
-type SparseReduceSumSparseAttr func(optionalAttr)
-
-// SparseReduceSumSparseKeepDims sets the optional keep_dims attribute to value.
+// Selects elements from `x` or `y`, depending on `condition`.
//
-// value: If true, retain reduced dimensions with length 1.
-// If not specified, defaults to false
-func SparseReduceSumSparseKeepDims(value bool) SparseReduceSumSparseAttr {
- return func(m optionalAttr) {
- m["keep_dims"] = value
- }
-}
-
-// Computes the sum of elements across dimensions of a SparseTensor.
+// The `x`, and `y` tensors must all have the same shape, and the
+// output will also have that shape.
//
-// This Op takes a SparseTensor and is the sparse counterpart to
-// `tf.reduce_sum()`. In contrast to SparseReduceSum, this Op returns a
-// SparseTensor.
+// The `condition` tensor must be a scalar if `x` and `y` are scalars.
+// If `x` and `y` are vectors or higher rank, then `condition` must be either a
+// scalar, a vector with size matching the first dimension of `x`, or must have
+// the same shape as `x`.
//
-// Reduces `sp_input` along the dimensions given in `reduction_axes`. Unless
-// `keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in
-// `reduction_axes`. If `keep_dims` is true, the reduced dimensions are retained
-// with length 1.
+// The `condition` tensor acts as a mask that chooses, based on the value at each
+// element, whether the corresponding element / row in the output should be
+// taken from `x` (if true) or `y` (if false).
//
-// If `reduction_axes` has no entries, all dimensions are reduced, and a tensor
-// with a single element is returned. Additionally, the axes can be negative,
-// which are interpreted according to the indexing rules in Python.
+// If `condition` is a vector and `x` and `y` are higher rank matrices, then
+// it chooses which row (outer dimension) to copy from `x` and `y`.
+// If `condition` has the same shape as `x` and `y`, then it chooses which
+// element to copy from `x` and `y`.
+//
+// For example:
+//
+// ```python
+// # 'condition' tensor is [[True, False]
+// # [False, True]]
+// # 't' is [[1, 2],
+// # [3, 4]]
+// # 'e' is [[5, 6],
+// # [7, 8]]
+// select(condition, t, e) # => [[1, 6], [7, 4]]
+//
+//
+// # 'condition' tensor is [True, False]
+// # 't' is [[1, 2],
+// # [3, 4]]
+// # 'e' is [[5, 6],
+// # [7, 8]]
+// select(condition, t, e) ==> [[1, 2],
+// [7, 8]]
+//
+// ```
//
// Arguments:
-// input_indices: 2-D. `N x R` matrix with the indices of non-empty values in a
-// SparseTensor, possibly not in canonical ordering.
-// input_values: 1-D. `N` non-empty values corresponding to `input_indices`.
-// input_shape: 1-D. Shape of the input SparseTensor.
-// reduction_axes: 1-D. Length-`K` vector containing the reduction axes.
-func SparseReduceSumSparse(scope *Scope, input_indices tf.Output, input_values tf.Output, input_shape tf.Output, reduction_axes tf.Output, optional ...SparseReduceSumSparseAttr) (output_indices tf.Output, output_values tf.Output, output_shape tf.Output) {
- if scope.Err() != nil {
- return
- }
- attrs := map[string]interface{}{}
- for _, a := range optional {
- a(attrs)
- }
- opspec := tf.OpSpec{
- Type: "SparseReduceSumSparse",
- Input: []tf.Input{
- input_indices, input_values, input_shape, reduction_axes,
- },
- Attrs: attrs,
- }
- op := scope.AddOperation(opspec)
- return op.Output(0), op.Output(1), op.Output(2)
-}
-
-// Returns x + y element-wise.
//
-// *NOTE*: `Add` supports broadcasting. `AddN` does not. More about broadcasting
-// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
-func AddV2(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) {
+// x: = A `Tensor` which may have the same shape as `condition`.
+// If `condition` is rank 1, `x` may have higher rank,
+// but its first dimension must match the size of `condition`.
+// y: = A `Tensor` with the same type and shape as `x`.
+//
+// Returns = A `Tensor` with the same type and shape as `x` and `y`.
+func Select(scope *Scope, condition tf.Output, x tf.Output, y tf.Output) (output tf.Output) {
if scope.Err() != nil {
return
}
opspec := tf.OpSpec{
- Type: "AddV2",
+ Type: "Select",
Input: []tf.Input{
- x, y,
+ condition, x, y,
},
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// Returns x + y element-wise.
+// Returns the truth value of x OR y element-wise.
//
-// *NOTE*: `Add` supports broadcasting. `AddN` does not. More about broadcasting
+// *NOTE*: `LogicalOr` supports broadcasting. More about broadcasting
// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
-func Add(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) {
+func LogicalOr(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) {
if scope.Err() != nil {
return
}
opspec := tf.OpSpec{
- Type: "Add",
+ Type: "LogicalOr",
Input: []tf.Input{
x, y,
},
return op.Output(0)
}
-// NthElementAttr is an optional argument to NthElement.
-type NthElementAttr func(optionalAttr)
-
-// NthElementReverse sets the optional reverse attribute to value.
+// Compute the regularized incomplete beta integral \\(I_x(a, b)\\).
//
-// value: When set to True, find the nth-largest value in the vector and vice
-// versa.
-// If not specified, defaults to false
-func NthElementReverse(value bool) NthElementAttr {
- return func(m optionalAttr) {
- m["reverse"] = value
- }
-}
-
-// Finds values of the `n`-th order statistic for the last dimension.
+// The regularized incomplete beta integral is defined as:
//
-// If the input is a vector (rank-1), finds the entries which is the nth-smallest
-// value in the vector and outputs their values as scalar tensor.
//
-// For matrices (resp. higher rank input), computes the entries which is the
-// nth-smallest value in each row (resp. vector along the last dimension). Thus,
+// \\(I_x(a, b) = \frac{B(x; a, b)}{B(a, b)}\\)
//
-// values.shape = input.shape[:-1]
+// where
//
-// Arguments:
-// input: 1-D or higher with last dimension at least `n+1`.
-// n: 0-D. Position of sorted vector to select along the last dimension (along
-// each row for matrices). Valid range of n is `[0, input.shape[:-1])`
//
-// Returns The `n`-th order statistic along each last dimensional slice.
-func NthElement(scope *Scope, input tf.Output, n tf.Output, optional ...NthElementAttr) (values tf.Output) {
+// \\(B(x; a, b) = \int_0^x t^{a-1} (1 - t)^{b-1} dt\\)
+//
+//
+// is the incomplete beta function and \\(B(a, b)\\) is the *complete*
+// beta function.
+func Betainc(scope *Scope, a tf.Output, b tf.Output, x tf.Output) (z tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{}
- for _, a := range optional {
- a(attrs)
- }
opspec := tf.OpSpec{
- Type: "NthElement",
+ Type: "Betainc",
Input: []tf.Input{
- input, n,
+ a, b, x,
},
- Attrs: attrs,
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// Computes the Max along segments of a tensor.
-//
-// Read @{$math_ops#segmentation$the section on segmentation} for an explanation of
-// segments.
-//
-// This operator is similar to the [unsorted segment sum operator](../../../api_docs/python/math_ops.md#UnsortedSegmentSum).
-// Instead of computing the sum over segments, it computes the maximum
-// such that:
+// Computes the sum along sparse segments of a tensor divided by the sqrt of N.
//
-// \\(output_i = \max_j data_j\\) where max is over `j` such
-// that `segment_ids[j] == i`.
+// N is the size of the segment being reduced.
//
-// If the maximum is empty for a given segment ID `i`, it outputs the smallest possible value for specific numeric type,
-// `output[i] = numeric_limits<T>::min()`.
+// Like `SparseSegmentSqrtN`, but allows missing ids in `segment_ids`. If an id is
+// misisng, the `output` tensor at that position will be zeroed.
//
-// <div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;">
-// <img style="width:100%" src="https://www.tensorflow.org/images/UnsortedSegmentMax.png" alt>
-// </div>
+// Read @{$math_ops#segmentation$the section on segmentation} for an explanation of
+// segments.
//
// Arguments:
//
-// segment_ids: A 1-D tensor whose rank is equal to the rank of `data`'s
-// first dimension.
-//
+// indices: A 1-D tensor. Has same rank as `segment_ids`.
+// segment_ids: A 1-D tensor. Values should be sorted and can be repeated.
+// num_segments: Should equal the number of distinct segment IDs.
//
// Returns Has same shape as data, except for dimension 0 which
-// has size `num_segments`.
-func UnsortedSegmentMax(scope *Scope, data tf.Output, segment_ids tf.Output, num_segments tf.Output) (output tf.Output) {
+// has size `k`, the number of segments.
+func SparseSegmentSqrtNWithNumSegments(scope *Scope, data tf.Output, indices tf.Output, segment_ids tf.Output, num_segments tf.Output) (output tf.Output) {
if scope.Err() != nil {
return
}
opspec := tf.OpSpec{
- Type: "UnsortedSegmentMax",
+ Type: "SparseSegmentSqrtNWithNumSegments",
Input: []tf.Input{
- data, segment_ids, num_segments,
+ data, indices, segment_ids, num_segments,
},
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// Computes exponential of x element-wise. \\(y = e^x\\).
-func Exp(scope *Scope, x tf.Output) (y tf.Output) {
+// Compute the upper regularized incomplete Gamma function `Q(a, x)`.
+//
+// The upper regularized incomplete Gamma function is defined as:
+//
+// \\(Q(a, x) = Gamma(a, x) / Gamma(a) = 1 - P(a, x)\\)
+//
+// where
+//
+// \\(Gamma(a, x) = int_{x}^{\infty} t^{a-1} exp(-t) dt\\)
+//
+// is the upper incomplete Gama function.
+//
+// Note, above `P(a, x)` (`Igamma`) is the lower regularized complete
+// Gamma function.
+func Igammac(scope *Scope, a tf.Output, x tf.Output) (z tf.Output) {
if scope.Err() != nil {
return
}
opspec := tf.OpSpec{
- Type: "Exp",
+ Type: "Igammac",
Input: []tf.Input{
- x,
+ a, x,
},
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// Returns an element-wise indication of the sign of a number.
-//
-// `y = sign(x) = -1` if `x < 0`; 0 if `x == 0`; 1 if `x > 0`.
+// LogUniformCandidateSamplerAttr is an optional argument to LogUniformCandidateSampler.
+type LogUniformCandidateSamplerAttr func(optionalAttr)
+
+// LogUniformCandidateSamplerSeed sets the optional seed attribute to value.
//
-// For complex numbers, `y = sign(x) = x / |x|` if `x != 0`, otherwise `y = 0`.
-func Sign(scope *Scope, x tf.Output) (y tf.Output) {
- if scope.Err() != nil {
- return
- }
- opspec := tf.OpSpec{
- Type: "Sign",
- Input: []tf.Input{
- x,
- },
+// value: If either seed or seed2 are set to be non-zero, the random number
+// generator is seeded by the given seed. Otherwise, it is seeded by a
+// random seed.
+// If not specified, defaults to 0
+func LogUniformCandidateSamplerSeed(value int64) LogUniformCandidateSamplerAttr {
+ return func(m optionalAttr) {
+ m["seed"] = value
}
- op := scope.AddOperation(opspec)
- return op.Output(0)
}
-// ArgMinAttr is an optional argument to ArgMin.
-type ArgMinAttr func(optionalAttr)
-
-// ArgMinOutputType sets the optional output_type attribute to value.
-// If not specified, defaults to DT_INT64
-func ArgMinOutputType(value tf.DataType) ArgMinAttr {
+// LogUniformCandidateSamplerSeed2 sets the optional seed2 attribute to value.
+//
+// value: An second seed to avoid seed collision.
+// If not specified, defaults to 0
+func LogUniformCandidateSamplerSeed2(value int64) LogUniformCandidateSamplerAttr {
return func(m optionalAttr) {
- m["output_type"] = value
+ m["seed2"] = value
}
}
-// Returns the index with the smallest value across dimensions of a tensor.
+// Generates labels for candidate sampling with a log-uniform distribution.
//
-// Note that in case of ties the identity of the return value is not guaranteed.
+// See explanations of candidate sampling and the data formats at
+// go/candidate-sampling.
+//
+// For each batch, this op picks a single set of sampled candidate labels.
+//
+// The advantages of sampling candidates per-batch are simplicity and the
+// possibility of efficient dense matrix multiplication. The disadvantage is that
+// the sampled candidates must be chosen independently of the context and of the
+// true labels.
//
// Arguments:
+// true_classes: A batch_size * num_true matrix, in which each row contains the
+// IDs of the num_true target_classes in the corresponding original label.
+// num_true: Number of true labels per context.
+// num_sampled: Number of candidates to randomly sample.
+// unique: If unique is true, we sample with rejection, so that all sampled
+// candidates in a batch are unique. This requires some approximation to
+// estimate the post-rejection sampling probabilities.
+// range_max: The sampler will sample integers from the interval [0, range_max).
//
-// dimension: int32 or int64, must be in the range `[-rank(input), rank(input))`.
-// Describes which dimension of the input Tensor to reduce across. For vectors,
-// use dimension = 0.
-func ArgMin(scope *Scope, input tf.Output, dimension tf.Output, optional ...ArgMinAttr) (output tf.Output) {
+// Returns A vector of length num_sampled, in which each element is
+// the ID of a sampled candidate.A batch_size * num_true matrix, representing
+// the number of times each candidate is expected to occur in a batch
+// of sampled candidates. If unique=true, then this is a probability.A vector of length num_sampled, for each sampled
+// candidate representing the number of times the candidate is expected
+// to occur in a batch of sampled candidates. If unique=true, then this is a
+// probability.
+func LogUniformCandidateSampler(scope *Scope, true_classes tf.Output, num_true int64, num_sampled int64, unique bool, range_max int64, optional ...LogUniformCandidateSamplerAttr) (sampled_candidates tf.Output, true_expected_count tf.Output, sampled_expected_count tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{}
+ attrs := map[string]interface{}{"num_true": num_true, "num_sampled": num_sampled, "unique": unique, "range_max": range_max}
for _, a := range optional {
a(attrs)
}
opspec := tf.OpSpec{
- Type: "ArgMin",
+ Type: "LogUniformCandidateSampler",
Input: []tf.Input{
- input, dimension,
+ true_classes,
},
Attrs: attrs,
}
op := scope.AddOperation(opspec)
- return op.Output(0)
+ return op.Output(0), op.Output(1), op.Output(2)
}
-// Convert the quantized 'input' tensor into a lower-precision 'output', using the
-//
-// output range specified with 'requested_output_min' and 'requested_output_max'.
-//
-// [input_min, input_max] are scalar floats that specify the range for the float
-// interpretation of the 'input' data. For example, if input_min is -1.0f and
-// input_max is 1.0f, and we are dealing with quint16 quantized data, then a 0
-// value in the 16-bit data should be interpreted as -1.0f, and a 65535 means 1.0f.
-//
-// Arguments:
-//
-// input_min: The float value that the minimum quantized input value represents.
-// input_max: The float value that the maximum quantized input value represents.
-// requested_output_min: The float value that the minimum quantized output value represents.
-// requested_output_max: The float value that the maximum quantized output value represents.
-// out_type: The type of the output. Should be a lower bit depth than Tinput.
+// Returns (x - y)(x - y) element-wise.
//
-// Returns The requested_output_min value is copied into this output.The requested_output_max value is copied into this output.
-func Requantize(scope *Scope, input tf.Output, input_min tf.Output, input_max tf.Output, requested_output_min tf.Output, requested_output_max tf.Output, out_type tf.DataType) (output tf.Output, output_min tf.Output, output_max tf.Output) {
+// *NOTE*: `SquaredDifference` supports broadcasting. More about broadcasting
+// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
+func SquaredDifference(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{"out_type": out_type}
opspec := tf.OpSpec{
- Type: "Requantize",
+ Type: "SquaredDifference",
Input: []tf.Input{
- input, input_min, input_max, requested_output_min, requested_output_max,
+ x, y,
},
- Attrs: attrs,
}
op := scope.AddOperation(opspec)
- return op.Output(0), op.Output(1), op.Output(2)
+ return op.Output(0)
}
-// Computes the determinant of one or more square matrices.
+// Forwards the input to the output.
//
-// The input is a tensor of shape `[..., M, M]` whose inner-most 2 dimensions
-// form square matrices. The output is a tensor containing the determinants
-// for all input submatrices `[..., :, :]`.
+// This operator represents the loop termination condition used by the
+// "pivot" switches of a loop.
//
// Arguments:
-// input: Shape is `[..., M, M]`.
+// input: A boolean scalar, representing the branch predicate of the Switch op.
//
-// Returns Shape is `[...]`.
-func MatrixDeterminant(scope *Scope, input tf.Output) (output tf.Output) {
+// Returns The same tensor as `input`.
+func LoopCond(scope *Scope, input tf.Output) (output tf.Output) {
if scope.Err() != nil {
return
}
opspec := tf.OpSpec{
- Type: "MatrixDeterminant",
+ Type: "LoopCond",
Input: []tf.Input{
input,
},
return op.Output(0)
}
-// Computes sin of x element-wise.
-func Sin(scope *Scope, x tf.Output) (y tf.Output) {
+// ApproximateEqualAttr is an optional argument to ApproximateEqual.
+type ApproximateEqualAttr func(optionalAttr)
+
+// ApproximateEqualTolerance sets the optional tolerance attribute to value.
+// If not specified, defaults to 1e-05
+func ApproximateEqualTolerance(value float32) ApproximateEqualAttr {
+ return func(m optionalAttr) {
+ m["tolerance"] = value
+ }
+}
+
+// Returns the truth value of abs(x-y) < tolerance element-wise.
+func ApproximateEqual(scope *Scope, x tf.Output, y tf.Output, optional ...ApproximateEqualAttr) (z tf.Output) {
if scope.Err() != nil {
return
}
+ attrs := map[string]interface{}{}
+ for _, a := range optional {
+ a(attrs)
+ }
opspec := tf.OpSpec{
- Type: "Sin",
+ Type: "ApproximateEqual",
Input: []tf.Input{
- x,
+ x, y,
},
+ Attrs: attrs,
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// Computes the complementary error function of `x` element-wise.
-func Erfc(scope *Scope, x tf.Output) (y tf.Output) {
+// Returns x / y element-wise.
+//
+// *NOTE*: `Div` supports broadcasting. More about broadcasting
+// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
+func Div(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) {
if scope.Err() != nil {
return
}
opspec := tf.OpSpec{
- Type: "Erfc",
+ Type: "Div",
Input: []tf.Input{
- x,
+ x, y,
},
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// Computes Psi, the derivative of Lgamma (the log of the absolute value of
+// Returns x * y element-wise.
//
-// `Gamma(x)`), element-wise.
-func Digamma(scope *Scope, x tf.Output) (y tf.Output) {
+// *NOTE*: `Multiply` supports broadcasting. More about broadcasting
+// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
+func Mul(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) {
if scope.Err() != nil {
return
}
opspec := tf.OpSpec{
- Type: "Digamma",
+ Type: "Mul",
Input: []tf.Input{
- x,
+ x, y,
},
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// Conv2DBackpropFilterAttr is an optional argument to Conv2DBackpropFilter.
-type Conv2DBackpropFilterAttr func(optionalAttr)
-
-// Conv2DBackpropFilterUseCudnnOnGpu sets the optional use_cudnn_on_gpu attribute to value.
-// If not specified, defaults to true
-func Conv2DBackpropFilterUseCudnnOnGpu(value bool) Conv2DBackpropFilterAttr {
- return func(m optionalAttr) {
- m["use_cudnn_on_gpu"] = value
- }
-}
+// BiasAddAttr is an optional argument to BiasAdd.
+type BiasAddAttr func(optionalAttr)
-// Conv2DBackpropFilterDataFormat sets the optional data_format attribute to value.
+// BiasAddDataFormat sets the optional data_format attribute to value.
//
// value: Specify the data format of the input and output data. With the
-// default format "NHWC", the data is stored in the order of:
-// [batch, in_height, in_width, in_channels].
+// default format "NHWC", the bias tensor will be added to the last dimension
+// of the value tensor.
// Alternatively, the format could be "NCHW", the data storage order of:
// [batch, in_channels, in_height, in_width].
+// The tensor will be added to "in_channels", the third-to-the-last
+// dimension.
// If not specified, defaults to "NHWC"
-func Conv2DBackpropFilterDataFormat(value string) Conv2DBackpropFilterAttr {
+func BiasAddDataFormat(value string) BiasAddAttr {
return func(m optionalAttr) {
m["data_format"] = value
}
}
-// Conv2DBackpropFilterDilations sets the optional dilations attribute to value.
+// Adds `bias` to `value`.
//
-// value: 1-D tensor of length 4. The dilation factor for each dimension of
-// `input`. If set to k > 1, there will be k-1 skipped cells between each filter
-// element on that dimension. The dimension order is determined by the value of
-// `data_format`, see above for details. Dilations in the batch and depth
-// dimensions must be 1.
-// If not specified, defaults to <i:1 i:1 i:1 i:1 >
-func Conv2DBackpropFilterDilations(value []int64) Conv2DBackpropFilterAttr {
- return func(m optionalAttr) {
- m["dilations"] = value
- }
-}
-
-// Computes the gradients of convolution with respect to the filter.
+// This is a special case of `tf.add` where `bias` is restricted to be 1-D.
+// Broadcasting is supported, so `value` may have any number of dimensions.
//
// Arguments:
-// input: 4-D with shape `[batch, in_height, in_width, in_channels]`.
-// filter_sizes: An integer vector representing the tensor shape of `filter`,
-// where `filter` is a 4-D
-// `[filter_height, filter_width, in_channels, out_channels]` tensor.
-// out_backprop: 4-D with shape `[batch, out_height, out_width, out_channels]`.
-// Gradients w.r.t. the output of the convolution.
-// strides: The stride of the sliding window for each dimension of the input
-// of the convolution. Must be in the same order as the dimension specified with
-// format.
-// padding: The type of padding algorithm to use.
+// value: Any number of dimensions.
+// bias: 1-D with size the last dimension of `value`.
//
-// Returns 4-D with shape
-// `[filter_height, filter_width, in_channels, out_channels]`. Gradient w.r.t.
-// the `filter` input of the convolution.
-func Conv2DBackpropFilter(scope *Scope, input tf.Output, filter_sizes tf.Output, out_backprop tf.Output, strides []int64, padding string, optional ...Conv2DBackpropFilterAttr) (output tf.Output) {
+// Returns Broadcasted sum of `value` and `bias`.
+func BiasAdd(scope *Scope, value tf.Output, bias tf.Output, optional ...BiasAddAttr) (output tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{"strides": strides, "padding": padding}
+ attrs := map[string]interface{}{}
for _, a := range optional {
a(attrs)
}
opspec := tf.OpSpec{
- Type: "Conv2DBackpropFilter",
+ Type: "BiasAdd",
Input: []tf.Input{
- input, filter_sizes, out_backprop,
+ value, bias,
},
Attrs: attrs,
}
return op.Output(0)
}
-// Returns the number of work units this Reader has finished processing.
+// SparseReduceSumSparseAttr is an optional argument to SparseReduceSumSparse.
+type SparseReduceSumSparseAttr func(optionalAttr)
+
+// SparseReduceSumSparseKeepDims sets the optional keep_dims attribute to value.
+//
+// value: If true, retain reduced dimensions with length 1.
+// If not specified, defaults to false
+func SparseReduceSumSparseKeepDims(value bool) SparseReduceSumSparseAttr {
+ return func(m optionalAttr) {
+ m["keep_dims"] = value
+ }
+}
+
+// Computes the sum of elements across dimensions of a SparseTensor.
+//
+// This Op takes a SparseTensor and is the sparse counterpart to
+// `tf.reduce_sum()`. In contrast to SparseReduceSum, this Op returns a
+// SparseTensor.
+//
+// Reduces `sp_input` along the dimensions given in `reduction_axes`. Unless
+// `keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in
+// `reduction_axes`. If `keep_dims` is true, the reduced dimensions are retained
+// with length 1.
+//
+// If `reduction_axes` has no entries, all dimensions are reduced, and a tensor
+// with a single element is returned. Additionally, the axes can be negative,
+// which are interpreted according to the indexing rules in Python.
//
// Arguments:
-// reader_handle: Handle to a Reader.
-func ReaderNumWorkUnitsCompletedV2(scope *Scope, reader_handle tf.Output) (units_completed tf.Output) {
+// input_indices: 2-D. `N x R` matrix with the indices of non-empty values in a
+// SparseTensor, possibly not in canonical ordering.
+// input_values: 1-D. `N` non-empty values corresponding to `input_indices`.
+// input_shape: 1-D. Shape of the input SparseTensor.
+// reduction_axes: 1-D. Length-`K` vector containing the reduction axes.
+func SparseReduceSumSparse(scope *Scope, input_indices tf.Output, input_values tf.Output, input_shape tf.Output, reduction_axes tf.Output, optional ...SparseReduceSumSparseAttr) (output_indices tf.Output, output_values tf.Output, output_shape tf.Output) {
if scope.Err() != nil {
return
}
+ attrs := map[string]interface{}{}
+ for _, a := range optional {
+ a(attrs)
+ }
opspec := tf.OpSpec{
- Type: "ReaderNumWorkUnitsCompletedV2",
+ Type: "SparseReduceSumSparse",
Input: []tf.Input{
- reader_handle,
+ input_indices, input_values, input_shape, reduction_axes,
},
+ Attrs: attrs,
}
op := scope.AddOperation(opspec)
- return op.Output(0)
+ return op.Output(0), op.Output(1), op.Output(2)
}
-// Computes the log of the absolute value of `Gamma(x)` element-wise.
-func Lgamma(scope *Scope, x tf.Output) (y tf.Output) {
+// Returns x + y element-wise.
+//
+// *NOTE*: `Add` supports broadcasting. `AddN` does not. More about broadcasting
+// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
+func AddV2(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) {
if scope.Err() != nil {
return
}
opspec := tf.OpSpec{
- Type: "Lgamma",
+ Type: "AddV2",
Input: []tf.Input{
- x,
+ x, y,
},
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// Computes the reverse mode backpropagated gradient of the Cholesky algorithm.
-//
-// For an explanation see "Differentiation of the Cholesky algorithm" by
-// Iain Murray http://arxiv.org/abs/1602.07527.
-//
-// Arguments:
-// l: Output of batch Cholesky algorithm l = cholesky(A). Shape is `[..., M, M]`.
-// Algorithm depends only on lower triangular part of the innermost matrices of
-// this tensor.
-// grad: df/dl where f is some scalar function. Shape is `[..., M, M]`.
-// Algorithm depends only on lower triangular part of the innermost matrices of
-// this tensor.
+// Returns x + y element-wise.
//
-// Returns Symmetrized version of df/dA . Shape is `[..., M, M]`
-func CholeskyGrad(scope *Scope, l tf.Output, grad tf.Output) (output tf.Output) {
+// *NOTE*: `Add` supports broadcasting. `AddN` does not. More about broadcasting
+// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
+func Add(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) {
if scope.Err() != nil {
return
}
opspec := tf.OpSpec{
- Type: "CholeskyGrad",
+ Type: "Add",
Input: []tf.Input{
- l, grad,
+ x, y,
},
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// Computes the mean along sparse segments of a tensor.
-//
-// Like `SparseSegmentMean`, but allows missing ids in `segment_ids`. If an id is
-// misisng, the `output` tensor at that position will be zeroed.
+// NthElementAttr is an optional argument to NthElement.
+type NthElementAttr func(optionalAttr)
+
+// NthElementReverse sets the optional reverse attribute to value.
//
-// Read @{$math_ops#segmentation$the section on segmentation} for an explanation of
-// segments.
+// value: When set to True, find the nth-largest value in the vector and vice
+// versa.
+// If not specified, defaults to false
+func NthElementReverse(value bool) NthElementAttr {
+ return func(m optionalAttr) {
+ m["reverse"] = value
+ }
+}
+
+// Finds values of the `n`-th order statistic for the last dimension.
//
-// Arguments:
+// If the input is a vector (rank-1), finds the entries which is the nth-smallest
+// value in the vector and outputs their values as scalar tensor.
//
-// indices: A 1-D tensor. Has same rank as `segment_ids`.
-// segment_ids: A 1-D tensor. Values should be sorted and can be repeated.
-// num_segments: Should equal the number of distinct segment IDs.
+// For matrices (resp. higher rank input), computes the entries which is the
+// nth-smallest value in each row (resp. vector along the last dimension). Thus,
//
-// Returns Has same shape as data, except for dimension 0 which has size
-// `num_segments`.
-func SparseSegmentMeanWithNumSegments(scope *Scope, data tf.Output, indices tf.Output, segment_ids tf.Output, num_segments tf.Output) (output tf.Output) {
+// values.shape = input.shape[:-1]
+//
+// Arguments:
+// input: 1-D or higher with last dimension at least `n+1`.
+// n: 0-D. Position of sorted vector to select along the last dimension (along
+// each row for matrices). Valid range of n is `[0, input.shape[:-1])`
+//
+// Returns The `n`-th order statistic along each last dimensional slice.
+func NthElement(scope *Scope, input tf.Output, n tf.Output, optional ...NthElementAttr) (values tf.Output) {
if scope.Err() != nil {
return
}
+ attrs := map[string]interface{}{}
+ for _, a := range optional {
+ a(attrs)
+ }
opspec := tf.OpSpec{
- Type: "SparseSegmentMeanWithNumSegments",
+ Type: "NthElement",
Input: []tf.Input{
- data, indices, segment_ids, num_segments,
+ input, n,
},
+ Attrs: attrs,
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// Computes hyperbolic cosine of x element-wise.
-func Cosh(scope *Scope, x tf.Output) (y tf.Output) {
+// Computes the Max along segments of a tensor.
+//
+// Read @{$math_ops#segmentation$the section on segmentation} for an explanation of
+// segments.
+//
+// This operator is similar to the [unsorted segment sum operator](../../../api_docs/python/math_ops.md#UnsortedSegmentSum).
+// Instead of computing the sum over segments, it computes the maximum
+// such that:
+//
+// \\(output_i = \max_j data_j\\) where max is over `j` such
+// that `segment_ids[j] == i`.
+//
+// If the maximum is empty for a given segment ID `i`, it outputs the smallest possible value for specific numeric type,
+// `output[i] = numeric_limits<T>::min()`.
+//
+// <div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;">
+// <img style="width:100%" src="https://www.tensorflow.org/images/UnsortedSegmentMax.png" alt>
+// </div>
+//
+// Arguments:
+//
+// segment_ids: A 1-D tensor whose rank is equal to the rank of `data`'s
+// first dimension.
+//
+//
+// Returns Has same shape as data, except for dimension 0 which
+// has size `num_segments`.
+func UnsortedSegmentMax(scope *Scope, data tf.Output, segment_ids tf.Output, num_segments tf.Output) (output tf.Output) {
if scope.Err() != nil {
return
}
opspec := tf.OpSpec{
- Type: "Cosh",
+ Type: "UnsortedSegmentMax",
Input: []tf.Input{
- x,
+ data, segment_ids, num_segments,
},
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// Creates a dataset that emits each dim-0 slice of `components` once.
-func TensorSliceDataset(scope *Scope, components []tf.Output, output_shapes []tf.Shape) (handle tf.Output) {
+// Computes exponential of x element-wise. \\(y = e^x\\).
+func Exp(scope *Scope, x tf.Output) (y tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{"output_shapes": output_shapes}
opspec := tf.OpSpec{
- Type: "TensorSliceDataset",
+ Type: "Exp",
Input: []tf.Input{
- tf.OutputList(components),
+ x,
},
- Attrs: attrs,
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// Computes natural logarithm of (1 + x) element-wise.
+// Returns an element-wise indication of the sign of a number.
//
-// I.e., \\(y = \log_e (1 + x)\\).
-func Log1p(scope *Scope, x tf.Output) (y tf.Output) {
+// `y = sign(x) = -1` if `x < 0`; 0 if `x == 0`; 1 if `x > 0`.
+//
+// For complex numbers, `y = sign(x) = x / |x|` if `x != 0`, otherwise `y = 0`.
+func Sign(scope *Scope, x tf.Output) (y tf.Output) {
if scope.Err() != nil {
return
}
opspec := tf.OpSpec{
- Type: "Log1p",
+ Type: "Sign",
Input: []tf.Input{
x,
},
return op.Output(0)
}
-// Computes rectified linear 6 gradients for a Relu6 operation.
+// ArgMinAttr is an optional argument to ArgMin.
+type ArgMinAttr func(optionalAttr)
+
+// ArgMinOutputType sets the optional output_type attribute to value.
+// If not specified, defaults to DT_INT64
+func ArgMinOutputType(value tf.DataType) ArgMinAttr {
+ return func(m optionalAttr) {
+ m["output_type"] = value
+ }
+}
+
+// Returns the index with the smallest value across dimensions of a tensor.
+//
+// Note that in case of ties the identity of the return value is not guaranteed.
//
// Arguments:
-// gradients: The backpropagated gradients to the corresponding Relu6 operation.
-// features: The features passed as input to the corresponding Relu6 operation, or
-// its output; using either one produces the same result.
//
-// Returns The gradients:
-// `gradients * (features > 0) * (features < 6)`.
-func Relu6Grad(scope *Scope, gradients tf.Output, features tf.Output) (backprops tf.Output) {
+// dimension: int32 or int64, must be in the range `[-rank(input), rank(input))`.
+// Describes which dimension of the input Tensor to reduce across. For vectors,
+// use dimension = 0.
+func ArgMin(scope *Scope, input tf.Output, dimension tf.Output, optional ...ArgMinAttr) (output tf.Output) {
if scope.Err() != nil {
return
}
+ attrs := map[string]interface{}{}
+ for _, a := range optional {
+ a(attrs)
+ }
opspec := tf.OpSpec{
- Type: "Relu6Grad",
+ Type: "ArgMin",
Input: []tf.Input{
- gradients, features,
+ input, dimension,
},
+ Attrs: attrs,
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// ResizeBicubicAttr is an optional argument to ResizeBicubic.
-type ResizeBicubicAttr func(optionalAttr)
-
-// ResizeBicubicAlignCorners sets the optional align_corners attribute to value.
+// Convert the quantized 'input' tensor into a lower-precision 'output', using the
//
-// value: If true, rescale input by (new_height - 1) / (height - 1), which
-// exactly aligns the 4 corners of images and resized images. If false, rescale
-// by new_height / height. Treat similarly the width dimension.
-// If not specified, defaults to false
-func ResizeBicubicAlignCorners(value bool) ResizeBicubicAttr {
- return func(m optionalAttr) {
- m["align_corners"] = value
+// output range specified with 'requested_output_min' and 'requested_output_max'.
+//
+// [input_min, input_max] are scalar floats that specify the range for the float
+// interpretation of the 'input' data. For example, if input_min is -1.0f and
+// input_max is 1.0f, and we are dealing with quint16 quantized data, then a 0
+// value in the 16-bit data should be interpreted as -1.0f, and a 65535 means 1.0f.
+//
+// Arguments:
+//
+// input_min: The float value that the minimum quantized input value represents.
+// input_max: The float value that the maximum quantized input value represents.
+// requested_output_min: The float value that the minimum quantized output value represents.
+// requested_output_max: The float value that the maximum quantized output value represents.
+// out_type: The type of the output. Should be a lower bit depth than Tinput.
+//
+// Returns The requested_output_min value is copied into this output.The requested_output_max value is copied into this output.
+func Requantize(scope *Scope, input tf.Output, input_min tf.Output, input_max tf.Output, requested_output_min tf.Output, requested_output_max tf.Output, out_type tf.DataType) (output tf.Output, output_min tf.Output, output_max tf.Output) {
+ if scope.Err() != nil {
+ return
+ }
+ attrs := map[string]interface{}{"out_type": out_type}
+ opspec := tf.OpSpec{
+ Type: "Requantize",
+ Input: []tf.Input{
+ input, input_min, input_max, requested_output_min, requested_output_max,
+ },
+ Attrs: attrs,
}
+ op := scope.AddOperation(opspec)
+ return op.Output(0), op.Output(1), op.Output(2)
}
-// Resize `images` to `size` using bicubic interpolation.
+// Computes the determinant of one or more square matrices.
//
-// Input images can be of different types but output images are always float.
+// The input is a tensor of shape `[..., M, M]` whose inner-most 2 dimensions
+// form square matrices. The output is a tensor containing the determinants
+// for all input submatrices `[..., :, :]`.
//
// Arguments:
-// images: 4-D with shape `[batch, height, width, channels]`.
-// size: = A 1-D int32 Tensor of 2 elements: `new_height, new_width`. The
-// new size for the images.
+// input: Shape is `[..., M, M]`.
//
-// Returns 4-D with shape
-// `[batch, new_height, new_width, channels]`.
-func ResizeBicubic(scope *Scope, images tf.Output, size tf.Output, optional ...ResizeBicubicAttr) (resized_images tf.Output) {
+// Returns Shape is `[...]`.
+func MatrixDeterminant(scope *Scope, input tf.Output) (output tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{}
- for _, a := range optional {
- a(attrs)
+ opspec := tf.OpSpec{
+ Type: "MatrixDeterminant",
+ Input: []tf.Input{
+ input,
+ },
+ }
+ op := scope.AddOperation(opspec)
+ return op.Output(0)
+}
+
+// Computes sin of x element-wise.
+func Sin(scope *Scope, x tf.Output) (y tf.Output) {
+ if scope.Err() != nil {
+ return
}
opspec := tf.OpSpec{
- Type: "ResizeBicubic",
+ Type: "Sin",
Input: []tf.Input{
- images, size,
+ x,
},
- Attrs: attrs,
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// Computes natural logarithm of x element-wise.
-//
-// I.e., \\(y = \log_e x\\).
-func Log(scope *Scope, x tf.Output) (y tf.Output) {
+// Computes the complementary error function of `x` element-wise.
+func Erfc(scope *Scope, x tf.Output) (y tf.Output) {
if scope.Err() != nil {
return
}
opspec := tf.OpSpec{
- Type: "Log",
+ Type: "Erfc",
Input: []tf.Input{
x,
},
return op.Output(0)
}
-// Rounds the values of a tensor to the nearest integer, element-wise.
+// Computes Psi, the derivative of Lgamma (the log of the absolute value of
//
-// Rounds half to even. Also known as bankers rounding. If you want to round
-// according to the current system rounding mode use std::cint.
-func Round(scope *Scope, x tf.Output) (y tf.Output) {
+// `Gamma(x)`), element-wise.
+func Digamma(scope *Scope, x tf.Output) (y tf.Output) {
if scope.Err() != nil {
return
}
opspec := tf.OpSpec{
- Type: "Round",
+ Type: "Digamma",
Input: []tf.Input{
x,
},
return op.Output(0)
}
-// RecordInputAttr is an optional argument to RecordInput.
-type RecordInputAttr func(optionalAttr)
+// Conv2DBackpropFilterAttr is an optional argument to Conv2DBackpropFilter.
+type Conv2DBackpropFilterAttr func(optionalAttr)
-// RecordInputFileRandomSeed sets the optional file_random_seed attribute to value.
-//
-// value: Random seeds used to produce randomized records.
-// If not specified, defaults to 301
-func RecordInputFileRandomSeed(value int64) RecordInputAttr {
+// Conv2DBackpropFilterUseCudnnOnGpu sets the optional use_cudnn_on_gpu attribute to value.
+// If not specified, defaults to true
+func Conv2DBackpropFilterUseCudnnOnGpu(value bool) Conv2DBackpropFilterAttr {
return func(m optionalAttr) {
- m["file_random_seed"] = value
+ m["use_cudnn_on_gpu"] = value
}
}
-// RecordInputFileShuffleShiftRatio sets the optional file_shuffle_shift_ratio attribute to value.
+// Conv2DBackpropFilterDataFormat sets the optional data_format attribute to value.
//
-// value: Shifts the list of files after the list is randomly
-// shuffled.
-// If not specified, defaults to 0
-func RecordInputFileShuffleShiftRatio(value float32) RecordInputAttr {
+// value: Specify the data format of the input and output data. With the
+// default format "NHWC", the data is stored in the order of:
+// [batch, in_height, in_width, in_channels].
+// Alternatively, the format could be "NCHW", the data storage order of:
+// [batch, in_channels, in_height, in_width].
+// If not specified, defaults to "NHWC"
+func Conv2DBackpropFilterDataFormat(value string) Conv2DBackpropFilterAttr {
return func(m optionalAttr) {
- m["file_shuffle_shift_ratio"] = value
+ m["data_format"] = value
}
}
-// RecordInputFileBufferSize sets the optional file_buffer_size attribute to value.
+// Conv2DBackpropFilterDilations sets the optional dilations attribute to value.
//
-// value: The randomization shuffling buffer.
-// If not specified, defaults to 10000
-func RecordInputFileBufferSize(value int64) RecordInputAttr {
+// value: 1-D tensor of length 4. The dilation factor for each dimension of
+// `input`. If set to k > 1, there will be k-1 skipped cells between each filter
+// element on that dimension. The dimension order is determined by the value of
+// `data_format`, see above for details. Dilations in the batch and depth
+// dimensions must be 1.
+// If not specified, defaults to <i:1 i:1 i:1 i:1 >
+func Conv2DBackpropFilterDilations(value []int64) Conv2DBackpropFilterAttr {
return func(m optionalAttr) {
- m["file_buffer_size"] = value
+ m["dilations"] = value
}
}
-// RecordInputFileParallelism sets the optional file_parallelism attribute to value.
-//
-// value: How many sstables are opened and concurrently iterated over.
-// If not specified, defaults to 16
-func RecordInputFileParallelism(value int64) RecordInputAttr {
- return func(m optionalAttr) {
- m["file_parallelism"] = value
- }
-}
-
-// RecordInputBatchSize sets the optional batch_size attribute to value.
-//
-// value: The batch size.
-// If not specified, defaults to 32
-func RecordInputBatchSize(value int64) RecordInputAttr {
- return func(m optionalAttr) {
- m["batch_size"] = value
- }
-}
-
-// RecordInputCompressionType sets the optional compression_type attribute to value.
-//
-// value: The type of compression for the file. Currently ZLIB and
-// GZIP are supported. Defaults to none.
-// If not specified, defaults to ""
-func RecordInputCompressionType(value string) RecordInputAttr {
- return func(m optionalAttr) {
- m["compression_type"] = value
- }
-}
-
-// Emits randomized records.
+// Computes the gradients of convolution with respect to the filter.
//
// Arguments:
-// file_pattern: Glob pattern for the data files.
+// input: 4-D with shape `[batch, in_height, in_width, in_channels]`.
+// filter_sizes: An integer vector representing the tensor shape of `filter`,
+// where `filter` is a 4-D
+// `[filter_height, filter_width, in_channels, out_channels]` tensor.
+// out_backprop: 4-D with shape `[batch, out_height, out_width, out_channels]`.
+// Gradients w.r.t. the output of the convolution.
+// strides: The stride of the sliding window for each dimension of the input
+// of the convolution. Must be in the same order as the dimension specified with
+// format.
+// padding: The type of padding algorithm to use.
//
-// Returns A tensor of shape [batch_size].
-func RecordInput(scope *Scope, file_pattern string, optional ...RecordInputAttr) (records tf.Output) {
+// Returns 4-D with shape
+// `[filter_height, filter_width, in_channels, out_channels]`. Gradient w.r.t.
+// the `filter` input of the convolution.
+func Conv2DBackpropFilter(scope *Scope, input tf.Output, filter_sizes tf.Output, out_backprop tf.Output, strides []int64, padding string, optional ...Conv2DBackpropFilterAttr) (output tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{"file_pattern": file_pattern}
+ attrs := map[string]interface{}{"strides": strides, "padding": padding}
for _, a := range optional {
a(attrs)
}
opspec := tf.OpSpec{
- Type: "RecordInput",
-
+ Type: "Conv2DBackpropFilter",
+ Input: []tf.Input{
+ input, filter_sizes, out_backprop,
+ },
Attrs: attrs,
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// Computes reciprocal of square root of x element-wise.
+// Returns the number of work units this Reader has finished processing.
//
-// I.e., \\(y = 1 / \sqrt{x}\\).
-func Rsqrt(scope *Scope, x tf.Output) (y tf.Output) {
+// Arguments:
+// reader_handle: Handle to a Reader.
+func ReaderNumWorkUnitsCompletedV2(scope *Scope, reader_handle tf.Output) (units_completed tf.Output) {
if scope.Err() != nil {
return
}
opspec := tf.OpSpec{
- Type: "Rsqrt",
+ Type: "ReaderNumWorkUnitsCompletedV2",
Input: []tf.Input{
- x,
+ reader_handle,
},
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// MatrixInverseAttr is an optional argument to MatrixInverse.
-type MatrixInverseAttr func(optionalAttr)
-
-// MatrixInverseAdjoint sets the optional adjoint attribute to value.
-// If not specified, defaults to false
-func MatrixInverseAdjoint(value bool) MatrixInverseAttr {
- return func(m optionalAttr) {
- m["adjoint"] = value
+// Computes the log of the absolute value of `Gamma(x)` element-wise.
+func Lgamma(scope *Scope, x tf.Output) (y tf.Output) {
+ if scope.Err() != nil {
+ return
}
+ opspec := tf.OpSpec{
+ Type: "Lgamma",
+ Input: []tf.Input{
+ x,
+ },
+ }
+ op := scope.AddOperation(opspec)
+ return op.Output(0)
}
-// Computes the inverse of one or more square invertible matrices or their
+// Computes the reverse mode backpropagated gradient of the Cholesky algorithm.
//
-// adjoints (conjugate transposes).
+// For an explanation see "Differentiation of the Cholesky algorithm" by
+// Iain Murray http://arxiv.org/abs/1602.07527.
//
-// The input is a tensor of shape `[..., M, M]` whose inner-most 2 dimensions
-// form square matrices. The output is a tensor of the same shape as the input
-// containing the inverse for all input submatrices `[..., :, :]`.
+// Arguments:
+// l: Output of batch Cholesky algorithm l = cholesky(A). Shape is `[..., M, M]`.
+// Algorithm depends only on lower triangular part of the innermost matrices of
+// this tensor.
+// grad: df/dl where f is some scalar function. Shape is `[..., M, M]`.
+// Algorithm depends only on lower triangular part of the innermost matrices of
+// this tensor.
//
-// The op uses LU decomposition with partial pivoting to compute the inverses.
+// Returns Symmetrized version of df/dA . Shape is `[..., M, M]`
+func CholeskyGrad(scope *Scope, l tf.Output, grad tf.Output) (output tf.Output) {
+ if scope.Err() != nil {
+ return
+ }
+ opspec := tf.OpSpec{
+ Type: "CholeskyGrad",
+ Input: []tf.Input{
+ l, grad,
+ },
+ }
+ op := scope.AddOperation(opspec)
+ return op.Output(0)
+}
+
+// Computes the mean along sparse segments of a tensor.
//
-// If a matrix is not invertible there is no guarantee what the op does. It
-// may detect the condition and raise an exception or it may simply return a
-// garbage result.
+// Like `SparseSegmentMean`, but allows missing ids in `segment_ids`. If an id is
+// misisng, the `output` tensor at that position will be zeroed.
+//
+// Read @{$math_ops#segmentation$the section on segmentation} for an explanation of
+// segments.
//
// Arguments:
-// input: Shape is `[..., M, M]`.
//
-// Returns Shape is `[..., M, M]`.
+// indices: A 1-D tensor. Has same rank as `segment_ids`.
+// segment_ids: A 1-D tensor. Values should be sorted and can be repeated.
+// num_segments: Should equal the number of distinct segment IDs.
//
-// @compatibility(numpy)
-// Equivalent to np.linalg.inv
-// @end_compatibility
-func MatrixInverse(scope *Scope, input tf.Output, optional ...MatrixInverseAttr) (output tf.Output) {
+// Returns Has same shape as data, except for dimension 0 which has size
+// `num_segments`.
+func SparseSegmentMeanWithNumSegments(scope *Scope, data tf.Output, indices tf.Output, segment_ids tf.Output, num_segments tf.Output) (output tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{}
- for _, a := range optional {
- a(attrs)
- }
opspec := tf.OpSpec{
- Type: "MatrixInverse",
+ Type: "SparseSegmentMeanWithNumSegments",
Input: []tf.Input{
- input,
+ data, indices, segment_ids, num_segments,
},
- Attrs: attrs,
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// Computes square of x element-wise.
-//
-// I.e., \\(y = x * x = x^2\\).
-func Square(scope *Scope, x tf.Output) (y tf.Output) {
+// Computes hyperbolic cosine of x element-wise.
+func Cosh(scope *Scope, x tf.Output) (y tf.Output) {
if scope.Err() != nil {
return
}
opspec := tf.OpSpec{
- Type: "Square",
+ Type: "Cosh",
Input: []tf.Input{
x,
},
return op.Output(0)
}
-// Computes exponential linear: `exp(features) - 1` if < 0, `features` otherwise.
-//
-// See [Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)
-// ](http://arxiv.org/abs/1511.07289)
-func Elu(scope *Scope, features tf.Output) (activations tf.Output) {
+// Creates a dataset that emits each dim-0 slice of `components` once.
+func TensorSliceDataset(scope *Scope, components []tf.Output, output_shapes []tf.Shape) (handle tf.Output) {
if scope.Err() != nil {
return
}
+ attrs := map[string]interface{}{"output_shapes": output_shapes}
opspec := tf.OpSpec{
- Type: "Elu",
+ Type: "TensorSliceDataset",
Input: []tf.Input{
- features,
+ tf.OutputList(components),
},
+ Attrs: attrs,
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// Computes the reciprocal of x element-wise.
+// Computes natural logarithm of (1 + x) element-wise.
//
-// I.e., \\(y = 1 / x\\).
-func Reciprocal(scope *Scope, x tf.Output) (y tf.Output) {
+// I.e., \\(y = \log_e (1 + x)\\).
+func Log1p(scope *Scope, x tf.Output) (y tf.Output) {
if scope.Err() != nil {
return
}
opspec := tf.OpSpec{
- Type: "Reciprocal",
+ Type: "Log1p",
Input: []tf.Input{
x,
},
return op.Output(0)
}
-// OrderedMapClearAttr is an optional argument to OrderedMapClear.
-type OrderedMapClearAttr func(optionalAttr)
-
-// OrderedMapClearCapacity sets the optional capacity attribute to value.
-// If not specified, defaults to 0
+// Computes rectified linear 6 gradients for a Relu6 operation.
//
-// REQUIRES: value >= 0
-func OrderedMapClearCapacity(value int64) OrderedMapClearAttr {
- return func(m optionalAttr) {
- m["capacity"] = value
- }
-}
-
-// OrderedMapClearMemoryLimit sets the optional memory_limit attribute to value.
-// If not specified, defaults to 0
+// Arguments:
+// gradients: The backpropagated gradients to the corresponding Relu6 operation.
+// features: The features passed as input to the corresponding Relu6 operation, or
+// its output; using either one produces the same result.
//
-// REQUIRES: value >= 0
-func OrderedMapClearMemoryLimit(value int64) OrderedMapClearAttr {
- return func(m optionalAttr) {
- m["memory_limit"] = value
+// Returns The gradients:
+// `gradients * (features > 0) * (features < 6)`.
+func Relu6Grad(scope *Scope, gradients tf.Output, features tf.Output) (backprops tf.Output) {
+ if scope.Err() != nil {
+ return
}
-}
-
-// OrderedMapClearContainer sets the optional container attribute to value.
-// If not specified, defaults to ""
-func OrderedMapClearContainer(value string) OrderedMapClearAttr {
- return func(m optionalAttr) {
- m["container"] = value
+ opspec := tf.OpSpec{
+ Type: "Relu6Grad",
+ Input: []tf.Input{
+ gradients, features,
+ },
}
+ op := scope.AddOperation(opspec)
+ return op.Output(0)
}
-// OrderedMapClearSharedName sets the optional shared_name attribute to value.
-// If not specified, defaults to ""
-func OrderedMapClearSharedName(value string) OrderedMapClearAttr {
+// ResizeBicubicAttr is an optional argument to ResizeBicubic.
+type ResizeBicubicAttr func(optionalAttr)
+
+// ResizeBicubicAlignCorners sets the optional align_corners attribute to value.
+//
+// value: If true, rescale input by (new_height - 1) / (height - 1), which
+// exactly aligns the 4 corners of images and resized images. If false, rescale
+// by new_height / height. Treat similarly the width dimension.
+// If not specified, defaults to false
+func ResizeBicubicAlignCorners(value bool) ResizeBicubicAttr {
return func(m optionalAttr) {
- m["shared_name"] = value
+ m["align_corners"] = value
}
}
-// Op removes all elements in the underlying container.
+// Resize `images` to `size` using bicubic interpolation.
//
-// Returns the created operation.
-func OrderedMapClear(scope *Scope, dtypes []tf.DataType, optional ...OrderedMapClearAttr) (o *tf.Operation) {
+// Input images can be of different types but output images are always float.
+//
+// Arguments:
+// images: 4-D with shape `[batch, height, width, channels]`.
+// size: = A 1-D int32 Tensor of 2 elements: `new_height, new_width`. The
+// new size for the images.
+//
+// Returns 4-D with shape
+// `[batch, new_height, new_width, channels]`.
+func ResizeBicubic(scope *Scope, images tf.Output, size tf.Output, optional ...ResizeBicubicAttr) (resized_images tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{"dtypes": dtypes}
+ attrs := map[string]interface{}{}
for _, a := range optional {
a(attrs)
}
opspec := tf.OpSpec{
- Type: "OrderedMapClear",
-
- Attrs: attrs,
- }
- return scope.AddOperation(opspec)
-}
-
-// Computes the reciprocal of x element-wise.
-//
-// I.e., \\(y = 1 / x\\).
-func Inv(scope *Scope, x tf.Output) (y tf.Output) {
- if scope.Err() != nil {
- return
- }
- opspec := tf.OpSpec{
- Type: "Inv",
+ Type: "ResizeBicubic",
Input: []tf.Input{
- x,
+ images, size,
},
+ Attrs: attrs,
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// ComplexAbsAttr is an optional argument to ComplexAbs.
-type ComplexAbsAttr func(optionalAttr)
-
-// ComplexAbsTout sets the optional Tout attribute to value.
-// If not specified, defaults to DT_FLOAT
-func ComplexAbsTout(value tf.DataType) ComplexAbsAttr {
- return func(m optionalAttr) {
- m["Tout"] = value
- }
-}
-
-// Computes the complex absolute value of a tensor.
+// Computes natural logarithm of x element-wise.
//
-// Given a tensor `x` of complex numbers, this operation returns a tensor of type
-// `float` or `double` that is the absolute value of each element in `x`. All
-// elements in `x` must be complex numbers of the form \\(a + bj\\). The absolute
-// value is computed as \\( \sqrt{a^2 + b^2}\\).
-func ComplexAbs(scope *Scope, x tf.Output, optional ...ComplexAbsAttr) (y tf.Output) {
+// I.e., \\(y = \log_e x\\).
+func Log(scope *Scope, x tf.Output) (y tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{}
- for _, a := range optional {
- a(attrs)
- }
opspec := tf.OpSpec{
- Type: "ComplexAbs",
+ Type: "Log",
Input: []tf.Input{
x,
},
- Attrs: attrs,
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// Returns the truth value of x AND y element-wise.
+// Rounds the values of a tensor to the nearest integer, element-wise.
//
-// *NOTE*: `LogicalAnd` supports broadcasting. More about broadcasting
-// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
-func LogicalAnd(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) {
+// Rounds half to even. Also known as bankers rounding. If you want to round
+// according to the current system rounding mode use std::cint.
+func Round(scope *Scope, x tf.Output) (y tf.Output) {
if scope.Err() != nil {
return
}
opspec := tf.OpSpec{
- Type: "LogicalAnd",
+ Type: "Round",
Input: []tf.Input{
- x, y,
+ x,
},
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// Cast x of type SrcT to y of DstT.
-func Cast(scope *Scope, x tf.Output, DstT tf.DataType) (y tf.Output) {
- if scope.Err() != nil {
- return
+// RecordInputAttr is an optional argument to RecordInput.
+type RecordInputAttr func(optionalAttr)
+
+// RecordInputFileRandomSeed sets the optional file_random_seed attribute to value.
+//
+// value: Random seeds used to produce randomized records.
+// If not specified, defaults to 301
+func RecordInputFileRandomSeed(value int64) RecordInputAttr {
+ return func(m optionalAttr) {
+ m["file_random_seed"] = value
}
- attrs := map[string]interface{}{"DstT": DstT}
- opspec := tf.OpSpec{
- Type: "Cast",
- Input: []tf.Input{
- x,
- },
- Attrs: attrs,
+}
+
+// RecordInputFileShuffleShiftRatio sets the optional file_shuffle_shift_ratio attribute to value.
+//
+// value: Shifts the list of files after the list is randomly
+// shuffled.
+// If not specified, defaults to 0
+func RecordInputFileShuffleShiftRatio(value float32) RecordInputAttr {
+ return func(m optionalAttr) {
+ m["file_shuffle_shift_ratio"] = value
}
- op := scope.AddOperation(opspec)
- return op.Output(0)
}
-// MaxAttr is an optional argument to Max.
-type MaxAttr func(optionalAttr)
+// RecordInputFileBufferSize sets the optional file_buffer_size attribute to value.
+//
+// value: The randomization shuffling buffer.
+// If not specified, defaults to 10000
+func RecordInputFileBufferSize(value int64) RecordInputAttr {
+ return func(m optionalAttr) {
+ m["file_buffer_size"] = value
+ }
+}
-// MaxKeepDims sets the optional keep_dims attribute to value.
+// RecordInputFileParallelism sets the optional file_parallelism attribute to value.
//
-// value: If true, retain reduced dimensions with length 1.
-// If not specified, defaults to false
-func MaxKeepDims(value bool) MaxAttr {
+// value: How many sstables are opened and concurrently iterated over.
+// If not specified, defaults to 16
+func RecordInputFileParallelism(value int64) RecordInputAttr {
return func(m optionalAttr) {
- m["keep_dims"] = value
+ m["file_parallelism"] = value
}
}
-// Computes the maximum of elements across dimensions of a tensor.
+// RecordInputBatchSize sets the optional batch_size attribute to value.
//
-// Reduces `input` along the dimensions given in `axis`. Unless
-// `keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in
-// `axis`. If `keep_dims` is true, the reduced dimensions are
-// retained with length 1.
+// value: The batch size.
+// If not specified, defaults to 32
+func RecordInputBatchSize(value int64) RecordInputAttr {
+ return func(m optionalAttr) {
+ m["batch_size"] = value
+ }
+}
+
+// RecordInputCompressionType sets the optional compression_type attribute to value.
+//
+// value: The type of compression for the file. Currently ZLIB and
+// GZIP are supported. Defaults to none.
+// If not specified, defaults to ""
+func RecordInputCompressionType(value string) RecordInputAttr {
+ return func(m optionalAttr) {
+ m["compression_type"] = value
+ }
+}
+
+// Emits randomized records.
//
// Arguments:
-// input: The tensor to reduce.
-// axis: The dimensions to reduce. Must be in the range
-// `[-rank(input), rank(input))`.
+// file_pattern: Glob pattern for the data files.
//
-// Returns The reduced tensor.
-func Max(scope *Scope, input tf.Output, axis tf.Output, optional ...MaxAttr) (output tf.Output) {
+// Returns A tensor of shape [batch_size].
+func RecordInput(scope *Scope, file_pattern string, optional ...RecordInputAttr) (records tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{}
+ attrs := map[string]interface{}{"file_pattern": file_pattern}
for _, a := range optional {
a(attrs)
}
opspec := tf.OpSpec{
- Type: "Max",
- Input: []tf.Input{
- input, axis,
- },
+ Type: "RecordInput",
+
Attrs: attrs,
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// Quantized Batch normalization.
-//
-// This op is deprecated and will be removed in the future. Prefer
-// `tf.nn.batch_normalization`.
-//
-// Arguments:
-// t: A 4D input Tensor.
-// t_min: The value represented by the lowest quantized input.
-// t_max: The value represented by the highest quantized input.
-// m: A 1D mean Tensor with size matching the last dimension of t.
-// This is the first output from tf.nn.moments,
-// or a saved moving average thereof.
-// m_min: The value represented by the lowest quantized mean.
-// m_max: The value represented by the highest quantized mean.
-// v: A 1D variance Tensor with size matching the last dimension of t.
-// This is the second output from tf.nn.moments,
-// or a saved moving average thereof.
-// v_min: The value represented by the lowest quantized variance.
-// v_max: The value represented by the highest quantized variance.
-// beta: A 1D beta Tensor with size matching the last dimension of t.
-// An offset to be added to the normalized tensor.
-// beta_min: The value represented by the lowest quantized offset.
-// beta_max: The value represented by the highest quantized offset.
-// gamma: A 1D gamma Tensor with size matching the last dimension of t.
-// If "scale_after_normalization" is true, this tensor will be multiplied
-// with the normalized tensor.
-// gamma_min: The value represented by the lowest quantized gamma.
-// gamma_max: The value represented by the highest quantized gamma.
+// Computes reciprocal of square root of x element-wise.
//
-// variance_epsilon: A small float number to avoid dividing by 0.
-// scale_after_normalization: A bool indicating whether the resulted tensor
-// needs to be multiplied with gamma.
-func QuantizedBatchNormWithGlobalNormalization(scope *Scope, t tf.Output, t_min tf.Output, t_max tf.Output, m tf.Output, m_min tf.Output, m_max tf.Output, v tf.Output, v_min tf.Output, v_max tf.Output, beta tf.Output, beta_min tf.Output, beta_max tf.Output, gamma tf.Output, gamma_min tf.Output, gamma_max tf.Output, out_type tf.DataType, variance_epsilon float32, scale_after_normalization bool) (result tf.Output, result_min tf.Output, result_max tf.Output) {
+// I.e., \\(y = 1 / \sqrt{x}\\).
+func Rsqrt(scope *Scope, x tf.Output) (y tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{"out_type": out_type, "variance_epsilon": variance_epsilon, "scale_after_normalization": scale_after_normalization}
opspec := tf.OpSpec{
- Type: "QuantizedBatchNormWithGlobalNormalization",
+ Type: "Rsqrt",
Input: []tf.Input{
- t, t_min, t_max, m, m_min, m_max, v, v_min, v_max, beta, beta_min, beta_max, gamma, gamma_min, gamma_max,
+ x,
},
- Attrs: attrs,
}
op := scope.AddOperation(opspec)
- return op.Output(0), op.Output(1), op.Output(2)
+ return op.Output(0)
}
-// HistogramFixedWidthAttr is an optional argument to HistogramFixedWidth.
-type HistogramFixedWidthAttr func(optionalAttr)
+// MatrixInverseAttr is an optional argument to MatrixInverse.
+type MatrixInverseAttr func(optionalAttr)
-// HistogramFixedWidthDtype sets the optional dtype attribute to value.
-// If not specified, defaults to DT_INT32
-func HistogramFixedWidthDtype(value tf.DataType) HistogramFixedWidthAttr {
+// MatrixInverseAdjoint sets the optional adjoint attribute to value.
+// If not specified, defaults to false
+func MatrixInverseAdjoint(value bool) MatrixInverseAttr {
return func(m optionalAttr) {
- m["dtype"] = value
+ m["adjoint"] = value
}
}
-// Return histogram of values.
+// Computes the inverse of one or more square invertible matrices or their
//
-// Given the tensor `values`, this operation returns a rank 1 histogram counting
-// the number of entries in `values` that fall into every bin. The bins are
-// equal width and determined by the arguments `value_range` and `nbins`.
+// adjoints (conjugate transposes).
//
-// ```python
-// # Bins will be: (-inf, 1), [1, 2), [2, 3), [3, 4), [4, inf)
-// nbins = 5
-// value_range = [0.0, 5.0]
-// new_values = [-1.0, 0.0, 1.5, 2.0, 5.0, 15]
+// The input is a tensor of shape `[..., M, M]` whose inner-most 2 dimensions
+// form square matrices. The output is a tensor of the same shape as the input
+// containing the inverse for all input submatrices `[..., :, :]`.
//
-// with tf.get_default_session() as sess:
-// hist = tf.histogram_fixed_width(new_values, value_range, nbins=5)
-// variables.global_variables_initializer().run()
-// sess.run(hist) => [2, 1, 1, 0, 2]
-// ```
+// The op uses LU decomposition with partial pivoting to compute the inverses.
+//
+// If a matrix is not invertible there is no guarantee what the op does. It
+// may detect the condition and raise an exception or it may simply return a
+// garbage result.
//
// Arguments:
-// values: Numeric `Tensor`.
-// value_range: Shape [2] `Tensor` of same `dtype` as `values`.
-// values <= value_range[0] will be mapped to hist[0],
-// values >= value_range[1] will be mapped to hist[-1].
-// nbins: Scalar `int32 Tensor`. Number of histogram bins.
+// input: Shape is `[..., M, M]`.
//
-// Returns A 1-D `Tensor` holding histogram of values.
-func HistogramFixedWidth(scope *Scope, values tf.Output, value_range tf.Output, nbins tf.Output, optional ...HistogramFixedWidthAttr) (out tf.Output) {
+// Returns Shape is `[..., M, M]`.
+//
+// @compatibility(numpy)
+// Equivalent to np.linalg.inv
+// @end_compatibility
+func MatrixInverse(scope *Scope, input tf.Output, optional ...MatrixInverseAttr) (output tf.Output) {
if scope.Err() != nil {
return
}
a(attrs)
}
opspec := tf.OpSpec{
- Type: "HistogramFixedWidth",
+ Type: "MatrixInverse",
Input: []tf.Input{
- values, value_range, nbins,
+ input,
},
Attrs: attrs,
}
return op.Output(0)
}
-// Adds Tensor 'bias' to Tensor 'input' for Quantized types.
-//
-// Broadcasts the values of bias on dimensions 0..N-2 of 'input'.
-//
-// Arguments:
+// Computes square of x element-wise.
//
-// bias: A 1D bias Tensor with size matching the last dimension of 'input'.
-// min_input: The float value that the lowest quantized input value represents.
-// max_input: The float value that the highest quantized input value represents.
-// min_bias: The float value that the lowest quantized bias value represents.
-// max_bias: The float value that the highest quantized bias value represents.
-//
-//
-// Returns The float value that the lowest quantized output value represents.The float value that the highest quantized output value represents.
-func QuantizedBiasAdd(scope *Scope, input tf.Output, bias tf.Output, min_input tf.Output, max_input tf.Output, min_bias tf.Output, max_bias tf.Output, out_type tf.DataType) (output tf.Output, min_out tf.Output, max_out tf.Output) {
+// I.e., \\(y = x * x = x^2\\).
+func Square(scope *Scope, x tf.Output) (y tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{"out_type": out_type}
opspec := tf.OpSpec{
- Type: "QuantizedBiasAdd",
+ Type: "Square",
Input: []tf.Input{
- input, bias, min_input, max_input, min_bias, max_bias,
+ x,
},
- Attrs: attrs,
}
op := scope.AddOperation(opspec)
- return op.Output(0), op.Output(1), op.Output(2)
+ return op.Output(0)
}
-// Produces the average pool of the input tensor for quantized types.
-//
-// Arguments:
-// input: 4-D with shape `[batch, height, width, channels]`.
-// min_input: The float value that the lowest quantized input value represents.
-// max_input: The float value that the highest quantized input value represents.
-// ksize: The size of the window for each dimension of the input tensor.
-// The length must be 4 to match the number of dimensions of the input.
-// strides: The stride of the sliding window for each dimension of the input
-// tensor. The length must be 4 to match the number of dimensions of the input.
-// padding: The type of padding algorithm to use.
+// Computes exponential linear: `exp(features) - 1` if < 0, `features` otherwise.
//
-// Returns The float value that the lowest quantized output value represents.The float value that the highest quantized output value represents.
-func QuantizedAvgPool(scope *Scope, input tf.Output, min_input tf.Output, max_input tf.Output, ksize []int64, strides []int64, padding string) (output tf.Output, min_output tf.Output, max_output tf.Output) {
+// See [Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)
+// ](http://arxiv.org/abs/1511.07289)
+func Elu(scope *Scope, features tf.Output) (activations tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{"ksize": ksize, "strides": strides, "padding": padding}
opspec := tf.OpSpec{
- Type: "QuantizedAvgPool",
+ Type: "Elu",
Input: []tf.Input{
- input, min_input, max_input,
+ features,
},
- Attrs: attrs,
}
op := scope.AddOperation(opspec)
- return op.Output(0), op.Output(1), op.Output(2)
+ return op.Output(0)
}
-// FractionalAvgPoolAttr is an optional argument to FractionalAvgPool.
-type FractionalAvgPoolAttr func(optionalAttr)
-
-// FractionalAvgPoolPseudoRandom sets the optional pseudo_random attribute to value.
+// Computes the reciprocal of x element-wise.
//
-// value: When set to True, generates the pooling sequence in a
-// pseudorandom fashion, otherwise, in a random fashion. Check paper [Benjamin
-// Graham, Fractional Max-Pooling](http://arxiv.org/abs/1412.6071) for
-// difference between pseudorandom and random.
-// If not specified, defaults to false
-func FractionalAvgPoolPseudoRandom(value bool) FractionalAvgPoolAttr {
- return func(m optionalAttr) {
- m["pseudo_random"] = value
+// I.e., \\(y = 1 / x\\).
+func Reciprocal(scope *Scope, x tf.Output) (y tf.Output) {
+ if scope.Err() != nil {
+ return
+ }
+ opspec := tf.OpSpec{
+ Type: "Reciprocal",
+ Input: []tf.Input{
+ x,
+ },
}
+ op := scope.AddOperation(opspec)
+ return op.Output(0)
}
-// FractionalAvgPoolOverlapping sets the optional overlapping attribute to value.
-//
-// value: When set to True, it means when pooling, the values at the boundary
-// of adjacent pooling cells are used by both cells. For example:
-//
-// `index 0 1 2 3 4`
-//
-// `value 20 5 16 3 7`
+// OrderedMapClearAttr is an optional argument to OrderedMapClear.
+type OrderedMapClearAttr func(optionalAttr)
+
+// OrderedMapClearCapacity sets the optional capacity attribute to value.
+// If not specified, defaults to 0
//
-// If the pooling sequence is [0, 2, 4], then 16, at index 2 will be used twice.
-// The result would be [41/3, 26/3] for fractional avg pooling.
-// If not specified, defaults to false
-func FractionalAvgPoolOverlapping(value bool) FractionalAvgPoolAttr {
+// REQUIRES: value >= 0
+func OrderedMapClearCapacity(value int64) OrderedMapClearAttr {
return func(m optionalAttr) {
- m["overlapping"] = value
+ m["capacity"] = value
}
}
-// FractionalAvgPoolDeterministic sets the optional deterministic attribute to value.
+// OrderedMapClearMemoryLimit sets the optional memory_limit attribute to value.
+// If not specified, defaults to 0
//
-// value: When set to True, a fixed pooling region will be used when
-// iterating over a FractionalAvgPool node in the computation graph. Mainly used
-// in unit test to make FractionalAvgPool deterministic.
-// If not specified, defaults to false
-func FractionalAvgPoolDeterministic(value bool) FractionalAvgPoolAttr {
+// REQUIRES: value >= 0
+func OrderedMapClearMemoryLimit(value int64) OrderedMapClearAttr {
return func(m optionalAttr) {
- m["deterministic"] = value
+ m["memory_limit"] = value
}
}
-// FractionalAvgPoolSeed sets the optional seed attribute to value.
-//
-// value: If either seed or seed2 are set to be non-zero, the random number
-// generator is seeded by the given seed. Otherwise, it is seeded by a
-// random seed.
-// If not specified, defaults to 0
-func FractionalAvgPoolSeed(value int64) FractionalAvgPoolAttr {
+// OrderedMapClearContainer sets the optional container attribute to value.
+// If not specified, defaults to ""
+func OrderedMapClearContainer(value string) OrderedMapClearAttr {
return func(m optionalAttr) {
- m["seed"] = value
+ m["container"] = value
}
}
-// FractionalAvgPoolSeed2 sets the optional seed2 attribute to value.
-//
-// value: An second seed to avoid seed collision.
-// If not specified, defaults to 0
-func FractionalAvgPoolSeed2(value int64) FractionalAvgPoolAttr {
+// OrderedMapClearSharedName sets the optional shared_name attribute to value.
+// If not specified, defaults to ""
+func OrderedMapClearSharedName(value string) OrderedMapClearAttr {
return func(m optionalAttr) {
- m["seed2"] = value
+ m["shared_name"] = value
}
}
-// Performs fractional average pooling on the input.
-//
-// Fractional average pooling is similar to Fractional max pooling in the pooling
-// region generation step. The only difference is that after pooling regions are
-// generated, a mean operation is performed instead of a max operation in each
-// pooling region.
-//
-// Arguments:
-// value: 4-D with shape `[batch, height, width, channels]`.
-// pooling_ratio: Pooling ratio for each dimension of `value`, currently only
-// supports row and col dimension and should be >= 1.0. For example, a valid
-// pooling ratio looks like [1.0, 1.44, 1.73, 1.0]. The first and last elements
-// must be 1.0 because we don't allow pooling on batch and channels
-// dimensions. 1.44 and 1.73 are pooling ratio on height and width dimensions
-// respectively.
+// Op removes all elements in the underlying container.
//
-// Returns output tensor after fractional avg pooling.row pooling sequence, needed to calculate gradient.column pooling sequence, needed to calculate gradient.
-func FractionalAvgPool(scope *Scope, value tf.Output, pooling_ratio []float32, optional ...FractionalAvgPoolAttr) (output tf.Output, row_pooling_sequence tf.Output, col_pooling_sequence tf.Output) {
+// Returns the created operation.
+func OrderedMapClear(scope *Scope, dtypes []tf.DataType, optional ...OrderedMapClearAttr) (o *tf.Operation) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{"pooling_ratio": pooling_ratio}
+ attrs := map[string]interface{}{"dtypes": dtypes}
for _, a := range optional {
a(attrs)
}
opspec := tf.OpSpec{
- Type: "FractionalAvgPool",
- Input: []tf.Input{
- value,
- },
- Attrs: attrs,
- }
- op := scope.AddOperation(opspec)
- return op.Output(0), op.Output(1), op.Output(2)
-}
-
-// RandomCropAttr is an optional argument to RandomCrop.
-type RandomCropAttr func(optionalAttr)
-
-// RandomCropSeed sets the optional seed attribute to value.
-//
-// value: If either seed or seed2 are set to be non-zero, the random number
-// generator is seeded by the given seed. Otherwise, it is seeded by a
-// random seed.
-// If not specified, defaults to 0
-func RandomCropSeed(value int64) RandomCropAttr {
- return func(m optionalAttr) {
- m["seed"] = value
- }
-}
+ Type: "OrderedMapClear",
-// RandomCropSeed2 sets the optional seed2 attribute to value.
-//
-// value: An second seed to avoid seed collision.
-// If not specified, defaults to 0
-func RandomCropSeed2(value int64) RandomCropAttr {
- return func(m optionalAttr) {
- m["seed2"] = value
+ Attrs: attrs,
}
+ return scope.AddOperation(opspec)
}
-// Randomly crop `image`.
-//
-// DEPRECATED at GraphDef version 8: Random crop is now pure Python
-//
-// `size` is a 1-D int64 tensor with 2 elements representing the crop height and
-// width. The values must be non negative.
-//
-// This Op picks a random location in `image` and crops a `height` by `width`
-// rectangle from that location. The random location is picked so the cropped
-// area will fit inside the original image.
-//
-// Arguments:
-// image: 3-D of shape `[height, width, channels]`.
-// size: 1-D of length 2 containing: `crop_height`, `crop_width`..
+// Computes the reciprocal of x element-wise.
//
-// Returns 3-D of shape `[crop_height, crop_width, channels].`
-func RandomCrop(scope *Scope, image tf.Output, size tf.Output, optional ...RandomCropAttr) (output tf.Output) {
+// I.e., \\(y = 1 / x\\).
+func Inv(scope *Scope, x tf.Output) (y tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{}
- for _, a := range optional {
- a(attrs)
- }
opspec := tf.OpSpec{
- Type: "RandomCrop",
+ Type: "Inv",
Input: []tf.Input{
- image, size,
+ x,
},
- Attrs: attrs,
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// TopKV2Attr is an optional argument to TopKV2.
-type TopKV2Attr func(optionalAttr)
+// ComplexAbsAttr is an optional argument to ComplexAbs.
+type ComplexAbsAttr func(optionalAttr)
-// TopKV2Sorted sets the optional sorted attribute to value.
-//
-// value: If true the resulting `k` elements will be sorted by the values in
-// descending order.
-// If not specified, defaults to true
-func TopKV2Sorted(value bool) TopKV2Attr {
+// ComplexAbsTout sets the optional Tout attribute to value.
+// If not specified, defaults to DT_FLOAT
+func ComplexAbsTout(value tf.DataType) ComplexAbsAttr {
return func(m optionalAttr) {
- m["sorted"] = value
+ m["Tout"] = value
}
}
-// Finds values and indices of the `k` largest elements for the last dimension.
-//
-// If the input is a vector (rank-1), finds the `k` largest entries in the vector
-// and outputs their values and indices as vectors. Thus `values[j]` is the
-// `j`-th largest entry in `input`, and its index is `indices[j]`.
-//
-// For matrices (resp. higher rank input), computes the top `k` entries in each
-// row (resp. vector along the last dimension). Thus,
-//
-// values.shape = indices.shape = input.shape[:-1] + [k]
-//
-// If two elements are equal, the lower-index element appears first.
-//
-// Arguments:
-// input: 1-D or higher with last dimension at least `k`.
-// k: 0-D. Number of top elements to look for along the last dimension (along each
-// row for matrices).
+// Computes the complex absolute value of a tensor.
//
-// Returns The `k` largest elements along each last dimensional slice.The indices of `values` within the last dimension of `input`.
-func TopKV2(scope *Scope, input tf.Output, k tf.Output, optional ...TopKV2Attr) (values tf.Output, indices tf.Output) {
+// Given a tensor `x` of complex numbers, this operation returns a tensor of type
+// `float` or `double` that is the absolute value of each element in `x`. All
+// elements in `x` must be complex numbers of the form \\(a + bj\\). The absolute
+// value is computed as \\( \sqrt{a^2 + b^2}\\).
+func ComplexAbs(scope *Scope, x tf.Output, optional ...ComplexAbsAttr) (y tf.Output) {
if scope.Err() != nil {
return
}
a(attrs)
}
opspec := tf.OpSpec{
- Type: "TopKV2",
+ Type: "ComplexAbs",
Input: []tf.Input{
- input, k,
+ x,
},
Attrs: attrs,
}
op := scope.AddOperation(opspec)
- return op.Output(0), op.Output(1)
+ return op.Output(0)
}
-// Returns x // y element-wise.
+// Returns the truth value of x AND y element-wise.
//
-// *NOTE*: `FloorDiv` supports broadcasting. More about broadcasting
+// *NOTE*: `LogicalAnd` supports broadcasting. More about broadcasting
// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
-func FloorDiv(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) {
+func LogicalAnd(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) {
if scope.Err() != nil {
return
}
opspec := tf.OpSpec{
- Type: "FloorDiv",
+ Type: "LogicalAnd",
Input: []tf.Input{
x, y,
},
return op.Output(0)
}
-// Returns a batched diagonal tensor with a given batched diagonal values.
-//
-// Given a `diagonal`, this operation returns a tensor with the `diagonal` and
-// everything else padded with zeros. The diagonal is computed as follows:
-//
-// Assume `diagonal` has `k` dimensions `[I, J, K, ..., N]`, then the output is a
-// tensor of rank `k+1` with dimensions [I, J, K, ..., N, N]` where:
-//
-// `output[i, j, k, ..., m, n] = 1{m=n} * diagonal[i, j, k, ..., n]`.
-//
-// For example:
-//
-// ```
-// # 'diagonal' is [[1, 2, 3, 4], [5, 6, 7, 8]]
-//
-// and diagonal.shape = (2, 4)
-//
-// tf.matrix_diag(diagonal) ==> [[[1, 0, 0, 0]
-// [0, 2, 0, 0]
-// [0, 0, 3, 0]
-// [0, 0, 0, 4]],
-// [[5, 0, 0, 0]
-// [0, 6, 0, 0]
-// [0, 0, 7, 0]
-// [0, 0, 0, 8]]]
-//
-// which has shape (2, 4, 4)
-// ```
-//
-// Arguments:
-// diagonal: Rank `k`, where `k >= 1`.
-//
-// Returns Rank `k+1`, with `output.shape = diagonal.shape + [diagonal.shape[-1]]`.
-func MatrixDiag(scope *Scope, diagonal tf.Output) (output tf.Output) {
+// Cast x of type SrcT to y of DstT.
+func Cast(scope *Scope, x tf.Output, DstT tf.DataType) (y tf.Output) {
if scope.Err() != nil {
return
}
+ attrs := map[string]interface{}{"DstT": DstT}
opspec := tf.OpSpec{
- Type: "MatrixDiag",
+ Type: "Cast",
Input: []tf.Input{
- diagonal,
+ x,
},
+ Attrs: attrs,
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// Returns the truth value of (x <= y) element-wise.
+// MaxAttr is an optional argument to Max.
+type MaxAttr func(optionalAttr)
+
+// MaxKeepDims sets the optional keep_dims attribute to value.
//
-// *NOTE*: `LessEqual` supports broadcasting. More about broadcasting
-// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
-func LessEqual(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) {
+// value: If true, retain reduced dimensions with length 1.
+// If not specified, defaults to false
+func MaxKeepDims(value bool) MaxAttr {
+ return func(m optionalAttr) {
+ m["keep_dims"] = value
+ }
+}
+
+// Computes the maximum of elements across dimensions of a tensor.
+//
+// Reduces `input` along the dimensions given in `axis`. Unless
+// `keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in
+// `axis`. If `keep_dims` is true, the reduced dimensions are
+// retained with length 1.
+//
+// Arguments:
+// input: The tensor to reduce.
+// axis: The dimensions to reduce. Must be in the range
+// `[-rank(input), rank(input))`.
+//
+// Returns The reduced tensor.
+func Max(scope *Scope, input tf.Output, axis tf.Output, optional ...MaxAttr) (output tf.Output) {
if scope.Err() != nil {
return
}
+ attrs := map[string]interface{}{}
+ for _, a := range optional {
+ a(attrs)
+ }
opspec := tf.OpSpec{
- Type: "LessEqual",
+ Type: "Max",
Input: []tf.Input{
- x, y,
+ input, axis,
},
+ Attrs: attrs,
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// Computes softmax activations.
-//
-// For each batch `i` and class `j` we have
+// Quantized Batch normalization.
//
-// softmax[i, j] = exp(logits[i, j]) / sum_j(exp(logits[i, j]))
+// This op is deprecated and will be removed in the future. Prefer
+// `tf.nn.batch_normalization`.
//
// Arguments:
-// logits: 2-D with shape `[batch_size, num_classes]`.
+// t: A 4D input Tensor.
+// t_min: The value represented by the lowest quantized input.
+// t_max: The value represented by the highest quantized input.
+// m: A 1D mean Tensor with size matching the last dimension of t.
+// This is the first output from tf.nn.moments,
+// or a saved moving average thereof.
+// m_min: The value represented by the lowest quantized mean.
+// m_max: The value represented by the highest quantized mean.
+// v: A 1D variance Tensor with size matching the last dimension of t.
+// This is the second output from tf.nn.moments,
+// or a saved moving average thereof.
+// v_min: The value represented by the lowest quantized variance.
+// v_max: The value represented by the highest quantized variance.
+// beta: A 1D beta Tensor with size matching the last dimension of t.
+// An offset to be added to the normalized tensor.
+// beta_min: The value represented by the lowest quantized offset.
+// beta_max: The value represented by the highest quantized offset.
+// gamma: A 1D gamma Tensor with size matching the last dimension of t.
+// If "scale_after_normalization" is true, this tensor will be multiplied
+// with the normalized tensor.
+// gamma_min: The value represented by the lowest quantized gamma.
+// gamma_max: The value represented by the highest quantized gamma.
//
-// Returns Same shape as `logits`.
-func Softmax(scope *Scope, logits tf.Output) (softmax tf.Output) {
+// variance_epsilon: A small float number to avoid dividing by 0.
+// scale_after_normalization: A bool indicating whether the resulted tensor
+// needs to be multiplied with gamma.
+func QuantizedBatchNormWithGlobalNormalization(scope *Scope, t tf.Output, t_min tf.Output, t_max tf.Output, m tf.Output, m_min tf.Output, m_max tf.Output, v tf.Output, v_min tf.Output, v_max tf.Output, beta tf.Output, beta_min tf.Output, beta_max tf.Output, gamma tf.Output, gamma_min tf.Output, gamma_max tf.Output, out_type tf.DataType, variance_epsilon float32, scale_after_normalization bool) (result tf.Output, result_min tf.Output, result_max tf.Output) {
if scope.Err() != nil {
return
}
+ attrs := map[string]interface{}{"out_type": out_type, "variance_epsilon": variance_epsilon, "scale_after_normalization": scale_after_normalization}
opspec := tf.OpSpec{
- Type: "Softmax",
+ Type: "QuantizedBatchNormWithGlobalNormalization",
Input: []tf.Input{
- logits,
+ t, t_min, t_max, m, m_min, m_max, v, v_min, v_max, beta, beta_min, beta_max, gamma, gamma_min, gamma_max,
},
+ Attrs: attrs,
}
op := scope.AddOperation(opspec)
- return op.Output(0)
+ return op.Output(0), op.Output(1), op.Output(2)
}
-// DecodeBmpAttr is an optional argument to DecodeBmp.
-type DecodeBmpAttr func(optionalAttr)
+// HistogramFixedWidthAttr is an optional argument to HistogramFixedWidth.
+type HistogramFixedWidthAttr func(optionalAttr)
-// DecodeBmpChannels sets the optional channels attribute to value.
-// If not specified, defaults to 0
-func DecodeBmpChannels(value int64) DecodeBmpAttr {
+// HistogramFixedWidthDtype sets the optional dtype attribute to value.
+// If not specified, defaults to DT_INT32
+func HistogramFixedWidthDtype(value tf.DataType) HistogramFixedWidthAttr {
return func(m optionalAttr) {
- m["channels"] = value
+ m["dtype"] = value
}
}
-// Decode the first frame of a BMP-encoded image to a uint8 tensor.
+// Return histogram of values.
//
-// The attr `channels` indicates the desired number of color channels for the
-// decoded image.
+// Given the tensor `values`, this operation returns a rank 1 histogram counting
+// the number of entries in `values` that fall into every bin. The bins are
+// equal width and determined by the arguments `value_range` and `nbins`.
//
-// Accepted values are:
+// ```python
+// # Bins will be: (-inf, 1), [1, 2), [2, 3), [3, 4), [4, inf)
+// nbins = 5
+// value_range = [0.0, 5.0]
+// new_values = [-1.0, 0.0, 1.5, 2.0, 5.0, 15]
//
-// * 0: Use the number of channels in the BMP-encoded image.
-// * 3: output an RGB image.
-// * 4: output an RGBA image.
+// with tf.get_default_session() as sess:
+// hist = tf.histogram_fixed_width(new_values, value_range, nbins=5)
+// variables.global_variables_initializer().run()
+// sess.run(hist) => [2, 1, 1, 0, 2]
+// ```
//
// Arguments:
-// contents: 0-D. The BMP-encoded image.
+// values: Numeric `Tensor`.
+// value_range: Shape [2] `Tensor` of same `dtype` as `values`.
+// values <= value_range[0] will be mapped to hist[0],
+// values >= value_range[1] will be mapped to hist[-1].
+// nbins: Scalar `int32 Tensor`. Number of histogram bins.
//
-// Returns 3-D with shape `[height, width, channels]`. RGB order
-func DecodeBmp(scope *Scope, contents tf.Output, optional ...DecodeBmpAttr) (image tf.Output) {
+// Returns A 1-D `Tensor` holding histogram of values.
+func HistogramFixedWidth(scope *Scope, values tf.Output, value_range tf.Output, nbins tf.Output, optional ...HistogramFixedWidthAttr) (out tf.Output) {
if scope.Err() != nil {
return
}
a(attrs)
}
opspec := tf.OpSpec{
- Type: "DecodeBmp",
+ Type: "HistogramFixedWidth",
Input: []tf.Input{
- contents,
+ values, value_range, nbins,
},
Attrs: attrs,
}
return op.Output(0)
}
-// Computes softsign gradients for a softsign operation.
+// Adds Tensor 'bias' to Tensor 'input' for Quantized types.
+//
+// Broadcasts the values of bias on dimensions 0..N-2 of 'input'.
//
// Arguments:
-// gradients: The backpropagated gradients to the corresponding softsign operation.
-// features: The features passed as input to the corresponding softsign operation.
//
-// Returns The gradients: `gradients / (1 + abs(features)) ** 2`.
-func SoftsignGrad(scope *Scope, gradients tf.Output, features tf.Output) (backprops tf.Output) {
+// bias: A 1D bias Tensor with size matching the last dimension of 'input'.
+// min_input: The float value that the lowest quantized input value represents.
+// max_input: The float value that the highest quantized input value represents.
+// min_bias: The float value that the lowest quantized bias value represents.
+// max_bias: The float value that the highest quantized bias value represents.
+//
+//
+// Returns The float value that the lowest quantized output value represents.The float value that the highest quantized output value represents.
+func QuantizedBiasAdd(scope *Scope, input tf.Output, bias tf.Output, min_input tf.Output, max_input tf.Output, min_bias tf.Output, max_bias tf.Output, out_type tf.DataType) (output tf.Output, min_out tf.Output, max_out tf.Output) {
if scope.Err() != nil {
return
}
+ attrs := map[string]interface{}{"out_type": out_type}
opspec := tf.OpSpec{
- Type: "SoftsignGrad",
+ Type: "QuantizedBiasAdd",
Input: []tf.Input{
- gradients, features,
+ input, bias, min_input, max_input, min_bias, max_bias,
},
+ Attrs: attrs,
}
op := scope.AddOperation(opspec)
- return op.Output(0)
+ return op.Output(0), op.Output(1), op.Output(2)
}
-// BatchMatMulAttr is an optional argument to BatchMatMul.
-type BatchMatMulAttr func(optionalAttr)
-
-// BatchMatMulAdjX sets the optional adj_x attribute to value.
+// Produces the average pool of the input tensor for quantized types.
//
-// value: If `True`, adjoint the slices of `x`. Defaults to `False`.
-// If not specified, defaults to false
-func BatchMatMulAdjX(value bool) BatchMatMulAttr {
- return func(m optionalAttr) {
- m["adj_x"] = value
+// Arguments:
+// input: 4-D with shape `[batch, height, width, channels]`.
+// min_input: The float value that the lowest quantized input value represents.
+// max_input: The float value that the highest quantized input value represents.
+// ksize: The size of the window for each dimension of the input tensor.
+// The length must be 4 to match the number of dimensions of the input.
+// strides: The stride of the sliding window for each dimension of the input
+// tensor. The length must be 4 to match the number of dimensions of the input.
+// padding: The type of padding algorithm to use.
+//
+// Returns The float value that the lowest quantized output value represents.The float value that the highest quantized output value represents.
+func QuantizedAvgPool(scope *Scope, input tf.Output, min_input tf.Output, max_input tf.Output, ksize []int64, strides []int64, padding string) (output tf.Output, min_output tf.Output, max_output tf.Output) {
+ if scope.Err() != nil {
+ return
}
+ attrs := map[string]interface{}{"ksize": ksize, "strides": strides, "padding": padding}
+ opspec := tf.OpSpec{
+ Type: "QuantizedAvgPool",
+ Input: []tf.Input{
+ input, min_input, max_input,
+ },
+ Attrs: attrs,
+ }
+ op := scope.AddOperation(opspec)
+ return op.Output(0), op.Output(1), op.Output(2)
}
-// BatchMatMulAdjY sets the optional adj_y attribute to value.
+// FractionalAvgPoolAttr is an optional argument to FractionalAvgPool.
+type FractionalAvgPoolAttr func(optionalAttr)
+
+// FractionalAvgPoolPseudoRandom sets the optional pseudo_random attribute to value.
//
-// value: If `True`, adjoint the slices of `y`. Defaults to `False`.
+// value: When set to True, generates the pooling sequence in a
+// pseudorandom fashion, otherwise, in a random fashion. Check paper [Benjamin
+// Graham, Fractional Max-Pooling](http://arxiv.org/abs/1412.6071) for
+// difference between pseudorandom and random.
// If not specified, defaults to false
-func BatchMatMulAdjY(value bool) BatchMatMulAttr {
+func FractionalAvgPoolPseudoRandom(value bool) FractionalAvgPoolAttr {
return func(m optionalAttr) {
- m["adj_y"] = value
+ m["pseudo_random"] = value
}
}
-// Multiplies slices of two tensors in batches.
+// FractionalAvgPoolOverlapping sets the optional overlapping attribute to value.
//
-// Multiplies all slices of `Tensor` `x` and `y` (each slice can be
-// viewed as an element of a batch), and arranges the individual results
-// in a single output tensor of the same batch size. Each of the
-// individual slices can optionally be adjointed (to adjoint a matrix
-// means to transpose and conjugate it) before multiplication by setting
-// the `adj_x` or `adj_y` flag to `True`, which are by default `False`.
+// value: When set to True, it means when pooling, the values at the boundary
+// of adjacent pooling cells are used by both cells. For example:
//
-// The input tensors `x` and `y` are 2-D or higher with shape `[..., r_x, c_x]`
-// and `[..., r_y, c_y]`.
+// `index 0 1 2 3 4`
//
-// The output tensor is 2-D or higher with shape `[..., r_o, c_o]`, where:
+// `value 20 5 16 3 7`
//
-// r_o = c_x if adj_x else r_x
-// c_o = r_y if adj_y else c_y
+// If the pooling sequence is [0, 2, 4], then 16, at index 2 will be used twice.
+// The result would be [41/3, 26/3] for fractional avg pooling.
+// If not specified, defaults to false
+func FractionalAvgPoolOverlapping(value bool) FractionalAvgPoolAttr {
+ return func(m optionalAttr) {
+ m["overlapping"] = value
+ }
+}
+
+// FractionalAvgPoolDeterministic sets the optional deterministic attribute to value.
//
-// It is computed as:
+// value: When set to True, a fixed pooling region will be used when
+// iterating over a FractionalAvgPool node in the computation graph. Mainly used
+// in unit test to make FractionalAvgPool deterministic.
+// If not specified, defaults to false
+func FractionalAvgPoolDeterministic(value bool) FractionalAvgPoolAttr {
+ return func(m optionalAttr) {
+ m["deterministic"] = value
+ }
+}
+
+// FractionalAvgPoolSeed sets the optional seed attribute to value.
//
-// output[..., :, :] = matrix(x[..., :, :]) * matrix(y[..., :, :])
+// value: If either seed or seed2 are set to be non-zero, the random number
+// generator is seeded by the given seed. Otherwise, it is seeded by a
+// random seed.
+// If not specified, defaults to 0
+func FractionalAvgPoolSeed(value int64) FractionalAvgPoolAttr {
+ return func(m optionalAttr) {
+ m["seed"] = value
+ }
+}
+
+// FractionalAvgPoolSeed2 sets the optional seed2 attribute to value.
+//
+// value: An second seed to avoid seed collision.
+// If not specified, defaults to 0
+func FractionalAvgPoolSeed2(value int64) FractionalAvgPoolAttr {
+ return func(m optionalAttr) {
+ m["seed2"] = value
+ }
+}
+
+// Performs fractional average pooling on the input.
+//
+// Fractional average pooling is similar to Fractional max pooling in the pooling
+// region generation step. The only difference is that after pooling regions are
+// generated, a mean operation is performed instead of a max operation in each
+// pooling region.
//
// Arguments:
-// x: 2-D or higher with shape `[..., r_x, c_x]`.
-// y: 2-D or higher with shape `[..., r_y, c_y]`.
+// value: 4-D with shape `[batch, height, width, channels]`.
+// pooling_ratio: Pooling ratio for each dimension of `value`, currently only
+// supports row and col dimension and should be >= 1.0. For example, a valid
+// pooling ratio looks like [1.0, 1.44, 1.73, 1.0]. The first and last elements
+// must be 1.0 because we don't allow pooling on batch and channels
+// dimensions. 1.44 and 1.73 are pooling ratio on height and width dimensions
+// respectively.
//
-// Returns 3-D or higher with shape `[..., r_o, c_o]`
-func BatchMatMul(scope *Scope, x tf.Output, y tf.Output, optional ...BatchMatMulAttr) (output tf.Output) {
+// Returns output tensor after fractional avg pooling.row pooling sequence, needed to calculate gradient.column pooling sequence, needed to calculate gradient.
+func FractionalAvgPool(scope *Scope, value tf.Output, pooling_ratio []float32, optional ...FractionalAvgPoolAttr) (output tf.Output, row_pooling_sequence tf.Output, col_pooling_sequence tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{}
+ attrs := map[string]interface{}{"pooling_ratio": pooling_ratio}
for _, a := range optional {
a(attrs)
}
opspec := tf.OpSpec{
- Type: "BatchMatMul",
+ Type: "FractionalAvgPool",
Input: []tf.Input{
- x, y,
+ value,
},
Attrs: attrs,
}
op := scope.AddOperation(opspec)
- return op.Output(0)
+ return op.Output(0), op.Output(1), op.Output(2)
}
-// Returns which elements of x are NaN.
+// RandomCropAttr is an optional argument to RandomCrop.
+type RandomCropAttr func(optionalAttr)
+
+// RandomCropSeed sets the optional seed attribute to value.
//
-// @compatibility(numpy)
-// Equivalent to np.isnan
-// @end_compatibility
-func IsNan(scope *Scope, x tf.Output) (y tf.Output) {
- if scope.Err() != nil {
- return
- }
- opspec := tf.OpSpec{
- Type: "IsNan",
- Input: []tf.Input{
- x,
- },
+// value: If either seed or seed2 are set to be non-zero, the random number
+// generator is seeded by the given seed. Otherwise, it is seeded by a
+// random seed.
+// If not specified, defaults to 0
+func RandomCropSeed(value int64) RandomCropAttr {
+ return func(m optionalAttr) {
+ m["seed"] = value
}
- op := scope.AddOperation(opspec)
- return op.Output(0)
}
-// Computes rectified linear gradients for a Relu operation.
-//
-// Arguments:
-// gradients: The backpropagated gradients to the corresponding Relu operation.
-// features: The features passed as input to the corresponding Relu operation, OR
-// the outputs of that operation (both work equivalently).
+// RandomCropSeed2 sets the optional seed2 attribute to value.
//
-// Returns `gradients * (features > 0)`.
-func ReluGrad(scope *Scope, gradients tf.Output, features tf.Output) (backprops tf.Output) {
- if scope.Err() != nil {
- return
- }
- opspec := tf.OpSpec{
- Type: "ReluGrad",
- Input: []tf.Input{
- gradients, features,
- },
+// value: An second seed to avoid seed collision.
+// If not specified, defaults to 0
+func RandomCropSeed2(value int64) RandomCropAttr {
+ return func(m optionalAttr) {
+ m["seed2"] = value
}
- op := scope.AddOperation(opspec)
- return op.Output(0)
}
-// Computes the gradient of morphological 2-D dilation with respect to the input.
+// Randomly crop `image`.
+//
+// DEPRECATED at GraphDef version 8: Random crop is now pure Python
+//
+// `size` is a 1-D int64 tensor with 2 elements representing the crop height and
+// width. The values must be non negative.
+//
+// This Op picks a random location in `image` and crops a `height` by `width`
+// rectangle from that location. The random location is picked so the cropped
+// area will fit inside the original image.
//
// Arguments:
-// input: 4-D with shape `[batch, in_height, in_width, depth]`.
-// filter: 3-D with shape `[filter_height, filter_width, depth]`.
-// out_backprop: 4-D with shape `[batch, out_height, out_width, depth]`.
-// strides: 1-D of length 4. The stride of the sliding window for each dimension of
-// the input tensor. Must be: `[1, stride_height, stride_width, 1]`.
-// rates: 1-D of length 4. The input stride for atrous morphological dilation.
-// Must be: `[1, rate_height, rate_width, 1]`.
-// padding: The type of padding algorithm to use.
+// image: 3-D of shape `[height, width, channels]`.
+// size: 1-D of length 2 containing: `crop_height`, `crop_width`..
//
-// Returns 4-D with shape `[batch, in_height, in_width, depth]`.
-func Dilation2DBackpropInput(scope *Scope, input tf.Output, filter tf.Output, out_backprop tf.Output, strides []int64, rates []int64, padding string) (in_backprop tf.Output) {
+// Returns 3-D of shape `[crop_height, crop_width, channels].`
+func RandomCrop(scope *Scope, image tf.Output, size tf.Output, optional ...RandomCropAttr) (output tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{"strides": strides, "rates": rates, "padding": padding}
+ attrs := map[string]interface{}{}
+ for _, a := range optional {
+ a(attrs)
+ }
opspec := tf.OpSpec{
- Type: "Dilation2DBackpropInput",
+ Type: "RandomCrop",
Input: []tf.Input{
- input, filter, out_backprop,
+ image, size,
},
Attrs: attrs,
}
return op.Output(0)
}
-// CTCBeamSearchDecoderAttr is an optional argument to CTCBeamSearchDecoder.
-type CTCBeamSearchDecoderAttr func(optionalAttr)
+// TopKV2Attr is an optional argument to TopKV2.
+type TopKV2Attr func(optionalAttr)
-// CTCBeamSearchDecoderMergeRepeated sets the optional merge_repeated attribute to value.
+// TopKV2Sorted sets the optional sorted attribute to value.
//
-// value: If true, merge repeated classes in output.
+// value: If true the resulting `k` elements will be sorted by the values in
+// descending order.
// If not specified, defaults to true
-func CTCBeamSearchDecoderMergeRepeated(value bool) CTCBeamSearchDecoderAttr {
+func TopKV2Sorted(value bool) TopKV2Attr {
return func(m optionalAttr) {
- m["merge_repeated"] = value
+ m["sorted"] = value
}
}
-// Performs beam search decoding on the logits given in input.
+// Finds values and indices of the `k` largest elements for the last dimension.
//
-// A note about the attribute merge_repeated: For the beam search decoder,
-// this means that if consecutive entries in a beam are the same, only
-// the first of these is emitted. That is, when the top path is "A B B B B",
-// "A B" is returned if merge_repeated = True but "A B B B B" is
-// returned if merge_repeated = False.
+// If the input is a vector (rank-1), finds the `k` largest entries in the vector
+// and outputs their values and indices as vectors. Thus `values[j]` is the
+// `j`-th largest entry in `input`, and its index is `indices[j]`.
+//
+// For matrices (resp. higher rank input), computes the top `k` entries in each
+// row (resp. vector along the last dimension). Thus,
+//
+// values.shape = indices.shape = input.shape[:-1] + [k]
+//
+// If two elements are equal, the lower-index element appears first.
//
// Arguments:
-// inputs: 3-D, shape: `(max_time x batch_size x num_classes)`, the logits.
-// sequence_length: A vector containing sequence lengths, size `(batch)`.
-// beam_width: A scalar >= 0 (beam search beam width).
-// top_paths: A scalar >= 0, <= beam_width (controls output size).
+// input: 1-D or higher with last dimension at least `k`.
+// k: 0-D. Number of top elements to look for along the last dimension (along each
+// row for matrices).
//
-// Returns A list (length: top_paths) of indices matrices. Matrix j,
-// size `(total_decoded_outputs[j] x 2)`, has indices of a
-// `SparseTensor<int64, 2>`. The rows store: [batch, time].A list (length: top_paths) of values vectors. Vector j,
-// size `(length total_decoded_outputs[j])`, has the values of a
-// `SparseTensor<int64, 2>`. The vector stores the decoded classes for beam j.A list (length: top_paths) of shape vector. Vector j,
-// size `(2)`, stores the shape of the decoded `SparseTensor[j]`.
-// Its values are: `[batch_size, max_decoded_length[j]]`.A matrix, shaped: `(batch_size x top_paths)`. The
-// sequence log-probabilities.
-func CTCBeamSearchDecoder(scope *Scope, inputs tf.Output, sequence_length tf.Output, beam_width int64, top_paths int64, optional ...CTCBeamSearchDecoderAttr) (decoded_indices []tf.Output, decoded_values []tf.Output, decoded_shape []tf.Output, log_probability tf.Output) {
+// Returns The `k` largest elements along each last dimensional slice.The indices of `values` within the last dimension of `input`.
+func TopKV2(scope *Scope, input tf.Output, k tf.Output, optional ...TopKV2Attr) (values tf.Output, indices tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{"beam_width": beam_width, "top_paths": top_paths}
+ attrs := map[string]interface{}{}
for _, a := range optional {
a(attrs)
}
opspec := tf.OpSpec{
- Type: "CTCBeamSearchDecoder",
+ Type: "TopKV2",
Input: []tf.Input{
- inputs, sequence_length,
+ input, k,
},
Attrs: attrs,
}
op := scope.AddOperation(opspec)
+ return op.Output(0), op.Output(1)
+}
+
+// Returns x // y element-wise.
+//
+// *NOTE*: `FloorDiv` supports broadcasting. More about broadcasting
+// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
+func FloorDiv(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) {
if scope.Err() != nil {
return
}
- var idx int
- var err error
- if decoded_indices, idx, err = makeOutputList(op, idx, "decoded_indices"); err != nil {
- scope.UpdateErr("CTCBeamSearchDecoder", err)
- return
- }
- if decoded_values, idx, err = makeOutputList(op, idx, "decoded_values"); err != nil {
- scope.UpdateErr("CTCBeamSearchDecoder", err)
- return
- }
- if decoded_shape, idx, err = makeOutputList(op, idx, "decoded_shape"); err != nil {
- scope.UpdateErr("CTCBeamSearchDecoder", err)
- return
+ opspec := tf.OpSpec{
+ Type: "FloorDiv",
+ Input: []tf.Input{
+ x, y,
+ },
}
- log_probability = op.Output(idx)
- return decoded_indices, decoded_values, decoded_shape, log_probability
+ op := scope.AddOperation(opspec)
+ return op.Output(0)
}
-// AudioSpectrogramAttr is an optional argument to AudioSpectrogram.
-type AudioSpectrogramAttr func(optionalAttr)
-
-// AudioSpectrogramMagnitudeSquared sets the optional magnitude_squared attribute to value.
+// Returns a batched diagonal tensor with a given batched diagonal values.
//
-// value: Whether to return the squared magnitude or just the
-// magnitude. Using squared magnitude can avoid extra calculations.
-// If not specified, defaults to false
-func AudioSpectrogramMagnitudeSquared(value bool) AudioSpectrogramAttr {
- return func(m optionalAttr) {
- m["magnitude_squared"] = value
- }
-}
-
-// Produces a visualization of audio data over time.
+// Given a `diagonal`, this operation returns a tensor with the `diagonal` and
+// everything else padded with zeros. The diagonal is computed as follows:
//
-// Spectrograms are a standard way of representing audio information as a series of
-// slices of frequency information, one slice for each window of time. By joining
-// these together into a sequence, they form a distinctive fingerprint of the sound
-// over time.
+// Assume `diagonal` has `k` dimensions `[I, J, K, ..., N]`, then the output is a
+// tensor of rank `k+1` with dimensions [I, J, K, ..., N, N]` where:
//
-// This op expects to receive audio data as an input, stored as floats in the range
-// -1 to 1, together with a window width in samples, and a stride specifying how
-// far to move the window between slices. From this it generates a three
-// dimensional output. The lowest dimension has an amplitude value for each
-// frequency during that time slice. The next dimension is time, with successive
-// frequency slices. The final dimension is for the channels in the input, so a
-// stereo audio input would have two here for example.
+// `output[i, j, k, ..., m, n] = 1{m=n} * diagonal[i, j, k, ..., n]`.
//
-// This means the layout when converted and saved as an image is rotated 90 degrees
-// clockwise from a typical spectrogram. Time is descending down the Y axis, and
-// the frequency decreases from left to right.
+// For example:
//
-// Each value in the result represents the square root of the sum of the real and
-// imaginary parts of an FFT on the current window of samples. In this way, the
-// lowest dimension represents the power of each frequency in the current window,
-// and adjacent windows are concatenated in the next dimension.
+// ```
+// # 'diagonal' is [[1, 2, 3, 4], [5, 6, 7, 8]]
//
-// To get a more intuitive and visual look at what this operation does, you can run
-// tensorflow/examples/wav_to_spectrogram to read in an audio file and save out the
-// resulting spectrogram as a PNG image.
+// and diagonal.shape = (2, 4)
+//
+// tf.matrix_diag(diagonal) ==> [[[1, 0, 0, 0]
+// [0, 2, 0, 0]
+// [0, 0, 3, 0]
+// [0, 0, 0, 4]],
+// [[5, 0, 0, 0]
+// [0, 6, 0, 0]
+// [0, 0, 7, 0]
+// [0, 0, 0, 8]]]
+//
+// which has shape (2, 4, 4)
+// ```
//
// Arguments:
-// input: Float representation of audio data.
-// window_size: How wide the input window is in samples. For the highest efficiency
-// this should be a power of two, but other values are accepted.
-// stride: How widely apart the center of adjacent sample windows should be.
+// diagonal: Rank `k`, where `k >= 1`.
//
-// Returns 3D representation of the audio frequencies as an image.
-func AudioSpectrogram(scope *Scope, input tf.Output, window_size int64, stride int64, optional ...AudioSpectrogramAttr) (spectrogram tf.Output) {
+// Returns Rank `k+1`, with `output.shape = diagonal.shape + [diagonal.shape[-1]]`.
+func MatrixDiag(scope *Scope, diagonal tf.Output) (output tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{"window_size": window_size, "stride": stride}
- for _, a := range optional {
- a(attrs)
- }
opspec := tf.OpSpec{
- Type: "AudioSpectrogram",
+ Type: "MatrixDiag",
Input: []tf.Input{
- input,
+ diagonal,
},
- Attrs: attrs,
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// Compute the polygamma function \\(\psi^{(n)}(x)\\).
-//
-// The polygamma function is defined as:
-//
-//
-// \\(\psi^{(n)}(x) = \frac{d^n}{dx^n} \psi(x)\\)
+// Returns the truth value of (x <= y) element-wise.
//
-// where \\(\psi(x)\\) is the digamma function.
-func Polygamma(scope *Scope, a tf.Output, x tf.Output) (z tf.Output) {
+// *NOTE*: `LessEqual` supports broadcasting. More about broadcasting
+// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
+func LessEqual(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) {
if scope.Err() != nil {
return
}
opspec := tf.OpSpec{
- Type: "Polygamma",
+ Type: "LessEqual",
Input: []tf.Input{
- a, x,
+ x, y,
},
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// Computes second-order gradients of the maxpooling function.
+// Computes softmax activations.
+//
+// For each batch `i` and class `j` we have
+//
+// softmax[i, j] = exp(logits[i, j]) / sum_j(exp(logits[i, j]))
//
// Arguments:
-// input: The original input.
-// grad: 4-D with shape `[batch, height, width, channels]`. Gradients w.r.t. the
-// input of `max_pool`.
-// argmax: The indices of the maximum values chosen for each output of `max_pool`.
-// ksize: The size of the window for each dimension of the input tensor.
-// strides: The stride of the sliding window for each dimension of the
-// input tensor.
-// padding: The type of padding algorithm to use.
+// logits: 2-D with shape `[batch_size, num_classes]`.
//
-// Returns Gradients of gradients w.r.t. the input of `max_pool`.
-func MaxPoolGradGradWithArgmax(scope *Scope, input tf.Output, grad tf.Output, argmax tf.Output, ksize []int64, strides []int64, padding string) (output tf.Output) {
+// Returns Same shape as `logits`.
+func Softmax(scope *Scope, logits tf.Output) (softmax tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{"ksize": ksize, "strides": strides, "padding": padding}
opspec := tf.OpSpec{
- Type: "MaxPoolGradGradWithArgmax",
+ Type: "Softmax",
Input: []tf.Input{
- input, grad, argmax,
+ logits,
},
- Attrs: attrs,
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// MaxPoolGradGradV2Attr is an optional argument to MaxPoolGradGradV2.
-type MaxPoolGradGradV2Attr func(optionalAttr)
+// DecodeBmpAttr is an optional argument to DecodeBmp.
+type DecodeBmpAttr func(optionalAttr)
-// MaxPoolGradGradV2DataFormat sets the optional data_format attribute to value.
-//
-// value: Specify the data format of the input and output data. With the
-// default format "NHWC", the data is stored in the order of:
-// [batch, in_height, in_width, in_channels].
-// Alternatively, the format could be "NCHW", the data storage order of:
-// [batch, in_channels, in_height, in_width].
-// If not specified, defaults to "NHWC"
-func MaxPoolGradGradV2DataFormat(value string) MaxPoolGradGradV2Attr {
+// DecodeBmpChannels sets the optional channels attribute to value.
+// If not specified, defaults to 0
+func DecodeBmpChannels(value int64) DecodeBmpAttr {
return func(m optionalAttr) {
- m["data_format"] = value
+ m["channels"] = value
}
}
-// Computes second-order gradients of the maxpooling function.
+// Decode the first frame of a BMP-encoded image to a uint8 tensor.
+//
+// The attr `channels` indicates the desired number of color channels for the
+// decoded image.
+//
+// Accepted values are:
+//
+// * 0: Use the number of channels in the BMP-encoded image.
+// * 3: output an RGB image.
+// * 4: output an RGBA image.
//
// Arguments:
-// orig_input: The original input tensor.
-// orig_output: The original output tensor.
-// grad: 4-D. Gradients of gradients w.r.t. the input of `max_pool`.
-// ksize: The size of the window for each dimension of the input tensor.
-// strides: The stride of the sliding window for each dimension of the
-// input tensor.
-// padding: The type of padding algorithm to use.
+// contents: 0-D. The BMP-encoded image.
//
-// Returns Gradients of gradients w.r.t. the input to `max_pool`.
-func MaxPoolGradGradV2(scope *Scope, orig_input tf.Output, orig_output tf.Output, grad tf.Output, ksize tf.Output, strides tf.Output, padding string, optional ...MaxPoolGradGradV2Attr) (output tf.Output) {
+// Returns 3-D with shape `[height, width, channels]`. RGB order
+func DecodeBmp(scope *Scope, contents tf.Output, optional ...DecodeBmpAttr) (image tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{"padding": padding}
+ attrs := map[string]interface{}{}
for _, a := range optional {
a(attrs)
}
opspec := tf.OpSpec{
- Type: "MaxPoolGradGradV2",
+ Type: "DecodeBmp",
Input: []tf.Input{
- orig_input, orig_output, grad, ksize, strides,
+ contents,
},
Attrs: attrs,
}
return op.Output(0)
}
-// Computes gradients of the maxpooling function.
+// Computes softsign gradients for a softsign operation.
//
// Arguments:
-// input: The original input.
-// grad: 4-D with shape `[batch, height, width, channels]`. Gradients w.r.t. the
-// output of `max_pool`.
-// argmax: The indices of the maximum values chosen for each output of `max_pool`.
-// ksize: The size of the window for each dimension of the input tensor.
-// strides: The stride of the sliding window for each dimension of the
-// input tensor.
-// padding: The type of padding algorithm to use.
+// gradients: The backpropagated gradients to the corresponding softsign operation.
+// features: The features passed as input to the corresponding softsign operation.
//
-// Returns Gradients w.r.t. the input of `max_pool`.
-func MaxPoolGradWithArgmax(scope *Scope, input tf.Output, grad tf.Output, argmax tf.Output, ksize []int64, strides []int64, padding string) (output tf.Output) {
+// Returns The gradients: `gradients / (1 + abs(features)) ** 2`.
+func SoftsignGrad(scope *Scope, gradients tf.Output, features tf.Output) (backprops tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{"ksize": ksize, "strides": strides, "padding": padding}
opspec := tf.OpSpec{
- Type: "MaxPoolGradWithArgmax",
+ Type: "SoftsignGrad",
Input: []tf.Input{
- input, grad, argmax,
+ gradients, features,
},
- Attrs: attrs,
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// AvgPool3DAttr is an optional argument to AvgPool3D.
-type AvgPool3DAttr func(optionalAttr)
+// BatchMatMulAttr is an optional argument to BatchMatMul.
+type BatchMatMulAttr func(optionalAttr)
-// AvgPool3DDataFormat sets the optional data_format attribute to value.
+// BatchMatMulAdjX sets the optional adj_x attribute to value.
//
-// value: The data format of the input and output data. With the
-// default format "NDHWC", the data is stored in the order of:
-// [batch, in_depth, in_height, in_width, in_channels].
-// Alternatively, the format could be "NCDHW", the data storage order is:
-// [batch, in_channels, in_depth, in_height, in_width].
-// If not specified, defaults to "NDHWC"
-func AvgPool3DDataFormat(value string) AvgPool3DAttr {
+// value: If `True`, adjoint the slices of `x`. Defaults to `False`.
+// If not specified, defaults to false
+func BatchMatMulAdjX(value bool) BatchMatMulAttr {
return func(m optionalAttr) {
- m["data_format"] = value
+ m["adj_x"] = value
}
}
-// Performs 3D average pooling on the input.
+// BatchMatMulAdjY sets the optional adj_y attribute to value.
+//
+// value: If `True`, adjoint the slices of `y`. Defaults to `False`.
+// If not specified, defaults to false
+func BatchMatMulAdjY(value bool) BatchMatMulAttr {
+ return func(m optionalAttr) {
+ m["adj_y"] = value
+ }
+}
+
+// Multiplies slices of two tensors in batches.
+//
+// Multiplies all slices of `Tensor` `x` and `y` (each slice can be
+// viewed as an element of a batch), and arranges the individual results
+// in a single output tensor of the same batch size. Each of the
+// individual slices can optionally be adjointed (to adjoint a matrix
+// means to transpose and conjugate it) before multiplication by setting
+// the `adj_x` or `adj_y` flag to `True`, which are by default `False`.
+//
+// The input tensors `x` and `y` are 2-D or higher with shape `[..., r_x, c_x]`
+// and `[..., r_y, c_y]`.
+//
+// The output tensor is 2-D or higher with shape `[..., r_o, c_o]`, where:
+//
+// r_o = c_x if adj_x else r_x
+// c_o = r_y if adj_y else c_y
+//
+// It is computed as:
+//
+// output[..., :, :] = matrix(x[..., :, :]) * matrix(y[..., :, :])
//
// Arguments:
-// input: Shape `[batch, depth, rows, cols, channels]` tensor to pool over.
-// ksize: 1-D tensor of length 5. The size of the window for each dimension of
-// the input tensor. Must have `ksize[0] = ksize[4] = 1`.
-// strides: 1-D tensor of length 5. The stride of the sliding window for each
-// dimension of `input`. Must have `strides[0] = strides[4] = 1`.
-// padding: The type of padding algorithm to use.
+// x: 2-D or higher with shape `[..., r_x, c_x]`.
+// y: 2-D or higher with shape `[..., r_y, c_y]`.
//
-// Returns The average pooled output tensor.
-func AvgPool3D(scope *Scope, input tf.Output, ksize []int64, strides []int64, padding string, optional ...AvgPool3DAttr) (output tf.Output) {
+// Returns 3-D or higher with shape `[..., r_o, c_o]`
+func BatchMatMul(scope *Scope, x tf.Output, y tf.Output, optional ...BatchMatMulAttr) (output tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{"ksize": ksize, "strides": strides, "padding": padding}
+ attrs := map[string]interface{}{}
for _, a := range optional {
a(attrs)
}
opspec := tf.OpSpec{
- Type: "AvgPool3D",
+ Type: "BatchMatMul",
Input: []tf.Input{
- input,
+ x, y,
},
Attrs: attrs,
}
return op.Output(0)
}
-// Returns element-wise remainder of division. This emulates C semantics in that
-//
-// the result here is consistent with a truncating divide. E.g.
-// `tf.truncatediv(x, y) * y + truncate_mod(x, y) = x`.
+// Returns which elements of x are NaN.
//
-// *NOTE*: `Mod` supports broadcasting. More about broadcasting
-// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
-func Mod(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) {
+// @compatibility(numpy)
+// Equivalent to np.isnan
+// @end_compatibility
+func IsNan(scope *Scope, x tf.Output) (y tf.Output) {
if scope.Err() != nil {
return
}
opspec := tf.OpSpec{
- Type: "Mod",
+ Type: "IsNan",
Input: []tf.Input{
- x, y,
+ x,
},
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// DepthToSpaceAttr is an optional argument to DepthToSpace.
-type DepthToSpaceAttr func(optionalAttr)
-
-// DepthToSpaceDataFormat sets the optional data_format attribute to value.
-// If not specified, defaults to "NHWC"
-func DepthToSpaceDataFormat(value string) DepthToSpaceAttr {
- return func(m optionalAttr) {
- m["data_format"] = value
- }
-}
-
-// DepthToSpace for tensors of type T.
-//
-// Rearranges data from depth into blocks of spatial data.
-// This is the reverse transformation of SpaceToDepth. More specifically,
-// this op outputs a copy of the input tensor where values from the `depth`
-// dimension are moved in spatial blocks to the `height` and `width` dimensions.
-// The attr `block_size` indicates the input block size and how the data is moved.
-//
-// * Chunks of data of size `block_size * block_size` from depth are rearranged
-// into non-overlapping blocks of size `block_size x block_size`
-// * The width the output tensor is `input_depth * block_size`, whereas the
-// height is `input_height * block_size`.
-// * The Y, X coordinates within each block of the output image are determined
-// by the high order component of the input channel index.
-// * The depth of the input tensor must be divisible by
-// `block_size * block_size`.
-//
-// The `data_format` attr specifies the layout of the input and output tensors
-// with the following options:
-// "NHWC": `[ batch, height, width, channels ]`
-// "NCHW": `[ batch, channels, height, width ]`
-// "NCHW_VECT_C":
-// `qint8 [ batch, channels / 4, height, width, 4 ]`
-//
-// It is useful to consider the operation as transforming a 6-D Tensor.
-// e.g. for data_format = NHWC,
-// Each element in the input tensor can be specified via 6 coordinates,
-// ordered by decreasing memory layout significance as:
-// n,iY,iX,bY,bX,oC (where n=batch index, iX, iY means X or Y coordinates
-// within the input image, bX, bY means coordinates
-// within the output block, oC means output channels).
-// The output would be the input transposed to the following layout:
-// n,iY,bY,iX,bX,oC
-//
-// This operation is useful for resizing the activations between convolutions
-// (but keeping all data), e.g. instead of pooling. It is also useful for training
-// purely convolutional models.
-//
-// For example, given an input of shape `[1, 1, 1, 4]`, data_format = "NHWC" and
-// block_size = 2:
-//
-// ```
-// x = [[[[1, 2, 3, 4]]]]
-//
-// ```
-//
-// This operation will output a tensor of shape `[1, 2, 2, 1]`:
-//
-// ```
-// [[[[1], [2]],
-// [[3], [4]]]]
-// ```
-//
-// Here, the input has a batch of 1 and each batch element has shape `[1, 1, 4]`,
-// the corresponding output will have 2x2 elements and will have a depth of
-// 1 channel (1 = `4 / (block_size * block_size)`).
-// The output element shape is `[2, 2, 1]`.
-//
-// For an input tensor with larger depth, here of shape `[1, 1, 1, 12]`, e.g.
-//
-// ```
-// x = [[[[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]]]]
-// ```
-//
-// This operation, for block size of 2, will return the following tensor of shape
-// `[1, 2, 2, 3]`
-//
-// ```
-// [[[[1, 2, 3], [4, 5, 6]],
-// [[7, 8, 9], [10, 11, 12]]]]
-//
-// ```
-//
-// Similarly, for the following input of shape `[1 2 2 4]`, and a block size of 2:
-//
-// ```
-// x = [[[[1, 2, 3, 4],
-// [5, 6, 7, 8]],
-// [[9, 10, 11, 12],
-// [13, 14, 15, 16]]]]
-// ```
-//
-// the operator will return the following tensor of shape `[1 4 4 1]`:
-//
-// ```
-// x = [[[ [1], [2], [5], [6]],
-// [ [3], [4], [7], [8]],
-// [ [9], [10], [13], [14]],
-// [ [11], [12], [15], [16]]]]
-//
-// ```
+// Computes rectified linear gradients for a Relu operation.
//
// Arguments:
+// gradients: The backpropagated gradients to the corresponding Relu operation.
+// features: The features passed as input to the corresponding Relu operation, OR
+// the outputs of that operation (both work equivalently).
//
-// block_size: The size of the spatial block, same as in Space2Depth.
-func DepthToSpace(scope *Scope, input tf.Output, block_size int64, optional ...DepthToSpaceAttr) (output tf.Output) {
+// Returns `gradients * (features > 0)`.
+func ReluGrad(scope *Scope, gradients tf.Output, features tf.Output) (backprops tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{"block_size": block_size}
- for _, a := range optional {
- a(attrs)
- }
opspec := tf.OpSpec{
- Type: "DepthToSpace",
+ Type: "ReluGrad",
Input: []tf.Input{
- input,
+ gradients, features,
},
- Attrs: attrs,
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// Conv3DBackpropInputV2Attr is an optional argument to Conv3DBackpropInputV2.
-type Conv3DBackpropInputV2Attr func(optionalAttr)
-
-// Conv3DBackpropInputV2DataFormat sets the optional data_format attribute to value.
-//
-// value: The data format of the input and output data. With the
-// default format "NDHWC", the data is stored in the order of:
-// [batch, in_depth, in_height, in_width, in_channels].
-// Alternatively, the format could be "NCDHW", the data storage order is:
-// [batch, in_channels, in_depth, in_height, in_width].
-// If not specified, defaults to "NDHWC"
-func Conv3DBackpropInputV2DataFormat(value string) Conv3DBackpropInputV2Attr {
- return func(m optionalAttr) {
- m["data_format"] = value
- }
-}
-
-// Conv3DBackpropInputV2Dilations sets the optional dilations attribute to value.
-//
-// value: 1-D tensor of length 5. The dilation factor for each dimension of
-// `input`. If set to k > 1, there will be k-1 skipped cells between each
-// filter element on that dimension. The dimension order is determined by the
-// value of `data_format`, see above for details. Dilations in the batch and
-// depth dimensions must be 1.
-// If not specified, defaults to <i:1 i:1 i:1 i:1 i:1 >
-func Conv3DBackpropInputV2Dilations(value []int64) Conv3DBackpropInputV2Attr {
- return func(m optionalAttr) {
- m["dilations"] = value
- }
-}
-
-// Computes the gradients of 3-D convolution with respect to the input.
+// Computes the gradient of morphological 2-D dilation with respect to the input.
//
// Arguments:
-// input_sizes: An integer vector representing the tensor shape of `input`,
-// where `input` is a 5-D
-// `[batch, depth, rows, cols, in_channels]` tensor.
-// filter: Shape `[depth, rows, cols, in_channels, out_channels]`.
-// `in_channels` must match between `input` and `filter`.
-// out_backprop: Backprop signal of shape `[batch, out_depth, out_rows, out_cols,
-// out_channels]`.
-// strides: 1-D tensor of length 5. The stride of the sliding window for each
-// dimension of `input`. Must have `strides[0] = strides[4] = 1`.
+// input: 4-D with shape `[batch, in_height, in_width, depth]`.
+// filter: 3-D with shape `[filter_height, filter_width, depth]`.
+// out_backprop: 4-D with shape `[batch, out_height, out_width, depth]`.
+// strides: 1-D of length 4. The stride of the sliding window for each dimension of
+// the input tensor. Must be: `[1, stride_height, stride_width, 1]`.
+// rates: 1-D of length 4. The input stride for atrous morphological dilation.
+// Must be: `[1, rate_height, rate_width, 1]`.
// padding: The type of padding algorithm to use.
-func Conv3DBackpropInputV2(scope *Scope, input_sizes tf.Output, filter tf.Output, out_backprop tf.Output, strides []int64, padding string, optional ...Conv3DBackpropInputV2Attr) (output tf.Output) {
+//
+// Returns 4-D with shape `[batch, in_height, in_width, depth]`.
+func Dilation2DBackpropInput(scope *Scope, input tf.Output, filter tf.Output, out_backprop tf.Output, strides []int64, rates []int64, padding string) (in_backprop tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{"strides": strides, "padding": padding}
- for _, a := range optional {
- a(attrs)
- }
+ attrs := map[string]interface{}{"strides": strides, "rates": rates, "padding": padding}
opspec := tf.OpSpec{
- Type: "Conv3DBackpropInputV2",
+ Type: "Dilation2DBackpropInput",
Input: []tf.Input{
- input_sizes, filter, out_backprop,
+ input, filter, out_backprop,
},
Attrs: attrs,
}
return op.Output(0)
}
-// Computes square root of x element-wise.
+// CTCBeamSearchDecoderAttr is an optional argument to CTCBeamSearchDecoder.
+type CTCBeamSearchDecoderAttr func(optionalAttr)
+
+// CTCBeamSearchDecoderMergeRepeated sets the optional merge_repeated attribute to value.
//
-// I.e., \\(y = \sqrt{x} = x^{1/2}\\).
-func Sqrt(scope *Scope, x tf.Output) (y tf.Output) {
- if scope.Err() != nil {
- return
- }
- opspec := tf.OpSpec{
- Type: "Sqrt",
- Input: []tf.Input{
- x,
- },
+// value: If true, merge repeated classes in output.
+// If not specified, defaults to true
+func CTCBeamSearchDecoderMergeRepeated(value bool) CTCBeamSearchDecoderAttr {
+ return func(m optionalAttr) {
+ m["merge_repeated"] = value
}
- op := scope.AddOperation(opspec)
- return op.Output(0)
}
-// Computes the gradients of 3-D convolution with respect to the filter.
+// Performs beam search decoding on the logits given in input.
//
-// DEPRECATED at GraphDef version 10: Use Conv3DBackpropFilterV2
+// A note about the attribute merge_repeated: For the beam search decoder,
+// this means that if consecutive entries in a beam are the same, only
+// the first of these is emitted. That is, when the top path is "A B B B B",
+// "A B" is returned if merge_repeated = True but "A B B B B" is
+// returned if merge_repeated = False.
//
// Arguments:
-// input: Shape `[batch, depth, rows, cols, in_channels]`.
-// filter: Shape `[depth, rows, cols, in_channels, out_channels]`.
-// `in_channels` must match between `input` and `filter`.
-// out_backprop: Backprop signal of shape `[batch, out_depth, out_rows, out_cols,
-// out_channels]`.
-// strides: 1-D tensor of length 5. The stride of the sliding window for each
-// dimension of `input`. Must have `strides[0] = strides[4] = 1`.
-// padding: The type of padding algorithm to use.
-func Conv3DBackpropFilter(scope *Scope, input tf.Output, filter tf.Output, out_backprop tf.Output, strides []int64, padding string) (output tf.Output) {
+// inputs: 3-D, shape: `(max_time x batch_size x num_classes)`, the logits.
+// sequence_length: A vector containing sequence lengths, size `(batch)`.
+// beam_width: A scalar >= 0 (beam search beam width).
+// top_paths: A scalar >= 0, <= beam_width (controls output size).
+//
+// Returns A list (length: top_paths) of indices matrices. Matrix j,
+// size `(total_decoded_outputs[j] x 2)`, has indices of a
+// `SparseTensor<int64, 2>`. The rows store: [batch, time].A list (length: top_paths) of values vectors. Vector j,
+// size `(length total_decoded_outputs[j])`, has the values of a
+// `SparseTensor<int64, 2>`. The vector stores the decoded classes for beam j.A list (length: top_paths) of shape vector. Vector j,
+// size `(2)`, stores the shape of the decoded `SparseTensor[j]`.
+// Its values are: `[batch_size, max_decoded_length[j]]`.A matrix, shaped: `(batch_size x top_paths)`. The
+// sequence log-probabilities.
+func CTCBeamSearchDecoder(scope *Scope, inputs tf.Output, sequence_length tf.Output, beam_width int64, top_paths int64, optional ...CTCBeamSearchDecoderAttr) (decoded_indices []tf.Output, decoded_values []tf.Output, decoded_shape []tf.Output, log_probability tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{"strides": strides, "padding": padding}
+ attrs := map[string]interface{}{"beam_width": beam_width, "top_paths": top_paths}
+ for _, a := range optional {
+ a(attrs)
+ }
opspec := tf.OpSpec{
- Type: "Conv3DBackpropFilter",
+ Type: "CTCBeamSearchDecoder",
Input: []tf.Input{
- input, filter, out_backprop,
+ inputs, sequence_length,
},
Attrs: attrs,
}
op := scope.AddOperation(opspec)
- return op.Output(0)
-}
-
-// Computes the gradient for the rsqrt of `x` wrt its input.
-//
-// Specifically, `grad = dy * -0.5 * y^3`, where `y = rsqrt(x)`, and `dy`
-// is the corresponding input gradient.
-func RsqrtGrad(scope *Scope, y tf.Output, dy tf.Output) (z tf.Output) {
if scope.Err() != nil {
return
}
- opspec := tf.OpSpec{
- Type: "RsqrtGrad",
- Input: []tf.Input{
- y, dy,
- },
+ var idx int
+ var err error
+ if decoded_indices, idx, err = makeOutputList(op, idx, "decoded_indices"); err != nil {
+ scope.UpdateErr("CTCBeamSearchDecoder", err)
+ return
}
- op := scope.AddOperation(opspec)
- return op.Output(0)
+ if decoded_values, idx, err = makeOutputList(op, idx, "decoded_values"); err != nil {
+ scope.UpdateErr("CTCBeamSearchDecoder", err)
+ return
+ }
+ if decoded_shape, idx, err = makeOutputList(op, idx, "decoded_shape"); err != nil {
+ scope.UpdateErr("CTCBeamSearchDecoder", err)
+ return
+ }
+ log_probability = op.Output(idx)
+ return decoded_indices, decoded_values, decoded_shape, log_probability
}
-// ReverseSequenceAttr is an optional argument to ReverseSequence.
-type ReverseSequenceAttr func(optionalAttr)
+// AudioSpectrogramAttr is an optional argument to AudioSpectrogram.
+type AudioSpectrogramAttr func(optionalAttr)
-// ReverseSequenceBatchDim sets the optional batch_dim attribute to value.
+// AudioSpectrogramMagnitudeSquared sets the optional magnitude_squared attribute to value.
//
-// value: The dimension along which reversal is performed.
-// If not specified, defaults to 0
-func ReverseSequenceBatchDim(value int64) ReverseSequenceAttr {
+// value: Whether to return the squared magnitude or just the
+// magnitude. Using squared magnitude can avoid extra calculations.
+// If not specified, defaults to false
+func AudioSpectrogramMagnitudeSquared(value bool) AudioSpectrogramAttr {
return func(m optionalAttr) {
- m["batch_dim"] = value
+ m["magnitude_squared"] = value
}
}
-// Reverses variable length slices.
-//
-// This op first slices `input` along the dimension `batch_dim`, and for each
-// slice `i`, reverses the first `seq_lengths[i]` elements along
-// the dimension `seq_dim`.
-//
-// The elements of `seq_lengths` must obey `seq_lengths[i] <= input.dims[seq_dim]`,
-// and `seq_lengths` must be a vector of length `input.dims[batch_dim]`.
-//
-// The output slice `i` along dimension `batch_dim` is then given by input
-// slice `i`, with the first `seq_lengths[i]` slices along dimension
-// `seq_dim` reversed.
-//
-// For example:
-//
-// ```
-// # Given this:
-// batch_dim = 0
-// seq_dim = 1
-// input.dims = (4, 8, ...)
-// seq_lengths = [7, 2, 3, 5]
-//
-// # then slices of input are reversed on seq_dim, but only up to seq_lengths:
-// output[0, 0:7, :, ...] = input[0, 7:0:-1, :, ...]
-// output[1, 0:2, :, ...] = input[1, 2:0:-1, :, ...]
-// output[2, 0:3, :, ...] = input[2, 3:0:-1, :, ...]
-// output[3, 0:5, :, ...] = input[3, 5:0:-1, :, ...]
+// Produces a visualization of audio data over time.
//
-// # while entries past seq_lens are copied through:
-// output[0, 7:, :, ...] = input[0, 7:, :, ...]
-// output[1, 2:, :, ...] = input[1, 2:, :, ...]
-// output[2, 3:, :, ...] = input[2, 3:, :, ...]
-// output[3, 2:, :, ...] = input[3, 2:, :, ...]
-// ```
+// Spectrograms are a standard way of representing audio information as a series of
+// slices of frequency information, one slice for each window of time. By joining
+// these together into a sequence, they form a distinctive fingerprint of the sound
+// over time.
//
-// In contrast, if:
+// This op expects to receive audio data as an input, stored as floats in the range
+// -1 to 1, together with a window width in samples, and a stride specifying how
+// far to move the window between slices. From this it generates a three
+// dimensional output. The lowest dimension has an amplitude value for each
+// frequency during that time slice. The next dimension is time, with successive
+// frequency slices. The final dimension is for the channels in the input, so a
+// stereo audio input would have two here for example.
//
-// ```
-// # Given this:
-// batch_dim = 2
-// seq_dim = 0
-// input.dims = (8, ?, 4, ...)
-// seq_lengths = [7, 2, 3, 5]
+// This means the layout when converted and saved as an image is rotated 90 degrees
+// clockwise from a typical spectrogram. Time is descending down the Y axis, and
+// the frequency decreases from left to right.
//
-// # then slices of input are reversed on seq_dim, but only up to seq_lengths:
-// output[0:7, :, 0, :, ...] = input[7:0:-1, :, 0, :, ...]
-// output[0:2, :, 1, :, ...] = input[2:0:-1, :, 1, :, ...]
-// output[0:3, :, 2, :, ...] = input[3:0:-1, :, 2, :, ...]
-// output[0:5, :, 3, :, ...] = input[5:0:-1, :, 3, :, ...]
+// Each value in the result represents the square root of the sum of the real and
+// imaginary parts of an FFT on the current window of samples. In this way, the
+// lowest dimension represents the power of each frequency in the current window,
+// and adjacent windows are concatenated in the next dimension.
//
-// # while entries past seq_lens are copied through:
-// output[7:, :, 0, :, ...] = input[7:, :, 0, :, ...]
-// output[2:, :, 1, :, ...] = input[2:, :, 1, :, ...]
-// output[3:, :, 2, :, ...] = input[3:, :, 2, :, ...]
-// output[2:, :, 3, :, ...] = input[2:, :, 3, :, ...]
-// ```
+// To get a more intuitive and visual look at what this operation does, you can run
+// tensorflow/examples/wav_to_spectrogram to read in an audio file and save out the
+// resulting spectrogram as a PNG image.
//
// Arguments:
-// input: The input to reverse.
-// seq_lengths: 1-D with length `input.dims(batch_dim)` and
-// `max(seq_lengths) <= input.dims(seq_dim)`
-// seq_dim: The dimension which is partially reversed.
+// input: Float representation of audio data.
+// window_size: How wide the input window is in samples. For the highest efficiency
+// this should be a power of two, but other values are accepted.
+// stride: How widely apart the center of adjacent sample windows should be.
//
-// Returns The partially reversed input. It has the same shape as `input`.
-func ReverseSequence(scope *Scope, input tf.Output, seq_lengths tf.Output, seq_dim int64, optional ...ReverseSequenceAttr) (output tf.Output) {
+// Returns 3D representation of the audio frequencies as an image.
+func AudioSpectrogram(scope *Scope, input tf.Output, window_size int64, stride int64, optional ...AudioSpectrogramAttr) (spectrogram tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{"seq_dim": seq_dim}
+ attrs := map[string]interface{}{"window_size": window_size, "stride": stride}
for _, a := range optional {
a(attrs)
}
opspec := tf.OpSpec{
- Type: "ReverseSequence",
+ Type: "AudioSpectrogram",
Input: []tf.Input{
- input, seq_lengths,
+ input,
},
Attrs: attrs,
}
return op.Output(0)
}
-// DepthwiseConv2dNativeAttr is an optional argument to DepthwiseConv2dNative.
-type DepthwiseConv2dNativeAttr func(optionalAttr)
-
-// DepthwiseConv2dNativeDataFormat sets the optional data_format attribute to value.
+// Compute the polygamma function \\(\psi^{(n)}(x)\\).
//
-// value: Specify the data format of the input and output data. With the
-// default format "NHWC", the data is stored in the order of:
-// [batch, height, width, channels].
-// Alternatively, the format could be "NCHW", the data storage order of:
-// [batch, channels, height, width].
-// If not specified, defaults to "NHWC"
-func DepthwiseConv2dNativeDataFormat(value string) DepthwiseConv2dNativeAttr {
- return func(m optionalAttr) {
- m["data_format"] = value
- }
-}
-
-// DepthwiseConv2dNativeDilations sets the optional dilations attribute to value.
+// The polygamma function is defined as:
//
-// value: 1-D tensor of length 4. The dilation factor for each dimension of
-// `input`. If set to k > 1, there will be k-1 skipped cells between each filter
-// element on that dimension. The dimension order is determined by the value of
-// `data_format`, see above for details. Dilations in the batch and depth
-// dimensions must be 1.
-// If not specified, defaults to <i:1 i:1 i:1 i:1 >
-func DepthwiseConv2dNativeDilations(value []int64) DepthwiseConv2dNativeAttr {
- return func(m optionalAttr) {
- m["dilations"] = value
+//
+// \\(\psi^{(n)}(x) = \frac{d^n}{dx^n} \psi(x)\\)
+//
+// where \\(\psi(x)\\) is the digamma function.
+func Polygamma(scope *Scope, a tf.Output, x tf.Output) (z tf.Output) {
+ if scope.Err() != nil {
+ return
+ }
+ opspec := tf.OpSpec{
+ Type: "Polygamma",
+ Input: []tf.Input{
+ a, x,
+ },
}
+ op := scope.AddOperation(opspec)
+ return op.Output(0)
}
-// Computes a 2-D depthwise convolution given 4-D `input` and `filter` tensors.
-//
-// Given an input tensor of shape `[batch, in_height, in_width, in_channels]`
-// and a filter / kernel tensor of shape
-// `[filter_height, filter_width, in_channels, channel_multiplier]`, containing
-// `in_channels` convolutional filters of depth 1, `depthwise_conv2d` applies
-// a different filter to each input channel (expanding from 1 channel to
-// `channel_multiplier` channels for each), then concatenates the results
-// together. Thus, the output has `in_channels * channel_multiplier` channels.
-//
-// ```
-// for k in 0..in_channels-1
-// for q in 0..channel_multiplier-1
-// output[b, i, j, k * channel_multiplier + q] =
-// sum_{di, dj} input[b, strides[1] * i + di, strides[2] * j + dj, k] *
-// filter[di, dj, k, q]
-// ```
-//
-// Must have `strides[0] = strides[3] = 1`. For the most common case of the same
-// horizontal and vertices strides, `strides = [1, stride, stride, 1]`.
+// Computes second-order gradients of the maxpooling function.
//
// Arguments:
-//
-//
-// strides: 1-D of length 4. The stride of the sliding window for each dimension
-// of `input`.
+// input: The original input.
+// grad: 4-D with shape `[batch, height, width, channels]`. Gradients w.r.t. the
+// input of `max_pool`.
+// argmax: The indices of the maximum values chosen for each output of `max_pool`.
+// ksize: The size of the window for each dimension of the input tensor.
+// strides: The stride of the sliding window for each dimension of the
+// input tensor.
// padding: The type of padding algorithm to use.
-func DepthwiseConv2dNative(scope *Scope, input tf.Output, filter tf.Output, strides []int64, padding string, optional ...DepthwiseConv2dNativeAttr) (output tf.Output) {
+//
+// Returns Gradients of gradients w.r.t. the input of `max_pool`.
+func MaxPoolGradGradWithArgmax(scope *Scope, input tf.Output, grad tf.Output, argmax tf.Output, ksize []int64, strides []int64, padding string) (output tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{"strides": strides, "padding": padding}
- for _, a := range optional {
- a(attrs)
- }
+ attrs := map[string]interface{}{"ksize": ksize, "strides": strides, "padding": padding}
opspec := tf.OpSpec{
- Type: "DepthwiseConv2dNative",
+ Type: "MaxPoolGradGradWithArgmax",
Input: []tf.Input{
- input, filter,
+ input, grad, argmax,
},
Attrs: attrs,
}
return op.Output(0)
}
-// MaxPoolGradV2Attr is an optional argument to MaxPoolGradV2.
-type MaxPoolGradV2Attr func(optionalAttr)
+// MaxPoolGradGradV2Attr is an optional argument to MaxPoolGradGradV2.
+type MaxPoolGradGradV2Attr func(optionalAttr)
-// MaxPoolGradV2DataFormat sets the optional data_format attribute to value.
+// MaxPoolGradGradV2DataFormat sets the optional data_format attribute to value.
//
// value: Specify the data format of the input and output data. With the
// default format "NHWC", the data is stored in the order of:
// Alternatively, the format could be "NCHW", the data storage order of:
// [batch, in_channels, in_height, in_width].
// If not specified, defaults to "NHWC"
-func MaxPoolGradV2DataFormat(value string) MaxPoolGradV2Attr {
+func MaxPoolGradGradV2DataFormat(value string) MaxPoolGradGradV2Attr {
return func(m optionalAttr) {
m["data_format"] = value
}
}
-// Computes gradients of the maxpooling function.
+// Computes second-order gradients of the maxpooling function.
//
// Arguments:
// orig_input: The original input tensor.
// orig_output: The original output tensor.
-// grad: 4-D. Gradients w.r.t. the output of `max_pool`.
+// grad: 4-D. Gradients of gradients w.r.t. the input of `max_pool`.
// ksize: The size of the window for each dimension of the input tensor.
// strides: The stride of the sliding window for each dimension of the
// input tensor.
// padding: The type of padding algorithm to use.
//
-// Returns Gradients w.r.t. the input to `max_pool`.
-func MaxPoolGradV2(scope *Scope, orig_input tf.Output, orig_output tf.Output, grad tf.Output, ksize tf.Output, strides tf.Output, padding string, optional ...MaxPoolGradV2Attr) (output tf.Output) {
+// Returns Gradients of gradients w.r.t. the input to `max_pool`.
+func MaxPoolGradGradV2(scope *Scope, orig_input tf.Output, orig_output tf.Output, grad tf.Output, ksize tf.Output, strides tf.Output, padding string, optional ...MaxPoolGradGradV2Attr) (output tf.Output) {
if scope.Err() != nil {
return
}
a(attrs)
}
opspec := tf.OpSpec{
- Type: "MaxPoolGradV2",
+ Type: "MaxPoolGradGradV2",
Input: []tf.Input{
orig_input, orig_output, grad, ksize, strides,
},
return op.Output(0)
}
-// Restore a reader to a previously saved state.
-//
-// Not all Readers support being restored, so this can produce an
-// Unimplemented error.
+// Computes gradients of the maxpooling function.
//
// Arguments:
-// reader_handle: Handle to a Reader.
-// state: Result of a ReaderSerializeState of a Reader with type
-// matching reader_handle.
+// input: The original input.
+// grad: 4-D with shape `[batch, height, width, channels]`. Gradients w.r.t. the
+// output of `max_pool`.
+// argmax: The indices of the maximum values chosen for each output of `max_pool`.
+// ksize: The size of the window for each dimension of the input tensor.
+// strides: The stride of the sliding window for each dimension of the
+// input tensor.
+// padding: The type of padding algorithm to use.
//
-// Returns the created operation.
-func ReaderRestoreStateV2(scope *Scope, reader_handle tf.Output, state tf.Output) (o *tf.Operation) {
+// Returns Gradients w.r.t. the input of `max_pool`.
+func MaxPoolGradWithArgmax(scope *Scope, input tf.Output, grad tf.Output, argmax tf.Output, ksize []int64, strides []int64, padding string) (output tf.Output) {
if scope.Err() != nil {
return
}
+ attrs := map[string]interface{}{"ksize": ksize, "strides": strides, "padding": padding}
opspec := tf.OpSpec{
- Type: "ReaderRestoreStateV2",
+ Type: "MaxPoolGradWithArgmax",
Input: []tf.Input{
- reader_handle, state,
+ input, grad, argmax,
},
+ Attrs: attrs,
}
- return scope.AddOperation(opspec)
+ op := scope.AddOperation(opspec)
+ return op.Output(0)
}
-// MaxPoolGradAttr is an optional argument to MaxPoolGrad.
-type MaxPoolGradAttr func(optionalAttr)
+// AvgPool3DAttr is an optional argument to AvgPool3D.
+type AvgPool3DAttr func(optionalAttr)
-// MaxPoolGradDataFormat sets the optional data_format attribute to value.
+// AvgPool3DDataFormat sets the optional data_format attribute to value.
//
-// value: Specify the data format of the input and output data. With the
-// default format "NHWC", the data is stored in the order of:
-// [batch, in_height, in_width, in_channels].
-// Alternatively, the format could be "NCHW", the data storage order of:
-// [batch, in_channels, in_height, in_width].
-// If not specified, defaults to "NHWC"
-func MaxPoolGradDataFormat(value string) MaxPoolGradAttr {
+// value: The data format of the input and output data. With the
+// default format "NDHWC", the data is stored in the order of:
+// [batch, in_depth, in_height, in_width, in_channels].
+// Alternatively, the format could be "NCDHW", the data storage order is:
+// [batch, in_channels, in_depth, in_height, in_width].
+// If not specified, defaults to "NDHWC"
+func AvgPool3DDataFormat(value string) AvgPool3DAttr {
return func(m optionalAttr) {
m["data_format"] = value
}
}
-// Computes gradients of the maxpooling function.
+// Performs 3D average pooling on the input.
//
// Arguments:
-// orig_input: The original input tensor.
-// orig_output: The original output tensor.
-// grad: 4-D. Gradients w.r.t. the output of `max_pool`.
-// ksize: The size of the window for each dimension of the input tensor.
-// strides: The stride of the sliding window for each dimension of the
-// input tensor.
+// input: Shape `[batch, depth, rows, cols, channels]` tensor to pool over.
+// ksize: 1-D tensor of length 5. The size of the window for each dimension of
+// the input tensor. Must have `ksize[0] = ksize[4] = 1`.
+// strides: 1-D tensor of length 5. The stride of the sliding window for each
+// dimension of `input`. Must have `strides[0] = strides[4] = 1`.
// padding: The type of padding algorithm to use.
//
-// Returns Gradients w.r.t. the input to `max_pool`.
-func MaxPoolGrad(scope *Scope, orig_input tf.Output, orig_output tf.Output, grad tf.Output, ksize []int64, strides []int64, padding string, optional ...MaxPoolGradAttr) (output tf.Output) {
+// Returns The average pooled output tensor.
+func AvgPool3D(scope *Scope, input tf.Output, ksize []int64, strides []int64, padding string, optional ...AvgPool3DAttr) (output tf.Output) {
if scope.Err() != nil {
return
}
a(attrs)
}
opspec := tf.OpSpec{
- Type: "MaxPoolGrad",
+ Type: "AvgPool3D",
Input: []tf.Input{
- orig_input, orig_output, grad,
+ input,
},
Attrs: attrs,
}
return op.Output(0)
}
-// CropAndResizeAttr is an optional argument to CropAndResize.
-type CropAndResizeAttr func(optionalAttr)
-
-// CropAndResizeMethod sets the optional method attribute to value.
-//
-// value: A string specifying the interpolation method. Only 'bilinear' is
-// supported for now.
-// If not specified, defaults to "bilinear"
-func CropAndResizeMethod(value string) CropAndResizeAttr {
- return func(m optionalAttr) {
- m["method"] = value
- }
-}
-
-// CropAndResizeExtrapolationValue sets the optional extrapolation_value attribute to value.
-//
-// value: Value used for extrapolation, when applicable.
-// If not specified, defaults to 0
-func CropAndResizeExtrapolationValue(value float32) CropAndResizeAttr {
- return func(m optionalAttr) {
- m["extrapolation_value"] = value
- }
-}
-
-// Extracts crops from the input image tensor and bilinearly resizes them (possibly
-//
-// with aspect ratio change) to a common output size specified by `crop_size`. This
-// is more general than the `crop_to_bounding_box` op which extracts a fixed size
-// slice from the input image and does not allow resizing or aspect ratio change.
-//
-// Returns a tensor with `crops` from the input `image` at positions defined at the
-// bounding box locations in `boxes`. The cropped boxes are all resized (with
-// bilinear interpolation) to a fixed `size = [crop_height, crop_width]`. The
-// result is a 4-D tensor `[num_boxes, crop_height, crop_width, depth]`. The
-// resizing is corner aligned. In particular, if `boxes = [[0, 0, 1, 1]]`, the
-// method will give identical results to using `tf.image.resize_bilinear()`
-// with `align_corners=True`.
+// Returns element-wise remainder of division. This emulates C semantics in that
//
-// Arguments:
-// image: A 4-D tensor of shape `[batch, image_height, image_width, depth]`.
-// Both `image_height` and `image_width` need to be positive.
-// boxes: A 2-D tensor of shape `[num_boxes, 4]`. The `i`-th row of the tensor
-// specifies the coordinates of a box in the `box_ind[i]` image and is specified
-// in normalized coordinates `[y1, x1, y2, x2]`. A normalized coordinate value of
-// `y` is mapped to the image coordinate at `y * (image_height - 1)`, so as the
-// `[0, 1]` interval of normalized image height is mapped to
-// `[0, image_height - 1]` in image height coordinates. We do allow `y1` > `y2`, in
-// which case the sampled crop is an up-down flipped version of the original
-// image. The width dimension is treated similarly. Normalized coordinates
-// outside the `[0, 1]` range are allowed, in which case we use
-// `extrapolation_value` to extrapolate the input image values.
-// box_ind: A 1-D tensor of shape `[num_boxes]` with int32 values in `[0, batch)`.
-// The value of `box_ind[i]` specifies the image that the `i`-th box refers to.
-// crop_size: A 1-D tensor of 2 elements, `size = [crop_height, crop_width]`. All
-// cropped image patches are resized to this size. The aspect ratio of the image
-// content is not preserved. Both `crop_height` and `crop_width` need to be
-// positive.
+// the result here is consistent with a truncating divide. E.g.
+// `tf.truncatediv(x, y) * y + truncate_mod(x, y) = x`.
//
-// Returns A 4-D tensor of shape `[num_boxes, crop_height, crop_width, depth]`.
-func CropAndResize(scope *Scope, image tf.Output, boxes tf.Output, box_ind tf.Output, crop_size tf.Output, optional ...CropAndResizeAttr) (crops tf.Output) {
+// *NOTE*: `Mod` supports broadcasting. More about broadcasting
+// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
+func Mod(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{}
- for _, a := range optional {
- a(attrs)
- }
opspec := tf.OpSpec{
- Type: "CropAndResize",
+ Type: "Mod",
Input: []tf.Input{
- image, boxes, box_ind, crop_size,
+ x, y,
},
- Attrs: attrs,
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// Fills empty rows in the input 2-D `SparseTensor` with a default value.
+// DepthToSpaceAttr is an optional argument to DepthToSpace.
+type DepthToSpaceAttr func(optionalAttr)
+
+// DepthToSpaceDataFormat sets the optional data_format attribute to value.
+// If not specified, defaults to "NHWC"
+func DepthToSpaceDataFormat(value string) DepthToSpaceAttr {
+ return func(m optionalAttr) {
+ m["data_format"] = value
+ }
+}
+
+// DepthToSpace for tensors of type T.
//
-// The input `SparseTensor` is represented via the tuple of inputs
-// (`indices`, `values`, `dense_shape`). The output `SparseTensor` has the
-// same `dense_shape` but with indices `output_indices` and values
-// `output_values`.
+// Rearranges data from depth into blocks of spatial data.
+// This is the reverse transformation of SpaceToDepth. More specifically,
+// this op outputs a copy of the input tensor where values from the `depth`
+// dimension are moved in spatial blocks to the `height` and `width` dimensions.
+// The attr `block_size` indicates the input block size and how the data is moved.
//
-// This op inserts a single entry for every row that doesn't have any values.
-// The index is created as `[row, 0, ..., 0]` and the inserted value
-// is `default_value`.
+// * Chunks of data of size `block_size * block_size` from depth are rearranged
+// into non-overlapping blocks of size `block_size x block_size`
+// * The width the output tensor is `input_depth * block_size`, whereas the
+// height is `input_height * block_size`.
+// * The Y, X coordinates within each block of the output image are determined
+// by the high order component of the input channel index.
+// * The depth of the input tensor must be divisible by
+// `block_size * block_size`.
//
-// For example, suppose `sp_input` has shape `[5, 6]` and non-empty values:
+// The `data_format` attr specifies the layout of the input and output tensors
+// with the following options:
+// "NHWC": `[ batch, height, width, channels ]`
+// "NCHW": `[ batch, channels, height, width ]`
+// "NCHW_VECT_C":
+// `qint8 [ batch, channels / 4, height, width, 4 ]`
//
-// [0, 1]: a
-// [0, 3]: b
-// [2, 0]: c
-// [3, 1]: d
+// It is useful to consider the operation as transforming a 6-D Tensor.
+// e.g. for data_format = NHWC,
+// Each element in the input tensor can be specified via 6 coordinates,
+// ordered by decreasing memory layout significance as:
+// n,iY,iX,bY,bX,oC (where n=batch index, iX, iY means X or Y coordinates
+// within the input image, bX, bY means coordinates
+// within the output block, oC means output channels).
+// The output would be the input transposed to the following layout:
+// n,iY,bY,iX,bX,oC
//
-// Rows 1 and 4 are empty, so the output will be of shape `[5, 6]` with values:
+// This operation is useful for resizing the activations between convolutions
+// (but keeping all data), e.g. instead of pooling. It is also useful for training
+// purely convolutional models.
//
-// [0, 1]: a
-// [0, 3]: b
-// [1, 0]: default_value
-// [2, 0]: c
-// [3, 1]: d
-// [4, 0]: default_value
+// For example, given an input of shape `[1, 1, 1, 4]`, data_format = "NHWC" and
+// block_size = 2:
//
-// The output `SparseTensor` will be in row-major order and will have the
-// same shape as the input.
+// ```
+// x = [[[[1, 2, 3, 4]]]]
//
-// This op also returns an indicator vector shaped `[dense_shape[0]]` such that
+// ```
//
-// empty_row_indicator[i] = True iff row i was an empty row.
+// This operation will output a tensor of shape `[1, 2, 2, 1]`:
//
-// And a reverse index map vector shaped `[indices.shape[0]]` that is used during
-// backpropagation,
+// ```
+// [[[[1], [2]],
+// [[3], [4]]]]
+// ```
//
-// reverse_index_map[j] = out_j s.t. indices[j, :] == output_indices[out_j, :]
+// Here, the input has a batch of 1 and each batch element has shape `[1, 1, 4]`,
+// the corresponding output will have 2x2 elements and will have a depth of
+// 1 channel (1 = `4 / (block_size * block_size)`).
+// The output element shape is `[2, 2, 1]`.
+//
+// For an input tensor with larger depth, here of shape `[1, 1, 1, 12]`, e.g.
+//
+// ```
+// x = [[[[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]]]]
+// ```
+//
+// This operation, for block size of 2, will return the following tensor of shape
+// `[1, 2, 2, 3]`
+//
+// ```
+// [[[[1, 2, 3], [4, 5, 6]],
+// [[7, 8, 9], [10, 11, 12]]]]
+//
+// ```
+//
+// Similarly, for the following input of shape `[1 2 2 4]`, and a block size of 2:
+//
+// ```
+// x = [[[[1, 2, 3, 4],
+// [5, 6, 7, 8]],
+// [[9, 10, 11, 12],
+// [13, 14, 15, 16]]]]
+// ```
+//
+// the operator will return the following tensor of shape `[1 4 4 1]`:
+//
+// ```
+// x = [[[ [1], [2], [5], [6]],
+// [ [3], [4], [7], [8]],
+// [ [9], [10], [13], [14]],
+// [ [11], [12], [15], [16]]]]
+//
+// ```
//
// Arguments:
-// indices: 2-D. the indices of the sparse tensor.
-// values: 1-D. the values of the sparse tensor.
-// dense_shape: 1-D. the shape of the sparse tensor.
-// default_value: 0-D. default value to insert into location `[row, 0, ..., 0]`
-// for rows missing from the input sparse tensor.
-// output indices: 2-D. the indices of the filled sparse tensor.
//
-// Returns 1-D. the values of the filled sparse tensor.1-D. whether the dense row was missing in the
-// input sparse tensor.1-D. a map from the input indices to the output indices.
-func SparseFillEmptyRows(scope *Scope, indices tf.Output, values tf.Output, dense_shape tf.Output, default_value tf.Output) (output_indices tf.Output, output_values tf.Output, empty_row_indicator tf.Output, reverse_index_map tf.Output) {
+// block_size: The size of the spatial block, same as in Space2Depth.
+func DepthToSpace(scope *Scope, input tf.Output, block_size int64, optional ...DepthToSpaceAttr) (output tf.Output) {
if scope.Err() != nil {
return
}
+ attrs := map[string]interface{}{"block_size": block_size}
+ for _, a := range optional {
+ a(attrs)
+ }
opspec := tf.OpSpec{
- Type: "SparseFillEmptyRows",
+ Type: "DepthToSpace",
Input: []tf.Input{
- indices, values, dense_shape, default_value,
+ input,
},
+ Attrs: attrs,
}
op := scope.AddOperation(opspec)
- return op.Output(0), op.Output(1), op.Output(2), op.Output(3)
+ return op.Output(0)
}
-// Reverses specific dimensions of a tensor.
-//
-// Given a `tensor`, and a `bool` tensor `dims` representing the dimensions
-// of `tensor`, this operation reverses each dimension i of `tensor` where
-// `dims[i]` is `True`.
-//
-// `tensor` can have up to 8 dimensions. The number of dimensions
-// of `tensor` must equal the number of elements in `dims`. In other words:
-//
-// `rank(tensor) = size(dims)`
-//
-// For example:
-//
-// ```
-// # tensor 't' is [[[[ 0, 1, 2, 3],
-// # [ 4, 5, 6, 7],
-// # [ 8, 9, 10, 11]],
-// # [[12, 13, 14, 15],
-// # [16, 17, 18, 19],
-// # [20, 21, 22, 23]]]]
-// # tensor 't' shape is [1, 2, 3, 4]
-//
-// # 'dims' is [False, False, False, True]
-// reverse(t, dims) ==> [[[[ 3, 2, 1, 0],
-// [ 7, 6, 5, 4],
-// [ 11, 10, 9, 8]],
-// [[15, 14, 13, 12],
-// [19, 18, 17, 16],
-// [23, 22, 21, 20]]]]
+// Conv3DBackpropInputV2Attr is an optional argument to Conv3DBackpropInputV2.
+type Conv3DBackpropInputV2Attr func(optionalAttr)
+
+// Conv3DBackpropInputV2DataFormat sets the optional data_format attribute to value.
//
-// # 'dims' is [False, True, False, False]
-// reverse(t, dims) ==> [[[[12, 13, 14, 15],
-// [16, 17, 18, 19],
-// [20, 21, 22, 23]
-// [[ 0, 1, 2, 3],
-// [ 4, 5, 6, 7],
-// [ 8, 9, 10, 11]]]]
+// value: The data format of the input and output data. With the
+// default format "NDHWC", the data is stored in the order of:
+// [batch, in_depth, in_height, in_width, in_channels].
+// Alternatively, the format could be "NCDHW", the data storage order is:
+// [batch, in_channels, in_depth, in_height, in_width].
+// If not specified, defaults to "NDHWC"
+func Conv3DBackpropInputV2DataFormat(value string) Conv3DBackpropInputV2Attr {
+ return func(m optionalAttr) {
+ m["data_format"] = value
+ }
+}
+
+// Conv3DBackpropInputV2Dilations sets the optional dilations attribute to value.
//
-// # 'dims' is [False, False, True, False]
-// reverse(t, dims) ==> [[[[8, 9, 10, 11],
-// [4, 5, 6, 7],
-// [0, 1, 2, 3]]
-// [[20, 21, 22, 23],
-// [16, 17, 18, 19],
-// [12, 13, 14, 15]]]]
-// ```
+// value: 1-D tensor of length 5. The dilation factor for each dimension of
+// `input`. If set to k > 1, there will be k-1 skipped cells between each
+// filter element on that dimension. The dimension order is determined by the
+// value of `data_format`, see above for details. Dilations in the batch and
+// depth dimensions must be 1.
+// If not specified, defaults to <i:1 i:1 i:1 i:1 i:1 >
+func Conv3DBackpropInputV2Dilations(value []int64) Conv3DBackpropInputV2Attr {
+ return func(m optionalAttr) {
+ m["dilations"] = value
+ }
+}
+
+// Computes the gradients of 3-D convolution with respect to the input.
//
// Arguments:
-// tensor: Up to 8-D.
-// dims: 1-D. The dimensions to reverse.
-//
-// Returns The same shape as `tensor`.
-func Reverse(scope *Scope, tensor tf.Output, dims tf.Output) (output tf.Output) {
+// input_sizes: An integer vector representing the tensor shape of `input`,
+// where `input` is a 5-D
+// `[batch, depth, rows, cols, in_channels]` tensor.
+// filter: Shape `[depth, rows, cols, in_channels, out_channels]`.
+// `in_channels` must match between `input` and `filter`.
+// out_backprop: Backprop signal of shape `[batch, out_depth, out_rows, out_cols,
+// out_channels]`.
+// strides: 1-D tensor of length 5. The stride of the sliding window for each
+// dimension of `input`. Must have `strides[0] = strides[4] = 1`.
+// padding: The type of padding algorithm to use.
+func Conv3DBackpropInputV2(scope *Scope, input_sizes tf.Output, filter tf.Output, out_backprop tf.Output, strides []int64, padding string, optional ...Conv3DBackpropInputV2Attr) (output tf.Output) {
if scope.Err() != nil {
return
}
+ attrs := map[string]interface{}{"strides": strides, "padding": padding}
+ for _, a := range optional {
+ a(attrs)
+ }
opspec := tf.OpSpec{
- Type: "Reverse",
+ Type: "Conv3DBackpropInputV2",
Input: []tf.Input{
- tensor, dims,
+ input_sizes, filter, out_backprop,
},
+ Attrs: attrs,
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// Computes log softmax activations.
-//
-// For each batch `i` and class `j` we have
-//
-// logsoftmax[i, j] = logits[i, j] - log(sum(exp(logits[i])))
-//
-// Arguments:
-// logits: 2-D with shape `[batch_size, num_classes]`.
+// Computes square root of x element-wise.
//
-// Returns Same shape as `logits`.
-func LogSoftmax(scope *Scope, logits tf.Output) (logsoftmax tf.Output) {
+// I.e., \\(y = \sqrt{x} = x^{1/2}\\).
+func Sqrt(scope *Scope, x tf.Output) (y tf.Output) {
if scope.Err() != nil {
return
}
opspec := tf.OpSpec{
- Type: "LogSoftmax",
+ Type: "Sqrt",
Input: []tf.Input{
- logits,
+ x,
},
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// Computes the inverse permutation of a tensor.
-//
-// This operation computes the inverse of an index permutation. It takes a 1-D
-// integer tensor `x`, which represents the indices of a zero-based array, and
-// swaps each value with its index position. In other words, for an output tensor
-// `y` and an input tensor `x`, this operation computes the following:
-//
-// `y[x[i]] = i for i in [0, 1, ..., len(x) - 1]`
-//
-// The values must include 0. There can be no duplicate values or negative values.
-//
-// For example:
+// Computes the gradients of 3-D convolution with respect to the filter.
//
-// ```
-// # tensor `x` is [3, 4, 0, 2, 1]
-// invert_permutation(x) ==> [2, 4, 3, 0, 1]
-// ```
+// DEPRECATED at GraphDef version 10: Use Conv3DBackpropFilterV2
//
// Arguments:
-// x: 1-D.
-//
-// Returns 1-D.
-func InvertPermutation(scope *Scope, x tf.Output) (y tf.Output) {
+// input: Shape `[batch, depth, rows, cols, in_channels]`.
+// filter: Shape `[depth, rows, cols, in_channels, out_channels]`.
+// `in_channels` must match between `input` and `filter`.
+// out_backprop: Backprop signal of shape `[batch, out_depth, out_rows, out_cols,
+// out_channels]`.
+// strides: 1-D tensor of length 5. The stride of the sliding window for each
+// dimension of `input`. Must have `strides[0] = strides[4] = 1`.
+// padding: The type of padding algorithm to use.
+func Conv3DBackpropFilter(scope *Scope, input tf.Output, filter tf.Output, out_backprop tf.Output, strides []int64, padding string) (output tf.Output) {
if scope.Err() != nil {
return
}
+ attrs := map[string]interface{}{"strides": strides, "padding": padding}
opspec := tf.OpSpec{
- Type: "InvertPermutation",
+ Type: "Conv3DBackpropFilter",
Input: []tf.Input{
- x,
+ input, filter, out_backprop,
},
+ Attrs: attrs,
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// BiasAddGradAttr is an optional argument to BiasAddGrad.
-type BiasAddGradAttr func(optionalAttr)
-
-// BiasAddGradDataFormat sets the optional data_format attribute to value.
-//
-// value: Specify the data format of the input and output data. With the
-// default format "NHWC", the bias tensor will be added to the last dimension
-// of the value tensor.
-// Alternatively, the format could be "NCHW", the data storage order of:
-// [batch, in_channels, in_height, in_width].
-// The tensor will be added to "in_channels", the third-to-the-last
-// dimension.
-// If not specified, defaults to "NHWC"
-func BiasAddGradDataFormat(value string) BiasAddGradAttr {
- return func(m optionalAttr) {
- m["data_format"] = value
- }
-}
-
-// The backward operation for "BiasAdd" on the "bias" tensor.
-//
-// It accumulates all the values from out_backprop into the feature dimension.
-// For NHWC data format, the feature dimension is the last. For NCHW data format,
-// the feature dimension is the third-to-last.
-//
-// Arguments:
-// out_backprop: Any number of dimensions.
+// Computes the gradient for the rsqrt of `x` wrt its input.
//
-// Returns 1-D with size the feature dimension of `out_backprop`.
-func BiasAddGrad(scope *Scope, out_backprop tf.Output, optional ...BiasAddGradAttr) (output tf.Output) {
+// Specifically, `grad = dy * -0.5 * y^3`, where `y = rsqrt(x)`, and `dy`
+// is the corresponding input gradient.
+func RsqrtGrad(scope *Scope, y tf.Output, dy tf.Output) (z tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{}
- for _, a := range optional {
- a(attrs)
- }
opspec := tf.OpSpec{
- Type: "BiasAddGrad",
+ Type: "RsqrtGrad",
Input: []tf.Input{
- out_backprop,
+ y, dy,
},
- Attrs: attrs,
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// FusedBatchNormV2Attr is an optional argument to FusedBatchNormV2.
-type FusedBatchNormV2Attr func(optionalAttr)
+// ReverseSequenceAttr is an optional argument to ReverseSequence.
+type ReverseSequenceAttr func(optionalAttr)
-// FusedBatchNormV2Epsilon sets the optional epsilon attribute to value.
+// ReverseSequenceBatchDim sets the optional batch_dim attribute to value.
//
-// value: A small float number added to the variance of x.
-// If not specified, defaults to 0.0001
-func FusedBatchNormV2Epsilon(value float32) FusedBatchNormV2Attr {
+// value: The dimension along which reversal is performed.
+// If not specified, defaults to 0
+func ReverseSequenceBatchDim(value int64) ReverseSequenceAttr {
return func(m optionalAttr) {
- m["epsilon"] = value
+ m["batch_dim"] = value
}
}
-// FusedBatchNormV2DataFormat sets the optional data_format attribute to value.
+// Reverses variable length slices.
//
-// value: The data format for x and y. Either "NHWC" (default) or "NCHW".
-// If not specified, defaults to "NHWC"
-func FusedBatchNormV2DataFormat(value string) FusedBatchNormV2Attr {
- return func(m optionalAttr) {
- m["data_format"] = value
- }
-}
-
-// FusedBatchNormV2IsTraining sets the optional is_training attribute to value.
+// This op first slices `input` along the dimension `batch_dim`, and for each
+// slice `i`, reverses the first `seq_lengths[i]` elements along
+// the dimension `seq_dim`.
//
-// value: A bool value to indicate the operation is for training (default)
-// or inference.
-// If not specified, defaults to true
-func FusedBatchNormV2IsTraining(value bool) FusedBatchNormV2Attr {
- return func(m optionalAttr) {
- m["is_training"] = value
- }
-}
-
-// Batch normalization.
+// The elements of `seq_lengths` must obey `seq_lengths[i] <= input.dims[seq_dim]`,
+// and `seq_lengths` must be a vector of length `input.dims[batch_dim]`.
//
-// Note that the size of 4D Tensors are defined by either "NHWC" or "NCHW".
-// The size of 1D Tensors matches the dimension C of the 4D Tensors.
+// The output slice `i` along dimension `batch_dim` is then given by input
+// slice `i`, with the first `seq_lengths[i]` slices along dimension
+// `seq_dim` reversed.
//
-// Arguments:
-// x: A 4D Tensor for input data.
-// scale: A 1D Tensor for scaling factor, to scale the normalized x.
-// offset: A 1D Tensor for offset, to shift to the normalized x.
-// mean: A 1D Tensor for population mean. Used for inference only;
-// must be empty for training.
-// variance: A 1D Tensor for population variance. Used for inference only;
-// must be empty for training.
+// For example:
//
-// Returns A 4D Tensor for output data.A 1D Tensor for the computed batch mean, to be used by TensorFlow
-// to compute the running mean.A 1D Tensor for the computed batch variance, to be used by
-// TensorFlow to compute the running variance.A 1D Tensor for the computed batch mean, to be reused
-// in the gradient computation.A 1D Tensor for the computed batch variance (inverted variance
-// in the cuDNN case), to be reused in the gradient computation.
-func FusedBatchNormV2(scope *Scope, x tf.Output, scale tf.Output, offset tf.Output, mean tf.Output, variance tf.Output, optional ...FusedBatchNormV2Attr) (y tf.Output, batch_mean tf.Output, batch_variance tf.Output, reserve_space_1 tf.Output, reserve_space_2 tf.Output) {
+// ```
+// # Given this:
+// batch_dim = 0
+// seq_dim = 1
+// input.dims = (4, 8, ...)
+// seq_lengths = [7, 2, 3, 5]
+//
+// # then slices of input are reversed on seq_dim, but only up to seq_lengths:
+// output[0, 0:7, :, ...] = input[0, 7:0:-1, :, ...]
+// output[1, 0:2, :, ...] = input[1, 2:0:-1, :, ...]
+// output[2, 0:3, :, ...] = input[2, 3:0:-1, :, ...]
+// output[3, 0:5, :, ...] = input[3, 5:0:-1, :, ...]
+//
+// # while entries past seq_lens are copied through:
+// output[0, 7:, :, ...] = input[0, 7:, :, ...]
+// output[1, 2:, :, ...] = input[1, 2:, :, ...]
+// output[2, 3:, :, ...] = input[2, 3:, :, ...]
+// output[3, 2:, :, ...] = input[3, 2:, :, ...]
+// ```
+//
+// In contrast, if:
+//
+// ```
+// # Given this:
+// batch_dim = 2
+// seq_dim = 0
+// input.dims = (8, ?, 4, ...)
+// seq_lengths = [7, 2, 3, 5]
+//
+// # then slices of input are reversed on seq_dim, but only up to seq_lengths:
+// output[0:7, :, 0, :, ...] = input[7:0:-1, :, 0, :, ...]
+// output[0:2, :, 1, :, ...] = input[2:0:-1, :, 1, :, ...]
+// output[0:3, :, 2, :, ...] = input[3:0:-1, :, 2, :, ...]
+// output[0:5, :, 3, :, ...] = input[5:0:-1, :, 3, :, ...]
+//
+// # while entries past seq_lens are copied through:
+// output[7:, :, 0, :, ...] = input[7:, :, 0, :, ...]
+// output[2:, :, 1, :, ...] = input[2:, :, 1, :, ...]
+// output[3:, :, 2, :, ...] = input[3:, :, 2, :, ...]
+// output[2:, :, 3, :, ...] = input[2:, :, 3, :, ...]
+// ```
+//
+// Arguments:
+// input: The input to reverse.
+// seq_lengths: 1-D with length `input.dims(batch_dim)` and
+// `max(seq_lengths) <= input.dims(seq_dim)`
+// seq_dim: The dimension which is partially reversed.
+//
+// Returns The partially reversed input. It has the same shape as `input`.
+func ReverseSequence(scope *Scope, input tf.Output, seq_lengths tf.Output, seq_dim int64, optional ...ReverseSequenceAttr) (output tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{}
+ attrs := map[string]interface{}{"seq_dim": seq_dim}
for _, a := range optional {
a(attrs)
}
opspec := tf.OpSpec{
- Type: "FusedBatchNormV2",
+ Type: "ReverseSequence",
Input: []tf.Input{
- x, scale, offset, mean, variance,
+ input, seq_lengths,
},
Attrs: attrs,
}
op := scope.AddOperation(opspec)
- return op.Output(0), op.Output(1), op.Output(2), op.Output(3), op.Output(4)
+ return op.Output(0)
}
-// Returns the rank of a tensor.
+// DepthwiseConv2dNativeAttr is an optional argument to DepthwiseConv2dNative.
+type DepthwiseConv2dNativeAttr func(optionalAttr)
+
+// DepthwiseConv2dNativeDataFormat sets the optional data_format attribute to value.
//
-// This operation returns an integer representing the rank of `input`.
+// value: Specify the data format of the input and output data. With the
+// default format "NHWC", the data is stored in the order of:
+// [batch, height, width, channels].
+// Alternatively, the format could be "NCHW", the data storage order of:
+// [batch, channels, height, width].
+// If not specified, defaults to "NHWC"
+func DepthwiseConv2dNativeDataFormat(value string) DepthwiseConv2dNativeAttr {
+ return func(m optionalAttr) {
+ m["data_format"] = value
+ }
+}
+
+// DepthwiseConv2dNativeDilations sets the optional dilations attribute to value.
//
-// For example:
+// value: 1-D tensor of length 4. The dilation factor for each dimension of
+// `input`. If set to k > 1, there will be k-1 skipped cells between each filter
+// element on that dimension. The dimension order is determined by the value of
+// `data_format`, see above for details. Dilations in the batch and depth
+// dimensions must be 1.
+// If not specified, defaults to <i:1 i:1 i:1 i:1 >
+func DepthwiseConv2dNativeDilations(value []int64) DepthwiseConv2dNativeAttr {
+ return func(m optionalAttr) {
+ m["dilations"] = value
+ }
+}
+
+// Computes a 2-D depthwise convolution given 4-D `input` and `filter` tensors.
+//
+// Given an input tensor of shape `[batch, in_height, in_width, in_channels]`
+// and a filter / kernel tensor of shape
+// `[filter_height, filter_width, in_channels, channel_multiplier]`, containing
+// `in_channels` convolutional filters of depth 1, `depthwise_conv2d` applies
+// a different filter to each input channel (expanding from 1 channel to
+// `channel_multiplier` channels for each), then concatenates the results
+// together. Thus, the output has `in_channels * channel_multiplier` channels.
//
// ```
-// # 't' is [[[1, 1, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]]]
-// # shape of tensor 't' is [2, 2, 3]
-// rank(t) ==> 3
+// for k in 0..in_channels-1
+// for q in 0..channel_multiplier-1
+// output[b, i, j, k * channel_multiplier + q] =
+// sum_{di, dj} input[b, strides[1] * i + di, strides[2] * j + dj, k] *
+// filter[di, dj, k, q]
// ```
//
-// **Note**: The rank of a tensor is not the same as the rank of a matrix. The rank
-// of a tensor is the number of indices required to uniquely select each element
-// of the tensor. Rank is also known as "order", "degree", or "ndims."
-func Rank(scope *Scope, input tf.Output) (output tf.Output) {
- if scope.Err() != nil {
- return
- }
- opspec := tf.OpSpec{
- Type: "Rank",
- Input: []tf.Input{
- input,
- },
- }
- op := scope.AddOperation(opspec)
- return op.Output(0)
-}
-
-// Transforms a Tensor into a serialized TensorProto proto.
+// Must have `strides[0] = strides[3] = 1`. For the most common case of the same
+// horizontal and vertices strides, `strides = [1, stride, stride, 1]`.
//
// Arguments:
-// tensor: A Tensor of type `T`.
//
-// Returns A serialized TensorProto proto of the input tensor.
-func SerializeTensor(scope *Scope, tensor tf.Output) (serialized tf.Output) {
+//
+// strides: 1-D of length 4. The stride of the sliding window for each dimension
+// of `input`.
+// padding: The type of padding algorithm to use.
+func DepthwiseConv2dNative(scope *Scope, input tf.Output, filter tf.Output, strides []int64, padding string, optional ...DepthwiseConv2dNativeAttr) (output tf.Output) {
if scope.Err() != nil {
return
}
+ attrs := map[string]interface{}{"strides": strides, "padding": padding}
+ for _, a := range optional {
+ a(attrs)
+ }
opspec := tf.OpSpec{
- Type: "SerializeTensor",
+ Type: "DepthwiseConv2dNative",
Input: []tf.Input{
- tensor,
+ input, filter,
},
+ Attrs: attrs,
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// MatrixSolveAttr is an optional argument to MatrixSolve.
-type MatrixSolveAttr func(optionalAttr)
+// MaxPoolGradV2Attr is an optional argument to MaxPoolGradV2.
+type MaxPoolGradV2Attr func(optionalAttr)
-// MatrixSolveAdjoint sets the optional adjoint attribute to value.
+// MaxPoolGradV2DataFormat sets the optional data_format attribute to value.
//
-// value: Boolean indicating whether to solve with `matrix` or its (block-wise)
-// adjoint.
-// If not specified, defaults to false
-func MatrixSolveAdjoint(value bool) MatrixSolveAttr {
+// value: Specify the data format of the input and output data. With the
+// default format "NHWC", the data is stored in the order of:
+// [batch, in_height, in_width, in_channels].
+// Alternatively, the format could be "NCHW", the data storage order of:
+// [batch, in_channels, in_height, in_width].
+// If not specified, defaults to "NHWC"
+func MaxPoolGradV2DataFormat(value string) MaxPoolGradV2Attr {
return func(m optionalAttr) {
- m["adjoint"] = value
+ m["data_format"] = value
}
}
-// Solves systems of linear equations.
-//
-// `Matrix` is a tensor of shape `[..., M, M]` whose inner-most 2 dimensions
-// form square matrices. `Rhs` is a tensor of shape `[..., M, K]`. The `output` is
-// a tensor shape `[..., M, K]`. If `adjoint` is `False` then each output matrix
-// satisfies `matrix[..., :, :] * output[..., :, :] = rhs[..., :, :]`.
-// If `adjoint` is `True` then each output matrix satisfies
-// `adjoint(matrix[..., :, :]) * output[..., :, :] = rhs[..., :, :]`.
+// Computes gradients of the maxpooling function.
//
// Arguments:
-// matrix: Shape is `[..., M, M]`.
-// rhs: Shape is `[..., M, K]`.
+// orig_input: The original input tensor.
+// orig_output: The original output tensor.
+// grad: 4-D. Gradients w.r.t. the output of `max_pool`.
+// ksize: The size of the window for each dimension of the input tensor.
+// strides: The stride of the sliding window for each dimension of the
+// input tensor.
+// padding: The type of padding algorithm to use.
//
-// Returns Shape is `[..., M, K]`.
-func MatrixSolve(scope *Scope, matrix tf.Output, rhs tf.Output, optional ...MatrixSolveAttr) (output tf.Output) {
+// Returns Gradients w.r.t. the input to `max_pool`.
+func MaxPoolGradV2(scope *Scope, orig_input tf.Output, orig_output tf.Output, grad tf.Output, ksize tf.Output, strides tf.Output, padding string, optional ...MaxPoolGradV2Attr) (output tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{}
+ attrs := map[string]interface{}{"padding": padding}
for _, a := range optional {
a(attrs)
}
opspec := tf.OpSpec{
- Type: "MatrixSolve",
+ Type: "MaxPoolGradV2",
Input: []tf.Input{
- matrix, rhs,
+ orig_input, orig_output, grad, ksize, strides,
},
Attrs: attrs,
}
return op.Output(0)
}
-// Computes acos of x element-wise.
-func Acos(scope *Scope, x tf.Output) (y tf.Output) {
- if scope.Err() != nil {
- return
- }
- opspec := tf.OpSpec{
- Type: "Acos",
- Input: []tf.Input{
- x,
- },
- }
- op := scope.AddOperation(opspec)
- return op.Output(0)
-}
-
-// Real-valued fast Fourier transform.
-//
-// Computes the 1-dimensional discrete Fourier transform of a real-valued signal
-// over the inner-most dimension of `input`.
-//
-// Since the DFT of a real signal is Hermitian-symmetric, `RFFT` only returns the
-// `fft_length / 2 + 1` unique components of the FFT: the zero-frequency term,
-// followed by the `fft_length / 2` positive-frequency terms.
+// Restore a reader to a previously saved state.
//
-// Along the axis `RFFT` is computed on, if `fft_length` is smaller than the
-// corresponding dimension of `input`, the dimension is cropped. If it is larger,
-// the dimension is padded with zeros.
+// Not all Readers support being restored, so this can produce an
+// Unimplemented error.
//
// Arguments:
-// input: A float32 tensor.
-// fft_length: An int32 tensor of shape [1]. The FFT length.
-//
-// Returns A complex64 tensor of the same rank as `input`. The inner-most
-// dimension of `input` is replaced with the `fft_length / 2 + 1` unique
-// frequency components of its 1D Fourier transform.
+// reader_handle: Handle to a Reader.
+// state: Result of a ReaderSerializeState of a Reader with type
+// matching reader_handle.
//
-// @compatibility(numpy)
-// Equivalent to np.fft.rfft
-// @end_compatibility
-func RFFT(scope *Scope, input tf.Output, fft_length tf.Output) (output tf.Output) {
+// Returns the created operation.
+func ReaderRestoreStateV2(scope *Scope, reader_handle tf.Output, state tf.Output) (o *tf.Operation) {
if scope.Err() != nil {
return
}
opspec := tf.OpSpec{
- Type: "RFFT",
+ Type: "ReaderRestoreStateV2",
Input: []tf.Input{
- input, fft_length,
+ reader_handle, state,
},
}
- op := scope.AddOperation(opspec)
- return op.Output(0)
+ return scope.AddOperation(opspec)
}
-// DepthwiseConv2dNativeBackpropFilterAttr is an optional argument to DepthwiseConv2dNativeBackpropFilter.
-type DepthwiseConv2dNativeBackpropFilterAttr func(optionalAttr)
+// MaxPoolGradAttr is an optional argument to MaxPoolGrad.
+type MaxPoolGradAttr func(optionalAttr)
-// DepthwiseConv2dNativeBackpropFilterDataFormat sets the optional data_format attribute to value.
+// MaxPoolGradDataFormat sets the optional data_format attribute to value.
//
// value: Specify the data format of the input and output data. With the
// default format "NHWC", the data is stored in the order of:
-// [batch, height, width, channels].
+// [batch, in_height, in_width, in_channels].
// Alternatively, the format could be "NCHW", the data storage order of:
-// [batch, channels, height, width].
+// [batch, in_channels, in_height, in_width].
// If not specified, defaults to "NHWC"
-func DepthwiseConv2dNativeBackpropFilterDataFormat(value string) DepthwiseConv2dNativeBackpropFilterAttr {
+func MaxPoolGradDataFormat(value string) MaxPoolGradAttr {
return func(m optionalAttr) {
m["data_format"] = value
}
}
-// DepthwiseConv2dNativeBackpropFilterDilations sets the optional dilations attribute to value.
-//
-// value: 1-D tensor of length 4. The dilation factor for each dimension of
-// `input`. If set to k > 1, there will be k-1 skipped cells between each filter
-// element on that dimension. The dimension order is determined by the value of
-// `data_format`, see above for details. Dilations in the batch and depth
-// dimensions must be 1.
-// If not specified, defaults to <i:1 i:1 i:1 i:1 >
-func DepthwiseConv2dNativeBackpropFilterDilations(value []int64) DepthwiseConv2dNativeBackpropFilterAttr {
- return func(m optionalAttr) {
- m["dilations"] = value
- }
-}
-
-// Computes the gradients of depthwise convolution with respect to the filter.
+// Computes gradients of the maxpooling function.
//
// Arguments:
-// input: 4-D with shape based on `data_format`. For example, if
-// `data_format` is 'NHWC' then `input` is a 4-D `[batch, in_height,
-// in_width, in_channels]` tensor.
-// filter_sizes: An integer vector representing the tensor shape of `filter`,
-// where `filter` is a 4-D
-// `[filter_height, filter_width, in_channels, depthwise_multiplier]` tensor.
-// out_backprop: 4-D with shape based on `data_format`.
-// For example, if `data_format` is 'NHWC' then
-// out_backprop shape is `[batch, out_height, out_width, out_channels]`.
-// Gradients w.r.t. the output of the convolution.
-// strides: The stride of the sliding window for each dimension of the input
-// of the convolution.
+// orig_input: The original input tensor.
+// orig_output: The original output tensor.
+// grad: 4-D. Gradients w.r.t. the output of `max_pool`.
+// ksize: The size of the window for each dimension of the input tensor.
+// strides: The stride of the sliding window for each dimension of the
+// input tensor.
// padding: The type of padding algorithm to use.
//
-// Returns 4-D with shape
-// `[filter_height, filter_width, in_channels, out_channels]`. Gradient w.r.t.
-// the `filter` input of the convolution.
-func DepthwiseConv2dNativeBackpropFilter(scope *Scope, input tf.Output, filter_sizes tf.Output, out_backprop tf.Output, strides []int64, padding string, optional ...DepthwiseConv2dNativeBackpropFilterAttr) (output tf.Output) {
+// Returns Gradients w.r.t. the input to `max_pool`.
+func MaxPoolGrad(scope *Scope, orig_input tf.Output, orig_output tf.Output, grad tf.Output, ksize []int64, strides []int64, padding string, optional ...MaxPoolGradAttr) (output tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{"strides": strides, "padding": padding}
+ attrs := map[string]interface{}{"ksize": ksize, "strides": strides, "padding": padding}
for _, a := range optional {
a(attrs)
}
opspec := tf.OpSpec{
- Type: "DepthwiseConv2dNativeBackpropFilter",
+ Type: "MaxPoolGrad",
Input: []tf.Input{
- input, filter_sizes, out_backprop,
+ orig_input, orig_output, grad,
},
Attrs: attrs,
}
return op.Output(0)
}
-// LRNGradAttr is an optional argument to LRNGrad.
-type LRNGradAttr func(optionalAttr)
+// CropAndResizeAttr is an optional argument to CropAndResize.
+type CropAndResizeAttr func(optionalAttr)
-// LRNGradDepthRadius sets the optional depth_radius attribute to value.
+// CropAndResizeMethod sets the optional method attribute to value.
//
-// value: A depth radius.
-// If not specified, defaults to 5
-func LRNGradDepthRadius(value int64) LRNGradAttr {
+// value: A string specifying the interpolation method. Only 'bilinear' is
+// supported for now.
+// If not specified, defaults to "bilinear"
+func CropAndResizeMethod(value string) CropAndResizeAttr {
return func(m optionalAttr) {
- m["depth_radius"] = value
+ m["method"] = value
}
}
-// LRNGradBias sets the optional bias attribute to value.
+// CropAndResizeExtrapolationValue sets the optional extrapolation_value attribute to value.
//
-// value: An offset (usually > 0 to avoid dividing by 0).
-// If not specified, defaults to 1
-func LRNGradBias(value float32) LRNGradAttr {
+// value: Value used for extrapolation, when applicable.
+// If not specified, defaults to 0
+func CropAndResizeExtrapolationValue(value float32) CropAndResizeAttr {
return func(m optionalAttr) {
- m["bias"] = value
+ m["extrapolation_value"] = value
}
}
-// LRNGradAlpha sets the optional alpha attribute to value.
+// Extracts crops from the input image tensor and bilinearly resizes them (possibly
//
-// value: A scale factor, usually positive.
-// If not specified, defaults to 1
-func LRNGradAlpha(value float32) LRNGradAttr {
- return func(m optionalAttr) {
- m["alpha"] = value
- }
-}
-
-// LRNGradBeta sets the optional beta attribute to value.
+// with aspect ratio change) to a common output size specified by `crop_size`. This
+// is more general than the `crop_to_bounding_box` op which extracts a fixed size
+// slice from the input image and does not allow resizing or aspect ratio change.
//
-// value: An exponent.
-// If not specified, defaults to 0.5
-func LRNGradBeta(value float32) LRNGradAttr {
- return func(m optionalAttr) {
- m["beta"] = value
- }
-}
-
-// Gradients for Local Response Normalization.
+// Returns a tensor with `crops` from the input `image` at positions defined at the
+// bounding box locations in `boxes`. The cropped boxes are all resized (with
+// bilinear interpolation) to a fixed `size = [crop_height, crop_width]`. The
+// result is a 4-D tensor `[num_boxes, crop_height, crop_width, depth]`. The
+// resizing is corner aligned. In particular, if `boxes = [[0, 0, 1, 1]]`, the
+// method will give identical results to using `tf.image.resize_bilinear()`
+// with `align_corners=True`.
//
// Arguments:
-// input_grads: 4-D with shape `[batch, height, width, channels]`.
-// input_image: 4-D with shape `[batch, height, width, channels]`.
-// output_image: 4-D with shape `[batch, height, width, channels]`.
+// image: A 4-D tensor of shape `[batch, image_height, image_width, depth]`.
+// Both `image_height` and `image_width` need to be positive.
+// boxes: A 2-D tensor of shape `[num_boxes, 4]`. The `i`-th row of the tensor
+// specifies the coordinates of a box in the `box_ind[i]` image and is specified
+// in normalized coordinates `[y1, x1, y2, x2]`. A normalized coordinate value of
+// `y` is mapped to the image coordinate at `y * (image_height - 1)`, so as the
+// `[0, 1]` interval of normalized image height is mapped to
+// `[0, image_height - 1]` in image height coordinates. We do allow `y1` > `y2`, in
+// which case the sampled crop is an up-down flipped version of the original
+// image. The width dimension is treated similarly. Normalized coordinates
+// outside the `[0, 1]` range are allowed, in which case we use
+// `extrapolation_value` to extrapolate the input image values.
+// box_ind: A 1-D tensor of shape `[num_boxes]` with int32 values in `[0, batch)`.
+// The value of `box_ind[i]` specifies the image that the `i`-th box refers to.
+// crop_size: A 1-D tensor of 2 elements, `size = [crop_height, crop_width]`. All
+// cropped image patches are resized to this size. The aspect ratio of the image
+// content is not preserved. Both `crop_height` and `crop_width` need to be
+// positive.
//
-// Returns The gradients for LRN.
-func LRNGrad(scope *Scope, input_grads tf.Output, input_image tf.Output, output_image tf.Output, optional ...LRNGradAttr) (output tf.Output) {
+// Returns A 4-D tensor of shape `[num_boxes, crop_height, crop_width, depth]`.
+func CropAndResize(scope *Scope, image tf.Output, boxes tf.Output, box_ind tf.Output, crop_size tf.Output, optional ...CropAndResizeAttr) (crops tf.Output) {
if scope.Err() != nil {
return
}
a(attrs)
}
opspec := tf.OpSpec{
- Type: "LRNGrad",
+ Type: "CropAndResize",
Input: []tf.Input{
- input_grads, input_image, output_image,
+ image, boxes, box_ind, crop_size,
},
Attrs: attrs,
}
return op.Output(0)
}
-// AnyAttr is an optional argument to Any.
-type AnyAttr func(optionalAttr)
-
-// AnyKeepDims sets the optional keep_dims attribute to value.
+// Fills empty rows in the input 2-D `SparseTensor` with a default value.
//
-// value: If true, retain reduced dimensions with length 1.
-// If not specified, defaults to false
-func AnyKeepDims(value bool) AnyAttr {
- return func(m optionalAttr) {
- m["keep_dims"] = value
- }
-}
-
-// Computes the "logical or" of elements across dimensions of a tensor.
+// The input `SparseTensor` is represented via the tuple of inputs
+// (`indices`, `values`, `dense_shape`). The output `SparseTensor` has the
+// same `dense_shape` but with indices `output_indices` and values
+// `output_values`.
//
-// Reduces `input` along the dimensions given in `axis`. Unless
-// `keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in
-// `axis`. If `keep_dims` is true, the reduced dimensions are
-// retained with length 1.
+// This op inserts a single entry for every row that doesn't have any values.
+// The index is created as `[row, 0, ..., 0]` and the inserted value
+// is `default_value`.
+//
+// For example, suppose `sp_input` has shape `[5, 6]` and non-empty values:
+//
+// [0, 1]: a
+// [0, 3]: b
+// [2, 0]: c
+// [3, 1]: d
+//
+// Rows 1 and 4 are empty, so the output will be of shape `[5, 6]` with values:
+//
+// [0, 1]: a
+// [0, 3]: b
+// [1, 0]: default_value
+// [2, 0]: c
+// [3, 1]: d
+// [4, 0]: default_value
+//
+// The output `SparseTensor` will be in row-major order and will have the
+// same shape as the input.
+//
+// This op also returns an indicator vector shaped `[dense_shape[0]]` such that
+//
+// empty_row_indicator[i] = True iff row i was an empty row.
+//
+// And a reverse index map vector shaped `[indices.shape[0]]` that is used during
+// backpropagation,
+//
+// reverse_index_map[j] = out_j s.t. indices[j, :] == output_indices[out_j, :]
//
// Arguments:
-// input: The tensor to reduce.
-// axis: The dimensions to reduce. Must be in the range
-// `[-rank(input), rank(input))`.
+// indices: 2-D. the indices of the sparse tensor.
+// values: 1-D. the values of the sparse tensor.
+// dense_shape: 1-D. the shape of the sparse tensor.
+// default_value: 0-D. default value to insert into location `[row, 0, ..., 0]`
+// for rows missing from the input sparse tensor.
+// output indices: 2-D. the indices of the filled sparse tensor.
//
-// Returns The reduced tensor.
-func Any(scope *Scope, input tf.Output, axis tf.Output, optional ...AnyAttr) (output tf.Output) {
+// Returns 1-D. the values of the filled sparse tensor.1-D. whether the dense row was missing in the
+// input sparse tensor.1-D. a map from the input indices to the output indices.
+func SparseFillEmptyRows(scope *Scope, indices tf.Output, values tf.Output, dense_shape tf.Output, default_value tf.Output) (output_indices tf.Output, output_values tf.Output, empty_row_indicator tf.Output, reverse_index_map tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{}
- for _, a := range optional {
- a(attrs)
- }
opspec := tf.OpSpec{
- Type: "Any",
+ Type: "SparseFillEmptyRows",
Input: []tf.Input{
- input, axis,
+ indices, values, dense_shape, default_value,
},
- Attrs: attrs,
}
op := scope.AddOperation(opspec)
- return op.Output(0)
+ return op.Output(0), op.Output(1), op.Output(2), op.Output(3)
}
-// ResourceApplyFtrlAttr is an optional argument to ResourceApplyFtrl.
-type ResourceApplyFtrlAttr func(optionalAttr)
-
-// ResourceApplyFtrlUseLocking sets the optional use_locking attribute to value.
+// Reverses specific dimensions of a tensor.
//
-// value: If `True`, updating of the var and accum tensors will be protected
-// by a lock; otherwise the behavior is undefined, but may exhibit less
-// contention.
-// If not specified, defaults to false
-func ResourceApplyFtrlUseLocking(value bool) ResourceApplyFtrlAttr {
- return func(m optionalAttr) {
- m["use_locking"] = value
- }
-}
-
-// Update '*var' according to the Ftrl-proximal scheme.
+// Given a `tensor`, and a `bool` tensor `dims` representing the dimensions
+// of `tensor`, this operation reverses each dimension i of `tensor` where
+// `dims[i]` is `True`.
//
-// accum_new = accum + grad * grad
-// linear += grad - (accum_new^(-lr_power) - accum^(-lr_power)) / lr * var
-// quadratic = 1.0 / (accum_new^(lr_power) * lr) + 2 * l2
-// var = (sign(linear) * l1 - linear) / quadratic if |linear| > l1 else 0.0
-// accum = accum_new
+// `tensor` can have up to 8 dimensions. The number of dimensions
+// of `tensor` must equal the number of elements in `dims`. In other words:
//
-// Arguments:
-// var_: Should be from a Variable().
-// accum: Should be from a Variable().
-// linear: Should be from a Variable().
-// grad: The gradient.
-// lr: Scaling factor. Must be a scalar.
-// l1: L1 regulariation. Must be a scalar.
-// l2: L2 regulariation. Must be a scalar.
-// lr_power: Scaling factor. Must be a scalar.
+// `rank(tensor) = size(dims)`
//
-// Returns the created operation.
-func ResourceApplyFtrl(scope *Scope, var_ tf.Output, accum tf.Output, linear tf.Output, grad tf.Output, lr tf.Output, l1 tf.Output, l2 tf.Output, lr_power tf.Output, optional ...ResourceApplyFtrlAttr) (o *tf.Operation) {
- if scope.Err() != nil {
- return
- }
- attrs := map[string]interface{}{}
- for _, a := range optional {
- a(attrs)
- }
- opspec := tf.OpSpec{
- Type: "ResourceApplyFtrl",
- Input: []tf.Input{
- var_, accum, linear, grad, lr, l1, l2, lr_power,
- },
- Attrs: attrs,
- }
- return scope.AddOperation(opspec)
-}
-
-// RandomUniformAttr is an optional argument to RandomUniform.
-type RandomUniformAttr func(optionalAttr)
-
-// RandomUniformSeed sets the optional seed attribute to value.
+// For example:
//
-// value: If either `seed` or `seed2` are set to be non-zero, the random number
-// generator is seeded by the given seed. Otherwise, it is seeded by a
-// random seed.
-// If not specified, defaults to 0
-func RandomUniformSeed(value int64) RandomUniformAttr {
- return func(m optionalAttr) {
- m["seed"] = value
- }
-}
-
-// RandomUniformSeed2 sets the optional seed2 attribute to value.
+// ```
+// # tensor 't' is [[[[ 0, 1, 2, 3],
+// # [ 4, 5, 6, 7],
+// # [ 8, 9, 10, 11]],
+// # [[12, 13, 14, 15],
+// # [16, 17, 18, 19],
+// # [20, 21, 22, 23]]]]
+// # tensor 't' shape is [1, 2, 3, 4]
//
-// value: A second seed to avoid seed collision.
-// If not specified, defaults to 0
-func RandomUniformSeed2(value int64) RandomUniformAttr {
- return func(m optionalAttr) {
- m["seed2"] = value
- }
-}
-
-// Outputs random values from a uniform distribution.
+// # 'dims' is [False, False, False, True]
+// reverse(t, dims) ==> [[[[ 3, 2, 1, 0],
+// [ 7, 6, 5, 4],
+// [ 11, 10, 9, 8]],
+// [[15, 14, 13, 12],
+// [19, 18, 17, 16],
+// [23, 22, 21, 20]]]]
//
-// The generated values follow a uniform distribution in the range `[0, 1)`. The
-// lower bound 0 is included in the range, while the upper bound 1 is excluded.
+// # 'dims' is [False, True, False, False]
+// reverse(t, dims) ==> [[[[12, 13, 14, 15],
+// [16, 17, 18, 19],
+// [20, 21, 22, 23]
+// [[ 0, 1, 2, 3],
+// [ 4, 5, 6, 7],
+// [ 8, 9, 10, 11]]]]
+//
+// # 'dims' is [False, False, True, False]
+// reverse(t, dims) ==> [[[[8, 9, 10, 11],
+// [4, 5, 6, 7],
+// [0, 1, 2, 3]]
+// [[20, 21, 22, 23],
+// [16, 17, 18, 19],
+// [12, 13, 14, 15]]]]
+// ```
//
// Arguments:
-// shape: The shape of the output tensor.
-// dtype: The type of the output.
+// tensor: Up to 8-D.
+// dims: 1-D. The dimensions to reverse.
//
-// Returns A tensor of the specified shape filled with uniform random values.
-func RandomUniform(scope *Scope, shape tf.Output, dtype tf.DataType, optional ...RandomUniformAttr) (output tf.Output) {
+// Returns The same shape as `tensor`.
+func Reverse(scope *Scope, tensor tf.Output, dims tf.Output) (output tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{"dtype": dtype}
- for _, a := range optional {
- a(attrs)
- }
opspec := tf.OpSpec{
- Type: "RandomUniform",
+ Type: "Reverse",
Input: []tf.Input{
- shape,
+ tensor, dims,
},
- Attrs: attrs,
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// AssertAttr is an optional argument to Assert.
-type AssertAttr func(optionalAttr)
-
-// AssertSummarize sets the optional summarize attribute to value.
+// Computes log softmax activations.
//
-// value: Print this many entries of each tensor.
-// If not specified, defaults to 3
-func AssertSummarize(value int64) AssertAttr {
- return func(m optionalAttr) {
- m["summarize"] = value
- }
-}
-
-// Asserts that the given condition is true.
+// For each batch `i` and class `j` we have
//
-// If `condition` evaluates to false, print the list of tensors in `data`.
-// `summarize` determines how many entries of the tensors to print.
+// logsoftmax[i, j] = logits[i, j] - log(sum(exp(logits[i])))
//
// Arguments:
-// condition: The condition to evaluate.
-// data: The tensors to print out when condition is false.
-//
-// Returns the created operation.
-func Assert(scope *Scope, condition tf.Output, data []tf.Output, optional ...AssertAttr) (o *tf.Operation) {
- if scope.Err() != nil {
- return
- }
- attrs := map[string]interface{}{}
- for _, a := range optional {
- a(attrs)
- }
- opspec := tf.OpSpec{
- Type: "Assert",
- Input: []tf.Input{
- condition, tf.OutputList(data),
- },
- Attrs: attrs,
- }
- return scope.AddOperation(opspec)
-}
-
-// Computes element-wise population count (a.k.a. popcount, bitsum, bitcount).
-//
-// For each entry in `x`, calculates the number of `1` (on) bits in the binary
-// representation of that entry.
+// logits: 2-D with shape `[batch_size, num_classes]`.
//
-// **NOTE**: It is more efficient to first `tf.bitcast` your tensors into
-// `int32` or `int64` and perform the bitcount on the result, than to feed in
-// 8- or 16-bit inputs and then aggregate the resulting counts.
-func PopulationCount(scope *Scope, x tf.Output) (y tf.Output) {
+// Returns Same shape as `logits`.
+func LogSoftmax(scope *Scope, logits tf.Output) (logsoftmax tf.Output) {
if scope.Err() != nil {
return
}
opspec := tf.OpSpec{
- Type: "PopulationCount",
+ Type: "LogSoftmax",
Input: []tf.Input{
- x,
+ logits,
},
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// Split a `SparseTensor` into `num_split` tensors along one dimension.
+// Computes the inverse permutation of a tensor.
//
-// If the `shape[split_dim]` is not an integer multiple of `num_split`. Slices
-// `[0 : shape[split_dim] % num_split]` gets one extra dimension.
-// For example, if `split_dim = 1` and `num_split = 2` and the input is
+// This operation computes the inverse of an index permutation. It takes a 1-D
+// integer tensor `x`, which represents the indices of a zero-based array, and
+// swaps each value with its index position. In other words, for an output tensor
+// `y` and an input tensor `x`, this operation computes the following:
//
-// input_tensor = shape = [2, 7]
-// [ a d e ]
-// [b c ]
+// `y[x[i]] = i for i in [0, 1, ..., len(x) - 1]`
//
-// Graphically the output tensors are:
+// The values must include 0. There can be no duplicate values or negative values.
//
-// output_tensor[0] = shape = [2, 4]
-// [ a ]
-// [b c ]
+// For example:
//
-// output_tensor[1] = shape = [2, 3]
-// [ d e ]
-// [ ]
+// ```
+// # tensor `x` is [3, 4, 0, 2, 1]
+// invert_permutation(x) ==> [2, 4, 3, 0, 1]
+// ```
//
// Arguments:
-// split_dim: 0-D. The dimension along which to split. Must be in the range
-// `[0, rank(shape))`.
-// indices: 2-D tensor represents the indices of the sparse tensor.
-// values: 1-D tensor represents the values of the sparse tensor.
-// shape: 1-D. tensor represents the shape of the sparse tensor.
-// output indices: A list of 1-D tensors represents the indices of the output
-// sparse tensors.
-// num_split: The number of ways to split.
+// x: 1-D.
//
-// Returns A list of 1-D tensors represents the values of the output sparse
-// tensors.A list of 1-D tensors represents the shape of the output sparse
-// tensors.
-func SparseSplit(scope *Scope, split_dim tf.Output, indices tf.Output, values tf.Output, shape tf.Output, num_split int64) (output_indices []tf.Output, output_values []tf.Output, output_shape []tf.Output) {
+// Returns 1-D.
+func InvertPermutation(scope *Scope, x tf.Output) (y tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{"num_split": num_split}
opspec := tf.OpSpec{
- Type: "SparseSplit",
+ Type: "InvertPermutation",
Input: []tf.Input{
- split_dim, indices, values, shape,
+ x,
},
- Attrs: attrs,
}
op := scope.AddOperation(opspec)
- if scope.Err() != nil {
- return
- }
- var idx int
- var err error
- if output_indices, idx, err = makeOutputList(op, idx, "output_indices"); err != nil {
- scope.UpdateErr("SparseSplit", err)
- return
- }
- if output_values, idx, err = makeOutputList(op, idx, "output_values"); err != nil {
- scope.UpdateErr("SparseSplit", err)
- return
- }
- if output_shape, idx, err = makeOutputList(op, idx, "output_shape"); err != nil {
- scope.UpdateErr("SparseSplit", err)
- return
- }
- return output_indices, output_values, output_shape
+ return op.Output(0)
}
-// RandomPoissonAttr is an optional argument to RandomPoisson.
-type RandomPoissonAttr func(optionalAttr)
-
-// RandomPoissonSeed sets the optional seed attribute to value.
-// If not specified, defaults to 0
-func RandomPoissonSeed(value int64) RandomPoissonAttr {
- return func(m optionalAttr) {
- m["seed"] = value
- }
-}
+// BiasAddGradAttr is an optional argument to BiasAddGrad.
+type BiasAddGradAttr func(optionalAttr)
-// RandomPoissonSeed2 sets the optional seed2 attribute to value.
-// If not specified, defaults to 0
-func RandomPoissonSeed2(value int64) RandomPoissonAttr {
+// BiasAddGradDataFormat sets the optional data_format attribute to value.
+//
+// value: Specify the data format of the input and output data. With the
+// default format "NHWC", the bias tensor will be added to the last dimension
+// of the value tensor.
+// Alternatively, the format could be "NCHW", the data storage order of:
+// [batch, in_channels, in_height, in_width].
+// The tensor will be added to "in_channels", the third-to-the-last
+// dimension.
+// If not specified, defaults to "NHWC"
+func BiasAddGradDataFormat(value string) BiasAddGradAttr {
return func(m optionalAttr) {
- m["seed2"] = value
+ m["data_format"] = value
}
}
-// Use RandomPoissonV2 instead.
+// The backward operation for "BiasAdd" on the "bias" tensor.
//
-// DEPRECATED at GraphDef version 25: Replaced by RandomPoissonV2
-func RandomPoisson(scope *Scope, shape tf.Output, rate tf.Output, optional ...RandomPoissonAttr) (output tf.Output) {
+// It accumulates all the values from out_backprop into the feature dimension.
+// For NHWC data format, the feature dimension is the last. For NCHW data format,
+// the feature dimension is the third-to-last.
+//
+// Arguments:
+// out_backprop: Any number of dimensions.
+//
+// Returns 1-D with size the feature dimension of `out_backprop`.
+func BiasAddGrad(scope *Scope, out_backprop tf.Output, optional ...BiasAddGradAttr) (output tf.Output) {
if scope.Err() != nil {
return
}
a(attrs)
}
opspec := tf.OpSpec{
- Type: "RandomPoisson",
+ Type: "BiasAddGrad",
Input: []tf.Input{
- shape, rate,
+ out_backprop,
},
Attrs: attrs,
}
return op.Output(0)
}
-// ResourceSparseApplyFtrlV2Attr is an optional argument to ResourceSparseApplyFtrlV2.
-type ResourceSparseApplyFtrlV2Attr func(optionalAttr)
+// FusedBatchNormV2Attr is an optional argument to FusedBatchNormV2.
+type FusedBatchNormV2Attr func(optionalAttr)
-// ResourceSparseApplyFtrlV2UseLocking sets the optional use_locking attribute to value.
+// FusedBatchNormV2Epsilon sets the optional epsilon attribute to value.
//
-// value: If `True`, updating of the var and accum tensors will be protected
-// by a lock; otherwise the behavior is undefined, but may exhibit less
-// contention.
-// If not specified, defaults to false
-func ResourceSparseApplyFtrlV2UseLocking(value bool) ResourceSparseApplyFtrlV2Attr {
+// value: A small float number added to the variance of x.
+// If not specified, defaults to 0.0001
+func FusedBatchNormV2Epsilon(value float32) FusedBatchNormV2Attr {
return func(m optionalAttr) {
- m["use_locking"] = value
+ m["epsilon"] = value
}
}
-// Update relevant entries in '*var' according to the Ftrl-proximal scheme.
+// FusedBatchNormV2DataFormat sets the optional data_format attribute to value.
//
-// That is for rows we have grad for, we update var, accum and linear as follows:
-// grad_with_shrinkage = grad + 2 * l2_shrinkage * var
-// accum_new = accum + grad_with_shrinkage * grad_with_shrinkage
-// linear += grad_with_shrinkage +
-// (accum_new^(-lr_power) - accum^(-lr_power)) / lr * var
-// quadratic = 1.0 / (accum_new^(lr_power) * lr) + 2 * l2
-// var = (sign(linear) * l1 - linear) / quadratic if |linear| > l1 else 0.0
-// accum = accum_new
+// value: The data format for x and y. Either "NHWC" (default) or "NCHW".
+// If not specified, defaults to "NHWC"
+func FusedBatchNormV2DataFormat(value string) FusedBatchNormV2Attr {
+ return func(m optionalAttr) {
+ m["data_format"] = value
+ }
+}
+
+// FusedBatchNormV2IsTraining sets the optional is_training attribute to value.
//
-// Arguments:
-// var_: Should be from a Variable().
-// accum: Should be from a Variable().
-// linear: Should be from a Variable().
-// grad: The gradient.
-// indices: A vector of indices into the first dimension of var and accum.
-// lr: Scaling factor. Must be a scalar.
-// l1: L1 regularization. Must be a scalar.
-// l2: L2 shrinkage regulariation. Must be a scalar.
+// value: A bool value to indicate the operation is for training (default)
+// or inference.
+// If not specified, defaults to true
+func FusedBatchNormV2IsTraining(value bool) FusedBatchNormV2Attr {
+ return func(m optionalAttr) {
+ m["is_training"] = value
+ }
+}
+
+// Batch normalization.
//
-// lr_power: Scaling factor. Must be a scalar.
+// Note that the size of 4D Tensors are defined by either "NHWC" or "NCHW".
+// The size of 1D Tensors matches the dimension C of the 4D Tensors.
//
-// Returns the created operation.
-func ResourceSparseApplyFtrlV2(scope *Scope, var_ tf.Output, accum tf.Output, linear tf.Output, grad tf.Output, indices tf.Output, lr tf.Output, l1 tf.Output, l2 tf.Output, l2_shrinkage tf.Output, lr_power tf.Output, optional ...ResourceSparseApplyFtrlV2Attr) (o *tf.Operation) {
+// Arguments:
+// x: A 4D Tensor for input data.
+// scale: A 1D Tensor for scaling factor, to scale the normalized x.
+// offset: A 1D Tensor for offset, to shift to the normalized x.
+// mean: A 1D Tensor for population mean. Used for inference only;
+// must be empty for training.
+// variance: A 1D Tensor for population variance. Used for inference only;
+// must be empty for training.
+//
+// Returns A 4D Tensor for output data.A 1D Tensor for the computed batch mean, to be used by TensorFlow
+// to compute the running mean.A 1D Tensor for the computed batch variance, to be used by
+// TensorFlow to compute the running variance.A 1D Tensor for the computed batch mean, to be reused
+// in the gradient computation.A 1D Tensor for the computed batch variance (inverted variance
+// in the cuDNN case), to be reused in the gradient computation.
+func FusedBatchNormV2(scope *Scope, x tf.Output, scale tf.Output, offset tf.Output, mean tf.Output, variance tf.Output, optional ...FusedBatchNormV2Attr) (y tf.Output, batch_mean tf.Output, batch_variance tf.Output, reserve_space_1 tf.Output, reserve_space_2 tf.Output) {
if scope.Err() != nil {
return
}
a(attrs)
}
opspec := tf.OpSpec{
- Type: "ResourceSparseApplyFtrlV2",
+ Type: "FusedBatchNormV2",
Input: []tf.Input{
- var_, accum, linear, grad, indices, lr, l1, l2, l2_shrinkage, lr_power,
+ x, scale, offset, mean, variance,
},
Attrs: attrs,
}
- return scope.AddOperation(opspec)
-}
-
-// Associates the given iterator with the given statistics aggregator.
-//
-// Returns the created operation.
-func IteratorSetStatsAggregator(scope *Scope, iterator_handle tf.Output, stats_aggregator_handle tf.Output) (o *tf.Operation) {
- if scope.Err() != nil {
- return
- }
- opspec := tf.OpSpec{
- Type: "IteratorSetStatsAggregator",
- Input: []tf.Input{
- iterator_handle, stats_aggregator_handle,
- },
- }
- return scope.AddOperation(opspec)
-}
-
-// DataFormatVecPermuteAttr is an optional argument to DataFormatVecPermute.
-type DataFormatVecPermuteAttr func(optionalAttr)
-
-// DataFormatVecPermuteSrcFormat sets the optional src_format attribute to value.
-//
-// value: source data format.
-// If not specified, defaults to "NHWC"
-func DataFormatVecPermuteSrcFormat(value string) DataFormatVecPermuteAttr {
- return func(m optionalAttr) {
- m["src_format"] = value
- }
+ op := scope.AddOperation(opspec)
+ return op.Output(0), op.Output(1), op.Output(2), op.Output(3), op.Output(4)
}
-// DataFormatVecPermuteDstFormat sets the optional dst_format attribute to value.
+// Returns the rank of a tensor.
//
-// value: destination data format.
-// If not specified, defaults to "NCHW"
-func DataFormatVecPermuteDstFormat(value string) DataFormatVecPermuteAttr {
- return func(m optionalAttr) {
- m["dst_format"] = value
- }
-}
-
-// Returns the permuted vector/tensor in the destination data format given the
+// This operation returns an integer representing the rank of `input`.
//
-// one in the source data format.
+// For example:
//
-// Arguments:
-// x: Vector of size 4 or Tensor of shape (4, 2) in source data format.
+// ```
+// # 't' is [[[1, 1, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]]]
+// # shape of tensor 't' is [2, 2, 3]
+// rank(t) ==> 3
+// ```
//
-// Returns Vector of size 4 or Tensor of shape (4, 2) in destination data format.
-func DataFormatVecPermute(scope *Scope, x tf.Output, optional ...DataFormatVecPermuteAttr) (y tf.Output) {
+// **Note**: The rank of a tensor is not the same as the rank of a matrix. The rank
+// of a tensor is the number of indices required to uniquely select each element
+// of the tensor. Rank is also known as "order", "degree", or "ndims."
+func Rank(scope *Scope, input tf.Output) (output tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{}
- for _, a := range optional {
- a(attrs)
- }
opspec := tf.OpSpec{
- Type: "DataFormatVecPermute",
+ Type: "Rank",
Input: []tf.Input{
- x,
+ input,
},
- Attrs: attrs,
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// Computes tan of x element-wise.
-func Tan(scope *Scope, x tf.Output) (y tf.Output) {
+// Transforms a Tensor into a serialized TensorProto proto.
+//
+// Arguments:
+// tensor: A Tensor of type `T`.
+//
+// Returns A serialized TensorProto proto of the input tensor.
+func SerializeTensor(scope *Scope, tensor tf.Output) (serialized tf.Output) {
if scope.Err() != nil {
return
}
opspec := tf.OpSpec{
- Type: "Tan",
+ Type: "SerializeTensor",
Input: []tf.Input{
- x,
+ tensor,
},
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// ResourceSparseApplyFtrlAttr is an optional argument to ResourceSparseApplyFtrl.
-type ResourceSparseApplyFtrlAttr func(optionalAttr)
+// MatrixSolveAttr is an optional argument to MatrixSolve.
+type MatrixSolveAttr func(optionalAttr)
-// ResourceSparseApplyFtrlUseLocking sets the optional use_locking attribute to value.
+// MatrixSolveAdjoint sets the optional adjoint attribute to value.
//
-// value: If `True`, updating of the var and accum tensors will be protected
-// by a lock; otherwise the behavior is undefined, but may exhibit less
-// contention.
+// value: Boolean indicating whether to solve with `matrix` or its (block-wise)
+// adjoint.
// If not specified, defaults to false
-func ResourceSparseApplyFtrlUseLocking(value bool) ResourceSparseApplyFtrlAttr {
+func MatrixSolveAdjoint(value bool) MatrixSolveAttr {
return func(m optionalAttr) {
- m["use_locking"] = value
+ m["adjoint"] = value
}
}
-// Update relevant entries in '*var' according to the Ftrl-proximal scheme.
+// Solves systems of linear equations.
//
-// That is for rows we have grad for, we update var, accum and linear as follows:
-// accum_new = accum + grad * grad
-// linear += grad + (accum_new^(-lr_power) - accum^(-lr_power)) / lr * var
-// quadratic = 1.0 / (accum_new^(lr_power) * lr) + 2 * l2
-// var = (sign(linear) * l1 - linear) / quadratic if |linear| > l1 else 0.0
-// accum = accum_new
+// `Matrix` is a tensor of shape `[..., M, M]` whose inner-most 2 dimensions
+// form square matrices. `Rhs` is a tensor of shape `[..., M, K]`. The `output` is
+// a tensor shape `[..., M, K]`. If `adjoint` is `False` then each output matrix
+// satisfies `matrix[..., :, :] * output[..., :, :] = rhs[..., :, :]`.
+// If `adjoint` is `True` then each output matrix satisfies
+// `adjoint(matrix[..., :, :]) * output[..., :, :] = rhs[..., :, :]`.
//
// Arguments:
-// var_: Should be from a Variable().
-// accum: Should be from a Variable().
-// linear: Should be from a Variable().
-// grad: The gradient.
-// indices: A vector of indices into the first dimension of var and accum.
-// lr: Scaling factor. Must be a scalar.
-// l1: L1 regularization. Must be a scalar.
-// l2: L2 regularization. Must be a scalar.
-// lr_power: Scaling factor. Must be a scalar.
+// matrix: Shape is `[..., M, M]`.
+// rhs: Shape is `[..., M, K]`.
//
-// Returns the created operation.
-func ResourceSparseApplyFtrl(scope *Scope, var_ tf.Output, accum tf.Output, linear tf.Output, grad tf.Output, indices tf.Output, lr tf.Output, l1 tf.Output, l2 tf.Output, lr_power tf.Output, optional ...ResourceSparseApplyFtrlAttr) (o *tf.Operation) {
+// Returns Shape is `[..., M, K]`.
+func MatrixSolve(scope *Scope, matrix tf.Output, rhs tf.Output, optional ...MatrixSolveAttr) (output tf.Output) {
if scope.Err() != nil {
return
}
a(attrs)
}
opspec := tf.OpSpec{
- Type: "ResourceSparseApplyFtrl",
+ Type: "MatrixSolve",
Input: []tf.Input{
- var_, accum, linear, grad, indices, lr, l1, l2, lr_power,
+ matrix, rhs,
},
Attrs: attrs,
}
- return scope.AddOperation(opspec)
+ op := scope.AddOperation(opspec)
+ return op.Output(0)
}
-// Returns which elements of x are Inf.
-//
-// @compatibility(numpy)
-// Equivalent to np.isinf
-// @end_compatibility
-func IsInf(scope *Scope, x tf.Output) (y tf.Output) {
+// Computes acos of x element-wise.
+func Acos(scope *Scope, x tf.Output) (y tf.Output) {
if scope.Err() != nil {
return
}
opspec := tf.OpSpec{
- Type: "IsInf",
+ Type: "Acos",
Input: []tf.Input{
x,
},
return op.Output(0)
}
-// Computes the sum along sparse segments of a tensor divided by the sqrt of N.
+// Real-valued fast Fourier transform.
//
-// N is the size of the segment being reduced.
+// Computes the 1-dimensional discrete Fourier transform of a real-valued signal
+// over the inner-most dimension of `input`.
//
-// Read @{$math_ops#segmentation$the section on segmentation} for an explanation of
-// segments.
+// Since the DFT of a real signal is Hermitian-symmetric, `RFFT` only returns the
+// `fft_length / 2 + 1` unique components of the FFT: the zero-frequency term,
+// followed by the `fft_length / 2` positive-frequency terms.
+//
+// Along the axis `RFFT` is computed on, if `fft_length` is smaller than the
+// corresponding dimension of `input`, the dimension is cropped. If it is larger,
+// the dimension is padded with zeros.
//
// Arguments:
+// input: A float32 tensor.
+// fft_length: An int32 tensor of shape [1]. The FFT length.
//
-// indices: A 1-D tensor. Has same rank as `segment_ids`.
-// segment_ids: A 1-D tensor. Values should be sorted and can be repeated.
+// Returns A complex64 tensor of the same rank as `input`. The inner-most
+// dimension of `input` is replaced with the `fft_length / 2 + 1` unique
+// frequency components of its 1D Fourier transform.
//
-// Returns Has same shape as data, except for dimension 0 which
-// has size `k`, the number of segments.
-func SparseSegmentSqrtN(scope *Scope, data tf.Output, indices tf.Output, segment_ids tf.Output) (output tf.Output) {
+// @compatibility(numpy)
+// Equivalent to np.fft.rfft
+// @end_compatibility
+func RFFT(scope *Scope, input tf.Output, fft_length tf.Output) (output tf.Output) {
if scope.Err() != nil {
return
}
opspec := tf.OpSpec{
- Type: "SparseSegmentSqrtN",
+ Type: "RFFT",
Input: []tf.Input{
- data, indices, segment_ids,
+ input, fft_length,
},
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// StatelessTruncatedNormalAttr is an optional argument to StatelessTruncatedNormal.
-type StatelessTruncatedNormalAttr func(optionalAttr)
+// DepthwiseConv2dNativeBackpropFilterAttr is an optional argument to DepthwiseConv2dNativeBackpropFilter.
+type DepthwiseConv2dNativeBackpropFilterAttr func(optionalAttr)
-// StatelessTruncatedNormalDtype sets the optional dtype attribute to value.
+// DepthwiseConv2dNativeBackpropFilterDataFormat sets the optional data_format attribute to value.
//
-// value: The type of the output.
-// If not specified, defaults to DT_FLOAT
-func StatelessTruncatedNormalDtype(value tf.DataType) StatelessTruncatedNormalAttr {
- return func(m optionalAttr) {
- m["dtype"] = value
- }
+// value: Specify the data format of the input and output data. With the
+// default format "NHWC", the data is stored in the order of:
+// [batch, height, width, channels].
+// Alternatively, the format could be "NCHW", the data storage order of:
+// [batch, channels, height, width].
+// If not specified, defaults to "NHWC"
+func DepthwiseConv2dNativeBackpropFilterDataFormat(value string) DepthwiseConv2dNativeBackpropFilterAttr {
+ return func(m optionalAttr) {
+ m["data_format"] = value
+ }
}
-// Outputs deterministic pseudorandom values from a truncated normal distribution.
-//
-// The generated values follow a normal distribution with mean 0 and standard
-// deviation 1, except that values whose magnitude is more than 2 standard
-// deviations from the mean are dropped and re-picked.
+// DepthwiseConv2dNativeBackpropFilterDilations sets the optional dilations attribute to value.
//
-// The outputs are a deterministic function of `shape` and `seed`.
+// value: 1-D tensor of length 4. The dilation factor for each dimension of
+// `input`. If set to k > 1, there will be k-1 skipped cells between each filter
+// element on that dimension. The dimension order is determined by the value of
+// `data_format`, see above for details. Dilations in the batch and depth
+// dimensions must be 1.
+// If not specified, defaults to <i:1 i:1 i:1 i:1 >
+func DepthwiseConv2dNativeBackpropFilterDilations(value []int64) DepthwiseConv2dNativeBackpropFilterAttr {
+ return func(m optionalAttr) {
+ m["dilations"] = value
+ }
+}
+
+// Computes the gradients of depthwise convolution with respect to the filter.
//
// Arguments:
-// shape: The shape of the output tensor.
-// seed: 2 seeds (shape [2]).
+// input: 4-D with shape based on `data_format`. For example, if
+// `data_format` is 'NHWC' then `input` is a 4-D `[batch, in_height,
+// in_width, in_channels]` tensor.
+// filter_sizes: An integer vector representing the tensor shape of `filter`,
+// where `filter` is a 4-D
+// `[filter_height, filter_width, in_channels, depthwise_multiplier]` tensor.
+// out_backprop: 4-D with shape based on `data_format`.
+// For example, if `data_format` is 'NHWC' then
+// out_backprop shape is `[batch, out_height, out_width, out_channels]`.
+// Gradients w.r.t. the output of the convolution.
+// strides: The stride of the sliding window for each dimension of the input
+// of the convolution.
+// padding: The type of padding algorithm to use.
//
-// Returns Random values with specified shape.
-func StatelessTruncatedNormal(scope *Scope, shape tf.Output, seed tf.Output, optional ...StatelessTruncatedNormalAttr) (output tf.Output) {
+// Returns 4-D with shape
+// `[filter_height, filter_width, in_channels, out_channels]`. Gradient w.r.t.
+// the `filter` input of the convolution.
+func DepthwiseConv2dNativeBackpropFilter(scope *Scope, input tf.Output, filter_sizes tf.Output, out_backprop tf.Output, strides []int64, padding string, optional ...DepthwiseConv2dNativeBackpropFilterAttr) (output tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{}
+ attrs := map[string]interface{}{"strides": strides, "padding": padding}
for _, a := range optional {
a(attrs)
}
opspec := tf.OpSpec{
- Type: "StatelessTruncatedNormal",
+ Type: "DepthwiseConv2dNativeBackpropFilter",
Input: []tf.Input{
- shape, seed,
+ input, filter_sizes, out_backprop,
},
Attrs: attrs,
}
return op.Output(0)
}
-// RestoreSliceAttr is an optional argument to RestoreSlice.
-type RestoreSliceAttr func(optionalAttr)
+// LRNGradAttr is an optional argument to LRNGrad.
+type LRNGradAttr func(optionalAttr)
-// RestoreSlicePreferredShard sets the optional preferred_shard attribute to value.
+// LRNGradDepthRadius sets the optional depth_radius attribute to value.
//
-// value: Index of file to open first if multiple files match
-// `file_pattern`. See the documentation for `Restore`.
-// If not specified, defaults to -1
-func RestoreSlicePreferredShard(value int64) RestoreSliceAttr {
+// value: A depth radius.
+// If not specified, defaults to 5
+func LRNGradDepthRadius(value int64) LRNGradAttr {
return func(m optionalAttr) {
- m["preferred_shard"] = value
+ m["depth_radius"] = value
}
}
-// Restores a tensor from checkpoint files.
+// LRNGradBias sets the optional bias attribute to value.
//
-// This is like `Restore` except that restored tensor can be listed as filling
-// only a slice of a larger tensor. `shape_and_slice` specifies the shape of the
-// larger tensor and the slice that the restored tensor covers.
+// value: An offset (usually > 0 to avoid dividing by 0).
+// If not specified, defaults to 1
+func LRNGradBias(value float32) LRNGradAttr {
+ return func(m optionalAttr) {
+ m["bias"] = value
+ }
+}
+
+// LRNGradAlpha sets the optional alpha attribute to value.
//
-// The `shape_and_slice` input has the same format as the
-// elements of the `shapes_and_slices` input of the `SaveSlices` op.
+// value: A scale factor, usually positive.
+// If not specified, defaults to 1
+func LRNGradAlpha(value float32) LRNGradAttr {
+ return func(m optionalAttr) {
+ m["alpha"] = value
+ }
+}
+
+// LRNGradBeta sets the optional beta attribute to value.
+//
+// value: An exponent.
+// If not specified, defaults to 0.5
+func LRNGradBeta(value float32) LRNGradAttr {
+ return func(m optionalAttr) {
+ m["beta"] = value
+ }
+}
+
+// Gradients for Local Response Normalization.
//
// Arguments:
-// file_pattern: Must have a single element. The pattern of the files from
-// which we read the tensor.
-// tensor_name: Must have a single element. The name of the tensor to be
-// restored.
-// shape_and_slice: Scalar. The shapes and slice specifications to use when
-// restoring a tensors.
-// dt: The type of the tensor to be restored.
+// input_grads: 4-D with shape `[batch, height, width, channels]`.
+// input_image: 4-D with shape `[batch, height, width, channels]`.
+// output_image: 4-D with shape `[batch, height, width, channels]`.
//
-// Returns The restored tensor.
-func RestoreSlice(scope *Scope, file_pattern tf.Output, tensor_name tf.Output, shape_and_slice tf.Output, dt tf.DataType, optional ...RestoreSliceAttr) (tensor tf.Output) {
+// Returns The gradients for LRN.
+func LRNGrad(scope *Scope, input_grads tf.Output, input_image tf.Output, output_image tf.Output, optional ...LRNGradAttr) (output tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{"dt": dt}
+ attrs := map[string]interface{}{}
for _, a := range optional {
a(attrs)
}
opspec := tf.OpSpec{
- Type: "RestoreSlice",
+ Type: "LRNGrad",
Input: []tf.Input{
- file_pattern, tensor_name, shape_and_slice,
+ input_grads, input_image, output_image,
},
Attrs: attrs,
}
return op.Output(0)
}
-// ImagAttr is an optional argument to Imag.
-type ImagAttr func(optionalAttr)
+// AnyAttr is an optional argument to Any.
+type AnyAttr func(optionalAttr)
-// ImagTout sets the optional Tout attribute to value.
-// If not specified, defaults to DT_FLOAT
-func ImagTout(value tf.DataType) ImagAttr {
+// AnyKeepDims sets the optional keep_dims attribute to value.
+//
+// value: If true, retain reduced dimensions with length 1.
+// If not specified, defaults to false
+func AnyKeepDims(value bool) AnyAttr {
return func(m optionalAttr) {
- m["Tout"] = value
+ m["keep_dims"] = value
}
}
-// Returns the imaginary part of a complex number.
+// Computes the "logical or" of elements across dimensions of a tensor.
//
-// Given a tensor `input` of complex numbers, this operation returns a tensor of
-// type `float` that is the imaginary part of each element in `input`. All
-// elements in `input` must be complex numbers of the form \\(a + bj\\), where *a*
-// is the real part and *b* is the imaginary part returned by this operation.
+// Reduces `input` along the dimensions given in `axis`. Unless
+// `keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in
+// `axis`. If `keep_dims` is true, the reduced dimensions are
+// retained with length 1.
//
-// For example:
+// Arguments:
+// input: The tensor to reduce.
+// axis: The dimensions to reduce. Must be in the range
+// `[-rank(input), rank(input))`.
//
-// ```
-// # tensor 'input' is [-2.25 + 4.75j, 3.25 + 5.75j]
-// tf.imag(input) ==> [4.75, 5.75]
-// ```
-func Imag(scope *Scope, input tf.Output, optional ...ImagAttr) (output tf.Output) {
+// Returns The reduced tensor.
+func Any(scope *Scope, input tf.Output, axis tf.Output, optional ...AnyAttr) (output tf.Output) {
if scope.Err() != nil {
return
}
a(attrs)
}
opspec := tf.OpSpec{
- Type: "Imag",
+ Type: "Any",
Input: []tf.Input{
- input,
+ input, axis,
},
Attrs: attrs,
}
return op.Output(0)
}
-// ComplexAttr is an optional argument to Complex.
-type ComplexAttr func(optionalAttr)
+// ResourceApplyFtrlAttr is an optional argument to ResourceApplyFtrl.
+type ResourceApplyFtrlAttr func(optionalAttr)
-// ComplexTout sets the optional Tout attribute to value.
-// If not specified, defaults to DT_COMPLEX64
-func ComplexTout(value tf.DataType) ComplexAttr {
+// ResourceApplyFtrlUseLocking sets the optional use_locking attribute to value.
+//
+// value: If `True`, updating of the var and accum tensors will be protected
+// by a lock; otherwise the behavior is undefined, but may exhibit less
+// contention.
+// If not specified, defaults to false
+func ResourceApplyFtrlUseLocking(value bool) ResourceApplyFtrlAttr {
return func(m optionalAttr) {
- m["Tout"] = value
+ m["use_locking"] = value
}
}
-// Converts two real numbers to a complex number.
-//
-// Given a tensor `real` representing the real part of a complex number, and a
-// tensor `imag` representing the imaginary part of a complex number, this
-// operation returns complex numbers elementwise of the form \\(a + bj\\), where
-// *a* represents the `real` part and *b* represents the `imag` part.
+// Update '*var' according to the Ftrl-proximal scheme.
//
-// The input tensors `real` and `imag` must have the same shape.
+// accum_new = accum + grad * grad
+// linear += grad - (accum_new^(-lr_power) - accum^(-lr_power)) / lr * var
+// quadratic = 1.0 / (accum_new^(lr_power) * lr) + 2 * l2
+// var = (sign(linear) * l1 - linear) / quadratic if |linear| > l1 else 0.0
+// accum = accum_new
//
-// For example:
+// Arguments:
+// var_: Should be from a Variable().
+// accum: Should be from a Variable().
+// linear: Should be from a Variable().
+// grad: The gradient.
+// lr: Scaling factor. Must be a scalar.
+// l1: L1 regulariation. Must be a scalar.
+// l2: L2 regulariation. Must be a scalar.
+// lr_power: Scaling factor. Must be a scalar.
//
-// ```
-// # tensor 'real' is [2.25, 3.25]
-// # tensor `imag` is [4.75, 5.75]
-// tf.complex(real, imag) ==> [[2.25 + 4.75j], [3.25 + 5.75j]]
-// ```
-func Complex(scope *Scope, real tf.Output, imag tf.Output, optional ...ComplexAttr) (out tf.Output) {
+// Returns the created operation.
+func ResourceApplyFtrl(scope *Scope, var_ tf.Output, accum tf.Output, linear tf.Output, grad tf.Output, lr tf.Output, l1 tf.Output, l2 tf.Output, lr_power tf.Output, optional ...ResourceApplyFtrlAttr) (o *tf.Operation) {
if scope.Err() != nil {
return
}
a(attrs)
}
opspec := tf.OpSpec{
- Type: "Complex",
+ Type: "ResourceApplyFtrl",
Input: []tf.Input{
- real, imag,
+ var_, accum, linear, grad, lr, l1, l2, lr_power,
},
Attrs: attrs,
}
- op := scope.AddOperation(opspec)
- return op.Output(0)
+ return scope.AddOperation(opspec)
}
-// UniqueWithCountsAttr is an optional argument to UniqueWithCounts.
-type UniqueWithCountsAttr func(optionalAttr)
+// RandomUniformAttr is an optional argument to RandomUniform.
+type RandomUniformAttr func(optionalAttr)
-// UniqueWithCountsOutIdx sets the optional out_idx attribute to value.
-// If not specified, defaults to DT_INT32
-func UniqueWithCountsOutIdx(value tf.DataType) UniqueWithCountsAttr {
+// RandomUniformSeed sets the optional seed attribute to value.
+//
+// value: If either `seed` or `seed2` are set to be non-zero, the random number
+// generator is seeded by the given seed. Otherwise, it is seeded by a
+// random seed.
+// If not specified, defaults to 0
+func RandomUniformSeed(value int64) RandomUniformAttr {
return func(m optionalAttr) {
- m["out_idx"] = value
+ m["seed"] = value
}
}
-// Finds unique elements in a 1-D tensor.
+// RandomUniformSeed2 sets the optional seed2 attribute to value.
//
-// This operation returns a tensor `y` containing all of the unique elements of `x`
-// sorted in the same order that they occur in `x`. This operation also returns a
-// tensor `idx` the same size as `x` that contains the index of each value of `x`
-// in the unique output `y`. Finally, it returns a third tensor `count` that
-// contains the count of each element of `y` in `x`. In other words:
+// value: A second seed to avoid seed collision.
+// If not specified, defaults to 0
+func RandomUniformSeed2(value int64) RandomUniformAttr {
+ return func(m optionalAttr) {
+ m["seed2"] = value
+ }
+}
+
+// Outputs random values from a uniform distribution.
//
-// `y[idx[i]] = x[i] for i in [0, 1,...,rank(x) - 1]`
-//
-// For example:
-//
-// ```
-// # tensor 'x' is [1, 1, 2, 4, 4, 4, 7, 8, 8]
-// y, idx, count = unique_with_counts(x)
-// y ==> [1, 2, 4, 7, 8]
-// idx ==> [0, 0, 1, 2, 2, 2, 3, 4, 4]
-// count ==> [2, 1, 3, 1, 2]
-// ```
+// The generated values follow a uniform distribution in the range `[0, 1)`. The
+// lower bound 0 is included in the range, while the upper bound 1 is excluded.
//
// Arguments:
-// x: 1-D.
+// shape: The shape of the output tensor.
+// dtype: The type of the output.
//
-// Returns 1-D.1-D.1-D.
-func UniqueWithCounts(scope *Scope, x tf.Output, optional ...UniqueWithCountsAttr) (y tf.Output, idx tf.Output, count tf.Output) {
+// Returns A tensor of the specified shape filled with uniform random values.
+func RandomUniform(scope *Scope, shape tf.Output, dtype tf.DataType, optional ...RandomUniformAttr) (output tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{}
+ attrs := map[string]interface{}{"dtype": dtype}
for _, a := range optional {
a(attrs)
}
opspec := tf.OpSpec{
- Type: "UniqueWithCounts",
+ Type: "RandomUniform",
Input: []tf.Input{
- x,
+ shape,
},
Attrs: attrs,
}
op := scope.AddOperation(opspec)
- return op.Output(0), op.Output(1), op.Output(2)
+ return op.Output(0)
}
-// StatelessRandomNormalAttr is an optional argument to StatelessRandomNormal.
-type StatelessRandomNormalAttr func(optionalAttr)
+// AssertAttr is an optional argument to Assert.
+type AssertAttr func(optionalAttr)
-// StatelessRandomNormalDtype sets the optional dtype attribute to value.
+// AssertSummarize sets the optional summarize attribute to value.
//
-// value: The type of the output.
-// If not specified, defaults to DT_FLOAT
-func StatelessRandomNormalDtype(value tf.DataType) StatelessRandomNormalAttr {
+// value: Print this many entries of each tensor.
+// If not specified, defaults to 3
+func AssertSummarize(value int64) AssertAttr {
return func(m optionalAttr) {
- m["dtype"] = value
+ m["summarize"] = value
}
}
-// Outputs deterministic pseudorandom values from a normal distribution.
-//
-// The generated values will have mean 0 and standard deviation 1.
+// Asserts that the given condition is true.
//
-// The outputs are a deterministic function of `shape` and `seed`.
+// If `condition` evaluates to false, print the list of tensors in `data`.
+// `summarize` determines how many entries of the tensors to print.
//
// Arguments:
-// shape: The shape of the output tensor.
-// seed: 2 seeds (shape [2]).
+// condition: The condition to evaluate.
+// data: The tensors to print out when condition is false.
//
-// Returns Random values with specified shape.
-func StatelessRandomNormal(scope *Scope, shape tf.Output, seed tf.Output, optional ...StatelessRandomNormalAttr) (output tf.Output) {
+// Returns the created operation.
+func Assert(scope *Scope, condition tf.Output, data []tf.Output, optional ...AssertAttr) (o *tf.Operation) {
if scope.Err() != nil {
return
}
a(attrs)
}
opspec := tf.OpSpec{
- Type: "StatelessRandomNormal",
+ Type: "Assert",
Input: []tf.Input{
- shape, seed,
+ condition, tf.OutputList(data),
},
Attrs: attrs,
}
- op := scope.AddOperation(opspec)
- return op.Output(0)
+ return scope.AddOperation(opspec)
}
-// Reshapes a quantized tensor as per the Reshape op.
-//
-// ```
-//
-// Arguments:
+// Computes element-wise population count (a.k.a. popcount, bitsum, bitcount).
//
-// shape: Defines the shape of the output tensor.
-// input_min: The minimum value of the input.
-// input_max: The maximum value of the input.
+// For each entry in `x`, calculates the number of `1` (on) bits in the binary
+// representation of that entry.
//
-// Returns This value is copied from input_min.This value is copied from input_max.
-func QuantizedReshape(scope *Scope, tensor tf.Output, shape tf.Output, input_min tf.Output, input_max tf.Output) (output tf.Output, output_min tf.Output, output_max tf.Output) {
+// **NOTE**: It is more efficient to first `tf.bitcast` your tensors into
+// `int32` or `int64` and perform the bitcount on the result, than to feed in
+// 8- or 16-bit inputs and then aggregate the resulting counts.
+func PopulationCount(scope *Scope, x tf.Output) (y tf.Output) {
if scope.Err() != nil {
return
}
opspec := tf.OpSpec{
- Type: "QuantizedReshape",
+ Type: "PopulationCount",
Input: []tf.Input{
- tensor, shape, input_min, input_max,
+ x,
},
}
op := scope.AddOperation(opspec)
- return op.Output(0), op.Output(1), op.Output(2)
-}
-
-// GatherAttr is an optional argument to Gather.
-type GatherAttr func(optionalAttr)
-
-// GatherValidateIndices sets the optional validate_indices attribute to value.
-// If not specified, defaults to true
-func GatherValidateIndices(value bool) GatherAttr {
- return func(m optionalAttr) {
- m["validate_indices"] = value
- }
+ return op.Output(0)
}
-// Gather slices from `params` according to `indices`.
+// Split a `SparseTensor` into `num_split` tensors along one dimension.
//
-// `indices` must be an integer tensor of any dimension (usually 0-D or 1-D).
-// Produces an output tensor with shape `indices.shape + params.shape[1:]` where:
+// If the `shape[split_dim]` is not an integer multiple of `num_split`. Slices
+// `[0 : shape[split_dim] % num_split]` gets one extra dimension.
+// For example, if `split_dim = 1` and `num_split = 2` and the input is
//
-// ```python
-// # Scalar indices
-// output[:, ..., :] = params[indices, :, ... :]
+// input_tensor = shape = [2, 7]
+// [ a d e ]
+// [b c ]
//
-// # Vector indices
-// output[i, :, ..., :] = params[indices[i], :, ... :]
+// Graphically the output tensors are:
//
-// # Higher rank indices
-// output[i, ..., j, :, ... :] = params[indices[i, ..., j], :, ..., :]
-// ```
+// output_tensor[0] = shape = [2, 4]
+// [ a ]
+// [b c ]
//
-// If `indices` is a permutation and `len(indices) == params.shape[0]` then
-// this operation will permute `params` accordingly.
+// output_tensor[1] = shape = [2, 3]
+// [ d e ]
+// [ ]
//
-// `validate_indices`: DEPRECATED. If this operation is assigned to CPU, values in
-// `indices` are always validated to be within range. If assigned to GPU,
-// out-of-bound indices result in safe but unspecified behavior, which may include
-// raising an error.
+// Arguments:
+// split_dim: 0-D. The dimension along which to split. Must be in the range
+// `[0, rank(shape))`.
+// indices: 2-D tensor represents the indices of the sparse tensor.
+// values: 1-D tensor represents the values of the sparse tensor.
+// shape: 1-D. tensor represents the shape of the sparse tensor.
+// output indices: A list of 1-D tensors represents the indices of the output
+// sparse tensors.
+// num_split: The number of ways to split.
//
-// <div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;">
-// <img style="width:100%" src="https://www.tensorflow.org/images/Gather.png" alt>
-// </div>
-func Gather(scope *Scope, params tf.Output, indices tf.Output, optional ...GatherAttr) (output tf.Output) {
+// Returns A list of 1-D tensors represents the values of the output sparse
+// tensors.A list of 1-D tensors represents the shape of the output sparse
+// tensors.
+func SparseSplit(scope *Scope, split_dim tf.Output, indices tf.Output, values tf.Output, shape tf.Output, num_split int64) (output_indices []tf.Output, output_values []tf.Output, output_shape []tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{}
- for _, a := range optional {
- a(attrs)
- }
+ attrs := map[string]interface{}{"num_split": num_split}
opspec := tf.OpSpec{
- Type: "Gather",
+ Type: "SparseSplit",
Input: []tf.Input{
- params, indices,
+ split_dim, indices, values, shape,
},
Attrs: attrs,
}
op := scope.AddOperation(opspec)
- return op.Output(0)
-}
-
-// Returns the truth value of (x != y) element-wise.
-//
-// *NOTE*: `NotEqual` supports broadcasting. More about broadcasting
-// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
-func NotEqual(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) {
if scope.Err() != nil {
return
}
- opspec := tf.OpSpec{
- Type: "NotEqual",
- Input: []tf.Input{
- x, y,
- },
+ var idx int
+ var err error
+ if output_indices, idx, err = makeOutputList(op, idx, "output_indices"); err != nil {
+ scope.UpdateErr("SparseSplit", err)
+ return
}
- op := scope.AddOperation(opspec)
- return op.Output(0)
+ if output_values, idx, err = makeOutputList(op, idx, "output_values"); err != nil {
+ scope.UpdateErr("SparseSplit", err)
+ return
+ }
+ if output_shape, idx, err = makeOutputList(op, idx, "output_shape"); err != nil {
+ scope.UpdateErr("SparseSplit", err)
+ return
+ }
+ return output_indices, output_values, output_shape
}
-// Inverse 3D real-valued fast Fourier transform.
-//
-// Computes the inverse 3-dimensional discrete Fourier transform of a real-valued
-// signal over the inner-most 3 dimensions of `input`.
-//
-// The inner-most 3 dimensions of `input` are assumed to be the result of `RFFT3D`:
-// The inner-most dimension contains the `fft_length / 2 + 1` unique components of
-// the DFT of a real-valued signal. If `fft_length` is not provided, it is computed
-// from the size of the inner-most 3 dimensions of `input`. If the FFT length used
-// to compute `input` is odd, it should be provided since it cannot be inferred
-// properly.
-//
-// Along each axis `IRFFT3D` is computed on, if `fft_length` (or
-// `fft_length / 2 + 1` for the inner-most dimension) is smaller than the
-// corresponding dimension of `input`, the dimension is cropped. If it is larger,
-// the dimension is padded with zeros.
-//
-// Arguments:
-// input: A complex64 tensor.
-// fft_length: An int32 tensor of shape [3]. The FFT length for each dimension.
-//
-// Returns A float32 tensor of the same rank as `input`. The inner-most 3
-// dimensions of `input` are replaced with the `fft_length` samples of their
-// inverse 3D real Fourier transform.
+// RandomPoissonAttr is an optional argument to RandomPoisson.
+type RandomPoissonAttr func(optionalAttr)
+
+// RandomPoissonSeed sets the optional seed attribute to value.
+// If not specified, defaults to 0
+func RandomPoissonSeed(value int64) RandomPoissonAttr {
+ return func(m optionalAttr) {
+ m["seed"] = value
+ }
+}
+
+// RandomPoissonSeed2 sets the optional seed2 attribute to value.
+// If not specified, defaults to 0
+func RandomPoissonSeed2(value int64) RandomPoissonAttr {
+ return func(m optionalAttr) {
+ m["seed2"] = value
+ }
+}
+
+// Use RandomPoissonV2 instead.
//
-// @compatibility(numpy)
-// Equivalent to np.irfftn with 3 dimensions.
-// @end_compatibility
-func IRFFT3D(scope *Scope, input tf.Output, fft_length tf.Output) (output tf.Output) {
+// DEPRECATED at GraphDef version 25: Replaced by RandomPoissonV2
+func RandomPoisson(scope *Scope, shape tf.Output, rate tf.Output, optional ...RandomPoissonAttr) (output tf.Output) {
if scope.Err() != nil {
return
}
+ attrs := map[string]interface{}{}
+ for _, a := range optional {
+ a(attrs)
+ }
opspec := tf.OpSpec{
- Type: "IRFFT3D",
+ Type: "RandomPoisson",
Input: []tf.Input{
- input, fft_length,
+ shape, rate,
},
+ Attrs: attrs,
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// StringSplitAttr is an optional argument to StringSplit.
-type StringSplitAttr func(optionalAttr)
+// ResourceSparseApplyFtrlV2Attr is an optional argument to ResourceSparseApplyFtrlV2.
+type ResourceSparseApplyFtrlV2Attr func(optionalAttr)
-// StringSplitSkipEmpty sets the optional skip_empty attribute to value.
+// ResourceSparseApplyFtrlV2UseLocking sets the optional use_locking attribute to value.
//
-// value: A `bool`. If `True`, skip the empty strings from the result.
-// If not specified, defaults to true
-func StringSplitSkipEmpty(value bool) StringSplitAttr {
+// value: If `True`, updating of the var and accum tensors will be protected
+// by a lock; otherwise the behavior is undefined, but may exhibit less
+// contention.
+// If not specified, defaults to false
+func ResourceSparseApplyFtrlV2UseLocking(value bool) ResourceSparseApplyFtrlV2Attr {
return func(m optionalAttr) {
- m["skip_empty"] = value
+ m["use_locking"] = value
}
}
-// Split elements of `input` based on `delimiter` into a `SparseTensor`.
-//
-// Let N be the size of source (typically N will be the batch size). Split each
-// element of `input` based on `delimiter` and return a `SparseTensor`
-// containing the splitted tokens. Empty tokens are ignored.
-//
-// `delimiter` can be empty, or a string of split characters. If `delimiter` is an
-// empty string, each element of `input` is split into individual single-byte
-// character strings, including splitting of UTF-8 multibyte sequences. Otherwise
-// every character of `delimiter` is a potential split point.
-//
-// For example:
-// N = 2, input[0] is 'hello world' and input[1] is 'a b c', then the output
-// will be
+// Update relevant entries in '*var' according to the Ftrl-proximal scheme.
//
-// indices = [0, 0;
-// 0, 1;
-// 1, 0;
-// 1, 1;
-// 1, 2]
-// shape = [2, 3]
-// values = ['hello', 'world', 'a', 'b', 'c']
+// That is for rows we have grad for, we update var, accum and linear as follows:
+// grad_with_shrinkage = grad + 2 * l2_shrinkage * var
+// accum_new = accum + grad_with_shrinkage * grad_with_shrinkage
+// linear += grad_with_shrinkage +
+// (accum_new^(-lr_power) - accum^(-lr_power)) / lr * var
+// quadratic = 1.0 / (accum_new^(lr_power) * lr) + 2 * l2
+// var = (sign(linear) * l1 - linear) / quadratic if |linear| > l1 else 0.0
+// accum = accum_new
//
// Arguments:
-// input: 1-D. Strings to split.
-// delimiter: 0-D. Delimiter characters (bytes), or empty string.
+// var_: Should be from a Variable().
+// accum: Should be from a Variable().
+// linear: Should be from a Variable().
+// grad: The gradient.
+// indices: A vector of indices into the first dimension of var and accum.
+// lr: Scaling factor. Must be a scalar.
+// l1: L1 regularization. Must be a scalar.
+// l2: L2 shrinkage regulariation. Must be a scalar.
//
-// Returns A dense matrix of int64 representing the indices of the sparse tensor.A vector of strings corresponding to the splited values.a length-2 vector of int64 representing the shape of the sparse
-// tensor, where the first value is N and the second value is the maximum number
-// of tokens in a single input entry.
-func StringSplit(scope *Scope, input tf.Output, delimiter tf.Output, optional ...StringSplitAttr) (indices tf.Output, values tf.Output, shape tf.Output) {
+// lr_power: Scaling factor. Must be a scalar.
+//
+// Returns the created operation.
+func ResourceSparseApplyFtrlV2(scope *Scope, var_ tf.Output, accum tf.Output, linear tf.Output, grad tf.Output, indices tf.Output, lr tf.Output, l1 tf.Output, l2 tf.Output, l2_shrinkage tf.Output, lr_power tf.Output, optional ...ResourceSparseApplyFtrlV2Attr) (o *tf.Operation) {
if scope.Err() != nil {
return
}
a(attrs)
}
opspec := tf.OpSpec{
- Type: "StringSplit",
+ Type: "ResourceSparseApplyFtrlV2",
Input: []tf.Input{
- input, delimiter,
+ var_, accum, linear, grad, indices, lr, l1, l2, l2_shrinkage, lr_power,
},
Attrs: attrs,
}
- op := scope.AddOperation(opspec)
- return op.Output(0), op.Output(1), op.Output(2)
+ return scope.AddOperation(opspec)
}
-// ResizeBilinearAttr is an optional argument to ResizeBilinear.
-type ResizeBilinearAttr func(optionalAttr)
+// Associates the given iterator with the given statistics aggregator.
+//
+// Returns the created operation.
+func IteratorSetStatsAggregator(scope *Scope, iterator_handle tf.Output, stats_aggregator_handle tf.Output) (o *tf.Operation) {
+ if scope.Err() != nil {
+ return
+ }
+ opspec := tf.OpSpec{
+ Type: "IteratorSetStatsAggregator",
+ Input: []tf.Input{
+ iterator_handle, stats_aggregator_handle,
+ },
+ }
+ return scope.AddOperation(opspec)
+}
-// ResizeBilinearAlignCorners sets the optional align_corners attribute to value.
+// DataFormatVecPermuteAttr is an optional argument to DataFormatVecPermute.
+type DataFormatVecPermuteAttr func(optionalAttr)
+
+// DataFormatVecPermuteSrcFormat sets the optional src_format attribute to value.
//
-// value: If true, rescale input by (new_height - 1) / (height - 1), which
-// exactly aligns the 4 corners of images and resized images. If false, rescale
-// by new_height / height. Treat similarly the width dimension.
-// If not specified, defaults to false
-func ResizeBilinearAlignCorners(value bool) ResizeBilinearAttr {
+// value: source data format.
+// If not specified, defaults to "NHWC"
+func DataFormatVecPermuteSrcFormat(value string) DataFormatVecPermuteAttr {
return func(m optionalAttr) {
- m["align_corners"] = value
+ m["src_format"] = value
}
}
-// Resize `images` to `size` using bilinear interpolation.
+// DataFormatVecPermuteDstFormat sets the optional dst_format attribute to value.
//
-// Input images can be of different types but output images are always float.
+// value: destination data format.
+// If not specified, defaults to "NCHW"
+func DataFormatVecPermuteDstFormat(value string) DataFormatVecPermuteAttr {
+ return func(m optionalAttr) {
+ m["dst_format"] = value
+ }
+}
+
+// Returns the permuted vector/tensor in the destination data format given the
+//
+// one in the source data format.
//
// Arguments:
-// images: 4-D with shape `[batch, height, width, channels]`.
-// size: = A 1-D int32 Tensor of 2 elements: `new_height, new_width`. The
-// new size for the images.
+// x: Vector of size 4 or Tensor of shape (4, 2) in source data format.
//
-// Returns 4-D with shape
-// `[batch, new_height, new_width, channels]`.
-func ResizeBilinear(scope *Scope, images tf.Output, size tf.Output, optional ...ResizeBilinearAttr) (resized_images tf.Output) {
+// Returns Vector of size 4 or Tensor of shape (4, 2) in destination data format.
+func DataFormatVecPermute(scope *Scope, x tf.Output, optional ...DataFormatVecPermuteAttr) (y tf.Output) {
if scope.Err() != nil {
return
}
a(attrs)
}
opspec := tf.OpSpec{
- Type: "ResizeBilinear",
+ Type: "DataFormatVecPermute",
Input: []tf.Input{
- images, size,
+ x,
},
Attrs: attrs,
}
return op.Output(0)
}
-// Computes softsign: `features / (abs(features) + 1)`.
-func Softsign(scope *Scope, features tf.Output) (activations tf.Output) {
+// Computes tan of x element-wise.
+func Tan(scope *Scope, x tf.Output) (y tf.Output) {
if scope.Err() != nil {
return
}
opspec := tf.OpSpec{
- Type: "Softsign",
+ Type: "Tan",
Input: []tf.Input{
- features,
+ x,
},
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// GenerateVocabRemappingAttr is an optional argument to GenerateVocabRemapping.
-type GenerateVocabRemappingAttr func(optionalAttr)
+// ResourceSparseApplyFtrlAttr is an optional argument to ResourceSparseApplyFtrl.
+type ResourceSparseApplyFtrlAttr func(optionalAttr)
-// GenerateVocabRemappingOldVocabSize sets the optional old_vocab_size attribute to value.
-//
-// value: Number of entries in the old vocab file to consider. If -1,
-// use the entire old vocabulary.
-// If not specified, defaults to -1
+// ResourceSparseApplyFtrlUseLocking sets the optional use_locking attribute to value.
//
-// REQUIRES: value >= -1
-func GenerateVocabRemappingOldVocabSize(value int64) GenerateVocabRemappingAttr {
+// value: If `True`, updating of the var and accum tensors will be protected
+// by a lock; otherwise the behavior is undefined, but may exhibit less
+// contention.
+// If not specified, defaults to false
+func ResourceSparseApplyFtrlUseLocking(value bool) ResourceSparseApplyFtrlAttr {
return func(m optionalAttr) {
- m["old_vocab_size"] = value
+ m["use_locking"] = value
}
}
-// Given a path to new and old vocabulary files, returns a remapping Tensor of
-//
-// length `num_new_vocab`, where `remapping[i]` contains the row number in the old
-// vocabulary that corresponds to row `i` in the new vocabulary (starting at line
-// `new_vocab_offset` and up to `num_new_vocab` entities), or `-1` if entry `i`
-// in the new vocabulary is not in the old vocabulary. The old vocabulary is
-// constrained to the first `old_vocab_size` entries if `old_vocab_size` is not the
-// default value of -1.
-//
-// `num_vocab_offset` enables
-// use in the partitioned variable case, and should generally be set through
-// examining partitioning info. The format of the files should be a text file,
-// with each line containing a single entity within the vocabulary.
-//
-// For example, with `new_vocab_file` a text file containing each of the following
-// elements on a single line: `[f0, f1, f2, f3]`, old_vocab_file = [f1, f0, f3],
-// `num_new_vocab = 3, new_vocab_offset = 1`, the returned remapping would be
-// `[0, -1, 2]`.
-//
-// The op also returns a count of how many entries in the new vocabulary
-// were present in the old vocabulary, which is used to calculate the number of
-// values to initialize in a weight matrix remapping
+// Update relevant entries in '*var' according to the Ftrl-proximal scheme.
//
-// This functionality can be used to remap both row vocabularies (typically,
-// features) and column vocabularies (typically, classes) from TensorFlow
-// checkpoints. Note that the partitioning logic relies on contiguous vocabularies
-// corresponding to div-partitioned variables. Moreover, the underlying remapping
-// uses an IndexTable (as opposed to an inexact CuckooTable), so client code should
-// use the corresponding index_table_from_file() as the FeatureColumn framework
-// does (as opposed to tf.feature_to_id(), which uses a CuckooTable).
+// That is for rows we have grad for, we update var, accum and linear as follows:
+// accum_new = accum + grad * grad
+// linear += grad + (accum_new^(-lr_power) - accum^(-lr_power)) / lr * var
+// quadratic = 1.0 / (accum_new^(lr_power) * lr) + 2 * l2
+// var = (sign(linear) * l1 - linear) / quadratic if |linear| > l1 else 0.0
+// accum = accum_new
//
// Arguments:
-// new_vocab_file: Path to the new vocab file.
-// old_vocab_file: Path to the old vocab file.
-// new_vocab_offset: How many entries into the new vocab file to start reading.
-// num_new_vocab: Number of entries in the new vocab file to remap.
+// var_: Should be from a Variable().
+// accum: Should be from a Variable().
+// linear: Should be from a Variable().
+// grad: The gradient.
+// indices: A vector of indices into the first dimension of var and accum.
+// lr: Scaling factor. Must be a scalar.
+// l1: L1 regularization. Must be a scalar.
+// l2: L2 regularization. Must be a scalar.
+// lr_power: Scaling factor. Must be a scalar.
//
-// Returns A Tensor of length num_new_vocab where the element at index i
-// is equal to the old ID that maps to the new ID i. This element is -1 for any
-// new ID that is not found in the old vocabulary.Number of new vocab entries found in old vocab.
-func GenerateVocabRemapping(scope *Scope, new_vocab_file tf.Output, old_vocab_file tf.Output, new_vocab_offset int64, num_new_vocab int64, optional ...GenerateVocabRemappingAttr) (remapping tf.Output, num_present tf.Output) {
+// Returns the created operation.
+func ResourceSparseApplyFtrl(scope *Scope, var_ tf.Output, accum tf.Output, linear tf.Output, grad tf.Output, indices tf.Output, lr tf.Output, l1 tf.Output, l2 tf.Output, lr_power tf.Output, optional ...ResourceSparseApplyFtrlAttr) (o *tf.Operation) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{"new_vocab_offset": new_vocab_offset, "num_new_vocab": num_new_vocab}
+ attrs := map[string]interface{}{}
for _, a := range optional {
a(attrs)
}
opspec := tf.OpSpec{
- Type: "GenerateVocabRemapping",
+ Type: "ResourceSparseApplyFtrl",
Input: []tf.Input{
- new_vocab_file, old_vocab_file,
+ var_, accum, linear, grad, indices, lr, l1, l2, lr_power,
},
Attrs: attrs,
}
- op := scope.AddOperation(opspec)
- return op.Output(0), op.Output(1)
+ return scope.AddOperation(opspec)
}
-// Assigns sparse updates to the variable referenced by `resource`.
-//
-// This operation computes
+// Returns which elements of x are Inf.
//
-// # Scalar indices
-// ref[indices, ...] = updates[...]
+// @compatibility(numpy)
+// Equivalent to np.isinf
+// @end_compatibility
+func IsInf(scope *Scope, x tf.Output) (y tf.Output) {
+ if scope.Err() != nil {
+ return
+ }
+ opspec := tf.OpSpec{
+ Type: "IsInf",
+ Input: []tf.Input{
+ x,
+ },
+ }
+ op := scope.AddOperation(opspec)
+ return op.Output(0)
+}
+
+// Computes the sum along sparse segments of a tensor divided by the sqrt of N.
//
-// # Vector indices (for each i)
-// ref[indices[i], ...] = updates[i, ...]
+// N is the size of the segment being reduced.
//
-// # High rank indices (for each i, ..., j)
-// ref[indices[i, ..., j], ...] = updates[i, ..., j, ...]
+// Read @{$math_ops#segmentation$the section on segmentation} for an explanation of
+// segments.
//
// Arguments:
-// resource: Should be from a `Variable` node.
-// indices: A tensor of indices into the first dimension of `ref`.
-// updates: A tensor of updated values to add to `ref`.
//
-// Returns the created operation.
-func ResourceScatterUpdate(scope *Scope, resource tf.Output, indices tf.Output, updates tf.Output) (o *tf.Operation) {
+// indices: A 1-D tensor. Has same rank as `segment_ids`.
+// segment_ids: A 1-D tensor. Values should be sorted and can be repeated.
+//
+// Returns Has same shape as data, except for dimension 0 which
+// has size `k`, the number of segments.
+func SparseSegmentSqrtN(scope *Scope, data tf.Output, indices tf.Output, segment_ids tf.Output) (output tf.Output) {
if scope.Err() != nil {
return
}
opspec := tf.OpSpec{
- Type: "ResourceScatterUpdate",
+ Type: "SparseSegmentSqrtN",
Input: []tf.Input{
- resource, indices, updates,
+ data, indices, segment_ids,
},
}
- return scope.AddOperation(opspec)
+ op := scope.AddOperation(opspec)
+ return op.Output(0)
}
-// AvgPoolGradAttr is an optional argument to AvgPoolGrad.
-type AvgPoolGradAttr func(optionalAttr)
+// StatelessTruncatedNormalAttr is an optional argument to StatelessTruncatedNormal.
+type StatelessTruncatedNormalAttr func(optionalAttr)
-// AvgPoolGradDataFormat sets the optional data_format attribute to value.
+// StatelessTruncatedNormalDtype sets the optional dtype attribute to value.
//
-// value: Specify the data format of the input and output data. With the
-// default format "NHWC", the data is stored in the order of:
-// [batch, in_height, in_width, in_channels].
-// Alternatively, the format could be "NCHW", the data storage order of:
-// [batch, in_channels, in_height, in_width].
-// If not specified, defaults to "NHWC"
-func AvgPoolGradDataFormat(value string) AvgPoolGradAttr {
+// value: The type of the output.
+// If not specified, defaults to DT_FLOAT
+func StatelessTruncatedNormalDtype(value tf.DataType) StatelessTruncatedNormalAttr {
return func(m optionalAttr) {
- m["data_format"] = value
+ m["dtype"] = value
}
}
-// Computes gradients of the average pooling function.
+// Outputs deterministic pseudorandom values from a truncated normal distribution.
+//
+// The generated values follow a normal distribution with mean 0 and standard
+// deviation 1, except that values whose magnitude is more than 2 standard
+// deviations from the mean are dropped and re-picked.
+//
+// The outputs are a deterministic function of `shape` and `seed`.
//
// Arguments:
-// orig_input_shape: 1-D. Shape of the original input to `avg_pool`.
-// grad: 4-D with shape `[batch, height, width, channels]`. Gradients w.r.t.
-// the output of `avg_pool`.
-// ksize: The size of the sliding window for each dimension of the input.
-// strides: The stride of the sliding window for each dimension of the input.
-// padding: The type of padding algorithm to use.
+// shape: The shape of the output tensor.
+// seed: 2 seeds (shape [2]).
//
-// Returns 4-D. Gradients w.r.t. the input of `avg_pool`.
-func AvgPoolGrad(scope *Scope, orig_input_shape tf.Output, grad tf.Output, ksize []int64, strides []int64, padding string, optional ...AvgPoolGradAttr) (output tf.Output) {
+// Returns Random values with specified shape.
+func StatelessTruncatedNormal(scope *Scope, shape tf.Output, seed tf.Output, optional ...StatelessTruncatedNormalAttr) (output tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{"ksize": ksize, "strides": strides, "padding": padding}
+ attrs := map[string]interface{}{}
for _, a := range optional {
a(attrs)
}
opspec := tf.OpSpec{
- Type: "AvgPoolGrad",
+ Type: "StatelessTruncatedNormal",
Input: []tf.Input{
- orig_input_shape, grad,
+ shape, seed,
},
Attrs: attrs,
}
return op.Output(0)
}
-// StageClearAttr is an optional argument to StageClear.
-type StageClearAttr func(optionalAttr)
+// RestoreSliceAttr is an optional argument to RestoreSlice.
+type RestoreSliceAttr func(optionalAttr)
-// StageClearCapacity sets the optional capacity attribute to value.
-// If not specified, defaults to 0
+// RestoreSlicePreferredShard sets the optional preferred_shard attribute to value.
//
-// REQUIRES: value >= 0
-func StageClearCapacity(value int64) StageClearAttr {
+// value: Index of file to open first if multiple files match
+// `file_pattern`. See the documentation for `Restore`.
+// If not specified, defaults to -1
+func RestoreSlicePreferredShard(value int64) RestoreSliceAttr {
return func(m optionalAttr) {
- m["capacity"] = value
+ m["preferred_shard"] = value
}
}
-// StageClearMemoryLimit sets the optional memory_limit attribute to value.
-// If not specified, defaults to 0
+// Restores a tensor from checkpoint files.
//
-// REQUIRES: value >= 0
-func StageClearMemoryLimit(value int64) StageClearAttr {
- return func(m optionalAttr) {
- m["memory_limit"] = value
- }
-}
-
-// StageClearContainer sets the optional container attribute to value.
-// If not specified, defaults to ""
-func StageClearContainer(value string) StageClearAttr {
- return func(m optionalAttr) {
- m["container"] = value
- }
-}
-
-// StageClearSharedName sets the optional shared_name attribute to value.
-// If not specified, defaults to ""
-func StageClearSharedName(value string) StageClearAttr {
- return func(m optionalAttr) {
- m["shared_name"] = value
- }
-}
-
-// Op removes all elements in the underlying container.
+// This is like `Restore` except that restored tensor can be listed as filling
+// only a slice of a larger tensor. `shape_and_slice` specifies the shape of the
+// larger tensor and the slice that the restored tensor covers.
//
-// Returns the created operation.
-func StageClear(scope *Scope, dtypes []tf.DataType, optional ...StageClearAttr) (o *tf.Operation) {
+// The `shape_and_slice` input has the same format as the
+// elements of the `shapes_and_slices` input of the `SaveSlices` op.
+//
+// Arguments:
+// file_pattern: Must have a single element. The pattern of the files from
+// which we read the tensor.
+// tensor_name: Must have a single element. The name of the tensor to be
+// restored.
+// shape_and_slice: Scalar. The shapes and slice specifications to use when
+// restoring a tensors.
+// dt: The type of the tensor to be restored.
+//
+// Returns The restored tensor.
+func RestoreSlice(scope *Scope, file_pattern tf.Output, tensor_name tf.Output, shape_and_slice tf.Output, dt tf.DataType, optional ...RestoreSliceAttr) (tensor tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{"dtypes": dtypes}
+ attrs := map[string]interface{}{"dt": dt}
for _, a := range optional {
a(attrs)
}
opspec := tf.OpSpec{
- Type: "StageClear",
-
+ Type: "RestoreSlice",
+ Input: []tf.Input{
+ file_pattern, tensor_name, shape_and_slice,
+ },
Attrs: attrs,
}
- return scope.AddOperation(opspec)
+ op := scope.AddOperation(opspec)
+ return op.Output(0)
}
-// ComputeAccidentalHitsAttr is an optional argument to ComputeAccidentalHits.
-type ComputeAccidentalHitsAttr func(optionalAttr)
-
-// ComputeAccidentalHitsSeed sets the optional seed attribute to value.
-//
-// value: If either seed or seed2 are set to be non-zero, the random number
-// generator is seeded by the given seed. Otherwise, it is seeded by a
-// random seed.
-// If not specified, defaults to 0
-func ComputeAccidentalHitsSeed(value int64) ComputeAccidentalHitsAttr {
- return func(m optionalAttr) {
- m["seed"] = value
- }
-}
+// ImagAttr is an optional argument to Imag.
+type ImagAttr func(optionalAttr)
-// ComputeAccidentalHitsSeed2 sets the optional seed2 attribute to value.
-//
-// value: An second seed to avoid seed collision.
-// If not specified, defaults to 0
-func ComputeAccidentalHitsSeed2(value int64) ComputeAccidentalHitsAttr {
+// ImagTout sets the optional Tout attribute to value.
+// If not specified, defaults to DT_FLOAT
+func ImagTout(value tf.DataType) ImagAttr {
return func(m optionalAttr) {
- m["seed2"] = value
+ m["Tout"] = value
}
}
-// Computes the ids of the positions in sampled_candidates that match true_labels.
+// Returns the imaginary part of a complex number.
//
-// When doing log-odds NCE, the result of this op should be passed through a
-// SparseToDense op, then added to the logits of the sampled candidates. This has
-// the effect of 'removing' the sampled labels that match the true labels by
-// making the classifier sure that they are sampled labels.
+// Given a tensor `input` of complex numbers, this operation returns a tensor of
+// type `float` that is the imaginary part of each element in `input`. All
+// elements in `input` must be complex numbers of the form \\(a + bj\\), where *a*
+// is the real part and *b* is the imaginary part returned by this operation.
//
-// Arguments:
-// true_classes: The true_classes output of UnpackSparseLabels.
-// sampled_candidates: The sampled_candidates output of CandidateSampler.
-// num_true: Number of true labels per context.
+// For example:
//
-// Returns A vector of indices corresponding to rows of true_candidates.A vector of IDs of positions in sampled_candidates that match a true_label
-// for the row with the corresponding index in indices.A vector of the same length as indices and ids, in which each element
-// is -FLOAT_MAX.
-func ComputeAccidentalHits(scope *Scope, true_classes tf.Output, sampled_candidates tf.Output, num_true int64, optional ...ComputeAccidentalHitsAttr) (indices tf.Output, ids tf.Output, weights tf.Output) {
+// ```
+// # tensor 'input' is [-2.25 + 4.75j, 3.25 + 5.75j]
+// tf.imag(input) ==> [4.75, 5.75]
+// ```
+func Imag(scope *Scope, input tf.Output, optional ...ImagAttr) (output tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{"num_true": num_true}
+ attrs := map[string]interface{}{}
for _, a := range optional {
a(attrs)
}
opspec := tf.OpSpec{
- Type: "ComputeAccidentalHits",
+ Type: "Imag",
Input: []tf.Input{
- true_classes, sampled_candidates,
+ input,
},
Attrs: attrs,
}
op := scope.AddOperation(opspec)
- return op.Output(0), op.Output(1), op.Output(2)
+ return op.Output(0)
}
-// CumsumAttr is an optional argument to Cumsum.
-type CumsumAttr func(optionalAttr)
-
-// CumsumExclusive sets the optional exclusive attribute to value.
-//
-// value: If `True`, perform exclusive cumsum.
-// If not specified, defaults to false
-func CumsumExclusive(value bool) CumsumAttr {
- return func(m optionalAttr) {
- m["exclusive"] = value
- }
-}
+// ComplexAttr is an optional argument to Complex.
+type ComplexAttr func(optionalAttr)
-// CumsumReverse sets the optional reverse attribute to value.
-//
-// value: A `bool` (default: False).
-// If not specified, defaults to false
-func CumsumReverse(value bool) CumsumAttr {
+// ComplexTout sets the optional Tout attribute to value.
+// If not specified, defaults to DT_COMPLEX64
+func ComplexTout(value tf.DataType) ComplexAttr {
return func(m optionalAttr) {
- m["reverse"] = value
+ m["Tout"] = value
}
}
-// Compute the cumulative sum of the tensor `x` along `axis`.
-//
-// By default, this op performs an inclusive cumsum, which means that the first
-// element of the input is identical to the first element of the output:
-//
-// ```python
-// tf.cumsum([a, b, c]) # => [a, a + b, a + b + c]
-// ```
+// Converts two real numbers to a complex number.
//
-// By setting the `exclusive` kwarg to `True`, an exclusive cumsum is
-// performed instead:
+// Given a tensor `real` representing the real part of a complex number, and a
+// tensor `imag` representing the imaginary part of a complex number, this
+// operation returns complex numbers elementwise of the form \\(a + bj\\), where
+// *a* represents the `real` part and *b* represents the `imag` part.
//
-// ```python
-// tf.cumsum([a, b, c], exclusive=True) # => [0, a, a + b]
-// ```
+// The input tensors `real` and `imag` must have the same shape.
//
-// By setting the `reverse` kwarg to `True`, the cumsum is performed in the
-// opposite direction:
+// For example:
//
-// ```python
-// tf.cumsum([a, b, c], reverse=True) # => [a + b + c, b + c, c]
// ```
-//
-// This is more efficient than using separate `tf.reverse` ops.
-//
-// The `reverse` and `exclusive` kwargs can also be combined:
-//
-// ```python
-// tf.cumsum([a, b, c], exclusive=True, reverse=True) # => [b + c, c, 0]
+// # tensor 'real' is [2.25, 3.25]
+// # tensor `imag` is [4.75, 5.75]
+// tf.complex(real, imag) ==> [[2.25 + 4.75j], [3.25 + 5.75j]]
// ```
-//
-// Arguments:
-// x: A `Tensor`. Must be one of the following types: `float32`, `float64`,
-// `int64`, `int32`, `uint8`, `uint16`, `int16`, `int8`, `complex64`,
-// `complex128`, `qint8`, `quint8`, `qint32`, `half`.
-// axis: A `Tensor` of type `int32` (default: 0). Must be in the range
-// `[-rank(x), rank(x))`.
-func Cumsum(scope *Scope, x tf.Output, axis tf.Output, optional ...CumsumAttr) (out tf.Output) {
+func Complex(scope *Scope, real tf.Output, imag tf.Output, optional ...ComplexAttr) (out tf.Output) {
if scope.Err() != nil {
return
}
a(attrs)
}
opspec := tf.OpSpec{
- Type: "Cumsum",
+ Type: "Complex",
Input: []tf.Input{
- x, axis,
+ real, imag,
},
Attrs: attrs,
}
return op.Output(0)
}
-// QuantizedRelu6Attr is an optional argument to QuantizedRelu6.
-type QuantizedRelu6Attr func(optionalAttr)
+// UniqueWithCountsAttr is an optional argument to UniqueWithCounts.
+type UniqueWithCountsAttr func(optionalAttr)
-// QuantizedRelu6OutType sets the optional out_type attribute to value.
-// If not specified, defaults to DT_QUINT8
-func QuantizedRelu6OutType(value tf.DataType) QuantizedRelu6Attr {
+// UniqueWithCountsOutIdx sets the optional out_idx attribute to value.
+// If not specified, defaults to DT_INT32
+func UniqueWithCountsOutIdx(value tf.DataType) UniqueWithCountsAttr {
return func(m optionalAttr) {
- m["out_type"] = value
+ m["out_idx"] = value
}
}
-// Computes Quantized Rectified Linear 6: `min(max(features, 0), 6)`
+// Finds unique elements in a 1-D tensor.
//
-// Arguments:
+// This operation returns a tensor `y` containing all of the unique elements of `x`
+// sorted in the same order that they occur in `x`. This operation also returns a
+// tensor `idx` the same size as `x` that contains the index of each value of `x`
+// in the unique output `y`. Finally, it returns a third tensor `count` that
+// contains the count of each element of `y` in `x`. In other words:
//
-// min_features: The float value that the lowest quantized value represents.
-// max_features: The float value that the highest quantized value represents.
+// `y[idx[i]] = x[i] for i in [0, 1,...,rank(x) - 1]`
//
-// Returns Has the same output shape as "features".The float value that the lowest quantized value represents.The float value that the highest quantized value represents.
-func QuantizedRelu6(scope *Scope, features tf.Output, min_features tf.Output, max_features tf.Output, optional ...QuantizedRelu6Attr) (activations tf.Output, min_activations tf.Output, max_activations tf.Output) {
+// For example:
+//
+// ```
+// # tensor 'x' is [1, 1, 2, 4, 4, 4, 7, 8, 8]
+// y, idx, count = unique_with_counts(x)
+// y ==> [1, 2, 4, 7, 8]
+// idx ==> [0, 0, 1, 2, 2, 2, 3, 4, 4]
+// count ==> [2, 1, 3, 1, 2]
+// ```
+//
+// Arguments:
+// x: 1-D.
+//
+// Returns 1-D.1-D.1-D.
+func UniqueWithCounts(scope *Scope, x tf.Output, optional ...UniqueWithCountsAttr) (y tf.Output, idx tf.Output, count tf.Output) {
if scope.Err() != nil {
return
}
a(attrs)
}
opspec := tf.OpSpec{
- Type: "QuantizedRelu6",
+ Type: "UniqueWithCounts",
Input: []tf.Input{
- features, min_features, max_features,
+ x,
},
Attrs: attrs,
}
return op.Output(0), op.Output(1), op.Output(2)
}
-// FixedLengthRecordReaderV2Attr is an optional argument to FixedLengthRecordReaderV2.
-type FixedLengthRecordReaderV2Attr func(optionalAttr)
-
-// FixedLengthRecordReaderV2HeaderBytes sets the optional header_bytes attribute to value.
-//
-// value: Number of bytes in the header, defaults to 0.
-// If not specified, defaults to 0
-func FixedLengthRecordReaderV2HeaderBytes(value int64) FixedLengthRecordReaderV2Attr {
- return func(m optionalAttr) {
- m["header_bytes"] = value
- }
-}
-
-// FixedLengthRecordReaderV2FooterBytes sets the optional footer_bytes attribute to value.
-//
-// value: Number of bytes in the footer, defaults to 0.
-// If not specified, defaults to 0
-func FixedLengthRecordReaderV2FooterBytes(value int64) FixedLengthRecordReaderV2Attr {
- return func(m optionalAttr) {
- m["footer_bytes"] = value
- }
-}
-
-// FixedLengthRecordReaderV2HopBytes sets the optional hop_bytes attribute to value.
-//
-// value: Number of bytes to hop before each read. Default of 0 means using
-// record_bytes.
-// If not specified, defaults to 0
-func FixedLengthRecordReaderV2HopBytes(value int64) FixedLengthRecordReaderV2Attr {
- return func(m optionalAttr) {
- m["hop_bytes"] = value
- }
-}
+// StatelessRandomNormalAttr is an optional argument to StatelessRandomNormal.
+type StatelessRandomNormalAttr func(optionalAttr)
-// FixedLengthRecordReaderV2Container sets the optional container attribute to value.
+// StatelessRandomNormalDtype sets the optional dtype attribute to value.
//
-// value: If non-empty, this reader is placed in the given container.
-// Otherwise, a default container is used.
-// If not specified, defaults to ""
-func FixedLengthRecordReaderV2Container(value string) FixedLengthRecordReaderV2Attr {
+// value: The type of the output.
+// If not specified, defaults to DT_FLOAT
+func StatelessRandomNormalDtype(value tf.DataType) StatelessRandomNormalAttr {
return func(m optionalAttr) {
- m["container"] = value
+ m["dtype"] = value
}
}
-// FixedLengthRecordReaderV2SharedName sets the optional shared_name attribute to value.
+// Outputs deterministic pseudorandom values from a normal distribution.
//
-// value: If non-empty, this reader is named in the given bucket
-// with this shared_name. Otherwise, the node name is used instead.
-// If not specified, defaults to ""
-func FixedLengthRecordReaderV2SharedName(value string) FixedLengthRecordReaderV2Attr {
- return func(m optionalAttr) {
- m["shared_name"] = value
- }
-}
-
-// FixedLengthRecordReaderV2Encoding sets the optional encoding attribute to value.
+// The generated values will have mean 0 and standard deviation 1.
//
-// value: The type of encoding for the file. Currently ZLIB and GZIP
-// are supported. Defaults to none.
-// If not specified, defaults to ""
-func FixedLengthRecordReaderV2Encoding(value string) FixedLengthRecordReaderV2Attr {
- return func(m optionalAttr) {
- m["encoding"] = value
- }
-}
-
-// A Reader that outputs fixed-length records from a file.
+// The outputs are a deterministic function of `shape` and `seed`.
//
// Arguments:
-// record_bytes: Number of bytes in the record.
+// shape: The shape of the output tensor.
+// seed: 2 seeds (shape [2]).
//
-// Returns The handle to reference the Reader.
-func FixedLengthRecordReaderV2(scope *Scope, record_bytes int64, optional ...FixedLengthRecordReaderV2Attr) (reader_handle tf.Output) {
+// Returns Random values with specified shape.
+func StatelessRandomNormal(scope *Scope, shape tf.Output, seed tf.Output, optional ...StatelessRandomNormalAttr) (output tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{"record_bytes": record_bytes}
+ attrs := map[string]interface{}{}
for _, a := range optional {
a(attrs)
}
opspec := tf.OpSpec{
- Type: "FixedLengthRecordReaderV2",
-
+ Type: "StatelessRandomNormal",
+ Input: []tf.Input{
+ shape, seed,
+ },
Attrs: attrs,
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// Converts each string in the input Tensor to its hash mod by a number of buckets.
-//
-// The hash function is deterministic on the content of the string within the
-// process.
+// Reshapes a quantized tensor as per the Reshape op.
//
-// Note that the hash function may change from time to time.
-// This functionality will be deprecated and it's recommended to use
-// `tf.string_to_hash_bucket_fast()` or `tf.string_to_hash_bucket_strong()`.
+// ```
//
// Arguments:
//
-// num_buckets: The number of buckets.
+// shape: Defines the shape of the output tensor.
+// input_min: The minimum value of the input.
+// input_max: The maximum value of the input.
//
-// Returns A Tensor of the same shape as the input `string_tensor`.
-func StringToHashBucket(scope *Scope, string_tensor tf.Output, num_buckets int64) (output tf.Output) {
+// Returns This value is copied from input_min.This value is copied from input_max.
+func QuantizedReshape(scope *Scope, tensor tf.Output, shape tf.Output, input_min tf.Output, input_max tf.Output) (output tf.Output, output_min tf.Output, output_max tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{"num_buckets": num_buckets}
opspec := tf.OpSpec{
- Type: "StringToHashBucket",
+ Type: "QuantizedReshape",
Input: []tf.Input{
- string_tensor,
+ tensor, shape, input_min, input_max,
},
- Attrs: attrs,
}
op := scope.AddOperation(opspec)
- return op.Output(0)
+ return op.Output(0), op.Output(1), op.Output(2)
}
-// Computes gradients for the exponential linear (Elu) operation.
+// GatherAttr is an optional argument to Gather.
+type GatherAttr func(optionalAttr)
+
+// GatherValidateIndices sets the optional validate_indices attribute to value.
+// If not specified, defaults to true
+func GatherValidateIndices(value bool) GatherAttr {
+ return func(m optionalAttr) {
+ m["validate_indices"] = value
+ }
+}
+
+// Gather slices from `params` according to `indices`.
//
-// Arguments:
-// gradients: The backpropagated gradients to the corresponding Elu operation.
-// outputs: The outputs of the corresponding Elu operation.
+// `indices` must be an integer tensor of any dimension (usually 0-D or 1-D).
+// Produces an output tensor with shape `indices.shape + params.shape[1:]` where:
//
-// Returns The gradients: `gradients * (outputs + 1)` if outputs < 0,
-// `gradients` otherwise.
-func EluGrad(scope *Scope, gradients tf.Output, outputs tf.Output) (backprops tf.Output) {
+// ```python
+// # Scalar indices
+// output[:, ..., :] = params[indices, :, ... :]
+//
+// # Vector indices
+// output[i, :, ..., :] = params[indices[i], :, ... :]
+//
+// # Higher rank indices
+// output[i, ..., j, :, ... :] = params[indices[i, ..., j], :, ..., :]
+// ```
+//
+// If `indices` is a permutation and `len(indices) == params.shape[0]` then
+// this operation will permute `params` accordingly.
+//
+// `validate_indices`: DEPRECATED. If this operation is assigned to CPU, values in
+// `indices` are always validated to be within range. If assigned to GPU,
+// out-of-bound indices result in safe but unspecified behavior, which may include
+// raising an error.
+//
+// <div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;">
+// <img style="width:100%" src="https://www.tensorflow.org/images/Gather.png" alt>
+// </div>
+func Gather(scope *Scope, params tf.Output, indices tf.Output, optional ...GatherAttr) (output tf.Output) {
if scope.Err() != nil {
return
}
+ attrs := map[string]interface{}{}
+ for _, a := range optional {
+ a(attrs)
+ }
opspec := tf.OpSpec{
- Type: "EluGrad",
+ Type: "Gather",
Input: []tf.Input{
- gradients, outputs,
+ params, indices,
},
+ Attrs: attrs,
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// Creates a dataset that contains `count` elements from the `input_dataset`.
-//
-// Arguments:
-//
-// count: A scalar representing the number of elements from the `input_dataset`
-// that should be taken. A value of `-1` indicates that all of `input_dataset`
-// is taken.
-//
+// Returns the truth value of (x != y) element-wise.
//
-func TakeDataset(scope *Scope, input_dataset tf.Output, count tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) {
+// *NOTE*: `NotEqual` supports broadcasting. More about broadcasting
+// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
+func NotEqual(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes}
opspec := tf.OpSpec{
- Type: "TakeDataset",
+ Type: "NotEqual",
Input: []tf.Input{
- input_dataset, count,
+ x, y,
},
- Attrs: attrs,
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// The gradient operator for the SparseAdd op.
+// Inverse 3D real-valued fast Fourier transform.
//
-// The SparseAdd op calculates A + B, where A, B, and the sum are all represented
-// as `SparseTensor` objects. This op takes in the upstream gradient w.r.t.
-// non-empty values of the sum, and outputs the gradients w.r.t. the non-empty
-// values of A and B.
+// Computes the inverse 3-dimensional discrete Fourier transform of a real-valued
+// signal over the inner-most 3 dimensions of `input`.
+//
+// The inner-most 3 dimensions of `input` are assumed to be the result of `RFFT3D`:
+// The inner-most dimension contains the `fft_length / 2 + 1` unique components of
+// the DFT of a real-valued signal. If `fft_length` is not provided, it is computed
+// from the size of the inner-most 3 dimensions of `input`. If the FFT length used
+// to compute `input` is odd, it should be provided since it cannot be inferred
+// properly.
+//
+// Along each axis `IRFFT3D` is computed on, if `fft_length` (or
+// `fft_length / 2 + 1` for the inner-most dimension) is smaller than the
+// corresponding dimension of `input`, the dimension is cropped. If it is larger,
+// the dimension is padded with zeros.
//
// Arguments:
-// backprop_val_grad: 1-D with shape `[nnz(sum)]`. The gradient with respect to
-// the non-empty values of the sum.
-// a_indices: 2-D. The `indices` of the `SparseTensor` A, size `[nnz(A), ndims]`.
-// b_indices: 2-D. The `indices` of the `SparseTensor` B, size `[nnz(B), ndims]`.
-// sum_indices: 2-D. The `indices` of the sum `SparseTensor`, size
-// `[nnz(sum), ndims]`.
+// input: A complex64 tensor.
+// fft_length: An int32 tensor of shape [3]. The FFT length for each dimension.
//
-// Returns 1-D with shape `[nnz(A)]`. The gradient with respect to the
-// non-empty values of A.1-D with shape `[nnz(B)]`. The gradient with respect to the
-// non-empty values of B.
-func SparseAddGrad(scope *Scope, backprop_val_grad tf.Output, a_indices tf.Output, b_indices tf.Output, sum_indices tf.Output) (a_val_grad tf.Output, b_val_grad tf.Output) {
+// Returns A float32 tensor of the same rank as `input`. The inner-most 3
+// dimensions of `input` are replaced with the `fft_length` samples of their
+// inverse 3D real Fourier transform.
+//
+// @compatibility(numpy)
+// Equivalent to np.irfftn with 3 dimensions.
+// @end_compatibility
+func IRFFT3D(scope *Scope, input tf.Output, fft_length tf.Output) (output tf.Output) {
if scope.Err() != nil {
return
}
opspec := tf.OpSpec{
- Type: "SparseAddGrad",
+ Type: "IRFFT3D",
Input: []tf.Input{
- backprop_val_grad, a_indices, b_indices, sum_indices,
+ input, fft_length,
},
}
op := scope.AddOperation(opspec)
- return op.Output(0), op.Output(1)
+ return op.Output(0)
}
-// Computes atan of x element-wise.
-func Atan(scope *Scope, x tf.Output) (y tf.Output) {
- if scope.Err() != nil {
- return
- }
- opspec := tf.OpSpec{
- Type: "Atan",
- Input: []tf.Input{
- x,
- },
+// StringSplitAttr is an optional argument to StringSplit.
+type StringSplitAttr func(optionalAttr)
+
+// StringSplitSkipEmpty sets the optional skip_empty attribute to value.
+//
+// value: A `bool`. If `True`, skip the empty strings from the result.
+// If not specified, defaults to true
+func StringSplitSkipEmpty(value bool) StringSplitAttr {
+ return func(m optionalAttr) {
+ m["skip_empty"] = value
}
- op := scope.AddOperation(opspec)
- return op.Output(0)
}
-// Encode audio data using the WAV file format.
+// Split elements of `input` based on `delimiter` into a `SparseTensor`.
//
-// This operation will generate a string suitable to be saved out to create a .wav
-// audio file. It will be encoded in the 16-bit PCM format. It takes in float
-// values in the range -1.0f to 1.0f, and any outside that value will be clamped to
-// that range.
+// Let N be the size of source (typically N will be the batch size). Split each
+// element of `input` based on `delimiter` and return a `SparseTensor`
+// containing the splitted tokens. Empty tokens are ignored.
//
-// `audio` is a 2-D float Tensor of shape `[length, channels]`.
-// `sample_rate` is a scalar Tensor holding the rate to use (e.g. 44100).
+// `delimiter` can be empty, or a string of split characters. If `delimiter` is an
+// empty string, each element of `input` is split into individual single-byte
+// character strings, including splitting of UTF-8 multibyte sequences. Otherwise
+// every character of `delimiter` is a potential split point.
+//
+// For example:
+// N = 2, input[0] is 'hello world' and input[1] is 'a b c', then the output
+// will be
+//
+// indices = [0, 0;
+// 0, 1;
+// 1, 0;
+// 1, 1;
+// 1, 2]
+// shape = [2, 3]
+// values = ['hello', 'world', 'a', 'b', 'c']
//
// Arguments:
-// audio: 2-D with shape `[length, channels]`.
-// sample_rate: Scalar containing the sample frequency.
+// input: 1-D. Strings to split.
+// delimiter: 0-D. Delimiter characters (bytes), or empty string.
//
-// Returns 0-D. WAV-encoded file contents.
-func EncodeWav(scope *Scope, audio tf.Output, sample_rate tf.Output) (contents tf.Output) {
+// Returns A dense matrix of int64 representing the indices of the sparse tensor.A vector of strings corresponding to the splited values.a length-2 vector of int64 representing the shape of the sparse
+// tensor, where the first value is N and the second value is the maximum number
+// of tokens in a single input entry.
+func StringSplit(scope *Scope, input tf.Output, delimiter tf.Output, optional ...StringSplitAttr) (indices tf.Output, values tf.Output, shape tf.Output) {
if scope.Err() != nil {
return
}
+ attrs := map[string]interface{}{}
+ for _, a := range optional {
+ a(attrs)
+ }
opspec := tf.OpSpec{
- Type: "EncodeWav",
+ Type: "StringSplit",
Input: []tf.Input{
- audio, sample_rate,
+ input, delimiter,
},
+ Attrs: attrs,
}
op := scope.AddOperation(opspec)
- return op.Output(0)
+ return op.Output(0), op.Output(1), op.Output(2)
}
-// Converts each string in the input Tensor to its hash mod by a number of buckets.
+// ResizeBilinearAttr is an optional argument to ResizeBilinear.
+type ResizeBilinearAttr func(optionalAttr)
+
+// ResizeBilinearAlignCorners sets the optional align_corners attribute to value.
//
-// The hash function is deterministic on the content of the string within the
-// process. The hash function is a keyed hash function, where attribute `key`
-// defines the key of the hash function. `key` is an array of 2 elements.
+// value: If true, rescale input by (new_height - 1) / (height - 1), which
+// exactly aligns the 4 corners of images and resized images. If false, rescale
+// by new_height / height. Treat similarly the width dimension.
+// If not specified, defaults to false
+func ResizeBilinearAlignCorners(value bool) ResizeBilinearAttr {
+ return func(m optionalAttr) {
+ m["align_corners"] = value
+ }
+}
+
+// Resize `images` to `size` using bilinear interpolation.
//
-// A strong hash is important when inputs may be malicious, e.g. URLs with
-// additional components. Adversaries could try to make their inputs hash to the
-// same bucket for a denial-of-service attack or to skew the results. A strong
-// hash prevents this by making it difficult, if not infeasible, to compute inputs
-// that hash to the same bucket. This comes at a cost of roughly 4x higher compute
-// time than `tf.string_to_hash_bucket_fast`.
+// Input images can be of different types but output images are always float.
//
// Arguments:
-// input: The strings to assign a hash bucket.
-// num_buckets: The number of buckets.
-// key: The key for the keyed hash function passed as a list of two uint64
-// elements.
+// images: 4-D with shape `[batch, height, width, channels]`.
+// size: = A 1-D int32 Tensor of 2 elements: `new_height, new_width`. The
+// new size for the images.
//
-// Returns A Tensor of the same shape as the input `string_tensor`.
-func StringToHashBucketStrong(scope *Scope, input tf.Output, num_buckets int64, key []int64) (output tf.Output) {
+// Returns 4-D with shape
+// `[batch, new_height, new_width, channels]`.
+func ResizeBilinear(scope *Scope, images tf.Output, size tf.Output, optional ...ResizeBilinearAttr) (resized_images tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{"num_buckets": num_buckets, "key": key}
+ attrs := map[string]interface{}{}
+ for _, a := range optional {
+ a(attrs)
+ }
opspec := tf.OpSpec{
- Type: "StringToHashBucketStrong",
+ Type: "ResizeBilinear",
Input: []tf.Input{
- input,
+ images, size,
},
Attrs: attrs,
}
return op.Output(0)
}
-// Applies softmax to a batched N-D `SparseTensor`.
-//
-// The inputs represent an N-D SparseTensor with logical shape `[..., B, C]`
-// (where `N >= 2`), and with indices sorted in the canonical lexicographic order.
-//
-// This op is equivalent to applying the normal `tf.nn.softmax()` to each innermost
-// logical submatrix with shape `[B, C]`, but with the catch that *the implicitly
-// zero elements do not participate*. Specifically, the algorithm is equivalent
-// to the following:
-//
-// (1) Applies `tf.nn.softmax()` to a densified view of each innermost submatrix
-// with shape `[B, C]`, along the size-C dimension;
-// (2) Masks out the original implicitly-zero locations;
-// (3) Renormalizes the remaining elements.
-//
-// Hence, the `SparseTensor` result has exactly the same non-zero indices and
-// shape.
-//
-// Arguments:
-// sp_indices: 2-D. `NNZ x R` matrix with the indices of non-empty values in a
-// SparseTensor, in canonical ordering.
-// sp_values: 1-D. `NNZ` non-empty values corresponding to `sp_indices`.
-// sp_shape: 1-D. Shape of the input SparseTensor.
-//
-// Returns 1-D. The `NNZ` values for the result `SparseTensor`.
-func SparseSoftmax(scope *Scope, sp_indices tf.Output, sp_values tf.Output, sp_shape tf.Output) (output tf.Output) {
+// Computes softsign: `features / (abs(features) + 1)`.
+func Softsign(scope *Scope, features tf.Output) (activations tf.Output) {
if scope.Err() != nil {
return
}
opspec := tf.OpSpec{
- Type: "SparseSoftmax",
+ Type: "Softsign",
Input: []tf.Input{
- sp_indices, sp_values, sp_shape,
+ features,
},
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// Partitions `data` into `num_partitions` tensors using indices from `partitions`.
-//
-// For each index tuple `js` of size `partitions.ndim`, the slice `data[js, ...]`
-// becomes part of `outputs[partitions[js]]`. The slices with `partitions[js] = i`
-// are placed in `outputs[i]` in lexicographic order of `js`, and the first
-// dimension of `outputs[i]` is the number of entries in `partitions` equal to `i`.
-// In detail,
-//
-// ```python
-// outputs[i].shape = [sum(partitions == i)] + data.shape[partitions.ndim:]
+// GenerateVocabRemappingAttr is an optional argument to GenerateVocabRemapping.
+type GenerateVocabRemappingAttr func(optionalAttr)
+
+// GenerateVocabRemappingOldVocabSize sets the optional old_vocab_size attribute to value.
//
-// outputs[i] = pack([data[js, ...] for js if partitions[js] == i])
-// ```
+// value: Number of entries in the old vocab file to consider. If -1,
+// use the entire old vocabulary.
+// If not specified, defaults to -1
//
-// `data.shape` must start with `partitions.shape`.
+// REQUIRES: value >= -1
+func GenerateVocabRemappingOldVocabSize(value int64) GenerateVocabRemappingAttr {
+ return func(m optionalAttr) {
+ m["old_vocab_size"] = value
+ }
+}
+
+// Given a path to new and old vocabulary files, returns a remapping Tensor of
//
-// For example:
+// length `num_new_vocab`, where `remapping[i]` contains the row number in the old
+// vocabulary that corresponds to row `i` in the new vocabulary (starting at line
+// `new_vocab_offset` and up to `num_new_vocab` entities), or `-1` if entry `i`
+// in the new vocabulary is not in the old vocabulary. The old vocabulary is
+// constrained to the first `old_vocab_size` entries if `old_vocab_size` is not the
+// default value of -1.
//
-// ```python
-// # Scalar partitions.
-// partitions = 1
-// num_partitions = 2
-// data = [10, 20]
-// outputs[0] = [] # Empty with shape [0, 2]
-// outputs[1] = [[10, 20]]
+// `num_vocab_offset` enables
+// use in the partitioned variable case, and should generally be set through
+// examining partitioning info. The format of the files should be a text file,
+// with each line containing a single entity within the vocabulary.
//
-// # Vector partitions.
-// partitions = [0, 0, 1, 1, 0]
-// num_partitions = 2
-// data = [10, 20, 30, 40, 50]
-// outputs[0] = [10, 20, 50]
-// outputs[1] = [30, 40]
-// ```
+// For example, with `new_vocab_file` a text file containing each of the following
+// elements on a single line: `[f0, f1, f2, f3]`, old_vocab_file = [f1, f0, f3],
+// `num_new_vocab = 3, new_vocab_offset = 1`, the returned remapping would be
+// `[0, -1, 2]`.
//
-// See `dynamic_stitch` for an example on how to merge partitions back.
+// The op also returns a count of how many entries in the new vocabulary
+// were present in the old vocabulary, which is used to calculate the number of
+// values to initialize in a weight matrix remapping
//
-// <div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;">
-// <img style="width:100%" src="https://www.tensorflow.org/images/DynamicPartition.png" alt>
-// </div>
+// This functionality can be used to remap both row vocabularies (typically,
+// features) and column vocabularies (typically, classes) from TensorFlow
+// checkpoints. Note that the partitioning logic relies on contiguous vocabularies
+// corresponding to div-partitioned variables. Moreover, the underlying remapping
+// uses an IndexTable (as opposed to an inexact CuckooTable), so client code should
+// use the corresponding index_table_from_file() as the FeatureColumn framework
+// does (as opposed to tf.feature_to_id(), which uses a CuckooTable).
//
// Arguments:
+// new_vocab_file: Path to the new vocab file.
+// old_vocab_file: Path to the old vocab file.
+// new_vocab_offset: How many entries into the new vocab file to start reading.
+// num_new_vocab: Number of entries in the new vocab file to remap.
//
-// partitions: Any shape. Indices in the range `[0, num_partitions)`.
-// num_partitions: The number of partitions to output.
-func DynamicPartition(scope *Scope, data tf.Output, partitions tf.Output, num_partitions int64) (outputs []tf.Output) {
+// Returns A Tensor of length num_new_vocab where the element at index i
+// is equal to the old ID that maps to the new ID i. This element is -1 for any
+// new ID that is not found in the old vocabulary.Number of new vocab entries found in old vocab.
+func GenerateVocabRemapping(scope *Scope, new_vocab_file tf.Output, old_vocab_file tf.Output, new_vocab_offset int64, num_new_vocab int64, optional ...GenerateVocabRemappingAttr) (remapping tf.Output, num_present tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{"num_partitions": num_partitions}
+ attrs := map[string]interface{}{"new_vocab_offset": new_vocab_offset, "num_new_vocab": num_new_vocab}
+ for _, a := range optional {
+ a(attrs)
+ }
opspec := tf.OpSpec{
- Type: "DynamicPartition",
+ Type: "GenerateVocabRemapping",
Input: []tf.Input{
- data, partitions,
+ new_vocab_file, old_vocab_file,
},
Attrs: attrs,
}
op := scope.AddOperation(opspec)
- if scope.Err() != nil {
- return
- }
- var idx int
- var err error
- if outputs, idx, err = makeOutputList(op, idx, "outputs"); err != nil {
- scope.UpdateErr("DynamicPartition", err)
- return
- }
- return outputs
+ return op.Output(0), op.Output(1)
}
-// ResourceApplyAdagradAttr is an optional argument to ResourceApplyAdagrad.
-type ResourceApplyAdagradAttr func(optionalAttr)
+// Assigns sparse updates to the variable referenced by `resource`.
+//
+// This operation computes
+//
+// # Scalar indices
+// ref[indices, ...] = updates[...]
+//
+// # Vector indices (for each i)
+// ref[indices[i], ...] = updates[i, ...]
+//
+// # High rank indices (for each i, ..., j)
+// ref[indices[i, ..., j], ...] = updates[i, ..., j, ...]
+//
+// Arguments:
+// resource: Should be from a `Variable` node.
+// indices: A tensor of indices into the first dimension of `ref`.
+// updates: A tensor of updated values to add to `ref`.
+//
+// Returns the created operation.
+func ResourceScatterUpdate(scope *Scope, resource tf.Output, indices tf.Output, updates tf.Output) (o *tf.Operation) {
+ if scope.Err() != nil {
+ return
+ }
+ opspec := tf.OpSpec{
+ Type: "ResourceScatterUpdate",
+ Input: []tf.Input{
+ resource, indices, updates,
+ },
+ }
+ return scope.AddOperation(opspec)
+}
-// ResourceApplyAdagradUseLocking sets the optional use_locking attribute to value.
+// AvgPoolGradAttr is an optional argument to AvgPoolGrad.
+type AvgPoolGradAttr func(optionalAttr)
+
+// AvgPoolGradDataFormat sets the optional data_format attribute to value.
//
-// value: If `True`, updating of the var and accum tensors will be protected
-// by a lock; otherwise the behavior is undefined, but may exhibit less
-// contention.
-// If not specified, defaults to false
-func ResourceApplyAdagradUseLocking(value bool) ResourceApplyAdagradAttr {
+// value: Specify the data format of the input and output data. With the
+// default format "NHWC", the data is stored in the order of:
+// [batch, in_height, in_width, in_channels].
+// Alternatively, the format could be "NCHW", the data storage order of:
+// [batch, in_channels, in_height, in_width].
+// If not specified, defaults to "NHWC"
+func AvgPoolGradDataFormat(value string) AvgPoolGradAttr {
return func(m optionalAttr) {
- m["use_locking"] = value
+ m["data_format"] = value
}
}
-// Update '*var' according to the adagrad scheme.
-//
-// accum += grad * grad
-// var -= lr * grad * (1 / sqrt(accum))
+// Computes gradients of the average pooling function.
//
// Arguments:
-// var_: Should be from a Variable().
-// accum: Should be from a Variable().
-// lr: Scaling factor. Must be a scalar.
-// grad: The gradient.
+// orig_input_shape: 1-D. Shape of the original input to `avg_pool`.
+// grad: 4-D with shape `[batch, height, width, channels]`. Gradients w.r.t.
+// the output of `avg_pool`.
+// ksize: The size of the sliding window for each dimension of the input.
+// strides: The stride of the sliding window for each dimension of the input.
+// padding: The type of padding algorithm to use.
//
-// Returns the created operation.
-func ResourceApplyAdagrad(scope *Scope, var_ tf.Output, accum tf.Output, lr tf.Output, grad tf.Output, optional ...ResourceApplyAdagradAttr) (o *tf.Operation) {
+// Returns 4-D. Gradients w.r.t. the input of `avg_pool`.
+func AvgPoolGrad(scope *Scope, orig_input_shape tf.Output, grad tf.Output, ksize []int64, strides []int64, padding string, optional ...AvgPoolGradAttr) (output tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{}
+ attrs := map[string]interface{}{"ksize": ksize, "strides": strides, "padding": padding}
for _, a := range optional {
a(attrs)
}
opspec := tf.OpSpec{
- Type: "ResourceApplyAdagrad",
+ Type: "AvgPoolGrad",
Input: []tf.Input{
- var_, accum, lr, grad,
+ orig_input_shape, grad,
},
Attrs: attrs,
}
+ op := scope.AddOperation(opspec)
+ return op.Output(0)
+}
+
+// StageClearAttr is an optional argument to StageClear.
+type StageClearAttr func(optionalAttr)
+
+// StageClearCapacity sets the optional capacity attribute to value.
+// If not specified, defaults to 0
+//
+// REQUIRES: value >= 0
+func StageClearCapacity(value int64) StageClearAttr {
+ return func(m optionalAttr) {
+ m["capacity"] = value
+ }
+}
+
+// StageClearMemoryLimit sets the optional memory_limit attribute to value.
+// If not specified, defaults to 0
+//
+// REQUIRES: value >= 0
+func StageClearMemoryLimit(value int64) StageClearAttr {
+ return func(m optionalAttr) {
+ m["memory_limit"] = value
+ }
+}
+
+// StageClearContainer sets the optional container attribute to value.
+// If not specified, defaults to ""
+func StageClearContainer(value string) StageClearAttr {
+ return func(m optionalAttr) {
+ m["container"] = value
+ }
+}
+
+// StageClearSharedName sets the optional shared_name attribute to value.
+// If not specified, defaults to ""
+func StageClearSharedName(value string) StageClearAttr {
+ return func(m optionalAttr) {
+ m["shared_name"] = value
+ }
+}
+
+// Op removes all elements in the underlying container.
+//
+// Returns the created operation.
+func StageClear(scope *Scope, dtypes []tf.DataType, optional ...StageClearAttr) (o *tf.Operation) {
+ if scope.Err() != nil {
+ return
+ }
+ attrs := map[string]interface{}{"dtypes": dtypes}
+ for _, a := range optional {
+ a(attrs)
+ }
+ opspec := tf.OpSpec{
+ Type: "StageClear",
+
+ Attrs: attrs,
+ }
return scope.AddOperation(opspec)
}
-// ResourceApplyPowerSignAttr is an optional argument to ResourceApplyPowerSign.
-type ResourceApplyPowerSignAttr func(optionalAttr)
+// ComputeAccidentalHitsAttr is an optional argument to ComputeAccidentalHits.
+type ComputeAccidentalHitsAttr func(optionalAttr)
-// ResourceApplyPowerSignUseLocking sets the optional use_locking attribute to value.
+// ComputeAccidentalHitsSeed sets the optional seed attribute to value.
//
-// value: If `True`, updating of the var and m tensors is
-// protected by a lock; otherwise the behavior is undefined, but may exhibit less
-// contention.
-// If not specified, defaults to false
-func ResourceApplyPowerSignUseLocking(value bool) ResourceApplyPowerSignAttr {
+// value: If either seed or seed2 are set to be non-zero, the random number
+// generator is seeded by the given seed. Otherwise, it is seeded by a
+// random seed.
+// If not specified, defaults to 0
+func ComputeAccidentalHitsSeed(value int64) ComputeAccidentalHitsAttr {
return func(m optionalAttr) {
- m["use_locking"] = value
+ m["seed"] = value
}
}
-// Update '*var' according to the AddSign update.
+// ComputeAccidentalHitsSeed2 sets the optional seed2 attribute to value.
//
-// m_t <- beta1 * m_{t-1} + (1 - beta1) * g
-// update <- exp(logbase * sign_decay * sign(g) * sign(m_t)) * g
-// variable <- variable - lr_t * update
+// value: An second seed to avoid seed collision.
+// If not specified, defaults to 0
+func ComputeAccidentalHitsSeed2(value int64) ComputeAccidentalHitsAttr {
+ return func(m optionalAttr) {
+ m["seed2"] = value
+ }
+}
+
+// Computes the ids of the positions in sampled_candidates that match true_labels.
+//
+// When doing log-odds NCE, the result of this op should be passed through a
+// SparseToDense op, then added to the logits of the sampled candidates. This has
+// the effect of 'removing' the sampled labels that match the true labels by
+// making the classifier sure that they are sampled labels.
//
// Arguments:
-// var_: Should be from a Variable().
-// m: Should be from a Variable().
-// lr: Scaling factor. Must be a scalar.
-// logbase: Must be a scalar.
-// sign_decay: Must be a scalar.
-// beta: Must be a scalar.
-// grad: The gradient.
+// true_classes: The true_classes output of UnpackSparseLabels.
+// sampled_candidates: The sampled_candidates output of CandidateSampler.
+// num_true: Number of true labels per context.
//
-// Returns the created operation.
-func ResourceApplyPowerSign(scope *Scope, var_ tf.Output, m tf.Output, lr tf.Output, logbase tf.Output, sign_decay tf.Output, beta tf.Output, grad tf.Output, optional ...ResourceApplyPowerSignAttr) (o *tf.Operation) {
+// Returns A vector of indices corresponding to rows of true_candidates.A vector of IDs of positions in sampled_candidates that match a true_label
+// for the row with the corresponding index in indices.A vector of the same length as indices and ids, in which each element
+// is -FLOAT_MAX.
+func ComputeAccidentalHits(scope *Scope, true_classes tf.Output, sampled_candidates tf.Output, num_true int64, optional ...ComputeAccidentalHitsAttr) (indices tf.Output, ids tf.Output, weights tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{}
+ attrs := map[string]interface{}{"num_true": num_true}
for _, a := range optional {
a(attrs)
}
opspec := tf.OpSpec{
- Type: "ResourceApplyPowerSign",
+ Type: "ComputeAccidentalHits",
Input: []tf.Input{
- var_, m, lr, logbase, sign_decay, beta, grad,
+ true_classes, sampled_candidates,
},
Attrs: attrs,
}
- return scope.AddOperation(opspec)
+ op := scope.AddOperation(opspec)
+ return op.Output(0), op.Output(1), op.Output(2)
}
-// CumprodAttr is an optional argument to Cumprod.
-type CumprodAttr func(optionalAttr)
+// CumsumAttr is an optional argument to Cumsum.
+type CumsumAttr func(optionalAttr)
-// CumprodExclusive sets the optional exclusive attribute to value.
+// CumsumExclusive sets the optional exclusive attribute to value.
//
-// value: If `True`, perform exclusive cumprod.
+// value: If `True`, perform exclusive cumsum.
// If not specified, defaults to false
-func CumprodExclusive(value bool) CumprodAttr {
+func CumsumExclusive(value bool) CumsumAttr {
return func(m optionalAttr) {
m["exclusive"] = value
}
}
-// CumprodReverse sets the optional reverse attribute to value.
+// CumsumReverse sets the optional reverse attribute to value.
//
// value: A `bool` (default: False).
// If not specified, defaults to false
-func CumprodReverse(value bool) CumprodAttr {
+func CumsumReverse(value bool) CumsumAttr {
return func(m optionalAttr) {
m["reverse"] = value
}
}
-// Compute the cumulative product of the tensor `x` along `axis`.
+// Compute the cumulative sum of the tensor `x` along `axis`.
//
-// By default, this op performs an inclusive cumprod, which means that the first
+// By default, this op performs an inclusive cumsum, which means that the first
// element of the input is identical to the first element of the output:
//
// ```python
-// tf.cumprod([a, b, c]) # => [a, a * b, a * b * c]
+// tf.cumsum([a, b, c]) # => [a, a + b, a + b + c]
// ```
//
-// By setting the `exclusive` kwarg to `True`, an exclusive cumprod is
+// By setting the `exclusive` kwarg to `True`, an exclusive cumsum is
// performed instead:
//
// ```python
-// tf.cumprod([a, b, c], exclusive=True) # => [1, a, a * b]
+// tf.cumsum([a, b, c], exclusive=True) # => [0, a, a + b]
// ```
//
-// By setting the `reverse` kwarg to `True`, the cumprod is performed in the
+// By setting the `reverse` kwarg to `True`, the cumsum is performed in the
// opposite direction:
//
// ```python
-// tf.cumprod([a, b, c], reverse=True) # => [a * b * c, b * c, c]
+// tf.cumsum([a, b, c], reverse=True) # => [a + b + c, b + c, c]
// ```
//
// This is more efficient than using separate `tf.reverse` ops.
// The `reverse` and `exclusive` kwargs can also be combined:
//
// ```python
-// tf.cumprod([a, b, c], exclusive=True, reverse=True) # => [b * c, c, 1]
+// tf.cumsum([a, b, c], exclusive=True, reverse=True) # => [b + c, c, 0]
// ```
//
// Arguments:
// `complex128`, `qint8`, `quint8`, `qint32`, `half`.
// axis: A `Tensor` of type `int32` (default: 0). Must be in the range
// `[-rank(x), rank(x))`.
-func Cumprod(scope *Scope, x tf.Output, axis tf.Output, optional ...CumprodAttr) (out tf.Output) {
+func Cumsum(scope *Scope, x tf.Output, axis tf.Output, optional ...CumsumAttr) (out tf.Output) {
if scope.Err() != nil {
return
}
a(attrs)
}
opspec := tf.OpSpec{
- Type: "Cumprod",
+ Type: "Cumsum",
Input: []tf.Input{
x, axis,
},
return op.Output(0)
}
-// Computes the mean along segments of a tensor.
-//
-// Read @{$math_ops#segmentation$the section on segmentation} for an explanation of
-// segments.
+// QuantizedRelu6Attr is an optional argument to QuantizedRelu6.
+type QuantizedRelu6Attr func(optionalAttr)
+
+// QuantizedRelu6OutType sets the optional out_type attribute to value.
+// If not specified, defaults to DT_QUINT8
+func QuantizedRelu6OutType(value tf.DataType) QuantizedRelu6Attr {
+ return func(m optionalAttr) {
+ m["out_type"] = value
+ }
+}
+
+// Computes Quantized Rectified Linear 6: `min(max(features, 0), 6)`
//
-// Computes a tensor such that
-// \\(output_i = \frac{\sum_j data_j}{N}\\) where `mean` is
-// over `j` such that `segment_ids[j] == i` and `N` is the total number of
-// values summed.
+// Arguments:
//
-// If the mean is empty for a given segment ID `i`, `output[i] = 0`.
+// min_features: The float value that the lowest quantized value represents.
+// max_features: The float value that the highest quantized value represents.
//
-// <div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;">
-// <img style="width:100%" src="https://www.tensorflow.org/images/SegmentMean.png" alt>
-// </div>
-//
-// Arguments:
-//
-// segment_ids: A 1-D tensor whose rank is equal to the rank of `data`'s
-// first dimension. Values should be sorted and can be repeated.
-//
-// Returns Has same shape as data, except for dimension 0 which
-// has size `k`, the number of segments.
-func SegmentMean(scope *Scope, data tf.Output, segment_ids tf.Output) (output tf.Output) {
+// Returns Has the same output shape as "features".The float value that the lowest quantized value represents.The float value that the highest quantized value represents.
+func QuantizedRelu6(scope *Scope, features tf.Output, min_features tf.Output, max_features tf.Output, optional ...QuantizedRelu6Attr) (activations tf.Output, min_activations tf.Output, max_activations tf.Output) {
if scope.Err() != nil {
return
}
+ attrs := map[string]interface{}{}
+ for _, a := range optional {
+ a(attrs)
+ }
opspec := tf.OpSpec{
- Type: "SegmentMean",
+ Type: "QuantizedRelu6",
Input: []tf.Input{
- data, segment_ids,
+ features, min_features, max_features,
},
+ Attrs: attrs,
}
op := scope.AddOperation(opspec)
- return op.Output(0)
+ return op.Output(0), op.Output(1), op.Output(2)
}
-// ResourceSparseApplyCenteredRMSPropAttr is an optional argument to ResourceSparseApplyCenteredRMSProp.
-type ResourceSparseApplyCenteredRMSPropAttr func(optionalAttr)
+// FixedLengthRecordReaderV2Attr is an optional argument to FixedLengthRecordReaderV2.
+type FixedLengthRecordReaderV2Attr func(optionalAttr)
-// ResourceSparseApplyCenteredRMSPropUseLocking sets the optional use_locking attribute to value.
+// FixedLengthRecordReaderV2HeaderBytes sets the optional header_bytes attribute to value.
//
-// value: If `True`, updating of the var, mg, ms, and mom tensors is
-// protected by a lock; otherwise the behavior is undefined, but may exhibit less
-// contention.
-// If not specified, defaults to false
-func ResourceSparseApplyCenteredRMSPropUseLocking(value bool) ResourceSparseApplyCenteredRMSPropAttr {
+// value: Number of bytes in the header, defaults to 0.
+// If not specified, defaults to 0
+func FixedLengthRecordReaderV2HeaderBytes(value int64) FixedLengthRecordReaderV2Attr {
return func(m optionalAttr) {
- m["use_locking"] = value
+ m["header_bytes"] = value
}
}
-// Update '*var' according to the centered RMSProp algorithm.
+// FixedLengthRecordReaderV2FooterBytes sets the optional footer_bytes attribute to value.
//
-// The centered RMSProp algorithm uses an estimate of the centered second moment
-// (i.e., the variance) for normalization, as opposed to regular RMSProp, which
-// uses the (uncentered) second moment. This often helps with training, but is
-// slightly more expensive in terms of computation and memory.
+// value: Number of bytes in the footer, defaults to 0.
+// If not specified, defaults to 0
+func FixedLengthRecordReaderV2FooterBytes(value int64) FixedLengthRecordReaderV2Attr {
+ return func(m optionalAttr) {
+ m["footer_bytes"] = value
+ }
+}
+
+// FixedLengthRecordReaderV2HopBytes sets the optional hop_bytes attribute to value.
//
-// Note that in dense implementation of this algorithm, mg, ms, and mom will
-// update even if the grad is zero, but in this sparse implementation, mg, ms,
-// and mom will not update in iterations during which the grad is zero.
+// value: Number of bytes to hop before each read. Default of 0 means using
+// record_bytes.
+// If not specified, defaults to 0
+func FixedLengthRecordReaderV2HopBytes(value int64) FixedLengthRecordReaderV2Attr {
+ return func(m optionalAttr) {
+ m["hop_bytes"] = value
+ }
+}
+
+// FixedLengthRecordReaderV2Container sets the optional container attribute to value.
//
-// mean_square = decay * mean_square + (1-decay) * gradient ** 2
-// mean_grad = decay * mean_grad + (1-decay) * gradient
-// Delta = learning_rate * gradient / sqrt(mean_square + epsilon - mean_grad ** 2)
+// value: If non-empty, this reader is placed in the given container.
+// Otherwise, a default container is used.
+// If not specified, defaults to ""
+func FixedLengthRecordReaderV2Container(value string) FixedLengthRecordReaderV2Attr {
+ return func(m optionalAttr) {
+ m["container"] = value
+ }
+}
+
+// FixedLengthRecordReaderV2SharedName sets the optional shared_name attribute to value.
//
-// ms <- rho * ms_{t-1} + (1-rho) * grad * grad
-// mom <- momentum * mom_{t-1} + lr * grad / sqrt(ms + epsilon)
-// var <- var - mom
+// value: If non-empty, this reader is named in the given bucket
+// with this shared_name. Otherwise, the node name is used instead.
+// If not specified, defaults to ""
+func FixedLengthRecordReaderV2SharedName(value string) FixedLengthRecordReaderV2Attr {
+ return func(m optionalAttr) {
+ m["shared_name"] = value
+ }
+}
+
+// FixedLengthRecordReaderV2Encoding sets the optional encoding attribute to value.
//
-// Arguments:
-// var_: Should be from a Variable().
-// mg: Should be from a Variable().
-// ms: Should be from a Variable().
-// mom: Should be from a Variable().
-// lr: Scaling factor. Must be a scalar.
-// rho: Decay rate. Must be a scalar.
+// value: The type of encoding for the file. Currently ZLIB and GZIP
+// are supported. Defaults to none.
+// If not specified, defaults to ""
+func FixedLengthRecordReaderV2Encoding(value string) FixedLengthRecordReaderV2Attr {
+ return func(m optionalAttr) {
+ m["encoding"] = value
+ }
+}
+
+// A Reader that outputs fixed-length records from a file.
//
-// epsilon: Ridge term. Must be a scalar.
-// grad: The gradient.
-// indices: A vector of indices into the first dimension of var, ms and mom.
+// Arguments:
+// record_bytes: Number of bytes in the record.
//
-// Returns the created operation.
-func ResourceSparseApplyCenteredRMSProp(scope *Scope, var_ tf.Output, mg tf.Output, ms tf.Output, mom tf.Output, lr tf.Output, rho tf.Output, momentum tf.Output, epsilon tf.Output, grad tf.Output, indices tf.Output, optional ...ResourceSparseApplyCenteredRMSPropAttr) (o *tf.Operation) {
+// Returns The handle to reference the Reader.
+func FixedLengthRecordReaderV2(scope *Scope, record_bytes int64, optional ...FixedLengthRecordReaderV2Attr) (reader_handle tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{}
+ attrs := map[string]interface{}{"record_bytes": record_bytes}
for _, a := range optional {
a(attrs)
}
opspec := tf.OpSpec{
- Type: "ResourceSparseApplyCenteredRMSProp",
- Input: []tf.Input{
- var_, mg, ms, mom, lr, rho, momentum, epsilon, grad, indices,
- },
+ Type: "FixedLengthRecordReaderV2",
+
Attrs: attrs,
}
- return scope.AddOperation(opspec)
+ op := scope.AddOperation(opspec)
+ return op.Output(0)
}
-// Creates a dataset that batches `batch_size` elements from `input_dataset`.
+// Converts each string in the input Tensor to its hash mod by a number of buckets.
//
-// Arguments:
+// The hash function is deterministic on the content of the string within the
+// process.
//
-// batch_size: A scalar representing the number of elements to accumulate in a
-// batch.
+// Note that the hash function may change from time to time.
+// This functionality will be deprecated and it's recommended to use
+// `tf.string_to_hash_bucket_fast()` or `tf.string_to_hash_bucket_strong()`.
//
+// Arguments:
//
-func BatchDataset(scope *Scope, input_dataset tf.Output, batch_size tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) {
+// num_buckets: The number of buckets.
+//
+// Returns A Tensor of the same shape as the input `string_tensor`.
+func StringToHashBucket(scope *Scope, string_tensor tf.Output, num_buckets int64) (output tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes}
+ attrs := map[string]interface{}{"num_buckets": num_buckets}
opspec := tf.OpSpec{
- Type: "BatchDataset",
+ Type: "StringToHashBucket",
Input: []tf.Input{
- input_dataset, batch_size,
+ string_tensor,
},
Attrs: attrs,
}
return op.Output(0)
}
-// Inverse fast Fourier transform.
-//
-// Computes the inverse 1-dimensional discrete Fourier transform over the
-// inner-most dimension of `input`.
+// Computes gradients for the exponential linear (Elu) operation.
//
// Arguments:
-// input: A complex64 tensor.
-//
-// Returns A complex64 tensor of the same shape as `input`. The inner-most
-// dimension of `input` is replaced with its inverse 1D Fourier transform.
+// gradients: The backpropagated gradients to the corresponding Elu operation.
+// outputs: The outputs of the corresponding Elu operation.
//
-// @compatibility(numpy)
-// Equivalent to np.fft.ifft
-// @end_compatibility
-func IFFT(scope *Scope, input tf.Output) (output tf.Output) {
+// Returns The gradients: `gradients * (outputs + 1)` if outputs < 0,
+// `gradients` otherwise.
+func EluGrad(scope *Scope, gradients tf.Output, outputs tf.Output) (backprops tf.Output) {
if scope.Err() != nil {
return
}
opspec := tf.OpSpec{
- Type: "IFFT",
+ Type: "EluGrad",
Input: []tf.Input{
- input,
+ gradients, outputs,
},
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// Generates values in an interval.
-//
-// A sequence of `num` evenly-spaced values are generated beginning at `start`.
-// If `num > 1`, the values in the sequence increase by `stop - start / num - 1`,
-// so that the last one is exactly `stop`.
+// Creates a dataset that contains `count` elements from the `input_dataset`.
//
-// For example:
+// Arguments:
//
-// ```
-// tf.linspace(10.0, 12.0, 3, name="linspace") => [ 10.0 11.0 12.0]
-// ```
+// count: A scalar representing the number of elements from the `input_dataset`
+// that should be taken. A value of `-1` indicates that all of `input_dataset`
+// is taken.
//
-// Arguments:
-// start: First entry in the range.
-// stop: Last entry in the range.
-// num: Number of values to generate.
//
-// Returns 1-D. The generated values.
-func LinSpace(scope *Scope, start tf.Output, stop tf.Output, num tf.Output) (output tf.Output) {
+func TakeDataset(scope *Scope, input_dataset tf.Output, count tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) {
if scope.Err() != nil {
return
}
+ attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes}
opspec := tf.OpSpec{
- Type: "LinSpace",
+ Type: "TakeDataset",
Input: []tf.Input{
- start, stop, num,
+ input_dataset, count,
},
+ Attrs: attrs,
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// DestroyResourceOpAttr is an optional argument to DestroyResourceOp.
-type DestroyResourceOpAttr func(optionalAttr)
-
-// DestroyResourceOpIgnoreLookupError sets the optional ignore_lookup_error attribute to value.
-//
-// value: whether to ignore the error when the resource
-// doesn't exist.
-// If not specified, defaults to true
-func DestroyResourceOpIgnoreLookupError(value bool) DestroyResourceOpAttr {
- return func(m optionalAttr) {
- m["ignore_lookup_error"] = value
- }
-}
-
-// Deletes the resource specified by the handle.
+// The gradient operator for the SparseAdd op.
//
-// All subsequent operations using the resource will result in a NotFound
-// error status.
+// The SparseAdd op calculates A + B, where A, B, and the sum are all represented
+// as `SparseTensor` objects. This op takes in the upstream gradient w.r.t.
+// non-empty values of the sum, and outputs the gradients w.r.t. the non-empty
+// values of A and B.
//
// Arguments:
-// resource: handle to the resource to delete.
+// backprop_val_grad: 1-D with shape `[nnz(sum)]`. The gradient with respect to
+// the non-empty values of the sum.
+// a_indices: 2-D. The `indices` of the `SparseTensor` A, size `[nnz(A), ndims]`.
+// b_indices: 2-D. The `indices` of the `SparseTensor` B, size `[nnz(B), ndims]`.
+// sum_indices: 2-D. The `indices` of the sum `SparseTensor`, size
+// `[nnz(sum), ndims]`.
//
-// Returns the created operation.
-func DestroyResourceOp(scope *Scope, resource tf.Output, optional ...DestroyResourceOpAttr) (o *tf.Operation) {
+// Returns 1-D with shape `[nnz(A)]`. The gradient with respect to the
+// non-empty values of A.1-D with shape `[nnz(B)]`. The gradient with respect to the
+// non-empty values of B.
+func SparseAddGrad(scope *Scope, backprop_val_grad tf.Output, a_indices tf.Output, b_indices tf.Output, sum_indices tf.Output) (a_val_grad tf.Output, b_val_grad tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{}
- for _, a := range optional {
- a(attrs)
- }
opspec := tf.OpSpec{
- Type: "DestroyResourceOp",
+ Type: "SparseAddGrad",
Input: []tf.Input{
- resource,
+ backprop_val_grad, a_indices, b_indices, sum_indices,
},
- Attrs: attrs,
}
- return scope.AddOperation(opspec)
+ op := scope.AddOperation(opspec)
+ return op.Output(0), op.Output(1)
}
-// LRNAttr is an optional argument to LRN.
-type LRNAttr func(optionalAttr)
-
-// LRNDepthRadius sets the optional depth_radius attribute to value.
-//
-// value: 0-D. Half-width of the 1-D normalization window.
-// If not specified, defaults to 5
-func LRNDepthRadius(value int64) LRNAttr {
- return func(m optionalAttr) {
- m["depth_radius"] = value
+// Computes atan of x element-wise.
+func Atan(scope *Scope, x tf.Output) (y tf.Output) {
+ if scope.Err() != nil {
+ return
}
-}
-
-// LRNBias sets the optional bias attribute to value.
-//
-// value: An offset (usually positive to avoid dividing by 0).
-// If not specified, defaults to 1
-func LRNBias(value float32) LRNAttr {
- return func(m optionalAttr) {
- m["bias"] = value
+ opspec := tf.OpSpec{
+ Type: "Atan",
+ Input: []tf.Input{
+ x,
+ },
}
+ op := scope.AddOperation(opspec)
+ return op.Output(0)
}
-// LRNAlpha sets the optional alpha attribute to value.
+// Encode audio data using the WAV file format.
//
-// value: A scale factor, usually positive.
-// If not specified, defaults to 1
-func LRNAlpha(value float32) LRNAttr {
- return func(m optionalAttr) {
- m["alpha"] = value
+// This operation will generate a string suitable to be saved out to create a .wav
+// audio file. It will be encoded in the 16-bit PCM format. It takes in float
+// values in the range -1.0f to 1.0f, and any outside that value will be clamped to
+// that range.
+//
+// `audio` is a 2-D float Tensor of shape `[length, channels]`.
+// `sample_rate` is a scalar Tensor holding the rate to use (e.g. 44100).
+//
+// Arguments:
+// audio: 2-D with shape `[length, channels]`.
+// sample_rate: Scalar containing the sample frequency.
+//
+// Returns 0-D. WAV-encoded file contents.
+func EncodeWav(scope *Scope, audio tf.Output, sample_rate tf.Output) (contents tf.Output) {
+ if scope.Err() != nil {
+ return
+ }
+ opspec := tf.OpSpec{
+ Type: "EncodeWav",
+ Input: []tf.Input{
+ audio, sample_rate,
+ },
}
+ op := scope.AddOperation(opspec)
+ return op.Output(0)
}
-// LRNBeta sets the optional beta attribute to value.
+// Converts each string in the input Tensor to its hash mod by a number of buckets.
//
-// value: An exponent.
-// If not specified, defaults to 0.5
-func LRNBeta(value float32) LRNAttr {
- return func(m optionalAttr) {
- m["beta"] = value
+// The hash function is deterministic on the content of the string within the
+// process. The hash function is a keyed hash function, where attribute `key`
+// defines the key of the hash function. `key` is an array of 2 elements.
+//
+// A strong hash is important when inputs may be malicious, e.g. URLs with
+// additional components. Adversaries could try to make their inputs hash to the
+// same bucket for a denial-of-service attack or to skew the results. A strong
+// hash prevents this by making it difficult, if not infeasible, to compute inputs
+// that hash to the same bucket. This comes at a cost of roughly 4x higher compute
+// time than `tf.string_to_hash_bucket_fast`.
+//
+// Arguments:
+// input: The strings to assign a hash bucket.
+// num_buckets: The number of buckets.
+// key: The key for the keyed hash function passed as a list of two uint64
+// elements.
+//
+// Returns A Tensor of the same shape as the input `string_tensor`.
+func StringToHashBucketStrong(scope *Scope, input tf.Output, num_buckets int64, key []int64) (output tf.Output) {
+ if scope.Err() != nil {
+ return
+ }
+ attrs := map[string]interface{}{"num_buckets": num_buckets, "key": key}
+ opspec := tf.OpSpec{
+ Type: "StringToHashBucketStrong",
+ Input: []tf.Input{
+ input,
+ },
+ Attrs: attrs,
}
+ op := scope.AddOperation(opspec)
+ return op.Output(0)
}
-// Local Response Normalization.
+// Applies softmax to a batched N-D `SparseTensor`.
//
-// The 4-D `input` tensor is treated as a 3-D array of 1-D vectors (along the last
-// dimension), and each vector is normalized independently. Within a given vector,
-// each component is divided by the weighted, squared sum of inputs within
-// `depth_radius`. In detail,
+// The inputs represent an N-D SparseTensor with logical shape `[..., B, C]`
+// (where `N >= 2`), and with indices sorted in the canonical lexicographic order.
//
-// sqr_sum[a, b, c, d] =
-// sum(input[a, b, c, d - depth_radius : d + depth_radius + 1] ** 2)
-// output = input / (bias + alpha * sqr_sum) ** beta
+// This op is equivalent to applying the normal `tf.nn.softmax()` to each innermost
+// logical submatrix with shape `[B, C]`, but with the catch that *the implicitly
+// zero elements do not participate*. Specifically, the algorithm is equivalent
+// to the following:
//
-// For details, see [Krizhevsky et al., ImageNet classification with deep
-// convolutional neural networks (NIPS 2012)](http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks).
+// (1) Applies `tf.nn.softmax()` to a densified view of each innermost submatrix
+// with shape `[B, C]`, along the size-C dimension;
+// (2) Masks out the original implicitly-zero locations;
+// (3) Renormalizes the remaining elements.
+//
+// Hence, the `SparseTensor` result has exactly the same non-zero indices and
+// shape.
//
// Arguments:
-// input: 4-D.
-func LRN(scope *Scope, input tf.Output, optional ...LRNAttr) (output tf.Output) {
+// sp_indices: 2-D. `NNZ x R` matrix with the indices of non-empty values in a
+// SparseTensor, in canonical ordering.
+// sp_values: 1-D. `NNZ` non-empty values corresponding to `sp_indices`.
+// sp_shape: 1-D. Shape of the input SparseTensor.
+//
+// Returns 1-D. The `NNZ` values for the result `SparseTensor`.
+func SparseSoftmax(scope *Scope, sp_indices tf.Output, sp_values tf.Output, sp_shape tf.Output) (output tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{}
- for _, a := range optional {
- a(attrs)
- }
opspec := tf.OpSpec{
- Type: "LRN",
+ Type: "SparseSoftmax",
Input: []tf.Input{
- input,
+ sp_indices, sp_values, sp_shape,
},
- Attrs: attrs,
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// Creates a dataset that zips together `input_datasets`.
-func ZipDataset(scope *Scope, input_datasets []tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) {
+// Partitions `data` into `num_partitions` tensors using indices from `partitions`.
+//
+// For each index tuple `js` of size `partitions.ndim`, the slice `data[js, ...]`
+// becomes part of `outputs[partitions[js]]`. The slices with `partitions[js] = i`
+// are placed in `outputs[i]` in lexicographic order of `js`, and the first
+// dimension of `outputs[i]` is the number of entries in `partitions` equal to `i`.
+// In detail,
+//
+// ```python
+// outputs[i].shape = [sum(partitions == i)] + data.shape[partitions.ndim:]
+//
+// outputs[i] = pack([data[js, ...] for js if partitions[js] == i])
+// ```
+//
+// `data.shape` must start with `partitions.shape`.
+//
+// For example:
+//
+// ```python
+// # Scalar partitions.
+// partitions = 1
+// num_partitions = 2
+// data = [10, 20]
+// outputs[0] = [] # Empty with shape [0, 2]
+// outputs[1] = [[10, 20]]
+//
+// # Vector partitions.
+// partitions = [0, 0, 1, 1, 0]
+// num_partitions = 2
+// data = [10, 20, 30, 40, 50]
+// outputs[0] = [10, 20, 50]
+// outputs[1] = [30, 40]
+// ```
+//
+// See `dynamic_stitch` for an example on how to merge partitions back.
+//
+// <div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;">
+// <img style="width:100%" src="https://www.tensorflow.org/images/DynamicPartition.png" alt>
+// </div>
+//
+// Arguments:
+//
+// partitions: Any shape. Indices in the range `[0, num_partitions)`.
+// num_partitions: The number of partitions to output.
+func DynamicPartition(scope *Scope, data tf.Output, partitions tf.Output, num_partitions int64) (outputs []tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes}
+ attrs := map[string]interface{}{"num_partitions": num_partitions}
opspec := tf.OpSpec{
- Type: "ZipDataset",
+ Type: "DynamicPartition",
Input: []tf.Input{
- tf.OutputList(input_datasets),
+ data, partitions,
},
Attrs: attrs,
}
op := scope.AddOperation(opspec)
- return op.Output(0)
+ if scope.Err() != nil {
+ return
+ }
+ var idx int
+ var err error
+ if outputs, idx, err = makeOutputList(op, idx, "outputs"); err != nil {
+ scope.UpdateErr("DynamicPartition", err)
+ return
+ }
+ return outputs
}
-// ResourceSparseApplyAdagradAttr is an optional argument to ResourceSparseApplyAdagrad.
-type ResourceSparseApplyAdagradAttr func(optionalAttr)
+// ResourceApplyAdagradAttr is an optional argument to ResourceApplyAdagrad.
+type ResourceApplyAdagradAttr func(optionalAttr)
-// ResourceSparseApplyAdagradUseLocking sets the optional use_locking attribute to value.
+// ResourceApplyAdagradUseLocking sets the optional use_locking attribute to value.
//
// value: If `True`, updating of the var and accum tensors will be protected
// by a lock; otherwise the behavior is undefined, but may exhibit less
// contention.
// If not specified, defaults to false
-func ResourceSparseApplyAdagradUseLocking(value bool) ResourceSparseApplyAdagradAttr {
+func ResourceApplyAdagradUseLocking(value bool) ResourceApplyAdagradAttr {
return func(m optionalAttr) {
m["use_locking"] = value
}
}
-// Update relevant entries in '*var' and '*accum' according to the adagrad scheme.
+// Update '*var' according to the adagrad scheme.
//
-// That is for rows we have grad for, we update var and accum as follows:
// accum += grad * grad
// var -= lr * grad * (1 / sqrt(accum))
//
// Arguments:
// var_: Should be from a Variable().
// accum: Should be from a Variable().
-// lr: Learning rate. Must be a scalar.
+// lr: Scaling factor. Must be a scalar.
// grad: The gradient.
-// indices: A vector of indices into the first dimension of var and accum.
//
// Returns the created operation.
-func ResourceSparseApplyAdagrad(scope *Scope, var_ tf.Output, accum tf.Output, lr tf.Output, grad tf.Output, indices tf.Output, optional ...ResourceSparseApplyAdagradAttr) (o *tf.Operation) {
+func ResourceApplyAdagrad(scope *Scope, var_ tf.Output, accum tf.Output, lr tf.Output, grad tf.Output, optional ...ResourceApplyAdagradAttr) (o *tf.Operation) {
if scope.Err() != nil {
return
}
a(attrs)
}
opspec := tf.OpSpec{
- Type: "ResourceSparseApplyAdagrad",
+ Type: "ResourceApplyAdagrad",
Input: []tf.Input{
- var_, accum, lr, grad, indices,
+ var_, accum, lr, grad,
},
Attrs: attrs,
}
return scope.AddOperation(opspec)
}
-// 2D real-valued fast Fourier transform.
-//
-// Computes the 2-dimensional discrete Fourier transform of a real-valued signal
-// over the inner-most 2 dimensions of `input`.
-//
-// Since the DFT of a real signal is Hermitian-symmetric, `RFFT2D` only returns the
-// `fft_length / 2 + 1` unique components of the FFT for the inner-most dimension
-// of `output`: the zero-frequency term, followed by the `fft_length / 2`
-// positive-frequency terms.
-//
-// Along each axis `RFFT2D` is computed on, if `fft_length` is smaller than the
-// corresponding dimension of `input`, the dimension is cropped. If it is larger,
-// the dimension is padded with zeros.
-//
-// Arguments:
-// input: A float32 tensor.
-// fft_length: An int32 tensor of shape [2]. The FFT length for each dimension.
-//
-// Returns A complex64 tensor of the same rank as `input`. The inner-most 2
-// dimensions of `input` are replaced with their 2D Fourier transform. The
-// inner-most dimension contains `fft_length / 2 + 1` unique frequency
-// components.
+// Return the shape of s0 op s1 with broadcast.
//
-// @compatibility(numpy)
-// Equivalent to np.fft.rfft2
-// @end_compatibility
-func RFFT2D(scope *Scope, input tf.Output, fft_length tf.Output) (output tf.Output) {
+// Given `s0` and `s1`, tensors that represent shapes, compute `r0`, the
+// broadcasted shape. `s0`, `s1` and `r0` are all integer vectors.
+func BroadcastArgs(scope *Scope, s0 tf.Output, s1 tf.Output) (r0 tf.Output) {
if scope.Err() != nil {
return
}
opspec := tf.OpSpec{
- Type: "RFFT2D",
+ Type: "BroadcastArgs",
Input: []tf.Input{
- input, fft_length,
+ s0, s1,
},
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// ResizeAreaAttr is an optional argument to ResizeArea.
-type ResizeAreaAttr func(optionalAttr)
+// DataFormatDimMapAttr is an optional argument to DataFormatDimMap.
+type DataFormatDimMapAttr func(optionalAttr)
-// ResizeAreaAlignCorners sets the optional align_corners attribute to value.
+// DataFormatDimMapSrcFormat sets the optional src_format attribute to value.
//
-// value: If true, rescale input by (new_height - 1) / (height - 1), which
-// exactly aligns the 4 corners of images and resized images. If false, rescale
-// by new_height / height. Treat similarly the width dimension.
-// If not specified, defaults to false
-func ResizeAreaAlignCorners(value bool) ResizeAreaAttr {
+// value: source data format.
+// If not specified, defaults to "NHWC"
+func DataFormatDimMapSrcFormat(value string) DataFormatDimMapAttr {
return func(m optionalAttr) {
- m["align_corners"] = value
+ m["src_format"] = value
}
}
-// Resize `images` to `size` using area interpolation.
+// DataFormatDimMapDstFormat sets the optional dst_format attribute to value.
//
-// Input images can be of different types but output images are always float.
+// value: destination data format.
+// If not specified, defaults to "NCHW"
+func DataFormatDimMapDstFormat(value string) DataFormatDimMapAttr {
+ return func(m optionalAttr) {
+ m["dst_format"] = value
+ }
+}
+
+// Returns the dimension index in the destination data format given the one in
//
-// Each output pixel is computed by first transforming the pixel's footprint into
-// the input tensor and then averaging the pixels that intersect the footprint. An
-// input pixel's contribution to the average is weighted by the fraction of its
-// area that intersects the footprint. This is the same as OpenCV's INTER_AREA.
+// the source data format.
//
// Arguments:
-// images: 4-D with shape `[batch, height, width, channels]`.
-// size: = A 1-D int32 Tensor of 2 elements: `new_height, new_width`. The
-// new size for the images.
+// x: A Tensor with each element as a dimension index in source data format.
+// Must be in the range [-4, 4).
//
-// Returns 4-D with shape
-// `[batch, new_height, new_width, channels]`.
-func ResizeArea(scope *Scope, images tf.Output, size tf.Output, optional ...ResizeAreaAttr) (resized_images tf.Output) {
+// Returns A Tensor with each element as a dimension index in destination data format.
+func DataFormatDimMap(scope *Scope, x tf.Output, optional ...DataFormatDimMapAttr) (y tf.Output) {
if scope.Err() != nil {
return
}
a(attrs)
}
opspec := tf.OpSpec{
- Type: "ResizeArea",
+ Type: "DataFormatDimMap",
Input: []tf.Input{
- images, size,
+ x,
},
Attrs: attrs,
}
return op.Output(0)
}
-// Pads a tensor with zeros.
-//
-// This operation pads a `input` with zeros according to the `paddings` you
-// specify. `paddings` is an integer tensor with shape `[Dn, 2]`, where n is the
-// rank of `input`. For each dimension D of `input`, `paddings[D, 0]` indicates
-// how many zeros to add before the contents of `input` in that dimension, and
-// `paddings[D, 1]` indicates how many zeros to add after the contents of `input`
-// in that dimension.
-//
-// The padded size of each dimension D of the output is:
-//
-// `paddings(D, 0) + input.dim_size(D) + paddings(D, 1)`
-//
-// For example:
+// ResourceApplyPowerSignAttr is an optional argument to ResourceApplyPowerSign.
+type ResourceApplyPowerSignAttr func(optionalAttr)
+
+// ResourceApplyPowerSignUseLocking sets the optional use_locking attribute to value.
//
-// ```
-// # 't' is [[1, 1], [2, 2]]
-// # 'paddings' is [[1, 1], [2, 2]]
-// # rank of 't' is 2
-// pad(t, paddings) ==> [[0, 0, 0, 0, 0, 0]
-// [0, 0, 1, 1, 0, 0]
-// [0, 0, 2, 2, 0, 0]
-// [0, 0, 0, 0, 0, 0]]
-// ```
-func Pad(scope *Scope, input tf.Output, paddings tf.Output) (output tf.Output) {
- if scope.Err() != nil {
- return
- }
- opspec := tf.OpSpec{
- Type: "Pad",
- Input: []tf.Input{
- input, paddings,
- },
+// value: If `True`, updating of the var and m tensors is
+// protected by a lock; otherwise the behavior is undefined, but may exhibit less
+// contention.
+// If not specified, defaults to false
+func ResourceApplyPowerSignUseLocking(value bool) ResourceApplyPowerSignAttr {
+ return func(m optionalAttr) {
+ m["use_locking"] = value
}
- op := scope.AddOperation(opspec)
- return op.Output(0)
}
-// Checks whether a resource handle-based variable has been initialized.
+// Update '*var' according to the AddSign update.
+//
+// m_t <- beta1 * m_{t-1} + (1 - beta1) * g
+// update <- exp(logbase * sign_decay * sign(g) * sign(m_t)) * g
+// variable <- variable - lr_t * update
//
// Arguments:
-// resource: the input resource handle.
+// var_: Should be from a Variable().
+// m: Should be from a Variable().
+// lr: Scaling factor. Must be a scalar.
+// logbase: Must be a scalar.
+// sign_decay: Must be a scalar.
+// beta: Must be a scalar.
+// grad: The gradient.
//
-// Returns a scalar boolean which is true if the variable has been
-// initialized.
-func VarIsInitializedOp(scope *Scope, resource tf.Output) (is_initialized tf.Output) {
+// Returns the created operation.
+func ResourceApplyPowerSign(scope *Scope, var_ tf.Output, m tf.Output, lr tf.Output, logbase tf.Output, sign_decay tf.Output, beta tf.Output, grad tf.Output, optional ...ResourceApplyPowerSignAttr) (o *tf.Operation) {
if scope.Err() != nil {
return
}
+ attrs := map[string]interface{}{}
+ for _, a := range optional {
+ a(attrs)
+ }
opspec := tf.OpSpec{
- Type: "VarIsInitializedOp",
+ Type: "ResourceApplyPowerSign",
Input: []tf.Input{
- resource,
+ var_, m, lr, logbase, sign_decay, beta, grad,
},
+ Attrs: attrs,
}
- op := scope.AddOperation(opspec)
- return op.Output(0)
+ return scope.AddOperation(opspec)
}
-// StatelessRandomUniformAttr is an optional argument to StatelessRandomUniform.
-type StatelessRandomUniformAttr func(optionalAttr)
+// CumprodAttr is an optional argument to Cumprod.
+type CumprodAttr func(optionalAttr)
-// StatelessRandomUniformDtype sets the optional dtype attribute to value.
+// CumprodExclusive sets the optional exclusive attribute to value.
//
-// value: The type of the output.
-// If not specified, defaults to DT_FLOAT
-func StatelessRandomUniformDtype(value tf.DataType) StatelessRandomUniformAttr {
+// value: If `True`, perform exclusive cumprod.
+// If not specified, defaults to false
+func CumprodExclusive(value bool) CumprodAttr {
return func(m optionalAttr) {
- m["dtype"] = value
+ m["exclusive"] = value
}
}
-// Outputs deterministic pseudorandom random values from a uniform distribution.
+// CumprodReverse sets the optional reverse attribute to value.
//
-// The generated values follow a uniform distribution in the range `[0, 1)`. The
-// lower bound 0 is included in the range, while the upper bound 1 is excluded.
+// value: A `bool` (default: False).
+// If not specified, defaults to false
+func CumprodReverse(value bool) CumprodAttr {
+ return func(m optionalAttr) {
+ m["reverse"] = value
+ }
+}
+
+// Compute the cumulative product of the tensor `x` along `axis`.
//
-// The outputs are a deterministic function of `shape` and `seed`.
+// By default, this op performs an inclusive cumprod, which means that the first
+// element of the input is identical to the first element of the output:
//
-// Arguments:
-// shape: The shape of the output tensor.
-// seed: 2 seeds (shape [2]).
+// ```python
+// tf.cumprod([a, b, c]) # => [a, a * b, a * b * c]
+// ```
//
-// Returns Random values with specified shape.
-func StatelessRandomUniform(scope *Scope, shape tf.Output, seed tf.Output, optional ...StatelessRandomUniformAttr) (output tf.Output) {
+// By setting the `exclusive` kwarg to `True`, an exclusive cumprod is
+// performed instead:
+//
+// ```python
+// tf.cumprod([a, b, c], exclusive=True) # => [1, a, a * b]
+// ```
+//
+// By setting the `reverse` kwarg to `True`, the cumprod is performed in the
+// opposite direction:
+//
+// ```python
+// tf.cumprod([a, b, c], reverse=True) # => [a * b * c, b * c, c]
+// ```
+//
+// This is more efficient than using separate `tf.reverse` ops.
+//
+// The `reverse` and `exclusive` kwargs can also be combined:
+//
+// ```python
+// tf.cumprod([a, b, c], exclusive=True, reverse=True) # => [b * c, c, 1]
+// ```
+//
+// Arguments:
+// x: A `Tensor`. Must be one of the following types: `float32`, `float64`,
+// `int64`, `int32`, `uint8`, `uint16`, `int16`, `int8`, `complex64`,
+// `complex128`, `qint8`, `quint8`, `qint32`, `half`.
+// axis: A `Tensor` of type `int32` (default: 0). Must be in the range
+// `[-rank(x), rank(x))`.
+func Cumprod(scope *Scope, x tf.Output, axis tf.Output, optional ...CumprodAttr) (out tf.Output) {
if scope.Err() != nil {
return
}
a(attrs)
}
opspec := tf.OpSpec{
- Type: "StatelessRandomUniform",
+ Type: "Cumprod",
Input: []tf.Input{
- shape, seed,
+ x, axis,
},
Attrs: attrs,
}
return op.Output(0)
}
-// Makes its input available to the next iteration.
+// Computes the mean along segments of a tensor.
+//
+// Read @{$math_ops#segmentation$the section on segmentation} for an explanation of
+// segments.
+//
+// Computes a tensor such that
+// \\(output_i = \frac{\sum_j data_j}{N}\\) where `mean` is
+// over `j` such that `segment_ids[j] == i` and `N` is the total number of
+// values summed.
+//
+// If the mean is empty for a given segment ID `i`, `output[i] = 0`.
+//
+// <div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;">
+// <img style="width:100%" src="https://www.tensorflow.org/images/SegmentMean.png" alt>
+// </div>
//
// Arguments:
-// data: The tensor to be made available to the next iteration.
//
-// Returns The same tensor as `data`.
-func NextIteration(scope *Scope, data tf.Output) (output tf.Output) {
+// segment_ids: A 1-D tensor whose rank is equal to the rank of `data`'s
+// first dimension. Values should be sorted and can be repeated.
+//
+// Returns Has same shape as data, except for dimension 0 which
+// has size `k`, the number of segments.
+func SegmentMean(scope *Scope, data tf.Output, segment_ids tf.Output) (output tf.Output) {
if scope.Err() != nil {
return
}
opspec := tf.OpSpec{
- Type: "NextIteration",
+ Type: "SegmentMean",
Input: []tf.Input{
- data,
+ data, segment_ids,
},
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// Output a fact about factorials.
-func Fact(scope *Scope) (fact tf.Output) {
- if scope.Err() != nil {
- return
- }
- opspec := tf.OpSpec{
- Type: "Fact",
- }
- op := scope.AddOperation(opspec)
- return op.Output(0)
-}
-
-// AngleAttr is an optional argument to Angle.
-type AngleAttr func(optionalAttr)
+// ResourceSparseApplyCenteredRMSPropAttr is an optional argument to ResourceSparseApplyCenteredRMSProp.
+type ResourceSparseApplyCenteredRMSPropAttr func(optionalAttr)
-// AngleTout sets the optional Tout attribute to value.
-// If not specified, defaults to DT_FLOAT
-func AngleTout(value tf.DataType) AngleAttr {
+// ResourceSparseApplyCenteredRMSPropUseLocking sets the optional use_locking attribute to value.
+//
+// value: If `True`, updating of the var, mg, ms, and mom tensors is
+// protected by a lock; otherwise the behavior is undefined, but may exhibit less
+// contention.
+// If not specified, defaults to false
+func ResourceSparseApplyCenteredRMSPropUseLocking(value bool) ResourceSparseApplyCenteredRMSPropAttr {
return func(m optionalAttr) {
- m["Tout"] = value
+ m["use_locking"] = value
}
}
-// Returns the argument of a complex number.
+// Update '*var' according to the centered RMSProp algorithm.
//
-// Given a tensor `input` of complex numbers, this operation returns a tensor of
-// type `float` that is the argument of each element in `input`. All elements in
-// `input` must be complex numbers of the form \\(a + bj\\), where *a*
-// is the real part and *b* is the imaginary part.
+// The centered RMSProp algorithm uses an estimate of the centered second moment
+// (i.e., the variance) for normalization, as opposed to regular RMSProp, which
+// uses the (uncentered) second moment. This often helps with training, but is
+// slightly more expensive in terms of computation and memory.
//
-// The argument returned by this operation is of the form \\(atan2(b, a)\\).
+// Note that in dense implementation of this algorithm, mg, ms, and mom will
+// update even if the grad is zero, but in this sparse implementation, mg, ms,
+// and mom will not update in iterations during which the grad is zero.
//
-// For example:
+// mean_square = decay * mean_square + (1-decay) * gradient ** 2
+// mean_grad = decay * mean_grad + (1-decay) * gradient
+// Delta = learning_rate * gradient / sqrt(mean_square + epsilon - mean_grad ** 2)
//
-// ```
-// # tensor 'input' is [-2.25 + 4.75j, 3.25 + 5.75j]
-// tf.angle(input) ==> [2.0132, 1.056]
-// ```
+// ms <- rho * ms_{t-1} + (1-rho) * grad * grad
+// mom <- momentum * mom_{t-1} + lr * grad / sqrt(ms + epsilon)
+// var <- var - mom
//
-// @compatibility(numpy)
-// Equivalent to np.angle.
-// @end_compatibility
-func Angle(scope *Scope, input tf.Output, optional ...AngleAttr) (output tf.Output) {
+// Arguments:
+// var_: Should be from a Variable().
+// mg: Should be from a Variable().
+// ms: Should be from a Variable().
+// mom: Should be from a Variable().
+// lr: Scaling factor. Must be a scalar.
+// rho: Decay rate. Must be a scalar.
+//
+// epsilon: Ridge term. Must be a scalar.
+// grad: The gradient.
+// indices: A vector of indices into the first dimension of var, ms and mom.
+//
+// Returns the created operation.
+func ResourceSparseApplyCenteredRMSProp(scope *Scope, var_ tf.Output, mg tf.Output, ms tf.Output, mom tf.Output, lr tf.Output, rho tf.Output, momentum tf.Output, epsilon tf.Output, grad tf.Output, indices tf.Output, optional ...ResourceSparseApplyCenteredRMSPropAttr) (o *tf.Operation) {
if scope.Err() != nil {
return
}
a(attrs)
}
opspec := tf.OpSpec{
- Type: "Angle",
+ Type: "ResourceSparseApplyCenteredRMSProp",
Input: []tf.Input{
- input,
+ var_, mg, ms, mom, lr, rho, momentum, epsilon, grad, indices,
},
Attrs: attrs,
}
- op := scope.AddOperation(opspec)
- return op.Output(0)
+ return scope.AddOperation(opspec)
}
-// VarHandleOpAttr is an optional argument to VarHandleOp.
-type VarHandleOpAttr func(optionalAttr)
-
-// VarHandleOpContainer sets the optional container attribute to value.
+// Creates a dataset that batches `batch_size` elements from `input_dataset`.
//
-// value: the container this variable is placed in.
-// If not specified, defaults to ""
-func VarHandleOpContainer(value string) VarHandleOpAttr {
- return func(m optionalAttr) {
- m["container"] = value
- }
-}
-
-// VarHandleOpSharedName sets the optional shared_name attribute to value.
+// Arguments:
//
-// value: the name by which this variable is referred to.
-// If not specified, defaults to ""
-func VarHandleOpSharedName(value string) VarHandleOpAttr {
- return func(m optionalAttr) {
- m["shared_name"] = value
- }
-}
-
-// Creates a handle to a Variable resource.
+// batch_size: A scalar representing the number of elements to accumulate in a
+// batch.
//
-// Arguments:
-// dtype: the type of this variable. Must agree with the dtypes
-// of all ops using this variable.
-// shape: The (possibly partially specified) shape of this variable.
-func VarHandleOp(scope *Scope, dtype tf.DataType, shape tf.Shape, optional ...VarHandleOpAttr) (resource tf.Output) {
+//
+func BatchDataset(scope *Scope, input_dataset tf.Output, batch_size tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{"dtype": dtype, "shape": shape}
- for _, a := range optional {
- a(attrs)
- }
+ attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes}
opspec := tf.OpSpec{
- Type: "VarHandleOp",
-
+ Type: "BatchDataset",
+ Input: []tf.Input{
+ input_dataset, batch_size,
+ },
Attrs: attrs,
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// Elementwise computes the bitwise XOR of `x` and `y`.
+// Inverse fast Fourier transform.
//
-// The result will have those bits set, that are different in `x` and `y`. The
-// computation is performed on the underlying representations of `x` and `y`.
-func BitwiseXor(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) {
+// Computes the inverse 1-dimensional discrete Fourier transform over the
+// inner-most dimension of `input`.
+//
+// Arguments:
+// input: A complex64 tensor.
+//
+// Returns A complex64 tensor of the same shape as `input`. The inner-most
+// dimension of `input` is replaced with its inverse 1D Fourier transform.
+//
+// @compatibility(numpy)
+// Equivalent to np.fft.ifft
+// @end_compatibility
+func IFFT(scope *Scope, input tf.Output) (output tf.Output) {
if scope.Err() != nil {
return
}
opspec := tf.OpSpec{
- Type: "BitwiseXor",
+ Type: "IFFT",
Input: []tf.Input{
- x, y,
+ input,
},
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// Deserialize `SparseTensor` objects.
-//
-// The input `serialized_sparse` must have the shape `[?, ?, ..., ?, 3]` where
-// the last dimension stores serialized `SparseTensor` objects and the other N
-// dimensions (N >= 0) correspond to a batch. The ranks of the original
-// `SparseTensor` objects must all match. When the final `SparseTensor` is
-// created, its rank is the rank of the incoming `SparseTensor` objects plus N;
-// the sparse tensors have been concatenated along new dimensions, one for each
-// batch.
-//
-// The output `SparseTensor` object's shape values for the original dimensions
-// are the max across the input `SparseTensor` objects' shape values for the
-// corresponding dimensions. The new dimensions match the size of the batch.
-//
-// The input `SparseTensor` objects' indices are assumed ordered in
-// standard lexicographic order. If this is not the case, after this
-// step run `SparseReorder` to restore index ordering.
-//
-// For example, if the serialized input is a `[2 x 3]` matrix representing two
-// original `SparseTensor` objects:
-//
-// index = [ 0]
-// [10]
-// [20]
-// values = [1, 2, 3]
-// shape = [50]
-//
-// and
+// Generates values in an interval.
//
-// index = [ 2]
-// [10]
-// values = [4, 5]
-// shape = [30]
+// A sequence of `num` evenly-spaced values are generated beginning at `start`.
+// If `num > 1`, the values in the sequence increase by `stop - start / num - 1`,
+// so that the last one is exactly `stop`.
//
-// then the final deserialized `SparseTensor` will be:
+// For example:
//
-// index = [0 0]
-// [0 10]
-// [0 20]
-// [1 2]
-// [1 10]
-// values = [1, 2, 3, 4, 5]
-// shape = [2 50]
+// ```
+// tf.linspace(10.0, 12.0, 3, name="linspace") => [ 10.0 11.0 12.0]
+// ```
//
// Arguments:
-// serialized_sparse: The serialized `SparseTensor` objects. The last dimension
-// must have 3 columns.
-// dtype: The `dtype` of the serialized `SparseTensor` objects.
-func DeserializeSparse(scope *Scope, serialized_sparse tf.Output, dtype tf.DataType) (sparse_indices tf.Output, sparse_values tf.Output, sparse_shape tf.Output) {
+// start: First entry in the range.
+// stop: Last entry in the range.
+// num: Number of values to generate.
+//
+// Returns 1-D. The generated values.
+func LinSpace(scope *Scope, start tf.Output, stop tf.Output, num tf.Output) (output tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{"dtype": dtype}
opspec := tf.OpSpec{
- Type: "DeserializeSparse",
+ Type: "LinSpace",
Input: []tf.Input{
- serialized_sparse,
+ start, stop, num,
},
- Attrs: attrs,
}
op := scope.AddOperation(opspec)
- return op.Output(0), op.Output(1), op.Output(2)
+ return op.Output(0)
}
-// ResourceApplyRMSPropAttr is an optional argument to ResourceApplyRMSProp.
-type ResourceApplyRMSPropAttr func(optionalAttr)
+// DestroyResourceOpAttr is an optional argument to DestroyResourceOp.
+type DestroyResourceOpAttr func(optionalAttr)
-// ResourceApplyRMSPropUseLocking sets the optional use_locking attribute to value.
+// DestroyResourceOpIgnoreLookupError sets the optional ignore_lookup_error attribute to value.
//
-// value: If `True`, updating of the var, ms, and mom tensors is protected
-// by a lock; otherwise the behavior is undefined, but may exhibit less
-// contention.
-// If not specified, defaults to false
-func ResourceApplyRMSPropUseLocking(value bool) ResourceApplyRMSPropAttr {
+// value: whether to ignore the error when the resource
+// doesn't exist.
+// If not specified, defaults to true
+func DestroyResourceOpIgnoreLookupError(value bool) DestroyResourceOpAttr {
return func(m optionalAttr) {
- m["use_locking"] = value
+ m["ignore_lookup_error"] = value
}
}
-// Update '*var' according to the RMSProp algorithm.
-//
-// Note that in dense implementation of this algorithm, ms and mom will
-// update even if the grad is zero, but in this sparse implementation, ms
-// and mom will not update in iterations during which the grad is zero.
-//
-// mean_square = decay * mean_square + (1-decay) * gradient ** 2
-// Delta = learning_rate * gradient / sqrt(mean_square + epsilon)
+// Deletes the resource specified by the handle.
//
-// ms <- rho * ms_{t-1} + (1-rho) * grad * grad
-// mom <- momentum * mom_{t-1} + lr * grad / sqrt(ms + epsilon)
-// var <- var - mom
+// All subsequent operations using the resource will result in a NotFound
+// error status.
//
// Arguments:
-// var_: Should be from a Variable().
-// ms: Should be from a Variable().
-// mom: Should be from a Variable().
-// lr: Scaling factor. Must be a scalar.
-// rho: Decay rate. Must be a scalar.
-//
-// epsilon: Ridge term. Must be a scalar.
-// grad: The gradient.
+// resource: handle to the resource to delete.
//
// Returns the created operation.
-func ResourceApplyRMSProp(scope *Scope, var_ tf.Output, ms tf.Output, mom tf.Output, lr tf.Output, rho tf.Output, momentum tf.Output, epsilon tf.Output, grad tf.Output, optional ...ResourceApplyRMSPropAttr) (o *tf.Operation) {
+func DestroyResourceOp(scope *Scope, resource tf.Output, optional ...DestroyResourceOpAttr) (o *tf.Operation) {
if scope.Err() != nil {
return
}
a(attrs)
}
opspec := tf.OpSpec{
- Type: "ResourceApplyRMSProp",
+ Type: "DestroyResourceOp",
Input: []tf.Input{
- var_, ms, mom, lr, rho, momentum, epsilon, grad,
+ resource,
},
Attrs: attrs,
}
return scope.AddOperation(opspec)
}
-// SizeAttr is an optional argument to Size.
-type SizeAttr func(optionalAttr)
+// LRNAttr is an optional argument to LRN.
+type LRNAttr func(optionalAttr)
-// SizeOutType sets the optional out_type attribute to value.
-// If not specified, defaults to DT_INT32
-func SizeOutType(value tf.DataType) SizeAttr {
+// LRNDepthRadius sets the optional depth_radius attribute to value.
+//
+// value: 0-D. Half-width of the 1-D normalization window.
+// If not specified, defaults to 5
+func LRNDepthRadius(value int64) LRNAttr {
return func(m optionalAttr) {
- m["out_type"] = value
+ m["depth_radius"] = value
}
}
-// Returns the size of a tensor.
+// LRNBias sets the optional bias attribute to value.
//
-// This operation returns an integer representing the number of elements in
-// `input`.
+// value: An offset (usually positive to avoid dividing by 0).
+// If not specified, defaults to 1
+func LRNBias(value float32) LRNAttr {
+ return func(m optionalAttr) {
+ m["bias"] = value
+ }
+}
+
+// LRNAlpha sets the optional alpha attribute to value.
//
-// For example:
+// value: A scale factor, usually positive.
+// If not specified, defaults to 1
+func LRNAlpha(value float32) LRNAttr {
+ return func(m optionalAttr) {
+ m["alpha"] = value
+ }
+}
+
+// LRNBeta sets the optional beta attribute to value.
//
-// ```
-// # 't' is [[[1, 1,, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]]]]
-// size(t) ==> 12
-// ```
-func Size(scope *Scope, input tf.Output, optional ...SizeAttr) (output tf.Output) {
+// value: An exponent.
+// If not specified, defaults to 0.5
+func LRNBeta(value float32) LRNAttr {
+ return func(m optionalAttr) {
+ m["beta"] = value
+ }
+}
+
+// Local Response Normalization.
+//
+// The 4-D `input` tensor is treated as a 3-D array of 1-D vectors (along the last
+// dimension), and each vector is normalized independently. Within a given vector,
+// each component is divided by the weighted, squared sum of inputs within
+// `depth_radius`. In detail,
+//
+// sqr_sum[a, b, c, d] =
+// sum(input[a, b, c, d - depth_radius : d + depth_radius + 1] ** 2)
+// output = input / (bias + alpha * sqr_sum) ** beta
+//
+// For details, see [Krizhevsky et al., ImageNet classification with deep
+// convolutional neural networks (NIPS 2012)](http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks).
+//
+// Arguments:
+// input: 4-D.
+func LRN(scope *Scope, input tf.Output, optional ...LRNAttr) (output tf.Output) {
if scope.Err() != nil {
return
}
a(attrs)
}
opspec := tf.OpSpec{
- Type: "Size",
+ Type: "LRN",
Input: []tf.Input{
input,
},
return op.Output(0)
}
-// ResourceScatterNdUpdateAttr is an optional argument to ResourceScatterNdUpdate.
-type ResourceScatterNdUpdateAttr func(optionalAttr)
+// Creates a dataset that zips together `input_datasets`.
+func ZipDataset(scope *Scope, input_datasets []tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) {
+ if scope.Err() != nil {
+ return
+ }
+ attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes}
+ opspec := tf.OpSpec{
+ Type: "ZipDataset",
+ Input: []tf.Input{
+ tf.OutputList(input_datasets),
+ },
+ Attrs: attrs,
+ }
+ op := scope.AddOperation(opspec)
+ return op.Output(0)
+}
-// ResourceScatterNdUpdateUseLocking sets the optional use_locking attribute to value.
+// ResourceSparseApplyAdagradAttr is an optional argument to ResourceSparseApplyAdagrad.
+type ResourceSparseApplyAdagradAttr func(optionalAttr)
+
+// ResourceSparseApplyAdagradUseLocking sets the optional use_locking attribute to value.
//
-// value: An optional bool. Defaults to True. If True, the assignment will
-// be protected by a lock; otherwise the behavior is undefined,
-// but may exhibit less contention.
-// If not specified, defaults to true
-func ResourceScatterNdUpdateUseLocking(value bool) ResourceScatterNdUpdateAttr {
+// value: If `True`, updating of the var and accum tensors will be protected
+// by a lock; otherwise the behavior is undefined, but may exhibit less
+// contention.
+// If not specified, defaults to false
+func ResourceSparseApplyAdagradUseLocking(value bool) ResourceSparseApplyAdagradAttr {
return func(m optionalAttr) {
m["use_locking"] = value
}
}
-// Applies sparse `updates` to individual values or slices within a given
-//
-// variable according to `indices`.
-//
-// `ref` is a `Tensor` with rank `P` and `indices` is a `Tensor` of rank `Q`.
-//
-// `indices` must be integer tensor, containing indices into `ref`.
-// It must be shape `[d_0, ..., d_{Q-2}, K]` where `0 < K <= P`.
-//
-// The innermost dimension of `indices` (with length `K`) corresponds to
-// indices into elements (if `K = P`) or slices (if `K < P`) along the `K`th
-// dimension of `ref`.
-//
-// `updates` is `Tensor` of rank `Q-1+P-K` with shape:
-//
-// ```
-// [d_0, ..., d_{Q-2}, ref.shape[K], ..., ref.shape[P-1]].
-// ```
-//
-// For example, say we want to update 4 scattered elements to a rank-1 tensor to
-// 8 elements. In Python, that update would look like this:
-//
-// ```python
-// ref = tfe.Variable([1, 2, 3, 4, 5, 6, 7, 8])
-// indices = tf.constant([[4], [3], [1] ,[7]])
-// updates = tf.constant([9, 10, 11, 12])
-// update = tf.scatter_nd_update(ref, indices, updates)
-// with tf.Session() as sess:
-// print sess.run(update)
-// ```
-//
-// The resulting update to ref would look like this:
-//
-// [1, 11, 3, 10, 9, 6, 7, 12]
+// Update relevant entries in '*var' and '*accum' according to the adagrad scheme.
//
-// See @{tf.scatter_nd} for more details about how to make updates to
-// slices.
+// That is for rows we have grad for, we update var and accum as follows:
+// accum += grad * grad
+// var -= lr * grad * (1 / sqrt(accum))
//
// Arguments:
-// ref: A resource handle. Must be from a VarHandleOp.
-// indices: A Tensor. Must be one of the following types: int32, int64.
-// A tensor of indices into ref.
-// updates: A Tensor. Must have the same type as ref. A tensor of updated
-// values to add to ref.
+// var_: Should be from a Variable().
+// accum: Should be from a Variable().
+// lr: Learning rate. Must be a scalar.
+// grad: The gradient.
+// indices: A vector of indices into the first dimension of var and accum.
//
// Returns the created operation.
-func ResourceScatterNdUpdate(scope *Scope, ref tf.Output, indices tf.Output, updates tf.Output, optional ...ResourceScatterNdUpdateAttr) (o *tf.Operation) {
+func ResourceSparseApplyAdagrad(scope *Scope, var_ tf.Output, accum tf.Output, lr tf.Output, grad tf.Output, indices tf.Output, optional ...ResourceSparseApplyAdagradAttr) (o *tf.Operation) {
if scope.Err() != nil {
return
}
a(attrs)
}
opspec := tf.OpSpec{
- Type: "ResourceScatterNdUpdate",
+ Type: "ResourceSparseApplyAdagrad",
Input: []tf.Input{
- ref, indices, updates,
+ var_, accum, lr, grad, indices,
},
Attrs: attrs,
}
return scope.AddOperation(opspec)
}
-// SqueezeAttr is an optional argument to Squeeze.
-type SqueezeAttr func(optionalAttr)
-
-// SqueezeAxis sets the optional axis attribute to value.
-//
-// value: If specified, only squeezes the dimensions listed. The dimension
-// index starts at 0. It is an error to squeeze a dimension that is not 1. Must
-// be in the range `[-rank(input), rank(input))`.
-// If not specified, defaults to <>
-//
-// REQUIRES: len(value) >= 0
-func SqueezeAxis(value []int64) SqueezeAttr {
- return func(m optionalAttr) {
- m["squeeze_dims"] = value
- }
-}
-
-// Removes dimensions of size 1 from the shape of a tensor.
-//
-// Given a tensor `input`, this operation returns a tensor of the same type with
-// all dimensions of size 1 removed. If you don't want to remove all size 1
-// dimensions, you can remove specific size 1 dimensions by specifying
-// `axis`.
-//
-// For example:
+// 2D real-valued fast Fourier transform.
//
-// ```
-// # 't' is a tensor of shape [1, 2, 1, 3, 1, 1]
-// shape(squeeze(t)) ==> [2, 3]
-// ```
+// Computes the 2-dimensional discrete Fourier transform of a real-valued signal
+// over the inner-most 2 dimensions of `input`.
//
-// Or, to remove specific size 1 dimensions:
+// Since the DFT of a real signal is Hermitian-symmetric, `RFFT2D` only returns the
+// `fft_length / 2 + 1` unique components of the FFT for the inner-most dimension
+// of `output`: the zero-frequency term, followed by the `fft_length / 2`
+// positive-frequency terms.
//
-// ```
-// # 't' is a tensor of shape [1, 2, 1, 3, 1, 1]
-// shape(squeeze(t, [2, 4])) ==> [1, 2, 3, 1]
-// ```
+// Along each axis `RFFT2D` is computed on, if `fft_length` is smaller than the
+// corresponding dimension of `input`, the dimension is cropped. If it is larger,
+// the dimension is padded with zeros.
//
// Arguments:
-// input: The `input` to squeeze.
+// input: A float32 tensor.
+// fft_length: An int32 tensor of shape [2]. The FFT length for each dimension.
//
-// Returns Contains the same data as `input`, but has one or more dimensions of
-// size 1 removed.
-func Squeeze(scope *Scope, input tf.Output, optional ...SqueezeAttr) (output tf.Output) {
+// Returns A complex64 tensor of the same rank as `input`. The inner-most 2
+// dimensions of `input` are replaced with their 2D Fourier transform. The
+// inner-most dimension contains `fft_length / 2 + 1` unique frequency
+// components.
+//
+// @compatibility(numpy)
+// Equivalent to np.fft.rfft2
+// @end_compatibility
+func RFFT2D(scope *Scope, input tf.Output, fft_length tf.Output) (output tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{}
- for _, a := range optional {
- a(attrs)
- }
opspec := tf.OpSpec{
- Type: "Squeeze",
+ Type: "RFFT2D",
Input: []tf.Input{
- input,
+ input, fft_length,
},
- Attrs: attrs,
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// ResourceApplyAdadeltaAttr is an optional argument to ResourceApplyAdadelta.
-type ResourceApplyAdadeltaAttr func(optionalAttr)
+// ResizeAreaAttr is an optional argument to ResizeArea.
+type ResizeAreaAttr func(optionalAttr)
-// ResourceApplyAdadeltaUseLocking sets the optional use_locking attribute to value.
+// ResizeAreaAlignCorners sets the optional align_corners attribute to value.
//
-// value: If True, updating of the var, accum and update_accum tensors will be protected by
-// a lock; otherwise the behavior is undefined, but may exhibit less contention.
+// value: If true, rescale input by (new_height - 1) / (height - 1), which
+// exactly aligns the 4 corners of images and resized images. If false, rescale
+// by new_height / height. Treat similarly the width dimension.
// If not specified, defaults to false
-func ResourceApplyAdadeltaUseLocking(value bool) ResourceApplyAdadeltaAttr {
+func ResizeAreaAlignCorners(value bool) ResizeAreaAttr {
return func(m optionalAttr) {
- m["use_locking"] = value
+ m["align_corners"] = value
}
}
-// Update '*var' according to the adadelta scheme.
+// Resize `images` to `size` using area interpolation.
//
-// accum = rho() * accum + (1 - rho()) * grad.square();
-// update = (update_accum + epsilon).sqrt() * (accum + epsilon()).rsqrt() * grad;
-// update_accum = rho() * update_accum + (1 - rho()) * update.square();
-// var -= update;
+// Input images can be of different types but output images are always float.
+//
+// Each output pixel is computed by first transforming the pixel's footprint into
+// the input tensor and then averaging the pixels that intersect the footprint. An
+// input pixel's contribution to the average is weighted by the fraction of its
+// area that intersects the footprint. This is the same as OpenCV's INTER_AREA.
//
// Arguments:
-// var_: Should be from a Variable().
-// accum: Should be from a Variable().
-// accum_update: Should be from a Variable().
-// lr: Scaling factor. Must be a scalar.
-// rho: Decay factor. Must be a scalar.
-// epsilon: Constant factor. Must be a scalar.
-// grad: The gradient.
+// images: 4-D with shape `[batch, height, width, channels]`.
+// size: = A 1-D int32 Tensor of 2 elements: `new_height, new_width`. The
+// new size for the images.
//
-// Returns the created operation.
-func ResourceApplyAdadelta(scope *Scope, var_ tf.Output, accum tf.Output, accum_update tf.Output, lr tf.Output, rho tf.Output, epsilon tf.Output, grad tf.Output, optional ...ResourceApplyAdadeltaAttr) (o *tf.Operation) {
+// Returns 4-D with shape
+// `[batch, new_height, new_width, channels]`.
+func ResizeArea(scope *Scope, images tf.Output, size tf.Output, optional ...ResizeAreaAttr) (resized_images tf.Output) {
if scope.Err() != nil {
return
}
a(attrs)
}
opspec := tf.OpSpec{
- Type: "ResourceApplyAdadelta",
+ Type: "ResizeArea",
Input: []tf.Input{
- var_, accum, accum_update, lr, rho, epsilon, grad,
+ images, size,
},
Attrs: attrs,
}
- return scope.AddOperation(opspec)
-}
-
-// NonMaxSuppressionAttr is an optional argument to NonMaxSuppression.
-type NonMaxSuppressionAttr func(optionalAttr)
-
-// NonMaxSuppressionIouThreshold sets the optional iou_threshold attribute to value.
-//
-// value: A float representing the threshold for deciding whether boxes
-// overlap too much with respect to IOU.
-// If not specified, defaults to 0.5
-func NonMaxSuppressionIouThreshold(value float32) NonMaxSuppressionAttr {
- return func(m optionalAttr) {
- m["iou_threshold"] = value
- }
+ op := scope.AddOperation(opspec)
+ return op.Output(0)
}
-// Greedily selects a subset of bounding boxes in descending order of score,
+// Pads a tensor with zeros.
//
-// pruning away boxes that have high intersection-over-union (IOU) overlap
-// with previously selected boxes. Bounding boxes are supplied as
-// [y1, x1, y2, x2], where (y1, x1) and (y2, x2) are the coordinates of any
-// diagonal pair of box corners and the coordinates can be provided as normalized
-// (i.e., lying in the interval [0, 1]) or absolute. Note that this algorithm
-// is agnostic to where the origin is in the coordinate system. Note that this
-// algorithm is invariant to orthogonal transformations and translations
-// of the coordinate system; thus translating or reflections of the coordinate
-// system result in the same boxes being selected by the algorithm.
-// The output of this operation is a set of integers indexing into the input
-// collection of bounding boxes representing the selected boxes. The bounding
-// box coordinates corresponding to the selected indices can then be obtained
-// using the `tf.gather operation`. For example:
-// selected_indices = tf.image.non_max_suppression(
-// boxes, scores, max_output_size, iou_threshold)
-// selected_boxes = tf.gather(boxes, selected_indices)
+// This operation pads a `input` with zeros according to the `paddings` you
+// specify. `paddings` is an integer tensor with shape `[Dn, 2]`, where n is the
+// rank of `input`. For each dimension D of `input`, `paddings[D, 0]` indicates
+// how many zeros to add before the contents of `input` in that dimension, and
+// `paddings[D, 1]` indicates how many zeros to add after the contents of `input`
+// in that dimension.
//
-// Arguments:
-// boxes: A 2-D float tensor of shape `[num_boxes, 4]`.
-// scores: A 1-D float tensor of shape `[num_boxes]` representing a single
-// score corresponding to each box (each row of boxes).
-// max_output_size: A scalar integer tensor representing the maximum number of
-// boxes to be selected by non max suppression.
+// The padded size of each dimension D of the output is:
//
-// Returns A 1-D integer tensor of shape `[M]` representing the selected
-// indices from the boxes tensor, where `M <= max_output_size`.
-func NonMaxSuppression(scope *Scope, boxes tf.Output, scores tf.Output, max_output_size tf.Output, optional ...NonMaxSuppressionAttr) (selected_indices tf.Output) {
- if scope.Err() != nil {
- return
- }
- attrs := map[string]interface{}{}
- for _, a := range optional {
- a(attrs)
- }
- opspec := tf.OpSpec{
- Type: "NonMaxSuppression",
- Input: []tf.Input{
- boxes, scores, max_output_size,
- },
- Attrs: attrs,
- }
- op := scope.AddOperation(opspec)
- return op.Output(0)
-}
-
-// Creates a dataset that emits `components` as a tuple of tensors once.
-func TensorDataset(scope *Scope, components []tf.Output, output_shapes []tf.Shape) (handle tf.Output) {
+// `paddings(D, 0) + input.dim_size(D) + paddings(D, 1)`
+//
+// For example:
+//
+// ```
+// # 't' is [[1, 1], [2, 2]]
+// # 'paddings' is [[1, 1], [2, 2]]
+// # rank of 't' is 2
+// pad(t, paddings) ==> [[0, 0, 0, 0, 0, 0]
+// [0, 0, 1, 1, 0, 0]
+// [0, 0, 2, 2, 0, 0]
+// [0, 0, 0, 0, 0, 0]]
+// ```
+func Pad(scope *Scope, input tf.Output, paddings tf.Output) (output tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{"output_shapes": output_shapes}
opspec := tf.OpSpec{
- Type: "TensorDataset",
+ Type: "Pad",
Input: []tf.Input{
- tf.OutputList(components),
+ input, paddings,
},
- Attrs: attrs,
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// Component-wise multiplies a SparseTensor by a dense Tensor.
-//
-// The output locations corresponding to the implicitly zero elements in the sparse
-// tensor will be zero (i.e., will not take up storage space), regardless of the
-// contents of the dense tensor (even if it's +/-INF and that INF*0 == NaN).
-//
-// *Limitation*: this Op only broadcasts the dense side to the sparse side, but not
-// the other direction.
+// Checks whether a resource handle-based variable has been initialized.
//
// Arguments:
-// sp_indices: 2-D. `N x R` matrix with the indices of non-empty values in a
-// SparseTensor, possibly not in canonical ordering.
-// sp_values: 1-D. `N` non-empty values corresponding to `sp_indices`.
-// sp_shape: 1-D. Shape of the input SparseTensor.
-// dense: `R`-D. The dense Tensor operand.
+// resource: the input resource handle.
//
-// Returns 1-D. The `N` values that are operated on.
-func SparseDenseCwiseMul(scope *Scope, sp_indices tf.Output, sp_values tf.Output, sp_shape tf.Output, dense tf.Output) (output tf.Output) {
+// Returns a scalar boolean which is true if the variable has been
+// initialized.
+func VarIsInitializedOp(scope *Scope, resource tf.Output) (is_initialized tf.Output) {
if scope.Err() != nil {
return
}
opspec := tf.OpSpec{
- Type: "SparseDenseCwiseMul",
+ Type: "VarIsInitializedOp",
Input: []tf.Input{
- sp_indices, sp_values, sp_shape, dense,
+ resource,
},
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// ResourceSparseApplyRMSPropAttr is an optional argument to ResourceSparseApplyRMSProp.
-type ResourceSparseApplyRMSPropAttr func(optionalAttr)
+// StatelessRandomUniformAttr is an optional argument to StatelessRandomUniform.
+type StatelessRandomUniformAttr func(optionalAttr)
-// ResourceSparseApplyRMSPropUseLocking sets the optional use_locking attribute to value.
+// StatelessRandomUniformDtype sets the optional dtype attribute to value.
//
-// value: If `True`, updating of the var, ms, and mom tensors is protected
-// by a lock; otherwise the behavior is undefined, but may exhibit less
-// contention.
-// If not specified, defaults to false
-func ResourceSparseApplyRMSPropUseLocking(value bool) ResourceSparseApplyRMSPropAttr {
+// value: The type of the output.
+// If not specified, defaults to DT_FLOAT
+func StatelessRandomUniformDtype(value tf.DataType) StatelessRandomUniformAttr {
return func(m optionalAttr) {
- m["use_locking"] = value
+ m["dtype"] = value
}
}
-// Update '*var' according to the RMSProp algorithm.
-//
-// Note that in dense implementation of this algorithm, ms and mom will
-// update even if the grad is zero, but in this sparse implementation, ms
-// and mom will not update in iterations during which the grad is zero.
+// Outputs deterministic pseudorandom random values from a uniform distribution.
//
-// mean_square = decay * mean_square + (1-decay) * gradient ** 2
-// Delta = learning_rate * gradient / sqrt(mean_square + epsilon)
+// The generated values follow a uniform distribution in the range `[0, 1)`. The
+// lower bound 0 is included in the range, while the upper bound 1 is excluded.
//
-// ms <- rho * ms_{t-1} + (1-rho) * grad * grad
-// mom <- momentum * mom_{t-1} + lr * grad / sqrt(ms + epsilon)
-// var <- var - mom
+// The outputs are a deterministic function of `shape` and `seed`.
//
// Arguments:
-// var_: Should be from a Variable().
-// ms: Should be from a Variable().
-// mom: Should be from a Variable().
-// lr: Scaling factor. Must be a scalar.
-// rho: Decay rate. Must be a scalar.
-//
-// epsilon: Ridge term. Must be a scalar.
-// grad: The gradient.
-// indices: A vector of indices into the first dimension of var, ms and mom.
+// shape: The shape of the output tensor.
+// seed: 2 seeds (shape [2]).
//
-// Returns the created operation.
-func ResourceSparseApplyRMSProp(scope *Scope, var_ tf.Output, ms tf.Output, mom tf.Output, lr tf.Output, rho tf.Output, momentum tf.Output, epsilon tf.Output, grad tf.Output, indices tf.Output, optional ...ResourceSparseApplyRMSPropAttr) (o *tf.Operation) {
+// Returns Random values with specified shape.
+func StatelessRandomUniform(scope *Scope, shape tf.Output, seed tf.Output, optional ...StatelessRandomUniformAttr) (output tf.Output) {
if scope.Err() != nil {
return
}
a(attrs)
}
opspec := tf.OpSpec{
- Type: "ResourceSparseApplyRMSProp",
+ Type: "StatelessRandomUniform",
Input: []tf.Input{
- var_, ms, mom, lr, rho, momentum, epsilon, grad, indices,
+ shape, seed,
},
Attrs: attrs,
}
- return scope.AddOperation(opspec)
+ op := scope.AddOperation(opspec)
+ return op.Output(0)
}
-// Returns the truth value of (x > y) element-wise.
+// Makes its input available to the next iteration.
//
-// *NOTE*: `Greater` supports broadcasting. More about broadcasting
-// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
-func Greater(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) {
+// Arguments:
+// data: The tensor to be made available to the next iteration.
+//
+// Returns The same tensor as `data`.
+func NextIteration(scope *Scope, data tf.Output) (output tf.Output) {
if scope.Err() != nil {
return
}
opspec := tf.OpSpec{
- Type: "Greater",
+ Type: "NextIteration",
Input: []tf.Input{
- x, y,
+ data,
},
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// SampleDistortedBoundingBoxAttr is an optional argument to SampleDistortedBoundingBox.
-type SampleDistortedBoundingBoxAttr func(optionalAttr)
+// Output a fact about factorials.
+func Fact(scope *Scope) (fact tf.Output) {
+ if scope.Err() != nil {
+ return
+ }
+ opspec := tf.OpSpec{
+ Type: "Fact",
+ }
+ op := scope.AddOperation(opspec)
+ return op.Output(0)
+}
-// SampleDistortedBoundingBoxSeed sets the optional seed attribute to value.
-//
-// value: If either `seed` or `seed2` are set to non-zero, the random number
-// generator is seeded by the given `seed`. Otherwise, it is seeded by a random
-// seed.
-// If not specified, defaults to 0
-func SampleDistortedBoundingBoxSeed(value int64) SampleDistortedBoundingBoxAttr {
+// AngleAttr is an optional argument to Angle.
+type AngleAttr func(optionalAttr)
+
+// AngleTout sets the optional Tout attribute to value.
+// If not specified, defaults to DT_FLOAT
+func AngleTout(value tf.DataType) AngleAttr {
return func(m optionalAttr) {
- m["seed"] = value
+ m["Tout"] = value
}
}
-// SampleDistortedBoundingBoxSeed2 sets the optional seed2 attribute to value.
+// Returns the argument of a complex number.
//
-// value: A second seed to avoid seed collision.
-// If not specified, defaults to 0
-func SampleDistortedBoundingBoxSeed2(value int64) SampleDistortedBoundingBoxAttr {
- return func(m optionalAttr) {
- m["seed2"] = value
+// Given a tensor `input` of complex numbers, this operation returns a tensor of
+// type `float` that is the argument of each element in `input`. All elements in
+// `input` must be complex numbers of the form \\(a + bj\\), where *a*
+// is the real part and *b* is the imaginary part.
+//
+// The argument returned by this operation is of the form \\(atan2(b, a)\\).
+//
+// For example:
+//
+// ```
+// # tensor 'input' is [-2.25 + 4.75j, 3.25 + 5.75j]
+// tf.angle(input) ==> [2.0132, 1.056]
+// ```
+//
+// @compatibility(numpy)
+// Equivalent to np.angle.
+// @end_compatibility
+func Angle(scope *Scope, input tf.Output, optional ...AngleAttr) (output tf.Output) {
+ if scope.Err() != nil {
+ return
+ }
+ attrs := map[string]interface{}{}
+ for _, a := range optional {
+ a(attrs)
+ }
+ opspec := tf.OpSpec{
+ Type: "Angle",
+ Input: []tf.Input{
+ input,
+ },
+ Attrs: attrs,
}
+ op := scope.AddOperation(opspec)
+ return op.Output(0)
}
-// SampleDistortedBoundingBoxMinObjectCovered sets the optional min_object_covered attribute to value.
+// VarHandleOpAttr is an optional argument to VarHandleOp.
+type VarHandleOpAttr func(optionalAttr)
+
+// VarHandleOpContainer sets the optional container attribute to value.
//
-// value: The cropped area of the image must contain at least this
-// fraction of any bounding box supplied. The value of this parameter should be
-// non-negative. In the case of 0, the cropped area does not need to overlap
-// any of the bounding boxes supplied.
-// If not specified, defaults to 0.1
-func SampleDistortedBoundingBoxMinObjectCovered(value float32) SampleDistortedBoundingBoxAttr {
+// value: the container this variable is placed in.
+// If not specified, defaults to ""
+func VarHandleOpContainer(value string) VarHandleOpAttr {
return func(m optionalAttr) {
- m["min_object_covered"] = value
+ m["container"] = value
}
}
-// SampleDistortedBoundingBoxAspectRatioRange sets the optional aspect_ratio_range attribute to value.
+// VarHandleOpSharedName sets the optional shared_name attribute to value.
//
-// value: The cropped area of the image must have an aspect ratio =
-// width / height within this range.
-// If not specified, defaults to <f:0.75 f:1.33 >
-func SampleDistortedBoundingBoxAspectRatioRange(value []float32) SampleDistortedBoundingBoxAttr {
+// value: the name by which this variable is referred to.
+// If not specified, defaults to ""
+func VarHandleOpSharedName(value string) VarHandleOpAttr {
return func(m optionalAttr) {
- m["aspect_ratio_range"] = value
+ m["shared_name"] = value
}
}
-// SampleDistortedBoundingBoxAreaRange sets the optional area_range attribute to value.
-//
-// value: The cropped area of the image must contain a fraction of the
-// supplied image within in this range.
-// If not specified, defaults to <f:0.05 f:1 >
-func SampleDistortedBoundingBoxAreaRange(value []float32) SampleDistortedBoundingBoxAttr {
- return func(m optionalAttr) {
- m["area_range"] = value
- }
-}
-
-// SampleDistortedBoundingBoxMaxAttempts sets the optional max_attempts attribute to value.
-//
-// value: Number of attempts at generating a cropped region of the image
-// of the specified constraints. After `max_attempts` failures, return the entire
-// image.
-// If not specified, defaults to 100
-func SampleDistortedBoundingBoxMaxAttempts(value int64) SampleDistortedBoundingBoxAttr {
- return func(m optionalAttr) {
- m["max_attempts"] = value
- }
-}
-
-// SampleDistortedBoundingBoxUseImageIfNoBoundingBoxes sets the optional use_image_if_no_bounding_boxes attribute to value.
-//
-// value: Controls behavior if no bounding boxes supplied.
-// If true, assume an implicit bounding box covering the whole input. If false,
-// raise an error.
-// If not specified, defaults to false
-func SampleDistortedBoundingBoxUseImageIfNoBoundingBoxes(value bool) SampleDistortedBoundingBoxAttr {
- return func(m optionalAttr) {
- m["use_image_if_no_bounding_boxes"] = value
- }
-}
-
-// Generate a single randomly distorted bounding box for an image.
-//
-// Bounding box annotations are often supplied in addition to ground-truth labels
-// in image recognition or object localization tasks. A common technique for
-// training such a system is to randomly distort an image while preserving
-// its content, i.e. *data augmentation*. This Op outputs a randomly distorted
-// localization of an object, i.e. bounding box, given an `image_size`,
-// `bounding_boxes` and a series of constraints.
-//
-// The output of this Op is a single bounding box that may be used to crop the
-// original image. The output is returned as 3 tensors: `begin`, `size` and
-// `bboxes`. The first 2 tensors can be fed directly into `tf.slice` to crop the
-// image. The latter may be supplied to `tf.image.draw_bounding_boxes` to visualize
-// what the bounding box looks like.
-//
-// Bounding boxes are supplied and returned as `[y_min, x_min, y_max, x_max]`. The
-// bounding box coordinates are floats in `[0.0, 1.0]` relative to the width and
-// height of the underlying image.
-//
-// For example,
-//
-// ```python
-// # Generate a single distorted bounding box.
-// begin, size, bbox_for_draw = tf.image.sample_distorted_bounding_box(
-// tf.shape(image),
-// bounding_boxes=bounding_boxes)
-//
-// # Draw the bounding box in an image summary.
-// image_with_box = tf.image.draw_bounding_boxes(tf.expand_dims(image, 0),
-// bbox_for_draw)
-// tf.summary.image('images_with_box', image_with_box)
-//
-// # Employ the bounding box to distort the image.
-// distorted_image = tf.slice(image, begin, size)
-// ```
-//
-// Note that if no bounding box information is available, setting
-// `use_image_if_no_bounding_boxes = true` will assume there is a single implicit
-// bounding box covering the whole image. If `use_image_if_no_bounding_boxes` is
-// false and no bounding boxes are supplied, an error is raised.
+// Creates a handle to a Variable resource.
//
// Arguments:
-// image_size: 1-D, containing `[height, width, channels]`.
-// bounding_boxes: 3-D with shape `[batch, N, 4]` describing the N bounding boxes
-// associated with the image.
-//
-// Returns 1-D, containing `[offset_height, offset_width, 0]`. Provide as input to
-// `tf.slice`.1-D, containing `[target_height, target_width, -1]`. Provide as input to
-// `tf.slice`.3-D with shape `[1, 1, 4]` containing the distorted bounding box.
-// Provide as input to `tf.image.draw_bounding_boxes`.
-func SampleDistortedBoundingBox(scope *Scope, image_size tf.Output, bounding_boxes tf.Output, optional ...SampleDistortedBoundingBoxAttr) (begin tf.Output, size tf.Output, bboxes tf.Output) {
+// dtype: the type of this variable. Must agree with the dtypes
+// of all ops using this variable.
+// shape: The (possibly partially specified) shape of this variable.
+func VarHandleOp(scope *Scope, dtype tf.DataType, shape tf.Shape, optional ...VarHandleOpAttr) (resource tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{}
+ attrs := map[string]interface{}{"dtype": dtype, "shape": shape}
for _, a := range optional {
a(attrs)
}
opspec := tf.OpSpec{
- Type: "SampleDistortedBoundingBox",
- Input: []tf.Input{
- image_size, bounding_boxes,
- },
+ Type: "VarHandleOp",
+
Attrs: attrs,
}
op := scope.AddOperation(opspec)
- return op.Output(0), op.Output(1), op.Output(2)
+ return op.Output(0)
}
-// Converts each string in the input Tensor to its hash mod by a number of buckets.
-//
-// The hash function is deterministic on the content of the string within the
-// process and will never change. However, it is not suitable for cryptography.
-// This function may be used when CPU time is scarce and inputs are trusted or
-// unimportant. There is a risk of adversaries constructing inputs that all hash
-// to the same bucket. To prevent this problem, use a strong hash function with
-// `tf.string_to_hash_bucket_strong`.
-//
-// Arguments:
-// input: The strings to assign a hash bucket.
-// num_buckets: The number of buckets.
+// Elementwise computes the bitwise XOR of `x` and `y`.
//
-// Returns A Tensor of the same shape as the input `string_tensor`.
-func StringToHashBucketFast(scope *Scope, input tf.Output, num_buckets int64) (output tf.Output) {
+// The result will have those bits set, that are different in `x` and `y`. The
+// computation is performed on the underlying representations of `x` and `y`.
+func BitwiseXor(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{"num_buckets": num_buckets}
opspec := tf.OpSpec{
- Type: "StringToHashBucketFast",
+ Type: "BitwiseXor",
Input: []tf.Input{
- input,
+ x, y,
},
- Attrs: attrs,
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// Returns the max of x and y (i.e. x > y ? x : y) element-wise.
+// Deserialize `SparseTensor` objects.
//
-// *NOTE*: `Maximum` supports broadcasting. More about broadcasting
-// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
-func Maximum(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) {
+// The input `serialized_sparse` must have the shape `[?, ?, ..., ?, 3]` where
+// the last dimension stores serialized `SparseTensor` objects and the other N
+// dimensions (N >= 0) correspond to a batch. The ranks of the original
+// `SparseTensor` objects must all match. When the final `SparseTensor` is
+// created, its rank is the rank of the incoming `SparseTensor` objects plus N;
+// the sparse tensors have been concatenated along new dimensions, one for each
+// batch.
+//
+// The output `SparseTensor` object's shape values for the original dimensions
+// are the max across the input `SparseTensor` objects' shape values for the
+// corresponding dimensions. The new dimensions match the size of the batch.
+//
+// The input `SparseTensor` objects' indices are assumed ordered in
+// standard lexicographic order. If this is not the case, after this
+// step run `SparseReorder` to restore index ordering.
+//
+// For example, if the serialized input is a `[2 x 3]` matrix representing two
+// original `SparseTensor` objects:
+//
+// index = [ 0]
+// [10]
+// [20]
+// values = [1, 2, 3]
+// shape = [50]
+//
+// and
+//
+// index = [ 2]
+// [10]
+// values = [4, 5]
+// shape = [30]
+//
+// then the final deserialized `SparseTensor` will be:
+//
+// index = [0 0]
+// [0 10]
+// [0 20]
+// [1 2]
+// [1 10]
+// values = [1, 2, 3, 4, 5]
+// shape = [2 50]
+//
+// Arguments:
+// serialized_sparse: The serialized `SparseTensor` objects. The last dimension
+// must have 3 columns.
+// dtype: The `dtype` of the serialized `SparseTensor` objects.
+func DeserializeSparse(scope *Scope, serialized_sparse tf.Output, dtype tf.DataType) (sparse_indices tf.Output, sparse_values tf.Output, sparse_shape tf.Output) {
if scope.Err() != nil {
return
}
+ attrs := map[string]interface{}{"dtype": dtype}
opspec := tf.OpSpec{
- Type: "Maximum",
+ Type: "DeserializeSparse",
Input: []tf.Input{
- x, y,
+ serialized_sparse,
},
+ Attrs: attrs,
}
op := scope.AddOperation(opspec)
- return op.Output(0)
+ return op.Output(0), op.Output(1), op.Output(2)
}
-// TensorArrayGatherV3Attr is an optional argument to TensorArrayGatherV3.
-type TensorArrayGatherV3Attr func(optionalAttr)
+// ResourceApplyRMSPropAttr is an optional argument to ResourceApplyRMSProp.
+type ResourceApplyRMSPropAttr func(optionalAttr)
-// TensorArrayGatherV3ElementShape sets the optional element_shape attribute to value.
+// ResourceApplyRMSPropUseLocking sets the optional use_locking attribute to value.
//
-// value: The expected shape of an element, if known. Used to
-// validate the shapes of TensorArray elements. If this shape is not
-// fully specified, gathering zero-size TensorArrays is an error.
-// If not specified, defaults to <unknown_rank:true >
-func TensorArrayGatherV3ElementShape(value tf.Shape) TensorArrayGatherV3Attr {
+// value: If `True`, updating of the var, ms, and mom tensors is protected
+// by a lock; otherwise the behavior is undefined, but may exhibit less
+// contention.
+// If not specified, defaults to false
+func ResourceApplyRMSPropUseLocking(value bool) ResourceApplyRMSPropAttr {
return func(m optionalAttr) {
- m["element_shape"] = value
+ m["use_locking"] = value
}
}
-// Gather specific elements from the TensorArray into output `value`.
+// Update '*var' according to the RMSProp algorithm.
//
-// All elements selected by `indices` must have the same shape.
+// Note that in dense implementation of this algorithm, ms and mom will
+// update even if the grad is zero, but in this sparse implementation, ms
+// and mom will not update in iterations during which the grad is zero.
+//
+// mean_square = decay * mean_square + (1-decay) * gradient ** 2
+// Delta = learning_rate * gradient / sqrt(mean_square + epsilon)
+//
+// ms <- rho * ms_{t-1} + (1-rho) * grad * grad
+// mom <- momentum * mom_{t-1} + lr * grad / sqrt(ms + epsilon)
+// var <- var - mom
//
// Arguments:
-// handle: The handle to a TensorArray.
-// indices: The locations in the TensorArray from which to read tensor elements.
-// flow_in: A float scalar that enforces proper chaining of operations.
-// dtype: The type of the elem that is returned.
+// var_: Should be from a Variable().
+// ms: Should be from a Variable().
+// mom: Should be from a Variable().
+// lr: Scaling factor. Must be a scalar.
+// rho: Decay rate. Must be a scalar.
//
-// Returns All of the elements in the TensorArray, concatenated along a new
-// axis (the new dimension 0).
-func TensorArrayGatherV3(scope *Scope, handle tf.Output, indices tf.Output, flow_in tf.Output, dtype tf.DataType, optional ...TensorArrayGatherV3Attr) (value tf.Output) {
+// epsilon: Ridge term. Must be a scalar.
+// grad: The gradient.
+//
+// Returns the created operation.
+func ResourceApplyRMSProp(scope *Scope, var_ tf.Output, ms tf.Output, mom tf.Output, lr tf.Output, rho tf.Output, momentum tf.Output, epsilon tf.Output, grad tf.Output, optional ...ResourceApplyRMSPropAttr) (o *tf.Operation) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{"dtype": dtype}
+ attrs := map[string]interface{}{}
for _, a := range optional {
a(attrs)
}
opspec := tf.OpSpec{
- Type: "TensorArrayGatherV3",
+ Type: "ResourceApplyRMSProp",
Input: []tf.Input{
- handle, indices, flow_in,
+ var_, ms, mom, lr, rho, momentum, epsilon, grad,
},
Attrs: attrs,
}
- op := scope.AddOperation(opspec)
- return op.Output(0)
+ return scope.AddOperation(opspec)
}
-// Returns x / y element-wise for integer types.
-//
-// Truncation designates that negative numbers will round fractional quantities
-// toward zero. I.e. -7 / 5 = -1. This matches C semantics but it is different
-// than Python semantics. See `FloorDiv` for a division function that matches
-// Python Semantics.
+// ResourceScatterNdUpdateAttr is an optional argument to ResourceScatterNdUpdate.
+type ResourceScatterNdUpdateAttr func(optionalAttr)
+
+// ResourceScatterNdUpdateUseLocking sets the optional use_locking attribute to value.
//
-// *NOTE*: `TruncateDiv` supports broadcasting. More about broadcasting
-// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
-func TruncateDiv(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) {
- if scope.Err() != nil {
- return
- }
- opspec := tf.OpSpec{
- Type: "TruncateDiv",
- Input: []tf.Input{
- x, y,
- },
+// value: An optional bool. Defaults to True. If True, the assignment will
+// be protected by a lock; otherwise the behavior is undefined,
+// but may exhibit less contention.
+// If not specified, defaults to true
+func ResourceScatterNdUpdateUseLocking(value bool) ResourceScatterNdUpdateAttr {
+ return func(m optionalAttr) {
+ m["use_locking"] = value
}
- op := scope.AddOperation(opspec)
- return op.Output(0)
}
-// Restores tensors from a V2 checkpoint.
-//
-// For backward compatibility with the V1 format, this Op currently allows
-// restoring from a V1 checkpoint as well:
-// - This Op first attempts to find the V2 index file pointed to by "prefix", and
-// if found proceed to read it as a V2 checkpoint;
-// - Otherwise the V1 read path is invoked.
-// Relying on this behavior is not recommended, as the ability to fall back to read
-// V1 might be deprecated and eventually removed.
-//
-// By default, restores the named tensors in full. If the caller wishes to restore
-// specific slices of stored tensors, "shape_and_slices" should be non-empty
-// strings and correspondingly well-formed.
+// Applies sparse `updates` to individual values or slices within a given
//
-// Callers must ensure all the named tensors are indeed stored in the checkpoint.
+// variable according to `indices`.
//
-// Arguments:
-// prefix: Must have a single element. The prefix of a V2 checkpoint.
-// tensor_names: shape {N}. The names of the tensors to be restored.
-// shape_and_slices: shape {N}. The slice specs of the tensors to be restored.
-// Empty strings indicate that they are non-partitioned tensors.
-// dtypes: shape {N}. The list of expected dtype for the tensors. Must match
-// those stored in the checkpoint.
+// `ref` is a `Tensor` with rank `P` and `indices` is a `Tensor` of rank `Q`.
//
-// Returns shape {N}. The restored tensors, whose shapes are read from the
-// checkpoint directly.
-func RestoreV2(scope *Scope, prefix tf.Output, tensor_names tf.Output, shape_and_slices tf.Output, dtypes []tf.DataType) (tensors []tf.Output) {
- if scope.Err() != nil {
- return
- }
- attrs := map[string]interface{}{"dtypes": dtypes}
- opspec := tf.OpSpec{
- Type: "RestoreV2",
- Input: []tf.Input{
- prefix, tensor_names, shape_and_slices,
- },
- Attrs: attrs,
- }
- op := scope.AddOperation(opspec)
- if scope.Err() != nil {
- return
- }
- var idx int
- var err error
- if tensors, idx, err = makeOutputList(op, idx, "tensors"); err != nil {
- scope.UpdateErr("RestoreV2", err)
- return
- }
- return tensors
-}
-
-// Creates a dataset that skips `count` elements from the `input_dataset`.
+// `indices` must be integer tensor, containing indices into `ref`.
+// It must be shape `[d_0, ..., d_{Q-2}, K]` where `0 < K <= P`.
//
-// Arguments:
+// The innermost dimension of `indices` (with length `K`) corresponds to
+// indices into elements (if `K = P`) or slices (if `K < P`) along the `K`th
+// dimension of `ref`.
//
-// count: A scalar representing the number of elements from the `input_dataset`
-// that should be skipped. If count is -1, skips everything.
+// `updates` is `Tensor` of rank `Q-1+P-K` with shape:
//
+// ```
+// [d_0, ..., d_{Q-2}, ref.shape[K], ..., ref.shape[P-1]].
+// ```
//
-func SkipDataset(scope *Scope, input_dataset tf.Output, count tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) {
- if scope.Err() != nil {
- return
- }
- attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes}
- opspec := tf.OpSpec{
- Type: "SkipDataset",
- Input: []tf.Input{
- input_dataset, count,
- },
- Attrs: attrs,
- }
- op := scope.AddOperation(opspec)
- return op.Output(0)
-}
-
-// Computes the maximum along segments of a tensor.
+// For example, say we want to update 4 scattered elements to a rank-1 tensor to
+// 8 elements. In Python, that update would look like this:
//
-// Read @{$math_ops#segmentation$the section on segmentation} for an explanation of
-// segments.
+// ```python
+// ref = tfe.Variable([1, 2, 3, 4, 5, 6, 7, 8])
+// indices = tf.constant([[4], [3], [1] ,[7]])
+// updates = tf.constant([9, 10, 11, 12])
+// update = tf.scatter_nd_update(ref, indices, updates)
+// with tf.Session() as sess:
+// print sess.run(update)
+// ```
//
-// Computes a tensor such that
-// \\(output_i = \max_j(data_j)\\) where `max` is over `j` such
-// that `segment_ids[j] == i`.
+// The resulting update to ref would look like this:
//
-// If the max is empty for a given segment ID `i`, `output[i] = 0`.
+// [1, 11, 3, 10, 9, 6, 7, 12]
//
-// <div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;">
-// <img style="width:100%" src="https://www.tensorflow.org/images/SegmentMax.png" alt>
-// </div>
+// See @{tf.scatter_nd} for more details about how to make updates to
+// slices.
//
// Arguments:
+// ref: A resource handle. Must be from a VarHandleOp.
+// indices: A Tensor. Must be one of the following types: int32, int64.
+// A tensor of indices into ref.
+// updates: A Tensor. Must have the same type as ref. A tensor of updated
+// values to add to ref.
//
-// segment_ids: A 1-D tensor whose rank is equal to the rank of `data`'s
-// first dimension. Values should be sorted and can be repeated.
-//
-// Returns Has same shape as data, except for dimension 0 which
-// has size `k`, the number of segments.
-func SegmentMax(scope *Scope, data tf.Output, segment_ids tf.Output) (output tf.Output) {
+// Returns the created operation.
+func ResourceScatterNdUpdate(scope *Scope, ref tf.Output, indices tf.Output, updates tf.Output, optional ...ResourceScatterNdUpdateAttr) (o *tf.Operation) {
if scope.Err() != nil {
return
}
- opspec := tf.OpSpec{
- Type: "SegmentMax",
- Input: []tf.Input{
- data, segment_ids,
- },
- }
- op := scope.AddOperation(opspec)
- return op.Output(0)
-}
-
-// Computes hyperbolic tangent of `x` element-wise.
-func Tanh(scope *Scope, x tf.Output) (y tf.Output) {
- if scope.Err() != nil {
- return
+ attrs := map[string]interface{}{}
+ for _, a := range optional {
+ a(attrs)
}
opspec := tf.OpSpec{
- Type: "Tanh",
+ Type: "ResourceScatterNdUpdate",
Input: []tf.Input{
- x,
+ ref, indices, updates,
},
+ Attrs: attrs,
}
- op := scope.AddOperation(opspec)
- return op.Output(0)
+ return scope.AddOperation(opspec)
}
-// Decode web-safe base64-encoded strings.
-//
-// Input may or may not have padding at the end. See EncodeBase64 for padding.
-// Web-safe means that input must use - and _ instead of + and /.
+// SqueezeAttr is an optional argument to Squeeze.
+type SqueezeAttr func(optionalAttr)
+
+// SqueezeAxis sets the optional axis attribute to value.
//
-// Arguments:
-// input: Base64 strings to decode.
+// value: If specified, only squeezes the dimensions listed. The dimension
+// index starts at 0. It is an error to squeeze a dimension that is not 1. Must
+// be in the range `[-rank(input), rank(input))`.
+// If not specified, defaults to <>
//
-// Returns Decoded strings.
-func DecodeBase64(scope *Scope, input tf.Output) (output tf.Output) {
- if scope.Err() != nil {
- return
- }
- opspec := tf.OpSpec{
- Type: "DecodeBase64",
- Input: []tf.Input{
- input,
- },
+// REQUIRES: len(value) >= 0
+func SqueezeAxis(value []int64) SqueezeAttr {
+ return func(m optionalAttr) {
+ m["squeeze_dims"] = value
}
- op := scope.AddOperation(opspec)
- return op.Output(0)
}
-// Store the input tensor in the state of the current session.
+// Removes dimensions of size 1 from the shape of a tensor.
+//
+// Given a tensor `input`, this operation returns a tensor of the same type with
+// all dimensions of size 1 removed. If you don't want to remove all size 1
+// dimensions, you can remove specific size 1 dimensions by specifying
+// `axis`.
+//
+// For example:
+//
+// ```
+// # 't' is a tensor of shape [1, 2, 1, 3, 1, 1]
+// shape(squeeze(t)) ==> [2, 3]
+// ```
+//
+// Or, to remove specific size 1 dimensions:
+//
+// ```
+// # 't' is a tensor of shape [1, 2, 1, 3, 1, 1]
+// shape(squeeze(t, [2, 4])) ==> [1, 2, 3, 1]
+// ```
//
// Arguments:
-// value: The tensor to be stored.
+// input: The `input` to squeeze.
//
-// Returns The handle for the tensor stored in the session state, represented
-// as a string.
-func GetSessionHandle(scope *Scope, value tf.Output) (handle tf.Output) {
+// Returns Contains the same data as `input`, but has one or more dimensions of
+// size 1 removed.
+func Squeeze(scope *Scope, input tf.Output, optional ...SqueezeAttr) (output tf.Output) {
if scope.Err() != nil {
return
}
+ attrs := map[string]interface{}{}
+ for _, a := range optional {
+ a(attrs)
+ }
opspec := tf.OpSpec{
- Type: "GetSessionHandle",
+ Type: "Squeeze",
Input: []tf.Input{
- value,
+ input,
},
+ Attrs: attrs,
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// ResourceSparseApplyProximalAdagradAttr is an optional argument to ResourceSparseApplyProximalAdagrad.
-type ResourceSparseApplyProximalAdagradAttr func(optionalAttr)
+// ResourceApplyAdadeltaAttr is an optional argument to ResourceApplyAdadelta.
+type ResourceApplyAdadeltaAttr func(optionalAttr)
-// ResourceSparseApplyProximalAdagradUseLocking sets the optional use_locking attribute to value.
+// ResourceApplyAdadeltaUseLocking sets the optional use_locking attribute to value.
//
-// value: If True, updating of the var and accum tensors will be protected by
+// value: If True, updating of the var, accum and update_accum tensors will be protected by
// a lock; otherwise the behavior is undefined, but may exhibit less contention.
// If not specified, defaults to false
-func ResourceSparseApplyProximalAdagradUseLocking(value bool) ResourceSparseApplyProximalAdagradAttr {
+func ResourceApplyAdadeltaUseLocking(value bool) ResourceApplyAdadeltaAttr {
return func(m optionalAttr) {
m["use_locking"] = value
}
}
-// Sparse update entries in '*var' and '*accum' according to FOBOS algorithm.
+// Update '*var' according to the adadelta scheme.
//
-// That is for rows we have grad for, we update var and accum as follows:
-// accum += grad * grad
-// prox_v = var
-// prox_v -= lr * grad * (1 / sqrt(accum))
-// var = sign(prox_v)/(1+lr*l2) * max{|prox_v|-lr*l1,0}
+// accum = rho() * accum + (1 - rho()) * grad.square();
+// update = (update_accum + epsilon).sqrt() * (accum + epsilon()).rsqrt() * grad;
+// update_accum = rho() * update_accum + (1 - rho()) * update.square();
+// var -= update;
//
// Arguments:
// var_: Should be from a Variable().
// accum: Should be from a Variable().
-// lr: Learning rate. Must be a scalar.
-// l1: L1 regularization. Must be a scalar.
-// l2: L2 regularization. Must be a scalar.
+// accum_update: Should be from a Variable().
+// lr: Scaling factor. Must be a scalar.
+// rho: Decay factor. Must be a scalar.
+// epsilon: Constant factor. Must be a scalar.
// grad: The gradient.
-// indices: A vector of indices into the first dimension of var and accum.
//
// Returns the created operation.
-func ResourceSparseApplyProximalAdagrad(scope *Scope, var_ tf.Output, accum tf.Output, lr tf.Output, l1 tf.Output, l2 tf.Output, grad tf.Output, indices tf.Output, optional ...ResourceSparseApplyProximalAdagradAttr) (o *tf.Operation) {
+func ResourceApplyAdadelta(scope *Scope, var_ tf.Output, accum tf.Output, accum_update tf.Output, lr tf.Output, rho tf.Output, epsilon tf.Output, grad tf.Output, optional ...ResourceApplyAdadeltaAttr) (o *tf.Operation) {
if scope.Err() != nil {
return
}
a(attrs)
}
opspec := tf.OpSpec{
- Type: "ResourceSparseApplyProximalAdagrad",
+ Type: "ResourceApplyAdadelta",
Input: []tf.Input{
- var_, accum, lr, l1, l2, grad, indices,
+ var_, accum, accum_update, lr, rho, epsilon, grad,
},
Attrs: attrs,
}
return scope.AddOperation(opspec)
}
-// Returns element-wise largest integer not greater than x.
-func Floor(scope *Scope, x tf.Output) (y tf.Output) {
+// NonMaxSuppressionAttr is an optional argument to NonMaxSuppression.
+type NonMaxSuppressionAttr func(optionalAttr)
+
+// NonMaxSuppressionIouThreshold sets the optional iou_threshold attribute to value.
+//
+// value: A float representing the threshold for deciding whether boxes
+// overlap too much with respect to IOU.
+// If not specified, defaults to 0.5
+func NonMaxSuppressionIouThreshold(value float32) NonMaxSuppressionAttr {
+ return func(m optionalAttr) {
+ m["iou_threshold"] = value
+ }
+}
+
+// Greedily selects a subset of bounding boxes in descending order of score,
+//
+// pruning away boxes that have high intersection-over-union (IOU) overlap
+// with previously selected boxes. Bounding boxes are supplied as
+// [y1, x1, y2, x2], where (y1, x1) and (y2, x2) are the coordinates of any
+// diagonal pair of box corners and the coordinates can be provided as normalized
+// (i.e., lying in the interval [0, 1]) or absolute. Note that this algorithm
+// is agnostic to where the origin is in the coordinate system. Note that this
+// algorithm is invariant to orthogonal transformations and translations
+// of the coordinate system; thus translating or reflections of the coordinate
+// system result in the same boxes being selected by the algorithm.
+// The output of this operation is a set of integers indexing into the input
+// collection of bounding boxes representing the selected boxes. The bounding
+// box coordinates corresponding to the selected indices can then be obtained
+// using the `tf.gather operation`. For example:
+// selected_indices = tf.image.non_max_suppression(
+// boxes, scores, max_output_size, iou_threshold)
+// selected_boxes = tf.gather(boxes, selected_indices)
+//
+// Arguments:
+// boxes: A 2-D float tensor of shape `[num_boxes, 4]`.
+// scores: A 1-D float tensor of shape `[num_boxes]` representing a single
+// score corresponding to each box (each row of boxes).
+// max_output_size: A scalar integer tensor representing the maximum number of
+// boxes to be selected by non max suppression.
+//
+// Returns A 1-D integer tensor of shape `[M]` representing the selected
+// indices from the boxes tensor, where `M <= max_output_size`.
+func NonMaxSuppression(scope *Scope, boxes tf.Output, scores tf.Output, max_output_size tf.Output, optional ...NonMaxSuppressionAttr) (selected_indices tf.Output) {
if scope.Err() != nil {
return
}
+ attrs := map[string]interface{}{}
+ for _, a := range optional {
+ a(attrs)
+ }
opspec := tf.OpSpec{
- Type: "Floor",
+ Type: "NonMaxSuppression",
Input: []tf.Input{
- x,
+ boxes, scores, max_output_size,
},
+ Attrs: attrs,
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// Computes the Gauss error function of `x` element-wise.
-func Erf(scope *Scope, x tf.Output) (y tf.Output) {
+// Creates a dataset that emits `components` as a tuple of tensors once.
+func TensorDataset(scope *Scope, components []tf.Output, output_shapes []tf.Shape) (handle tf.Output) {
if scope.Err() != nil {
return
}
+ attrs := map[string]interface{}{"output_shapes": output_shapes}
opspec := tf.OpSpec{
- Type: "Erf",
+ Type: "TensorDataset",
Input: []tf.Input{
- x,
+ tf.OutputList(components),
},
+ Attrs: attrs,
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// OneHotAttr is an optional argument to OneHot.
-type OneHotAttr func(optionalAttr)
+// Component-wise multiplies a SparseTensor by a dense Tensor.
+//
+// The output locations corresponding to the implicitly zero elements in the sparse
+// tensor will be zero (i.e., will not take up storage space), regardless of the
+// contents of the dense tensor (even if it's +/-INF and that INF*0 == NaN).
+//
+// *Limitation*: this Op only broadcasts the dense side to the sparse side, but not
+// the other direction.
+//
+// Arguments:
+// sp_indices: 2-D. `N x R` matrix with the indices of non-empty values in a
+// SparseTensor, possibly not in canonical ordering.
+// sp_values: 1-D. `N` non-empty values corresponding to `sp_indices`.
+// sp_shape: 1-D. Shape of the input SparseTensor.
+// dense: `R`-D. The dense Tensor operand.
+//
+// Returns 1-D. The `N` values that are operated on.
+func SparseDenseCwiseMul(scope *Scope, sp_indices tf.Output, sp_values tf.Output, sp_shape tf.Output, dense tf.Output) (output tf.Output) {
+ if scope.Err() != nil {
+ return
+ }
+ opspec := tf.OpSpec{
+ Type: "SparseDenseCwiseMul",
+ Input: []tf.Input{
+ sp_indices, sp_values, sp_shape, dense,
+ },
+ }
+ op := scope.AddOperation(opspec)
+ return op.Output(0)
+}
-// OneHotAxis sets the optional axis attribute to value.
+// ResourceSparseApplyRMSPropAttr is an optional argument to ResourceSparseApplyRMSProp.
+type ResourceSparseApplyRMSPropAttr func(optionalAttr)
+
+// ResourceSparseApplyRMSPropUseLocking sets the optional use_locking attribute to value.
//
-// value: The axis to fill (default: -1, a new inner-most axis).
-// If not specified, defaults to -1
-func OneHotAxis(value int64) OneHotAttr {
+// value: If `True`, updating of the var, ms, and mom tensors is protected
+// by a lock; otherwise the behavior is undefined, but may exhibit less
+// contention.
+// If not specified, defaults to false
+func ResourceSparseApplyRMSPropUseLocking(value bool) ResourceSparseApplyRMSPropAttr {
return func(m optionalAttr) {
- m["axis"] = value
+ m["use_locking"] = value
}
}
-// Returns a one-hot tensor.
-//
-// The locations represented by indices in `indices` take value `on_value`,
-// while all other locations take value `off_value`.
-//
-// If the input `indices` is rank `N`, the output will have rank `N+1`,
-// The new axis is created at dimension `axis` (default: the new axis is
-// appended at the end).
-//
-// If `indices` is a scalar the output shape will be a vector of length `depth`.
-//
-// If `indices` is a vector of length `features`, the output shape will be:
-// ```
-// features x depth if axis == -1
-// depth x features if axis == 0
-// ```
-//
-// If `indices` is a matrix (batch) with shape `[batch, features]`,
-// the output shape will be:
-// ```
-// batch x features x depth if axis == -1
-// batch x depth x features if axis == 1
-// depth x batch x features if axis == 0
-// ```
-//
-//
-// Examples
-// =========
-//
-// Suppose that
-//
-// ```
-// indices = [0, 2, -1, 1]
-// depth = 3
-// on_value = 5.0
-// off_value = 0.0
-// axis = -1
-// ```
-//
-// Then output is `[4 x 3]`:
-//
-// ```output =
-// [5.0 0.0 0.0] // one_hot(0)
-// [0.0 0.0 5.0] // one_hot(2)
-// [0.0 0.0 0.0] // one_hot(-1)
-// [0.0 5.0 0.0] // one_hot(1)
-// ```
-//
-// Suppose that
-//
-// ```
-// indices = [0, 2, -1, 1]
-// depth = 3
-// on_value = 0.0
-// off_value = 3.0
-// axis = 0
-// ```
-//
-// Then output is `[3 x 4]`:
-//
-// ```output =
-// [0.0 3.0 3.0 3.0]
-// [3.0 3.0 3.0 0.0]
-// [3.0 3.0 3.0 3.0]
-// [3.0 0.0 3.0 3.0]
-// // ^ one_hot(0)
-// // ^ one_hot(2)
-// // ^ one_hot(-1)
-// // ^ one_hot(1)
-// ```
-// Suppose that
+// Update '*var' according to the RMSProp algorithm.
//
-// ```
-// indices = [[0, 2], [1, -1]]
-// depth = 3
-// on_value = 1.0
-// off_value = 0.0
-// axis = -1
-// ```
+// Note that in dense implementation of this algorithm, ms and mom will
+// update even if the grad is zero, but in this sparse implementation, ms
+// and mom will not update in iterations during which the grad is zero.
//
-// Then output is `[2 x 2 x 3]`:
+// mean_square = decay * mean_square + (1-decay) * gradient ** 2
+// Delta = learning_rate * gradient / sqrt(mean_square + epsilon)
//
-// ```output =
-// [
-// [1.0, 0.0, 0.0] // one_hot(0)
-// [0.0, 0.0, 1.0] // one_hot(2)
-// ][
-// [0.0, 1.0, 0.0] // one_hot(1)
-// [0.0, 0.0, 0.0] // one_hot(-1)
-// ]```
+// ms <- rho * ms_{t-1} + (1-rho) * grad * grad
+// mom <- momentum * mom_{t-1} + lr * grad / sqrt(ms + epsilon)
+// var <- var - mom
//
// Arguments:
-// indices: A tensor of indices.
-// depth: A scalar defining the depth of the one hot dimension.
-// on_value: A scalar defining the value to fill in output when `indices[j] = i`.
-// off_value: A scalar defining the value to fill in output when `indices[j] != i`.
+// var_: Should be from a Variable().
+// ms: Should be from a Variable().
+// mom: Should be from a Variable().
+// lr: Scaling factor. Must be a scalar.
+// rho: Decay rate. Must be a scalar.
//
-// Returns The one-hot tensor.
-func OneHot(scope *Scope, indices tf.Output, depth tf.Output, on_value tf.Output, off_value tf.Output, optional ...OneHotAttr) (output tf.Output) {
+// epsilon: Ridge term. Must be a scalar.
+// grad: The gradient.
+// indices: A vector of indices into the first dimension of var, ms and mom.
+//
+// Returns the created operation.
+func ResourceSparseApplyRMSProp(scope *Scope, var_ tf.Output, ms tf.Output, mom tf.Output, lr tf.Output, rho tf.Output, momentum tf.Output, epsilon tf.Output, grad tf.Output, indices tf.Output, optional ...ResourceSparseApplyRMSPropAttr) (o *tf.Operation) {
if scope.Err() != nil {
return
}
a(attrs)
}
opspec := tf.OpSpec{
- Type: "OneHot",
+ Type: "ResourceSparseApplyRMSProp",
Input: []tf.Input{
- indices, depth, on_value, off_value,
+ var_, ms, mom, lr, rho, momentum, epsilon, grad, indices,
},
Attrs: attrs,
}
- op := scope.AddOperation(opspec)
- return op.Output(0)
+ return scope.AddOperation(opspec)
}
-// Reads the value of a variable.
-//
-// The tensor returned by this operation is immutable.
-//
-// The value returned by this operation is guaranteed to be influenced by all the
-// writes on which this operation depends directly or indirectly, and to not be
-// influenced by any of the writes which depend directly or indirectly on this
-// operation.
+// Returns the truth value of (x > y) element-wise.
//
-// Arguments:
-// resource: handle to the resource in which to store the variable.
-// dtype: the dtype of the value.
-func ReadVariableOp(scope *Scope, resource tf.Output, dtype tf.DataType) (value tf.Output) {
+// *NOTE*: `Greater` supports broadcasting. More about broadcasting
+// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
+func Greater(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{"dtype": dtype}
opspec := tf.OpSpec{
- Type: "ReadVariableOp",
+ Type: "Greater",
Input: []tf.Input{
- resource,
+ x, y,
},
- Attrs: attrs,
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// MaxPool3DGradAttr is an optional argument to MaxPool3DGrad.
-type MaxPool3DGradAttr func(optionalAttr)
+// SampleDistortedBoundingBoxAttr is an optional argument to SampleDistortedBoundingBox.
+type SampleDistortedBoundingBoxAttr func(optionalAttr)
-// MaxPool3DGradDataFormat sets the optional data_format attribute to value.
+// SampleDistortedBoundingBoxSeed sets the optional seed attribute to value.
//
-// value: The data format of the input and output data. With the
-// default format "NDHWC", the data is stored in the order of:
-// [batch, in_depth, in_height, in_width, in_channels].
-// Alternatively, the format could be "NCDHW", the data storage order is:
-// [batch, in_channels, in_depth, in_height, in_width].
-// If not specified, defaults to "NDHWC"
-func MaxPool3DGradDataFormat(value string) MaxPool3DGradAttr {
+// value: If either `seed` or `seed2` are set to non-zero, the random number
+// generator is seeded by the given `seed`. Otherwise, it is seeded by a random
+// seed.
+// If not specified, defaults to 0
+func SampleDistortedBoundingBoxSeed(value int64) SampleDistortedBoundingBoxAttr {
return func(m optionalAttr) {
- m["data_format"] = value
+ m["seed"] = value
}
}
-// Computes gradients of max pooling function.
+// SampleDistortedBoundingBoxSeed2 sets the optional seed2 attribute to value.
//
-// Arguments:
-// orig_input: The original input tensor.
-// orig_output: The original output tensor.
-// grad: Output backprop of shape `[batch, depth, rows, cols, channels]`.
-// ksize: 1-D tensor of length 5. The size of the window for each dimension of
-// the input tensor. Must have `ksize[0] = ksize[4] = 1`.
-// strides: 1-D tensor of length 5. The stride of the sliding window for each
-// dimension of `input`. Must have `strides[0] = strides[4] = 1`.
-// padding: The type of padding algorithm to use.
-func MaxPool3DGrad(scope *Scope, orig_input tf.Output, orig_output tf.Output, grad tf.Output, ksize []int64, strides []int64, padding string, optional ...MaxPool3DGradAttr) (output tf.Output) {
- if scope.Err() != nil {
- return
+// value: A second seed to avoid seed collision.
+// If not specified, defaults to 0
+func SampleDistortedBoundingBoxSeed2(value int64) SampleDistortedBoundingBoxAttr {
+ return func(m optionalAttr) {
+ m["seed2"] = value
}
- attrs := map[string]interface{}{"ksize": ksize, "strides": strides, "padding": padding}
- for _, a := range optional {
- a(attrs)
+}
+
+// SampleDistortedBoundingBoxMinObjectCovered sets the optional min_object_covered attribute to value.
+//
+// value: The cropped area of the image must contain at least this
+// fraction of any bounding box supplied. The value of this parameter should be
+// non-negative. In the case of 0, the cropped area does not need to overlap
+// any of the bounding boxes supplied.
+// If not specified, defaults to 0.1
+func SampleDistortedBoundingBoxMinObjectCovered(value float32) SampleDistortedBoundingBoxAttr {
+ return func(m optionalAttr) {
+ m["min_object_covered"] = value
}
- opspec := tf.OpSpec{
- Type: "MaxPool3DGrad",
- Input: []tf.Input{
- orig_input, orig_output, grad,
- },
- Attrs: attrs,
+}
+
+// SampleDistortedBoundingBoxAspectRatioRange sets the optional aspect_ratio_range attribute to value.
+//
+// value: The cropped area of the image must have an aspect ratio =
+// width / height within this range.
+// If not specified, defaults to <f:0.75 f:1.33 >
+func SampleDistortedBoundingBoxAspectRatioRange(value []float32) SampleDistortedBoundingBoxAttr {
+ return func(m optionalAttr) {
+ m["aspect_ratio_range"] = value
}
- op := scope.AddOperation(opspec)
- return op.Output(0)
}
-// SparseReduceSumAttr is an optional argument to SparseReduceSum.
-type SparseReduceSumAttr func(optionalAttr)
+// SampleDistortedBoundingBoxAreaRange sets the optional area_range attribute to value.
+//
+// value: The cropped area of the image must contain a fraction of the
+// supplied image within in this range.
+// If not specified, defaults to <f:0.05 f:1 >
+func SampleDistortedBoundingBoxAreaRange(value []float32) SampleDistortedBoundingBoxAttr {
+ return func(m optionalAttr) {
+ m["area_range"] = value
+ }
+}
-// SparseReduceSumKeepDims sets the optional keep_dims attribute to value.
+// SampleDistortedBoundingBoxMaxAttempts sets the optional max_attempts attribute to value.
//
-// value: If true, retain reduced dimensions with length 1.
+// value: Number of attempts at generating a cropped region of the image
+// of the specified constraints. After `max_attempts` failures, return the entire
+// image.
+// If not specified, defaults to 100
+func SampleDistortedBoundingBoxMaxAttempts(value int64) SampleDistortedBoundingBoxAttr {
+ return func(m optionalAttr) {
+ m["max_attempts"] = value
+ }
+}
+
+// SampleDistortedBoundingBoxUseImageIfNoBoundingBoxes sets the optional use_image_if_no_bounding_boxes attribute to value.
+//
+// value: Controls behavior if no bounding boxes supplied.
+// If true, assume an implicit bounding box covering the whole input. If false,
+// raise an error.
// If not specified, defaults to false
-func SparseReduceSumKeepDims(value bool) SparseReduceSumAttr {
+func SampleDistortedBoundingBoxUseImageIfNoBoundingBoxes(value bool) SampleDistortedBoundingBoxAttr {
return func(m optionalAttr) {
- m["keep_dims"] = value
+ m["use_image_if_no_bounding_boxes"] = value
}
}
-// Computes the sum of elements across dimensions of a SparseTensor.
+// Generate a single randomly distorted bounding box for an image.
//
-// This Op takes a SparseTensor and is the sparse counterpart to
-// `tf.reduce_sum()`. In particular, this Op also returns a dense `Tensor`
-// instead of a sparse one.
+// Bounding box annotations are often supplied in addition to ground-truth labels
+// in image recognition or object localization tasks. A common technique for
+// training such a system is to randomly distort an image while preserving
+// its content, i.e. *data augmentation*. This Op outputs a randomly distorted
+// localization of an object, i.e. bounding box, given an `image_size`,
+// `bounding_boxes` and a series of constraints.
//
-// Reduces `sp_input` along the dimensions given in `reduction_axes`. Unless
-// `keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in
-// `reduction_axes`. If `keep_dims` is true, the reduced dimensions are retained
-// with length 1.
+// The output of this Op is a single bounding box that may be used to crop the
+// original image. The output is returned as 3 tensors: `begin`, `size` and
+// `bboxes`. The first 2 tensors can be fed directly into `tf.slice` to crop the
+// image. The latter may be supplied to `tf.image.draw_bounding_boxes` to visualize
+// what the bounding box looks like.
//
-// If `reduction_axes` has no entries, all dimensions are reduced, and a tensor
-// with a single element is returned. Additionally, the axes can be negative,
-// which are interpreted according to the indexing rules in Python.
+// Bounding boxes are supplied and returned as `[y_min, x_min, y_max, x_max]`. The
+// bounding box coordinates are floats in `[0.0, 1.0]` relative to the width and
+// height of the underlying image.
+//
+// For example,
+//
+// ```python
+// # Generate a single distorted bounding box.
+// begin, size, bbox_for_draw = tf.image.sample_distorted_bounding_box(
+// tf.shape(image),
+// bounding_boxes=bounding_boxes)
+//
+// # Draw the bounding box in an image summary.
+// image_with_box = tf.image.draw_bounding_boxes(tf.expand_dims(image, 0),
+// bbox_for_draw)
+// tf.summary.image('images_with_box', image_with_box)
+//
+// # Employ the bounding box to distort the image.
+// distorted_image = tf.slice(image, begin, size)
+// ```
+//
+// Note that if no bounding box information is available, setting
+// `use_image_if_no_bounding_boxes = true` will assume there is a single implicit
+// bounding box covering the whole image. If `use_image_if_no_bounding_boxes` is
+// false and no bounding boxes are supplied, an error is raised.
//
// Arguments:
-// input_indices: 2-D. `N x R` matrix with the indices of non-empty values in a
-// SparseTensor, possibly not in canonical ordering.
-// input_values: 1-D. `N` non-empty values corresponding to `input_indices`.
-// input_shape: 1-D. Shape of the input SparseTensor.
-// reduction_axes: 1-D. Length-`K` vector containing the reduction axes.
+// image_size: 1-D, containing `[height, width, channels]`.
+// bounding_boxes: 3-D with shape `[batch, N, 4]` describing the N bounding boxes
+// associated with the image.
//
-// Returns `R-K`-D. The reduced Tensor.
-func SparseReduceSum(scope *Scope, input_indices tf.Output, input_values tf.Output, input_shape tf.Output, reduction_axes tf.Output, optional ...SparseReduceSumAttr) (output tf.Output) {
+// Returns 1-D, containing `[offset_height, offset_width, 0]`. Provide as input to
+// `tf.slice`.1-D, containing `[target_height, target_width, -1]`. Provide as input to
+// `tf.slice`.3-D with shape `[1, 1, 4]` containing the distorted bounding box.
+// Provide as input to `tf.image.draw_bounding_boxes`.
+func SampleDistortedBoundingBox(scope *Scope, image_size tf.Output, bounding_boxes tf.Output, optional ...SampleDistortedBoundingBoxAttr) (begin tf.Output, size tf.Output, bboxes tf.Output) {
if scope.Err() != nil {
return
}
a(attrs)
}
opspec := tf.OpSpec{
- Type: "SparseReduceSum",
+ Type: "SampleDistortedBoundingBox",
Input: []tf.Input{
- input_indices, input_values, input_shape, reduction_axes,
+ image_size, bounding_boxes,
},
Attrs: attrs,
}
op := scope.AddOperation(opspec)
- return op.Output(0)
+ return op.Output(0), op.Output(1), op.Output(2)
}
-// Returns element-wise remainder of division. This emulates C semantics in that
+// Converts each string in the input Tensor to its hash mod by a number of buckets.
//
-// the result here is consistent with a truncating divide. E.g. `truncate(x / y) *
-// y + truncate_mod(x, y) = x`.
+// The hash function is deterministic on the content of the string within the
+// process and will never change. However, it is not suitable for cryptography.
+// This function may be used when CPU time is scarce and inputs are trusted or
+// unimportant. There is a risk of adversaries constructing inputs that all hash
+// to the same bucket. To prevent this problem, use a strong hash function with
+// `tf.string_to_hash_bucket_strong`.
//
-// *NOTE*: `TruncateMod` supports broadcasting. More about broadcasting
-// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
-func TruncateMod(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) {
+// Arguments:
+// input: The strings to assign a hash bucket.
+// num_buckets: The number of buckets.
+//
+// Returns A Tensor of the same shape as the input `string_tensor`.
+func StringToHashBucketFast(scope *Scope, input tf.Output, num_buckets int64) (output tf.Output) {
if scope.Err() != nil {
return
}
+ attrs := map[string]interface{}{"num_buckets": num_buckets}
opspec := tf.OpSpec{
- Type: "TruncateMod",
+ Type: "StringToHashBucketFast",
Input: []tf.Input{
- x, y,
+ input,
},
+ Attrs: attrs,
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// Inverse 2D real-valued fast Fourier transform.
-//
-// Computes the inverse 2-dimensional discrete Fourier transform of a real-valued
-// signal over the inner-most 2 dimensions of `input`.
-//
-// The inner-most 2 dimensions of `input` are assumed to be the result of `RFFT2D`:
-// The inner-most dimension contains the `fft_length / 2 + 1` unique components of
-// the DFT of a real-valued signal. If `fft_length` is not provided, it is computed
-// from the size of the inner-most 2 dimensions of `input`. If the FFT length used
-// to compute `input` is odd, it should be provided since it cannot be inferred
-// properly.
-//
-// Along each axis `IRFFT2D` is computed on, if `fft_length` (or
-// `fft_length / 2 + 1` for the inner-most dimension) is smaller than the
-// corresponding dimension of `input`, the dimension is cropped. If it is larger,
-// the dimension is padded with zeros.
-//
-// Arguments:
-// input: A complex64 tensor.
-// fft_length: An int32 tensor of shape [2]. The FFT length for each dimension.
-//
-// Returns A float32 tensor of the same rank as `input`. The inner-most 2
-// dimensions of `input` are replaced with the `fft_length` samples of their
-// inverse 2D Fourier transform.
+// Returns the max of x and y (i.e. x > y ? x : y) element-wise.
//
-// @compatibility(numpy)
-// Equivalent to np.fft.irfft2
-// @end_compatibility
-func IRFFT2D(scope *Scope, input tf.Output, fft_length tf.Output) (output tf.Output) {
+// *NOTE*: `Maximum` supports broadcasting. More about broadcasting
+// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
+func Maximum(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) {
if scope.Err() != nil {
return
}
opspec := tf.OpSpec{
- Type: "IRFFT2D",
+ Type: "Maximum",
Input: []tf.Input{
- input, fft_length,
+ x, y,
},
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// DecodeJpegAttr is an optional argument to DecodeJpeg.
-type DecodeJpegAttr func(optionalAttr)
+// TensorArrayGatherV3Attr is an optional argument to TensorArrayGatherV3.
+type TensorArrayGatherV3Attr func(optionalAttr)
-// DecodeJpegChannels sets the optional channels attribute to value.
+// TensorArrayGatherV3ElementShape sets the optional element_shape attribute to value.
//
-// value: Number of color channels for the decoded image.
-// If not specified, defaults to 0
-func DecodeJpegChannels(value int64) DecodeJpegAttr {
+// value: The expected shape of an element, if known. Used to
+// validate the shapes of TensorArray elements. If this shape is not
+// fully specified, gathering zero-size TensorArrays is an error.
+// If not specified, defaults to <unknown_rank:true >
+func TensorArrayGatherV3ElementShape(value tf.Shape) TensorArrayGatherV3Attr {
return func(m optionalAttr) {
- m["channels"] = value
+ m["element_shape"] = value
}
}
-// DecodeJpegRatio sets the optional ratio attribute to value.
+// Gather specific elements from the TensorArray into output `value`.
//
-// value: Downscaling ratio.
-// If not specified, defaults to 1
-func DecodeJpegRatio(value int64) DecodeJpegAttr {
- return func(m optionalAttr) {
- m["ratio"] = value
- }
-}
-
-// DecodeJpegFancyUpscaling sets the optional fancy_upscaling attribute to value.
+// All elements selected by `indices` must have the same shape.
//
-// value: If true use a slower but nicer upscaling of the
-// chroma planes (yuv420/422 only).
-// If not specified, defaults to true
-func DecodeJpegFancyUpscaling(value bool) DecodeJpegAttr {
- return func(m optionalAttr) {
- m["fancy_upscaling"] = value
- }
-}
-
-// DecodeJpegTryRecoverTruncated sets the optional try_recover_truncated attribute to value.
+// Arguments:
+// handle: The handle to a TensorArray.
+// indices: The locations in the TensorArray from which to read tensor elements.
+// flow_in: A float scalar that enforces proper chaining of operations.
+// dtype: The type of the elem that is returned.
//
-// value: If true try to recover an image from truncated input.
-// If not specified, defaults to false
-func DecodeJpegTryRecoverTruncated(value bool) DecodeJpegAttr {
- return func(m optionalAttr) {
- m["try_recover_truncated"] = value
+// Returns All of the elements in the TensorArray, concatenated along a new
+// axis (the new dimension 0).
+func TensorArrayGatherV3(scope *Scope, handle tf.Output, indices tf.Output, flow_in tf.Output, dtype tf.DataType, optional ...TensorArrayGatherV3Attr) (value tf.Output) {
+ if scope.Err() != nil {
+ return
}
-}
-
-// DecodeJpegAcceptableFraction sets the optional acceptable_fraction attribute to value.
-//
-// value: The minimum required fraction of lines before a truncated
-// input is accepted.
-// If not specified, defaults to 1
-func DecodeJpegAcceptableFraction(value float32) DecodeJpegAttr {
- return func(m optionalAttr) {
- m["acceptable_fraction"] = value
+ attrs := map[string]interface{}{"dtype": dtype}
+ for _, a := range optional {
+ a(attrs)
}
-}
-
-// DecodeJpegDctMethod sets the optional dct_method attribute to value.
-//
-// value: string specifying a hint about the algorithm used for
-// decompression. Defaults to "" which maps to a system-specific
-// default. Currently valid values are ["INTEGER_FAST",
-// "INTEGER_ACCURATE"]. The hint may be ignored (e.g., the internal
-// jpeg library changes to a version that does not have that specific
-// option.)
-// If not specified, defaults to ""
-func DecodeJpegDctMethod(value string) DecodeJpegAttr {
- return func(m optionalAttr) {
- m["dct_method"] = value
+ opspec := tf.OpSpec{
+ Type: "TensorArrayGatherV3",
+ Input: []tf.Input{
+ handle, indices, flow_in,
+ },
+ Attrs: attrs,
}
+ op := scope.AddOperation(opspec)
+ return op.Output(0)
}
-// Decode a JPEG-encoded image to a uint8 tensor.
-//
-// The attr `channels` indicates the desired number of color channels for the
-// decoded image.
-//
-// Accepted values are:
-//
-// * 0: Use the number of channels in the JPEG-encoded image.
-// * 1: output a grayscale image.
-// * 3: output an RGB image.
-//
-// If needed, the JPEG-encoded image is transformed to match the requested number
-// of color channels.
-//
-// The attr `ratio` allows downscaling the image by an integer factor during
-// decoding. Allowed values are: 1, 2, 4, and 8. This is much faster than
-// downscaling the image later.
-//
-//
-// This op also supports decoding PNGs and non-animated GIFs since the interface is
-// the same, though it is cleaner to use `tf.image.decode_image`.
+// Returns x / y element-wise for integer types.
//
-// Arguments:
-// contents: 0-D. The JPEG-encoded image.
+// Truncation designates that negative numbers will round fractional quantities
+// toward zero. I.e. -7 / 5 = -1. This matches C semantics but it is different
+// than Python semantics. See `FloorDiv` for a division function that matches
+// Python Semantics.
//
-// Returns 3-D with shape `[height, width, channels]`..
-func DecodeJpeg(scope *Scope, contents tf.Output, optional ...DecodeJpegAttr) (image tf.Output) {
+// *NOTE*: `TruncateDiv` supports broadcasting. More about broadcasting
+// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
+func TruncateDiv(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{}
- for _, a := range optional {
- a(attrs)
- }
opspec := tf.OpSpec{
- Type: "DecodeJpeg",
+ Type: "TruncateDiv",
Input: []tf.Input{
- contents,
+ x, y,
},
- Attrs: attrs,
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// Transforms a vector of brain.Example protos (as strings) into typed tensors.
+// Restores tensors from a V2 checkpoint.
+//
+// For backward compatibility with the V1 format, this Op currently allows
+// restoring from a V1 checkpoint as well:
+// - This Op first attempts to find the V2 index file pointed to by "prefix", and
+// if found proceed to read it as a V2 checkpoint;
+// - Otherwise the V1 read path is invoked.
+// Relying on this behavior is not recommended, as the ability to fall back to read
+// V1 might be deprecated and eventually removed.
+//
+// By default, restores the named tensors in full. If the caller wishes to restore
+// specific slices of stored tensors, "shape_and_slices" should be non-empty
+// strings and correspondingly well-formed.
+//
+// Callers must ensure all the named tensors are indeed stored in the checkpoint.
//
// Arguments:
-// serialized: A vector containing a batch of binary serialized Example protos.
-// names: A vector containing the names of the serialized protos.
-// May contain, for example, table key (descriptive) names for the
-// corresponding serialized protos. These are purely useful for debugging
-// purposes, and the presence of values here has no effect on the output.
-// May also be an empty vector if no names are available.
-// If non-empty, this vector must be the same length as "serialized".
-// sparse_keys: A list of Nsparse string Tensors (scalars).
-// The keys expected in the Examples' features associated with sparse values.
-// dense_keys: A list of Ndense string Tensors (scalars).
-// The keys expected in the Examples' features associated with dense values.
-// dense_defaults: A list of Ndense Tensors (some may be empty).
-// dense_defaults[j] provides default values
-// when the example's feature_map lacks dense_key[j]. If an empty Tensor is
-// provided for dense_defaults[j], then the Feature dense_keys[j] is required.
-// The input type is inferred from dense_defaults[j], even when it's empty.
-// If dense_defaults[j] is not empty, and dense_shapes[j] is fully defined,
-// then the shape of dense_defaults[j] must match that of dense_shapes[j].
-// If dense_shapes[j] has an undefined major dimension (variable strides dense
-// feature), dense_defaults[j] must contain a single element:
-// the padding element.
-// sparse_types: A list of Nsparse types; the data types of data in each Feature
-// given in sparse_keys.
-// Currently the ParseExample supports DT_FLOAT (FloatList),
-// DT_INT64 (Int64List), and DT_STRING (BytesList).
-// dense_shapes: A list of Ndense shapes; the shapes of data in each Feature
-// given in dense_keys.
-// The number of elements in the Feature corresponding to dense_key[j]
-// must always equal dense_shapes[j].NumEntries().
-// If dense_shapes[j] == (D0, D1, ..., DN) then the shape of output
-// Tensor dense_values[j] will be (|serialized|, D0, D1, ..., DN):
-// The dense outputs are just the inputs row-stacked by batch.
-// This works for dense_shapes[j] = (-1, D1, ..., DN). In this case
-// the shape of the output Tensor dense_values[j] will be
-// (|serialized|, M, D1, .., DN), where M is the maximum number of blocks
-// of elements of length D1 * .... * DN, across all minibatch entries
-// in the input. Any minibatch entry with less than M blocks of elements of
-// length D1 * ... * DN will be padded with the corresponding default_value
-// scalar element along the second dimension.
-func ParseExample(scope *Scope, serialized tf.Output, names tf.Output, sparse_keys []tf.Output, dense_keys []tf.Output, dense_defaults []tf.Output, sparse_types []tf.DataType, dense_shapes []tf.Shape) (sparse_indices []tf.Output, sparse_values []tf.Output, sparse_shapes []tf.Output, dense_values []tf.Output) {
+// prefix: Must have a single element. The prefix of a V2 checkpoint.
+// tensor_names: shape {N}. The names of the tensors to be restored.
+// shape_and_slices: shape {N}. The slice specs of the tensors to be restored.
+// Empty strings indicate that they are non-partitioned tensors.
+// dtypes: shape {N}. The list of expected dtype for the tensors. Must match
+// those stored in the checkpoint.
+//
+// Returns shape {N}. The restored tensors, whose shapes are read from the
+// checkpoint directly.
+func RestoreV2(scope *Scope, prefix tf.Output, tensor_names tf.Output, shape_and_slices tf.Output, dtypes []tf.DataType) (tensors []tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{"sparse_types": sparse_types, "dense_shapes": dense_shapes}
+ attrs := map[string]interface{}{"dtypes": dtypes}
opspec := tf.OpSpec{
- Type: "ParseExample",
+ Type: "RestoreV2",
Input: []tf.Input{
- serialized, names, tf.OutputList(sparse_keys), tf.OutputList(dense_keys), tf.OutputList(dense_defaults),
+ prefix, tensor_names, shape_and_slices,
},
Attrs: attrs,
}
}
var idx int
var err error
- if sparse_indices, idx, err = makeOutputList(op, idx, "sparse_indices"); err != nil {
- scope.UpdateErr("ParseExample", err)
- return
- }
- if sparse_values, idx, err = makeOutputList(op, idx, "sparse_values"); err != nil {
- scope.UpdateErr("ParseExample", err)
- return
- }
- if sparse_shapes, idx, err = makeOutputList(op, idx, "sparse_shapes"); err != nil {
- scope.UpdateErr("ParseExample", err)
- return
- }
- if dense_values, idx, err = makeOutputList(op, idx, "dense_values"); err != nil {
- scope.UpdateErr("ParseExample", err)
+ if tensors, idx, err = makeOutputList(op, idx, "tensors"); err != nil {
+ scope.UpdateErr("RestoreV2", err)
return
}
- return sparse_indices, sparse_values, sparse_shapes, dense_values
-}
-
-// VariableShapeAttr is an optional argument to VariableShape.
-type VariableShapeAttr func(optionalAttr)
-
-// VariableShapeOutType sets the optional out_type attribute to value.
-// If not specified, defaults to DT_INT32
-func VariableShapeOutType(value tf.DataType) VariableShapeAttr {
- return func(m optionalAttr) {
- m["out_type"] = value
- }
+ return tensors
}
-// Returns the shape of the variable pointed to by `resource`.
+// Creates a dataset that skips `count` elements from the `input_dataset`.
//
-// This operation returns a 1-D integer tensor representing the shape of `input`.
+// Arguments:
//
-// For example:
+// count: A scalar representing the number of elements from the `input_dataset`
+// that should be skipped. If count is -1, skips everything.
//
-// ```
-// # 't' is [[[1, 1, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]]]
-// shape(t) ==> [2, 2, 3]
-// ```
-func VariableShape(scope *Scope, input tf.Output, optional ...VariableShapeAttr) (output tf.Output) {
+//
+func SkipDataset(scope *Scope, input_dataset tf.Output, count tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{}
- for _, a := range optional {
- a(attrs)
- }
+ attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes}
opspec := tf.OpSpec{
- Type: "VariableShape",
+ Type: "SkipDataset",
Input: []tf.Input{
- input,
+ input_dataset, count,
},
Attrs: attrs,
}
return op.Output(0)
}
-// Computes softmax cross entropy cost and gradients to backpropagate.
+// Computes the maximum along segments of a tensor.
//
-// Unlike `SoftmaxCrossEntropyWithLogits`, this operation does not accept
-// a matrix of label probabilities, but rather a single label per row
-// of features. This label is considered to have probability 1.0 for the
-// given row.
+// Read @{$math_ops#segmentation$the section on segmentation} for an explanation of
+// segments.
//
-// Inputs are the logits, not probabilities.
+// Computes a tensor such that
+// \\(output_i = \max_j(data_j)\\) where `max` is over `j` such
+// that `segment_ids[j] == i`.
+//
+// If the max is empty for a given segment ID `i`, `output[i] = 0`.
+//
+// <div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;">
+// <img style="width:100%" src="https://www.tensorflow.org/images/SegmentMax.png" alt>
+// </div>
//
// Arguments:
-// features: batch_size x num_classes matrix
-// labels: batch_size vector with values in [0, num_classes).
-// This is the label for the given minibatch entry.
//
-// Returns Per example loss (batch_size vector).backpropagated gradients (batch_size x num_classes matrix).
-func SparseSoftmaxCrossEntropyWithLogits(scope *Scope, features tf.Output, labels tf.Output) (loss tf.Output, backprop tf.Output) {
- if scope.Err() != nil {
+// segment_ids: A 1-D tensor whose rank is equal to the rank of `data`'s
+// first dimension. Values should be sorted and can be repeated.
+//
+// Returns Has same shape as data, except for dimension 0 which
+// has size `k`, the number of segments.
+func SegmentMax(scope *Scope, data tf.Output, segment_ids tf.Output) (output tf.Output) {
+ if scope.Err() != nil {
return
}
opspec := tf.OpSpec{
- Type: "SparseSoftmaxCrossEntropyWithLogits",
+ Type: "SegmentMax",
Input: []tf.Input{
- features, labels,
+ data, segment_ids,
},
}
op := scope.AddOperation(opspec)
- return op.Output(0), op.Output(1)
+ return op.Output(0)
}
-// Fast Fourier transform.
-//
-// Computes the 1-dimensional discrete Fourier transform over the inner-most
-// dimension of `input`.
-//
-// Arguments:
-// input: A complex64 tensor.
-//
-// Returns A complex64 tensor of the same shape as `input`. The inner-most
-// dimension of `input` is replaced with its 1D Fourier transform.
-//
-// @compatibility(numpy)
-// Equivalent to np.fft.fft
-// @end_compatibility
-func FFT(scope *Scope, input tf.Output) (output tf.Output) {
+// Computes hyperbolic tangent of `x` element-wise.
+func Tanh(scope *Scope, x tf.Output) (y tf.Output) {
if scope.Err() != nil {
return
}
opspec := tf.OpSpec{
- Type: "FFT",
+ Type: "Tanh",
Input: []tf.Input{
- input,
+ x,
},
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// Transforms a serialized tensorflow.TensorProto proto into a Tensor.
+// Decode web-safe base64-encoded strings.
+//
+// Input may or may not have padding at the end. See EncodeBase64 for padding.
+// Web-safe means that input must use - and _ instead of + and /.
//
// Arguments:
-// serialized: A scalar string containing a serialized TensorProto proto.
-// out_type: The type of the serialized tensor. The provided type must match the
-// type of the serialized tensor and no implicit conversion will take place.
+// input: Base64 strings to decode.
//
-// Returns A Tensor of type `out_type`.
-func ParseTensor(scope *Scope, serialized tf.Output, out_type tf.DataType) (output tf.Output) {
+// Returns Decoded strings.
+func DecodeBase64(scope *Scope, input tf.Output) (output tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{"out_type": out_type}
opspec := tf.OpSpec{
- Type: "ParseTensor",
+ Type: "DecodeBase64",
Input: []tf.Input{
- serialized,
+ input,
},
- Attrs: attrs,
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// MaxPoolWithArgmaxAttr is an optional argument to MaxPoolWithArgmax.
-type MaxPoolWithArgmaxAttr func(optionalAttr)
-
-// MaxPoolWithArgmaxTargmax sets the optional Targmax attribute to value.
-// If not specified, defaults to DT_INT64
-func MaxPoolWithArgmaxTargmax(value tf.DataType) MaxPoolWithArgmaxAttr {
- return func(m optionalAttr) {
- m["Targmax"] = value
- }
-}
-
-// Performs max pooling on the input and outputs both max values and indices.
-//
-// The indices in `argmax` are flattened, so that a maximum value at position
-// `[b, y, x, c]` becomes flattened index
-// `((b * height + y) * width + x) * channels + c`.
-//
-// The indices returned are always in `[0, height) x [0, width)` before flattening,
-// even if padding is involved and the mathematically correct answer is outside
-// (either negative or too large). This is a bug, but fixing it is difficult to do
-// in a safe backwards compatible way, especially due to flattening.
+// Store the input tensor in the state of the current session.
//
// Arguments:
-// input: 4-D with shape `[batch, height, width, channels]`. Input to pool over.
-// ksize: The size of the window for each dimension of the input tensor.
-// strides: The stride of the sliding window for each dimension of the
-// input tensor.
-// padding: The type of padding algorithm to use.
+// value: The tensor to be stored.
//
-// Returns The max pooled output tensor.4-D. The flattened indices of the max values chosen for each output.
-func MaxPoolWithArgmax(scope *Scope, input tf.Output, ksize []int64, strides []int64, padding string, optional ...MaxPoolWithArgmaxAttr) (output tf.Output, argmax tf.Output) {
+// Returns The handle for the tensor stored in the session state, represented
+// as a string.
+func GetSessionHandle(scope *Scope, value tf.Output) (handle tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{"ksize": ksize, "strides": strides, "padding": padding}
- for _, a := range optional {
- a(attrs)
- }
opspec := tf.OpSpec{
- Type: "MaxPoolWithArgmax",
+ Type: "GetSessionHandle",
Input: []tf.Input{
- input,
+ value,
},
- Attrs: attrs,
}
op := scope.AddOperation(opspec)
- return op.Output(0), op.Output(1)
+ return op.Output(0)
}
-// ResourceSparseApplyAdagradDAAttr is an optional argument to ResourceSparseApplyAdagradDA.
-type ResourceSparseApplyAdagradDAAttr func(optionalAttr)
+// ResourceSparseApplyProximalAdagradAttr is an optional argument to ResourceSparseApplyProximalAdagrad.
+type ResourceSparseApplyProximalAdagradAttr func(optionalAttr)
-// ResourceSparseApplyAdagradDAUseLocking sets the optional use_locking attribute to value.
+// ResourceSparseApplyProximalAdagradUseLocking sets the optional use_locking attribute to value.
//
// value: If True, updating of the var and accum tensors will be protected by
// a lock; otherwise the behavior is undefined, but may exhibit less contention.
// If not specified, defaults to false
-func ResourceSparseApplyAdagradDAUseLocking(value bool) ResourceSparseApplyAdagradDAAttr {
+func ResourceSparseApplyProximalAdagradUseLocking(value bool) ResourceSparseApplyProximalAdagradAttr {
return func(m optionalAttr) {
m["use_locking"] = value
}
}
-// Update entries in '*var' and '*accum' according to the proximal adagrad scheme.
+// Sparse update entries in '*var' and '*accum' according to FOBOS algorithm.
+//
+// That is for rows we have grad for, we update var and accum as follows:
+// accum += grad * grad
+// prox_v = var
+// prox_v -= lr * grad * (1 / sqrt(accum))
+// var = sign(prox_v)/(1+lr*l2) * max{|prox_v|-lr*l1,0}
//
// Arguments:
// var_: Should be from a Variable().
-// gradient_accumulator: Should be from a Variable().
-// gradient_squared_accumulator: Should be from a Variable().
-// grad: The gradient.
-// indices: A vector of indices into the first dimension of var and accum.
+// accum: Should be from a Variable().
// lr: Learning rate. Must be a scalar.
// l1: L1 regularization. Must be a scalar.
// l2: L2 regularization. Must be a scalar.
-// global_step: Training step number. Must be a scalar.
+// grad: The gradient.
+// indices: A vector of indices into the first dimension of var and accum.
//
// Returns the created operation.
-func ResourceSparseApplyAdagradDA(scope *Scope, var_ tf.Output, gradient_accumulator tf.Output, gradient_squared_accumulator tf.Output, grad tf.Output, indices tf.Output, lr tf.Output, l1 tf.Output, l2 tf.Output, global_step tf.Output, optional ...ResourceSparseApplyAdagradDAAttr) (o *tf.Operation) {
+func ResourceSparseApplyProximalAdagrad(scope *Scope, var_ tf.Output, accum tf.Output, lr tf.Output, l1 tf.Output, l2 tf.Output, grad tf.Output, indices tf.Output, optional ...ResourceSparseApplyProximalAdagradAttr) (o *tf.Operation) {
if scope.Err() != nil {
return
}
a(attrs)
}
opspec := tf.OpSpec{
- Type: "ResourceSparseApplyAdagradDA",
+ Type: "ResourceSparseApplyProximalAdagrad",
Input: []tf.Input{
- var_, gradient_accumulator, gradient_squared_accumulator, grad, indices, lr, l1, l2, global_step,
+ var_, accum, lr, l1, l2, grad, indices,
},
Attrs: attrs,
}
return scope.AddOperation(opspec)
}
-// EncodeJpegAttr is an optional argument to EncodeJpeg.
-type EncodeJpegAttr func(optionalAttr)
-
-// EncodeJpegFormat sets the optional format attribute to value.
-//
-// value: Per pixel image format.
-// If not specified, defaults to ""
-func EncodeJpegFormat(value string) EncodeJpegAttr {
- return func(m optionalAttr) {
- m["format"] = value
+// Returns element-wise largest integer not greater than x.
+func Floor(scope *Scope, x tf.Output) (y tf.Output) {
+ if scope.Err() != nil {
+ return
}
-}
-
-// EncodeJpegQuality sets the optional quality attribute to value.
-//
-// value: Quality of the compression from 0 to 100 (higher is better and slower).
-// If not specified, defaults to 95
-func EncodeJpegQuality(value int64) EncodeJpegAttr {
- return func(m optionalAttr) {
- m["quality"] = value
+ opspec := tf.OpSpec{
+ Type: "Floor",
+ Input: []tf.Input{
+ x,
+ },
}
+ op := scope.AddOperation(opspec)
+ return op.Output(0)
}
-// EncodeJpegProgressive sets the optional progressive attribute to value.
-//
-// value: If True, create a JPEG that loads progressively (coarse to fine).
-// If not specified, defaults to false
-func EncodeJpegProgressive(value bool) EncodeJpegAttr {
- return func(m optionalAttr) {
- m["progressive"] = value
+// Computes the Gauss error function of `x` element-wise.
+func Erf(scope *Scope, x tf.Output) (y tf.Output) {
+ if scope.Err() != nil {
+ return
}
-}
-
-// EncodeJpegOptimizeSize sets the optional optimize_size attribute to value.
-//
-// value: If True, spend CPU/RAM to reduce size with no quality change.
-// If not specified, defaults to false
-func EncodeJpegOptimizeSize(value bool) EncodeJpegAttr {
- return func(m optionalAttr) {
- m["optimize_size"] = value
+ opspec := tf.OpSpec{
+ Type: "Erf",
+ Input: []tf.Input{
+ x,
+ },
}
+ op := scope.AddOperation(opspec)
+ return op.Output(0)
}
-// EncodeJpegChromaDownsampling sets the optional chroma_downsampling attribute to value.
-//
-// value: See http://en.wikipedia.org/wiki/Chroma_subsampling.
-// If not specified, defaults to true
-func EncodeJpegChromaDownsampling(value bool) EncodeJpegAttr {
- return func(m optionalAttr) {
- m["chroma_downsampling"] = value
- }
-}
+// OneHotAttr is an optional argument to OneHot.
+type OneHotAttr func(optionalAttr)
-// EncodeJpegDensityUnit sets the optional density_unit attribute to value.
+// OneHotAxis sets the optional axis attribute to value.
//
-// value: Unit used to specify `x_density` and `y_density`:
-// pixels per inch (`'in'`) or centimeter (`'cm'`).
-// If not specified, defaults to "in"
-func EncodeJpegDensityUnit(value string) EncodeJpegAttr {
+// value: The axis to fill (default: -1, a new inner-most axis).
+// If not specified, defaults to -1
+func OneHotAxis(value int64) OneHotAttr {
return func(m optionalAttr) {
- m["density_unit"] = value
+ m["axis"] = value
}
}
-// EncodeJpegXDensity sets the optional x_density attribute to value.
+// Returns a one-hot tensor.
//
-// value: Horizontal pixels per density unit.
-// If not specified, defaults to 300
-func EncodeJpegXDensity(value int64) EncodeJpegAttr {
- return func(m optionalAttr) {
- m["x_density"] = value
- }
-}
-
-// EncodeJpegYDensity sets the optional y_density attribute to value.
+// The locations represented by indices in `indices` take value `on_value`,
+// while all other locations take value `off_value`.
//
-// value: Vertical pixels per density unit.
-// If not specified, defaults to 300
-func EncodeJpegYDensity(value int64) EncodeJpegAttr {
- return func(m optionalAttr) {
- m["y_density"] = value
- }
-}
-
-// EncodeJpegXmpMetadata sets the optional xmp_metadata attribute to value.
+// If the input `indices` is rank `N`, the output will have rank `N+1`,
+// The new axis is created at dimension `axis` (default: the new axis is
+// appended at the end).
//
-// value: If not empty, embed this XMP metadata in the image header.
-// If not specified, defaults to ""
-func EncodeJpegXmpMetadata(value string) EncodeJpegAttr {
- return func(m optionalAttr) {
- m["xmp_metadata"] = value
- }
-}
-
-// JPEG-encode an image.
+// If `indices` is a scalar the output shape will be a vector of length `depth`.
//
-// `image` is a 3-D uint8 Tensor of shape `[height, width, channels]`.
+// If `indices` is a vector of length `features`, the output shape will be:
+// ```
+// features x depth if axis == -1
+// depth x features if axis == 0
+// ```
//
-// The attr `format` can be used to override the color format of the encoded
-// output. Values can be:
+// If `indices` is a matrix (batch) with shape `[batch, features]`,
+// the output shape will be:
+// ```
+// batch x features x depth if axis == -1
+// batch x depth x features if axis == 1
+// depth x batch x features if axis == 0
+// ```
//
-// * `''`: Use a default format based on the number of channels in the image.
-// * `grayscale`: Output a grayscale JPEG image. The `channels` dimension
-// of `image` must be 1.
-// * `rgb`: Output an RGB JPEG image. The `channels` dimension
-// of `image` must be 3.
//
-// If `format` is not specified or is the empty string, a default format is picked
-// in function of the number of channels in `image`:
+// Examples
+// =========
//
-// * 1: Output a grayscale image.
-// * 3: Output an RGB image.
+// Suppose that
+//
+// ```
+// indices = [0, 2, -1, 1]
+// depth = 3
+// on_value = 5.0
+// off_value = 0.0
+// axis = -1
+// ```
+//
+// Then output is `[4 x 3]`:
+//
+// ```output =
+// [5.0 0.0 0.0] // one_hot(0)
+// [0.0 0.0 5.0] // one_hot(2)
+// [0.0 0.0 0.0] // one_hot(-1)
+// [0.0 5.0 0.0] // one_hot(1)
+// ```
+//
+// Suppose that
+//
+// ```
+// indices = [0, 2, -1, 1]
+// depth = 3
+// on_value = 0.0
+// off_value = 3.0
+// axis = 0
+// ```
+//
+// Then output is `[3 x 4]`:
+//
+// ```output =
+// [0.0 3.0 3.0 3.0]
+// [3.0 3.0 3.0 0.0]
+// [3.0 3.0 3.0 3.0]
+// [3.0 0.0 3.0 3.0]
+// // ^ one_hot(0)
+// // ^ one_hot(2)
+// // ^ one_hot(-1)
+// // ^ one_hot(1)
+// ```
+// Suppose that
+//
+// ```
+// indices = [[0, 2], [1, -1]]
+// depth = 3
+// on_value = 1.0
+// off_value = 0.0
+// axis = -1
+// ```
+//
+// Then output is `[2 x 2 x 3]`:
+//
+// ```output =
+// [
+// [1.0, 0.0, 0.0] // one_hot(0)
+// [0.0, 0.0, 1.0] // one_hot(2)
+// ][
+// [0.0, 1.0, 0.0] // one_hot(1)
+// [0.0, 0.0, 0.0] // one_hot(-1)
+// ]```
//
// Arguments:
-// image: 3-D with shape `[height, width, channels]`.
+// indices: A tensor of indices.
+// depth: A scalar defining the depth of the one hot dimension.
+// on_value: A scalar defining the value to fill in output when `indices[j] = i`.
+// off_value: A scalar defining the value to fill in output when `indices[j] != i`.
//
-// Returns 0-D. JPEG-encoded image.
-func EncodeJpeg(scope *Scope, image tf.Output, optional ...EncodeJpegAttr) (contents tf.Output) {
+// Returns The one-hot tensor.
+func OneHot(scope *Scope, indices tf.Output, depth tf.Output, on_value tf.Output, off_value tf.Output, optional ...OneHotAttr) (output tf.Output) {
if scope.Err() != nil {
return
}
a(attrs)
}
opspec := tf.OpSpec{
- Type: "EncodeJpeg",
+ Type: "OneHot",
Input: []tf.Input{
- image,
+ indices, depth, on_value, off_value,
},
Attrs: attrs,
}
return op.Output(0)
}
-// MultinomialAttr is an optional argument to Multinomial.
-type MultinomialAttr func(optionalAttr)
+// Reads the value of a variable.
+//
+// The tensor returned by this operation is immutable.
+//
+// The value returned by this operation is guaranteed to be influenced by all the
+// writes on which this operation depends directly or indirectly, and to not be
+// influenced by any of the writes which depend directly or indirectly on this
+// operation.
+//
+// Arguments:
+// resource: handle to the resource in which to store the variable.
+// dtype: the dtype of the value.
+func ReadVariableOp(scope *Scope, resource tf.Output, dtype tf.DataType) (value tf.Output) {
+ if scope.Err() != nil {
+ return
+ }
+ attrs := map[string]interface{}{"dtype": dtype}
+ opspec := tf.OpSpec{
+ Type: "ReadVariableOp",
+ Input: []tf.Input{
+ resource,
+ },
+ Attrs: attrs,
+ }
+ op := scope.AddOperation(opspec)
+ return op.Output(0)
+}
-// MultinomialSeed sets the optional seed attribute to value.
+// MaxPool3DGradAttr is an optional argument to MaxPool3DGrad.
+type MaxPool3DGradAttr func(optionalAttr)
+
+// MaxPool3DGradDataFormat sets the optional data_format attribute to value.
//
-// value: If either seed or seed2 is set to be non-zero, the internal random number
-// generator is seeded by the given seed. Otherwise, a random seed is used.
-// If not specified, defaults to 0
-func MultinomialSeed(value int64) MultinomialAttr {
+// value: The data format of the input and output data. With the
+// default format "NDHWC", the data is stored in the order of:
+// [batch, in_depth, in_height, in_width, in_channels].
+// Alternatively, the format could be "NCDHW", the data storage order is:
+// [batch, in_channels, in_depth, in_height, in_width].
+// If not specified, defaults to "NDHWC"
+func MaxPool3DGradDataFormat(value string) MaxPool3DGradAttr {
return func(m optionalAttr) {
- m["seed"] = value
+ m["data_format"] = value
}
}
-// MultinomialSeed2 sets the optional seed2 attribute to value.
+// Computes gradients of max pooling function.
//
-// value: A second seed to avoid seed collision.
-// If not specified, defaults to 0
-func MultinomialSeed2(value int64) MultinomialAttr {
- return func(m optionalAttr) {
- m["seed2"] = value
+// Arguments:
+// orig_input: The original input tensor.
+// orig_output: The original output tensor.
+// grad: Output backprop of shape `[batch, depth, rows, cols, channels]`.
+// ksize: 1-D tensor of length 5. The size of the window for each dimension of
+// the input tensor. Must have `ksize[0] = ksize[4] = 1`.
+// strides: 1-D tensor of length 5. The stride of the sliding window for each
+// dimension of `input`. Must have `strides[0] = strides[4] = 1`.
+// padding: The type of padding algorithm to use.
+func MaxPool3DGrad(scope *Scope, orig_input tf.Output, orig_output tf.Output, grad tf.Output, ksize []int64, strides []int64, padding string, optional ...MaxPool3DGradAttr) (output tf.Output) {
+ if scope.Err() != nil {
+ return
+ }
+ attrs := map[string]interface{}{"ksize": ksize, "strides": strides, "padding": padding}
+ for _, a := range optional {
+ a(attrs)
+ }
+ opspec := tf.OpSpec{
+ Type: "MaxPool3DGrad",
+ Input: []tf.Input{
+ orig_input, orig_output, grad,
+ },
+ Attrs: attrs,
}
+ op := scope.AddOperation(opspec)
+ return op.Output(0)
}
-// MultinomialOutputDtype sets the optional output_dtype attribute to value.
-// If not specified, defaults to DT_INT64
-func MultinomialOutputDtype(value tf.DataType) MultinomialAttr {
+// SparseReduceSumAttr is an optional argument to SparseReduceSum.
+type SparseReduceSumAttr func(optionalAttr)
+
+// SparseReduceSumKeepDims sets the optional keep_dims attribute to value.
+//
+// value: If true, retain reduced dimensions with length 1.
+// If not specified, defaults to false
+func SparseReduceSumKeepDims(value bool) SparseReduceSumAttr {
return func(m optionalAttr) {
- m["output_dtype"] = value
+ m["keep_dims"] = value
}
}
-// Draws samples from a multinomial distribution.
+// Computes the sum of elements across dimensions of a SparseTensor.
+//
+// This Op takes a SparseTensor and is the sparse counterpart to
+// `tf.reduce_sum()`. In particular, this Op also returns a dense `Tensor`
+// instead of a sparse one.
+//
+// Reduces `sp_input` along the dimensions given in `reduction_axes`. Unless
+// `keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in
+// `reduction_axes`. If `keep_dims` is true, the reduced dimensions are retained
+// with length 1.
+//
+// If `reduction_axes` has no entries, all dimensions are reduced, and a tensor
+// with a single element is returned. Additionally, the axes can be negative,
+// which are interpreted according to the indexing rules in Python.
//
// Arguments:
-// logits: 2-D Tensor with shape `[batch_size, num_classes]`. Each slice `[i, :]`
-// represents the unnormalized log probabilities for all classes.
-// num_samples: 0-D. Number of independent samples to draw for each row slice.
+// input_indices: 2-D. `N x R` matrix with the indices of non-empty values in a
+// SparseTensor, possibly not in canonical ordering.
+// input_values: 1-D. `N` non-empty values corresponding to `input_indices`.
+// input_shape: 1-D. Shape of the input SparseTensor.
+// reduction_axes: 1-D. Length-`K` vector containing the reduction axes.
//
-// Returns 2-D Tensor with shape `[batch_size, num_samples]`. Each slice `[i, :]`
-// contains the drawn class labels with range `[0, num_classes)`.
-func Multinomial(scope *Scope, logits tf.Output, num_samples tf.Output, optional ...MultinomialAttr) (output tf.Output) {
+// Returns `R-K`-D. The reduced Tensor.
+func SparseReduceSum(scope *Scope, input_indices tf.Output, input_values tf.Output, input_shape tf.Output, reduction_axes tf.Output, optional ...SparseReduceSumAttr) (output tf.Output) {
if scope.Err() != nil {
return
}
a(attrs)
}
opspec := tf.OpSpec{
- Type: "Multinomial",
+ Type: "SparseReduceSum",
Input: []tf.Input{
- logits, num_samples,
+ input_indices, input_values, input_shape, reduction_axes,
},
Attrs: attrs,
}
return op.Output(0)
}
-// Returns the truth value of NOT x element-wise.
-func LogicalNot(scope *Scope, x tf.Output) (y tf.Output) {
+// Returns element-wise remainder of division. This emulates C semantics in that
+//
+// the result here is consistent with a truncating divide. E.g. `truncate(x / y) *
+// y + truncate_mod(x, y) = x`.
+//
+// *NOTE*: `TruncateMod` supports broadcasting. More about broadcasting
+// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
+func TruncateMod(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) {
if scope.Err() != nil {
return
}
opspec := tf.OpSpec{
- Type: "LogicalNot",
+ Type: "TruncateMod",
Input: []tf.Input{
- x,
+ x, y,
},
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// 3D real-valued fast Fourier transform.
+// Inverse 2D real-valued fast Fourier transform.
//
-// Computes the 3-dimensional discrete Fourier transform of a real-valued signal
-// over the inner-most 3 dimensions of `input`.
+// Computes the inverse 2-dimensional discrete Fourier transform of a real-valued
+// signal over the inner-most 2 dimensions of `input`.
//
-// Since the DFT of a real signal is Hermitian-symmetric, `RFFT3D` only returns the
-// `fft_length / 2 + 1` unique components of the FFT for the inner-most dimension
-// of `output`: the zero-frequency term, followed by the `fft_length / 2`
-// positive-frequency terms.
+// The inner-most 2 dimensions of `input` are assumed to be the result of `RFFT2D`:
+// The inner-most dimension contains the `fft_length / 2 + 1` unique components of
+// the DFT of a real-valued signal. If `fft_length` is not provided, it is computed
+// from the size of the inner-most 2 dimensions of `input`. If the FFT length used
+// to compute `input` is odd, it should be provided since it cannot be inferred
+// properly.
//
-// Along each axis `RFFT3D` is computed on, if `fft_length` is smaller than the
+// Along each axis `IRFFT2D` is computed on, if `fft_length` (or
+// `fft_length / 2 + 1` for the inner-most dimension) is smaller than the
// corresponding dimension of `input`, the dimension is cropped. If it is larger,
// the dimension is padded with zeros.
//
// Arguments:
-// input: A float32 tensor.
-// fft_length: An int32 tensor of shape [3]. The FFT length for each dimension.
+// input: A complex64 tensor.
+// fft_length: An int32 tensor of shape [2]. The FFT length for each dimension.
//
-// Returns A complex64 tensor of the same rank as `input`. The inner-most 3
-// dimensions of `input` are replaced with the their 3D Fourier transform. The
-// inner-most dimension contains `fft_length / 2 + 1` unique frequency
-// components.
+// Returns A float32 tensor of the same rank as `input`. The inner-most 2
+// dimensions of `input` are replaced with the `fft_length` samples of their
+// inverse 2D Fourier transform.
//
// @compatibility(numpy)
-// Equivalent to np.fft.rfftn with 3 dimensions.
+// Equivalent to np.fft.irfft2
// @end_compatibility
-func RFFT3D(scope *Scope, input tf.Output, fft_length tf.Output) (output tf.Output) {
+func IRFFT2D(scope *Scope, input tf.Output, fft_length tf.Output) (output tf.Output) {
if scope.Err() != nil {
return
}
opspec := tf.OpSpec{
- Type: "RFFT3D",
+ Type: "IRFFT2D",
Input: []tf.Input{
input, fft_length,
},
return op.Output(0)
}
-// TensorArrayV3Attr is an optional argument to TensorArrayV3.
-type TensorArrayV3Attr func(optionalAttr)
+// DecodeJpegAttr is an optional argument to DecodeJpeg.
+type DecodeJpegAttr func(optionalAttr)
-// TensorArrayV3ElementShape sets the optional element_shape attribute to value.
+// DecodeJpegChannels sets the optional channels attribute to value.
//
-// value: The expected shape of an element, if known. Used to
-// validate the shapes of TensorArray elements. If this shape is not
-// fully specified, gathering zero-size TensorArrays is an error.
-// If not specified, defaults to <unknown_rank:true >
-func TensorArrayV3ElementShape(value tf.Shape) TensorArrayV3Attr {
+// value: Number of color channels for the decoded image.
+// If not specified, defaults to 0
+func DecodeJpegChannels(value int64) DecodeJpegAttr {
return func(m optionalAttr) {
- m["element_shape"] = value
+ m["channels"] = value
}
}
-// TensorArrayV3DynamicSize sets the optional dynamic_size attribute to value.
+// DecodeJpegRatio sets the optional ratio attribute to value.
//
-// value: A boolean that determines whether writes to the TensorArray
-// are allowed to grow the size. By default, this is not allowed.
-// If not specified, defaults to false
-func TensorArrayV3DynamicSize(value bool) TensorArrayV3Attr {
+// value: Downscaling ratio.
+// If not specified, defaults to 1
+func DecodeJpegRatio(value int64) DecodeJpegAttr {
return func(m optionalAttr) {
- m["dynamic_size"] = value
+ m["ratio"] = value
}
}
-// TensorArrayV3ClearAfterRead sets the optional clear_after_read attribute to value.
+// DecodeJpegFancyUpscaling sets the optional fancy_upscaling attribute to value.
//
-// value: If true (default), Tensors in the TensorArray are cleared
-// after being read. This disables multiple read semantics but allows early
-// release of memory.
+// value: If true use a slower but nicer upscaling of the
+// chroma planes (yuv420/422 only).
// If not specified, defaults to true
-func TensorArrayV3ClearAfterRead(value bool) TensorArrayV3Attr {
+func DecodeJpegFancyUpscaling(value bool) DecodeJpegAttr {
return func(m optionalAttr) {
- m["clear_after_read"] = value
+ m["fancy_upscaling"] = value
}
}
-// TensorArrayV3IdenticalElementShapes sets the optional identical_element_shapes attribute to value.
+// DecodeJpegTryRecoverTruncated sets the optional try_recover_truncated attribute to value.
//
-// value: If true (default is false), then all
-// elements in the TensorArray will be expected to have have identical shapes.
-// This allows certain behaviors, like dynamically checking for
-// consistent shapes on write, and being able to fill in properly
-// shaped zero tensors on stack -- even if the element_shape attribute
-// is not fully defined.
+// value: If true try to recover an image from truncated input.
// If not specified, defaults to false
-func TensorArrayV3IdenticalElementShapes(value bool) TensorArrayV3Attr {
+func DecodeJpegTryRecoverTruncated(value bool) DecodeJpegAttr {
return func(m optionalAttr) {
- m["identical_element_shapes"] = value
+ m["try_recover_truncated"] = value
}
}
-// TensorArrayV3TensorArrayName sets the optional tensor_array_name attribute to value.
+// DecodeJpegAcceptableFraction sets the optional acceptable_fraction attribute to value.
//
-// value: Overrides the name used for the temporary tensor_array
-// resource. Default value is the name of the 'TensorArray' op (which
-// is guaranteed unique).
+// value: The minimum required fraction of lines before a truncated
+// input is accepted.
+// If not specified, defaults to 1
+func DecodeJpegAcceptableFraction(value float32) DecodeJpegAttr {
+ return func(m optionalAttr) {
+ m["acceptable_fraction"] = value
+ }
+}
+
+// DecodeJpegDctMethod sets the optional dct_method attribute to value.
+//
+// value: string specifying a hint about the algorithm used for
+// decompression. Defaults to "" which maps to a system-specific
+// default. Currently valid values are ["INTEGER_FAST",
+// "INTEGER_ACCURATE"]. The hint may be ignored (e.g., the internal
+// jpeg library changes to a version that does not have that specific
+// option.)
// If not specified, defaults to ""
-func TensorArrayV3TensorArrayName(value string) TensorArrayV3Attr {
+func DecodeJpegDctMethod(value string) DecodeJpegAttr {
return func(m optionalAttr) {
- m["tensor_array_name"] = value
+ m["dct_method"] = value
}
}
-// An array of Tensors of given size.
+// Decode a JPEG-encoded image to a uint8 tensor.
//
-// Write data via Write and read via Read or Pack.
+// The attr `channels` indicates the desired number of color channels for the
+// decoded image.
+//
+// Accepted values are:
+//
+// * 0: Use the number of channels in the JPEG-encoded image.
+// * 1: output a grayscale image.
+// * 3: output an RGB image.
+//
+// If needed, the JPEG-encoded image is transformed to match the requested number
+// of color channels.
+//
+// The attr `ratio` allows downscaling the image by an integer factor during
+// decoding. Allowed values are: 1, 2, 4, and 8. This is much faster than
+// downscaling the image later.
+//
+//
+// This op also supports decoding PNGs and non-animated GIFs since the interface is
+// the same, though it is cleaner to use `tf.image.decode_image`.
//
// Arguments:
-// size: The size of the array.
-// dtype: The type of the elements on the tensor_array.
+// contents: 0-D. The JPEG-encoded image.
//
-// Returns The handle to the TensorArray.A scalar used to control gradient flow.
-func TensorArrayV3(scope *Scope, size tf.Output, dtype tf.DataType, optional ...TensorArrayV3Attr) (handle tf.Output, flow tf.Output) {
+// Returns 3-D with shape `[height, width, channels]`..
+func DecodeJpeg(scope *Scope, contents tf.Output, optional ...DecodeJpegAttr) (image tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{"dtype": dtype}
+ attrs := map[string]interface{}{}
for _, a := range optional {
a(attrs)
}
opspec := tf.OpSpec{
- Type: "TensorArrayV3",
+ Type: "DecodeJpeg",
Input: []tf.Input{
- size,
+ contents,
},
Attrs: attrs,
}
op := scope.AddOperation(opspec)
- return op.Output(0), op.Output(1)
+ return op.Output(0)
}
-// MaxPool3DAttr is an optional argument to MaxPool3D.
-type MaxPool3DAttr func(optionalAttr)
-
-// MaxPool3DDataFormat sets the optional data_format attribute to value.
+// Transforms a vector of brain.Example protos (as strings) into typed tensors.
//
-// value: The data format of the input and output data. With the
-// default format "NDHWC", the data is stored in the order of:
-// [batch, in_depth, in_height, in_width, in_channels].
-// Alternatively, the format could be "NCDHW", the data storage order is:
-// [batch, in_channels, in_depth, in_height, in_width].
-// If not specified, defaults to "NDHWC"
-func MaxPool3DDataFormat(value string) MaxPool3DAttr {
+// Arguments:
+// serialized: A vector containing a batch of binary serialized Example protos.
+// names: A vector containing the names of the serialized protos.
+// May contain, for example, table key (descriptive) names for the
+// corresponding serialized protos. These are purely useful for debugging
+// purposes, and the presence of values here has no effect on the output.
+// May also be an empty vector if no names are available.
+// If non-empty, this vector must be the same length as "serialized".
+// sparse_keys: A list of Nsparse string Tensors (scalars).
+// The keys expected in the Examples' features associated with sparse values.
+// dense_keys: A list of Ndense string Tensors (scalars).
+// The keys expected in the Examples' features associated with dense values.
+// dense_defaults: A list of Ndense Tensors (some may be empty).
+// dense_defaults[j] provides default values
+// when the example's feature_map lacks dense_key[j]. If an empty Tensor is
+// provided for dense_defaults[j], then the Feature dense_keys[j] is required.
+// The input type is inferred from dense_defaults[j], even when it's empty.
+// If dense_defaults[j] is not empty, and dense_shapes[j] is fully defined,
+// then the shape of dense_defaults[j] must match that of dense_shapes[j].
+// If dense_shapes[j] has an undefined major dimension (variable strides dense
+// feature), dense_defaults[j] must contain a single element:
+// the padding element.
+// sparse_types: A list of Nsparse types; the data types of data in each Feature
+// given in sparse_keys.
+// Currently the ParseExample supports DT_FLOAT (FloatList),
+// DT_INT64 (Int64List), and DT_STRING (BytesList).
+// dense_shapes: A list of Ndense shapes; the shapes of data in each Feature
+// given in dense_keys.
+// The number of elements in the Feature corresponding to dense_key[j]
+// must always equal dense_shapes[j].NumEntries().
+// If dense_shapes[j] == (D0, D1, ..., DN) then the shape of output
+// Tensor dense_values[j] will be (|serialized|, D0, D1, ..., DN):
+// The dense outputs are just the inputs row-stacked by batch.
+// This works for dense_shapes[j] = (-1, D1, ..., DN). In this case
+// the shape of the output Tensor dense_values[j] will be
+// (|serialized|, M, D1, .., DN), where M is the maximum number of blocks
+// of elements of length D1 * .... * DN, across all minibatch entries
+// in the input. Any minibatch entry with less than M blocks of elements of
+// length D1 * ... * DN will be padded with the corresponding default_value
+// scalar element along the second dimension.
+func ParseExample(scope *Scope, serialized tf.Output, names tf.Output, sparse_keys []tf.Output, dense_keys []tf.Output, dense_defaults []tf.Output, sparse_types []tf.DataType, dense_shapes []tf.Shape) (sparse_indices []tf.Output, sparse_values []tf.Output, sparse_shapes []tf.Output, dense_values []tf.Output) {
+ if scope.Err() != nil {
+ return
+ }
+ attrs := map[string]interface{}{"sparse_types": sparse_types, "dense_shapes": dense_shapes}
+ opspec := tf.OpSpec{
+ Type: "ParseExample",
+ Input: []tf.Input{
+ serialized, names, tf.OutputList(sparse_keys), tf.OutputList(dense_keys), tf.OutputList(dense_defaults),
+ },
+ Attrs: attrs,
+ }
+ op := scope.AddOperation(opspec)
+ if scope.Err() != nil {
+ return
+ }
+ var idx int
+ var err error
+ if sparse_indices, idx, err = makeOutputList(op, idx, "sparse_indices"); err != nil {
+ scope.UpdateErr("ParseExample", err)
+ return
+ }
+ if sparse_values, idx, err = makeOutputList(op, idx, "sparse_values"); err != nil {
+ scope.UpdateErr("ParseExample", err)
+ return
+ }
+ if sparse_shapes, idx, err = makeOutputList(op, idx, "sparse_shapes"); err != nil {
+ scope.UpdateErr("ParseExample", err)
+ return
+ }
+ if dense_values, idx, err = makeOutputList(op, idx, "dense_values"); err != nil {
+ scope.UpdateErr("ParseExample", err)
+ return
+ }
+ return sparse_indices, sparse_values, sparse_shapes, dense_values
+}
+
+// VariableShapeAttr is an optional argument to VariableShape.
+type VariableShapeAttr func(optionalAttr)
+
+// VariableShapeOutType sets the optional out_type attribute to value.
+// If not specified, defaults to DT_INT32
+func VariableShapeOutType(value tf.DataType) VariableShapeAttr {
return func(m optionalAttr) {
- m["data_format"] = value
+ m["out_type"] = value
}
}
-// Performs 3D max pooling on the input.
+// Returns the shape of the variable pointed to by `resource`.
//
-// Arguments:
-// input: Shape `[batch, depth, rows, cols, channels]` tensor to pool over.
-// ksize: 1-D tensor of length 5. The size of the window for each dimension of
-// the input tensor. Must have `ksize[0] = ksize[4] = 1`.
-// strides: 1-D tensor of length 5. The stride of the sliding window for each
-// dimension of `input`. Must have `strides[0] = strides[4] = 1`.
-// padding: The type of padding algorithm to use.
+// This operation returns a 1-D integer tensor representing the shape of `input`.
//
-// Returns The max pooled output tensor.
-func MaxPool3D(scope *Scope, input tf.Output, ksize []int64, strides []int64, padding string, optional ...MaxPool3DAttr) (output tf.Output) {
+// For example:
+//
+// ```
+// # 't' is [[[1, 1, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]]]
+// shape(t) ==> [2, 2, 3]
+// ```
+func VariableShape(scope *Scope, input tf.Output, optional ...VariableShapeAttr) (output tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{"ksize": ksize, "strides": strides, "padding": padding}
+ attrs := map[string]interface{}{}
for _, a := range optional {
a(attrs)
}
opspec := tf.OpSpec{
- Type: "MaxPool3D",
+ Type: "VariableShape",
Input: []tf.Input{
input,
},
return op.Output(0)
}
-// Computes the gradients of 3-D convolution with respect to the input.
+// Computes softmax cross entropy cost and gradients to backpropagate.
//
-// DEPRECATED at GraphDef version 10: Use Conv3DBackpropInputV2
+// Unlike `SoftmaxCrossEntropyWithLogits`, this operation does not accept
+// a matrix of label probabilities, but rather a single label per row
+// of features. This label is considered to have probability 1.0 for the
+// given row.
+//
+// Inputs are the logits, not probabilities.
//
// Arguments:
-// input: Shape `[batch, depth, rows, cols, in_channels]`.
-// filter: Shape `[depth, rows, cols, in_channels, out_channels]`.
-// `in_channels` must match between `input` and `filter`.
-// out_backprop: Backprop signal of shape `[batch, out_depth, out_rows, out_cols,
-// out_channels]`.
-// strides: 1-D tensor of length 5. The stride of the sliding window for each
-// dimension of `input`. Must have `strides[0] = strides[4] = 1`.
-// padding: The type of padding algorithm to use.
-func Conv3DBackpropInput(scope *Scope, input tf.Output, filter tf.Output, out_backprop tf.Output, strides []int64, padding string) (output tf.Output) {
- if scope.Err() != nil {
- return
- }
- attrs := map[string]interface{}{"strides": strides, "padding": padding}
- opspec := tf.OpSpec{
- Type: "Conv3DBackpropInput",
- Input: []tf.Input{
- input, filter, out_backprop,
- },
- Attrs: attrs,
- }
- op := scope.AddOperation(opspec)
- return op.Output(0)
-}
-
-// ResourceApplyProximalAdagradAttr is an optional argument to ResourceApplyProximalAdagrad.
-type ResourceApplyProximalAdagradAttr func(optionalAttr)
-
-// ResourceApplyProximalAdagradUseLocking sets the optional use_locking attribute to value.
-//
-// value: If True, updating of the var and accum tensors will be protected by
-// a lock; otherwise the behavior is undefined, but may exhibit less contention.
-// If not specified, defaults to false
-func ResourceApplyProximalAdagradUseLocking(value bool) ResourceApplyProximalAdagradAttr {
- return func(m optionalAttr) {
- m["use_locking"] = value
- }
-}
-
-// Update '*var' and '*accum' according to FOBOS with Adagrad learning rate.
-//
-// accum += grad * grad
-// prox_v = var - lr * grad * (1 / sqrt(accum))
-// var = sign(prox_v)/(1+lr*l2) * max{|prox_v|-lr*l1,0}
-//
-// Arguments:
-// var_: Should be from a Variable().
-// accum: Should be from a Variable().
-// lr: Scaling factor. Must be a scalar.
-// l1: L1 regularization. Must be a scalar.
-// l2: L2 regularization. Must be a scalar.
-// grad: The gradient.
+// features: batch_size x num_classes matrix
+// labels: batch_size vector with values in [0, num_classes).
+// This is the label for the given minibatch entry.
//
-// Returns the created operation.
-func ResourceApplyProximalAdagrad(scope *Scope, var_ tf.Output, accum tf.Output, lr tf.Output, l1 tf.Output, l2 tf.Output, grad tf.Output, optional ...ResourceApplyProximalAdagradAttr) (o *tf.Operation) {
+// Returns Per example loss (batch_size vector).backpropagated gradients (batch_size x num_classes matrix).
+func SparseSoftmaxCrossEntropyWithLogits(scope *Scope, features tf.Output, labels tf.Output) (loss tf.Output, backprop tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{}
- for _, a := range optional {
- a(attrs)
- }
opspec := tf.OpSpec{
- Type: "ResourceApplyProximalAdagrad",
+ Type: "SparseSoftmaxCrossEntropyWithLogits",
Input: []tf.Input{
- var_, accum, lr, l1, l2, grad,
+ features, labels,
},
- Attrs: attrs,
- }
- return scope.AddOperation(opspec)
-}
-
-// MutableHashTableOfTensorsV2Attr is an optional argument to MutableHashTableOfTensorsV2.
-type MutableHashTableOfTensorsV2Attr func(optionalAttr)
-
-// MutableHashTableOfTensorsV2Container sets the optional container attribute to value.
-//
-// value: If non-empty, this table is placed in the given container.
-// Otherwise, a default container is used.
-// If not specified, defaults to ""
-func MutableHashTableOfTensorsV2Container(value string) MutableHashTableOfTensorsV2Attr {
- return func(m optionalAttr) {
- m["container"] = value
- }
-}
-
-// MutableHashTableOfTensorsV2SharedName sets the optional shared_name attribute to value.
-//
-// value: If non-empty, this table is shared under the given name across
-// multiple sessions.
-// If not specified, defaults to ""
-func MutableHashTableOfTensorsV2SharedName(value string) MutableHashTableOfTensorsV2Attr {
- return func(m optionalAttr) {
- m["shared_name"] = value
- }
-}
-
-// MutableHashTableOfTensorsV2UseNodeNameSharing sets the optional use_node_name_sharing attribute to value.
-// If not specified, defaults to false
-func MutableHashTableOfTensorsV2UseNodeNameSharing(value bool) MutableHashTableOfTensorsV2Attr {
- return func(m optionalAttr) {
- m["use_node_name_sharing"] = value
- }
-}
-
-// MutableHashTableOfTensorsV2ValueShape sets the optional value_shape attribute to value.
-// If not specified, defaults to <>
-func MutableHashTableOfTensorsV2ValueShape(value tf.Shape) MutableHashTableOfTensorsV2Attr {
- return func(m optionalAttr) {
- m["value_shape"] = value
- }
-}
-
-// Creates an empty hash table.
-//
-// This op creates a mutable hash table, specifying the type of its keys and
-// values. Each value must be a vector. Data can be inserted into the table using
-// the insert operations. It does not support the initialization operation.
-//
-// Arguments:
-// key_dtype: Type of the table keys.
-// value_dtype: Type of the table values.
-//
-// Returns Handle to a table.
-func MutableHashTableOfTensorsV2(scope *Scope, key_dtype tf.DataType, value_dtype tf.DataType, optional ...MutableHashTableOfTensorsV2Attr) (table_handle tf.Output) {
- if scope.Err() != nil {
- return
- }
- attrs := map[string]interface{}{"key_dtype": key_dtype, "value_dtype": value_dtype}
- for _, a := range optional {
- a(attrs)
- }
- opspec := tf.OpSpec{
- Type: "MutableHashTableOfTensorsV2",
-
- Attrs: attrs,
}
op := scope.AddOperation(opspec)
- return op.Output(0)
+ return op.Output(0), op.Output(1)
}
-// Inverse 2D fast Fourier transform.
+// Fast Fourier transform.
//
-// Computes the inverse 2-dimensional discrete Fourier transform over the
-// inner-most 2 dimensions of `input`.
+// Computes the 1-dimensional discrete Fourier transform over the inner-most
+// dimension of `input`.
//
// Arguments:
// input: A complex64 tensor.
//
-// Returns A complex64 tensor of the same shape as `input`. The inner-most 2
-// dimensions of `input` are replaced with their inverse 2D Fourier transform.
+// Returns A complex64 tensor of the same shape as `input`. The inner-most
+// dimension of `input` is replaced with its 1D Fourier transform.
//
// @compatibility(numpy)
-// Equivalent to np.fft.ifft2
+// Equivalent to np.fft.fft
// @end_compatibility
-func IFFT2D(scope *Scope, input tf.Output) (output tf.Output) {
+func FFT(scope *Scope, input tf.Output) (output tf.Output) {
if scope.Err() != nil {
return
}
opspec := tf.OpSpec{
- Type: "IFFT2D",
+ Type: "FFT",
Input: []tf.Input{
input,
},
return op.Output(0)
}
-// Creates a tensor filled with a scalar value.
-//
-// This operation creates a tensor of shape `dims` and fills it with `value`.
-//
-// For example:
-//
-// ```
-// # Output tensor has shape [2, 3].
-// fill([2, 3], 9) ==> [[9, 9, 9]
-// [9, 9, 9]]
-// ```
+// Transforms a serialized tensorflow.TensorProto proto into a Tensor.
//
// Arguments:
-// dims: 1-D. Represents the shape of the output tensor.
-// value: 0-D (scalar). Value to fill the returned tensor.
+// serialized: A scalar string containing a serialized TensorProto proto.
+// out_type: The type of the serialized tensor. The provided type must match the
+// type of the serialized tensor and no implicit conversion will take place.
//
-// @compatibility(numpy)
-// Equivalent to np.full
-// @end_compatibility
-func Fill(scope *Scope, dims tf.Output, value tf.Output) (output tf.Output) {
+// Returns A Tensor of type `out_type`.
+func ParseTensor(scope *Scope, serialized tf.Output, out_type tf.DataType) (output tf.Output) {
if scope.Err() != nil {
return
}
+ attrs := map[string]interface{}{"out_type": out_type}
opspec := tf.OpSpec{
- Type: "Fill",
+ Type: "ParseTensor",
Input: []tf.Input{
- dims, value,
+ serialized,
},
+ Attrs: attrs,
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// 2D fast Fourier transform.
+// MaxPoolWithArgmaxAttr is an optional argument to MaxPoolWithArgmax.
+type MaxPoolWithArgmaxAttr func(optionalAttr)
+
+// MaxPoolWithArgmaxTargmax sets the optional Targmax attribute to value.
+// If not specified, defaults to DT_INT64
+func MaxPoolWithArgmaxTargmax(value tf.DataType) MaxPoolWithArgmaxAttr {
+ return func(m optionalAttr) {
+ m["Targmax"] = value
+ }
+}
+
+// Performs max pooling on the input and outputs both max values and indices.
//
-// Computes the 2-dimensional discrete Fourier transform over the inner-most
-// 2 dimensions of `input`.
+// The indices in `argmax` are flattened, so that a maximum value at position
+// `[b, y, x, c]` becomes flattened index
+// `((b * height + y) * width + x) * channels + c`.
//
-// Arguments:
-// input: A complex64 tensor.
+// The indices returned are always in `[0, height) x [0, width)` before flattening,
+// even if padding is involved and the mathematically correct answer is outside
+// (either negative or too large). This is a bug, but fixing it is difficult to do
+// in a safe backwards compatible way, especially due to flattening.
//
-// Returns A complex64 tensor of the same shape as `input`. The inner-most 2
-// dimensions of `input` are replaced with their 2D Fourier transform.
+// Arguments:
+// input: 4-D with shape `[batch, height, width, channels]`. Input to pool over.
+// ksize: The size of the window for each dimension of the input tensor.
+// strides: The stride of the sliding window for each dimension of the
+// input tensor.
+// padding: The type of padding algorithm to use.
//
-// @compatibility(numpy)
-// Equivalent to np.fft.fft2
-// @end_compatibility
-func FFT2D(scope *Scope, input tf.Output) (output tf.Output) {
+// Returns The max pooled output tensor.4-D. The flattened indices of the max values chosen for each output.
+func MaxPoolWithArgmax(scope *Scope, input tf.Output, ksize []int64, strides []int64, padding string, optional ...MaxPoolWithArgmaxAttr) (output tf.Output, argmax tf.Output) {
if scope.Err() != nil {
return
}
+ attrs := map[string]interface{}{"ksize": ksize, "strides": strides, "padding": padding}
+ for _, a := range optional {
+ a(attrs)
+ }
opspec := tf.OpSpec{
- Type: "FFT2D",
+ Type: "MaxPoolWithArgmax",
Input: []tf.Input{
input,
},
+ Attrs: attrs,
}
op := scope.AddOperation(opspec)
- return op.Output(0)
+ return op.Output(0), op.Output(1)
}
-// ResourceApplyProximalGradientDescentAttr is an optional argument to ResourceApplyProximalGradientDescent.
-type ResourceApplyProximalGradientDescentAttr func(optionalAttr)
+// ResourceSparseApplyAdagradDAAttr is an optional argument to ResourceSparseApplyAdagradDA.
+type ResourceSparseApplyAdagradDAAttr func(optionalAttr)
-// ResourceApplyProximalGradientDescentUseLocking sets the optional use_locking attribute to value.
+// ResourceSparseApplyAdagradDAUseLocking sets the optional use_locking attribute to value.
//
-// value: If True, the subtraction will be protected by a lock;
-// otherwise the behavior is undefined, but may exhibit less contention.
+// value: If True, updating of the var and accum tensors will be protected by
+// a lock; otherwise the behavior is undefined, but may exhibit less contention.
// If not specified, defaults to false
-func ResourceApplyProximalGradientDescentUseLocking(value bool) ResourceApplyProximalGradientDescentAttr {
+func ResourceSparseApplyAdagradDAUseLocking(value bool) ResourceSparseApplyAdagradDAAttr {
return func(m optionalAttr) {
m["use_locking"] = value
}
}
-// Update '*var' as FOBOS algorithm with fixed learning rate.
-//
-// prox_v = var - alpha * delta
-// var = sign(prox_v)/(1+alpha*l2) * max{|prox_v|-alpha*l1,0}
+// Update entries in '*var' and '*accum' according to the proximal adagrad scheme.
//
// Arguments:
// var_: Should be from a Variable().
-// alpha: Scaling factor. Must be a scalar.
+// gradient_accumulator: Should be from a Variable().
+// gradient_squared_accumulator: Should be from a Variable().
+// grad: The gradient.
+// indices: A vector of indices into the first dimension of var and accum.
+// lr: Learning rate. Must be a scalar.
// l1: L1 regularization. Must be a scalar.
// l2: L2 regularization. Must be a scalar.
-// delta: The change.
+// global_step: Training step number. Must be a scalar.
//
// Returns the created operation.
-func ResourceApplyProximalGradientDescent(scope *Scope, var_ tf.Output, alpha tf.Output, l1 tf.Output, l2 tf.Output, delta tf.Output, optional ...ResourceApplyProximalGradientDescentAttr) (o *tf.Operation) {
+func ResourceSparseApplyAdagradDA(scope *Scope, var_ tf.Output, gradient_accumulator tf.Output, gradient_squared_accumulator tf.Output, grad tf.Output, indices tf.Output, lr tf.Output, l1 tf.Output, l2 tf.Output, global_step tf.Output, optional ...ResourceSparseApplyAdagradDAAttr) (o *tf.Operation) {
if scope.Err() != nil {
return
}
a(attrs)
}
opspec := tf.OpSpec{
- Type: "ResourceApplyProximalGradientDescent",
+ Type: "ResourceSparseApplyAdagradDA",
Input: []tf.Input{
- var_, alpha, l1, l2, delta,
+ var_, gradient_accumulator, gradient_squared_accumulator, grad, indices, lr, l1, l2, global_step,
},
Attrs: attrs,
}
return scope.AddOperation(opspec)
}
-// Computes the gradient for the sqrt of `x` wrt its input.
+// EncodeJpegAttr is an optional argument to EncodeJpeg.
+type EncodeJpegAttr func(optionalAttr)
+
+// EncodeJpegFormat sets the optional format attribute to value.
//
-// Specifically, `grad = dy * 0.5 / y`, where `y = sqrt(x)`, and `dy`
-// is the corresponding input gradient.
-func SqrtGrad(scope *Scope, y tf.Output, dy tf.Output) (z tf.Output) {
- if scope.Err() != nil {
- return
+// value: Per pixel image format.
+// If not specified, defaults to ""
+func EncodeJpegFormat(value string) EncodeJpegAttr {
+ return func(m optionalAttr) {
+ m["format"] = value
}
- opspec := tf.OpSpec{
- Type: "SqrtGrad",
- Input: []tf.Input{
- y, dy,
- },
+}
+
+// EncodeJpegQuality sets the optional quality attribute to value.
+//
+// value: Quality of the compression from 0 to 100 (higher is better and slower).
+// If not specified, defaults to 95
+func EncodeJpegQuality(value int64) EncodeJpegAttr {
+ return func(m optionalAttr) {
+ m["quality"] = value
}
- op := scope.AddOperation(opspec)
- return op.Output(0)
}
-// Get the value of the tensor specified by its handle.
+// EncodeJpegProgressive sets the optional progressive attribute to value.
//
-// Arguments:
-// handle: The handle for a tensor stored in the session state.
-// dtype: The type of the output value.
+// value: If True, create a JPEG that loads progressively (coarse to fine).
+// If not specified, defaults to false
+func EncodeJpegProgressive(value bool) EncodeJpegAttr {
+ return func(m optionalAttr) {
+ m["progressive"] = value
+ }
+}
+
+// EncodeJpegOptimizeSize sets the optional optimize_size attribute to value.
//
-// Returns The tensor for the given handle.
-func GetSessionTensor(scope *Scope, handle tf.Output, dtype tf.DataType) (value tf.Output) {
- if scope.Err() != nil {
- return
+// value: If True, spend CPU/RAM to reduce size with no quality change.
+// If not specified, defaults to false
+func EncodeJpegOptimizeSize(value bool) EncodeJpegAttr {
+ return func(m optionalAttr) {
+ m["optimize_size"] = value
}
- attrs := map[string]interface{}{"dtype": dtype}
- opspec := tf.OpSpec{
- Type: "GetSessionTensor",
- Input: []tf.Input{
- handle,
- },
- Attrs: attrs,
+}
+
+// EncodeJpegChromaDownsampling sets the optional chroma_downsampling attribute to value.
+//
+// value: See http://en.wikipedia.org/wiki/Chroma_subsampling.
+// If not specified, defaults to true
+func EncodeJpegChromaDownsampling(value bool) EncodeJpegAttr {
+ return func(m optionalAttr) {
+ m["chroma_downsampling"] = value
}
- op := scope.AddOperation(opspec)
- return op.Output(0)
}
-// Returns x - y element-wise.
+// EncodeJpegDensityUnit sets the optional density_unit attribute to value.
//
-// *NOTE*: `Subtract` supports broadcasting. More about broadcasting
-// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
-func Sub(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) {
- if scope.Err() != nil {
- return
+// value: Unit used to specify `x_density` and `y_density`:
+// pixels per inch (`'in'`) or centimeter (`'cm'`).
+// If not specified, defaults to "in"
+func EncodeJpegDensityUnit(value string) EncodeJpegAttr {
+ return func(m optionalAttr) {
+ m["density_unit"] = value
}
- opspec := tf.OpSpec{
- Type: "Sub",
- Input: []tf.Input{
- x, y,
- },
+}
+
+// EncodeJpegXDensity sets the optional x_density attribute to value.
+//
+// value: Horizontal pixels per density unit.
+// If not specified, defaults to 300
+func EncodeJpegXDensity(value int64) EncodeJpegAttr {
+ return func(m optionalAttr) {
+ m["x_density"] = value
}
- op := scope.AddOperation(opspec)
- return op.Output(0)
}
-// Computes softmax cross entropy cost and gradients to backpropagate.
+// EncodeJpegYDensity sets the optional y_density attribute to value.
//
-// Inputs are the logits, not probabilities.
+// value: Vertical pixels per density unit.
+// If not specified, defaults to 300
+func EncodeJpegYDensity(value int64) EncodeJpegAttr {
+ return func(m optionalAttr) {
+ m["y_density"] = value
+ }
+}
+
+// EncodeJpegXmpMetadata sets the optional xmp_metadata attribute to value.
+//
+// value: If not empty, embed this XMP metadata in the image header.
+// If not specified, defaults to ""
+func EncodeJpegXmpMetadata(value string) EncodeJpegAttr {
+ return func(m optionalAttr) {
+ m["xmp_metadata"] = value
+ }
+}
+
+// JPEG-encode an image.
+//
+// `image` is a 3-D uint8 Tensor of shape `[height, width, channels]`.
+//
+// The attr `format` can be used to override the color format of the encoded
+// output. Values can be:
+//
+// * `''`: Use a default format based on the number of channels in the image.
+// * `grayscale`: Output a grayscale JPEG image. The `channels` dimension
+// of `image` must be 1.
+// * `rgb`: Output an RGB JPEG image. The `channels` dimension
+// of `image` must be 3.
+//
+// If `format` is not specified or is the empty string, a default format is picked
+// in function of the number of channels in `image`:
+//
+// * 1: Output a grayscale image.
+// * 3: Output an RGB image.
//
// Arguments:
-// features: batch_size x num_classes matrix
-// labels: batch_size x num_classes matrix
-// The caller must ensure that each batch of labels represents a valid
-// probability distribution.
+// image: 3-D with shape `[height, width, channels]`.
//
-// Returns Per example loss (batch_size vector).backpropagated gradients (batch_size x num_classes matrix).
-func SoftmaxCrossEntropyWithLogits(scope *Scope, features tf.Output, labels tf.Output) (loss tf.Output, backprop tf.Output) {
+// Returns 0-D. JPEG-encoded image.
+func EncodeJpeg(scope *Scope, image tf.Output, optional ...EncodeJpegAttr) (contents tf.Output) {
if scope.Err() != nil {
return
}
+ attrs := map[string]interface{}{}
+ for _, a := range optional {
+ a(attrs)
+ }
opspec := tf.OpSpec{
- Type: "SoftmaxCrossEntropyWithLogits",
+ Type: "EncodeJpeg",
Input: []tf.Input{
- features, labels,
+ image,
},
+ Attrs: attrs,
}
op := scope.AddOperation(opspec)
- return op.Output(0), op.Output(1)
+ return op.Output(0)
}
-// ReduceJoinAttr is an optional argument to ReduceJoin.
-type ReduceJoinAttr func(optionalAttr)
+// MultinomialAttr is an optional argument to Multinomial.
+type MultinomialAttr func(optionalAttr)
-// ReduceJoinKeepDims sets the optional keep_dims attribute to value.
+// MultinomialSeed sets the optional seed attribute to value.
//
-// value: If `True`, retain reduced dimensions with length `1`.
-// If not specified, defaults to false
-func ReduceJoinKeepDims(value bool) ReduceJoinAttr {
+// value: If either seed or seed2 is set to be non-zero, the internal random number
+// generator is seeded by the given seed. Otherwise, a random seed is used.
+// If not specified, defaults to 0
+func MultinomialSeed(value int64) MultinomialAttr {
return func(m optionalAttr) {
- m["keep_dims"] = value
+ m["seed"] = value
}
}
-// ReduceJoinSeparator sets the optional separator attribute to value.
+// MultinomialSeed2 sets the optional seed2 attribute to value.
//
-// value: The separator to use when joining.
-// If not specified, defaults to ""
-func ReduceJoinSeparator(value string) ReduceJoinAttr {
+// value: A second seed to avoid seed collision.
+// If not specified, defaults to 0
+func MultinomialSeed2(value int64) MultinomialAttr {
return func(m optionalAttr) {
- m["separator"] = value
+ m["seed2"] = value
}
}
-// Joins a string Tensor across the given dimensions.
-//
-// Computes the string join across dimensions in the given string Tensor of shape
-// `[d_0, d_1, ..., d_n-1]`. Returns a new Tensor created by joining the input
-// strings with the given separator (default: empty string). Negative indices are
-// counted backwards from the end, with `-1` being equivalent to `n - 1`.
-//
-// For example:
-//
-// ```python
-// # tensor `a` is [["a", "b"], ["c", "d"]]
-// tf.reduce_join(a, 0) ==> ["ac", "bd"]
-// tf.reduce_join(a, 1) ==> ["ab", "cd"]
-// tf.reduce_join(a, -2) = tf.reduce_join(a, 0) ==> ["ac", "bd"]
-// tf.reduce_join(a, -1) = tf.reduce_join(a, 1) ==> ["ab", "cd"]
-// tf.reduce_join(a, 0, keep_dims=True) ==> [["ac", "bd"]]
-// tf.reduce_join(a, 1, keep_dims=True) ==> [["ab"], ["cd"]]
-// tf.reduce_join(a, 0, separator=".") ==> ["a.c", "b.d"]
-// tf.reduce_join(a, [0, 1]) ==> ["acbd"]
-// tf.reduce_join(a, [1, 0]) ==> ["abcd"]
-// tf.reduce_join(a, []) ==> ["abcd"]
-// ```
+// MultinomialOutputDtype sets the optional output_dtype attribute to value.
+// If not specified, defaults to DT_INT64
+func MultinomialOutputDtype(value tf.DataType) MultinomialAttr {
+ return func(m optionalAttr) {
+ m["output_dtype"] = value
+ }
+}
+
+// Draws samples from a multinomial distribution.
//
// Arguments:
-// inputs: The input to be joined. All reduced indices must have non-zero size.
-// reduction_indices: The dimensions to reduce over. Dimensions are reduced in the
-// order specified. Omitting `reduction_indices` is equivalent to passing
-// `[n-1, n-2, ..., 0]`. Negative indices from `-n` to `-1` are supported.
+// logits: 2-D Tensor with shape `[batch_size, num_classes]`. Each slice `[i, :]`
+// represents the unnormalized log probabilities for all classes.
+// num_samples: 0-D. Number of independent samples to draw for each row slice.
//
-// Returns Has shape equal to that of the input with reduced dimensions removed or
-// set to `1` depending on `keep_dims`.
-func ReduceJoin(scope *Scope, inputs tf.Output, reduction_indices tf.Output, optional ...ReduceJoinAttr) (output tf.Output) {
+// Returns 2-D Tensor with shape `[batch_size, num_samples]`. Each slice `[i, :]`
+// contains the drawn class labels with range `[0, num_classes)`.
+func Multinomial(scope *Scope, logits tf.Output, num_samples tf.Output, optional ...MultinomialAttr) (output tf.Output) {
if scope.Err() != nil {
return
}
a(attrs)
}
opspec := tf.OpSpec{
- Type: "ReduceJoin",
+ Type: "Multinomial",
Input: []tf.Input{
- inputs, reduction_indices,
+ logits, num_samples,
},
Attrs: attrs,
}
return op.Output(0)
}
-// Computes cos of x element-wise.
-func Cos(scope *Scope, x tf.Output) (y tf.Output) {
+// Returns the truth value of NOT x element-wise.
+func LogicalNot(scope *Scope, x tf.Output) (y tf.Output) {
if scope.Err() != nil {
return
}
opspec := tf.OpSpec{
- Type: "Cos",
+ Type: "LogicalNot",
Input: []tf.Input{
x,
},
return op.Output(0)
}
-// FusedBatchNormGradAttr is an optional argument to FusedBatchNormGrad.
-type FusedBatchNormGradAttr func(optionalAttr)
-
-// FusedBatchNormGradEpsilon sets the optional epsilon attribute to value.
+// 3D real-valued fast Fourier transform.
//
-// value: A small float number added to the variance of x.
-// If not specified, defaults to 0.0001
-func FusedBatchNormGradEpsilon(value float32) FusedBatchNormGradAttr {
- return func(m optionalAttr) {
- m["epsilon"] = value
- }
-}
-
-// FusedBatchNormGradDataFormat sets the optional data_format attribute to value.
+// Computes the 3-dimensional discrete Fourier transform of a real-valued signal
+// over the inner-most 3 dimensions of `input`.
//
-// value: The data format for y_backprop, x, x_backprop.
-// Either "NHWC" (default) or "NCHW".
-// If not specified, defaults to "NHWC"
-func FusedBatchNormGradDataFormat(value string) FusedBatchNormGradAttr {
- return func(m optionalAttr) {
- m["data_format"] = value
- }
+// Since the DFT of a real signal is Hermitian-symmetric, `RFFT3D` only returns the
+// `fft_length / 2 + 1` unique components of the FFT for the inner-most dimension
+// of `output`: the zero-frequency term, followed by the `fft_length / 2`
+// positive-frequency terms.
+//
+// Along each axis `RFFT3D` is computed on, if `fft_length` is smaller than the
+// corresponding dimension of `input`, the dimension is cropped. If it is larger,
+// the dimension is padded with zeros.
+//
+// Arguments:
+// input: A float32 tensor.
+// fft_length: An int32 tensor of shape [3]. The FFT length for each dimension.
+//
+// Returns A complex64 tensor of the same rank as `input`. The inner-most 3
+// dimensions of `input` are replaced with the their 3D Fourier transform. The
+// inner-most dimension contains `fft_length / 2 + 1` unique frequency
+// components.
+//
+// @compatibility(numpy)
+// Equivalent to np.fft.rfftn with 3 dimensions.
+// @end_compatibility
+func RFFT3D(scope *Scope, input tf.Output, fft_length tf.Output) (output tf.Output) {
+ if scope.Err() != nil {
+ return
+ }
+ opspec := tf.OpSpec{
+ Type: "RFFT3D",
+ Input: []tf.Input{
+ input, fft_length,
+ },
+ }
+ op := scope.AddOperation(opspec)
+ return op.Output(0)
}
-// FusedBatchNormGradIsTraining sets the optional is_training attribute to value.
+// TensorArrayV3Attr is an optional argument to TensorArrayV3.
+type TensorArrayV3Attr func(optionalAttr)
+
+// TensorArrayV3ElementShape sets the optional element_shape attribute to value.
//
-// value: A bool value to indicate the operation is for training (default)
-// or inference.
+// value: The expected shape of an element, if known. Used to
+// validate the shapes of TensorArray elements. If this shape is not
+// fully specified, gathering zero-size TensorArrays is an error.
+// If not specified, defaults to <unknown_rank:true >
+func TensorArrayV3ElementShape(value tf.Shape) TensorArrayV3Attr {
+ return func(m optionalAttr) {
+ m["element_shape"] = value
+ }
+}
+
+// TensorArrayV3DynamicSize sets the optional dynamic_size attribute to value.
+//
+// value: A boolean that determines whether writes to the TensorArray
+// are allowed to grow the size. By default, this is not allowed.
+// If not specified, defaults to false
+func TensorArrayV3DynamicSize(value bool) TensorArrayV3Attr {
+ return func(m optionalAttr) {
+ m["dynamic_size"] = value
+ }
+}
+
+// TensorArrayV3ClearAfterRead sets the optional clear_after_read attribute to value.
+//
+// value: If true (default), Tensors in the TensorArray are cleared
+// after being read. This disables multiple read semantics but allows early
+// release of memory.
// If not specified, defaults to true
-func FusedBatchNormGradIsTraining(value bool) FusedBatchNormGradAttr {
+func TensorArrayV3ClearAfterRead(value bool) TensorArrayV3Attr {
return func(m optionalAttr) {
- m["is_training"] = value
+ m["clear_after_read"] = value
}
}
-// Gradient for batch normalization.
+// TensorArrayV3IdenticalElementShapes sets the optional identical_element_shapes attribute to value.
//
-// Note that the size of 4D Tensors are defined by either "NHWC" or "NCHW".
-// The size of 1D Tensors matches the dimension C of the 4D Tensors.
+// value: If true (default is false), then all
+// elements in the TensorArray will be expected to have have identical shapes.
+// This allows certain behaviors, like dynamically checking for
+// consistent shapes on write, and being able to fill in properly
+// shaped zero tensors on stack -- even if the element_shape attribute
+// is not fully defined.
+// If not specified, defaults to false
+func TensorArrayV3IdenticalElementShapes(value bool) TensorArrayV3Attr {
+ return func(m optionalAttr) {
+ m["identical_element_shapes"] = value
+ }
+}
+
+// TensorArrayV3TensorArrayName sets the optional tensor_array_name attribute to value.
+//
+// value: Overrides the name used for the temporary tensor_array
+// resource. Default value is the name of the 'TensorArray' op (which
+// is guaranteed unique).
+// If not specified, defaults to ""
+func TensorArrayV3TensorArrayName(value string) TensorArrayV3Attr {
+ return func(m optionalAttr) {
+ m["tensor_array_name"] = value
+ }
+}
+
+// An array of Tensors of given size.
+//
+// Write data via Write and read via Read or Pack.
//
// Arguments:
-// y_backprop: A 4D Tensor for the gradient with respect to y.
-// x: A 4D Tensor for input data.
-// scale: A 1D Tensor for scaling factor, to scale the normalized x.
-// reserve_space_1: When is_training is True, a 1D Tensor for the computed batch
-// mean to be reused in gradient computation. When is_training is
-// False, a 1D Tensor for the population mean to be reused in both
-// 1st and 2nd order gradient computation.
-// reserve_space_2: When is_training is True, a 1D Tensor for the computed batch
-// variance (inverted variance in the cuDNN case) to be reused in
-// gradient computation. When is_training is False, a 1D Tensor
-// for the population variance to be reused in both 1st and 2nd
-// order gradient computation.
+// size: The size of the array.
+// dtype: The type of the elements on the tensor_array.
//
-// Returns A 4D Tensor for the gradient with respect to x.A 1D Tensor for the gradient with respect to scale.A 1D Tensor for the gradient with respect to offset.Unused placeholder to match the mean input in FusedBatchNorm.Unused placeholder to match the variance input
-// in FusedBatchNorm.
-func FusedBatchNormGrad(scope *Scope, y_backprop tf.Output, x tf.Output, scale tf.Output, reserve_space_1 tf.Output, reserve_space_2 tf.Output, optional ...FusedBatchNormGradAttr) (x_backprop tf.Output, scale_backprop tf.Output, offset_backprop tf.Output, reserve_space_3 tf.Output, reserve_space_4 tf.Output) {
+// Returns The handle to the TensorArray.A scalar used to control gradient flow.
+func TensorArrayV3(scope *Scope, size tf.Output, dtype tf.DataType, optional ...TensorArrayV3Attr) (handle tf.Output, flow tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{}
+ attrs := map[string]interface{}{"dtype": dtype}
for _, a := range optional {
a(attrs)
}
opspec := tf.OpSpec{
- Type: "FusedBatchNormGrad",
+ Type: "TensorArrayV3",
Input: []tf.Input{
- y_backprop, x, scale, reserve_space_1, reserve_space_2,
+ size,
},
Attrs: attrs,
}
op := scope.AddOperation(opspec)
- return op.Output(0), op.Output(1), op.Output(2), op.Output(3), op.Output(4)
+ return op.Output(0), op.Output(1)
}
-// TopKAttr is an optional argument to TopK.
-type TopKAttr func(optionalAttr)
+// MaxPool3DAttr is an optional argument to MaxPool3D.
+type MaxPool3DAttr func(optionalAttr)
-// TopKSorted sets the optional sorted attribute to value.
+// MaxPool3DDataFormat sets the optional data_format attribute to value.
//
-// value: If true the resulting `k` elements will be sorted by the values in
-// descending order.
-// If not specified, defaults to true
-func TopKSorted(value bool) TopKAttr {
+// value: The data format of the input and output data. With the
+// default format "NDHWC", the data is stored in the order of:
+// [batch, in_depth, in_height, in_width, in_channels].
+// Alternatively, the format could be "NCDHW", the data storage order is:
+// [batch, in_channels, in_depth, in_height, in_width].
+// If not specified, defaults to "NDHWC"
+func MaxPool3DDataFormat(value string) MaxPool3DAttr {
return func(m optionalAttr) {
- m["sorted"] = value
+ m["data_format"] = value
}
}
-// Finds values and indices of the `k` largest elements for the last dimension.
-//
-// DEPRECATED at GraphDef version 7: Use TopKV2 instead
-//
-// If the input is a vector (rank-1), finds the `k` largest entries in the vector
-// and outputs their values and indices as vectors. Thus `values[j]` is the
-// `j`-th largest entry in `input`, and its index is `indices[j]`.
-//
-// For matrices (resp. higher rank input), computes the top `k` entries in each
-// row (resp. vector along the last dimension). Thus,
-//
-// values.shape = indices.shape = input.shape[:-1] + [k]
-//
-// If two elements are equal, the lower-index element appears first.
-//
-// If `k` varies dynamically, use `TopKV2` below.
+// Performs 3D max pooling on the input.
//
// Arguments:
-// input: 1-D or higher with last dimension at least `k`.
-// k: Number of top elements to look for along the last dimension (along each
-// row for matrices).
+// input: Shape `[batch, depth, rows, cols, channels]` tensor to pool over.
+// ksize: 1-D tensor of length 5. The size of the window for each dimension of
+// the input tensor. Must have `ksize[0] = ksize[4] = 1`.
+// strides: 1-D tensor of length 5. The stride of the sliding window for each
+// dimension of `input`. Must have `strides[0] = strides[4] = 1`.
+// padding: The type of padding algorithm to use.
//
-// Returns The `k` largest elements along each last dimensional slice.The indices of `values` within the last dimension of `input`.
-func TopK(scope *Scope, input tf.Output, k int64, optional ...TopKAttr) (values tf.Output, indices tf.Output) {
+// Returns The max pooled output tensor.
+func MaxPool3D(scope *Scope, input tf.Output, ksize []int64, strides []int64, padding string, optional ...MaxPool3DAttr) (output tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{"k": k}
+ attrs := map[string]interface{}{"ksize": ksize, "strides": strides, "padding": padding}
for _, a := range optional {
a(attrs)
}
opspec := tf.OpSpec{
- Type: "TopK",
+ Type: "MaxPool3D",
Input: []tf.Input{
input,
},
Attrs: attrs,
}
op := scope.AddOperation(opspec)
- return op.Output(0), op.Output(1)
+ return op.Output(0)
}
-// Compute the Hurwitz zeta function \\(\zeta(x, q)\\).
-//
-// The Hurwitz zeta function is defined as:
+// Computes the gradients of 3-D convolution with respect to the input.
//
+// DEPRECATED at GraphDef version 10: Use Conv3DBackpropInputV2
//
-// \\(\zeta(x, q) = \sum_{n=0}^{\infty} (q + n)^{-x}\\)
-func Zeta(scope *Scope, x tf.Output, q tf.Output) (z tf.Output) {
+// Arguments:
+// input: Shape `[batch, depth, rows, cols, in_channels]`.
+// filter: Shape `[depth, rows, cols, in_channels, out_channels]`.
+// `in_channels` must match between `input` and `filter`.
+// out_backprop: Backprop signal of shape `[batch, out_depth, out_rows, out_cols,
+// out_channels]`.
+// strides: 1-D tensor of length 5. The stride of the sliding window for each
+// dimension of `input`. Must have `strides[0] = strides[4] = 1`.
+// padding: The type of padding algorithm to use.
+func Conv3DBackpropInput(scope *Scope, input tf.Output, filter tf.Output, out_backprop tf.Output, strides []int64, padding string) (output tf.Output) {
if scope.Err() != nil {
return
}
+ attrs := map[string]interface{}{"strides": strides, "padding": padding}
opspec := tf.OpSpec{
- Type: "Zeta",
+ Type: "Conv3DBackpropInput",
Input: []tf.Input{
- x, q,
+ input, filter, out_backprop,
},
+ Attrs: attrs,
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// ProdAttr is an optional argument to Prod.
-type ProdAttr func(optionalAttr)
+// ResourceApplyProximalAdagradAttr is an optional argument to ResourceApplyProximalAdagrad.
+type ResourceApplyProximalAdagradAttr func(optionalAttr)
-// ProdKeepDims sets the optional keep_dims attribute to value.
+// ResourceApplyProximalAdagradUseLocking sets the optional use_locking attribute to value.
//
-// value: If true, retain reduced dimensions with length 1.
+// value: If True, updating of the var and accum tensors will be protected by
+// a lock; otherwise the behavior is undefined, but may exhibit less contention.
// If not specified, defaults to false
-func ProdKeepDims(value bool) ProdAttr {
+func ResourceApplyProximalAdagradUseLocking(value bool) ResourceApplyProximalAdagradAttr {
return func(m optionalAttr) {
- m["keep_dims"] = value
+ m["use_locking"] = value
}
}
-// Computes the product of elements across dimensions of a tensor.
+// Update '*var' and '*accum' according to FOBOS with Adagrad learning rate.
//
-// Reduces `input` along the dimensions given in `axis`. Unless
-// `keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in
-// `axis`. If `keep_dims` is true, the reduced dimensions are
-// retained with length 1.
+// accum += grad * grad
+// prox_v = var - lr * grad * (1 / sqrt(accum))
+// var = sign(prox_v)/(1+lr*l2) * max{|prox_v|-lr*l1,0}
//
// Arguments:
-// input: The tensor to reduce.
-// axis: The dimensions to reduce. Must be in the range
-// `[-rank(input), rank(input))`.
+// var_: Should be from a Variable().
+// accum: Should be from a Variable().
+// lr: Scaling factor. Must be a scalar.
+// l1: L1 regularization. Must be a scalar.
+// l2: L2 regularization. Must be a scalar.
+// grad: The gradient.
//
-// Returns The reduced tensor.
-func Prod(scope *Scope, input tf.Output, axis tf.Output, optional ...ProdAttr) (output tf.Output) {
+// Returns the created operation.
+func ResourceApplyProximalAdagrad(scope *Scope, var_ tf.Output, accum tf.Output, lr tf.Output, l1 tf.Output, l2 tf.Output, grad tf.Output, optional ...ResourceApplyProximalAdagradAttr) (o *tf.Operation) {
if scope.Err() != nil {
return
}
a(attrs)
}
opspec := tf.OpSpec{
- Type: "Prod",
+ Type: "ResourceApplyProximalAdagrad",
Input: []tf.Input{
- input, axis,
+ var_, accum, lr, l1, l2, grad,
},
Attrs: attrs,
}
- op := scope.AddOperation(opspec)
- return op.Output(0)
+ return scope.AddOperation(opspec)
}
-// FusedResizeAndPadConv2DAttr is an optional argument to FusedResizeAndPadConv2D.
-type FusedResizeAndPadConv2DAttr func(optionalAttr)
+// MutableHashTableOfTensorsV2Attr is an optional argument to MutableHashTableOfTensorsV2.
+type MutableHashTableOfTensorsV2Attr func(optionalAttr)
-// FusedResizeAndPadConv2DResizeAlignCorners sets the optional resize_align_corners attribute to value.
+// MutableHashTableOfTensorsV2Container sets the optional container attribute to value.
//
-// value: If true, rescale input by (new_height - 1) / (height - 1),
-// which exactly aligns the 4 corners of images and resized images. If false, rescale
-// by new_height / height. Treat similarly the width dimension.
+// value: If non-empty, this table is placed in the given container.
+// Otherwise, a default container is used.
+// If not specified, defaults to ""
+func MutableHashTableOfTensorsV2Container(value string) MutableHashTableOfTensorsV2Attr {
+ return func(m optionalAttr) {
+ m["container"] = value
+ }
+}
+
+// MutableHashTableOfTensorsV2SharedName sets the optional shared_name attribute to value.
+//
+// value: If non-empty, this table is shared under the given name across
+// multiple sessions.
+// If not specified, defaults to ""
+func MutableHashTableOfTensorsV2SharedName(value string) MutableHashTableOfTensorsV2Attr {
+ return func(m optionalAttr) {
+ m["shared_name"] = value
+ }
+}
+
+// MutableHashTableOfTensorsV2UseNodeNameSharing sets the optional use_node_name_sharing attribute to value.
// If not specified, defaults to false
-func FusedResizeAndPadConv2DResizeAlignCorners(value bool) FusedResizeAndPadConv2DAttr {
+func MutableHashTableOfTensorsV2UseNodeNameSharing(value bool) MutableHashTableOfTensorsV2Attr {
return func(m optionalAttr) {
- m["resize_align_corners"] = value
+ m["use_node_name_sharing"] = value
}
}
-// Performs a resize and padding as a preprocess during a convolution.
+// MutableHashTableOfTensorsV2ValueShape sets the optional value_shape attribute to value.
+// If not specified, defaults to <>
+func MutableHashTableOfTensorsV2ValueShape(value tf.Shape) MutableHashTableOfTensorsV2Attr {
+ return func(m optionalAttr) {
+ m["value_shape"] = value
+ }
+}
+
+// Creates an empty hash table.
//
-// It's often possible to do spatial transformations more efficiently as part of
-// the packing stage of a convolution, so this op allows for an optimized
-// implementation where these stages are fused together. This prevents the need to
-// write out the intermediate results as whole tensors, reducing memory pressure,
-// and we can get some latency gains by merging the transformation calculations.
-// The data_format attribute for Conv2D isn't supported by this op, and defaults to
-// 'NHWC' order.
-// Internally this op uses a single per-graph scratch buffer, which means that it
-// will block if multiple versions are being run in parallel. This is because this
-// operator is primarily an optimization to minimize memory usage.
+// This op creates a mutable hash table, specifying the type of its keys and
+// values. Each value must be a vector. Data can be inserted into the table using
+// the insert operations. It does not support the initialization operation.
//
// Arguments:
-// input: 4-D with shape `[batch, in_height, in_width, in_channels]`.
-// size: A 1-D int32 Tensor of 2 elements: `new_height, new_width`. The
-// new size for the images.
-// paddings: A two-column matrix specifying the padding sizes. The number of
-// rows must be the same as the rank of `input`.
-// filter: 4-D with shape
-// `[filter_height, filter_width, in_channels, out_channels]`.
+// key_dtype: Type of the table keys.
+// value_dtype: Type of the table values.
//
-// strides: 1-D of length 4. The stride of the sliding window for each dimension
-// of `input`. Must be in the same order as the dimension specified with format.
-// padding: The type of padding algorithm to use.
-func FusedResizeAndPadConv2D(scope *Scope, input tf.Output, size tf.Output, paddings tf.Output, filter tf.Output, mode string, strides []int64, padding string, optional ...FusedResizeAndPadConv2DAttr) (output tf.Output) {
+// Returns Handle to a table.
+func MutableHashTableOfTensorsV2(scope *Scope, key_dtype tf.DataType, value_dtype tf.DataType, optional ...MutableHashTableOfTensorsV2Attr) (table_handle tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{"mode": mode, "strides": strides, "padding": padding}
+ attrs := map[string]interface{}{"key_dtype": key_dtype, "value_dtype": value_dtype}
for _, a := range optional {
a(attrs)
}
opspec := tf.OpSpec{
- Type: "FusedResizeAndPadConv2D",
- Input: []tf.Input{
- input, size, paddings, filter,
- },
+ Type: "MutableHashTableOfTensorsV2",
+
Attrs: attrs,
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// Inverse 3D fast Fourier transform.
+// Inverse 2D fast Fourier transform.
//
-// Computes the inverse 3-dimensional discrete Fourier transform over the
-// inner-most 3 dimensions of `input`.
+// Computes the inverse 2-dimensional discrete Fourier transform over the
+// inner-most 2 dimensions of `input`.
//
// Arguments:
// input: A complex64 tensor.
//
-// Returns A complex64 tensor of the same shape as `input`. The inner-most 3
-// dimensions of `input` are replaced with their inverse 3D Fourier transform.
+// Returns A complex64 tensor of the same shape as `input`. The inner-most 2
+// dimensions of `input` are replaced with their inverse 2D Fourier transform.
//
// @compatibility(numpy)
-// Equivalent to np.fft.ifftn with 3 dimensions.
+// Equivalent to np.fft.ifft2
// @end_compatibility
-func IFFT3D(scope *Scope, input tf.Output) (output tf.Output) {
+func IFFT2D(scope *Scope, input tf.Output) (output tf.Output) {
if scope.Err() != nil {
return
}
opspec := tf.OpSpec{
- Type: "IFFT3D",
+ Type: "IFFT2D",
Input: []tf.Input{
input,
},
return op.Output(0)
}
-// Adds `bias` to `value`.
+// Creates a tensor filled with a scalar value.
//
-// This is a deprecated version of BiasAdd and will be soon removed.
+// This operation creates a tensor of shape `dims` and fills it with `value`.
//
-// This is a special case of `tf.add` where `bias` is restricted to be 1-D.
-// Broadcasting is supported, so `value` may have any number of dimensions.
+// For example:
+//
+// ```
+// # Output tensor has shape [2, 3].
+// fill([2, 3], 9) ==> [[9, 9, 9]
+// [9, 9, 9]]
+// ```
//
// Arguments:
-// value: Any number of dimensions.
-// bias: 1-D with size the last dimension of `value`.
+// dims: 1-D. Represents the shape of the output tensor.
+// value: 0-D (scalar). Value to fill the returned tensor.
//
-// Returns Broadcasted sum of `value` and `bias`.
-func BiasAddV1(scope *Scope, value tf.Output, bias tf.Output) (output tf.Output) {
+// @compatibility(numpy)
+// Equivalent to np.full
+// @end_compatibility
+func Fill(scope *Scope, dims tf.Output, value tf.Output) (output tf.Output) {
if scope.Err() != nil {
return
}
opspec := tf.OpSpec{
- Type: "BiasAddV1",
+ Type: "Fill",
Input: []tf.Input{
- value, bias,
+ dims, value,
},
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// Reverses specific dimensions of a tensor.
-//
-// NOTE `tf.reverse` has now changed behavior in preparation for 1.0.
-// `tf.reverse_v2` is currently an alias that will be deprecated before TF 1.0.
-//
-// Given a `tensor`, and a `int32` tensor `axis` representing the set of
-// dimensions of `tensor` to reverse. This operation reverses each dimension
-// `i` for which there exists `j` s.t. `axis[j] == i`.
-//
-// `tensor` can have up to 8 dimensions. The number of dimensions specified
-// in `axis` may be 0 or more entries. If an index is specified more than
-// once, a InvalidArgument error is raised.
-//
-// For example:
-//
-// ```
-// # tensor 't' is [[[[ 0, 1, 2, 3],
-// # [ 4, 5, 6, 7],
-// # [ 8, 9, 10, 11]],
-// # [[12, 13, 14, 15],
-// # [16, 17, 18, 19],
-// # [20, 21, 22, 23]]]]
-// # tensor 't' shape is [1, 2, 3, 4]
-//
-// # 'dims' is [3] or 'dims' is [-1]
-// reverse(t, dims) ==> [[[[ 3, 2, 1, 0],
-// [ 7, 6, 5, 4],
-// [ 11, 10, 9, 8]],
-// [[15, 14, 13, 12],
-// [19, 18, 17, 16],
-// [23, 22, 21, 20]]]]
-//
-// # 'dims' is '[1]' (or 'dims' is '[-3]')
-// reverse(t, dims) ==> [[[[12, 13, 14, 15],
-// [16, 17, 18, 19],
-// [20, 21, 22, 23]
-// [[ 0, 1, 2, 3],
-// [ 4, 5, 6, 7],
-// [ 8, 9, 10, 11]]]]
+// 2D fast Fourier transform.
//
-// # 'dims' is '[2]' (or 'dims' is '[-2]')
-// reverse(t, dims) ==> [[[[8, 9, 10, 11],
-// [4, 5, 6, 7],
-// [0, 1, 2, 3]]
-// [[20, 21, 22, 23],
-// [16, 17, 18, 19],
-// [12, 13, 14, 15]]]]
-// ```
+// Computes the 2-dimensional discrete Fourier transform over the inner-most
+// 2 dimensions of `input`.
//
// Arguments:
-// tensor: Up to 8-D.
-// axis: 1-D. The indices of the dimensions to reverse. Must be in the range
-// `[-rank(tensor), rank(tensor))`.
+// input: A complex64 tensor.
//
-// Returns The same shape as `tensor`.
-func ReverseV2(scope *Scope, tensor tf.Output, axis tf.Output) (output tf.Output) {
+// Returns A complex64 tensor of the same shape as `input`. The inner-most 2
+// dimensions of `input` are replaced with their 2D Fourier transform.
+//
+// @compatibility(numpy)
+// Equivalent to np.fft.fft2
+// @end_compatibility
+func FFT2D(scope *Scope, input tf.Output) (output tf.Output) {
if scope.Err() != nil {
return
}
opspec := tf.OpSpec{
- Type: "ReverseV2",
+ Type: "FFT2D",
Input: []tf.Input{
- tensor, axis,
+ input,
},
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// RealAttr is an optional argument to Real.
-type RealAttr func(optionalAttr)
+// ResourceApplyProximalGradientDescentAttr is an optional argument to ResourceApplyProximalGradientDescent.
+type ResourceApplyProximalGradientDescentAttr func(optionalAttr)
-// RealTout sets the optional Tout attribute to value.
-// If not specified, defaults to DT_FLOAT
-func RealTout(value tf.DataType) RealAttr {
+// ResourceApplyProximalGradientDescentUseLocking sets the optional use_locking attribute to value.
+//
+// value: If True, the subtraction will be protected by a lock;
+// otherwise the behavior is undefined, but may exhibit less contention.
+// If not specified, defaults to false
+func ResourceApplyProximalGradientDescentUseLocking(value bool) ResourceApplyProximalGradientDescentAttr {
return func(m optionalAttr) {
- m["Tout"] = value
+ m["use_locking"] = value
}
}
-// Returns the real part of a complex number.
+// Update '*var' as FOBOS algorithm with fixed learning rate.
//
-// Given a tensor `input` of complex numbers, this operation returns a tensor of
-// type `float` that is the real part of each element in `input`. All elements in
-// `input` must be complex numbers of the form \\(a + bj\\), where *a* is the real
-// part returned by this operation and *b* is the imaginary part.
+// prox_v = var - alpha * delta
+// var = sign(prox_v)/(1+alpha*l2) * max{|prox_v|-alpha*l1,0}
//
-// For example:
+// Arguments:
+// var_: Should be from a Variable().
+// alpha: Scaling factor. Must be a scalar.
+// l1: L1 regularization. Must be a scalar.
+// l2: L2 regularization. Must be a scalar.
+// delta: The change.
//
-// ```
-// # tensor 'input' is [-2.25 + 4.75j, 3.25 + 5.75j]
-// tf.real(input) ==> [-2.25, 3.25]
-// ```
-func Real(scope *Scope, input tf.Output, optional ...RealAttr) (output tf.Output) {
+// Returns the created operation.
+func ResourceApplyProximalGradientDescent(scope *Scope, var_ tf.Output, alpha tf.Output, l1 tf.Output, l2 tf.Output, delta tf.Output, optional ...ResourceApplyProximalGradientDescentAttr) (o *tf.Operation) {
if scope.Err() != nil {
return
}
a(attrs)
}
opspec := tf.OpSpec{
- Type: "Real",
+ Type: "ResourceApplyProximalGradientDescent",
Input: []tf.Input{
- input,
+ var_, alpha, l1, l2, delta,
},
Attrs: attrs,
}
- op := scope.AddOperation(opspec)
- return op.Output(0)
+ return scope.AddOperation(opspec)
}
-// AudioSummaryAttr is an optional argument to AudioSummary.
-type AudioSummaryAttr func(optionalAttr)
-
-// AudioSummaryMaxOutputs sets the optional max_outputs attribute to value.
-//
-// value: Max number of batch elements to generate audio for.
-// If not specified, defaults to 3
+// Computes the gradient for the sqrt of `x` wrt its input.
//
-// REQUIRES: value >= 1
-func AudioSummaryMaxOutputs(value int64) AudioSummaryAttr {
- return func(m optionalAttr) {
- m["max_outputs"] = value
+// Specifically, `grad = dy * 0.5 / y`, where `y = sqrt(x)`, and `dy`
+// is the corresponding input gradient.
+func SqrtGrad(scope *Scope, y tf.Output, dy tf.Output) (z tf.Output) {
+ if scope.Err() != nil {
+ return
+ }
+ opspec := tf.OpSpec{
+ Type: "SqrtGrad",
+ Input: []tf.Input{
+ y, dy,
+ },
}
+ op := scope.AddOperation(opspec)
+ return op.Output(0)
}
-// Outputs a `Summary` protocol buffer with audio.
-//
-// DEPRECATED at GraphDef version 15: Use AudioSummaryV2.
+// Get the value of the tensor specified by its handle.
//
-// The summary has up to `max_outputs` summary values containing audio. The
-// audio is built from `tensor` which must be 3-D with shape `[batch_size,
-// frames, channels]` or 2-D with shape `[batch_size, frames]`. The values are
-// assumed to be in the range of `[-1.0, 1.0]` with a sample rate of `sample_rate`.
+// Arguments:
+// handle: The handle for a tensor stored in the session state.
+// dtype: The type of the output value.
//
-// The `tag` argument is a scalar `Tensor` of type `string`. It is used to
-// build the `tag` of the summary values:
+// Returns The tensor for the given handle.
+func GetSessionTensor(scope *Scope, handle tf.Output, dtype tf.DataType) (value tf.Output) {
+ if scope.Err() != nil {
+ return
+ }
+ attrs := map[string]interface{}{"dtype": dtype}
+ opspec := tf.OpSpec{
+ Type: "GetSessionTensor",
+ Input: []tf.Input{
+ handle,
+ },
+ Attrs: attrs,
+ }
+ op := scope.AddOperation(opspec)
+ return op.Output(0)
+}
+
+// Returns x - y element-wise.
//
-// * If `max_outputs` is 1, the summary value tag is '*tag*/audio'.
-// * If `max_outputs` is greater than 1, the summary value tags are
-// generated sequentially as '*tag*/audio/0', '*tag*/audio/1', etc.
+// *NOTE*: `Subtract` supports broadcasting. More about broadcasting
+// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
+func Sub(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) {
+ if scope.Err() != nil {
+ return
+ }
+ opspec := tf.OpSpec{
+ Type: "Sub",
+ Input: []tf.Input{
+ x, y,
+ },
+ }
+ op := scope.AddOperation(opspec)
+ return op.Output(0)
+}
+
+// Computes softmax cross entropy cost and gradients to backpropagate.
+//
+// Inputs are the logits, not probabilities.
//
// Arguments:
-// tag: Scalar. Used to build the `tag` attribute of the summary values.
-// tensor: 2-D of shape `[batch_size, frames]`.
-// sample_rate: The sample rate of the signal in hertz.
+// features: batch_size x num_classes matrix
+// labels: batch_size x num_classes matrix
+// The caller must ensure that each batch of labels represents a valid
+// probability distribution.
//
-// Returns Scalar. Serialized `Summary` protocol buffer.
-func AudioSummary(scope *Scope, tag tf.Output, tensor tf.Output, sample_rate float32, optional ...AudioSummaryAttr) (summary tf.Output) {
+// Returns Per example loss (batch_size vector).backpropagated gradients (batch_size x num_classes matrix).
+func SoftmaxCrossEntropyWithLogits(scope *Scope, features tf.Output, labels tf.Output) (loss tf.Output, backprop tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{"sample_rate": sample_rate}
- for _, a := range optional {
- a(attrs)
- }
opspec := tf.OpSpec{
- Type: "AudioSummary",
+ Type: "SoftmaxCrossEntropyWithLogits",
Input: []tf.Input{
- tag, tensor,
+ features, labels,
},
- Attrs: attrs,
}
op := scope.AddOperation(opspec)
- return op.Output(0)
+ return op.Output(0), op.Output(1)
}
-// QrAttr is an optional argument to Qr.
-type QrAttr func(optionalAttr)
+// ReduceJoinAttr is an optional argument to ReduceJoin.
+type ReduceJoinAttr func(optionalAttr)
-// QrFullMatrices sets the optional full_matrices attribute to value.
+// ReduceJoinKeepDims sets the optional keep_dims attribute to value.
//
-// value: If true, compute full-sized `q` and `r`. If false
-// (the default), compute only the leading `P` columns of `q`.
+// value: If `True`, retain reduced dimensions with length `1`.
// If not specified, defaults to false
-func QrFullMatrices(value bool) QrAttr {
+func ReduceJoinKeepDims(value bool) ReduceJoinAttr {
return func(m optionalAttr) {
- m["full_matrices"] = value
+ m["keep_dims"] = value
}
}
-// Computes the QR decompositions of one or more matrices.
+// ReduceJoinSeparator sets the optional separator attribute to value.
//
-// Computes the QR decomposition of each inner matrix in `tensor` such that
-// `tensor[..., :, :] = q[..., :, :] * r[..., :,:])`
+// value: The separator to use when joining.
+// If not specified, defaults to ""
+func ReduceJoinSeparator(value string) ReduceJoinAttr {
+ return func(m optionalAttr) {
+ m["separator"] = value
+ }
+}
+
+// Joins a string Tensor across the given dimensions.
+//
+// Computes the string join across dimensions in the given string Tensor of shape
+// `[d_0, d_1, ..., d_n-1]`. Returns a new Tensor created by joining the input
+// strings with the given separator (default: empty string). Negative indices are
+// counted backwards from the end, with `-1` being equivalent to `n - 1`.
+//
+// For example:
//
// ```python
-// # a is a tensor.
-// # q is a tensor of orthonormal matrices.
-// # r is a tensor of upper triangular matrices.
-// q, r = qr(a)
-// q_full, r_full = qr(a, full_matrices=True)
+// # tensor `a` is [["a", "b"], ["c", "d"]]
+// tf.reduce_join(a, 0) ==> ["ac", "bd"]
+// tf.reduce_join(a, 1) ==> ["ab", "cd"]
+// tf.reduce_join(a, -2) = tf.reduce_join(a, 0) ==> ["ac", "bd"]
+// tf.reduce_join(a, -1) = tf.reduce_join(a, 1) ==> ["ab", "cd"]
+// tf.reduce_join(a, 0, keep_dims=True) ==> [["ac", "bd"]]
+// tf.reduce_join(a, 1, keep_dims=True) ==> [["ab"], ["cd"]]
+// tf.reduce_join(a, 0, separator=".") ==> ["a.c", "b.d"]
+// tf.reduce_join(a, [0, 1]) ==> ["acbd"]
+// tf.reduce_join(a, [1, 0]) ==> ["abcd"]
+// tf.reduce_join(a, []) ==> ["abcd"]
// ```
//
// Arguments:
-// input: A tensor of shape `[..., M, N]` whose inner-most 2 dimensions
-// form matrices of size `[M, N]`. Let `P` be the minimum of `M` and `N`.
+// inputs: The input to be joined. All reduced indices must have non-zero size.
+// reduction_indices: The dimensions to reduce over. Dimensions are reduced in the
+// order specified. Omitting `reduction_indices` is equivalent to passing
+// `[n-1, n-2, ..., 0]`. Negative indices from `-n` to `-1` are supported.
//
-// Returns Orthonormal basis for range of `a`. If `full_matrices` is `False` then
-// shape is `[..., M, P]`; if `full_matrices` is `True` then shape is
-// `[..., M, M]`.Triangular factor. If `full_matrices` is `False` then shape is
-// `[..., P, N]`. If `full_matrices` is `True` then shape is `[..., M, N]`.
-func Qr(scope *Scope, input tf.Output, optional ...QrAttr) (q tf.Output, r tf.Output) {
+// Returns Has shape equal to that of the input with reduced dimensions removed or
+// set to `1` depending on `keep_dims`.
+func ReduceJoin(scope *Scope, inputs tf.Output, reduction_indices tf.Output, optional ...ReduceJoinAttr) (output tf.Output) {
if scope.Err() != nil {
return
}
a(attrs)
}
opspec := tf.OpSpec{
- Type: "Qr",
+ Type: "ReduceJoin",
Input: []tf.Input{
- input,
+ inputs, reduction_indices,
},
Attrs: attrs,
}
op := scope.AddOperation(opspec)
- return op.Output(0), op.Output(1)
+ return op.Output(0)
}
-// Records the bytes size of each element of `input_dataset` in a StatsAggregator.
-func BytesProducedStatsDataset(scope *Scope, input_dataset tf.Output, tag tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) {
+// Computes cos of x element-wise.
+func Cos(scope *Scope, x tf.Output) (y tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes}
opspec := tf.OpSpec{
- Type: "BytesProducedStatsDataset",
+ Type: "Cos",
Input: []tf.Input{
- input_dataset, tag,
+ x,
},
- Attrs: attrs,
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// ResourceSparseApplyProximalGradientDescentAttr is an optional argument to ResourceSparseApplyProximalGradientDescent.
-type ResourceSparseApplyProximalGradientDescentAttr func(optionalAttr)
+// FusedBatchNormGradAttr is an optional argument to FusedBatchNormGrad.
+type FusedBatchNormGradAttr func(optionalAttr)
-// ResourceSparseApplyProximalGradientDescentUseLocking sets the optional use_locking attribute to value.
+// FusedBatchNormGradEpsilon sets the optional epsilon attribute to value.
//
-// value: If True, the subtraction will be protected by a lock;
-// otherwise the behavior is undefined, but may exhibit less contention.
-// If not specified, defaults to false
-func ResourceSparseApplyProximalGradientDescentUseLocking(value bool) ResourceSparseApplyProximalGradientDescentAttr {
+// value: A small float number added to the variance of x.
+// If not specified, defaults to 0.0001
+func FusedBatchNormGradEpsilon(value float32) FusedBatchNormGradAttr {
return func(m optionalAttr) {
- m["use_locking"] = value
+ m["epsilon"] = value
}
}
-// Sparse update '*var' as FOBOS algorithm with fixed learning rate.
-//
-// That is for rows we have grad for, we update var as follows:
-// prox_v = var - alpha * grad
-// var = sign(prox_v)/(1+alpha*l2) * max{|prox_v|-alpha*l1,0}
-//
-// Arguments:
-// var_: Should be from a Variable().
-// alpha: Scaling factor. Must be a scalar.
-// l1: L1 regularization. Must be a scalar.
-// l2: L2 regularization. Must be a scalar.
-// grad: The gradient.
-// indices: A vector of indices into the first dimension of var and accum.
+// FusedBatchNormGradDataFormat sets the optional data_format attribute to value.
//
-// Returns the created operation.
-func ResourceSparseApplyProximalGradientDescent(scope *Scope, var_ tf.Output, alpha tf.Output, l1 tf.Output, l2 tf.Output, grad tf.Output, indices tf.Output, optional ...ResourceSparseApplyProximalGradientDescentAttr) (o *tf.Operation) {
- if scope.Err() != nil {
- return
- }
- attrs := map[string]interface{}{}
- for _, a := range optional {
- a(attrs)
- }
- opspec := tf.OpSpec{
- Type: "ResourceSparseApplyProximalGradientDescent",
- Input: []tf.Input{
- var_, alpha, l1, l2, grad, indices,
- },
- Attrs: attrs,
+// value: The data format for y_backprop, x, x_backprop.
+// Either "NHWC" (default) or "NCHW".
+// If not specified, defaults to "NHWC"
+func FusedBatchNormGradDataFormat(value string) FusedBatchNormGradAttr {
+ return func(m optionalAttr) {
+ m["data_format"] = value
}
- return scope.AddOperation(opspec)
}
-// MeanAttr is an optional argument to Mean.
-type MeanAttr func(optionalAttr)
-
-// MeanKeepDims sets the optional keep_dims attribute to value.
+// FusedBatchNormGradIsTraining sets the optional is_training attribute to value.
//
-// value: If true, retain reduced dimensions with length 1.
-// If not specified, defaults to false
-func MeanKeepDims(value bool) MeanAttr {
+// value: A bool value to indicate the operation is for training (default)
+// or inference.
+// If not specified, defaults to true
+func FusedBatchNormGradIsTraining(value bool) FusedBatchNormGradAttr {
return func(m optionalAttr) {
- m["keep_dims"] = value
+ m["is_training"] = value
}
}
-// Computes the mean of elements across dimensions of a tensor.
+// Gradient for batch normalization.
//
-// Reduces `input` along the dimensions given in `axis`. Unless
-// `keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in
-// `axis`. If `keep_dims` is true, the reduced dimensions are
-// retained with length 1.
+// Note that the size of 4D Tensors are defined by either "NHWC" or "NCHW".
+// The size of 1D Tensors matches the dimension C of the 4D Tensors.
//
// Arguments:
-// input: The tensor to reduce.
-// axis: The dimensions to reduce. Must be in the range
-// `[-rank(input), rank(input))`.
+// y_backprop: A 4D Tensor for the gradient with respect to y.
+// x: A 4D Tensor for input data.
+// scale: A 1D Tensor for scaling factor, to scale the normalized x.
+// reserve_space_1: When is_training is True, a 1D Tensor for the computed batch
+// mean to be reused in gradient computation. When is_training is
+// False, a 1D Tensor for the population mean to be reused in both
+// 1st and 2nd order gradient computation.
+// reserve_space_2: When is_training is True, a 1D Tensor for the computed batch
+// variance (inverted variance in the cuDNN case) to be reused in
+// gradient computation. When is_training is False, a 1D Tensor
+// for the population variance to be reused in both 1st and 2nd
+// order gradient computation.
//
-// Returns The reduced tensor.
-func Mean(scope *Scope, input tf.Output, axis tf.Output, optional ...MeanAttr) (output tf.Output) {
+// Returns A 4D Tensor for the gradient with respect to x.A 1D Tensor for the gradient with respect to scale.A 1D Tensor for the gradient with respect to offset.Unused placeholder to match the mean input in FusedBatchNorm.Unused placeholder to match the variance input
+// in FusedBatchNorm.
+func FusedBatchNormGrad(scope *Scope, y_backprop tf.Output, x tf.Output, scale tf.Output, reserve_space_1 tf.Output, reserve_space_2 tf.Output, optional ...FusedBatchNormGradAttr) (x_backprop tf.Output, scale_backprop tf.Output, offset_backprop tf.Output, reserve_space_3 tf.Output, reserve_space_4 tf.Output) {
if scope.Err() != nil {
return
}
a(attrs)
}
opspec := tf.OpSpec{
- Type: "Mean",
+ Type: "FusedBatchNormGrad",
Input: []tf.Input{
- input, axis,
+ y_backprop, x, scale, reserve_space_1, reserve_space_2,
},
Attrs: attrs,
}
op := scope.AddOperation(opspec)
- return op.Output(0)
+ return op.Output(0), op.Output(1), op.Output(2), op.Output(3), op.Output(4)
}
-// InitializeTableFromTextFileV2Attr is an optional argument to InitializeTableFromTextFileV2.
-type InitializeTableFromTextFileV2Attr func(optionalAttr)
+// TopKAttr is an optional argument to TopK.
+type TopKAttr func(optionalAttr)
-// InitializeTableFromTextFileV2VocabSize sets the optional vocab_size attribute to value.
+// TopKSorted sets the optional sorted attribute to value.
//
-// value: Number of elements of the file, use -1 if unknown.
-// If not specified, defaults to -1
-//
-// REQUIRES: value >= -1
-func InitializeTableFromTextFileV2VocabSize(value int64) InitializeTableFromTextFileV2Attr {
+// value: If true the resulting `k` elements will be sorted by the values in
+// descending order.
+// If not specified, defaults to true
+func TopKSorted(value bool) TopKAttr {
return func(m optionalAttr) {
- m["vocab_size"] = value
+ m["sorted"] = value
}
}
-// InitializeTableFromTextFileV2Delimiter sets the optional delimiter attribute to value.
+// Finds values and indices of the `k` largest elements for the last dimension.
//
-// value: Delimiter to separate fields in a line.
-// If not specified, defaults to "\t"
-func InitializeTableFromTextFileV2Delimiter(value string) InitializeTableFromTextFileV2Attr {
- return func(m optionalAttr) {
- m["delimiter"] = value
- }
-}
-
-// Initializes a table from a text file.
+// DEPRECATED at GraphDef version 7: Use TopKV2 instead
//
-// It inserts one key-value pair into the table for each line of the file.
-// The key and value is extracted from the whole line content, elements from the
-// split line based on `delimiter` or the line number (starting from zero).
-// Where to extract the key and value from a line is specified by `key_index` and
-// `value_index`.
+// If the input is a vector (rank-1), finds the `k` largest entries in the vector
+// and outputs their values and indices as vectors. Thus `values[j]` is the
+// `j`-th largest entry in `input`, and its index is `indices[j]`.
//
-// - A value of -1 means use the line number(starting from zero), expects `int64`.
-// - A value of -2 means use the whole line content, expects `string`.
-// - A value >= 0 means use the index (starting at zero) of the split line based
-// on `delimiter`.
+// For matrices (resp. higher rank input), computes the top `k` entries in each
+// row (resp. vector along the last dimension). Thus,
+//
+// values.shape = indices.shape = input.shape[:-1] + [k]
+//
+// If two elements are equal, the lower-index element appears first.
+//
+// If `k` varies dynamically, use `TopKV2` below.
//
// Arguments:
-// table_handle: Handle to a table which will be initialized.
-// filename: Filename of a vocabulary text file.
-// key_index: Column index in a line to get the table `key` values from.
-// value_index: Column index that represents information of a line to get the table
-// `value` values from.
+// input: 1-D or higher with last dimension at least `k`.
+// k: Number of top elements to look for along the last dimension (along each
+// row for matrices).
//
-// Returns the created operation.
-func InitializeTableFromTextFileV2(scope *Scope, table_handle tf.Output, filename tf.Output, key_index int64, value_index int64, optional ...InitializeTableFromTextFileV2Attr) (o *tf.Operation) {
+// Returns The `k` largest elements along each last dimensional slice.The indices of `values` within the last dimension of `input`.
+func TopK(scope *Scope, input tf.Output, k int64, optional ...TopKAttr) (values tf.Output, indices tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{"key_index": key_index, "value_index": value_index}
+ attrs := map[string]interface{}{"k": k}
for _, a := range optional {
a(attrs)
}
opspec := tf.OpSpec{
- Type: "InitializeTableFromTextFileV2",
+ Type: "TopK",
Input: []tf.Input{
- table_handle, filename,
+ input,
},
Attrs: attrs,
}
- return scope.AddOperation(opspec)
-}
-
-// QuantizedReluAttr is an optional argument to QuantizedRelu.
-type QuantizedReluAttr func(optionalAttr)
-
-// QuantizedReluOutType sets the optional out_type attribute to value.
-// If not specified, defaults to DT_QUINT8
-func QuantizedReluOutType(value tf.DataType) QuantizedReluAttr {
- return func(m optionalAttr) {
- m["out_type"] = value
- }
+ op := scope.AddOperation(opspec)
+ return op.Output(0), op.Output(1)
}
-// Computes Quantized Rectified Linear: `max(features, 0)`
+// Compute the Hurwitz zeta function \\(\zeta(x, q)\\).
//
-// Arguments:
+// The Hurwitz zeta function is defined as:
//
-// min_features: The float value that the lowest quantized value represents.
-// max_features: The float value that the highest quantized value represents.
//
-// Returns Has the same output shape as "features".The float value that the lowest quantized value represents.The float value that the highest quantized value represents.
-func QuantizedRelu(scope *Scope, features tf.Output, min_features tf.Output, max_features tf.Output, optional ...QuantizedReluAttr) (activations tf.Output, min_activations tf.Output, max_activations tf.Output) {
+// \\(\zeta(x, q) = \sum_{n=0}^{\infty} (q + n)^{-x}\\)
+func Zeta(scope *Scope, x tf.Output, q tf.Output) (z tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{}
- for _, a := range optional {
- a(attrs)
- }
opspec := tf.OpSpec{
- Type: "QuantizedRelu",
+ Type: "Zeta",
Input: []tf.Input{
- features, min_features, max_features,
+ x, q,
},
- Attrs: attrs,
}
op := scope.AddOperation(opspec)
- return op.Output(0), op.Output(1), op.Output(2)
+ return op.Output(0)
}
-// Reshapes a SparseTensor to represent values in a new dense shape.
-//
-// This operation has the same semantics as reshape on the represented dense
-// tensor. The `input_indices` are recomputed based on the requested `new_shape`.
-//
-// If one component of `new_shape` is the special value -1, the size of that
-// dimension is computed so that the total dense size remains constant. At
-// most one component of `new_shape` can be -1. The number of dense elements
-// implied by `new_shape` must be the same as the number of dense elements
-// originally implied by `input_shape`.
+// ProdAttr is an optional argument to Prod.
+type ProdAttr func(optionalAttr)
+
+// ProdKeepDims sets the optional keep_dims attribute to value.
//
-// Reshaping does not affect the order of values in the SparseTensor.
+// value: If true, retain reduced dimensions with length 1.
+// If not specified, defaults to false
+func ProdKeepDims(value bool) ProdAttr {
+ return func(m optionalAttr) {
+ m["keep_dims"] = value
+ }
+}
+
+// Computes the product of elements across dimensions of a tensor.
//
-// If the input tensor has rank `R_in` and `N` non-empty values, and `new_shape`
-// has length `R_out`, then `input_indices` has shape `[N, R_in]`,
-// `input_shape` has length `R_in`, `output_indices` has shape `[N, R_out]`, and
-// `output_shape` has length `R_out`.
+// Reduces `input` along the dimensions given in `axis`. Unless
+// `keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in
+// `axis`. If `keep_dims` is true, the reduced dimensions are
+// retained with length 1.
//
// Arguments:
-// input_indices: 2-D. `N x R_in` matrix with the indices of non-empty values in a
-// SparseTensor.
-// input_shape: 1-D. `R_in` vector with the input SparseTensor's dense shape.
-// new_shape: 1-D. `R_out` vector with the requested new dense shape.
+// input: The tensor to reduce.
+// axis: The dimensions to reduce. Must be in the range
+// `[-rank(input), rank(input))`.
//
-// Returns 2-D. `N x R_out` matrix with the updated indices of non-empty
-// values in the output SparseTensor.1-D. `R_out` vector with the full dense shape of the output
-// SparseTensor. This is the same as `new_shape` but with any -1 dimensions
-// filled in.
-func SparseReshape(scope *Scope, input_indices tf.Output, input_shape tf.Output, new_shape tf.Output) (output_indices tf.Output, output_shape tf.Output) {
+// Returns The reduced tensor.
+func Prod(scope *Scope, input tf.Output, axis tf.Output, optional ...ProdAttr) (output tf.Output) {
if scope.Err() != nil {
return
}
- opspec := tf.OpSpec{
- Type: "SparseReshape",
- Input: []tf.Input{
- input_indices, input_shape, new_shape,
- },
- }
- op := scope.AddOperation(opspec)
- return op.Output(0), op.Output(1)
-}
-
-// Deprecated. Use TensorArraySplitV3
-//
-// DEPRECATED at GraphDef version 26: Use TensorArraySplitV3
-func TensorArraySplitV2(scope *Scope, handle tf.Output, value tf.Output, lengths tf.Output, flow_in tf.Output) (flow_out tf.Output) {
- if scope.Err() != nil {
- return
+ attrs := map[string]interface{}{}
+ for _, a := range optional {
+ a(attrs)
}
opspec := tf.OpSpec{
- Type: "TensorArraySplitV2",
+ Type: "Prod",
Input: []tf.Input{
- handle, value, lengths, flow_in,
+ input, axis,
},
+ Attrs: attrs,
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// PackAttr is an optional argument to Pack.
-type PackAttr func(optionalAttr)
+// FusedResizeAndPadConv2DAttr is an optional argument to FusedResizeAndPadConv2D.
+type FusedResizeAndPadConv2DAttr func(optionalAttr)
-// PackAxis sets the optional axis attribute to value.
+// FusedResizeAndPadConv2DResizeAlignCorners sets the optional resize_align_corners attribute to value.
//
-// value: Dimension along which to pack. Negative values wrap around, so the
-// valid range is `[-(R+1), R+1)`.
-// If not specified, defaults to 0
-func PackAxis(value int64) PackAttr {
+// value: If true, rescale input by (new_height - 1) / (height - 1),
+// which exactly aligns the 4 corners of images and resized images. If false, rescale
+// by new_height / height. Treat similarly the width dimension.
+// If not specified, defaults to false
+func FusedResizeAndPadConv2DResizeAlignCorners(value bool) FusedResizeAndPadConv2DAttr {
return func(m optionalAttr) {
- m["axis"] = value
+ m["resize_align_corners"] = value
}
}
-// Packs a list of `N` rank-`R` tensors into one rank-`(R+1)` tensor.
-//
-// Packs the `N` tensors in `values` into a tensor with rank one higher than each
-// tensor in `values`, by packing them along the `axis` dimension.
-// Given a list of tensors of shape `(A, B, C)`;
-//
-// if `axis == 0` then the `output` tensor will have the shape `(N, A, B, C)`.
-// if `axis == 1` then the `output` tensor will have the shape `(A, N, B, C)`.
-// Etc.
-//
-// For example:
-//
-// ```
-// # 'x' is [1, 4]
-// # 'y' is [2, 5]
-// # 'z' is [3, 6]
-// pack([x, y, z]) => [[1, 4], [2, 5], [3, 6]] # Pack along first dim.
-// pack([x, y, z], axis=1) => [[1, 2, 3], [4, 5, 6]]
-// ```
+// Performs a resize and padding as a preprocess during a convolution.
//
-// This is the opposite of `unpack`.
+// It's often possible to do spatial transformations more efficiently as part of
+// the packing stage of a convolution, so this op allows for an optimized
+// implementation where these stages are fused together. This prevents the need to
+// write out the intermediate results as whole tensors, reducing memory pressure,
+// and we can get some latency gains by merging the transformation calculations.
+// The data_format attribute for Conv2D isn't supported by this op, and defaults to
+// 'NHWC' order.
+// Internally this op uses a single per-graph scratch buffer, which means that it
+// will block if multiple versions are being run in parallel. This is because this
+// operator is primarily an optimization to minimize memory usage.
//
// Arguments:
-// values: Must be of same shape and type.
+// input: 4-D with shape `[batch, in_height, in_width, in_channels]`.
+// size: A 1-D int32 Tensor of 2 elements: `new_height, new_width`. The
+// new size for the images.
+// paddings: A two-column matrix specifying the padding sizes. The number of
+// rows must be the same as the rank of `input`.
+// filter: 4-D with shape
+// `[filter_height, filter_width, in_channels, out_channels]`.
//
-// Returns The packed tensor.
-func Pack(scope *Scope, values []tf.Output, optional ...PackAttr) (output tf.Output) {
+// strides: 1-D of length 4. The stride of the sliding window for each dimension
+// of `input`. Must be in the same order as the dimension specified with format.
+// padding: The type of padding algorithm to use.
+func FusedResizeAndPadConv2D(scope *Scope, input tf.Output, size tf.Output, paddings tf.Output, filter tf.Output, mode string, strides []int64, padding string, optional ...FusedResizeAndPadConv2DAttr) (output tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{}
+ attrs := map[string]interface{}{"mode": mode, "strides": strides, "padding": padding}
for _, a := range optional {
a(attrs)
}
opspec := tf.OpSpec{
- Type: "Pack",
+ Type: "FusedResizeAndPadConv2D",
Input: []tf.Input{
- tf.OutputList(values),
+ input, size, paddings, filter,
},
Attrs: attrs,
}
return op.Output(0)
}
-// Reorders a SparseTensor into the canonical, row-major ordering.
-//
-// Note that by convention, all sparse ops preserve the canonical ordering along
-// increasing dimension number. The only time ordering can be violated is during
-// manual manipulation of the indices and values vectors to add entries.
-//
-// Reordering does not affect the shape of the SparseTensor.
+// Inverse 3D fast Fourier transform.
//
-// If the tensor has rank `R` and `N` non-empty values, `input_indices` has
-// shape `[N, R]`, input_values has length `N`, and input_shape has length `R`.
+// Computes the inverse 3-dimensional discrete Fourier transform over the
+// inner-most 3 dimensions of `input`.
//
// Arguments:
-// input_indices: 2-D. `N x R` matrix with the indices of non-empty values in a
-// SparseTensor, possibly not in canonical ordering.
-// input_values: 1-D. `N` non-empty values corresponding to `input_indices`.
-// input_shape: 1-D. Shape of the input SparseTensor.
+// input: A complex64 tensor.
//
-// Returns 2-D. `N x R` matrix with the same indices as input_indices, but
-// in canonical row-major ordering.1-D. `N` non-empty values corresponding to `output_indices`.
-func SparseReorder(scope *Scope, input_indices tf.Output, input_values tf.Output, input_shape tf.Output) (output_indices tf.Output, output_values tf.Output) {
- if scope.Err() != nil {
- return
- }
+// Returns A complex64 tensor of the same shape as `input`. The inner-most 3
+// dimensions of `input` are replaced with their inverse 3D Fourier transform.
+//
+// @compatibility(numpy)
+// Equivalent to np.fft.ifftn with 3 dimensions.
+// @end_compatibility
+func IFFT3D(scope *Scope, input tf.Output) (output tf.Output) {
+ if scope.Err() != nil {
+ return
+ }
opspec := tf.OpSpec{
- Type: "SparseReorder",
+ Type: "IFFT3D",
Input: []tf.Input{
- input_indices, input_values, input_shape,
+ input,
},
}
op := scope.AddOperation(opspec)
- return op.Output(0), op.Output(1)
+ return op.Output(0)
}
-// Computes rectified linear: `max(features, 0)`.
-func Relu(scope *Scope, features tf.Output) (activations tf.Output) {
+// Adds `bias` to `value`.
+//
+// This is a deprecated version of BiasAdd and will be soon removed.
+//
+// This is a special case of `tf.add` where `bias` is restricted to be 1-D.
+// Broadcasting is supported, so `value` may have any number of dimensions.
+//
+// Arguments:
+// value: Any number of dimensions.
+// bias: 1-D with size the last dimension of `value`.
+//
+// Returns Broadcasted sum of `value` and `bias`.
+func BiasAddV1(scope *Scope, value tf.Output, bias tf.Output) (output tf.Output) {
if scope.Err() != nil {
return
}
opspec := tf.OpSpec{
- Type: "Relu",
+ Type: "BiasAddV1",
Input: []tf.Input{
- features,
+ value, bias,
},
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// ResourceApplyAddSignAttr is an optional argument to ResourceApplyAddSign.
-type ResourceApplyAddSignAttr func(optionalAttr)
-
-// ResourceApplyAddSignUseLocking sets the optional use_locking attribute to value.
+// Reverses specific dimensions of a tensor.
//
-// value: If `True`, updating of the var and m tensors is
-// protected by a lock; otherwise the behavior is undefined, but may exhibit less
-// contention.
-// If not specified, defaults to false
-func ResourceApplyAddSignUseLocking(value bool) ResourceApplyAddSignAttr {
- return func(m optionalAttr) {
- m["use_locking"] = value
- }
-}
-
-// Update '*var' according to the AddSign update.
+// NOTE `tf.reverse` has now changed behavior in preparation for 1.0.
+// `tf.reverse_v2` is currently an alias that will be deprecated before TF 1.0.
//
-// m_t <- beta1 * m_{t-1} + (1 - beta1) * g
-// update <- (alpha + sign_decay * sign(g) *sign(m)) * g
-// variable <- variable - lr_t * update
+// Given a `tensor`, and a `int32` tensor `axis` representing the set of
+// dimensions of `tensor` to reverse. This operation reverses each dimension
+// `i` for which there exists `j` s.t. `axis[j] == i`.
+//
+// `tensor` can have up to 8 dimensions. The number of dimensions specified
+// in `axis` may be 0 or more entries. If an index is specified more than
+// once, a InvalidArgument error is raised.
+//
+// For example:
+//
+// ```
+// # tensor 't' is [[[[ 0, 1, 2, 3],
+// # [ 4, 5, 6, 7],
+// # [ 8, 9, 10, 11]],
+// # [[12, 13, 14, 15],
+// # [16, 17, 18, 19],
+// # [20, 21, 22, 23]]]]
+// # tensor 't' shape is [1, 2, 3, 4]
+//
+// # 'dims' is [3] or 'dims' is [-1]
+// reverse(t, dims) ==> [[[[ 3, 2, 1, 0],
+// [ 7, 6, 5, 4],
+// [ 11, 10, 9, 8]],
+// [[15, 14, 13, 12],
+// [19, 18, 17, 16],
+// [23, 22, 21, 20]]]]
+//
+// # 'dims' is '[1]' (or 'dims' is '[-3]')
+// reverse(t, dims) ==> [[[[12, 13, 14, 15],
+// [16, 17, 18, 19],
+// [20, 21, 22, 23]
+// [[ 0, 1, 2, 3],
+// [ 4, 5, 6, 7],
+// [ 8, 9, 10, 11]]]]
+//
+// # 'dims' is '[2]' (or 'dims' is '[-2]')
+// reverse(t, dims) ==> [[[[8, 9, 10, 11],
+// [4, 5, 6, 7],
+// [0, 1, 2, 3]]
+// [[20, 21, 22, 23],
+// [16, 17, 18, 19],
+// [12, 13, 14, 15]]]]
+// ```
//
// Arguments:
-// var_: Should be from a Variable().
-// m: Should be from a Variable().
-// lr: Scaling factor. Must be a scalar.
-// alpha: Must be a scalar.
-// sign_decay: Must be a scalar.
-// beta: Must be a scalar.
-// grad: The gradient.
+// tensor: Up to 8-D.
+// axis: 1-D. The indices of the dimensions to reverse. Must be in the range
+// `[-rank(tensor), rank(tensor))`.
//
-// Returns the created operation.
-func ResourceApplyAddSign(scope *Scope, var_ tf.Output, m tf.Output, lr tf.Output, alpha tf.Output, sign_decay tf.Output, beta tf.Output, grad tf.Output, optional ...ResourceApplyAddSignAttr) (o *tf.Operation) {
+// Returns The same shape as `tensor`.
+func ReverseV2(scope *Scope, tensor tf.Output, axis tf.Output) (output tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{}
- for _, a := range optional {
- a(attrs)
- }
opspec := tf.OpSpec{
- Type: "ResourceApplyAddSign",
+ Type: "ReverseV2",
Input: []tf.Input{
- var_, m, lr, alpha, sign_decay, beta, grad,
+ tensor, axis,
},
- Attrs: attrs,
}
- return scope.AddOperation(opspec)
+ op := scope.AddOperation(opspec)
+ return op.Output(0)
}
-// FractionalMaxPoolGradAttr is an optional argument to FractionalMaxPoolGrad.
-type FractionalMaxPoolGradAttr func(optionalAttr)
+// RealAttr is an optional argument to Real.
+type RealAttr func(optionalAttr)
-// FractionalMaxPoolGradOverlapping sets the optional overlapping attribute to value.
-//
-// value: When set to True, it means when pooling, the values at the boundary
-// of adjacent pooling cells are used by both cells. For example:
-//
-// `index 0 1 2 3 4`
-//
-// `value 20 5 16 3 7`
-//
-// If the pooling sequence is [0, 2, 4], then 16, at index 2 will be used twice.
-// The result would be [20, 16] for fractional max pooling.
-// If not specified, defaults to false
-func FractionalMaxPoolGradOverlapping(value bool) FractionalMaxPoolGradAttr {
+// RealTout sets the optional Tout attribute to value.
+// If not specified, defaults to DT_FLOAT
+func RealTout(value tf.DataType) RealAttr {
return func(m optionalAttr) {
- m["overlapping"] = value
+ m["Tout"] = value
}
}
-// Computes gradient of the FractionalMaxPool function.
+// Returns the real part of a complex number.
//
-// Arguments:
-// orig_input: Original input for `fractional_max_pool`
-// orig_output: Original output for `fractional_max_pool`
-// out_backprop: 4-D with shape `[batch, height, width, channels]`. Gradients
-// w.r.t. the output of `fractional_max_pool`.
-// row_pooling_sequence: row pooling sequence, form pooling region with
-// col_pooling_sequence.
-// col_pooling_sequence: column pooling sequence, form pooling region with
-// row_pooling sequence.
+// Given a tensor `input` of complex numbers, this operation returns a tensor of
+// type `float` that is the real part of each element in `input`. All elements in
+// `input` must be complex numbers of the form \\(a + bj\\), where *a* is the real
+// part returned by this operation and *b* is the imaginary part.
//
-// Returns 4-D. Gradients w.r.t. the input of `fractional_max_pool`.
-func FractionalMaxPoolGrad(scope *Scope, orig_input tf.Output, orig_output tf.Output, out_backprop tf.Output, row_pooling_sequence tf.Output, col_pooling_sequence tf.Output, optional ...FractionalMaxPoolGradAttr) (output tf.Output) {
+// For example:
+//
+// ```
+// # tensor 'input' is [-2.25 + 4.75j, 3.25 + 5.75j]
+// tf.real(input) ==> [-2.25, 3.25]
+// ```
+func Real(scope *Scope, input tf.Output, optional ...RealAttr) (output tf.Output) {
if scope.Err() != nil {
return
}
a(attrs)
}
opspec := tf.OpSpec{
- Type: "FractionalMaxPoolGrad",
+ Type: "Real",
Input: []tf.Input{
- orig_input, orig_output, out_backprop, row_pooling_sequence, col_pooling_sequence,
+ input,
},
Attrs: attrs,
}
return op.Output(0)
}
-// ResourceApplyAdagradDAAttr is an optional argument to ResourceApplyAdagradDA.
-type ResourceApplyAdagradDAAttr func(optionalAttr)
+// AudioSummaryAttr is an optional argument to AudioSummary.
+type AudioSummaryAttr func(optionalAttr)
-// ResourceApplyAdagradDAUseLocking sets the optional use_locking attribute to value.
+// AudioSummaryMaxOutputs sets the optional max_outputs attribute to value.
//
-// value: If True, updating of the var and accum tensors will be protected by
-// a lock; otherwise the behavior is undefined, but may exhibit less contention.
-// If not specified, defaults to false
-func ResourceApplyAdagradDAUseLocking(value bool) ResourceApplyAdagradDAAttr {
+// value: Max number of batch elements to generate audio for.
+// If not specified, defaults to 3
+//
+// REQUIRES: value >= 1
+func AudioSummaryMaxOutputs(value int64) AudioSummaryAttr {
return func(m optionalAttr) {
- m["use_locking"] = value
+ m["max_outputs"] = value
}
}
-// Update '*var' according to the proximal adagrad scheme.
+// Outputs a `Summary` protocol buffer with audio.
+//
+// DEPRECATED at GraphDef version 15: Use AudioSummaryV2.
+//
+// The summary has up to `max_outputs` summary values containing audio. The
+// audio is built from `tensor` which must be 3-D with shape `[batch_size,
+// frames, channels]` or 2-D with shape `[batch_size, frames]`. The values are
+// assumed to be in the range of `[-1.0, 1.0]` with a sample rate of `sample_rate`.
+//
+// The `tag` argument is a scalar `Tensor` of type `string`. It is used to
+// build the `tag` of the summary values:
+//
+// * If `max_outputs` is 1, the summary value tag is '*tag*/audio'.
+// * If `max_outputs` is greater than 1, the summary value tags are
+// generated sequentially as '*tag*/audio/0', '*tag*/audio/1', etc.
//
// Arguments:
-// var_: Should be from a Variable().
-// gradient_accumulator: Should be from a Variable().
-// gradient_squared_accumulator: Should be from a Variable().
-// grad: The gradient.
-// lr: Scaling factor. Must be a scalar.
-// l1: L1 regularization. Must be a scalar.
-// l2: L2 regularization. Must be a scalar.
-// global_step: Training step number. Must be a scalar.
+// tag: Scalar. Used to build the `tag` attribute of the summary values.
+// tensor: 2-D of shape `[batch_size, frames]`.
+// sample_rate: The sample rate of the signal in hertz.
//
-// Returns the created operation.
-func ResourceApplyAdagradDA(scope *Scope, var_ tf.Output, gradient_accumulator tf.Output, gradient_squared_accumulator tf.Output, grad tf.Output, lr tf.Output, l1 tf.Output, l2 tf.Output, global_step tf.Output, optional ...ResourceApplyAdagradDAAttr) (o *tf.Operation) {
+// Returns Scalar. Serialized `Summary` protocol buffer.
+func AudioSummary(scope *Scope, tag tf.Output, tensor tf.Output, sample_rate float32, optional ...AudioSummaryAttr) (summary tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{}
+ attrs := map[string]interface{}{"sample_rate": sample_rate}
for _, a := range optional {
a(attrs)
}
opspec := tf.OpSpec{
- Type: "ResourceApplyAdagradDA",
+ Type: "AudioSummary",
Input: []tf.Input{
- var_, gradient_accumulator, gradient_squared_accumulator, grad, lr, l1, l2, global_step,
+ tag, tensor,
},
Attrs: attrs,
}
- return scope.AddOperation(opspec)
+ op := scope.AddOperation(opspec)
+ return op.Output(0)
}
-// SparseReduceMaxSparseAttr is an optional argument to SparseReduceMaxSparse.
-type SparseReduceMaxSparseAttr func(optionalAttr)
+// QrAttr is an optional argument to Qr.
+type QrAttr func(optionalAttr)
-// SparseReduceMaxSparseKeepDims sets the optional keep_dims attribute to value.
+// QrFullMatrices sets the optional full_matrices attribute to value.
//
-// value: If true, retain reduced dimensions with length 1.
+// value: If true, compute full-sized `q` and `r`. If false
+// (the default), compute only the leading `P` columns of `q`.
// If not specified, defaults to false
-func SparseReduceMaxSparseKeepDims(value bool) SparseReduceMaxSparseAttr {
+func QrFullMatrices(value bool) QrAttr {
return func(m optionalAttr) {
- m["keep_dims"] = value
+ m["full_matrices"] = value
}
}
-// Computes the max of elements across dimensions of a SparseTensor.
+// Computes the QR decompositions of one or more matrices.
//
-// This Op takes a SparseTensor and is the sparse counterpart to
-// `tf.reduce_max()`. In contrast to SparseReduceMax, this Op returns a
-// SparseTensor.
+// Computes the QR decomposition of each inner matrix in `tensor` such that
+// `tensor[..., :, :] = q[..., :, :] * r[..., :,:])`
//
-// Reduces `sp_input` along the dimensions given in `reduction_axes`. Unless
-// `keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in
-// `reduction_axes`. If `keep_dims` is true, the reduced dimensions are retained
-// with length 1.
-//
-// If `reduction_axes` has no entries, all dimensions are reduced, and a tensor
-// with a single element is returned. Additionally, the axes can be negative,
-// which are interpreted according to the indexing rules in Python.
+// ```python
+// # a is a tensor.
+// # q is a tensor of orthonormal matrices.
+// # r is a tensor of upper triangular matrices.
+// q, r = qr(a)
+// q_full, r_full = qr(a, full_matrices=True)
+// ```
//
// Arguments:
-// input_indices: 2-D. `N x R` matrix with the indices of non-empty values in a
-// SparseTensor, possibly not in canonical ordering.
-// input_values: 1-D. `N` non-empty values corresponding to `input_indices`.
-// input_shape: 1-D. Shape of the input SparseTensor.
-// reduction_axes: 1-D. Length-`K` vector containing the reduction axes.
-func SparseReduceMaxSparse(scope *Scope, input_indices tf.Output, input_values tf.Output, input_shape tf.Output, reduction_axes tf.Output, optional ...SparseReduceMaxSparseAttr) (output_indices tf.Output, output_values tf.Output, output_shape tf.Output) {
+// input: A tensor of shape `[..., M, N]` whose inner-most 2 dimensions
+// form matrices of size `[M, N]`. Let `P` be the minimum of `M` and `N`.
+//
+// Returns Orthonormal basis for range of `a`. If `full_matrices` is `False` then
+// shape is `[..., M, P]`; if `full_matrices` is `True` then shape is
+// `[..., M, M]`.Triangular factor. If `full_matrices` is `False` then shape is
+// `[..., P, N]`. If `full_matrices` is `True` then shape is `[..., M, N]`.
+func Qr(scope *Scope, input tf.Output, optional ...QrAttr) (q tf.Output, r tf.Output) {
if scope.Err() != nil {
return
}
a(attrs)
}
opspec := tf.OpSpec{
- Type: "SparseReduceMaxSparse",
+ Type: "Qr",
Input: []tf.Input{
- input_indices, input_values, input_shape, reduction_axes,
+ input,
},
Attrs: attrs,
}
op := scope.AddOperation(opspec)
- return op.Output(0), op.Output(1), op.Output(2)
+ return op.Output(0), op.Output(1)
}
-// Creates a dataset that emits the outputs of `input_dataset` `count` times.
-//
-// Arguments:
-//
-// count: A scalar representing the number of times that `input_dataset` should
-// be repeated. A value of `-1` indicates that it should be repeated infinitely.
-//
-//
-func RepeatDataset(scope *Scope, input_dataset tf.Output, count tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) {
+// Records the bytes size of each element of `input_dataset` in a StatsAggregator.
+func BytesProducedStatsDataset(scope *Scope, input_dataset tf.Output, tag tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) {
if scope.Err() != nil {
return
}
attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes}
opspec := tf.OpSpec{
- Type: "RepeatDataset",
+ Type: "BytesProducedStatsDataset",
Input: []tf.Input{
- input_dataset, count,
+ input_dataset, tag,
},
Attrs: attrs,
}
return op.Output(0)
}
-// AddManySparseToTensorsMapAttr is an optional argument to AddManySparseToTensorsMap.
-type AddManySparseToTensorsMapAttr func(optionalAttr)
-
-// AddManySparseToTensorsMapContainer sets the optional container attribute to value.
-//
-// value: The container name for the `SparseTensorsMap` created by this op.
-// If not specified, defaults to ""
-func AddManySparseToTensorsMapContainer(value string) AddManySparseToTensorsMapAttr {
- return func(m optionalAttr) {
- m["container"] = value
- }
-}
+// ResourceSparseApplyProximalGradientDescentAttr is an optional argument to ResourceSparseApplyProximalGradientDescent.
+type ResourceSparseApplyProximalGradientDescentAttr func(optionalAttr)
-// AddManySparseToTensorsMapSharedName sets the optional shared_name attribute to value.
+// ResourceSparseApplyProximalGradientDescentUseLocking sets the optional use_locking attribute to value.
//
-// value: The shared name for the `SparseTensorsMap` created by this op.
-// If blank, the new Operation's unique name is used.
-// If not specified, defaults to ""
-func AddManySparseToTensorsMapSharedName(value string) AddManySparseToTensorsMapAttr {
+// value: If True, the subtraction will be protected by a lock;
+// otherwise the behavior is undefined, but may exhibit less contention.
+// If not specified, defaults to false
+func ResourceSparseApplyProximalGradientDescentUseLocking(value bool) ResourceSparseApplyProximalGradientDescentAttr {
return func(m optionalAttr) {
- m["shared_name"] = value
+ m["use_locking"] = value
}
}
-// Add an `N`-minibatch `SparseTensor` to a `SparseTensorsMap`, return `N` handles.
-//
-// A `SparseTensor` of rank `R` is represented by three tensors: `sparse_indices`,
-// `sparse_values`, and `sparse_shape`, where
-//
-// ```sparse_indices.shape[1] == sparse_shape.shape[0] == R```
-//
-// An `N`-minibatch of `SparseTensor` objects is represented as a `SparseTensor`
-// having a first `sparse_indices` column taking values between `[0, N)`, where
-// the minibatch size `N == sparse_shape[0]`.
-//
-// The input `SparseTensor` must have rank `R` greater than 1, and the first
-// dimension is treated as the minibatch dimension. Elements of the `SparseTensor`
-// must be sorted in increasing order of this first dimension. The stored
-// `SparseTensor` objects pointed to by each row of the output `sparse_handles`
-// will have rank `R-1`.
+// Sparse update '*var' as FOBOS algorithm with fixed learning rate.
//
-// The `SparseTensor` values can then be read out as part of a minibatch by passing
-// the given keys as vector elements to `TakeManySparseFromTensorsMap`. To ensure
-// the correct `SparseTensorsMap` is accessed, ensure that the same
-// `container` and `shared_name` are passed to that Op. If no `shared_name`
-// is provided here, instead use the *name* of the Operation created by calling
-// `AddManySparseToTensorsMap` as the `shared_name` passed to
-// `TakeManySparseFromTensorsMap`. Ensure the Operations are colocated.
+// That is for rows we have grad for, we update var as follows:
+// prox_v = var - alpha * grad
+// var = sign(prox_v)/(1+alpha*l2) * max{|prox_v|-alpha*l1,0}
//
// Arguments:
-// sparse_indices: 2-D. The `indices` of the minibatch `SparseTensor`.
-// `sparse_indices[:, 0]` must be ordered values in `[0, N)`.
-// sparse_values: 1-D. The `values` of the minibatch `SparseTensor`.
-// sparse_shape: 1-D. The `shape` of the minibatch `SparseTensor`.
-// The minibatch size `N == sparse_shape[0]`.
+// var_: Should be from a Variable().
+// alpha: Scaling factor. Must be a scalar.
+// l1: L1 regularization. Must be a scalar.
+// l2: L2 regularization. Must be a scalar.
+// grad: The gradient.
+// indices: A vector of indices into the first dimension of var and accum.
//
-// Returns 1-D. The handles of the `SparseTensor` now stored in the
-// `SparseTensorsMap`. Shape: `[N]`.
-func AddManySparseToTensorsMap(scope *Scope, sparse_indices tf.Output, sparse_values tf.Output, sparse_shape tf.Output, optional ...AddManySparseToTensorsMapAttr) (sparse_handles tf.Output) {
+// Returns the created operation.
+func ResourceSparseApplyProximalGradientDescent(scope *Scope, var_ tf.Output, alpha tf.Output, l1 tf.Output, l2 tf.Output, grad tf.Output, indices tf.Output, optional ...ResourceSparseApplyProximalGradientDescentAttr) (o *tf.Operation) {
if scope.Err() != nil {
return
}
a(attrs)
}
opspec := tf.OpSpec{
- Type: "AddManySparseToTensorsMap",
+ Type: "ResourceSparseApplyProximalGradientDescent",
Input: []tf.Input{
- sparse_indices, sparse_values, sparse_shape,
+ var_, alpha, l1, l2, grad, indices,
},
Attrs: attrs,
}
- op := scope.AddOperation(opspec)
- return op.Output(0)
-}
-
-// Concatenates tensors along one dimension.
-//
-// Arguments:
-// values: List of `N` Tensors to concatenate. Their ranks and types must match,
-// and their sizes must match in all dimensions except `concat_dim`.
-// axis: 0-D. The dimension along which to concatenate. Must be in the
-// range [-rank(values), rank(values)).
-//
-// Returns A `Tensor` with the concatenation of values stacked along the
-// `concat_dim` dimension. This tensor's shape matches that of `values` except
-// in `concat_dim` where it has the sum of the sizes.
-func ConcatV2(scope *Scope, values []tf.Output, axis tf.Output) (output tf.Output) {
- if scope.Err() != nil {
- return
- }
- opspec := tf.OpSpec{
- Type: "ConcatV2",
- Input: []tf.Input{
- tf.OutputList(values), axis,
- },
- }
- op := scope.AddOperation(opspec)
- return op.Output(0)
-}
-
-// Reads and outputs the entire contents of the input filename.
-func ReadFile(scope *Scope, filename tf.Output) (contents tf.Output) {
- if scope.Err() != nil {
- return
- }
- opspec := tf.OpSpec{
- Type: "ReadFile",
- Input: []tf.Input{
- filename,
- },
- }
- op := scope.AddOperation(opspec)
- return op.Output(0)
+ return scope.AddOperation(opspec)
}
-// MinAttr is an optional argument to Min.
-type MinAttr func(optionalAttr)
+// MeanAttr is an optional argument to Mean.
+type MeanAttr func(optionalAttr)
-// MinKeepDims sets the optional keep_dims attribute to value.
+// MeanKeepDims sets the optional keep_dims attribute to value.
//
// value: If true, retain reduced dimensions with length 1.
// If not specified, defaults to false
-func MinKeepDims(value bool) MinAttr {
+func MeanKeepDims(value bool) MeanAttr {
return func(m optionalAttr) {
m["keep_dims"] = value
}
}
-// Computes the minimum of elements across dimensions of a tensor.
+// Computes the mean of elements across dimensions of a tensor.
//
// Reduces `input` along the dimensions given in `axis`. Unless
// `keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in
// `[-rank(input), rank(input))`.
//
// Returns The reduced tensor.
-func Min(scope *Scope, input tf.Output, axis tf.Output, optional ...MinAttr) (output tf.Output) {
+func Mean(scope *Scope, input tf.Output, axis tf.Output, optional ...MeanAttr) (output tf.Output) {
if scope.Err() != nil {
return
}
a(attrs)
}
opspec := tf.OpSpec{
- Type: "Min",
+ Type: "Mean",
Input: []tf.Input{
input, axis,
},
return op.Output(0)
}
-// Shuffle dimensions of x according to a permutation.
+// InitializeTableFromTextFileV2Attr is an optional argument to InitializeTableFromTextFileV2.
+type InitializeTableFromTextFileV2Attr func(optionalAttr)
+
+// InitializeTableFromTextFileV2VocabSize sets the optional vocab_size attribute to value.
//
-// The output `y` has the same rank as `x`. The shapes of `x` and `y` satisfy:
-// `y.shape[i] == x.shape[perm[i]] for i in [0, 1, ..., rank(x) - 1]`
-func Transpose(scope *Scope, x tf.Output, perm tf.Output) (y tf.Output) {
- if scope.Err() != nil {
- return
+// value: Number of elements of the file, use -1 if unknown.
+// If not specified, defaults to -1
+//
+// REQUIRES: value >= -1
+func InitializeTableFromTextFileV2VocabSize(value int64) InitializeTableFromTextFileV2Attr {
+ return func(m optionalAttr) {
+ m["vocab_size"] = value
}
- opspec := tf.OpSpec{
- Type: "Transpose",
- Input: []tf.Input{
- x, perm,
- },
+}
+
+// InitializeTableFromTextFileV2Delimiter sets the optional delimiter attribute to value.
+//
+// value: Delimiter to separate fields in a line.
+// If not specified, defaults to "\t"
+func InitializeTableFromTextFileV2Delimiter(value string) InitializeTableFromTextFileV2Attr {
+ return func(m optionalAttr) {
+ m["delimiter"] = value
}
- op := scope.AddOperation(opspec)
- return op.Output(0)
}
-// Computes sigmoid of `x` element-wise.
+// Initializes a table from a text file.
//
-// Specifically, `y = 1 / (1 + exp(-x))`.
-func Sigmoid(scope *Scope, x tf.Output) (y tf.Output) {
+// It inserts one key-value pair into the table for each line of the file.
+// The key and value is extracted from the whole line content, elements from the
+// split line based on `delimiter` or the line number (starting from zero).
+// Where to extract the key and value from a line is specified by `key_index` and
+// `value_index`.
+//
+// - A value of -1 means use the line number(starting from zero), expects `int64`.
+// - A value of -2 means use the whole line content, expects `string`.
+// - A value >= 0 means use the index (starting at zero) of the split line based
+// on `delimiter`.
+//
+// Arguments:
+// table_handle: Handle to a table which will be initialized.
+// filename: Filename of a vocabulary text file.
+// key_index: Column index in a line to get the table `key` values from.
+// value_index: Column index that represents information of a line to get the table
+// `value` values from.
+//
+// Returns the created operation.
+func InitializeTableFromTextFileV2(scope *Scope, table_handle tf.Output, filename tf.Output, key_index int64, value_index int64, optional ...InitializeTableFromTextFileV2Attr) (o *tf.Operation) {
if scope.Err() != nil {
return
}
+ attrs := map[string]interface{}{"key_index": key_index, "value_index": value_index}
+ for _, a := range optional {
+ a(attrs)
+ }
opspec := tf.OpSpec{
- Type: "Sigmoid",
+ Type: "InitializeTableFromTextFileV2",
Input: []tf.Input{
- x,
+ table_handle, filename,
},
+ Attrs: attrs,
}
- op := scope.AddOperation(opspec)
- return op.Output(0)
+ return scope.AddOperation(opspec)
}
-// FusedBatchNormAttr is an optional argument to FusedBatchNorm.
-type FusedBatchNormAttr func(optionalAttr)
+// QuantizedReluAttr is an optional argument to QuantizedRelu.
+type QuantizedReluAttr func(optionalAttr)
-// FusedBatchNormEpsilon sets the optional epsilon attribute to value.
-//
-// value: A small float number added to the variance of x.
-// If not specified, defaults to 0.0001
-func FusedBatchNormEpsilon(value float32) FusedBatchNormAttr {
- return func(m optionalAttr) {
- m["epsilon"] = value
- }
-}
-
-// FusedBatchNormDataFormat sets the optional data_format attribute to value.
-//
-// value: The data format for x and y. Either "NHWC" (default) or "NCHW".
-// If not specified, defaults to "NHWC"
-func FusedBatchNormDataFormat(value string) FusedBatchNormAttr {
- return func(m optionalAttr) {
- m["data_format"] = value
- }
-}
-
-// FusedBatchNormIsTraining sets the optional is_training attribute to value.
-//
-// value: A bool value to indicate the operation is for training (default)
-// or inference.
-// If not specified, defaults to true
-func FusedBatchNormIsTraining(value bool) FusedBatchNormAttr {
+// QuantizedReluOutType sets the optional out_type attribute to value.
+// If not specified, defaults to DT_QUINT8
+func QuantizedReluOutType(value tf.DataType) QuantizedReluAttr {
return func(m optionalAttr) {
- m["is_training"] = value
+ m["out_type"] = value
}
}
-// Batch normalization.
-//
-// Note that the size of 4D Tensors are defined by either "NHWC" or "NCHW".
-// The size of 1D Tensors matches the dimension C of the 4D Tensors.
+// Computes Quantized Rectified Linear: `max(features, 0)`
//
// Arguments:
-// x: A 4D Tensor for input data.
-// scale: A 1D Tensor for scaling factor, to scale the normalized x.
-// offset: A 1D Tensor for offset, to shift to the normalized x.
-// mean: A 1D Tensor for population mean. Used for inference only;
-// must be empty for training.
-// variance: A 1D Tensor for population variance. Used for inference only;
-// must be empty for training.
//
-// Returns A 4D Tensor for output data.A 1D Tensor for the computed batch mean, to be used by TensorFlow
-// to compute the running mean.A 1D Tensor for the computed batch variance, to be used by
-// TensorFlow to compute the running variance.A 1D Tensor for the computed batch mean, to be reused
-// in the gradient computation.A 1D Tensor for the computed batch variance (inverted variance
-// in the cuDNN case), to be reused in the gradient computation.
-func FusedBatchNorm(scope *Scope, x tf.Output, scale tf.Output, offset tf.Output, mean tf.Output, variance tf.Output, optional ...FusedBatchNormAttr) (y tf.Output, batch_mean tf.Output, batch_variance tf.Output, reserve_space_1 tf.Output, reserve_space_2 tf.Output) {
+// min_features: The float value that the lowest quantized value represents.
+// max_features: The float value that the highest quantized value represents.
+//
+// Returns Has the same output shape as "features".The float value that the lowest quantized value represents.The float value that the highest quantized value represents.
+func QuantizedRelu(scope *Scope, features tf.Output, min_features tf.Output, max_features tf.Output, optional ...QuantizedReluAttr) (activations tf.Output, min_activations tf.Output, max_activations tf.Output) {
if scope.Err() != nil {
return
}
a(attrs)
}
opspec := tf.OpSpec{
- Type: "FusedBatchNorm",
+ Type: "QuantizedRelu",
Input: []tf.Input{
- x, scale, offset, mean, variance,
+ features, min_features, max_features,
},
Attrs: attrs,
}
op := scope.AddOperation(opspec)
- return op.Output(0), op.Output(1), op.Output(2), op.Output(3), op.Output(4)
+ return op.Output(0), op.Output(1), op.Output(2)
}
-// RandomStandardNormalAttr is an optional argument to RandomStandardNormal.
-type RandomStandardNormalAttr func(optionalAttr)
-
-// RandomStandardNormalSeed sets the optional seed attribute to value.
+// Reshapes a SparseTensor to represent values in a new dense shape.
//
-// value: If either `seed` or `seed2` are set to be non-zero, the random number
-// generator is seeded by the given seed. Otherwise, it is seeded by a
-// random seed.
-// If not specified, defaults to 0
-func RandomStandardNormalSeed(value int64) RandomStandardNormalAttr {
- return func(m optionalAttr) {
- m["seed"] = value
- }
-}
-
-// RandomStandardNormalSeed2 sets the optional seed2 attribute to value.
+// This operation has the same semantics as reshape on the represented dense
+// tensor. The `input_indices` are recomputed based on the requested `new_shape`.
//
-// value: A second seed to avoid seed collision.
-// If not specified, defaults to 0
-func RandomStandardNormalSeed2(value int64) RandomStandardNormalAttr {
- return func(m optionalAttr) {
- m["seed2"] = value
- }
-}
-
-// Outputs random values from a normal distribution.
+// If one component of `new_shape` is the special value -1, the size of that
+// dimension is computed so that the total dense size remains constant. At
+// most one component of `new_shape` can be -1. The number of dense elements
+// implied by `new_shape` must be the same as the number of dense elements
+// originally implied by `input_shape`.
//
-// The generated values will have mean 0 and standard deviation 1.
+// Reshaping does not affect the order of values in the SparseTensor.
+//
+// If the input tensor has rank `R_in` and `N` non-empty values, and `new_shape`
+// has length `R_out`, then `input_indices` has shape `[N, R_in]`,
+// `input_shape` has length `R_in`, `output_indices` has shape `[N, R_out]`, and
+// `output_shape` has length `R_out`.
//
// Arguments:
-// shape: The shape of the output tensor.
-// dtype: The type of the output.
+// input_indices: 2-D. `N x R_in` matrix with the indices of non-empty values in a
+// SparseTensor.
+// input_shape: 1-D. `R_in` vector with the input SparseTensor's dense shape.
+// new_shape: 1-D. `R_out` vector with the requested new dense shape.
//
-// Returns A tensor of the specified shape filled with random normal values.
-func RandomStandardNormal(scope *Scope, shape tf.Output, dtype tf.DataType, optional ...RandomStandardNormalAttr) (output tf.Output) {
+// Returns 2-D. `N x R_out` matrix with the updated indices of non-empty
+// values in the output SparseTensor.1-D. `R_out` vector with the full dense shape of the output
+// SparseTensor. This is the same as `new_shape` but with any -1 dimensions
+// filled in.
+func SparseReshape(scope *Scope, input_indices tf.Output, input_shape tf.Output, new_shape tf.Output) (output_indices tf.Output, output_shape tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{"dtype": dtype}
- for _, a := range optional {
- a(attrs)
- }
opspec := tf.OpSpec{
- Type: "RandomStandardNormal",
+ Type: "SparseReshape",
Input: []tf.Input{
- shape,
+ input_indices, input_shape, new_shape,
},
- Attrs: attrs,
}
op := scope.AddOperation(opspec)
- return op.Output(0)
+ return op.Output(0), op.Output(1)
}
-// Component-wise divides a SparseTensor by a dense Tensor.
-//
-// *Limitation*: this Op only broadcasts the dense side to the sparse side, but not
-// the other direction.
-//
-// Arguments:
-// sp_indices: 2-D. `N x R` matrix with the indices of non-empty values in a
-// SparseTensor, possibly not in canonical ordering.
-// sp_values: 1-D. `N` non-empty values corresponding to `sp_indices`.
-// sp_shape: 1-D. Shape of the input SparseTensor.
-// dense: `R`-D. The dense Tensor operand.
+// Deprecated. Use TensorArraySplitV3
//
-// Returns 1-D. The `N` values that are operated on.
-func SparseDenseCwiseDiv(scope *Scope, sp_indices tf.Output, sp_values tf.Output, sp_shape tf.Output, dense tf.Output) (output tf.Output) {
+// DEPRECATED at GraphDef version 26: Use TensorArraySplitV3
+func TensorArraySplitV2(scope *Scope, handle tf.Output, value tf.Output, lengths tf.Output, flow_in tf.Output) (flow_out tf.Output) {
if scope.Err() != nil {
return
}
opspec := tf.OpSpec{
- Type: "SparseDenseCwiseDiv",
+ Type: "TensorArraySplitV2",
Input: []tf.Input{
- sp_indices, sp_values, sp_shape, dense,
+ handle, value, lengths, flow_in,
},
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// FractionalAvgPoolGradAttr is an optional argument to FractionalAvgPoolGrad.
-type FractionalAvgPoolGradAttr func(optionalAttr)
+// PackAttr is an optional argument to Pack.
+type PackAttr func(optionalAttr)
-// FractionalAvgPoolGradOverlapping sets the optional overlapping attribute to value.
-//
-// value: When set to True, it means when pooling, the values at the boundary
-// of adjacent pooling cells are used by both cells. For example:
-//
-// `index 0 1 2 3 4`
-//
-// `value 20 5 16 3 7`
+// PackAxis sets the optional axis attribute to value.
//
-// If the pooling sequence is [0, 2, 4], then 16, at index 2 will be used twice.
-// The result would be [41/3, 26/3] for fractional avg pooling.
-// If not specified, defaults to false
-func FractionalAvgPoolGradOverlapping(value bool) FractionalAvgPoolGradAttr {
+// value: Dimension along which to pack. Negative values wrap around, so the
+// valid range is `[-(R+1), R+1)`.
+// If not specified, defaults to 0
+func PackAxis(value int64) PackAttr {
return func(m optionalAttr) {
- m["overlapping"] = value
+ m["axis"] = value
}
}
-// Computes gradient of the FractionalAvgPool function.
+// Packs a list of `N` rank-`R` tensors into one rank-`(R+1)` tensor.
//
-// Unlike FractionalMaxPoolGrad, we don't need to find arg_max for
-// FractionalAvgPoolGrad, we just need to evenly back-propagate each element of
-// out_backprop to those indices that form the same pooling cell. Therefore, we
-// just need to know the shape of original input tensor, instead of the whole
-// tensor.
+// Packs the `N` tensors in `values` into a tensor with rank one higher than each
+// tensor in `values`, by packing them along the `axis` dimension.
+// Given a list of tensors of shape `(A, B, C)`;
+//
+// if `axis == 0` then the `output` tensor will have the shape `(N, A, B, C)`.
+// if `axis == 1` then the `output` tensor will have the shape `(A, N, B, C)`.
+// Etc.
+//
+// For example:
+//
+// ```
+// # 'x' is [1, 4]
+// # 'y' is [2, 5]
+// # 'z' is [3, 6]
+// pack([x, y, z]) => [[1, 4], [2, 5], [3, 6]] # Pack along first dim.
+// pack([x, y, z], axis=1) => [[1, 2, 3], [4, 5, 6]]
+// ```
+//
+// This is the opposite of `unpack`.
//
// Arguments:
-// orig_input_tensor_shape: Original input tensor shape for `fractional_avg_pool`
-// out_backprop: 4-D with shape `[batch, height, width, channels]`. Gradients
-// w.r.t. the output of `fractional_avg_pool`.
-// row_pooling_sequence: row pooling sequence, form pooling region with
-// col_pooling_sequence.
-// col_pooling_sequence: column pooling sequence, form pooling region with
-// row_pooling sequence.
+// values: Must be of same shape and type.
//
-// Returns 4-D. Gradients w.r.t. the input of `fractional_avg_pool`.
-func FractionalAvgPoolGrad(scope *Scope, orig_input_tensor_shape tf.Output, out_backprop tf.Output, row_pooling_sequence tf.Output, col_pooling_sequence tf.Output, optional ...FractionalAvgPoolGradAttr) (output tf.Output) {
+// Returns The packed tensor.
+func Pack(scope *Scope, values []tf.Output, optional ...PackAttr) (output tf.Output) {
if scope.Err() != nil {
return
}
a(attrs)
}
opspec := tf.OpSpec{
- Type: "FractionalAvgPoolGrad",
+ Type: "Pack",
Input: []tf.Input{
- orig_input_tensor_shape, out_backprop, row_pooling_sequence, col_pooling_sequence,
+ tf.OutputList(values),
},
Attrs: attrs,
}
return op.Output(0)
}
-// Concatenates tensors along one dimension.
+// Reorders a SparseTensor into the canonical, row-major ordering.
+//
+// Note that by convention, all sparse ops preserve the canonical ordering along
+// increasing dimension number. The only time ordering can be violated is during
+// manual manipulation of the indices and values vectors to add entries.
+//
+// Reordering does not affect the shape of the SparseTensor.
+//
+// If the tensor has rank `R` and `N` non-empty values, `input_indices` has
+// shape `[N, R]`, input_values has length `N`, and input_shape has length `R`.
//
// Arguments:
-// concat_dim: 0-D. The dimension along which to concatenate. Must be in the
-// range [0, rank(values)).
-// values: The `N` Tensors to concatenate. Their ranks and types must match,
-// and their sizes must match in all dimensions except `concat_dim`.
+// input_indices: 2-D. `N x R` matrix with the indices of non-empty values in a
+// SparseTensor, possibly not in canonical ordering.
+// input_values: 1-D. `N` non-empty values corresponding to `input_indices`.
+// input_shape: 1-D. Shape of the input SparseTensor.
//
-// Returns A `Tensor` with the concatenation of values stacked along the
-// `concat_dim` dimension. This tensor's shape matches that of `values` except
-// in `concat_dim` where it has the sum of the sizes.
-func Concat(scope *Scope, concat_dim tf.Output, values []tf.Output) (output tf.Output) {
+// Returns 2-D. `N x R` matrix with the same indices as input_indices, but
+// in canonical row-major ordering.1-D. `N` non-empty values corresponding to `output_indices`.
+func SparseReorder(scope *Scope, input_indices tf.Output, input_values tf.Output, input_shape tf.Output) (output_indices tf.Output, output_values tf.Output) {
if scope.Err() != nil {
return
}
opspec := tf.OpSpec{
- Type: "Concat",
+ Type: "SparseReorder",
Input: []tf.Input{
- concat_dim, tf.OutputList(values),
+ input_indices, input_values, input_shape,
+ },
+ }
+ op := scope.AddOperation(opspec)
+ return op.Output(0), op.Output(1)
+}
+
+// Computes rectified linear: `max(features, 0)`.
+func Relu(scope *Scope, features tf.Output) (activations tf.Output) {
+ if scope.Err() != nil {
+ return
+ }
+ opspec := tf.OpSpec{
+ Type: "Relu",
+ Input: []tf.Input{
+ features,
},
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// ResourceApplyMomentumAttr is an optional argument to ResourceApplyMomentum.
-type ResourceApplyMomentumAttr func(optionalAttr)
+// ResourceApplyAddSignAttr is an optional argument to ResourceApplyAddSign.
+type ResourceApplyAddSignAttr func(optionalAttr)
-// ResourceApplyMomentumUseLocking sets the optional use_locking attribute to value.
+// ResourceApplyAddSignUseLocking sets the optional use_locking attribute to value.
//
-// value: If `True`, updating of the var and accum tensors will be protected
-// by a lock; otherwise the behavior is undefined, but may exhibit less
+// value: If `True`, updating of the var and m tensors is
+// protected by a lock; otherwise the behavior is undefined, but may exhibit less
// contention.
// If not specified, defaults to false
-func ResourceApplyMomentumUseLocking(value bool) ResourceApplyMomentumAttr {
+func ResourceApplyAddSignUseLocking(value bool) ResourceApplyAddSignAttr {
return func(m optionalAttr) {
m["use_locking"] = value
}
}
-// ResourceApplyMomentumUseNesterov sets the optional use_nesterov attribute to value.
-//
-// value: If `True`, the tensor passed to compute grad will be
-// var - lr * momentum * accum, so in the end, the var you get is actually
-// var - lr * momentum * accum.
-// If not specified, defaults to false
-func ResourceApplyMomentumUseNesterov(value bool) ResourceApplyMomentumAttr {
- return func(m optionalAttr) {
- m["use_nesterov"] = value
- }
-}
-
-// Update '*var' according to the momentum scheme. Set use_nesterov = True if you
-//
-// want to use Nesterov momentum.
+// Update '*var' according to the AddSign update.
//
-// accum = accum * momentum + grad
-// var -= lr * accum
+// m_t <- beta1 * m_{t-1} + (1 - beta1) * g
+// update <- (alpha + sign_decay * sign(g) *sign(m)) * g
+// variable <- variable - lr_t * update
//
// Arguments:
// var_: Should be from a Variable().
-// accum: Should be from a Variable().
+// m: Should be from a Variable().
// lr: Scaling factor. Must be a scalar.
+// alpha: Must be a scalar.
+// sign_decay: Must be a scalar.
+// beta: Must be a scalar.
// grad: The gradient.
-// momentum: Momentum. Must be a scalar.
//
// Returns the created operation.
-func ResourceApplyMomentum(scope *Scope, var_ tf.Output, accum tf.Output, lr tf.Output, grad tf.Output, momentum tf.Output, optional ...ResourceApplyMomentumAttr) (o *tf.Operation) {
+func ResourceApplyAddSign(scope *Scope, var_ tf.Output, m tf.Output, lr tf.Output, alpha tf.Output, sign_decay tf.Output, beta tf.Output, grad tf.Output, optional ...ResourceApplyAddSignAttr) (o *tf.Operation) {
if scope.Err() != nil {
return
}
a(attrs)
}
opspec := tf.OpSpec{
- Type: "ResourceApplyMomentum",
+ Type: "ResourceApplyAddSign",
Input: []tf.Input{
- var_, accum, lr, grad, momentum,
+ var_, m, lr, alpha, sign_decay, beta, grad,
},
Attrs: attrs,
}
return scope.AddOperation(opspec)
}
-// MaxPoolGradGradAttr is an optional argument to MaxPoolGradGrad.
-type MaxPoolGradGradAttr func(optionalAttr)
+// FractionalMaxPoolGradAttr is an optional argument to FractionalMaxPoolGrad.
+type FractionalMaxPoolGradAttr func(optionalAttr)
-// MaxPoolGradGradDataFormat sets the optional data_format attribute to value.
+// FractionalMaxPoolGradOverlapping sets the optional overlapping attribute to value.
//
-// value: Specify the data format of the input and output data. With the
-// default format "NHWC", the data is stored in the order of:
-// [batch, in_height, in_width, in_channels].
-// Alternatively, the format could be "NCHW", the data storage order of:
-// [batch, in_channels, in_height, in_width].
-// If not specified, defaults to "NHWC"
-func MaxPoolGradGradDataFormat(value string) MaxPoolGradGradAttr {
+// value: When set to True, it means when pooling, the values at the boundary
+// of adjacent pooling cells are used by both cells. For example:
+//
+// `index 0 1 2 3 4`
+//
+// `value 20 5 16 3 7`
+//
+// If the pooling sequence is [0, 2, 4], then 16, at index 2 will be used twice.
+// The result would be [20, 16] for fractional max pooling.
+// If not specified, defaults to false
+func FractionalMaxPoolGradOverlapping(value bool) FractionalMaxPoolGradAttr {
return func(m optionalAttr) {
- m["data_format"] = value
+ m["overlapping"] = value
}
}
-// Computes second-order gradients of the maxpooling function.
+// Computes gradient of the FractionalMaxPool function.
//
// Arguments:
-// orig_input: The original input tensor.
-// orig_output: The original output tensor.
-// grad: 4-D. Gradients of gradients w.r.t. the input of `max_pool`.
-// ksize: The size of the window for each dimension of the input tensor.
-// strides: The stride of the sliding window for each dimension of the
-// input tensor.
-// padding: The type of padding algorithm to use.
+// orig_input: Original input for `fractional_max_pool`
+// orig_output: Original output for `fractional_max_pool`
+// out_backprop: 4-D with shape `[batch, height, width, channels]`. Gradients
+// w.r.t. the output of `fractional_max_pool`.
+// row_pooling_sequence: row pooling sequence, form pooling region with
+// col_pooling_sequence.
+// col_pooling_sequence: column pooling sequence, form pooling region with
+// row_pooling sequence.
//
-// Returns Gradients of gradients w.r.t. the input to `max_pool`.
-func MaxPoolGradGrad(scope *Scope, orig_input tf.Output, orig_output tf.Output, grad tf.Output, ksize []int64, strides []int64, padding string, optional ...MaxPoolGradGradAttr) (output tf.Output) {
+// Returns 4-D. Gradients w.r.t. the input of `fractional_max_pool`.
+func FractionalMaxPoolGrad(scope *Scope, orig_input tf.Output, orig_output tf.Output, out_backprop tf.Output, row_pooling_sequence tf.Output, col_pooling_sequence tf.Output, optional ...FractionalMaxPoolGradAttr) (output tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{"ksize": ksize, "strides": strides, "padding": padding}
+ attrs := map[string]interface{}{}
for _, a := range optional {
a(attrs)
}
opspec := tf.OpSpec{
- Type: "MaxPoolGradGrad",
+ Type: "FractionalMaxPoolGrad",
Input: []tf.Input{
- orig_input, orig_output, grad,
+ orig_input, orig_output, out_backprop, row_pooling_sequence, col_pooling_sequence,
},
Attrs: attrs,
}
return op.Output(0)
}
-// Returns element-wise integer closest to x.
-//
-// If the result is midway between two representable values,
-// the even representable is chosen.
-// For example:
-//
-// ```
-// rint(-1.5) ==> -2.0
-// rint(0.5000001) ==> 1.0
-// rint([-1.7, -1.5, -0.2, 0.2, 1.5, 1.7, 2.0]) ==> [-2., -2., -0., 0., 2., 2., 2.]
-// ```
-func Rint(scope *Scope, x tf.Output) (y tf.Output) {
- if scope.Err() != nil {
- return
- }
- opspec := tf.OpSpec{
- Type: "Rint",
- Input: []tf.Input{
- x,
- },
- }
- op := scope.AddOperation(opspec)
- return op.Output(0)
-}
-
-// OrderedMapUnstageNoKeyAttr is an optional argument to OrderedMapUnstageNoKey.
-type OrderedMapUnstageNoKeyAttr func(optionalAttr)
+// ResourceApplyAdagradDAAttr is an optional argument to ResourceApplyAdagradDA.
+type ResourceApplyAdagradDAAttr func(optionalAttr)
-// OrderedMapUnstageNoKeyCapacity sets the optional capacity attribute to value.
-// If not specified, defaults to 0
+// ResourceApplyAdagradDAUseLocking sets the optional use_locking attribute to value.
//
-// REQUIRES: value >= 0
-func OrderedMapUnstageNoKeyCapacity(value int64) OrderedMapUnstageNoKeyAttr {
+// value: If True, updating of the var and accum tensors will be protected by
+// a lock; otherwise the behavior is undefined, but may exhibit less contention.
+// If not specified, defaults to false
+func ResourceApplyAdagradDAUseLocking(value bool) ResourceApplyAdagradDAAttr {
return func(m optionalAttr) {
- m["capacity"] = value
+ m["use_locking"] = value
}
}
-// OrderedMapUnstageNoKeyMemoryLimit sets the optional memory_limit attribute to value.
-// If not specified, defaults to 0
+// Update '*var' according to the proximal adagrad scheme.
//
-// REQUIRES: value >= 0
-func OrderedMapUnstageNoKeyMemoryLimit(value int64) OrderedMapUnstageNoKeyAttr {
- return func(m optionalAttr) {
- m["memory_limit"] = value
- }
-}
-
-// OrderedMapUnstageNoKeyContainer sets the optional container attribute to value.
-// If not specified, defaults to ""
-func OrderedMapUnstageNoKeyContainer(value string) OrderedMapUnstageNoKeyAttr {
- return func(m optionalAttr) {
- m["container"] = value
- }
-}
-
-// OrderedMapUnstageNoKeySharedName sets the optional shared_name attribute to value.
-// If not specified, defaults to ""
-func OrderedMapUnstageNoKeySharedName(value string) OrderedMapUnstageNoKeyAttr {
- return func(m optionalAttr) {
- m["shared_name"] = value
- }
-}
-
-// Op removes and returns the (key, value) element with the smallest
+// Arguments:
+// var_: Should be from a Variable().
+// gradient_accumulator: Should be from a Variable().
+// gradient_squared_accumulator: Should be from a Variable().
+// grad: The gradient.
+// lr: Scaling factor. Must be a scalar.
+// l1: L1 regularization. Must be a scalar.
+// l2: L2 regularization. Must be a scalar.
+// global_step: Training step number. Must be a scalar.
//
-// key from the underlying container. If the underlying container
-// does not contain elements, the op will block until it does.
-func OrderedMapUnstageNoKey(scope *Scope, indices tf.Output, dtypes []tf.DataType, optional ...OrderedMapUnstageNoKeyAttr) (key tf.Output, values []tf.Output) {
+// Returns the created operation.
+func ResourceApplyAdagradDA(scope *Scope, var_ tf.Output, gradient_accumulator tf.Output, gradient_squared_accumulator tf.Output, grad tf.Output, lr tf.Output, l1 tf.Output, l2 tf.Output, global_step tf.Output, optional ...ResourceApplyAdagradDAAttr) (o *tf.Operation) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{"dtypes": dtypes}
+ attrs := map[string]interface{}{}
for _, a := range optional {
a(attrs)
}
opspec := tf.OpSpec{
- Type: "OrderedMapUnstageNoKey",
+ Type: "ResourceApplyAdagradDA",
Input: []tf.Input{
- indices,
+ var_, gradient_accumulator, gradient_squared_accumulator, grad, lr, l1, l2, global_step,
},
Attrs: attrs,
}
- op := scope.AddOperation(opspec)
- if scope.Err() != nil {
- return
- }
- var idx int
- var err error
- key = op.Output(idx)
- if values, idx, err = makeOutputList(op, idx, "values"); err != nil {
- scope.UpdateErr("OrderedMapUnstageNoKey", err)
- return
- }
- return key, values
+ return scope.AddOperation(opspec)
}
-// MaxPool3DGradGradAttr is an optional argument to MaxPool3DGradGrad.
-type MaxPool3DGradGradAttr func(optionalAttr)
+// SparseReduceMaxSparseAttr is an optional argument to SparseReduceMaxSparse.
+type SparseReduceMaxSparseAttr func(optionalAttr)
-// MaxPool3DGradGradDataFormat sets the optional data_format attribute to value.
+// SparseReduceMaxSparseKeepDims sets the optional keep_dims attribute to value.
//
-// value: The data format of the input and output data. With the
-// default format "NDHWC", the data is stored in the order of:
-// [batch, in_depth, in_height, in_width, in_channels].
-// Alternatively, the format could be "NCDHW", the data storage order is:
-// [batch, in_channels, in_depth, in_height, in_width].
-// If not specified, defaults to "NDHWC"
-func MaxPool3DGradGradDataFormat(value string) MaxPool3DGradGradAttr {
+// value: If true, retain reduced dimensions with length 1.
+// If not specified, defaults to false
+func SparseReduceMaxSparseKeepDims(value bool) SparseReduceMaxSparseAttr {
return func(m optionalAttr) {
- m["data_format"] = value
+ m["keep_dims"] = value
}
}
-// Computes second-order gradients of the maxpooling function.
+// Computes the max of elements across dimensions of a SparseTensor.
//
-// Arguments:
-// orig_input: The original input tensor.
-// orig_output: The original output tensor.
-// grad: Output backprop of shape `[batch, depth, rows, cols, channels]`.
-// ksize: 1-D tensor of length 5. The size of the window for each dimension of
-// the input tensor. Must have `ksize[0] = ksize[4] = 1`.
-// strides: 1-D tensor of length 5. The stride of the sliding window for each
-// dimension of `input`. Must have `strides[0] = strides[4] = 1`.
-// padding: The type of padding algorithm to use.
+// This Op takes a SparseTensor and is the sparse counterpart to
+// `tf.reduce_max()`. In contrast to SparseReduceMax, this Op returns a
+// SparseTensor.
//
-// Returns Gradients of gradients w.r.t. the input to `max_pool`.
-func MaxPool3DGradGrad(scope *Scope, orig_input tf.Output, orig_output tf.Output, grad tf.Output, ksize []int64, strides []int64, padding string, optional ...MaxPool3DGradGradAttr) (output tf.Output) {
+// Reduces `sp_input` along the dimensions given in `reduction_axes`. Unless
+// `keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in
+// `reduction_axes`. If `keep_dims` is true, the reduced dimensions are retained
+// with length 1.
+//
+// If `reduction_axes` has no entries, all dimensions are reduced, and a tensor
+// with a single element is returned. Additionally, the axes can be negative,
+// which are interpreted according to the indexing rules in Python.
+//
+// Arguments:
+// input_indices: 2-D. `N x R` matrix with the indices of non-empty values in a
+// SparseTensor, possibly not in canonical ordering.
+// input_values: 1-D. `N` non-empty values corresponding to `input_indices`.
+// input_shape: 1-D. Shape of the input SparseTensor.
+// reduction_axes: 1-D. Length-`K` vector containing the reduction axes.
+func SparseReduceMaxSparse(scope *Scope, input_indices tf.Output, input_values tf.Output, input_shape tf.Output, reduction_axes tf.Output, optional ...SparseReduceMaxSparseAttr) (output_indices tf.Output, output_values tf.Output, output_shape tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{"ksize": ksize, "strides": strides, "padding": padding}
+ attrs := map[string]interface{}{}
for _, a := range optional {
a(attrs)
}
opspec := tf.OpSpec{
- Type: "MaxPool3DGradGrad",
+ Type: "SparseReduceMaxSparse",
Input: []tf.Input{
- orig_input, orig_output, grad,
+ input_indices, input_values, input_shape, reduction_axes,
},
Attrs: attrs,
}
op := scope.AddOperation(opspec)
- return op.Output(0)
+ return op.Output(0), op.Output(1), op.Output(2)
}
-// Conv3DBackpropFilterV2Attr is an optional argument to Conv3DBackpropFilterV2.
-type Conv3DBackpropFilterV2Attr func(optionalAttr)
-
-// Conv3DBackpropFilterV2DataFormat sets the optional data_format attribute to value.
+// Creates a dataset that emits the outputs of `input_dataset` `count` times.
//
-// value: The data format of the input and output data. With the
-// default format "NDHWC", the data is stored in the order of:
-// [batch, in_depth, in_height, in_width, in_channels].
-// Alternatively, the format could be "NCDHW", the data storage order is:
-// [batch, in_channels, in_depth, in_height, in_width].
-// If not specified, defaults to "NDHWC"
-func Conv3DBackpropFilterV2DataFormat(value string) Conv3DBackpropFilterV2Attr {
+// Arguments:
+//
+// count: A scalar representing the number of times that `input_dataset` should
+// be repeated. A value of `-1` indicates that it should be repeated infinitely.
+//
+//
+func RepeatDataset(scope *Scope, input_dataset tf.Output, count tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) {
+ if scope.Err() != nil {
+ return
+ }
+ attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes}
+ opspec := tf.OpSpec{
+ Type: "RepeatDataset",
+ Input: []tf.Input{
+ input_dataset, count,
+ },
+ Attrs: attrs,
+ }
+ op := scope.AddOperation(opspec)
+ return op.Output(0)
+}
+
+// AddManySparseToTensorsMapAttr is an optional argument to AddManySparseToTensorsMap.
+type AddManySparseToTensorsMapAttr func(optionalAttr)
+
+// AddManySparseToTensorsMapContainer sets the optional container attribute to value.
+//
+// value: The container name for the `SparseTensorsMap` created by this op.
+// If not specified, defaults to ""
+func AddManySparseToTensorsMapContainer(value string) AddManySparseToTensorsMapAttr {
return func(m optionalAttr) {
- m["data_format"] = value
+ m["container"] = value
}
}
-// Conv3DBackpropFilterV2Dilations sets the optional dilations attribute to value.
+// AddManySparseToTensorsMapSharedName sets the optional shared_name attribute to value.
//
-// value: 1-D tensor of length 5. The dilation factor for each dimension of
-// `input`. If set to k > 1, there will be k-1 skipped cells between each
-// filter element on that dimension. The dimension order is determined by the
-// value of `data_format`, see above for details. Dilations in the batch and
-// depth dimensions must be 1.
-// If not specified, defaults to <i:1 i:1 i:1 i:1 i:1 >
-func Conv3DBackpropFilterV2Dilations(value []int64) Conv3DBackpropFilterV2Attr {
+// value: The shared name for the `SparseTensorsMap` created by this op.
+// If blank, the new Operation's unique name is used.
+// If not specified, defaults to ""
+func AddManySparseToTensorsMapSharedName(value string) AddManySparseToTensorsMapAttr {
return func(m optionalAttr) {
- m["dilations"] = value
+ m["shared_name"] = value
}
}
-// Computes the gradients of 3-D convolution with respect to the filter.
+// Add an `N`-minibatch `SparseTensor` to a `SparseTensorsMap`, return `N` handles.
+//
+// A `SparseTensor` of rank `R` is represented by three tensors: `sparse_indices`,
+// `sparse_values`, and `sparse_shape`, where
+//
+// ```sparse_indices.shape[1] == sparse_shape.shape[0] == R```
+//
+// An `N`-minibatch of `SparseTensor` objects is represented as a `SparseTensor`
+// having a first `sparse_indices` column taking values between `[0, N)`, where
+// the minibatch size `N == sparse_shape[0]`.
+//
+// The input `SparseTensor` must have rank `R` greater than 1, and the first
+// dimension is treated as the minibatch dimension. Elements of the `SparseTensor`
+// must be sorted in increasing order of this first dimension. The stored
+// `SparseTensor` objects pointed to by each row of the output `sparse_handles`
+// will have rank `R-1`.
+//
+// The `SparseTensor` values can then be read out as part of a minibatch by passing
+// the given keys as vector elements to `TakeManySparseFromTensorsMap`. To ensure
+// the correct `SparseTensorsMap` is accessed, ensure that the same
+// `container` and `shared_name` are passed to that Op. If no `shared_name`
+// is provided here, instead use the *name* of the Operation created by calling
+// `AddManySparseToTensorsMap` as the `shared_name` passed to
+// `TakeManySparseFromTensorsMap`. Ensure the Operations are colocated.
//
// Arguments:
-// input: Shape `[batch, depth, rows, cols, in_channels]`.
-// filter_sizes: An integer vector representing the tensor shape of `filter`,
-// where `filter` is a 5-D
-// `[filter_depth, filter_height, filter_width, in_channels, out_channels]`
-// tensor.
-// out_backprop: Backprop signal of shape `[batch, out_depth, out_rows, out_cols,
-// out_channels]`.
-// strides: 1-D tensor of length 5. The stride of the sliding window for each
-// dimension of `input`. Must have `strides[0] = strides[4] = 1`.
-// padding: The type of padding algorithm to use.
-func Conv3DBackpropFilterV2(scope *Scope, input tf.Output, filter_sizes tf.Output, out_backprop tf.Output, strides []int64, padding string, optional ...Conv3DBackpropFilterV2Attr) (output tf.Output) {
+// sparse_indices: 2-D. The `indices` of the minibatch `SparseTensor`.
+// `sparse_indices[:, 0]` must be ordered values in `[0, N)`.
+// sparse_values: 1-D. The `values` of the minibatch `SparseTensor`.
+// sparse_shape: 1-D. The `shape` of the minibatch `SparseTensor`.
+// The minibatch size `N == sparse_shape[0]`.
+//
+// Returns 1-D. The handles of the `SparseTensor` now stored in the
+// `SparseTensorsMap`. Shape: `[N]`.
+func AddManySparseToTensorsMap(scope *Scope, sparse_indices tf.Output, sparse_values tf.Output, sparse_shape tf.Output, optional ...AddManySparseToTensorsMapAttr) (sparse_handles tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{"strides": strides, "padding": padding}
+ attrs := map[string]interface{}{}
for _, a := range optional {
a(attrs)
}
opspec := tf.OpSpec{
- Type: "Conv3DBackpropFilterV2",
+ Type: "AddManySparseToTensorsMap",
Input: []tf.Input{
- input, filter_sizes, out_backprop,
+ sparse_indices, sparse_values, sparse_shape,
},
Attrs: attrs,
}
return op.Output(0)
}
-// Execute a sub graph on a remote processor.
-//
-// The graph specifications(such as graph itself, input tensors and output names)
-// are stored as a serialized protocol buffer of RemoteFusedGraphExecuteInfo
-// as serialized_remote_fused_graph_execute_info.
-// The specifications will be passed to a dedicated registered
-// remote fused graph executor. The executor will send the graph specifications
-// to a remote processor and execute that graph. The execution results
-// will be passed to consumer nodes as outputs of this node.
+// Concatenates tensors along one dimension.
//
// Arguments:
-// inputs: Arbitrary number of tensors with arbitrary data types
-//
-// serialized_remote_fused_graph_execute_info: Serialized protocol buffer
-// of RemoteFusedGraphExecuteInfo which contains graph specifications.
+// values: List of `N` Tensors to concatenate. Their ranks and types must match,
+// and their sizes must match in all dimensions except `concat_dim`.
+// axis: 0-D. The dimension along which to concatenate. Must be in the
+// range [-rank(values), rank(values)).
//
-// Returns Arbitrary number of tensors with arbitrary data types
-func RemoteFusedGraphExecute(scope *Scope, inputs []tf.Output, Toutputs []tf.DataType, serialized_remote_fused_graph_execute_info string) (outputs []tf.Output) {
+// Returns A `Tensor` with the concatenation of values stacked along the
+// `concat_dim` dimension. This tensor's shape matches that of `values` except
+// in `concat_dim` where it has the sum of the sizes.
+func ConcatV2(scope *Scope, values []tf.Output, axis tf.Output) (output tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{"Toutputs": Toutputs, "serialized_remote_fused_graph_execute_info": serialized_remote_fused_graph_execute_info}
opspec := tf.OpSpec{
- Type: "RemoteFusedGraphExecute",
+ Type: "ConcatV2",
Input: []tf.Input{
- tf.OutputList(inputs),
+ tf.OutputList(values), axis,
},
- Attrs: attrs,
}
op := scope.AddOperation(opspec)
+ return op.Output(0)
+}
+
+// Reads and outputs the entire contents of the input filename.
+func ReadFile(scope *Scope, filename tf.Output) (contents tf.Output) {
if scope.Err() != nil {
return
}
- var idx int
- var err error
- if outputs, idx, err = makeOutputList(op, idx, "outputs"); err != nil {
- scope.UpdateErr("RemoteFusedGraphExecute", err)
- return
+ opspec := tf.OpSpec{
+ Type: "ReadFile",
+ Input: []tf.Input{
+ filename,
+ },
}
- return outputs
+ op := scope.AddOperation(opspec)
+ return op.Output(0)
}
-// SerializeManySparseAttr is an optional argument to SerializeManySparse.
-type SerializeManySparseAttr func(optionalAttr)
+// MinAttr is an optional argument to Min.
+type MinAttr func(optionalAttr)
-// SerializeManySparseOutType sets the optional out_type attribute to value.
+// MinKeepDims sets the optional keep_dims attribute to value.
//
-// value: The `dtype` to use for serialization; the supported types are `string`
-// (default) and `variant`.
-// If not specified, defaults to DT_STRING
-func SerializeManySparseOutType(value tf.DataType) SerializeManySparseAttr {
+// value: If true, retain reduced dimensions with length 1.
+// If not specified, defaults to false
+func MinKeepDims(value bool) MinAttr {
return func(m optionalAttr) {
- m["out_type"] = value
+ m["keep_dims"] = value
}
}
-// Serialize an `N`-minibatch `SparseTensor` into an `[N, 3]` `Tensor` object.
-//
-// The `SparseTensor` must have rank `R` greater than 1, and the first dimension
-// is treated as the minibatch dimension. Elements of the `SparseTensor`
-// must be sorted in increasing order of this first dimension. The serialized
-// `SparseTensor` objects going into each row of `serialized_sparse` will have
-// rank `R-1`.
+// Computes the minimum of elements across dimensions of a tensor.
//
-// The minibatch size `N` is extracted from `sparse_shape[0]`.
+// Reduces `input` along the dimensions given in `axis`. Unless
+// `keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in
+// `axis`. If `keep_dims` is true, the reduced dimensions are
+// retained with length 1.
//
// Arguments:
-// sparse_indices: 2-D. The `indices` of the minibatch `SparseTensor`.
-// sparse_values: 1-D. The `values` of the minibatch `SparseTensor`.
-// sparse_shape: 1-D. The `shape` of the minibatch `SparseTensor`.
-func SerializeManySparse(scope *Scope, sparse_indices tf.Output, sparse_values tf.Output, sparse_shape tf.Output, optional ...SerializeManySparseAttr) (serialized_sparse tf.Output) {
+// input: The tensor to reduce.
+// axis: The dimensions to reduce. Must be in the range
+// `[-rank(input), rank(input))`.
+//
+// Returns The reduced tensor.
+func Min(scope *Scope, input tf.Output, axis tf.Output, optional ...MinAttr) (output tf.Output) {
if scope.Err() != nil {
return
}
a(attrs)
}
opspec := tf.OpSpec{
- Type: "SerializeManySparse",
+ Type: "Min",
Input: []tf.Input{
- sparse_indices, sparse_values, sparse_shape,
+ input, axis,
},
Attrs: attrs,
}
return op.Output(0)
}
-// Computes inverse hyperbolic cosine of x element-wise.
-func Acosh(scope *Scope, x tf.Output) (y tf.Output) {
+// Shuffle dimensions of x according to a permutation.
+//
+// The output `y` has the same rank as `x`. The shapes of `x` and `y` satisfy:
+// `y.shape[i] == x.shape[perm[i]] for i in [0, 1, ..., rank(x) - 1]`
+func Transpose(scope *Scope, x tf.Output, perm tf.Output) (y tf.Output) {
if scope.Err() != nil {
return
}
opspec := tf.OpSpec{
- Type: "Acosh",
+ Type: "Transpose",
+ Input: []tf.Input{
+ x, perm,
+ },
+ }
+ op := scope.AddOperation(opspec)
+ return op.Output(0)
+}
+
+// Computes sigmoid of `x` element-wise.
+//
+// Specifically, `y = 1 / (1 + exp(-x))`.
+func Sigmoid(scope *Scope, x tf.Output) (y tf.Output) {
+ if scope.Err() != nil {
+ return
+ }
+ opspec := tf.OpSpec{
+ Type: "Sigmoid",
Input: []tf.Input{
x,
},
return op.Output(0)
}
-// TensorArrayV2Attr is an optional argument to TensorArrayV2.
-type TensorArrayV2Attr func(optionalAttr)
+// FusedBatchNormAttr is an optional argument to FusedBatchNorm.
+type FusedBatchNormAttr func(optionalAttr)
-// TensorArrayV2ElementShape sets the optional element_shape attribute to value.
-// If not specified, defaults to <unknown_rank:true >
-func TensorArrayV2ElementShape(value tf.Shape) TensorArrayV2Attr {
+// FusedBatchNormEpsilon sets the optional epsilon attribute to value.
+//
+// value: A small float number added to the variance of x.
+// If not specified, defaults to 0.0001
+func FusedBatchNormEpsilon(value float32) FusedBatchNormAttr {
return func(m optionalAttr) {
- m["element_shape"] = value
+ m["epsilon"] = value
}
}
-// TensorArrayV2DynamicSize sets the optional dynamic_size attribute to value.
-// If not specified, defaults to false
-func TensorArrayV2DynamicSize(value bool) TensorArrayV2Attr {
+// FusedBatchNormDataFormat sets the optional data_format attribute to value.
+//
+// value: The data format for x and y. Either "NHWC" (default) or "NCHW".
+// If not specified, defaults to "NHWC"
+func FusedBatchNormDataFormat(value string) FusedBatchNormAttr {
return func(m optionalAttr) {
- m["dynamic_size"] = value
+ m["data_format"] = value
}
}
-// TensorArrayV2ClearAfterRead sets the optional clear_after_read attribute to value.
+// FusedBatchNormIsTraining sets the optional is_training attribute to value.
+//
+// value: A bool value to indicate the operation is for training (default)
+// or inference.
// If not specified, defaults to true
-func TensorArrayV2ClearAfterRead(value bool) TensorArrayV2Attr {
+func FusedBatchNormIsTraining(value bool) FusedBatchNormAttr {
return func(m optionalAttr) {
- m["clear_after_read"] = value
+ m["is_training"] = value
}
}
-// TensorArrayV2TensorArrayName sets the optional tensor_array_name attribute to value.
-// If not specified, defaults to ""
-func TensorArrayV2TensorArrayName(value string) TensorArrayV2Attr {
- return func(m optionalAttr) {
- m["tensor_array_name"] = value
- }
-}
-
-// Deprecated. Use TensorArrayV3
+// Batch normalization.
//
-// DEPRECATED at GraphDef version 26: Use TensorArrayV3
-func TensorArrayV2(scope *Scope, size tf.Output, dtype tf.DataType, optional ...TensorArrayV2Attr) (handle tf.Output) {
+// Note that the size of 4D Tensors are defined by either "NHWC" or "NCHW".
+// The size of 1D Tensors matches the dimension C of the 4D Tensors.
+//
+// Arguments:
+// x: A 4D Tensor for input data.
+// scale: A 1D Tensor for scaling factor, to scale the normalized x.
+// offset: A 1D Tensor for offset, to shift to the normalized x.
+// mean: A 1D Tensor for population mean. Used for inference only;
+// must be empty for training.
+// variance: A 1D Tensor for population variance. Used for inference only;
+// must be empty for training.
+//
+// Returns A 4D Tensor for output data.A 1D Tensor for the computed batch mean, to be used by TensorFlow
+// to compute the running mean.A 1D Tensor for the computed batch variance, to be used by
+// TensorFlow to compute the running variance.A 1D Tensor for the computed batch mean, to be reused
+// in the gradient computation.A 1D Tensor for the computed batch variance (inverted variance
+// in the cuDNN case), to be reused in the gradient computation.
+func FusedBatchNorm(scope *Scope, x tf.Output, scale tf.Output, offset tf.Output, mean tf.Output, variance tf.Output, optional ...FusedBatchNormAttr) (y tf.Output, batch_mean tf.Output, batch_variance tf.Output, reserve_space_1 tf.Output, reserve_space_2 tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{"dtype": dtype}
+ attrs := map[string]interface{}{}
for _, a := range optional {
a(attrs)
}
opspec := tf.OpSpec{
- Type: "TensorArrayV2",
+ Type: "FusedBatchNorm",
Input: []tf.Input{
- size,
+ x, scale, offset, mean, variance,
},
Attrs: attrs,
}
op := scope.AddOperation(opspec)
- return op.Output(0)
+ return op.Output(0), op.Output(1), op.Output(2), op.Output(3), op.Output(4)
}
-// DecodeCSVAttr is an optional argument to DecodeCSV.
-type DecodeCSVAttr func(optionalAttr)
-
-// DecodeCSVFieldDelim sets the optional field_delim attribute to value.
-//
-// value: char delimiter to separate fields in a record.
-// If not specified, defaults to ","
-func DecodeCSVFieldDelim(value string) DecodeCSVAttr {
- return func(m optionalAttr) {
- m["field_delim"] = value
- }
-}
+// RandomStandardNormalAttr is an optional argument to RandomStandardNormal.
+type RandomStandardNormalAttr func(optionalAttr)
-// DecodeCSVUseQuoteDelim sets the optional use_quote_delim attribute to value.
+// RandomStandardNormalSeed sets the optional seed attribute to value.
//
-// value: If false, treats double quotation marks as regular
-// characters inside of the string fields (ignoring RFC 4180, Section 2,
-// Bullet 5).
-// If not specified, defaults to true
-func DecodeCSVUseQuoteDelim(value bool) DecodeCSVAttr {
+// value: If either `seed` or `seed2` are set to be non-zero, the random number
+// generator is seeded by the given seed. Otherwise, it is seeded by a
+// random seed.
+// If not specified, defaults to 0
+func RandomStandardNormalSeed(value int64) RandomStandardNormalAttr {
return func(m optionalAttr) {
- m["use_quote_delim"] = value
+ m["seed"] = value
}
}
-// DecodeCSVNaValue sets the optional na_value attribute to value.
+// RandomStandardNormalSeed2 sets the optional seed2 attribute to value.
//
-// value: Additional string to recognize as NA/NaN.
-// If not specified, defaults to ""
-func DecodeCSVNaValue(value string) DecodeCSVAttr {
+// value: A second seed to avoid seed collision.
+// If not specified, defaults to 0
+func RandomStandardNormalSeed2(value int64) RandomStandardNormalAttr {
return func(m optionalAttr) {
- m["na_value"] = value
+ m["seed2"] = value
}
}
-// Convert CSV records to tensors. Each column maps to one tensor.
+// Outputs random values from a normal distribution.
//
-// RFC 4180 format is expected for the CSV records.
-// (https://tools.ietf.org/html/rfc4180)
-// Note that we allow leading and trailing spaces with int or float field.
+// The generated values will have mean 0 and standard deviation 1.
//
// Arguments:
-// records: Each string is a record/row in the csv and all records should have
-// the same format.
-// record_defaults: One tensor per column of the input record, with either a
-// scalar default value for that column or empty if the column is required.
+// shape: The shape of the output tensor.
+// dtype: The type of the output.
//
-// Returns Each tensor will have the same shape as records.
-func DecodeCSV(scope *Scope, records tf.Output, record_defaults []tf.Output, optional ...DecodeCSVAttr) (output []tf.Output) {
+// Returns A tensor of the specified shape filled with random normal values.
+func RandomStandardNormal(scope *Scope, shape tf.Output, dtype tf.DataType, optional ...RandomStandardNormalAttr) (output tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{}
+ attrs := map[string]interface{}{"dtype": dtype}
for _, a := range optional {
a(attrs)
}
opspec := tf.OpSpec{
- Type: "DecodeCSV",
+ Type: "RandomStandardNormal",
Input: []tf.Input{
- records, tf.OutputList(record_defaults),
+ shape,
},
Attrs: attrs,
}
op := scope.AddOperation(opspec)
- if scope.Err() != nil {
- return
- }
- var idx int
- var err error
- if output, idx, err = makeOutputList(op, idx, "output"); err != nil {
- scope.UpdateErr("DecodeCSV", err)
- return
- }
- return output
+ return op.Output(0)
}
-// MapClearAttr is an optional argument to MapClear.
-type MapClearAttr func(optionalAttr)
-
-// MapClearCapacity sets the optional capacity attribute to value.
-// If not specified, defaults to 0
+// Component-wise divides a SparseTensor by a dense Tensor.
//
-// REQUIRES: value >= 0
-func MapClearCapacity(value int64) MapClearAttr {
- return func(m optionalAttr) {
- m["capacity"] = value
- }
-}
-
-// MapClearMemoryLimit sets the optional memory_limit attribute to value.
-// If not specified, defaults to 0
+// *Limitation*: this Op only broadcasts the dense side to the sparse side, but not
+// the other direction.
//
-// REQUIRES: value >= 0
-func MapClearMemoryLimit(value int64) MapClearAttr {
- return func(m optionalAttr) {
- m["memory_limit"] = value
- }
-}
-
-// MapClearContainer sets the optional container attribute to value.
-// If not specified, defaults to ""
-func MapClearContainer(value string) MapClearAttr {
- return func(m optionalAttr) {
- m["container"] = value
- }
-}
-
-// MapClearSharedName sets the optional shared_name attribute to value.
-// If not specified, defaults to ""
-func MapClearSharedName(value string) MapClearAttr {
- return func(m optionalAttr) {
- m["shared_name"] = value
- }
-}
-
-// Op removes all elements in the underlying container.
+// Arguments:
+// sp_indices: 2-D. `N x R` matrix with the indices of non-empty values in a
+// SparseTensor, possibly not in canonical ordering.
+// sp_values: 1-D. `N` non-empty values corresponding to `sp_indices`.
+// sp_shape: 1-D. Shape of the input SparseTensor.
+// dense: `R`-D. The dense Tensor operand.
//
-// Returns the created operation.
-func MapClear(scope *Scope, dtypes []tf.DataType, optional ...MapClearAttr) (o *tf.Operation) {
+// Returns 1-D. The `N` values that are operated on.
+func SparseDenseCwiseDiv(scope *Scope, sp_indices tf.Output, sp_values tf.Output, sp_shape tf.Output, dense tf.Output) (output tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{"dtypes": dtypes}
- for _, a := range optional {
- a(attrs)
- }
opspec := tf.OpSpec{
- Type: "MapClear",
-
- Attrs: attrs,
+ Type: "SparseDenseCwiseDiv",
+ Input: []tf.Input{
+ sp_indices, sp_values, sp_shape, dense,
+ },
}
- return scope.AddOperation(opspec)
+ op := scope.AddOperation(opspec)
+ return op.Output(0)
}
-// ThreadUnsafeUnigramCandidateSamplerAttr is an optional argument to ThreadUnsafeUnigramCandidateSampler.
-type ThreadUnsafeUnigramCandidateSamplerAttr func(optionalAttr)
+// FractionalAvgPoolGradAttr is an optional argument to FractionalAvgPoolGrad.
+type FractionalAvgPoolGradAttr func(optionalAttr)
-// ThreadUnsafeUnigramCandidateSamplerSeed sets the optional seed attribute to value.
+// FractionalAvgPoolGradOverlapping sets the optional overlapping attribute to value.
//
-// value: If either seed or seed2 are set to be non-zero, the random number
-// generator is seeded by the given seed. Otherwise, it is seeded by a
-// random seed.
-// If not specified, defaults to 0
-func ThreadUnsafeUnigramCandidateSamplerSeed(value int64) ThreadUnsafeUnigramCandidateSamplerAttr {
- return func(m optionalAttr) {
- m["seed"] = value
- }
-}
-
-// ThreadUnsafeUnigramCandidateSamplerSeed2 sets the optional seed2 attribute to value.
+// value: When set to True, it means when pooling, the values at the boundary
+// of adjacent pooling cells are used by both cells. For example:
//
-// value: An second seed to avoid seed collision.
-// If not specified, defaults to 0
-func ThreadUnsafeUnigramCandidateSamplerSeed2(value int64) ThreadUnsafeUnigramCandidateSamplerAttr {
+// `index 0 1 2 3 4`
+//
+// `value 20 5 16 3 7`
+//
+// If the pooling sequence is [0, 2, 4], then 16, at index 2 will be used twice.
+// The result would be [41/3, 26/3] for fractional avg pooling.
+// If not specified, defaults to false
+func FractionalAvgPoolGradOverlapping(value bool) FractionalAvgPoolGradAttr {
return func(m optionalAttr) {
- m["seed2"] = value
+ m["overlapping"] = value
}
}
-// Generates labels for candidate sampling with a learned unigram distribution.
-//
-// See explanations of candidate sampling and the data formats at
-// go/candidate-sampling.
-//
-// For each batch, this op picks a single set of sampled candidate labels.
+// Computes gradient of the FractionalAvgPool function.
//
-// The advantages of sampling candidates per-batch are simplicity and the
-// possibility of efficient dense matrix multiplication. The disadvantage is that
-// the sampled candidates must be chosen independently of the context and of the
-// true labels.
+// Unlike FractionalMaxPoolGrad, we don't need to find arg_max for
+// FractionalAvgPoolGrad, we just need to evenly back-propagate each element of
+// out_backprop to those indices that form the same pooling cell. Therefore, we
+// just need to know the shape of original input tensor, instead of the whole
+// tensor.
//
// Arguments:
-// true_classes: A batch_size * num_true matrix, in which each row contains the
-// IDs of the num_true target_classes in the corresponding original label.
-// num_true: Number of true labels per context.
-// num_sampled: Number of candidates to randomly sample.
-// unique: If unique is true, we sample with rejection, so that all sampled
-// candidates in a batch are unique. This requires some approximation to
-// estimate the post-rejection sampling probabilities.
-// range_max: The sampler will sample integers from the interval [0, range_max).
+// orig_input_tensor_shape: Original input tensor shape for `fractional_avg_pool`
+// out_backprop: 4-D with shape `[batch, height, width, channels]`. Gradients
+// w.r.t. the output of `fractional_avg_pool`.
+// row_pooling_sequence: row pooling sequence, form pooling region with
+// col_pooling_sequence.
+// col_pooling_sequence: column pooling sequence, form pooling region with
+// row_pooling sequence.
//
-// Returns A vector of length num_sampled, in which each element is
-// the ID of a sampled candidate.A batch_size * num_true matrix, representing
-// the number of times each candidate is expected to occur in a batch
-// of sampled candidates. If unique=true, then this is a probability.A vector of length num_sampled, for each sampled
-// candidate representing the number of times the candidate is expected
-// to occur in a batch of sampled candidates. If unique=true, then this is a
-// probability.
-func ThreadUnsafeUnigramCandidateSampler(scope *Scope, true_classes tf.Output, num_true int64, num_sampled int64, unique bool, range_max int64, optional ...ThreadUnsafeUnigramCandidateSamplerAttr) (sampled_candidates tf.Output, true_expected_count tf.Output, sampled_expected_count tf.Output) {
+// Returns 4-D. Gradients w.r.t. the input of `fractional_avg_pool`.
+func FractionalAvgPoolGrad(scope *Scope, orig_input_tensor_shape tf.Output, out_backprop tf.Output, row_pooling_sequence tf.Output, col_pooling_sequence tf.Output, optional ...FractionalAvgPoolGradAttr) (output tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{"num_true": num_true, "num_sampled": num_sampled, "unique": unique, "range_max": range_max}
+ attrs := map[string]interface{}{}
for _, a := range optional {
a(attrs)
}
opspec := tf.OpSpec{
- Type: "ThreadUnsafeUnigramCandidateSampler",
+ Type: "FractionalAvgPoolGrad",
Input: []tf.Input{
- true_classes,
+ orig_input_tensor_shape, out_backprop, row_pooling_sequence, col_pooling_sequence,
},
Attrs: attrs,
}
op := scope.AddOperation(opspec)
- return op.Output(0), op.Output(1), op.Output(2)
+ return op.Output(0)
}
-// MaxPoolV2Attr is an optional argument to MaxPoolV2.
-type MaxPoolV2Attr func(optionalAttr)
-
-// MaxPoolV2DataFormat sets the optional data_format attribute to value.
-//
-// value: Specify the data format of the input and output data. With the
-// default format "NHWC", the data is stored in the order of:
-// [batch, in_height, in_width, in_channels].
-// Alternatively, the format could be "NCHW", the data storage order of:
-// [batch, in_channels, in_height, in_width].
-// If not specified, defaults to "NHWC"
-func MaxPoolV2DataFormat(value string) MaxPoolV2Attr {
- return func(m optionalAttr) {
- m["data_format"] = value
- }
-}
-
-// Performs max pooling on the input.
+// Concatenates tensors along one dimension.
//
// Arguments:
-// input: 4-D input to pool over.
-// ksize: The size of the window for each dimension of the input tensor.
-// strides: The stride of the sliding window for each dimension of the
-// input tensor.
-// padding: The type of padding algorithm to use.
+// concat_dim: 0-D. The dimension along which to concatenate. Must be in the
+// range [0, rank(values)).
+// values: The `N` Tensors to concatenate. Their ranks and types must match,
+// and their sizes must match in all dimensions except `concat_dim`.
//
-// Returns The max pooled output tensor.
-func MaxPoolV2(scope *Scope, input tf.Output, ksize tf.Output, strides tf.Output, padding string, optional ...MaxPoolV2Attr) (output tf.Output) {
+// Returns A `Tensor` with the concatenation of values stacked along the
+// `concat_dim` dimension. This tensor's shape matches that of `values` except
+// in `concat_dim` where it has the sum of the sizes.
+func Concat(scope *Scope, concat_dim tf.Output, values []tf.Output) (output tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{"padding": padding}
- for _, a := range optional {
- a(attrs)
- }
opspec := tf.OpSpec{
- Type: "MaxPoolV2",
+ Type: "Concat",
Input: []tf.Input{
- input, ksize, strides,
+ concat_dim, tf.OutputList(values),
},
- Attrs: attrs,
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// MutableDenseHashTableV2Attr is an optional argument to MutableDenseHashTableV2.
-type MutableDenseHashTableV2Attr func(optionalAttr)
+// ResourceApplyMomentumAttr is an optional argument to ResourceApplyMomentum.
+type ResourceApplyMomentumAttr func(optionalAttr)
-// MutableDenseHashTableV2Container sets the optional container attribute to value.
+// ResourceApplyMomentumUseLocking sets the optional use_locking attribute to value.
//
-// value: If non-empty, this table is placed in the given container.
-// Otherwise, a default container is used.
-// If not specified, defaults to ""
-func MutableDenseHashTableV2Container(value string) MutableDenseHashTableV2Attr {
+// value: If `True`, updating of the var and accum tensors will be protected
+// by a lock; otherwise the behavior is undefined, but may exhibit less
+// contention.
+// If not specified, defaults to false
+func ResourceApplyMomentumUseLocking(value bool) ResourceApplyMomentumAttr {
return func(m optionalAttr) {
- m["container"] = value
+ m["use_locking"] = value
}
}
-// MutableDenseHashTableV2SharedName sets the optional shared_name attribute to value.
+// ResourceApplyMomentumUseNesterov sets the optional use_nesterov attribute to value.
//
-// value: If non-empty, this table is shared under the given name across
-// multiple sessions.
-// If not specified, defaults to ""
-func MutableDenseHashTableV2SharedName(value string) MutableDenseHashTableV2Attr {
- return func(m optionalAttr) {
- m["shared_name"] = value
- }
-}
-
-// MutableDenseHashTableV2UseNodeNameSharing sets the optional use_node_name_sharing attribute to value.
+// value: If `True`, the tensor passed to compute grad will be
+// var - lr * momentum * accum, so in the end, the var you get is actually
+// var - lr * momentum * accum.
// If not specified, defaults to false
-func MutableDenseHashTableV2UseNodeNameSharing(value bool) MutableDenseHashTableV2Attr {
+func ResourceApplyMomentumUseNesterov(value bool) ResourceApplyMomentumAttr {
return func(m optionalAttr) {
- m["use_node_name_sharing"] = value
+ m["use_nesterov"] = value
}
}
-// MutableDenseHashTableV2ValueShape sets the optional value_shape attribute to value.
+// Update '*var' according to the momentum scheme. Set use_nesterov = True if you
//
-// value: The shape of each value.
-// If not specified, defaults to <>
-func MutableDenseHashTableV2ValueShape(value tf.Shape) MutableDenseHashTableV2Attr {
- return func(m optionalAttr) {
- m["value_shape"] = value
- }
-}
-
-// MutableDenseHashTableV2InitialNumBuckets sets the optional initial_num_buckets attribute to value.
+// want to use Nesterov momentum.
//
-// value: The initial number of hash table buckets. Must be a power
-// to 2.
-// If not specified, defaults to 131072
-func MutableDenseHashTableV2InitialNumBuckets(value int64) MutableDenseHashTableV2Attr {
- return func(m optionalAttr) {
- m["initial_num_buckets"] = value
+// accum = accum * momentum + grad
+// var -= lr * accum
+//
+// Arguments:
+// var_: Should be from a Variable().
+// accum: Should be from a Variable().
+// lr: Scaling factor. Must be a scalar.
+// grad: The gradient.
+// momentum: Momentum. Must be a scalar.
+//
+// Returns the created operation.
+func ResourceApplyMomentum(scope *Scope, var_ tf.Output, accum tf.Output, lr tf.Output, grad tf.Output, momentum tf.Output, optional ...ResourceApplyMomentumAttr) (o *tf.Operation) {
+ if scope.Err() != nil {
+ return
+ }
+ attrs := map[string]interface{}{}
+ for _, a := range optional {
+ a(attrs)
+ }
+ opspec := tf.OpSpec{
+ Type: "ResourceApplyMomentum",
+ Input: []tf.Input{
+ var_, accum, lr, grad, momentum,
+ },
+ Attrs: attrs,
}
+ return scope.AddOperation(opspec)
}
-// MutableDenseHashTableV2MaxLoadFactor sets the optional max_load_factor attribute to value.
+// MaxPoolGradGradAttr is an optional argument to MaxPoolGradGrad.
+type MaxPoolGradGradAttr func(optionalAttr)
+
+// MaxPoolGradGradDataFormat sets the optional data_format attribute to value.
//
-// value: The maximum ratio between number of entries and number of
-// buckets before growing the table. Must be between 0 and 1.
-// If not specified, defaults to 0.8
-func MutableDenseHashTableV2MaxLoadFactor(value float32) MutableDenseHashTableV2Attr {
+// value: Specify the data format of the input and output data. With the
+// default format "NHWC", the data is stored in the order of:
+// [batch, in_height, in_width, in_channels].
+// Alternatively, the format could be "NCHW", the data storage order of:
+// [batch, in_channels, in_height, in_width].
+// If not specified, defaults to "NHWC"
+func MaxPoolGradGradDataFormat(value string) MaxPoolGradGradAttr {
return func(m optionalAttr) {
- m["max_load_factor"] = value
+ m["data_format"] = value
}
}
-// Creates an empty hash table that uses tensors as the backing store.
-//
-// It uses "open addressing" with quadratic reprobing to resolve
-// collisions.
-//
-// This op creates a mutable hash table, specifying the type of its keys and
-// values. Each value must be a scalar. Data can be inserted into the table using
-// the insert operations. It does not support the initialization operation.
+// Computes second-order gradients of the maxpooling function.
//
// Arguments:
-// empty_key: The key used to represent empty key buckets internally. Must not
-// be used in insert or lookup operations.
-// value_dtype: Type of the table values.
+// orig_input: The original input tensor.
+// orig_output: The original output tensor.
+// grad: 4-D. Gradients of gradients w.r.t. the input of `max_pool`.
+// ksize: The size of the window for each dimension of the input tensor.
+// strides: The stride of the sliding window for each dimension of the
+// input tensor.
+// padding: The type of padding algorithm to use.
//
-// Returns Handle to a table.
-func MutableDenseHashTableV2(scope *Scope, empty_key tf.Output, value_dtype tf.DataType, optional ...MutableDenseHashTableV2Attr) (table_handle tf.Output) {
+// Returns Gradients of gradients w.r.t. the input to `max_pool`.
+func MaxPoolGradGrad(scope *Scope, orig_input tf.Output, orig_output tf.Output, grad tf.Output, ksize []int64, strides []int64, padding string, optional ...MaxPoolGradGradAttr) (output tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{"value_dtype": value_dtype}
+ attrs := map[string]interface{}{"ksize": ksize, "strides": strides, "padding": padding}
for _, a := range optional {
a(attrs)
}
opspec := tf.OpSpec{
- Type: "MutableDenseHashTableV2",
+ Type: "MaxPoolGradGrad",
Input: []tf.Input{
- empty_key,
+ orig_input, orig_output, grad,
},
Attrs: attrs,
}
return op.Output(0)
}
-// StageSizeAttr is an optional argument to StageSize.
-type StageSizeAttr func(optionalAttr)
+// Returns element-wise integer closest to x.
+//
+// If the result is midway between two representable values,
+// the even representable is chosen.
+// For example:
+//
+// ```
+// rint(-1.5) ==> -2.0
+// rint(0.5000001) ==> 1.0
+// rint([-1.7, -1.5, -0.2, 0.2, 1.5, 1.7, 2.0]) ==> [-2., -2., -0., 0., 2., 2., 2.]
+// ```
+func Rint(scope *Scope, x tf.Output) (y tf.Output) {
+ if scope.Err() != nil {
+ return
+ }
+ opspec := tf.OpSpec{
+ Type: "Rint",
+ Input: []tf.Input{
+ x,
+ },
+ }
+ op := scope.AddOperation(opspec)
+ return op.Output(0)
+}
-// StageSizeCapacity sets the optional capacity attribute to value.
+// OrderedMapUnstageNoKeyAttr is an optional argument to OrderedMapUnstageNoKey.
+type OrderedMapUnstageNoKeyAttr func(optionalAttr)
+
+// OrderedMapUnstageNoKeyCapacity sets the optional capacity attribute to value.
// If not specified, defaults to 0
//
// REQUIRES: value >= 0
-func StageSizeCapacity(value int64) StageSizeAttr {
+func OrderedMapUnstageNoKeyCapacity(value int64) OrderedMapUnstageNoKeyAttr {
return func(m optionalAttr) {
m["capacity"] = value
}
}
-// StageSizeMemoryLimit sets the optional memory_limit attribute to value.
+// OrderedMapUnstageNoKeyMemoryLimit sets the optional memory_limit attribute to value.
// If not specified, defaults to 0
//
// REQUIRES: value >= 0
-func StageSizeMemoryLimit(value int64) StageSizeAttr {
+func OrderedMapUnstageNoKeyMemoryLimit(value int64) OrderedMapUnstageNoKeyAttr {
return func(m optionalAttr) {
m["memory_limit"] = value
}
}
-// StageSizeContainer sets the optional container attribute to value.
+// OrderedMapUnstageNoKeyContainer sets the optional container attribute to value.
// If not specified, defaults to ""
-func StageSizeContainer(value string) StageSizeAttr {
+func OrderedMapUnstageNoKeyContainer(value string) OrderedMapUnstageNoKeyAttr {
return func(m optionalAttr) {
m["container"] = value
}
}
-// StageSizeSharedName sets the optional shared_name attribute to value.
+// OrderedMapUnstageNoKeySharedName sets the optional shared_name attribute to value.
// If not specified, defaults to ""
-func StageSizeSharedName(value string) StageSizeAttr {
+func OrderedMapUnstageNoKeySharedName(value string) OrderedMapUnstageNoKeyAttr {
return func(m optionalAttr) {
m["shared_name"] = value
}
}
-// Op returns the number of elements in the underlying container.
-func StageSize(scope *Scope, dtypes []tf.DataType, optional ...StageSizeAttr) (size tf.Output) {
+// Op removes and returns the (key, value) element with the smallest
+//
+// key from the underlying container. If the underlying container
+// does not contain elements, the op will block until it does.
+func OrderedMapUnstageNoKey(scope *Scope, indices tf.Output, dtypes []tf.DataType, optional ...OrderedMapUnstageNoKeyAttr) (key tf.Output, values []tf.Output) {
if scope.Err() != nil {
return
}
a(attrs)
}
opspec := tf.OpSpec{
- Type: "StageSize",
-
+ Type: "OrderedMapUnstageNoKey",
+ Input: []tf.Input{
+ indices,
+ },
Attrs: attrs,
}
op := scope.AddOperation(opspec)
- return op.Output(0)
-}
-
-// Produces the max pool of the input tensor for quantized types.
-//
-// Arguments:
-// input: The 4D (batch x rows x cols x depth) Tensor to MaxReduce over.
-// min_input: The float value that the lowest quantized input value represents.
-// max_input: The float value that the highest quantized input value represents.
-// ksize: The size of the window for each dimension of the input tensor.
-// The length must be 4 to match the number of dimensions of the input.
-// strides: The stride of the sliding window for each dimension of the input
-// tensor. The length must be 4 to match the number of dimensions of the input.
-// padding: The type of padding algorithm to use.
-//
-// Returns The float value that the lowest quantized output value represents.The float value that the highest quantized output value represents.
-func QuantizedMaxPool(scope *Scope, input tf.Output, min_input tf.Output, max_input tf.Output, ksize []int64, strides []int64, padding string) (output tf.Output, min_output tf.Output, max_output tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{"ksize": ksize, "strides": strides, "padding": padding}
- opspec := tf.OpSpec{
- Type: "QuantizedMaxPool",
- Input: []tf.Input{
- input, min_input, max_input,
- },
- Attrs: attrs,
+ var idx int
+ var err error
+ key = op.Output(idx)
+ if values, idx, err = makeOutputList(op, idx, "values"); err != nil {
+ scope.UpdateErr("OrderedMapUnstageNoKey", err)
+ return
}
- op := scope.AddOperation(opspec)
- return op.Output(0), op.Output(1), op.Output(2)
+ return key, values
}
-// Computes softplus: `log(exp(features) + 1)`.
-func Softplus(scope *Scope, features tf.Output) (activations tf.Output) {
- if scope.Err() != nil {
- return
- }
- opspec := tf.OpSpec{
- Type: "Softplus",
- Input: []tf.Input{
- features,
- },
- }
- op := scope.AddOperation(opspec)
- return op.Output(0)
-}
-
-// Computes exponential of x - 1 element-wise.
-//
-// I.e., \\(y = (\exp x) - 1\\).
-func Expm1(scope *Scope, x tf.Output) (y tf.Output) {
- if scope.Err() != nil {
- return
- }
- opspec := tf.OpSpec{
- Type: "Expm1",
- Input: []tf.Input{
- x,
- },
- }
- op := scope.AddOperation(opspec)
- return op.Output(0)
-}
-
-// Returns the number of records this Reader has produced.
-//
-// This is the same as the number of ReaderRead executions that have
-// succeeded.
-//
-// Arguments:
-// reader_handle: Handle to a Reader.
-func ReaderNumRecordsProducedV2(scope *Scope, reader_handle tf.Output) (records_produced tf.Output) {
- if scope.Err() != nil {
- return
- }
- opspec := tf.OpSpec{
- Type: "ReaderNumRecordsProducedV2",
- Input: []tf.Input{
- reader_handle,
- },
- }
- op := scope.AddOperation(opspec)
- return op.Output(0)
-}
+// MaxPool3DGradGradAttr is an optional argument to MaxPool3DGradGrad.
+type MaxPool3DGradGradAttr func(optionalAttr)
-// Computes the sum along segments of a tensor.
-//
-// Read @{$math_ops#segmentation$the section on segmentation} for an explanation of
-// segments.
-//
-// Computes a tensor such that
-// \\(output_i = \sum_j data_j\\) where sum is over `j` such
-// that `segment_ids[j] == i`.
-//
-// If the sum is empty for a given segment ID `i`, `output[i] = 0`.
-//
-// <div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;">
-// <img style="width:100%" src="https://www.tensorflow.org/images/SegmentSum.png" alt>
-// </div>
-//
-// Arguments:
-//
-// segment_ids: A 1-D tensor whose rank is equal to the rank of `data`'s
-// first dimension. Values should be sorted and can be repeated.
+// MaxPool3DGradGradDataFormat sets the optional data_format attribute to value.
//
-// Returns Has same shape as data, except for dimension 0 which
-// has size `k`, the number of segments.
-func SegmentSum(scope *Scope, data tf.Output, segment_ids tf.Output) (output tf.Output) {
- if scope.Err() != nil {
- return
- }
- opspec := tf.OpSpec{
- Type: "SegmentSum",
- Input: []tf.Input{
- data, segment_ids,
- },
+// value: The data format of the input and output data. With the
+// default format "NDHWC", the data is stored in the order of:
+// [batch, in_depth, in_height, in_width, in_channels].
+// Alternatively, the format could be "NCDHW", the data storage order is:
+// [batch, in_channels, in_depth, in_height, in_width].
+// If not specified, defaults to "NDHWC"
+func MaxPool3DGradGradDataFormat(value string) MaxPool3DGradGradAttr {
+ return func(m optionalAttr) {
+ m["data_format"] = value
}
- op := scope.AddOperation(opspec)
- return op.Output(0)
}
-// Creates a dataset that emits the lines of one or more text files.
+// Computes second-order gradients of the maxpooling function.
//
// Arguments:
-// filenames: A scalar or a vector containing the name(s) of the file(s) to be
-// read.
-// compression_type: A scalar containing either (i) the empty string (no
-// compression), (ii) "ZLIB", or (iii) "GZIP".
-// buffer_size: A scalar containing the number of bytes to buffer.
-func TextLineDataset(scope *Scope, filenames tf.Output, compression_type tf.Output, buffer_size tf.Output) (handle tf.Output) {
- if scope.Err() != nil {
- return
- }
- opspec := tf.OpSpec{
- Type: "TextLineDataset",
- Input: []tf.Input{
- filenames, compression_type, buffer_size,
- },
- }
- op := scope.AddOperation(opspec)
- return op.Output(0)
-}
-
-// Computes gradients for SparseSegmentMean.
-//
-// Returns tensor "output" with same shape as grad, except for dimension 0 whose
-// value is output_dim0.
+// orig_input: The original input tensor.
+// orig_output: The original output tensor.
+// grad: Output backprop of shape `[batch, depth, rows, cols, channels]`.
+// ksize: 1-D tensor of length 5. The size of the window for each dimension of
+// the input tensor. Must have `ksize[0] = ksize[4] = 1`.
+// strides: 1-D tensor of length 5. The stride of the sliding window for each
+// dimension of `input`. Must have `strides[0] = strides[4] = 1`.
+// padding: The type of padding algorithm to use.
//
-// Arguments:
-// grad: gradient propagated to the SparseSegmentMean op.
-// indices: indices passed to the corresponding SparseSegmentMean op.
-// segment_ids: segment_ids passed to the corresponding SparseSegmentMean op.
-// output_dim0: dimension 0 of "data" passed to SparseSegmentMean op.
-func SparseSegmentMeanGrad(scope *Scope, grad tf.Output, indices tf.Output, segment_ids tf.Output, output_dim0 tf.Output) (output tf.Output) {
+// Returns Gradients of gradients w.r.t. the input to `max_pool`.
+func MaxPool3DGradGrad(scope *Scope, orig_input tf.Output, orig_output tf.Output, grad tf.Output, ksize []int64, strides []int64, padding string, optional ...MaxPool3DGradGradAttr) (output tf.Output) {
if scope.Err() != nil {
return
}
- opspec := tf.OpSpec{
- Type: "SparseSegmentMeanGrad",
- Input: []tf.Input{
- grad, indices, segment_ids, output_dim0,
- },
- }
- op := scope.AddOperation(opspec)
- return op.Output(0)
-}
-
-// Returns the truth value of (x >= y) element-wise.
-//
-// *NOTE*: `GreaterEqual` supports broadcasting. More about broadcasting
-// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
-func GreaterEqual(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) {
- if scope.Err() != nil {
- return
+ attrs := map[string]interface{}{"ksize": ksize, "strides": strides, "padding": padding}
+ for _, a := range optional {
+ a(attrs)
}
opspec := tf.OpSpec{
- Type: "GreaterEqual",
+ Type: "MaxPool3DGradGrad",
Input: []tf.Input{
- x, y,
+ orig_input, orig_output, grad,
},
+ Attrs: attrs,
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// Conv3DAttr is an optional argument to Conv3D.
-type Conv3DAttr func(optionalAttr)
+// Conv3DBackpropFilterV2Attr is an optional argument to Conv3DBackpropFilterV2.
+type Conv3DBackpropFilterV2Attr func(optionalAttr)
-// Conv3DDataFormat sets the optional data_format attribute to value.
+// Conv3DBackpropFilterV2DataFormat sets the optional data_format attribute to value.
//
// value: The data format of the input and output data. With the
// default format "NDHWC", the data is stored in the order of:
// Alternatively, the format could be "NCDHW", the data storage order is:
// [batch, in_channels, in_depth, in_height, in_width].
// If not specified, defaults to "NDHWC"
-func Conv3DDataFormat(value string) Conv3DAttr {
+func Conv3DBackpropFilterV2DataFormat(value string) Conv3DBackpropFilterV2Attr {
return func(m optionalAttr) {
m["data_format"] = value
}
}
-// Conv3DDilations sets the optional dilations attribute to value.
+// Conv3DBackpropFilterV2Dilations sets the optional dilations attribute to value.
//
// value: 1-D tensor of length 5. The dilation factor for each dimension of
// `input`. If set to k > 1, there will be k-1 skipped cells between each
// value of `data_format`, see above for details. Dilations in the batch and
// depth dimensions must be 1.
// If not specified, defaults to <i:1 i:1 i:1 i:1 i:1 >
-func Conv3DDilations(value []int64) Conv3DAttr {
+func Conv3DBackpropFilterV2Dilations(value []int64) Conv3DBackpropFilterV2Attr {
return func(m optionalAttr) {
m["dilations"] = value
}
}
-// Computes a 3-D convolution given 5-D `input` and `filter` tensors.
-//
-// In signal processing, cross-correlation is a measure of similarity of
-// two waveforms as a function of a time-lag applied to one of them. This
-// is also known as a sliding dot product or sliding inner-product.
-//
-// Our Conv3D implements a form of cross-correlation.
+// Computes the gradients of 3-D convolution with respect to the filter.
//
// Arguments:
-// input: Shape `[batch, in_depth, in_height, in_width, in_channels]`.
-// filter: Shape `[filter_depth, filter_height, filter_width, in_channels,
-// out_channels]`. `in_channels` must match between `input` and `filter`.
+// input: Shape `[batch, depth, rows, cols, in_channels]`.
+// filter_sizes: An integer vector representing the tensor shape of `filter`,
+// where `filter` is a 5-D
+// `[filter_depth, filter_height, filter_width, in_channels, out_channels]`
+// tensor.
+// out_backprop: Backprop signal of shape `[batch, out_depth, out_rows, out_cols,
+// out_channels]`.
// strides: 1-D tensor of length 5. The stride of the sliding window for each
// dimension of `input`. Must have `strides[0] = strides[4] = 1`.
// padding: The type of padding algorithm to use.
-func Conv3D(scope *Scope, input tf.Output, filter tf.Output, strides []int64, padding string, optional ...Conv3DAttr) (output tf.Output) {
+func Conv3DBackpropFilterV2(scope *Scope, input tf.Output, filter_sizes tf.Output, out_backprop tf.Output, strides []int64, padding string, optional ...Conv3DBackpropFilterV2Attr) (output tf.Output) {
if scope.Err() != nil {
return
}
a(attrs)
}
opspec := tf.OpSpec{
- Type: "Conv3D",
+ Type: "Conv3DBackpropFilterV2",
Input: []tf.Input{
- input, filter,
+ input, filter_sizes, out_backprop,
},
Attrs: attrs,
}
return op.Output(0)
}
-// Adds up a SparseTensor and a dense Tensor, using these special rules:
-//
-// (1) Broadcasts the dense side to have the same shape as the sparse side, if
-// eligible;
-// (2) Then, only the dense values pointed to by the indices of the SparseTensor
-// participate in the cwise addition.
+// Execute a sub graph on a remote processor.
//
-// By these rules, the result is a logical SparseTensor with exactly the same
-// indices and shape, but possibly with different non-zero values. The output of
-// this Op is the resultant non-zero values.
+// The graph specifications(such as graph itself, input tensors and output names)
+// are stored as a serialized protocol buffer of RemoteFusedGraphExecuteInfo
+// as serialized_remote_fused_graph_execute_info.
+// The specifications will be passed to a dedicated registered
+// remote fused graph executor. The executor will send the graph specifications
+// to a remote processor and execute that graph. The execution results
+// will be passed to consumer nodes as outputs of this node.
//
// Arguments:
-// sp_indices: 2-D. `N x R` matrix with the indices of non-empty values in a
-// SparseTensor, possibly not in canonical ordering.
-// sp_values: 1-D. `N` non-empty values corresponding to `sp_indices`.
-// sp_shape: 1-D. Shape of the input SparseTensor.
-// dense: `R`-D. The dense Tensor operand.
+// inputs: Arbitrary number of tensors with arbitrary data types
//
-// Returns 1-D. The `N` values that are operated on.
-func SparseDenseCwiseAdd(scope *Scope, sp_indices tf.Output, sp_values tf.Output, sp_shape tf.Output, dense tf.Output) (output tf.Output) {
+// serialized_remote_fused_graph_execute_info: Serialized protocol buffer
+// of RemoteFusedGraphExecuteInfo which contains graph specifications.
+//
+// Returns Arbitrary number of tensors with arbitrary data types
+func RemoteFusedGraphExecute(scope *Scope, inputs []tf.Output, Toutputs []tf.DataType, serialized_remote_fused_graph_execute_info string) (outputs []tf.Output) {
if scope.Err() != nil {
return
}
+ attrs := map[string]interface{}{"Toutputs": Toutputs, "serialized_remote_fused_graph_execute_info": serialized_remote_fused_graph_execute_info}
opspec := tf.OpSpec{
- Type: "SparseDenseCwiseAdd",
+ Type: "RemoteFusedGraphExecute",
Input: []tf.Input{
- sp_indices, sp_values, sp_shape, dense,
+ tf.OutputList(inputs),
},
+ Attrs: attrs,
}
op := scope.AddOperation(opspec)
- return op.Output(0)
-}
-
-// Read an element from the TensorArray into output `value`.
-//
-// Arguments:
-// handle: The handle to a TensorArray.
-//
-// flow_in: A float scalar that enforces proper chaining of operations.
-// dtype: The type of the elem that is returned.
-//
-// Returns The tensor that is read from the TensorArray.
-func TensorArrayReadV3(scope *Scope, handle tf.Output, index tf.Output, flow_in tf.Output, dtype tf.DataType) (value tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{"dtype": dtype}
- opspec := tf.OpSpec{
- Type: "TensorArrayReadV3",
- Input: []tf.Input{
- handle, index, flow_in,
- },
- Attrs: attrs,
+ var idx int
+ var err error
+ if outputs, idx, err = makeOutputList(op, idx, "outputs"); err != nil {
+ scope.UpdateErr("RemoteFusedGraphExecute", err)
+ return
}
- op := scope.AddOperation(opspec)
- return op.Output(0)
+ return outputs
}
-// QuantizeV2Attr is an optional argument to QuantizeV2.
-type QuantizeV2Attr func(optionalAttr)
-
-// QuantizeV2Mode sets the optional mode attribute to value.
-// If not specified, defaults to "MIN_COMBINED"
-func QuantizeV2Mode(value string) QuantizeV2Attr {
- return func(m optionalAttr) {
- m["mode"] = value
- }
-}
+// SerializeManySparseAttr is an optional argument to SerializeManySparse.
+type SerializeManySparseAttr func(optionalAttr)
-// QuantizeV2RoundMode sets the optional round_mode attribute to value.
-// If not specified, defaults to "HALF_AWAY_FROM_ZERO"
-func QuantizeV2RoundMode(value string) QuantizeV2Attr {
+// SerializeManySparseOutType sets the optional out_type attribute to value.
+//
+// value: The `dtype` to use for serialization; the supported types are `string`
+// (default) and `variant`.
+// If not specified, defaults to DT_STRING
+func SerializeManySparseOutType(value tf.DataType) SerializeManySparseAttr {
return func(m optionalAttr) {
- m["round_mode"] = value
+ m["out_type"] = value
}
}
-// Quantize the 'input' tensor of type float to 'output' tensor of type 'T'.
-//
-// [min_range, max_range] are scalar floats that specify the range for
-// the 'input' data. The 'mode' attribute controls exactly which calculations are
-// used to convert the float values to their quantized equivalents. The
-// 'round_mode' attribute controls which rounding tie-breaking algorithm is used
-// when rounding float values to their quantized equivalents.
-//
-// In 'MIN_COMBINED' mode, each value of the tensor will undergo the following:
-//
-// ```
-// out[i] = (in[i] - min_range) * range(T) / (max_range - min_range)
-// if T == qint8, out[i] -= (range(T) + 1) / 2.0
-// ```
-// here `range(T) = numeric_limits<T>::max() - numeric_limits<T>::min()`
-//
-// *MIN_COMBINED Mode Example*
-//
-// Assume the input is type float and has a possible range of [0.0, 6.0] and the
-// output type is quint8 ([0, 255]). The min_range and max_range values should be
-// specified as 0.0 and 6.0. Quantizing from float to quint8 will multiply each
-// value of the input by 255/6 and cast to quint8.
-//
-// If the output type was qint8 ([-128, 127]), the operation will additionally
-// subtract each value by 128 prior to casting, so that the range of values aligns
-// with the range of qint8.
-//
-// If the mode is 'MIN_FIRST', then this approach is used:
-//
-// ```
-// num_discrete_values = 1 << (# of bits in T)
-// range_adjust = num_discrete_values / (num_discrete_values - 1)
-// range = (range_max - range_min) * range_adjust
-// range_scale = num_discrete_values / range
-// quantized = round(input * range_scale) - round(range_min * range_scale) +
-// numeric_limits<T>::min()
-// quantized = max(quantized, numeric_limits<T>::min())
-// quantized = min(quantized, numeric_limits<T>::max())
-// ```
-//
-// The biggest difference between this and MIN_COMBINED is that the minimum range
-// is rounded first, before it's subtracted from the rounded value. With
-// MIN_COMBINED, a small bias is introduced where repeated iterations of quantizing
-// and dequantizing will introduce a larger and larger error.
-//
-// *SCALED mode Example*
-//
-// `SCALED` mode matches the quantization approach used in
-// `QuantizeAndDequantize{V2|V3}`.
-//
-// If the mode is `SCALED`, we do not use the full range of the output type,
-// choosing to elide the lowest possible value for symmetry (e.g., output range is
-// -127 to 127, not -128 to 127 for signed 8 bit quantization), so that 0.0 maps to
-// 0.
-//
-// We first find the range of values in our tensor. The
-// range we use is always centered on 0, so we find m such that
-// ```c++
-// m = max(abs(input_min), abs(input_max))
-// ```
-//
-// Our input tensor range is then `[-m, m]`.
-//
-// Next, we choose our fixed-point quantization buckets, `[min_fixed, max_fixed]`.
-// If T is signed, this is
-// ```
-// num_bits = sizeof(T) * 8
-// [min_fixed, max_fixed] =
-// [-(1 << (num_bits - 1) - 1), (1 << (num_bits - 1)) - 1]
-// ```
-//
-// Otherwise, if T is unsigned, the fixed-point range is
-// ```
-// [min_fixed, max_fixed] = [0, (1 << num_bits) - 1]
-// ```
-//
-// From this we compute our scaling factor, s:
-// ```c++
-// s = (max_fixed - min_fixed) / (2 * m)
-// ```
+// Serialize an `N`-minibatch `SparseTensor` into an `[N, 3]` `Tensor` object.
//
-// Now we can quantize the elements of our tensor:
-// ```c++
-// result = round(input * s)
-// ```
+// The `SparseTensor` must have rank `R` greater than 1, and the first dimension
+// is treated as the minibatch dimension. Elements of the `SparseTensor`
+// must be sorted in increasing order of this first dimension. The serialized
+// `SparseTensor` objects going into each row of `serialized_sparse` will have
+// rank `R-1`.
//
-// One thing to watch out for is that the operator may choose to adjust the
-// requested minimum and maximum values slightly during the quantization process,
-// so you should always use the output ports as the range for further calculations.
-// For example, if the requested minimum and maximum values are close to equal,
-// they will be separated by a small epsilon value to prevent ill-formed quantized
-// buffers from being created. Otherwise, you can end up with buffers where all the
-// quantized values map to the same float value, which causes problems for
-// operations that have to perform further calculations on them.
+// The minibatch size `N` is extracted from `sparse_shape[0]`.
//
// Arguments:
-//
-// min_range: The minimum scalar value possibly produced for the input.
-// max_range: The maximum scalar value possibly produced for the input.
-//
-//
-// Returns The quantized data produced from the float input.The actual minimum scalar value used for the output.The actual maximum scalar value used for the output.
-func QuantizeV2(scope *Scope, input tf.Output, min_range tf.Output, max_range tf.Output, T tf.DataType, optional ...QuantizeV2Attr) (output tf.Output, output_min tf.Output, output_max tf.Output) {
+// sparse_indices: 2-D. The `indices` of the minibatch `SparseTensor`.
+// sparse_values: 1-D. The `values` of the minibatch `SparseTensor`.
+// sparse_shape: 1-D. The `shape` of the minibatch `SparseTensor`.
+func SerializeManySparse(scope *Scope, sparse_indices tf.Output, sparse_values tf.Output, sparse_shape tf.Output, optional ...SerializeManySparseAttr) (serialized_sparse tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{"T": T}
+ attrs := map[string]interface{}{}
for _, a := range optional {
a(attrs)
}
opspec := tf.OpSpec{
- Type: "QuantizeV2",
+ Type: "SerializeManySparse",
Input: []tf.Input{
- input, min_range, max_range,
+ sparse_indices, sparse_values, sparse_shape,
},
Attrs: attrs,
}
op := scope.AddOperation(opspec)
- return op.Output(0), op.Output(1), op.Output(2)
+ return op.Output(0)
}
-// Returns the truth value of (x < y) element-wise.
-//
-// *NOTE*: `Less` supports broadcasting. More about broadcasting
-// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
-func Less(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) {
+// Computes inverse hyperbolic cosine of x element-wise.
+func Acosh(scope *Scope, x tf.Output) (y tf.Output) {
if scope.Err() != nil {
return
}
opspec := tf.OpSpec{
- Type: "Less",
+ Type: "Acosh",
Input: []tf.Input{
- x, y,
+ x,
},
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// QuantizedReluXAttr is an optional argument to QuantizedReluX.
-type QuantizedReluXAttr func(optionalAttr)
+// TensorArrayV2Attr is an optional argument to TensorArrayV2.
+type TensorArrayV2Attr func(optionalAttr)
-// QuantizedReluXOutType sets the optional out_type attribute to value.
-// If not specified, defaults to DT_QUINT8
-func QuantizedReluXOutType(value tf.DataType) QuantizedReluXAttr {
+// TensorArrayV2ElementShape sets the optional element_shape attribute to value.
+// If not specified, defaults to <unknown_rank:true >
+func TensorArrayV2ElementShape(value tf.Shape) TensorArrayV2Attr {
return func(m optionalAttr) {
- m["out_type"] = value
+ m["element_shape"] = value
}
}
-// Computes Quantized Rectified Linear X: `min(max(features, 0), max_value)`
-//
-// Arguments:
-//
-//
-// min_features: The float value that the lowest quantized value represents.
-// max_features: The float value that the highest quantized value represents.
+// TensorArrayV2DynamicSize sets the optional dynamic_size attribute to value.
+// If not specified, defaults to false
+func TensorArrayV2DynamicSize(value bool) TensorArrayV2Attr {
+ return func(m optionalAttr) {
+ m["dynamic_size"] = value
+ }
+}
+
+// TensorArrayV2ClearAfterRead sets the optional clear_after_read attribute to value.
+// If not specified, defaults to true
+func TensorArrayV2ClearAfterRead(value bool) TensorArrayV2Attr {
+ return func(m optionalAttr) {
+ m["clear_after_read"] = value
+ }
+}
+
+// TensorArrayV2TensorArrayName sets the optional tensor_array_name attribute to value.
+// If not specified, defaults to ""
+func TensorArrayV2TensorArrayName(value string) TensorArrayV2Attr {
+ return func(m optionalAttr) {
+ m["tensor_array_name"] = value
+ }
+}
+
+// Deprecated. Use TensorArrayV3
//
-// Returns Has the same output shape as "features".The float value that the lowest quantized value represents.The float value that the highest quantized value represents.
-func QuantizedReluX(scope *Scope, features tf.Output, max_value tf.Output, min_features tf.Output, max_features tf.Output, optional ...QuantizedReluXAttr) (activations tf.Output, min_activations tf.Output, max_activations tf.Output) {
+// DEPRECATED at GraphDef version 26: Use TensorArrayV3
+func TensorArrayV2(scope *Scope, size tf.Output, dtype tf.DataType, optional ...TensorArrayV2Attr) (handle tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{}
+ attrs := map[string]interface{}{"dtype": dtype}
for _, a := range optional {
a(attrs)
}
opspec := tf.OpSpec{
- Type: "QuantizedReluX",
+ Type: "TensorArrayV2",
Input: []tf.Input{
- features, max_value, min_features, max_features,
+ size,
},
Attrs: attrs,
}
op := scope.AddOperation(opspec)
- return op.Output(0), op.Output(1), op.Output(2)
+ return op.Output(0)
}
-// WholeFileReaderV2Attr is an optional argument to WholeFileReaderV2.
-type WholeFileReaderV2Attr func(optionalAttr)
+// DecodeCSVAttr is an optional argument to DecodeCSV.
+type DecodeCSVAttr func(optionalAttr)
-// WholeFileReaderV2Container sets the optional container attribute to value.
+// DecodeCSVFieldDelim sets the optional field_delim attribute to value.
//
-// value: If non-empty, this reader is placed in the given container.
-// Otherwise, a default container is used.
-// If not specified, defaults to ""
-func WholeFileReaderV2Container(value string) WholeFileReaderV2Attr {
+// value: char delimiter to separate fields in a record.
+// If not specified, defaults to ","
+func DecodeCSVFieldDelim(value string) DecodeCSVAttr {
return func(m optionalAttr) {
- m["container"] = value
+ m["field_delim"] = value
}
}
-// WholeFileReaderV2SharedName sets the optional shared_name attribute to value.
+// DecodeCSVUseQuoteDelim sets the optional use_quote_delim attribute to value.
//
-// value: If non-empty, this reader is named in the given bucket
-// with this shared_name. Otherwise, the node name is used instead.
+// value: If false, treats double quotation marks as regular
+// characters inside of the string fields (ignoring RFC 4180, Section 2,
+// Bullet 5).
+// If not specified, defaults to true
+func DecodeCSVUseQuoteDelim(value bool) DecodeCSVAttr {
+ return func(m optionalAttr) {
+ m["use_quote_delim"] = value
+ }
+}
+
+// DecodeCSVNaValue sets the optional na_value attribute to value.
+//
+// value: Additional string to recognize as NA/NaN.
// If not specified, defaults to ""
-func WholeFileReaderV2SharedName(value string) WholeFileReaderV2Attr {
+func DecodeCSVNaValue(value string) DecodeCSVAttr {
return func(m optionalAttr) {
- m["shared_name"] = value
+ m["na_value"] = value
}
}
-// A Reader that outputs the entire contents of a file as a value.
+// Convert CSV records to tensors. Each column maps to one tensor.
//
-// To use, enqueue filenames in a Queue. The output of ReaderRead will
-// be a filename (key) and the contents of that file (value).
+// RFC 4180 format is expected for the CSV records.
+// (https://tools.ietf.org/html/rfc4180)
+// Note that we allow leading and trailing spaces with int or float field.
//
-// Returns The handle to reference the Reader.
-func WholeFileReaderV2(scope *Scope, optional ...WholeFileReaderV2Attr) (reader_handle tf.Output) {
+// Arguments:
+// records: Each string is a record/row in the csv and all records should have
+// the same format.
+// record_defaults: One tensor per column of the input record, with either a
+// scalar default value for that column or empty if the column is required.
+//
+// Returns Each tensor will have the same shape as records.
+func DecodeCSV(scope *Scope, records tf.Output, record_defaults []tf.Output, optional ...DecodeCSVAttr) (output []tf.Output) {
if scope.Err() != nil {
return
}
a(attrs)
}
opspec := tf.OpSpec{
- Type: "WholeFileReaderV2",
-
- Attrs: attrs,
- }
- op := scope.AddOperation(opspec)
- return op.Output(0)
-}
-
-// Transforms a tf.Example proto (as a string) into typed tensors.
-//
-// Arguments:
-// serialized: A vector containing a batch of binary serialized Example protos.
-// dense_defaults: A list of Tensors (some may be empty), whose length matches
-// the length of `dense_keys`. dense_defaults[j] provides default values
-// when the example's feature_map lacks dense_key[j]. If an empty Tensor is
-// provided for dense_defaults[j], then the Feature dense_keys[j] is required.
-// The input type is inferred from dense_defaults[j], even when it's empty.
-// If dense_defaults[j] is not empty, and dense_shapes[j] is fully defined,
-// then the shape of dense_defaults[j] must match that of dense_shapes[j].
-// If dense_shapes[j] has an undefined major dimension (variable strides dense
-// feature), dense_defaults[j] must contain a single element:
-// the padding element.
-// num_sparse: The number of sparse features to be parsed from the example. This
-// must match the lengths of `sparse_keys` and `sparse_types`.
-// sparse_keys: A list of `num_sparse` strings.
-// The keys expected in the Examples' features associated with sparse values.
-// dense_keys: The keys expected in the Examples' features associated with dense
-// values.
-// sparse_types: A list of `num_sparse` types; the data types of data in each
-// Feature given in sparse_keys.
-// Currently the ParseSingleExample op supports DT_FLOAT (FloatList),
-// DT_INT64 (Int64List), and DT_STRING (BytesList).
-// dense_shapes: The shapes of data in each Feature given in dense_keys.
-// The length of this list must match the length of `dense_keys`. The
-// number of elements in the Feature corresponding to dense_key[j] must
-// always equal dense_shapes[j].NumEntries(). If dense_shapes[j] ==
-// (D0, D1, ..., DN) then the shape of output Tensor dense_values[j]
-// will be (D0, D1, ..., DN): In the case dense_shapes[j] = (-1, D1,
-// ..., DN), the shape of the output Tensor dense_values[j] will be (M,
-// D1, .., DN), where M is the number of blocks of elements of length
-// D1 * .... * DN, in the input.
-func ParseSingleExample(scope *Scope, serialized tf.Output, dense_defaults []tf.Output, num_sparse int64, sparse_keys []string, dense_keys []string, sparse_types []tf.DataType, dense_shapes []tf.Shape) (sparse_indices []tf.Output, sparse_values []tf.Output, sparse_shapes []tf.Output, dense_values []tf.Output) {
- if scope.Err() != nil {
- return
- }
- attrs := map[string]interface{}{"num_sparse": num_sparse, "sparse_keys": sparse_keys, "dense_keys": dense_keys, "sparse_types": sparse_types, "dense_shapes": dense_shapes}
- opspec := tf.OpSpec{
- Type: "ParseSingleExample",
+ Type: "DecodeCSV",
Input: []tf.Input{
- serialized, tf.OutputList(dense_defaults),
+ records, tf.OutputList(record_defaults),
},
Attrs: attrs,
}
}
var idx int
var err error
- if sparse_indices, idx, err = makeOutputList(op, idx, "sparse_indices"); err != nil {
- scope.UpdateErr("ParseSingleExample", err)
+ if output, idx, err = makeOutputList(op, idx, "output"); err != nil {
+ scope.UpdateErr("DecodeCSV", err)
return
}
- if sparse_values, idx, err = makeOutputList(op, idx, "sparse_values"); err != nil {
- scope.UpdateErr("ParseSingleExample", err)
- return
+ return output
+}
+
+// MapClearAttr is an optional argument to MapClear.
+type MapClearAttr func(optionalAttr)
+
+// MapClearCapacity sets the optional capacity attribute to value.
+// If not specified, defaults to 0
+//
+// REQUIRES: value >= 0
+func MapClearCapacity(value int64) MapClearAttr {
+ return func(m optionalAttr) {
+ m["capacity"] = value
}
- if sparse_shapes, idx, err = makeOutputList(op, idx, "sparse_shapes"); err != nil {
- scope.UpdateErr("ParseSingleExample", err)
- return
+}
+
+// MapClearMemoryLimit sets the optional memory_limit attribute to value.
+// If not specified, defaults to 0
+//
+// REQUIRES: value >= 0
+func MapClearMemoryLimit(value int64) MapClearAttr {
+ return func(m optionalAttr) {
+ m["memory_limit"] = value
}
- if dense_values, idx, err = makeOutputList(op, idx, "dense_values"); err != nil {
- scope.UpdateErr("ParseSingleExample", err)
+}
+
+// MapClearContainer sets the optional container attribute to value.
+// If not specified, defaults to ""
+func MapClearContainer(value string) MapClearAttr {
+ return func(m optionalAttr) {
+ m["container"] = value
+ }
+}
+
+// MapClearSharedName sets the optional shared_name attribute to value.
+// If not specified, defaults to ""
+func MapClearSharedName(value string) MapClearAttr {
+ return func(m optionalAttr) {
+ m["shared_name"] = value
+ }
+}
+
+// Op removes all elements in the underlying container.
+//
+// Returns the created operation.
+func MapClear(scope *Scope, dtypes []tf.DataType, optional ...MapClearAttr) (o *tf.Operation) {
+ if scope.Err() != nil {
return
}
- return sparse_indices, sparse_values, sparse_shapes, dense_values
+ attrs := map[string]interface{}{"dtypes": dtypes}
+ for _, a := range optional {
+ a(attrs)
+ }
+ opspec := tf.OpSpec{
+ Type: "MapClear",
+
+ Attrs: attrs,
+ }
+ return scope.AddOperation(opspec)
}
-// QuantizedConv2DAttr is an optional argument to QuantizedConv2D.
-type QuantizedConv2DAttr func(optionalAttr)
+// ThreadUnsafeUnigramCandidateSamplerAttr is an optional argument to ThreadUnsafeUnigramCandidateSampler.
+type ThreadUnsafeUnigramCandidateSamplerAttr func(optionalAttr)
-// QuantizedConv2DOutType sets the optional out_type attribute to value.
-// If not specified, defaults to DT_QINT32
-func QuantizedConv2DOutType(value tf.DataType) QuantizedConv2DAttr {
+// ThreadUnsafeUnigramCandidateSamplerSeed sets the optional seed attribute to value.
+//
+// value: If either seed or seed2 are set to be non-zero, the random number
+// generator is seeded by the given seed. Otherwise, it is seeded by a
+// random seed.
+// If not specified, defaults to 0
+func ThreadUnsafeUnigramCandidateSamplerSeed(value int64) ThreadUnsafeUnigramCandidateSamplerAttr {
return func(m optionalAttr) {
- m["out_type"] = value
+ m["seed"] = value
}
}
-// QuantizedConv2DDilations sets the optional dilations attribute to value.
+// ThreadUnsafeUnigramCandidateSamplerSeed2 sets the optional seed2 attribute to value.
//
-// value: 1-D tensor of length 4. The dilation factor for each dimension of
-// `input`. If set to k > 1, there will be k-1 skipped cells between each
-// filter element on that dimension. The dimension order is determined by the
-// value of `data_format`, see above for details. Dilations in the batch and
-// depth dimensions must be 1.
-// If not specified, defaults to <i:1 i:1 i:1 i:1 >
-func QuantizedConv2DDilations(value []int64) QuantizedConv2DAttr {
+// value: An second seed to avoid seed collision.
+// If not specified, defaults to 0
+func ThreadUnsafeUnigramCandidateSamplerSeed2(value int64) ThreadUnsafeUnigramCandidateSamplerAttr {
return func(m optionalAttr) {
- m["dilations"] = value
+ m["seed2"] = value
}
}
-// Computes a 2D convolution given quantized 4D input and filter tensors.
+// Generates labels for candidate sampling with a learned unigram distribution.
//
-// The inputs are quantized tensors where the lowest value represents the real
-// number of the associated minimum, and the highest represents the maximum.
-// This means that you can only interpret the quantized output in the same way, by
-// taking the returned minimum and maximum values into account.
+// See explanations of candidate sampling and the data formats at
+// go/candidate-sampling.
//
-// Arguments:
+// For each batch, this op picks a single set of sampled candidate labels.
//
-// filter: filter's input_depth dimension must match input's depth dimensions.
-// min_input: The float value that the lowest quantized input value represents.
-// max_input: The float value that the highest quantized input value represents.
-// min_filter: The float value that the lowest quantized filter value represents.
-// max_filter: The float value that the highest quantized filter value represents.
-// strides: The stride of the sliding window for each dimension of the input
-// tensor.
-// padding: The type of padding algorithm to use.
+// The advantages of sampling candidates per-batch are simplicity and the
+// possibility of efficient dense matrix multiplication. The disadvantage is that
+// the sampled candidates must be chosen independently of the context and of the
+// true labels.
//
-// Returns The float value that the lowest quantized output value represents.The float value that the highest quantized output value represents.
-func QuantizedConv2D(scope *Scope, input tf.Output, filter tf.Output, min_input tf.Output, max_input tf.Output, min_filter tf.Output, max_filter tf.Output, strides []int64, padding string, optional ...QuantizedConv2DAttr) (output tf.Output, min_output tf.Output, max_output tf.Output) {
+// Arguments:
+// true_classes: A batch_size * num_true matrix, in which each row contains the
+// IDs of the num_true target_classes in the corresponding original label.
+// num_true: Number of true labels per context.
+// num_sampled: Number of candidates to randomly sample.
+// unique: If unique is true, we sample with rejection, so that all sampled
+// candidates in a batch are unique. This requires some approximation to
+// estimate the post-rejection sampling probabilities.
+// range_max: The sampler will sample integers from the interval [0, range_max).
+//
+// Returns A vector of length num_sampled, in which each element is
+// the ID of a sampled candidate.A batch_size * num_true matrix, representing
+// the number of times each candidate is expected to occur in a batch
+// of sampled candidates. If unique=true, then this is a probability.A vector of length num_sampled, for each sampled
+// candidate representing the number of times the candidate is expected
+// to occur in a batch of sampled candidates. If unique=true, then this is a
+// probability.
+func ThreadUnsafeUnigramCandidateSampler(scope *Scope, true_classes tf.Output, num_true int64, num_sampled int64, unique bool, range_max int64, optional ...ThreadUnsafeUnigramCandidateSamplerAttr) (sampled_candidates tf.Output, true_expected_count tf.Output, sampled_expected_count tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{"strides": strides, "padding": padding}
+ attrs := map[string]interface{}{"num_true": num_true, "num_sampled": num_sampled, "unique": unique, "range_max": range_max}
for _, a := range optional {
a(attrs)
}
opspec := tf.OpSpec{
- Type: "QuantizedConv2D",
+ Type: "ThreadUnsafeUnigramCandidateSampler",
Input: []tf.Input{
- input, filter, min_input, max_input, min_filter, max_filter,
+ true_classes,
},
Attrs: attrs,
}
return op.Output(0), op.Output(1), op.Output(2)
}
-// ResourceGatherAttr is an optional argument to ResourceGather.
-type ResourceGatherAttr func(optionalAttr)
+// MaxPoolV2Attr is an optional argument to MaxPoolV2.
+type MaxPoolV2Attr func(optionalAttr)
-// ResourceGatherValidateIndices sets the optional validate_indices attribute to value.
-// If not specified, defaults to true
-func ResourceGatherValidateIndices(value bool) ResourceGatherAttr {
+// MaxPoolV2DataFormat sets the optional data_format attribute to value.
+//
+// value: Specify the data format of the input and output data. With the
+// default format "NHWC", the data is stored in the order of:
+// [batch, in_height, in_width, in_channels].
+// Alternatively, the format could be "NCHW", the data storage order of:
+// [batch, in_channels, in_height, in_width].
+// If not specified, defaults to "NHWC"
+func MaxPoolV2DataFormat(value string) MaxPoolV2Attr {
return func(m optionalAttr) {
- m["validate_indices"] = value
+ m["data_format"] = value
}
}
-// Gather slices from the variable pointed to by `resource` according to `indices`.
-//
-// `indices` must be an integer tensor of any dimension (usually 0-D or 1-D).
-// Produces an output tensor with shape `indices.shape + params.shape[1:]` where:
-//
-// ```python
-// # Scalar indices
-// output[:, ..., :] = params[indices, :, ... :]
+// Performs max pooling on the input.
//
-// # Vector indices
-// output[i, :, ..., :] = params[indices[i], :, ... :]
+// Arguments:
+// input: 4-D input to pool over.
+// ksize: The size of the window for each dimension of the input tensor.
+// strides: The stride of the sliding window for each dimension of the
+// input tensor.
+// padding: The type of padding algorithm to use.
//
-// # Higher rank indices
-// output[i, ..., j, :, ... :] = params[indices[i, ..., j], :, ..., :]
-// ```
-func ResourceGather(scope *Scope, resource tf.Output, indices tf.Output, dtype tf.DataType, optional ...ResourceGatherAttr) (output tf.Output) {
+// Returns The max pooled output tensor.
+func MaxPoolV2(scope *Scope, input tf.Output, ksize tf.Output, strides tf.Output, padding string, optional ...MaxPoolV2Attr) (output tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{"dtype": dtype}
+ attrs := map[string]interface{}{"padding": padding}
for _, a := range optional {
a(attrs)
}
opspec := tf.OpSpec{
- Type: "ResourceGather",
+ Type: "MaxPoolV2",
Input: []tf.Input{
- resource, indices,
+ input, ksize, strides,
},
Attrs: attrs,
}
return op.Output(0)
}
-// Delete the TensorArray from its resource container.
-//
-// This enables the user to close and release the resource in the middle
-// of a step/run.
+// MutableDenseHashTableV2Attr is an optional argument to MutableDenseHashTableV2.
+type MutableDenseHashTableV2Attr func(optionalAttr)
+
+// MutableDenseHashTableV2Container sets the optional container attribute to value.
//
-// Arguments:
-// handle: The handle to a TensorArray (output of TensorArray or TensorArrayGrad).
+// value: If non-empty, this table is placed in the given container.
+// Otherwise, a default container is used.
+// If not specified, defaults to ""
+func MutableDenseHashTableV2Container(value string) MutableDenseHashTableV2Attr {
+ return func(m optionalAttr) {
+ m["container"] = value
+ }
+}
+
+// MutableDenseHashTableV2SharedName sets the optional shared_name attribute to value.
//
-// Returns the created operation.
-func TensorArrayCloseV3(scope *Scope, handle tf.Output) (o *tf.Operation) {
- if scope.Err() != nil {
- return
+// value: If non-empty, this table is shared under the given name across
+// multiple sessions.
+// If not specified, defaults to ""
+func MutableDenseHashTableV2SharedName(value string) MutableDenseHashTableV2Attr {
+ return func(m optionalAttr) {
+ m["shared_name"] = value
}
- opspec := tf.OpSpec{
- Type: "TensorArrayCloseV3",
- Input: []tf.Input{
- handle,
- },
+}
+
+// MutableDenseHashTableV2UseNodeNameSharing sets the optional use_node_name_sharing attribute to value.
+// If not specified, defaults to false
+func MutableDenseHashTableV2UseNodeNameSharing(value bool) MutableDenseHashTableV2Attr {
+ return func(m optionalAttr) {
+ m["use_node_name_sharing"] = value
}
- return scope.AddOperation(opspec)
}
-// Adds two `SparseTensor` objects to produce another `SparseTensor`.
+// MutableDenseHashTableV2ValueShape sets the optional value_shape attribute to value.
//
-// The input `SparseTensor` objects' indices are assumed ordered in standard
-// lexicographic order. If this is not the case, before this step run
-// `SparseReorder` to restore index ordering.
+// value: The shape of each value.
+// If not specified, defaults to <>
+func MutableDenseHashTableV2ValueShape(value tf.Shape) MutableDenseHashTableV2Attr {
+ return func(m optionalAttr) {
+ m["value_shape"] = value
+ }
+}
+
+// MutableDenseHashTableV2InitialNumBuckets sets the optional initial_num_buckets attribute to value.
//
-// By default, if two values sum to zero at some index, the output `SparseTensor`
-// would still include that particular location in its index, storing a zero in the
-// corresponding value slot. To override this, callers can specify `thresh`,
-// indicating that if the sum has a magnitude strictly smaller than `thresh`, its
-// corresponding value and index would then not be included. In particular,
-// `thresh == 0` (default) means everything is kept and actual thresholding happens
-// only for a positive value.
+// value: The initial number of hash table buckets. Must be a power
+// to 2.
+// If not specified, defaults to 131072
+func MutableDenseHashTableV2InitialNumBuckets(value int64) MutableDenseHashTableV2Attr {
+ return func(m optionalAttr) {
+ m["initial_num_buckets"] = value
+ }
+}
+
+// MutableDenseHashTableV2MaxLoadFactor sets the optional max_load_factor attribute to value.
//
-// In the following shapes, `nnz` is the count after taking `thresh` into account.
+// value: The maximum ratio between number of entries and number of
+// buckets before growing the table. Must be between 0 and 1.
+// If not specified, defaults to 0.8
+func MutableDenseHashTableV2MaxLoadFactor(value float32) MutableDenseHashTableV2Attr {
+ return func(m optionalAttr) {
+ m["max_load_factor"] = value
+ }
+}
+
+// Creates an empty hash table that uses tensors as the backing store.
+//
+// It uses "open addressing" with quadratic reprobing to resolve
+// collisions.
+//
+// This op creates a mutable hash table, specifying the type of its keys and
+// values. Each value must be a scalar. Data can be inserted into the table using
+// the insert operations. It does not support the initialization operation.
//
// Arguments:
-// a_indices: 2-D. The `indices` of the first `SparseTensor`, size `[nnz, ndims]` Matrix.
-// a_values: 1-D. The `values` of the first `SparseTensor`, size `[nnz]` Vector.
-// a_shape: 1-D. The `shape` of the first `SparseTensor`, size `[ndims]` Vector.
-// b_indices: 2-D. The `indices` of the second `SparseTensor`, size `[nnz, ndims]` Matrix.
-// b_values: 1-D. The `values` of the second `SparseTensor`, size `[nnz]` Vector.
-// b_shape: 1-D. The `shape` of the second `SparseTensor`, size `[ndims]` Vector.
-// thresh: 0-D. The magnitude threshold that determines if an output value/index
-// pair takes space.
-func SparseAdd(scope *Scope, a_indices tf.Output, a_values tf.Output, a_shape tf.Output, b_indices tf.Output, b_values tf.Output, b_shape tf.Output, thresh tf.Output) (sum_indices tf.Output, sum_values tf.Output, sum_shape tf.Output) {
+// empty_key: The key used to represent empty key buckets internally. Must not
+// be used in insert or lookup operations.
+// value_dtype: Type of the table values.
+//
+// Returns Handle to a table.
+func MutableDenseHashTableV2(scope *Scope, empty_key tf.Output, value_dtype tf.DataType, optional ...MutableDenseHashTableV2Attr) (table_handle tf.Output) {
if scope.Err() != nil {
return
}
+ attrs := map[string]interface{}{"value_dtype": value_dtype}
+ for _, a := range optional {
+ a(attrs)
+ }
opspec := tf.OpSpec{
- Type: "SparseAdd",
+ Type: "MutableDenseHashTableV2",
Input: []tf.Input{
- a_indices, a_values, a_shape, b_indices, b_values, b_shape, thresh,
+ empty_key,
},
+ Attrs: attrs,
}
op := scope.AddOperation(opspec)
- return op.Output(0), op.Output(1), op.Output(2)
+ return op.Output(0)
}
-// OrderedMapPeekAttr is an optional argument to OrderedMapPeek.
-type OrderedMapPeekAttr func(optionalAttr)
+// StageSizeAttr is an optional argument to StageSize.
+type StageSizeAttr func(optionalAttr)
-// OrderedMapPeekCapacity sets the optional capacity attribute to value.
+// StageSizeCapacity sets the optional capacity attribute to value.
// If not specified, defaults to 0
//
// REQUIRES: value >= 0
-func OrderedMapPeekCapacity(value int64) OrderedMapPeekAttr {
+func StageSizeCapacity(value int64) StageSizeAttr {
return func(m optionalAttr) {
m["capacity"] = value
}
}
-// OrderedMapPeekMemoryLimit sets the optional memory_limit attribute to value.
+// StageSizeMemoryLimit sets the optional memory_limit attribute to value.
// If not specified, defaults to 0
//
// REQUIRES: value >= 0
-func OrderedMapPeekMemoryLimit(value int64) OrderedMapPeekAttr {
+func StageSizeMemoryLimit(value int64) StageSizeAttr {
return func(m optionalAttr) {
m["memory_limit"] = value
}
}
-// OrderedMapPeekContainer sets the optional container attribute to value.
+// StageSizeContainer sets the optional container attribute to value.
// If not specified, defaults to ""
-func OrderedMapPeekContainer(value string) OrderedMapPeekAttr {
+func StageSizeContainer(value string) StageSizeAttr {
return func(m optionalAttr) {
m["container"] = value
}
}
-// OrderedMapPeekSharedName sets the optional shared_name attribute to value.
+// StageSizeSharedName sets the optional shared_name attribute to value.
// If not specified, defaults to ""
-func OrderedMapPeekSharedName(value string) OrderedMapPeekAttr {
+func StageSizeSharedName(value string) StageSizeAttr {
return func(m optionalAttr) {
m["shared_name"] = value
}
}
-// Op peeks at the values at the specified key. If the
-//
-// underlying container does not contain this key
-// this op will block until it does. This Op is optimized for
-// performance.
-func OrderedMapPeek(scope *Scope, key tf.Output, indices tf.Output, dtypes []tf.DataType, optional ...OrderedMapPeekAttr) (values []tf.Output) {
+// Op returns the number of elements in the underlying container.
+func StageSize(scope *Scope, dtypes []tf.DataType, optional ...StageSizeAttr) (size tf.Output) {
if scope.Err() != nil {
return
}
a(attrs)
}
opspec := tf.OpSpec{
- Type: "OrderedMapPeek",
- Input: []tf.Input{
- key, indices,
- },
+ Type: "StageSize",
+
Attrs: attrs,
}
op := scope.AddOperation(opspec)
+ return op.Output(0)
+}
+
+// Produces the max pool of the input tensor for quantized types.
+//
+// Arguments:
+// input: The 4D (batch x rows x cols x depth) Tensor to MaxReduce over.
+// min_input: The float value that the lowest quantized input value represents.
+// max_input: The float value that the highest quantized input value represents.
+// ksize: The size of the window for each dimension of the input tensor.
+// The length must be 4 to match the number of dimensions of the input.
+// strides: The stride of the sliding window for each dimension of the input
+// tensor. The length must be 4 to match the number of dimensions of the input.
+// padding: The type of padding algorithm to use.
+//
+// Returns The float value that the lowest quantized output value represents.The float value that the highest quantized output value represents.
+func QuantizedMaxPool(scope *Scope, input tf.Output, min_input tf.Output, max_input tf.Output, ksize []int64, strides []int64, padding string) (output tf.Output, min_output tf.Output, max_output tf.Output) {
if scope.Err() != nil {
return
}
- var idx int
- var err error
- if values, idx, err = makeOutputList(op, idx, "values"); err != nil {
- scope.UpdateErr("OrderedMapPeek", err)
- return
+ attrs := map[string]interface{}{"ksize": ksize, "strides": strides, "padding": padding}
+ opspec := tf.OpSpec{
+ Type: "QuantizedMaxPool",
+ Input: []tf.Input{
+ input, min_input, max_input,
+ },
+ Attrs: attrs,
}
- return values
+ op := scope.AddOperation(opspec)
+ return op.Output(0), op.Output(1), op.Output(2)
}
-// DecodeAndCropJpegAttr is an optional argument to DecodeAndCropJpeg.
-type DecodeAndCropJpegAttr func(optionalAttr)
-
-// DecodeAndCropJpegChannels sets the optional channels attribute to value.
-//
-// value: Number of color channels for the decoded image.
-// If not specified, defaults to 0
-func DecodeAndCropJpegChannels(value int64) DecodeAndCropJpegAttr {
- return func(m optionalAttr) {
- m["channels"] = value
+// Computes softplus: `log(exp(features) + 1)`.
+func Softplus(scope *Scope, features tf.Output) (activations tf.Output) {
+ if scope.Err() != nil {
+ return
}
-}
-
-// DecodeAndCropJpegRatio sets the optional ratio attribute to value.
-//
-// value: Downscaling ratio.
-// If not specified, defaults to 1
-func DecodeAndCropJpegRatio(value int64) DecodeAndCropJpegAttr {
- return func(m optionalAttr) {
- m["ratio"] = value
+ opspec := tf.OpSpec{
+ Type: "Softplus",
+ Input: []tf.Input{
+ features,
+ },
}
+ op := scope.AddOperation(opspec)
+ return op.Output(0)
}
-// DecodeAndCropJpegFancyUpscaling sets the optional fancy_upscaling attribute to value.
+// Computes exponential of x - 1 element-wise.
//
-// value: If true use a slower but nicer upscaling of the
-// chroma planes (yuv420/422 only).
-// If not specified, defaults to true
-func DecodeAndCropJpegFancyUpscaling(value bool) DecodeAndCropJpegAttr {
- return func(m optionalAttr) {
- m["fancy_upscaling"] = value
+// I.e., \\(y = (\exp x) - 1\\).
+func Expm1(scope *Scope, x tf.Output) (y tf.Output) {
+ if scope.Err() != nil {
+ return
+ }
+ opspec := tf.OpSpec{
+ Type: "Expm1",
+ Input: []tf.Input{
+ x,
+ },
}
+ op := scope.AddOperation(opspec)
+ return op.Output(0)
}
-// DecodeAndCropJpegTryRecoverTruncated sets the optional try_recover_truncated attribute to value.
+// Returns the number of records this Reader has produced.
//
-// value: If true try to recover an image from truncated input.
-// If not specified, defaults to false
-func DecodeAndCropJpegTryRecoverTruncated(value bool) DecodeAndCropJpegAttr {
- return func(m optionalAttr) {
- m["try_recover_truncated"] = value
+// This is the same as the number of ReaderRead executions that have
+// succeeded.
+//
+// Arguments:
+// reader_handle: Handle to a Reader.
+func ReaderNumRecordsProducedV2(scope *Scope, reader_handle tf.Output) (records_produced tf.Output) {
+ if scope.Err() != nil {
+ return
+ }
+ opspec := tf.OpSpec{
+ Type: "ReaderNumRecordsProducedV2",
+ Input: []tf.Input{
+ reader_handle,
+ },
}
+ op := scope.AddOperation(opspec)
+ return op.Output(0)
}
-// DecodeAndCropJpegAcceptableFraction sets the optional acceptable_fraction attribute to value.
+// Computes the sum along segments of a tensor.
//
-// value: The minimum required fraction of lines before a truncated
-// input is accepted.
-// If not specified, defaults to 1
-func DecodeAndCropJpegAcceptableFraction(value float32) DecodeAndCropJpegAttr {
- return func(m optionalAttr) {
- m["acceptable_fraction"] = value
- }
-}
-
-// DecodeAndCropJpegDctMethod sets the optional dct_method attribute to value.
-//
-// value: string specifying a hint about the algorithm used for
-// decompression. Defaults to "" which maps to a system-specific
-// default. Currently valid values are ["INTEGER_FAST",
-// "INTEGER_ACCURATE"]. The hint may be ignored (e.g., the internal
-// jpeg library changes to a version that does not have that specific
-// option.)
-// If not specified, defaults to ""
-func DecodeAndCropJpegDctMethod(value string) DecodeAndCropJpegAttr {
- return func(m optionalAttr) {
- m["dct_method"] = value
- }
-}
-
-// Decode and Crop a JPEG-encoded image to a uint8 tensor.
-//
-// The attr `channels` indicates the desired number of color channels for the
-// decoded image.
-//
-// Accepted values are:
-//
-// * 0: Use the number of channels in the JPEG-encoded image.
-// * 1: output a grayscale image.
-// * 3: output an RGB image.
-//
-// If needed, the JPEG-encoded image is transformed to match the requested number
-// of color channels.
+// Read @{$math_ops#segmentation$the section on segmentation} for an explanation of
+// segments.
//
-// The attr `ratio` allows downscaling the image by an integer factor during
-// decoding. Allowed values are: 1, 2, 4, and 8. This is much faster than
-// downscaling the image later.
+// Computes a tensor such that
+// \\(output_i = \sum_j data_j\\) where sum is over `j` such
+// that `segment_ids[j] == i`.
//
+// If the sum is empty for a given segment ID `i`, `output[i] = 0`.
//
-// It is equivalent to a combination of decode and crop, but much faster by only
-// decoding partial jpeg image.
+// <div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;">
+// <img style="width:100%" src="https://www.tensorflow.org/images/SegmentSum.png" alt>
+// </div>
//
// Arguments:
-// contents: 0-D. The JPEG-encoded image.
-// crop_window: 1-D. The crop window: [crop_y, crop_x, crop_height, crop_width].
//
-// Returns 3-D with shape `[height, width, channels]`..
-func DecodeAndCropJpeg(scope *Scope, contents tf.Output, crop_window tf.Output, optional ...DecodeAndCropJpegAttr) (image tf.Output) {
+// segment_ids: A 1-D tensor whose rank is equal to the rank of `data`'s
+// first dimension. Values should be sorted and can be repeated.
+//
+// Returns Has same shape as data, except for dimension 0 which
+// has size `k`, the number of segments.
+func SegmentSum(scope *Scope, data tf.Output, segment_ids tf.Output) (output tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{}
- for _, a := range optional {
- a(attrs)
- }
opspec := tf.OpSpec{
- Type: "DecodeAndCropJpeg",
+ Type: "SegmentSum",
Input: []tf.Input{
- contents, crop_window,
+ data, segment_ids,
},
- Attrs: attrs,
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// AllCandidateSamplerAttr is an optional argument to AllCandidateSampler.
-type AllCandidateSamplerAttr func(optionalAttr)
-
-// AllCandidateSamplerSeed sets the optional seed attribute to value.
-//
-// value: If either seed or seed2 are set to be non-zero, the random number
-// generator is seeded by the given seed. Otherwise, it is seeded by a
-// random seed.
-// If not specified, defaults to 0
-func AllCandidateSamplerSeed(value int64) AllCandidateSamplerAttr {
- return func(m optionalAttr) {
- m["seed"] = value
- }
-}
-
-// AllCandidateSamplerSeed2 sets the optional seed2 attribute to value.
-//
-// value: An second seed to avoid seed collision.
-// If not specified, defaults to 0
-func AllCandidateSamplerSeed2(value int64) AllCandidateSamplerAttr {
- return func(m optionalAttr) {
- m["seed2"] = value
- }
-}
-
-// Generates labels for candidate sampling with a learned unigram distribution.
-//
-// See explanations of candidate sampling and the data formats at
-// go/candidate-sampling.
-//
-// For each batch, this op picks a single set of sampled candidate labels.
-//
-// The advantages of sampling candidates per-batch are simplicity and the
-// possibility of efficient dense matrix multiplication. The disadvantage is that
-// the sampled candidates must be chosen independently of the context and of the
-// true labels.
+// Creates a dataset that emits the lines of one or more text files.
//
// Arguments:
-// true_classes: A batch_size * num_true matrix, in which each row contains the
-// IDs of the num_true target_classes in the corresponding original label.
-// num_true: Number of true labels per context.
-// num_sampled: Number of candidates to produce.
-// unique: If unique is true, we sample with rejection, so that all sampled
-// candidates in a batch are unique. This requires some approximation to
-// estimate the post-rejection sampling probabilities.
-//
-// Returns A vector of length num_sampled, in which each element is
-// the ID of a sampled candidate.A batch_size * num_true matrix, representing
-// the number of times each candidate is expected to occur in a batch
-// of sampled candidates. If unique=true, then this is a probability.A vector of length num_sampled, for each sampled
-// candidate representing the number of times the candidate is expected
-// to occur in a batch of sampled candidates. If unique=true, then this is a
-// probability.
-func AllCandidateSampler(scope *Scope, true_classes tf.Output, num_true int64, num_sampled int64, unique bool, optional ...AllCandidateSamplerAttr) (sampled_candidates tf.Output, true_expected_count tf.Output, sampled_expected_count tf.Output) {
+// filenames: A scalar or a vector containing the name(s) of the file(s) to be
+// read.
+// compression_type: A scalar containing either (i) the empty string (no
+// compression), (ii) "ZLIB", or (iii) "GZIP".
+// buffer_size: A scalar containing the number of bytes to buffer.
+func TextLineDataset(scope *Scope, filenames tf.Output, compression_type tf.Output, buffer_size tf.Output) (handle tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{"num_true": num_true, "num_sampled": num_sampled, "unique": unique}
- for _, a := range optional {
- a(attrs)
- }
opspec := tf.OpSpec{
- Type: "AllCandidateSampler",
+ Type: "TextLineDataset",
Input: []tf.Input{
- true_classes,
+ filenames, compression_type, buffer_size,
},
- Attrs: attrs,
}
op := scope.AddOperation(opspec)
- return op.Output(0), op.Output(1), op.Output(2)
+ return op.Output(0)
}
-// Saves the input tensors to disk.
-//
-// The size of `tensor_names` must match the number of tensors in `data`. `data[i]`
-// is written to `filename` with name `tensor_names[i]`.
+// Computes gradients for SparseSegmentMean.
//
-// See also `SaveSlices`.
+// Returns tensor "output" with same shape as grad, except for dimension 0 whose
+// value is output_dim0.
//
// Arguments:
-// filename: Must have a single element. The name of the file to which we write
-// the tensor.
-// tensor_names: Shape `[N]`. The names of the tensors to be saved.
-// data: `N` tensors to save.
-//
-// Returns the created operation.
-func Save(scope *Scope, filename tf.Output, tensor_names tf.Output, data []tf.Output) (o *tf.Operation) {
+// grad: gradient propagated to the SparseSegmentMean op.
+// indices: indices passed to the corresponding SparseSegmentMean op.
+// segment_ids: segment_ids passed to the corresponding SparseSegmentMean op.
+// output_dim0: dimension 0 of "data" passed to SparseSegmentMean op.
+func SparseSegmentMeanGrad(scope *Scope, grad tf.Output, indices tf.Output, segment_ids tf.Output, output_dim0 tf.Output) (output tf.Output) {
if scope.Err() != nil {
return
}
opspec := tf.OpSpec{
- Type: "Save",
+ Type: "SparseSegmentMeanGrad",
Input: []tf.Input{
- filename, tensor_names, tf.OutputList(data),
+ grad, indices, segment_ids, output_dim0,
},
}
- return scope.AddOperation(opspec)
+ op := scope.AddOperation(opspec)
+ return op.Output(0)
}
-// Returns element-wise remainder of division. When `x < 0` xor `y < 0` is
-//
-// true, this follows Python semantics in that the result here is consistent
-// with a flooring divide. E.g. `floor(x / y) * y + mod(x, y) = x`.
+// Returns the truth value of (x >= y) element-wise.
//
-// *NOTE*: `FloorMod` supports broadcasting. More about broadcasting
+// *NOTE*: `GreaterEqual` supports broadcasting. More about broadcasting
// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
-func FloorMod(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) {
+func GreaterEqual(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) {
if scope.Err() != nil {
return
}
opspec := tf.OpSpec{
- Type: "FloorMod",
+ Type: "GreaterEqual",
Input: []tf.Input{
x, y,
},
return op.Output(0)
}
-// SparseTensorDenseMatMulAttr is an optional argument to SparseTensorDenseMatMul.
-type SparseTensorDenseMatMulAttr func(optionalAttr)
+// Conv3DAttr is an optional argument to Conv3D.
+type Conv3DAttr func(optionalAttr)
-// SparseTensorDenseMatMulAdjointA sets the optional adjoint_a attribute to value.
+// Conv3DDataFormat sets the optional data_format attribute to value.
//
-// value: Use the adjoint of A in the matrix multiply. If A is complex, this
-// is transpose(conj(A)). Otherwise it's transpose(A).
-// If not specified, defaults to false
-func SparseTensorDenseMatMulAdjointA(value bool) SparseTensorDenseMatMulAttr {
+// value: The data format of the input and output data. With the
+// default format "NDHWC", the data is stored in the order of:
+// [batch, in_depth, in_height, in_width, in_channels].
+// Alternatively, the format could be "NCDHW", the data storage order is:
+// [batch, in_channels, in_depth, in_height, in_width].
+// If not specified, defaults to "NDHWC"
+func Conv3DDataFormat(value string) Conv3DAttr {
return func(m optionalAttr) {
- m["adjoint_a"] = value
+ m["data_format"] = value
}
}
-// SparseTensorDenseMatMulAdjointB sets the optional adjoint_b attribute to value.
+// Conv3DDilations sets the optional dilations attribute to value.
//
-// value: Use the adjoint of B in the matrix multiply. If B is complex, this
-// is transpose(conj(B)). Otherwise it's transpose(B).
-// If not specified, defaults to false
-func SparseTensorDenseMatMulAdjointB(value bool) SparseTensorDenseMatMulAttr {
+// value: 1-D tensor of length 5. The dilation factor for each dimension of
+// `input`. If set to k > 1, there will be k-1 skipped cells between each
+// filter element on that dimension. The dimension order is determined by the
+// value of `data_format`, see above for details. Dilations in the batch and
+// depth dimensions must be 1.
+// If not specified, defaults to <i:1 i:1 i:1 i:1 i:1 >
+func Conv3DDilations(value []int64) Conv3DAttr {
return func(m optionalAttr) {
- m["adjoint_b"] = value
+ m["dilations"] = value
}
}
-// Multiply SparseTensor (of rank 2) "A" by dense matrix "B".
+// Computes a 3-D convolution given 5-D `input` and `filter` tensors.
//
-// No validity checking is performed on the indices of A. However, the following
-// input format is recommended for optimal behavior:
+// In signal processing, cross-correlation is a measure of similarity of
+// two waveforms as a function of a time-lag applied to one of them. This
+// is also known as a sliding dot product or sliding inner-product.
//
-// if adjoint_a == false:
-// A should be sorted in lexicographically increasing order. Use SparseReorder
-// if you're not sure.
-// if adjoint_a == true:
-// A should be sorted in order of increasing dimension 1 (i.e., "column major"
-// order instead of "row major" order).
+// Our Conv3D implements a form of cross-correlation.
//
// Arguments:
-// a_indices: 2-D. The `indices` of the `SparseTensor`, size `[nnz, 2]` Matrix.
-// a_values: 1-D. The `values` of the `SparseTensor`, size `[nnz]` Vector.
-// a_shape: 1-D. The `shape` of the `SparseTensor`, size `[2]` Vector.
-// b: 2-D. A dense Matrix.
-func SparseTensorDenseMatMul(scope *Scope, a_indices tf.Output, a_values tf.Output, a_shape tf.Output, b tf.Output, optional ...SparseTensorDenseMatMulAttr) (product tf.Output) {
+// input: Shape `[batch, in_depth, in_height, in_width, in_channels]`.
+// filter: Shape `[filter_depth, filter_height, filter_width, in_channels,
+// out_channels]`. `in_channels` must match between `input` and `filter`.
+// strides: 1-D tensor of length 5. The stride of the sliding window for each
+// dimension of `input`. Must have `strides[0] = strides[4] = 1`.
+// padding: The type of padding algorithm to use.
+func Conv3D(scope *Scope, input tf.Output, filter tf.Output, strides []int64, padding string, optional ...Conv3DAttr) (output tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{}
+ attrs := map[string]interface{}{"strides": strides, "padding": padding}
for _, a := range optional {
a(attrs)
}
opspec := tf.OpSpec{
- Type: "SparseTensorDenseMatMul",
+ Type: "Conv3D",
Input: []tf.Input{
- a_indices, a_values, a_shape, b,
+ input, filter,
},
Attrs: attrs,
}
return op.Output(0)
}
-// Deserialize and concatenate `SparseTensors` from a serialized minibatch.
+// Adds up a SparseTensor and a dense Tensor, using these special rules:
//
-// The input `serialized_sparse` must be a string matrix of shape `[N x 3]` where
-// `N` is the minibatch size and the rows correspond to packed outputs of
-// `SerializeSparse`. The ranks of the original `SparseTensor` objects
-// must all match. When the final `SparseTensor` is created, it has rank one
-// higher than the ranks of the incoming `SparseTensor` objects
-// (they have been concatenated along a new row dimension).
+// (1) Broadcasts the dense side to have the same shape as the sparse side, if
+// eligible;
+// (2) Then, only the dense values pointed to by the indices of the SparseTensor
+// participate in the cwise addition.
//
-// The output `SparseTensor` object's shape values for all dimensions but the
-// first are the max across the input `SparseTensor` objects' shape values
-// for the corresponding dimensions. Its first shape value is `N`, the minibatch
-// size.
-//
-// The input `SparseTensor` objects' indices are assumed ordered in
-// standard lexicographic order. If this is not the case, after this
-// step run `SparseReorder` to restore index ordering.
-//
-// For example, if the serialized input is a `[2 x 3]` matrix representing two
-// original `SparseTensor` objects:
-//
-// index = [ 0]
-// [10]
-// [20]
-// values = [1, 2, 3]
-// shape = [50]
+// By these rules, the result is a logical SparseTensor with exactly the same
+// indices and shape, but possibly with different non-zero values. The output of
+// this Op is the resultant non-zero values.
//
-// and
+// Arguments:
+// sp_indices: 2-D. `N x R` matrix with the indices of non-empty values in a
+// SparseTensor, possibly not in canonical ordering.
+// sp_values: 1-D. `N` non-empty values corresponding to `sp_indices`.
+// sp_shape: 1-D. Shape of the input SparseTensor.
+// dense: `R`-D. The dense Tensor operand.
//
-// index = [ 2]
-// [10]
-// values = [4, 5]
-// shape = [30]
+// Returns 1-D. The `N` values that are operated on.
+func SparseDenseCwiseAdd(scope *Scope, sp_indices tf.Output, sp_values tf.Output, sp_shape tf.Output, dense tf.Output) (output tf.Output) {
+ if scope.Err() != nil {
+ return
+ }
+ opspec := tf.OpSpec{
+ Type: "SparseDenseCwiseAdd",
+ Input: []tf.Input{
+ sp_indices, sp_values, sp_shape, dense,
+ },
+ }
+ op := scope.AddOperation(opspec)
+ return op.Output(0)
+}
+
+// Read an element from the TensorArray into output `value`.
//
-// then the final deserialized `SparseTensor` will be:
+// Arguments:
+// handle: The handle to a TensorArray.
//
-// index = [0 0]
-// [0 10]
-// [0 20]
-// [1 2]
-// [1 10]
-// values = [1, 2, 3, 4, 5]
-// shape = [2 50]
+// flow_in: A float scalar that enforces proper chaining of operations.
+// dtype: The type of the elem that is returned.
//
-// Arguments:
-// serialized_sparse: 2-D, The `N` serialized `SparseTensor` objects.
-// Must have 3 columns.
-// dtype: The `dtype` of the serialized `SparseTensor` objects.
-func DeserializeManySparse(scope *Scope, serialized_sparse tf.Output, dtype tf.DataType) (sparse_indices tf.Output, sparse_values tf.Output, sparse_shape tf.Output) {
+// Returns The tensor that is read from the TensorArray.
+func TensorArrayReadV3(scope *Scope, handle tf.Output, index tf.Output, flow_in tf.Output, dtype tf.DataType) (value tf.Output) {
if scope.Err() != nil {
return
}
attrs := map[string]interface{}{"dtype": dtype}
opspec := tf.OpSpec{
- Type: "DeserializeManySparse",
+ Type: "TensorArrayReadV3",
Input: []tf.Input{
- serialized_sparse,
+ handle, index, flow_in,
},
Attrs: attrs,
}
op := scope.AddOperation(opspec)
- return op.Output(0), op.Output(1), op.Output(2)
+ return op.Output(0)
}
-// StringJoinAttr is an optional argument to StringJoin.
-type StringJoinAttr func(optionalAttr)
+// QuantizeV2Attr is an optional argument to QuantizeV2.
+type QuantizeV2Attr func(optionalAttr)
-// StringJoinSeparator sets the optional separator attribute to value.
-//
-// value: string, an optional join separator.
-// If not specified, defaults to ""
-func StringJoinSeparator(value string) StringJoinAttr {
+// QuantizeV2Mode sets the optional mode attribute to value.
+// If not specified, defaults to "MIN_COMBINED"
+func QuantizeV2Mode(value string) QuantizeV2Attr {
return func(m optionalAttr) {
- m["separator"] = value
+ m["mode"] = value
}
}
-// Joins the strings in the given list of string tensors into one tensor;
+// QuantizeV2RoundMode sets the optional round_mode attribute to value.
+// If not specified, defaults to "HALF_AWAY_FROM_ZERO"
+func QuantizeV2RoundMode(value string) QuantizeV2Attr {
+ return func(m optionalAttr) {
+ m["round_mode"] = value
+ }
+}
+
+// Quantize the 'input' tensor of type float to 'output' tensor of type 'T'.
//
-// with the given separator (default is an empty separator).
+// [min_range, max_range] are scalar floats that specify the range for
+// the 'input' data. The 'mode' attribute controls exactly which calculations are
+// used to convert the float values to their quantized equivalents. The
+// 'round_mode' attribute controls which rounding tie-breaking algorithm is used
+// when rounding float values to their quantized equivalents.
+//
+// In 'MIN_COMBINED' mode, each value of the tensor will undergo the following:
+//
+// ```
+// out[i] = (in[i] - min_range) * range(T) / (max_range - min_range)
+// if T == qint8, out[i] -= (range(T) + 1) / 2.0
+// ```
+// here `range(T) = numeric_limits<T>::max() - numeric_limits<T>::min()`
+//
+// *MIN_COMBINED Mode Example*
+//
+// Assume the input is type float and has a possible range of [0.0, 6.0] and the
+// output type is quint8 ([0, 255]). The min_range and max_range values should be
+// specified as 0.0 and 6.0. Quantizing from float to quint8 will multiply each
+// value of the input by 255/6 and cast to quint8.
+//
+// If the output type was qint8 ([-128, 127]), the operation will additionally
+// subtract each value by 128 prior to casting, so that the range of values aligns
+// with the range of qint8.
+//
+// If the mode is 'MIN_FIRST', then this approach is used:
+//
+// ```
+// num_discrete_values = 1 << (# of bits in T)
+// range_adjust = num_discrete_values / (num_discrete_values - 1)
+// range = (range_max - range_min) * range_adjust
+// range_scale = num_discrete_values / range
+// quantized = round(input * range_scale) - round(range_min * range_scale) +
+// numeric_limits<T>::min()
+// quantized = max(quantized, numeric_limits<T>::min())
+// quantized = min(quantized, numeric_limits<T>::max())
+// ```
+//
+// The biggest difference between this and MIN_COMBINED is that the minimum range
+// is rounded first, before it's subtracted from the rounded value. With
+// MIN_COMBINED, a small bias is introduced where repeated iterations of quantizing
+// and dequantizing will introduce a larger and larger error.
+//
+// *SCALED mode Example*
+//
+// `SCALED` mode matches the quantization approach used in
+// `QuantizeAndDequantize{V2|V3}`.
+//
+// If the mode is `SCALED`, we do not use the full range of the output type,
+// choosing to elide the lowest possible value for symmetry (e.g., output range is
+// -127 to 127, not -128 to 127 for signed 8 bit quantization), so that 0.0 maps to
+// 0.
+//
+// We first find the range of values in our tensor. The
+// range we use is always centered on 0, so we find m such that
+// ```c++
+// m = max(abs(input_min), abs(input_max))
+// ```
+//
+// Our input tensor range is then `[-m, m]`.
+//
+// Next, we choose our fixed-point quantization buckets, `[min_fixed, max_fixed]`.
+// If T is signed, this is
+// ```
+// num_bits = sizeof(T) * 8
+// [min_fixed, max_fixed] =
+// [-(1 << (num_bits - 1) - 1), (1 << (num_bits - 1)) - 1]
+// ```
+//
+// Otherwise, if T is unsigned, the fixed-point range is
+// ```
+// [min_fixed, max_fixed] = [0, (1 << num_bits) - 1]
+// ```
+//
+// From this we compute our scaling factor, s:
+// ```c++
+// s = (max_fixed - min_fixed) / (2 * m)
+// ```
+//
+// Now we can quantize the elements of our tensor:
+// ```c++
+// result = round(input * s)
+// ```
+//
+// One thing to watch out for is that the operator may choose to adjust the
+// requested minimum and maximum values slightly during the quantization process,
+// so you should always use the output ports as the range for further calculations.
+// For example, if the requested minimum and maximum values are close to equal,
+// they will be separated by a small epsilon value to prevent ill-formed quantized
+// buffers from being created. Otherwise, you can end up with buffers where all the
+// quantized values map to the same float value, which causes problems for
+// operations that have to perform further calculations on them.
//
// Arguments:
-// inputs: A list of string tensors. The tensors must all have the same shape,
-// or be scalars. Scalars may be mixed in; these will be broadcast to the shape
-// of non-scalar inputs.
-func StringJoin(scope *Scope, inputs []tf.Output, optional ...StringJoinAttr) (output tf.Output) {
+//
+// min_range: The minimum scalar value possibly produced for the input.
+// max_range: The maximum scalar value possibly produced for the input.
+//
+//
+// Returns The quantized data produced from the float input.The actual minimum scalar value used for the output.The actual maximum scalar value used for the output.
+func QuantizeV2(scope *Scope, input tf.Output, min_range tf.Output, max_range tf.Output, T tf.DataType, optional ...QuantizeV2Attr) (output tf.Output, output_min tf.Output, output_max tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{}
+ attrs := map[string]interface{}{"T": T}
for _, a := range optional {
a(attrs)
}
opspec := tf.OpSpec{
- Type: "StringJoin",
+ Type: "QuantizeV2",
Input: []tf.Input{
- tf.OutputList(inputs),
+ input, min_range, max_range,
},
Attrs: attrs,
}
op := scope.AddOperation(opspec)
- return op.Output(0)
+ return op.Output(0), op.Output(1), op.Output(2)
}
-// Returns immutable tensor from memory region.
-//
-// The current implementation memmaps the tensor from a file.
+// Returns the truth value of (x < y) element-wise.
//
-// Arguments:
-// dtype: Type of the returned tensor.
-// shape: Shape of the returned tensor.
-// memory_region_name: Name of readonly memory region used by the tensor, see
-// NewReadOnlyMemoryRegionFromFile in tensorflow::Env.
-func ImmutableConst(scope *Scope, dtype tf.DataType, shape tf.Shape, memory_region_name string) (tensor tf.Output) {
+// *NOTE*: `Less` supports broadcasting. More about broadcasting
+// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
+func Less(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{"dtype": dtype, "shape": shape, "memory_region_name": memory_region_name}
opspec := tf.OpSpec{
- Type: "ImmutableConst",
-
- Attrs: attrs,
+ Type: "Less",
+ Input: []tf.Input{
+ x, y,
+ },
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// Inverse real-valued fast Fourier transform.
+// QuantizedReluXAttr is an optional argument to QuantizedReluX.
+type QuantizedReluXAttr func(optionalAttr)
+
+// QuantizedReluXOutType sets the optional out_type attribute to value.
+// If not specified, defaults to DT_QUINT8
+func QuantizedReluXOutType(value tf.DataType) QuantizedReluXAttr {
+ return func(m optionalAttr) {
+ m["out_type"] = value
+ }
+}
+
+// Computes Quantized Rectified Linear X: `min(max(features, 0), max_value)`
//
-// Computes the inverse 1-dimensional discrete Fourier transform of a real-valued
-// signal over the inner-most dimension of `input`.
+// Arguments:
//
-// The inner-most dimension of `input` is assumed to be the result of `RFFT`: the
-// `fft_length / 2 + 1` unique components of the DFT of a real-valued signal. If
-// `fft_length` is not provided, it is computed from the size of the inner-most
-// dimension of `input` (`fft_length = 2 * (inner - 1)`). If the FFT length used to
-// compute `input` is odd, it should be provided since it cannot be inferred
-// properly.
//
-// Along the axis `IRFFT` is computed on, if `fft_length / 2 + 1` is smaller
-// than the corresponding dimension of `input`, the dimension is cropped. If it is
-// larger, the dimension is padded with zeros.
+// min_features: The float value that the lowest quantized value represents.
+// max_features: The float value that the highest quantized value represents.
//
-// Arguments:
-// input: A complex64 tensor.
-// fft_length: An int32 tensor of shape [1]. The FFT length.
-//
-// Returns A float32 tensor of the same rank as `input`. The inner-most
-// dimension of `input` is replaced with the `fft_length` samples of its inverse
-// 1D Fourier transform.
-//
-// @compatibility(numpy)
-// Equivalent to np.fft.irfft
-// @end_compatibility
-func IRFFT(scope *Scope, input tf.Output, fft_length tf.Output) (output tf.Output) {
+// Returns Has the same output shape as "features".The float value that the lowest quantized value represents.The float value that the highest quantized value represents.
+func QuantizedReluX(scope *Scope, features tf.Output, max_value tf.Output, min_features tf.Output, max_features tf.Output, optional ...QuantizedReluXAttr) (activations tf.Output, min_activations tf.Output, max_activations tf.Output) {
if scope.Err() != nil {
return
}
+ attrs := map[string]interface{}{}
+ for _, a := range optional {
+ a(attrs)
+ }
opspec := tf.OpSpec{
- Type: "IRFFT",
+ Type: "QuantizedReluX",
Input: []tf.Input{
- input, fft_length,
+ features, max_value, min_features, max_features,
},
+ Attrs: attrs,
}
op := scope.AddOperation(opspec)
- return op.Output(0)
+ return op.Output(0), op.Output(1), op.Output(2)
}
-// Concatenates a list of `SparseTensor` along the specified dimension.
-//
-// Concatenation is with respect to the dense versions of these sparse tensors.
-// It is assumed that each input is a `SparseTensor` whose elements are ordered
-// along increasing dimension number.
-//
-// All inputs' shapes must match, except for the concat dimension. The
-// `indices`, `values`, and `shapes` lists must have the same length.
-//
-// The output shape is identical to the inputs', except along the concat
-// dimension, where it is the sum of the inputs' sizes along that dimension.
-//
-// The output elements will be resorted to preserve the sort order along
-// increasing dimension number.
-//
-// This op runs in `O(M log M)` time, where `M` is the total number of non-empty
-// values across all inputs. This is due to the need for an internal sort in
-// order to concatenate efficiently across an arbitrary dimension.
-//
-// For example, if `concat_dim = 1` and the inputs are
-//
-// sp_inputs[0]: shape = [2, 3]
-// [0, 2]: "a"
-// [1, 0]: "b"
-// [1, 1]: "c"
-//
-// sp_inputs[1]: shape = [2, 4]
-// [0, 1]: "d"
-// [0, 2]: "e"
-//
-// then the output will be
-//
-// shape = [2, 7]
-// [0, 2]: "a"
-// [0, 4]: "d"
-// [0, 5]: "e"
-// [1, 0]: "b"
-// [1, 1]: "c"
+// QuantizedConv2DAttr is an optional argument to QuantizedConv2D.
+type QuantizedConv2DAttr func(optionalAttr)
+
+// QuantizedConv2DOutType sets the optional out_type attribute to value.
+// If not specified, defaults to DT_QINT32
+func QuantizedConv2DOutType(value tf.DataType) QuantizedConv2DAttr {
+ return func(m optionalAttr) {
+ m["out_type"] = value
+ }
+}
+
+// QuantizedConv2DDilations sets the optional dilations attribute to value.
//
-// Graphically this is equivalent to doing
+// value: 1-D tensor of length 4. The dilation factor for each dimension of
+// `input`. If set to k > 1, there will be k-1 skipped cells between each
+// filter element on that dimension. The dimension order is determined by the
+// value of `data_format`, see above for details. Dilations in the batch and
+// depth dimensions must be 1.
+// If not specified, defaults to <i:1 i:1 i:1 i:1 >
+func QuantizedConv2DDilations(value []int64) QuantizedConv2DAttr {
+ return func(m optionalAttr) {
+ m["dilations"] = value
+ }
+}
+
+// Computes a 2D convolution given quantized 4D input and filter tensors.
//
-// [ a] concat [ d e ] = [ a d e ]
-// [b c ] [ ] [b c ]
+// The inputs are quantized tensors where the lowest value represents the real
+// number of the associated minimum, and the highest represents the maximum.
+// This means that you can only interpret the quantized output in the same way, by
+// taking the returned minimum and maximum values into account.
//
// Arguments:
-// indices: 2-D. Indices of each input `SparseTensor`.
-// values: 1-D. Non-empty values of each `SparseTensor`.
-// shapes: 1-D. Shapes of each `SparseTensor`.
-// concat_dim: Dimension to concatenate along. Must be in range [-rank, rank),
-// where rank is the number of dimensions in each input `SparseTensor`.
//
-// Returns 2-D. Indices of the concatenated `SparseTensor`.1-D. Non-empty values of the concatenated `SparseTensor`.1-D. Shape of the concatenated `SparseTensor`.
-func SparseConcat(scope *Scope, indices []tf.Output, values []tf.Output, shapes []tf.Output, concat_dim int64) (output_indices tf.Output, output_values tf.Output, output_shape tf.Output) {
+// filter: filter's input_depth dimension must match input's depth dimensions.
+// min_input: The float value that the lowest quantized input value represents.
+// max_input: The float value that the highest quantized input value represents.
+// min_filter: The float value that the lowest quantized filter value represents.
+// max_filter: The float value that the highest quantized filter value represents.
+// strides: The stride of the sliding window for each dimension of the input
+// tensor.
+// padding: The type of padding algorithm to use.
+//
+// Returns The float value that the lowest quantized output value represents.The float value that the highest quantized output value represents.
+func QuantizedConv2D(scope *Scope, input tf.Output, filter tf.Output, min_input tf.Output, max_input tf.Output, min_filter tf.Output, max_filter tf.Output, strides []int64, padding string, optional ...QuantizedConv2DAttr) (output tf.Output, min_output tf.Output, max_output tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{"concat_dim": concat_dim}
+ attrs := map[string]interface{}{"strides": strides, "padding": padding}
+ for _, a := range optional {
+ a(attrs)
+ }
opspec := tf.OpSpec{
- Type: "SparseConcat",
+ Type: "QuantizedConv2D",
Input: []tf.Input{
- tf.OutputList(indices), tf.OutputList(values), tf.OutputList(shapes),
+ input, filter, min_input, max_input, min_filter, max_filter,
},
Attrs: attrs,
}
return op.Output(0), op.Output(1), op.Output(2)
}
-// Generates sparse cross from a list of sparse and dense tensors.
-//
-// The op takes two lists, one of 2D `SparseTensor` and one of 2D `Tensor`, each
-// representing features of one feature column. It outputs a 2D `SparseTensor` with
-// the batchwise crosses of these features.
-//
-// For example, if the inputs are
-//
-// inputs[0]: SparseTensor with shape = [2, 2]
-// [0, 0]: "a"
-// [1, 0]: "b"
-// [1, 1]: "c"
-//
-// inputs[1]: SparseTensor with shape = [2, 1]
-// [0, 0]: "d"
-// [1, 0]: "e"
-//
-// inputs[2]: Tensor [["f"], ["g"]]
-//
-// then the output will be
-//
-// shape = [2, 2]
-// [0, 0]: "a_X_d_X_f"
-// [1, 0]: "b_X_e_X_g"
-// [1, 1]: "c_X_e_X_g"
-//
-// if hashed_output=true then the output will be
-//
-// shape = [2, 2]
-// [0, 0]: FingerprintCat64(
-// Fingerprint64("f"), FingerprintCat64(
-// Fingerprint64("d"), Fingerprint64("a")))
-// [1, 0]: FingerprintCat64(
-// Fingerprint64("g"), FingerprintCat64(
-// Fingerprint64("e"), Fingerprint64("b")))
-// [1, 1]: FingerprintCat64(
-// Fingerprint64("g"), FingerprintCat64(
-// Fingerprint64("e"), Fingerprint64("c")))
+// ResourceGatherAttr is an optional argument to ResourceGather.
+type ResourceGatherAttr func(optionalAttr)
+
+// ResourceGatherValidateIndices sets the optional validate_indices attribute to value.
+// If not specified, defaults to true
+func ResourceGatherValidateIndices(value bool) ResourceGatherAttr {
+ return func(m optionalAttr) {
+ m["validate_indices"] = value
+ }
+}
+
+// Gather slices from the variable pointed to by `resource` according to `indices`.
//
-// Arguments:
-// indices: 2-D. Indices of each input `SparseTensor`.
-// values: 1-D. values of each `SparseTensor`.
-// shapes: 1-D. Shapes of each `SparseTensor`.
-// dense_inputs: 2-D. Columns represented by dense `Tensor`.
-// hashed_output: If true, returns the hash of the cross instead of the string.
-// This will allow us avoiding string manipulations.
-// num_buckets: It is used if hashed_output is true.
-// output = hashed_value%num_buckets if num_buckets > 0 else hashed_value.
-// hash_key: Specify the hash_key that will be used by the `FingerprintCat64`
-// function to combine the crosses fingerprints.
+// `indices` must be an integer tensor of any dimension (usually 0-D or 1-D).
+// Produces an output tensor with shape `indices.shape + params.shape[1:]` where:
//
+// ```python
+// # Scalar indices
+// output[:, ..., :] = params[indices, :, ... :]
//
+// # Vector indices
+// output[i, :, ..., :] = params[indices[i], :, ... :]
//
-// Returns 2-D. Indices of the concatenated `SparseTensor`.1-D. Non-empty values of the concatenated or hashed
-// `SparseTensor`.1-D. Shape of the concatenated `SparseTensor`.
-func SparseCross(scope *Scope, indices []tf.Output, values []tf.Output, shapes []tf.Output, dense_inputs []tf.Output, hashed_output bool, num_buckets int64, hash_key int64, out_type tf.DataType, internal_type tf.DataType) (output_indices tf.Output, output_values tf.Output, output_shape tf.Output) {
+// # Higher rank indices
+// output[i, ..., j, :, ... :] = params[indices[i, ..., j], :, ..., :]
+// ```
+func ResourceGather(scope *Scope, resource tf.Output, indices tf.Output, dtype tf.DataType, optional ...ResourceGatherAttr) (output tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{"hashed_output": hashed_output, "num_buckets": num_buckets, "hash_key": hash_key, "out_type": out_type, "internal_type": internal_type}
+ attrs := map[string]interface{}{"dtype": dtype}
+ for _, a := range optional {
+ a(attrs)
+ }
opspec := tf.OpSpec{
- Type: "SparseCross",
+ Type: "ResourceGather",
Input: []tf.Input{
- tf.OutputList(indices), tf.OutputList(values), tf.OutputList(shapes), tf.OutputList(dense_inputs),
+ resource, indices,
},
Attrs: attrs,
}
op := scope.AddOperation(opspec)
- return op.Output(0), op.Output(1), op.Output(2)
+ return op.Output(0)
}
-// Concatenates quantized tensors along one dimension.
+// Delete the TensorArray from its resource container.
+//
+// This enables the user to close and release the resource in the middle
+// of a step/run.
//
// Arguments:
-// concat_dim: 0-D. The dimension along which to concatenate. Must be in the
-// range [0, rank(values)).
-// values: The `N` Tensors to concatenate. Their ranks and types must match,
-// and their sizes must match in all dimensions except `concat_dim`.
-// input_mins: The minimum scalar values for each of the input tensors.
-// input_maxes: The maximum scalar values for each of the input tensors.
+// handle: The handle to a TensorArray (output of TensorArray or TensorArrayGrad).
//
-// Returns A `Tensor` with the concatenation of values stacked along the
-// `concat_dim` dimension. This tensor's shape matches that of `values` except
-// in `concat_dim` where it has the sum of the sizes.The float value that the minimum quantized output value represents.The float value that the maximum quantized output value represents.
-func QuantizedConcat(scope *Scope, concat_dim tf.Output, values []tf.Output, input_mins []tf.Output, input_maxes []tf.Output) (output tf.Output, output_min tf.Output, output_max tf.Output) {
+// Returns the created operation.
+func TensorArrayCloseV3(scope *Scope, handle tf.Output) (o *tf.Operation) {
if scope.Err() != nil {
return
}
opspec := tf.OpSpec{
- Type: "QuantizedConcat",
+ Type: "TensorArrayCloseV3",
Input: []tf.Input{
- concat_dim, tf.OutputList(values), tf.OutputList(input_mins), tf.OutputList(input_maxes),
+ handle,
},
}
- op := scope.AddOperation(opspec)
- return op.Output(0), op.Output(1), op.Output(2)
+ return scope.AddOperation(opspec)
}
-// Slice a `SparseTensor` based on the `start` and `size`.
-//
-// For example, if the input is
+// Adds two `SparseTensor` objects to produce another `SparseTensor`.
//
-// input_tensor = shape = [2, 7]
-// [ a d e ]
-// [b c ]
+// The input `SparseTensor` objects' indices are assumed ordered in standard
+// lexicographic order. If this is not the case, before this step run
+// `SparseReorder` to restore index ordering.
//
-// Graphically the output tensors are:
+// By default, if two values sum to zero at some index, the output `SparseTensor`
+// would still include that particular location in its index, storing a zero in the
+// corresponding value slot. To override this, callers can specify `thresh`,
+// indicating that if the sum has a magnitude strictly smaller than `thresh`, its
+// corresponding value and index would then not be included. In particular,
+// `thresh == 0` (default) means everything is kept and actual thresholding happens
+// only for a positive value.
//
-// sparse_slice([0, 0], [2, 4]) = shape = [2, 4]
-// [ a ]
-// [b c ]
-//
-// sparse_slice([0, 4], [2, 3]) = shape = [2, 3]
-// [ d e ]
-// [ ]
+// In the following shapes, `nnz` is the count after taking `thresh` into account.
//
// Arguments:
-// indices: 2-D tensor represents the indices of the sparse tensor.
-// values: 1-D tensor represents the values of the sparse tensor.
-// shape: 1-D. tensor represents the shape of the sparse tensor.
-// start: 1-D. tensor represents the start of the slice.
-// size: 1-D. tensor represents the size of the slice.
-// output indices: A list of 1-D tensors represents the indices of the output
-// sparse tensors.
-//
-// Returns A list of 1-D tensors represents the values of the output sparse
-// tensors.A list of 1-D tensors represents the shape of the output sparse
-// tensors.
-func SparseSlice(scope *Scope, indices tf.Output, values tf.Output, shape tf.Output, start tf.Output, size tf.Output) (output_indices tf.Output, output_values tf.Output, output_shape tf.Output) {
+// a_indices: 2-D. The `indices` of the first `SparseTensor`, size `[nnz, ndims]` Matrix.
+// a_values: 1-D. The `values` of the first `SparseTensor`, size `[nnz]` Vector.
+// a_shape: 1-D. The `shape` of the first `SparseTensor`, size `[ndims]` Vector.
+// b_indices: 2-D. The `indices` of the second `SparseTensor`, size `[nnz, ndims]` Matrix.
+// b_values: 1-D. The `values` of the second `SparseTensor`, size `[nnz]` Vector.
+// b_shape: 1-D. The `shape` of the second `SparseTensor`, size `[ndims]` Vector.
+// thresh: 0-D. The magnitude threshold that determines if an output value/index
+// pair takes space.
+func SparseAdd(scope *Scope, a_indices tf.Output, a_values tf.Output, a_shape tf.Output, b_indices tf.Output, b_values tf.Output, b_shape tf.Output, thresh tf.Output) (sum_indices tf.Output, sum_values tf.Output, sum_shape tf.Output) {
if scope.Err() != nil {
return
}
opspec := tf.OpSpec{
- Type: "SparseSlice",
+ Type: "SparseAdd",
Input: []tf.Input{
- indices, values, shape, start, size,
+ a_indices, a_values, a_shape, b_indices, b_values, b_shape, thresh,
},
}
op := scope.AddOperation(opspec)
return op.Output(0), op.Output(1), op.Output(2)
}
-// Adds up a `SparseTensor` and a dense `Tensor`, producing a dense `Tensor`.
+// OrderedMapPeekAttr is an optional argument to OrderedMapPeek.
+type OrderedMapPeekAttr func(optionalAttr)
+
+// OrderedMapPeekCapacity sets the optional capacity attribute to value.
+// If not specified, defaults to 0
//
-// This Op does not require `a_indices` be sorted in standard lexicographic order.
+// REQUIRES: value >= 0
+func OrderedMapPeekCapacity(value int64) OrderedMapPeekAttr {
+ return func(m optionalAttr) {
+ m["capacity"] = value
+ }
+}
+
+// OrderedMapPeekMemoryLimit sets the optional memory_limit attribute to value.
+// If not specified, defaults to 0
//
-// Arguments:
-// a_indices: 2-D. The `indices` of the `SparseTensor`, with shape `[nnz, ndims]`.
-// a_values: 1-D. The `values` of the `SparseTensor`, with shape `[nnz]`.
-// a_shape: 1-D. The `shape` of the `SparseTensor`, with shape `[ndims]`.
-// b: `ndims`-D Tensor. With shape `a_shape`.
-func SparseTensorDenseAdd(scope *Scope, a_indices tf.Output, a_values tf.Output, a_shape tf.Output, b tf.Output) (output tf.Output) {
+// REQUIRES: value >= 0
+func OrderedMapPeekMemoryLimit(value int64) OrderedMapPeekAttr {
+ return func(m optionalAttr) {
+ m["memory_limit"] = value
+ }
+}
+
+// OrderedMapPeekContainer sets the optional container attribute to value.
+// If not specified, defaults to ""
+func OrderedMapPeekContainer(value string) OrderedMapPeekAttr {
+ return func(m optionalAttr) {
+ m["container"] = value
+ }
+}
+
+// OrderedMapPeekSharedName sets the optional shared_name attribute to value.
+// If not specified, defaults to ""
+func OrderedMapPeekSharedName(value string) OrderedMapPeekAttr {
+ return func(m optionalAttr) {
+ m["shared_name"] = value
+ }
+}
+
+// Op peeks at the values at the specified key. If the
+//
+// underlying container does not contain this key
+// this op will block until it does. This Op is optimized for
+// performance.
+func OrderedMapPeek(scope *Scope, key tf.Output, indices tf.Output, dtypes []tf.DataType, optional ...OrderedMapPeekAttr) (values []tf.Output) {
if scope.Err() != nil {
return
}
+ attrs := map[string]interface{}{"dtypes": dtypes}
+ for _, a := range optional {
+ a(attrs)
+ }
opspec := tf.OpSpec{
- Type: "SparseTensorDenseAdd",
+ Type: "OrderedMapPeek",
Input: []tf.Input{
- a_indices, a_values, a_shape, b,
+ key, indices,
},
+ Attrs: attrs,
}
op := scope.AddOperation(opspec)
- return op.Output(0)
+ if scope.Err() != nil {
+ return
+ }
+ var idx int
+ var err error
+ if values, idx, err = makeOutputList(op, idx, "values"); err != nil {
+ scope.UpdateErr("OrderedMapPeek", err)
+ return
+ }
+ return values
}
-// Returns the set of files matching one or more glob patterns.
+// DecodeAndCropJpegAttr is an optional argument to DecodeAndCropJpeg.
+type DecodeAndCropJpegAttr func(optionalAttr)
+
+// DecodeAndCropJpegChannels sets the optional channels attribute to value.
//
-// Note that this routine only supports wildcard characters in the
-// basename portion of the pattern, not in the directory portion.
+// value: Number of color channels for the decoded image.
+// If not specified, defaults to 0
+func DecodeAndCropJpegChannels(value int64) DecodeAndCropJpegAttr {
+ return func(m optionalAttr) {
+ m["channels"] = value
+ }
+}
+
+// DecodeAndCropJpegRatio sets the optional ratio attribute to value.
//
-// Arguments:
-// pattern: Shell wildcard pattern(s). Scalar or vector of type string.
+// value: Downscaling ratio.
+// If not specified, defaults to 1
+func DecodeAndCropJpegRatio(value int64) DecodeAndCropJpegAttr {
+ return func(m optionalAttr) {
+ m["ratio"] = value
+ }
+}
+
+// DecodeAndCropJpegFancyUpscaling sets the optional fancy_upscaling attribute to value.
//
-// Returns A vector of matching filenames.
-func MatchingFiles(scope *Scope, pattern tf.Output) (filenames tf.Output) {
- if scope.Err() != nil {
- return
+// value: If true use a slower but nicer upscaling of the
+// chroma planes (yuv420/422 only).
+// If not specified, defaults to true
+func DecodeAndCropJpegFancyUpscaling(value bool) DecodeAndCropJpegAttr {
+ return func(m optionalAttr) {
+ m["fancy_upscaling"] = value
}
- opspec := tf.OpSpec{
- Type: "MatchingFiles",
- Input: []tf.Input{
- pattern,
- },
+}
+
+// DecodeAndCropJpegTryRecoverTruncated sets the optional try_recover_truncated attribute to value.
+//
+// value: If true try to recover an image from truncated input.
+// If not specified, defaults to false
+func DecodeAndCropJpegTryRecoverTruncated(value bool) DecodeAndCropJpegAttr {
+ return func(m optionalAttr) {
+ m["try_recover_truncated"] = value
}
- op := scope.AddOperation(opspec)
- return op.Output(0)
}
-// MatrixSolveLsAttr is an optional argument to MatrixSolveLs.
-type MatrixSolveLsAttr func(optionalAttr)
+// DecodeAndCropJpegAcceptableFraction sets the optional acceptable_fraction attribute to value.
+//
+// value: The minimum required fraction of lines before a truncated
+// input is accepted.
+// If not specified, defaults to 1
+func DecodeAndCropJpegAcceptableFraction(value float32) DecodeAndCropJpegAttr {
+ return func(m optionalAttr) {
+ m["acceptable_fraction"] = value
+ }
+}
-// MatrixSolveLsFast sets the optional fast attribute to value.
-// If not specified, defaults to true
-func MatrixSolveLsFast(value bool) MatrixSolveLsAttr {
+// DecodeAndCropJpegDctMethod sets the optional dct_method attribute to value.
+//
+// value: string specifying a hint about the algorithm used for
+// decompression. Defaults to "" which maps to a system-specific
+// default. Currently valid values are ["INTEGER_FAST",
+// "INTEGER_ACCURATE"]. The hint may be ignored (e.g., the internal
+// jpeg library changes to a version that does not have that specific
+// option.)
+// If not specified, defaults to ""
+func DecodeAndCropJpegDctMethod(value string) DecodeAndCropJpegAttr {
return func(m optionalAttr) {
- m["fast"] = value
+ m["dct_method"] = value
}
}
-// Solves one or more linear least-squares problems.
+// Decode and Crop a JPEG-encoded image to a uint8 tensor.
//
-// `matrix` is a tensor of shape `[..., M, N]` whose inner-most 2 dimensions
-// form real or complex matrices of size `[M, N]`. `Rhs` is a tensor of the same
-// type as `matrix` and shape `[..., M, K]`.
-// The output is a tensor shape `[..., N, K]` where each output matrix solves
-// each of the equations
-// `matrix[..., :, :]` * `output[..., :, :]` = `rhs[..., :, :]`
-// in the least squares sense.
+// The attr `channels` indicates the desired number of color channels for the
+// decoded image.
//
-// We use the following notation for (complex) matrix and right-hand sides
-// in the batch:
+// Accepted values are:
//
-// `matrix`=\\(A \in \mathbb{C}^{m \times n}\\),
-// `rhs`=\\(B \in \mathbb{C}^{m \times k}\\),
-// `output`=\\(X \in \mathbb{C}^{n \times k}\\),
-// `l2_regularizer`=\\(\lambda \in \mathbb{R}\\).
+// * 0: Use the number of channels in the JPEG-encoded image.
+// * 1: output a grayscale image.
+// * 3: output an RGB image.
//
-// If `fast` is `True`, then the solution is computed by solving the normal
-// equations using Cholesky decomposition. Specifically, if \\(m \ge n\\) then
-// \\(X = (A^H A + \lambda I)^{-1} A^H B\\), which solves the least-squares
-// problem \\(X = \mathrm{argmin}_{Z \in \Re^{n \times k} } ||A Z - B||_F^2 +
-// \lambda ||Z||_F^2\\). If \\(m \lt n\\) then `output` is computed as
-// \\(X = A^H (A A^H + \lambda I)^{-1} B\\), which (for \\(\lambda = 0\\)) is the
-// minimum-norm solution to the under-determined linear system, i.e.
-// \\(X = \mathrm{argmin}_{Z \in \mathbb{C}^{n \times k} } ||Z||_F^2 \\),
-// subject to \\(A Z = B\\). Notice that the fast path is only numerically stable
-// when \\(A\\) is numerically full rank and has a condition number
-// \\(\mathrm{cond}(A) \lt \frac{1}{\sqrt{\epsilon_{mach} } }\\) or\\(\lambda\\) is
-// sufficiently large.
+// If needed, the JPEG-encoded image is transformed to match the requested number
+// of color channels.
//
-// If `fast` is `False` an algorithm based on the numerically robust complete
-// orthogonal decomposition is used. This computes the minimum-norm
-// least-squares solution, even when \\(A\\) is rank deficient. This path is
-// typically 6-7 times slower than the fast path. If `fast` is `False` then
-// `l2_regularizer` is ignored.
+// The attr `ratio` allows downscaling the image by an integer factor during
+// decoding. Allowed values are: 1, 2, 4, and 8. This is much faster than
+// downscaling the image later.
//
-// Arguments:
-// matrix: Shape is `[..., M, N]`.
-// rhs: Shape is `[..., M, K]`.
-// l2_regularizer: Scalar tensor.
//
-// @compatibility(numpy)
-// Equivalent to np.linalg.lstsq
-// @end_compatibility
+// It is equivalent to a combination of decode and crop, but much faster by only
+// decoding partial jpeg image.
//
-// Returns Shape is `[..., N, K]`.
-func MatrixSolveLs(scope *Scope, matrix tf.Output, rhs tf.Output, l2_regularizer tf.Output, optional ...MatrixSolveLsAttr) (output tf.Output) {
+// Arguments:
+// contents: 0-D. The JPEG-encoded image.
+// crop_window: 1-D. The crop window: [crop_y, crop_x, crop_height, crop_width].
+//
+// Returns 3-D with shape `[height, width, channels]`..
+func DecodeAndCropJpeg(scope *Scope, contents tf.Output, crop_window tf.Output, optional ...DecodeAndCropJpegAttr) (image tf.Output) {
if scope.Err() != nil {
return
}
a(attrs)
}
opspec := tf.OpSpec{
- Type: "MatrixSolveLs",
+ Type: "DecodeAndCropJpeg",
Input: []tf.Input{
- matrix, rhs, l2_regularizer,
+ contents, crop_window,
},
Attrs: attrs,
}
return op.Output(0)
}
-// Elementwise computes the bitwise OR of `x` and `y`.
+// AllCandidateSamplerAttr is an optional argument to AllCandidateSampler.
+type AllCandidateSamplerAttr func(optionalAttr)
+
+// AllCandidateSamplerSeed sets the optional seed attribute to value.
//
-// The result will have those bits set, that are set in `x`, `y` or both. The
-// computation is performed on the underlying representations of `x` and `y`.
-func BitwiseOr(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) {
- if scope.Err() != nil {
- return
- }
- opspec := tf.OpSpec{
- Type: "BitwiseOr",
- Input: []tf.Input{
- x, y,
- },
+// value: If either seed or seed2 are set to be non-zero, the random number
+// generator is seeded by the given seed. Otherwise, it is seeded by a
+// random seed.
+// If not specified, defaults to 0
+func AllCandidateSamplerSeed(value int64) AllCandidateSamplerAttr {
+ return func(m optionalAttr) {
+ m["seed"] = value
}
- op := scope.AddOperation(opspec)
- return op.Output(0)
}
-// SparseToSparseSetOperationAttr is an optional argument to SparseToSparseSetOperation.
-type SparseToSparseSetOperationAttr func(optionalAttr)
-
-// SparseToSparseSetOperationValidateIndices sets the optional validate_indices attribute to value.
-// If not specified, defaults to true
-func SparseToSparseSetOperationValidateIndices(value bool) SparseToSparseSetOperationAttr {
+// AllCandidateSamplerSeed2 sets the optional seed2 attribute to value.
+//
+// value: An second seed to avoid seed collision.
+// If not specified, defaults to 0
+func AllCandidateSamplerSeed2(value int64) AllCandidateSamplerAttr {
return func(m optionalAttr) {
- m["validate_indices"] = value
+ m["seed2"] = value
}
}
-// Applies set operation along last dimension of 2 `SparseTensor` inputs.
-//
-// See SetOperationOp::SetOperationFromContext for values of `set_operation`.
-//
-// If `validate_indices` is `True`, `SparseToSparseSetOperation` validates the
-// order and range of `set1` and `set2` indices.
-//
-// Input `set1` is a `SparseTensor` represented by `set1_indices`, `set1_values`,
-// and `set1_shape`. For `set1` ranked `n`, 1st `n-1` dimensions must be the same
-// as `set2`. Dimension `n` contains values in a set, duplicates are allowed but
-// ignored.
+// Generates labels for candidate sampling with a learned unigram distribution.
//
-// Input `set2` is a `SparseTensor` represented by `set2_indices`, `set2_values`,
-// and `set2_shape`. For `set2` ranked `n`, 1st `n-1` dimensions must be the same
-// as `set1`. Dimension `n` contains values in a set, duplicates are allowed but
-// ignored.
+// See explanations of candidate sampling and the data formats at
+// go/candidate-sampling.
//
-// If `validate_indices` is `True`, this op validates the order and range of `set1`
-// and `set2` indices.
+// For each batch, this op picks a single set of sampled candidate labels.
//
-// Output `result` is a `SparseTensor` represented by `result_indices`,
-// `result_values`, and `result_shape`. For `set1` and `set2` ranked `n`, this
-// has rank `n` and the same 1st `n-1` dimensions as `set1` and `set2`. The `nth`
-// dimension contains the result of `set_operation` applied to the corresponding
-// `[0...n-1]` dimension of `set`.
+// The advantages of sampling candidates per-batch are simplicity and the
+// possibility of efficient dense matrix multiplication. The disadvantage is that
+// the sampled candidates must be chosen independently of the context and of the
+// true labels.
//
// Arguments:
-// set1_indices: 2D `Tensor`, indices of a `SparseTensor`. Must be in row-major
-// order.
-// set1_values: 1D `Tensor`, values of a `SparseTensor`. Must be in row-major
-// order.
-// set1_shape: 1D `Tensor`, shape of a `SparseTensor`. `set1_shape[0...n-1]` must
-// be the same as `set2_shape[0...n-1]`, `set1_shape[n]` is the
-// max set size across `0...n-1` dimensions.
-// set2_indices: 2D `Tensor`, indices of a `SparseTensor`. Must be in row-major
-// order.
-// set2_values: 1D `Tensor`, values of a `SparseTensor`. Must be in row-major
-// order.
-// set2_shape: 1D `Tensor`, shape of a `SparseTensor`. `set2_shape[0...n-1]` must
-// be the same as `set1_shape[0...n-1]`, `set2_shape[n]` is the
-// max set size across `0...n-1` dimensions.
-//
+// true_classes: A batch_size * num_true matrix, in which each row contains the
+// IDs of the num_true target_classes in the corresponding original label.
+// num_true: Number of true labels per context.
+// num_sampled: Number of candidates to produce.
+// unique: If unique is true, we sample with rejection, so that all sampled
+// candidates in a batch are unique. This requires some approximation to
+// estimate the post-rejection sampling probabilities.
//
-// Returns 2D indices of a `SparseTensor`.1D values of a `SparseTensor`.1D `Tensor` shape of a `SparseTensor`. `result_shape[0...n-1]` is
-// the same as the 1st `n-1` dimensions of `set1` and `set2`, `result_shape[n]`
-// is the max result set size across all `0...n-1` dimensions.
-func SparseToSparseSetOperation(scope *Scope, set1_indices tf.Output, set1_values tf.Output, set1_shape tf.Output, set2_indices tf.Output, set2_values tf.Output, set2_shape tf.Output, set_operation string, optional ...SparseToSparseSetOperationAttr) (result_indices tf.Output, result_values tf.Output, result_shape tf.Output) {
+// Returns A vector of length num_sampled, in which each element is
+// the ID of a sampled candidate.A batch_size * num_true matrix, representing
+// the number of times each candidate is expected to occur in a batch
+// of sampled candidates. If unique=true, then this is a probability.A vector of length num_sampled, for each sampled
+// candidate representing the number of times the candidate is expected
+// to occur in a batch of sampled candidates. If unique=true, then this is a
+// probability.
+func AllCandidateSampler(scope *Scope, true_classes tf.Output, num_true int64, num_sampled int64, unique bool, optional ...AllCandidateSamplerAttr) (sampled_candidates tf.Output, true_expected_count tf.Output, sampled_expected_count tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{"set_operation": set_operation}
+ attrs := map[string]interface{}{"num_true": num_true, "num_sampled": num_sampled, "unique": unique}
for _, a := range optional {
a(attrs)
}
opspec := tf.OpSpec{
- Type: "SparseToSparseSetOperation",
+ Type: "AllCandidateSampler",
Input: []tf.Input{
- set1_indices, set1_values, set1_shape, set2_indices, set2_values, set2_shape,
+ true_classes,
},
Attrs: attrs,
}
return op.Output(0), op.Output(1), op.Output(2)
}
-// Computes numerical negative value element-wise.
+// Saves the input tensors to disk.
//
-// I.e., \\(y = -x\\).
-func Neg(scope *Scope, x tf.Output) (y tf.Output) {
+// The size of `tensor_names` must match the number of tensors in `data`. `data[i]`
+// is written to `filename` with name `tensor_names[i]`.
+//
+// See also `SaveSlices`.
+//
+// Arguments:
+// filename: Must have a single element. The name of the file to which we write
+// the tensor.
+// tensor_names: Shape `[N]`. The names of the tensors to be saved.
+// data: `N` tensors to save.
+//
+// Returns the created operation.
+func Save(scope *Scope, filename tf.Output, tensor_names tf.Output, data []tf.Output) (o *tf.Operation) {
if scope.Err() != nil {
return
}
opspec := tf.OpSpec{
- Type: "Neg",
+ Type: "Save",
Input: []tf.Input{
- x,
+ filename, tensor_names, tf.OutputList(data),
+ },
+ }
+ return scope.AddOperation(opspec)
+}
+
+// Returns element-wise remainder of division. When `x < 0` xor `y < 0` is
+//
+// true, this follows Python semantics in that the result here is consistent
+// with a flooring divide. E.g. `floor(x / y) * y + mod(x, y) = x`.
+//
+// *NOTE*: `FloorMod` supports broadcasting. More about broadcasting
+// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
+func FloorMod(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) {
+ if scope.Err() != nil {
+ return
+ }
+ opspec := tf.OpSpec{
+ Type: "FloorMod",
+ Input: []tf.Input{
+ x, y,
},
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// FakeQuantWithMinMaxVarsAttr is an optional argument to FakeQuantWithMinMaxVars.
-type FakeQuantWithMinMaxVarsAttr func(optionalAttr)
+// SparseTensorDenseMatMulAttr is an optional argument to SparseTensorDenseMatMul.
+type SparseTensorDenseMatMulAttr func(optionalAttr)
-// FakeQuantWithMinMaxVarsNumBits sets the optional num_bits attribute to value.
-// If not specified, defaults to 8
-func FakeQuantWithMinMaxVarsNumBits(value int64) FakeQuantWithMinMaxVarsAttr {
+// SparseTensorDenseMatMulAdjointA sets the optional adjoint_a attribute to value.
+//
+// value: Use the adjoint of A in the matrix multiply. If A is complex, this
+// is transpose(conj(A)). Otherwise it's transpose(A).
+// If not specified, defaults to false
+func SparseTensorDenseMatMulAdjointA(value bool) SparseTensorDenseMatMulAttr {
return func(m optionalAttr) {
- m["num_bits"] = value
+ m["adjoint_a"] = value
}
}
-// FakeQuantWithMinMaxVarsNarrowRange sets the optional narrow_range attribute to value.
+// SparseTensorDenseMatMulAdjointB sets the optional adjoint_b attribute to value.
+//
+// value: Use the adjoint of B in the matrix multiply. If B is complex, this
+// is transpose(conj(B)). Otherwise it's transpose(B).
// If not specified, defaults to false
-func FakeQuantWithMinMaxVarsNarrowRange(value bool) FakeQuantWithMinMaxVarsAttr {
+func SparseTensorDenseMatMulAdjointB(value bool) SparseTensorDenseMatMulAttr {
return func(m optionalAttr) {
- m["narrow_range"] = value
+ m["adjoint_b"] = value
}
}
-// Fake-quantize the 'inputs' tensor of type float via global float scalars `min`
+// Multiply SparseTensor (of rank 2) "A" by dense matrix "B".
//
-// and `max` to 'outputs' tensor of same shape as `inputs`.
+// No validity checking is performed on the indices of A. However, the following
+// input format is recommended for optimal behavior:
//
-// `[min; max]` define the clamping range for the `inputs` data.
-// `inputs` values are quantized into the quantization range (`[0; 2^num_bits - 1]`
-// when `narrow_range` is false and `[1; 2^num_bits - 1]` when it is true) and
-// then de-quantized and output as floats in `[min; max]` interval.
-// `num_bits` is the bitwidth of the quantization; between 2 and 8, inclusive.
+// if adjoint_a == false:
+// A should be sorted in lexicographically increasing order. Use SparseReorder
+// if you're not sure.
+// if adjoint_a == true:
+// A should be sorted in order of increasing dimension 1 (i.e., "column major"
+// order instead of "row major" order).
//
-// This operation has a gradient and thus allows for training `min` and `max`
-// values.
-func FakeQuantWithMinMaxVars(scope *Scope, inputs tf.Output, min tf.Output, max tf.Output, optional ...FakeQuantWithMinMaxVarsAttr) (outputs tf.Output) {
+// Arguments:
+// a_indices: 2-D. The `indices` of the `SparseTensor`, size `[nnz, 2]` Matrix.
+// a_values: 1-D. The `values` of the `SparseTensor`, size `[nnz]` Vector.
+// a_shape: 1-D. The `shape` of the `SparseTensor`, size `[2]` Vector.
+// b: 2-D. A dense Matrix.
+func SparseTensorDenseMatMul(scope *Scope, a_indices tf.Output, a_values tf.Output, a_shape tf.Output, b tf.Output, optional ...SparseTensorDenseMatMulAttr) (product tf.Output) {
if scope.Err() != nil {
return
}
a(attrs)
}
opspec := tf.OpSpec{
- Type: "FakeQuantWithMinMaxVars",
+ Type: "SparseTensorDenseMatMul",
Input: []tf.Input{
- inputs, min, max,
+ a_indices, a_values, a_shape, b,
},
Attrs: attrs,
}
return op.Output(0)
}
-// Returns the element-wise min of two SparseTensors.
+// Deserialize and concatenate `SparseTensors` from a serialized minibatch.
//
-// Assumes the two SparseTensors have the same shape, i.e., no broadcasting.
+// The input `serialized_sparse` must be a string matrix of shape `[N x 3]` where
+// `N` is the minibatch size and the rows correspond to packed outputs of
+// `SerializeSparse`. The ranks of the original `SparseTensor` objects
+// must all match. When the final `SparseTensor` is created, it has rank one
+// higher than the ranks of the incoming `SparseTensor` objects
+// (they have been concatenated along a new row dimension).
//
-// Arguments:
-// a_indices: 2-D. `N x R` matrix with the indices of non-empty values in a
-// SparseTensor, in the canonical lexicographic ordering.
-// a_values: 1-D. `N` non-empty values corresponding to `a_indices`.
-// a_shape: 1-D. Shape of the input SparseTensor.
-// b_indices: counterpart to `a_indices` for the other operand.
-// b_values: counterpart to `a_values` for the other operand; must be of the same dtype.
-// b_shape: counterpart to `a_shape` for the other operand; the two shapes must be equal.
+// The output `SparseTensor` object's shape values for all dimensions but the
+// first are the max across the input `SparseTensor` objects' shape values
+// for the corresponding dimensions. Its first shape value is `N`, the minibatch
+// size.
//
-// Returns 2-D. The indices of the output SparseTensor.1-D. The values of the output SparseTensor.
-func SparseSparseMinimum(scope *Scope, a_indices tf.Output, a_values tf.Output, a_shape tf.Output, b_indices tf.Output, b_values tf.Output, b_shape tf.Output) (output_indices tf.Output, output_values tf.Output) {
+// The input `SparseTensor` objects' indices are assumed ordered in
+// standard lexicographic order. If this is not the case, after this
+// step run `SparseReorder` to restore index ordering.
+//
+// For example, if the serialized input is a `[2 x 3]` matrix representing two
+// original `SparseTensor` objects:
+//
+// index = [ 0]
+// [10]
+// [20]
+// values = [1, 2, 3]
+// shape = [50]
+//
+// and
+//
+// index = [ 2]
+// [10]
+// values = [4, 5]
+// shape = [30]
+//
+// then the final deserialized `SparseTensor` will be:
+//
+// index = [0 0]
+// [0 10]
+// [0 20]
+// [1 2]
+// [1 10]
+// values = [1, 2, 3, 4, 5]
+// shape = [2 50]
+//
+// Arguments:
+// serialized_sparse: 2-D, The `N` serialized `SparseTensor` objects.
+// Must have 3 columns.
+// dtype: The `dtype` of the serialized `SparseTensor` objects.
+func DeserializeManySparse(scope *Scope, serialized_sparse tf.Output, dtype tf.DataType) (sparse_indices tf.Output, sparse_values tf.Output, sparse_shape tf.Output) {
if scope.Err() != nil {
return
}
+ attrs := map[string]interface{}{"dtype": dtype}
opspec := tf.OpSpec{
- Type: "SparseSparseMinimum",
+ Type: "DeserializeManySparse",
Input: []tf.Input{
- a_indices, a_values, a_shape, b_indices, b_values, b_shape,
+ serialized_sparse,
},
+ Attrs: attrs,
}
op := scope.AddOperation(opspec)
- return op.Output(0), op.Output(1)
+ return op.Output(0), op.Output(1), op.Output(2)
}
-// Constructs a tensor by tiling a given tensor.
-//
-// This operation creates a new tensor by replicating `input` `multiples` times.
-// The output tensor's i'th dimension has `input.dims(i) * multiples[i]` elements,
-// and the values of `input` are replicated `multiples[i]` times along the 'i'th
-// dimension. For example, tiling `[a b c d]` by `[2]` produces
-// `[a b c d a b c d]`.
-//
-// Arguments:
-// input: 1-D or higher.
-// multiples: 1-D. Length must be the same as the number of dimensions in `input`
-func Tile(scope *Scope, input tf.Output, multiples tf.Output) (output tf.Output) {
- if scope.Err() != nil {
- return
- }
- opspec := tf.OpSpec{
- Type: "Tile",
- Input: []tf.Input{
- input, multiples,
- },
- }
- op := scope.AddOperation(opspec)
- return op.Output(0)
-}
-
-// TakeManySparseFromTensorsMapAttr is an optional argument to TakeManySparseFromTensorsMap.
-type TakeManySparseFromTensorsMapAttr func(optionalAttr)
-
-// TakeManySparseFromTensorsMapContainer sets the optional container attribute to value.
-//
-// value: The container name for the `SparseTensorsMap` read by this op.
-// If not specified, defaults to ""
-func TakeManySparseFromTensorsMapContainer(value string) TakeManySparseFromTensorsMapAttr {
- return func(m optionalAttr) {
- m["container"] = value
- }
-}
+// StringJoinAttr is an optional argument to StringJoin.
+type StringJoinAttr func(optionalAttr)
-// TakeManySparseFromTensorsMapSharedName sets the optional shared_name attribute to value.
+// StringJoinSeparator sets the optional separator attribute to value.
//
-// value: The shared name for the `SparseTensorsMap` read by this op.
-// It should not be blank; rather the `shared_name` or unique Operation name
-// of the Op that created the original `SparseTensorsMap` should be used.
+// value: string, an optional join separator.
// If not specified, defaults to ""
-func TakeManySparseFromTensorsMapSharedName(value string) TakeManySparseFromTensorsMapAttr {
+func StringJoinSeparator(value string) StringJoinAttr {
return func(m optionalAttr) {
- m["shared_name"] = value
+ m["separator"] = value
}
}
-// Read `SparseTensors` from a `SparseTensorsMap` and concatenate them.
-//
-// The input `sparse_handles` must be an `int64` matrix of shape `[N, 1]` where
-// `N` is the minibatch size and the rows correspond to the output handles of
-// `AddSparseToTensorsMap` or `AddManySparseToTensorsMap`. The ranks of the
-// original `SparseTensor` objects that went into the given input ops must all
-// match. When the final `SparseTensor` is created, it has rank one
-// higher than the ranks of the incoming `SparseTensor` objects
-// (they have been concatenated along a new row dimension on the left).
-//
-// The output `SparseTensor` object's shape values for all dimensions but the
-// first are the max across the input `SparseTensor` objects' shape values
-// for the corresponding dimensions. Its first shape value is `N`, the minibatch
-// size.
-//
-// The input `SparseTensor` objects' indices are assumed ordered in
-// standard lexicographic order. If this is not the case, after this
-// step run `SparseReorder` to restore index ordering.
-//
-// For example, if the handles represent an input, which is a `[2, 3]` matrix
-// representing two original `SparseTensor` objects:
-//
-// ```
-// index = [ 0]
-// [10]
-// [20]
-// values = [1, 2, 3]
-// shape = [50]
-// ```
-//
-// and
-//
-// ```
-// index = [ 2]
-// [10]
-// values = [4, 5]
-// shape = [30]
-// ```
-//
-// then the final `SparseTensor` will be:
+// Joins the strings in the given list of string tensors into one tensor;
//
-// ```
-// index = [0 0]
-// [0 10]
-// [0 20]
-// [1 2]
-// [1 10]
-// values = [1, 2, 3, 4, 5]
-// shape = [2 50]
-// ```
+// with the given separator (default is an empty separator).
//
// Arguments:
-// sparse_handles: 1-D, The `N` serialized `SparseTensor` objects.
-// Shape: `[N]`.
-// dtype: The `dtype` of the `SparseTensor` objects stored in the
-// `SparseTensorsMap`.
-//
-// Returns 2-D. The `indices` of the minibatch `SparseTensor`.1-D. The `values` of the minibatch `SparseTensor`.1-D. The `shape` of the minibatch `SparseTensor`.
-func TakeManySparseFromTensorsMap(scope *Scope, sparse_handles tf.Output, dtype tf.DataType, optional ...TakeManySparseFromTensorsMapAttr) (sparse_indices tf.Output, sparse_values tf.Output, sparse_shape tf.Output) {
+// inputs: A list of string tensors. The tensors must all have the same shape,
+// or be scalars. Scalars may be mixed in; these will be broadcast to the shape
+// of non-scalar inputs.
+func StringJoin(scope *Scope, inputs []tf.Output, optional ...StringJoinAttr) (output tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{"dtype": dtype}
+ attrs := map[string]interface{}{}
for _, a := range optional {
a(attrs)
}
opspec := tf.OpSpec{
- Type: "TakeManySparseFromTensorsMap",
+ Type: "StringJoin",
Input: []tf.Input{
- sparse_handles,
+ tf.OutputList(inputs),
},
Attrs: attrs,
}
op := scope.AddOperation(opspec)
- return op.Output(0), op.Output(1), op.Output(2)
+ return op.Output(0)
}
-// MaxPoolAttr is an optional argument to MaxPool.
-type MaxPoolAttr func(optionalAttr)
-
-// MaxPoolDataFormat sets the optional data_format attribute to value.
+// Returns immutable tensor from memory region.
//
-// value: Specify the data format of the input and output data. With the
-// default format "NHWC", the data is stored in the order of:
-// [batch, in_height, in_width, in_channels].
-// Alternatively, the format could be "NCHW", the data storage order of:
-// [batch, in_channels, in_height, in_width].
-// If not specified, defaults to "NHWC"
-func MaxPoolDataFormat(value string) MaxPoolAttr {
- return func(m optionalAttr) {
- m["data_format"] = value
- }
-}
-
-// Performs max pooling on the input.
+// The current implementation memmaps the tensor from a file.
//
// Arguments:
-// input: 4-D input to pool over.
-// ksize: The size of the window for each dimension of the input tensor.
-// strides: The stride of the sliding window for each dimension of the
-// input tensor.
-// padding: The type of padding algorithm to use.
-//
-// Returns The max pooled output tensor.
-func MaxPool(scope *Scope, input tf.Output, ksize []int64, strides []int64, padding string, optional ...MaxPoolAttr) (output tf.Output) {
+// dtype: Type of the returned tensor.
+// shape: Shape of the returned tensor.
+// memory_region_name: Name of readonly memory region used by the tensor, see
+// NewReadOnlyMemoryRegionFromFile in tensorflow::Env.
+func ImmutableConst(scope *Scope, dtype tf.DataType, shape tf.Shape, memory_region_name string) (tensor tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{"ksize": ksize, "strides": strides, "padding": padding}
- for _, a := range optional {
- a(attrs)
- }
+ attrs := map[string]interface{}{"dtype": dtype, "shape": shape, "memory_region_name": memory_region_name}
opspec := tf.OpSpec{
- Type: "MaxPool",
- Input: []tf.Input{
- input,
- },
+ Type: "ImmutableConst",
+
Attrs: attrs,
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// Says whether the targets are in the top `K` predictions.
-//
-// This outputs a `batch_size` bool array, an entry `out[i]` is `true` if the
-// prediction for the target class is among the top `k` predictions among
-// all predictions for example `i`. Note that the behavior of `InTopK` differs
-// from the `TopK` op in its handling of ties; if multiple classes have the
-// same prediction value and straddle the top-`k` boundary, all of those
-// classes are considered to be in the top `k`.
+// Inverse real-valued fast Fourier transform.
//
-// More formally, let
+// Computes the inverse 1-dimensional discrete Fourier transform of a real-valued
+// signal over the inner-most dimension of `input`.
//
-// \\(predictions_i\\) be the predictions for all classes for example `i`,
-// \\(targets_i\\) be the target class for example `i`,
-// \\(out_i\\) be the output for example `i`,
+// The inner-most dimension of `input` is assumed to be the result of `RFFT`: the
+// `fft_length / 2 + 1` unique components of the DFT of a real-valued signal. If
+// `fft_length` is not provided, it is computed from the size of the inner-most
+// dimension of `input` (`fft_length = 2 * (inner - 1)`). If the FFT length used to
+// compute `input` is odd, it should be provided since it cannot be inferred
+// properly.
//
-// $$out_i = predictions_{i, targets_i} \in TopKIncludingTies(predictions_i)$$
+// Along the axis `IRFFT` is computed on, if `fft_length / 2 + 1` is smaller
+// than the corresponding dimension of `input`, the dimension is cropped. If it is
+// larger, the dimension is padded with zeros.
//
// Arguments:
-// predictions: A `batch_size` x `classes` tensor.
-// targets: A `batch_size` vector of class ids.
-// k: Number of top elements to look at for computing precision.
+// input: A complex64 tensor.
+// fft_length: An int32 tensor of shape [1]. The FFT length.
//
-// Returns Computed precision at `k` as a `bool Tensor`.
-func InTopKV2(scope *Scope, predictions tf.Output, targets tf.Output, k tf.Output) (precision tf.Output) {
+// Returns A float32 tensor of the same rank as `input`. The inner-most
+// dimension of `input` is replaced with the `fft_length` samples of its inverse
+// 1D Fourier transform.
+//
+// @compatibility(numpy)
+// Equivalent to np.fft.irfft
+// @end_compatibility
+func IRFFT(scope *Scope, input tf.Output, fft_length tf.Output) (output tf.Output) {
if scope.Err() != nil {
return
}
opspec := tf.OpSpec{
- Type: "InTopKV2",
+ Type: "IRFFT",
Input: []tf.Input{
- predictions, targets, k,
+ input, fft_length,
},
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// Assigns a new value to a variable.
+// Concatenates a list of `SparseTensor` along the specified dimension.
//
-// Any ReadVariableOp with a control dependency on this op is guaranteed to return
-// this value or a subsequent newer value of the variable.
+// Concatenation is with respect to the dense versions of these sparse tensors.
+// It is assumed that each input is a `SparseTensor` whose elements are ordered
+// along increasing dimension number.
//
-// Arguments:
-// resource: handle to the resource in which to store the variable.
-// value: the value to set the new tensor to use.
+// All inputs' shapes must match, except for the concat dimension. The
+// `indices`, `values`, and `shapes` lists must have the same length.
//
-// Returns the created operation.
-func AssignVariableOp(scope *Scope, resource tf.Output, value tf.Output) (o *tf.Operation) {
- if scope.Err() != nil {
- return
- }
- opspec := tf.OpSpec{
- Type: "AssignVariableOp",
- Input: []tf.Input{
- resource, value,
- },
- }
- return scope.AddOperation(opspec)
-}
-
-// Returns a tensor of ones with the same shape and type as x.
+// The output shape is identical to the inputs', except along the concat
+// dimension, where it is the sum of the inputs' sizes along that dimension.
//
-// Arguments:
-// x: a tensor of type T.
+// The output elements will be resorted to preserve the sort order along
+// increasing dimension number.
//
-// Returns a tensor of the same shape and type as x but filled with ones.
-func OnesLike(scope *Scope, x tf.Output) (y tf.Output) {
- if scope.Err() != nil {
- return
- }
- opspec := tf.OpSpec{
- Type: "OnesLike",
- Input: []tf.Input{
- x,
- },
- }
- op := scope.AddOperation(opspec)
- return op.Output(0)
-}
-
-// The gradient of SparseFillEmptyRows.
+// This op runs in `O(M log M)` time, where `M` is the total number of non-empty
+// values across all inputs. This is due to the need for an internal sort in
+// order to concatenate efficiently across an arbitrary dimension.
//
-// Takes vectors reverse_index_map, shaped `[N]`, and grad_values,
-// shaped `[N_full]`, where `N_full >= N` and copies data into either
-// `d_values` or `d_default_value`. Here `d_values` is shaped `[N]` and
-// `d_default_value` is a scalar.
+// For example, if `concat_dim = 1` and the inputs are
//
-// d_values[j] = grad_values[reverse_index_map[j]]
-// d_default_value = sum_{k : 0 .. N_full - 1} (
-// grad_values[k] * 1{k not in reverse_index_map})
+// sp_inputs[0]: shape = [2, 3]
+// [0, 2]: "a"
+// [1, 0]: "b"
+// [1, 1]: "c"
+//
+// sp_inputs[1]: shape = [2, 4]
+// [0, 1]: "d"
+// [0, 2]: "e"
+//
+// then the output will be
+//
+// shape = [2, 7]
+// [0, 2]: "a"
+// [0, 4]: "d"
+// [0, 5]: "e"
+// [1, 0]: "b"
+// [1, 1]: "c"
+//
+// Graphically this is equivalent to doing
+//
+// [ a] concat [ d e ] = [ a d e ]
+// [b c ] [ ] [b c ]
//
// Arguments:
-// reverse_index_map: 1-D. The reverse index map from SparseFillEmptyRows.
-// grad_values: 1-D. The gradients from backprop.
+// indices: 2-D. Indices of each input `SparseTensor`.
+// values: 1-D. Non-empty values of each `SparseTensor`.
+// shapes: 1-D. Shapes of each `SparseTensor`.
+// concat_dim: Dimension to concatenate along. Must be in range [-rank, rank),
+// where rank is the number of dimensions in each input `SparseTensor`.
//
-// Returns 1-D. The backprop into values.0-D. The backprop into default_value.
-func SparseFillEmptyRowsGrad(scope *Scope, reverse_index_map tf.Output, grad_values tf.Output) (d_values tf.Output, d_default_value tf.Output) {
+// Returns 2-D. Indices of the concatenated `SparseTensor`.1-D. Non-empty values of the concatenated `SparseTensor`.1-D. Shape of the concatenated `SparseTensor`.
+func SparseConcat(scope *Scope, indices []tf.Output, values []tf.Output, shapes []tf.Output, concat_dim int64) (output_indices tf.Output, output_values tf.Output, output_shape tf.Output) {
if scope.Err() != nil {
return
}
+ attrs := map[string]interface{}{"concat_dim": concat_dim}
opspec := tf.OpSpec{
- Type: "SparseFillEmptyRowsGrad",
+ Type: "SparseConcat",
Input: []tf.Input{
- reverse_index_map, grad_values,
+ tf.OutputList(indices), tf.OutputList(values), tf.OutputList(shapes),
},
+ Attrs: attrs,
}
op := scope.AddOperation(opspec)
- return op.Output(0), op.Output(1)
+ return op.Output(0), op.Output(1), op.Output(2)
}
-// Computes scaled exponential linear: `scale * alpha * (exp(features) - 1)`
+// Generates sparse cross from a list of sparse and dense tensors.
//
-// if < 0, `scale * features` otherwise.
+// The op takes two lists, one of 2D `SparseTensor` and one of 2D `Tensor`, each
+// representing features of one feature column. It outputs a 2D `SparseTensor` with
+// the batchwise crosses of these features.
//
-// See [Self-Normalizing Neural Networks](https://arxiv.org/abs/1706.02515)
-func Selu(scope *Scope, features tf.Output) (activations tf.Output) {
+// For example, if the inputs are
+//
+// inputs[0]: SparseTensor with shape = [2, 2]
+// [0, 0]: "a"
+// [1, 0]: "b"
+// [1, 1]: "c"
+//
+// inputs[1]: SparseTensor with shape = [2, 1]
+// [0, 0]: "d"
+// [1, 0]: "e"
+//
+// inputs[2]: Tensor [["f"], ["g"]]
+//
+// then the output will be
+//
+// shape = [2, 2]
+// [0, 0]: "a_X_d_X_f"
+// [1, 0]: "b_X_e_X_g"
+// [1, 1]: "c_X_e_X_g"
+//
+// if hashed_output=true then the output will be
+//
+// shape = [2, 2]
+// [0, 0]: FingerprintCat64(
+// Fingerprint64("f"), FingerprintCat64(
+// Fingerprint64("d"), Fingerprint64("a")))
+// [1, 0]: FingerprintCat64(
+// Fingerprint64("g"), FingerprintCat64(
+// Fingerprint64("e"), Fingerprint64("b")))
+// [1, 1]: FingerprintCat64(
+// Fingerprint64("g"), FingerprintCat64(
+// Fingerprint64("e"), Fingerprint64("c")))
+//
+// Arguments:
+// indices: 2-D. Indices of each input `SparseTensor`.
+// values: 1-D. values of each `SparseTensor`.
+// shapes: 1-D. Shapes of each `SparseTensor`.
+// dense_inputs: 2-D. Columns represented by dense `Tensor`.
+// hashed_output: If true, returns the hash of the cross instead of the string.
+// This will allow us avoiding string manipulations.
+// num_buckets: It is used if hashed_output is true.
+// output = hashed_value%num_buckets if num_buckets > 0 else hashed_value.
+// hash_key: Specify the hash_key that will be used by the `FingerprintCat64`
+// function to combine the crosses fingerprints.
+//
+//
+//
+// Returns 2-D. Indices of the concatenated `SparseTensor`.1-D. Non-empty values of the concatenated or hashed
+// `SparseTensor`.1-D. Shape of the concatenated `SparseTensor`.
+func SparseCross(scope *Scope, indices []tf.Output, values []tf.Output, shapes []tf.Output, dense_inputs []tf.Output, hashed_output bool, num_buckets int64, hash_key int64, out_type tf.DataType, internal_type tf.DataType) (output_indices tf.Output, output_values tf.Output, output_shape tf.Output) {
if scope.Err() != nil {
return
}
+ attrs := map[string]interface{}{"hashed_output": hashed_output, "num_buckets": num_buckets, "hash_key": hash_key, "out_type": out_type, "internal_type": internal_type}
opspec := tf.OpSpec{
- Type: "Selu",
+ Type: "SparseCross",
Input: []tf.Input{
- features,
+ tf.OutputList(indices), tf.OutputList(values), tf.OutputList(shapes), tf.OutputList(dense_inputs),
},
+ Attrs: attrs,
}
op := scope.AddOperation(opspec)
- return op.Output(0)
-}
-
-// SetSizeAttr is an optional argument to SetSize.
-type SetSizeAttr func(optionalAttr)
-
-// SetSizeValidateIndices sets the optional validate_indices attribute to value.
-// If not specified, defaults to true
-func SetSizeValidateIndices(value bool) SetSizeAttr {
- return func(m optionalAttr) {
- m["validate_indices"] = value
- }
+ return op.Output(0), op.Output(1), op.Output(2)
}
-// Number of unique elements along last dimension of input `set`.
-//
-// Input `set` is a `SparseTensor` represented by `set_indices`, `set_values`,
-// and `set_shape`. The last dimension contains values in a set, duplicates are
-// allowed but ignored.
-//
-// If `validate_indices` is `True`, this op validates the order and range of `set`
-// indices.
+// Concatenates quantized tensors along one dimension.
//
// Arguments:
-// set_indices: 2D `Tensor`, indices of a `SparseTensor`.
-// set_values: 1D `Tensor`, values of a `SparseTensor`.
-// set_shape: 1D `Tensor`, shape of a `SparseTensor`.
+// concat_dim: 0-D. The dimension along which to concatenate. Must be in the
+// range [0, rank(values)).
+// values: The `N` Tensors to concatenate. Their ranks and types must match,
+// and their sizes must match in all dimensions except `concat_dim`.
+// input_mins: The minimum scalar values for each of the input tensors.
+// input_maxes: The maximum scalar values for each of the input tensors.
//
-// Returns For `set` ranked `n`, this is a `Tensor` with rank `n-1`, and the same 1st
-// `n-1` dimensions as `set`. Each value is the number of unique elements in
-// the corresponding `[0...n-1]` dimension of `set`.
-func SetSize(scope *Scope, set_indices tf.Output, set_values tf.Output, set_shape tf.Output, optional ...SetSizeAttr) (size tf.Output) {
+// Returns A `Tensor` with the concatenation of values stacked along the
+// `concat_dim` dimension. This tensor's shape matches that of `values` except
+// in `concat_dim` where it has the sum of the sizes.The float value that the minimum quantized output value represents.The float value that the maximum quantized output value represents.
+func QuantizedConcat(scope *Scope, concat_dim tf.Output, values []tf.Output, input_mins []tf.Output, input_maxes []tf.Output) (output tf.Output, output_min tf.Output, output_max tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{}
- for _, a := range optional {
- a(attrs)
- }
opspec := tf.OpSpec{
- Type: "SetSize",
+ Type: "QuantizedConcat",
Input: []tf.Input{
- set_indices, set_values, set_shape,
+ concat_dim, tf.OutputList(values), tf.OutputList(input_mins), tf.OutputList(input_maxes),
},
- Attrs: attrs,
}
op := scope.AddOperation(opspec)
- return op.Output(0)
+ return op.Output(0), op.Output(1), op.Output(2)
}
-// Computes the sign and the log of the absolute value of the determinant of
+// Slice a `SparseTensor` based on the `start` and `size`.
//
-// one or more square matrices.
+// For example, if the input is
//
-// The input is a tensor of shape `[N, M, M]` whose inner-most 2 dimensions
-// form square matrices. The outputs are two tensors containing the signs and
-// absolute values of the log determinants for all N input submatrices
-// `[..., :, :]` such that the determinant = sign*exp(log_abs_determinant).
-// The log_abs_determinant is computed as det(P)*sum(log(diag(LU))) where LU
-// is the LU decomposition of the input and P is the corresponding
-// permutation matrix.
+// input_tensor = shape = [2, 7]
+// [ a d e ]
+// [b c ]
+//
+// Graphically the output tensors are:
+//
+// sparse_slice([0, 0], [2, 4]) = shape = [2, 4]
+// [ a ]
+// [b c ]
+//
+// sparse_slice([0, 4], [2, 3]) = shape = [2, 3]
+// [ d e ]
+// [ ]
//
// Arguments:
-// input: Shape is `[N, M, M]`.
+// indices: 2-D tensor represents the indices of the sparse tensor.
+// values: 1-D tensor represents the values of the sparse tensor.
+// shape: 1-D. tensor represents the shape of the sparse tensor.
+// start: 1-D. tensor represents the start of the slice.
+// size: 1-D. tensor represents the size of the slice.
+// output indices: A list of 1-D tensors represents the indices of the output
+// sparse tensors.
//
-// Returns The signs of the log determinants of the inputs. Shape is `[N]`.The logs of the absolute values of the determinants
-// of the N input matrices. Shape is `[N]`.
-func LogMatrixDeterminant(scope *Scope, input tf.Output) (sign tf.Output, log_abs_determinant tf.Output) {
+// Returns A list of 1-D tensors represents the values of the output sparse
+// tensors.A list of 1-D tensors represents the shape of the output sparse
+// tensors.
+func SparseSlice(scope *Scope, indices tf.Output, values tf.Output, shape tf.Output, start tf.Output, size tf.Output) (output_indices tf.Output, output_values tf.Output, output_shape tf.Output) {
if scope.Err() != nil {
return
}
opspec := tf.OpSpec{
- Type: "LogMatrixDeterminant",
+ Type: "SparseSlice",
Input: []tf.Input{
- input,
+ indices, values, shape, start, size,
},
}
op := scope.AddOperation(opspec)
- return op.Output(0), op.Output(1)
-}
-
-// SumAttr is an optional argument to Sum.
-type SumAttr func(optionalAttr)
-
-// SumKeepDims sets the optional keep_dims attribute to value.
-//
-// value: If true, retain reduced dimensions with length 1.
-// If not specified, defaults to false
-func SumKeepDims(value bool) SumAttr {
- return func(m optionalAttr) {
- m["keep_dims"] = value
- }
+ return op.Output(0), op.Output(1), op.Output(2)
}
-// Computes the sum of elements across dimensions of a tensor.
+// Adds up a `SparseTensor` and a dense `Tensor`, producing a dense `Tensor`.
//
-// Reduces `input` along the dimensions given in `axis`. Unless
-// `keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in
-// `axis`. If `keep_dims` is true, the reduced dimensions are
-// retained with length 1.
+// This Op does not require `a_indices` be sorted in standard lexicographic order.
//
// Arguments:
-// input: The tensor to reduce.
-// axis: The dimensions to reduce. Must be in the range
-// `[-rank(input), rank(input))`.
-//
-// Returns The reduced tensor.
-func Sum(scope *Scope, input tf.Output, axis tf.Output, optional ...SumAttr) (output tf.Output) {
+// a_indices: 2-D. The `indices` of the `SparseTensor`, with shape `[nnz, ndims]`.
+// a_values: 1-D. The `values` of the `SparseTensor`, with shape `[nnz]`.
+// a_shape: 1-D. The `shape` of the `SparseTensor`, with shape `[ndims]`.
+// b: `ndims`-D Tensor. With shape `a_shape`.
+func SparseTensorDenseAdd(scope *Scope, a_indices tf.Output, a_values tf.Output, a_shape tf.Output, b tf.Output) (output tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{}
- for _, a := range optional {
- a(attrs)
- }
opspec := tf.OpSpec{
- Type: "Sum",
+ Type: "SparseTensorDenseAdd",
Input: []tf.Input{
- input, axis,
+ a_indices, a_values, a_shape, b,
},
- Attrs: attrs,
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// Delete the tensor specified by its handle in the session.
+// Returns the set of files matching one or more glob patterns.
+//
+// Note that this routine only supports wildcard characters in the
+// basename portion of the pattern, not in the directory portion.
//
// Arguments:
-// handle: The handle for a tensor stored in the session state.
+// pattern: Shell wildcard pattern(s). Scalar or vector of type string.
//
-// Returns the created operation.
-func DeleteSessionTensor(scope *Scope, handle tf.Output) (o *tf.Operation) {
+// Returns A vector of matching filenames.
+func MatchingFiles(scope *Scope, pattern tf.Output) (filenames tf.Output) {
if scope.Err() != nil {
return
}
opspec := tf.OpSpec{
- Type: "DeleteSessionTensor",
+ Type: "MatchingFiles",
Input: []tf.Input{
- handle,
+ pattern,
},
}
- return scope.AddOperation(opspec)
+ op := scope.AddOperation(opspec)
+ return op.Output(0)
}
-// L2 Loss.
+// MatrixSolveLsAttr is an optional argument to MatrixSolveLs.
+type MatrixSolveLsAttr func(optionalAttr)
+
+// MatrixSolveLsFast sets the optional fast attribute to value.
+// If not specified, defaults to true
+func MatrixSolveLsFast(value bool) MatrixSolveLsAttr {
+ return func(m optionalAttr) {
+ m["fast"] = value
+ }
+}
+
+// Solves one or more linear least-squares problems.
//
-// Computes half the L2 norm of a tensor without the `sqrt`:
+// `matrix` is a tensor of shape `[..., M, N]` whose inner-most 2 dimensions
+// form real or complex matrices of size `[M, N]`. `Rhs` is a tensor of the same
+// type as `matrix` and shape `[..., M, K]`.
+// The output is a tensor shape `[..., N, K]` where each output matrix solves
+// each of the equations
+// `matrix[..., :, :]` * `output[..., :, :]` = `rhs[..., :, :]`
+// in the least squares sense.
//
-// output = sum(t ** 2) / 2
+// We use the following notation for (complex) matrix and right-hand sides
+// in the batch:
+//
+// `matrix`=\\(A \in \mathbb{C}^{m \times n}\\),
+// `rhs`=\\(B \in \mathbb{C}^{m \times k}\\),
+// `output`=\\(X \in \mathbb{C}^{n \times k}\\),
+// `l2_regularizer`=\\(\lambda \in \mathbb{R}\\).
+//
+// If `fast` is `True`, then the solution is computed by solving the normal
+// equations using Cholesky decomposition. Specifically, if \\(m \ge n\\) then
+// \\(X = (A^H A + \lambda I)^{-1} A^H B\\), which solves the least-squares
+// problem \\(X = \mathrm{argmin}_{Z \in \Re^{n \times k} } ||A Z - B||_F^2 +
+// \lambda ||Z||_F^2\\). If \\(m \lt n\\) then `output` is computed as
+// \\(X = A^H (A A^H + \lambda I)^{-1} B\\), which (for \\(\lambda = 0\\)) is the
+// minimum-norm solution to the under-determined linear system, i.e.
+// \\(X = \mathrm{argmin}_{Z \in \mathbb{C}^{n \times k} } ||Z||_F^2 \\),
+// subject to \\(A Z = B\\). Notice that the fast path is only numerically stable
+// when \\(A\\) is numerically full rank and has a condition number
+// \\(\mathrm{cond}(A) \lt \frac{1}{\sqrt{\epsilon_{mach} } }\\) or\\(\lambda\\) is
+// sufficiently large.
+//
+// If `fast` is `False` an algorithm based on the numerically robust complete
+// orthogonal decomposition is used. This computes the minimum-norm
+// least-squares solution, even when \\(A\\) is rank deficient. This path is
+// typically 6-7 times slower than the fast path. If `fast` is `False` then
+// `l2_regularizer` is ignored.
//
// Arguments:
-// t: Typically 2-D, but may have any dimensions.
+// matrix: Shape is `[..., M, N]`.
+// rhs: Shape is `[..., M, K]`.
+// l2_regularizer: Scalar tensor.
//
-// Returns 0-D.
-func L2Loss(scope *Scope, t tf.Output) (output tf.Output) {
+// @compatibility(numpy)
+// Equivalent to np.linalg.lstsq
+// @end_compatibility
+//
+// Returns Shape is `[..., N, K]`.
+func MatrixSolveLs(scope *Scope, matrix tf.Output, rhs tf.Output, l2_regularizer tf.Output, optional ...MatrixSolveLsAttr) (output tf.Output) {
if scope.Err() != nil {
return
}
+ attrs := map[string]interface{}{}
+ for _, a := range optional {
+ a(attrs)
+ }
opspec := tf.OpSpec{
- Type: "L2Loss",
+ Type: "MatrixSolveLs",
Input: []tf.Input{
- t,
+ matrix, rhs, l2_regularizer,
},
+ Attrs: attrs,
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// DenseToSparseSetOperationAttr is an optional argument to DenseToSparseSetOperation.
-type DenseToSparseSetOperationAttr func(optionalAttr)
+// Elementwise computes the bitwise OR of `x` and `y`.
+//
+// The result will have those bits set, that are set in `x`, `y` or both. The
+// computation is performed on the underlying representations of `x` and `y`.
+func BitwiseOr(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) {
+ if scope.Err() != nil {
+ return
+ }
+ opspec := tf.OpSpec{
+ Type: "BitwiseOr",
+ Input: []tf.Input{
+ x, y,
+ },
+ }
+ op := scope.AddOperation(opspec)
+ return op.Output(0)
+}
-// DenseToSparseSetOperationValidateIndices sets the optional validate_indices attribute to value.
+// SparseToSparseSetOperationAttr is an optional argument to SparseToSparseSetOperation.
+type SparseToSparseSetOperationAttr func(optionalAttr)
+
+// SparseToSparseSetOperationValidateIndices sets the optional validate_indices attribute to value.
// If not specified, defaults to true
-func DenseToSparseSetOperationValidateIndices(value bool) DenseToSparseSetOperationAttr {
+func SparseToSparseSetOperationValidateIndices(value bool) SparseToSparseSetOperationAttr {
return func(m optionalAttr) {
m["validate_indices"] = value
}
}
-// Applies set operation along last dimension of `Tensor` and `SparseTensor`.
+// Applies set operation along last dimension of 2 `SparseTensor` inputs.
//
// See SetOperationOp::SetOperationFromContext for values of `set_operation`.
//
+// If `validate_indices` is `True`, `SparseToSparseSetOperation` validates the
+// order and range of `set1` and `set2` indices.
+//
+// Input `set1` is a `SparseTensor` represented by `set1_indices`, `set1_values`,
+// and `set1_shape`. For `set1` ranked `n`, 1st `n-1` dimensions must be the same
+// as `set2`. Dimension `n` contains values in a set, duplicates are allowed but
+// ignored.
+//
// Input `set2` is a `SparseTensor` represented by `set2_indices`, `set2_values`,
// and `set2_shape`. For `set2` ranked `n`, 1st `n-1` dimensions must be the same
// as `set1`. Dimension `n` contains values in a set, duplicates are allowed but
// ignored.
//
-// If `validate_indices` is `True`, this op validates the order and range of `set2`
-// indices.
+// If `validate_indices` is `True`, this op validates the order and range of `set1`
+// and `set2` indices.
//
// Output `result` is a `SparseTensor` represented by `result_indices`,
// `result_values`, and `result_shape`. For `set1` and `set2` ranked `n`, this
// `[0...n-1]` dimension of `set`.
//
// Arguments:
-// set1: `Tensor` with rank `n`. 1st `n-1` dimensions must be the same as `set2`.
-// Dimension `n` contains values in a set, duplicates are allowed but ignored.
+// set1_indices: 2D `Tensor`, indices of a `SparseTensor`. Must be in row-major
+// order.
+// set1_values: 1D `Tensor`, values of a `SparseTensor`. Must be in row-major
+// order.
+// set1_shape: 1D `Tensor`, shape of a `SparseTensor`. `set1_shape[0...n-1]` must
+// be the same as `set2_shape[0...n-1]`, `set1_shape[n]` is the
+// max set size across `0...n-1` dimensions.
// set2_indices: 2D `Tensor`, indices of a `SparseTensor`. Must be in row-major
// order.
// set2_values: 1D `Tensor`, values of a `SparseTensor`. Must be in row-major
// order.
// set2_shape: 1D `Tensor`, shape of a `SparseTensor`. `set2_shape[0...n-1]` must
-// be the same as the 1st `n-1` dimensions of `set1`, `result_shape[n]` is the
-// max set size across `n-1` dimensions.
+// be the same as `set1_shape[0...n-1]`, `set2_shape[n]` is the
+// max set size across `0...n-1` dimensions.
//
//
// Returns 2D indices of a `SparseTensor`.1D values of a `SparseTensor`.1D `Tensor` shape of a `SparseTensor`. `result_shape[0...n-1]` is
// the same as the 1st `n-1` dimensions of `set1` and `set2`, `result_shape[n]`
// is the max result set size across all `0...n-1` dimensions.
-func DenseToSparseSetOperation(scope *Scope, set1 tf.Output, set2_indices tf.Output, set2_values tf.Output, set2_shape tf.Output, set_operation string, optional ...DenseToSparseSetOperationAttr) (result_indices tf.Output, result_values tf.Output, result_shape tf.Output) {
+func SparseToSparseSetOperation(scope *Scope, set1_indices tf.Output, set1_values tf.Output, set1_shape tf.Output, set2_indices tf.Output, set2_values tf.Output, set2_shape tf.Output, set_operation string, optional ...SparseToSparseSetOperationAttr) (result_indices tf.Output, result_values tf.Output, result_shape tf.Output) {
if scope.Err() != nil {
return
}
a(attrs)
}
opspec := tf.OpSpec{
- Type: "DenseToSparseSetOperation",
+ Type: "SparseToSparseSetOperation",
Input: []tf.Input{
- set1, set2_indices, set2_values, set2_shape,
+ set1_indices, set1_values, set1_shape, set2_indices, set2_values, set2_shape,
},
Attrs: attrs,
}
return op.Output(0), op.Output(1), op.Output(2)
}
-// Subtracts a value from the current value of a variable.
-//
-// Any ReadVariableOp which depends directly or indirectly on this assign is
-// guaranteed to see the incremented value or a subsequent newer one.
-//
-// Outputs the incremented value, which can be used to totally order the
-// increments to this variable.
-//
-// Arguments:
-// resource: handle to the resource in which to store the variable.
-// value: the value by which the variable will be incremented.
+// Computes numerical negative value element-wise.
//
-// Returns the created operation.
-func AssignSubVariableOp(scope *Scope, resource tf.Output, value tf.Output) (o *tf.Operation) {
+// I.e., \\(y = -x\\).
+func Neg(scope *Scope, x tf.Output) (y tf.Output) {
if scope.Err() != nil {
return
}
opspec := tf.OpSpec{
- Type: "AssignSubVariableOp",
+ Type: "Neg",
Input: []tf.Input{
- resource, value,
+ x,
},
}
- return scope.AddOperation(opspec)
+ op := scope.AddOperation(opspec)
+ return op.Output(0)
}
-// RestoreAttr is an optional argument to Restore.
-type RestoreAttr func(optionalAttr)
+// FakeQuantWithMinMaxVarsAttr is an optional argument to FakeQuantWithMinMaxVars.
+type FakeQuantWithMinMaxVarsAttr func(optionalAttr)
-// RestorePreferredShard sets the optional preferred_shard attribute to value.
-//
-// value: Index of file to open first if multiple files match
-// `file_pattern`.
-// If not specified, defaults to -1
-func RestorePreferredShard(value int64) RestoreAttr {
+// FakeQuantWithMinMaxVarsNumBits sets the optional num_bits attribute to value.
+// If not specified, defaults to 8
+func FakeQuantWithMinMaxVarsNumBits(value int64) FakeQuantWithMinMaxVarsAttr {
return func(m optionalAttr) {
- m["preferred_shard"] = value
+ m["num_bits"] = value
}
}
-// Restores a tensor from checkpoint files.
-//
-// Reads a tensor stored in one or several files. If there are several files (for
-// instance because a tensor was saved as slices), `file_pattern` may contain
-// wildcard symbols (`*` and `?`) in the filename portion only, not in the
-// directory portion.
-//
-// If a `file_pattern` matches several files, `preferred_shard` can be used to hint
-// in which file the requested tensor is likely to be found. This op will first
-// open the file at index `preferred_shard` in the list of matching files and try
-// to restore tensors from that file. Only if some tensors or tensor slices are
-// not found in that first file, then the Op opens all the files. Setting
-// `preferred_shard` to match the value passed as the `shard` input
-// of a matching `Save` Op may speed up Restore. This attribute only affects
-// performance, not correctness. The default value -1 means files are processed in
-// order.
+// FakeQuantWithMinMaxVarsNarrowRange sets the optional narrow_range attribute to value.
+// If not specified, defaults to false
+func FakeQuantWithMinMaxVarsNarrowRange(value bool) FakeQuantWithMinMaxVarsAttr {
+ return func(m optionalAttr) {
+ m["narrow_range"] = value
+ }
+}
+
+// Fake-quantize the 'inputs' tensor of type float via global float scalars `min`
//
-// See also `RestoreSlice`.
+// and `max` to 'outputs' tensor of same shape as `inputs`.
//
-// Arguments:
-// file_pattern: Must have a single element. The pattern of the files from
-// which we read the tensor.
-// tensor_name: Must have a single element. The name of the tensor to be
-// restored.
-// dt: The type of the tensor to be restored.
+// `[min; max]` define the clamping range for the `inputs` data.
+// `inputs` values are quantized into the quantization range (`[0; 2^num_bits - 1]`
+// when `narrow_range` is false and `[1; 2^num_bits - 1]` when it is true) and
+// then de-quantized and output as floats in `[min; max]` interval.
+// `num_bits` is the bitwidth of the quantization; between 2 and 8, inclusive.
//
-// Returns The restored tensor.
-func Restore(scope *Scope, file_pattern tf.Output, tensor_name tf.Output, dt tf.DataType, optional ...RestoreAttr) (tensor tf.Output) {
+// This operation has a gradient and thus allows for training `min` and `max`
+// values.
+func FakeQuantWithMinMaxVars(scope *Scope, inputs tf.Output, min tf.Output, max tf.Output, optional ...FakeQuantWithMinMaxVarsAttr) (outputs tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{"dt": dt}
+ attrs := map[string]interface{}{}
for _, a := range optional {
a(attrs)
}
opspec := tf.OpSpec{
- Type: "Restore",
+ Type: "FakeQuantWithMinMaxVars",
Input: []tf.Input{
- file_pattern, tensor_name,
+ inputs, min, max,
},
Attrs: attrs,
}
return op.Output(0)
}
-// QuantizedResizeBilinearAttr is an optional argument to QuantizedResizeBilinear.
-type QuantizedResizeBilinearAttr func(optionalAttr)
-
-// QuantizedResizeBilinearAlignCorners sets the optional align_corners attribute to value.
-//
-// value: If true, rescale input by (new_height - 1) / (height - 1), which
-// exactly aligns the 4 corners of images and resized images. If false, rescale
-// by new_height / height. Treat similarly the width dimension.
-// If not specified, defaults to false
-func QuantizedResizeBilinearAlignCorners(value bool) QuantizedResizeBilinearAttr {
- return func(m optionalAttr) {
- m["align_corners"] = value
- }
-}
-
-// Resize quantized `images` to `size` using quantized bilinear interpolation.
+// Returns the element-wise min of two SparseTensors.
//
-// Input images and output images must be quantized types.
+// Assumes the two SparseTensors have the same shape, i.e., no broadcasting.
//
// Arguments:
-// images: 4-D with shape `[batch, height, width, channels]`.
-// size: = A 1-D int32 Tensor of 2 elements: `new_height, new_width`. The
-// new size for the images.
-//
-//
+// a_indices: 2-D. `N x R` matrix with the indices of non-empty values in a
+// SparseTensor, in the canonical lexicographic ordering.
+// a_values: 1-D. `N` non-empty values corresponding to `a_indices`.
+// a_shape: 1-D. Shape of the input SparseTensor.
+// b_indices: counterpart to `a_indices` for the other operand.
+// b_values: counterpart to `a_values` for the other operand; must be of the same dtype.
+// b_shape: counterpart to `a_shape` for the other operand; the two shapes must be equal.
//
-// Returns 4-D with shape
-// `[batch, new_height, new_width, channels]`.
-func QuantizedResizeBilinear(scope *Scope, images tf.Output, size tf.Output, min tf.Output, max tf.Output, optional ...QuantizedResizeBilinearAttr) (resized_images tf.Output, out_min tf.Output, out_max tf.Output) {
+// Returns 2-D. The indices of the output SparseTensor.1-D. The values of the output SparseTensor.
+func SparseSparseMinimum(scope *Scope, a_indices tf.Output, a_values tf.Output, a_shape tf.Output, b_indices tf.Output, b_values tf.Output, b_shape tf.Output) (output_indices tf.Output, output_values tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{}
- for _, a := range optional {
- a(attrs)
- }
opspec := tf.OpSpec{
- Type: "QuantizedResizeBilinear",
+ Type: "SparseSparseMinimum",
Input: []tf.Input{
- images, size, min, max,
+ a_indices, a_values, a_shape, b_indices, b_values, b_shape,
},
- Attrs: attrs,
}
op := scope.AddOperation(opspec)
- return op.Output(0), op.Output(1), op.Output(2)
+ return op.Output(0), op.Output(1)
}
-// Computes the minimum along segments of a tensor.
-//
-// Read @{$math_ops#segmentation$the section on segmentation} for an explanation of
-// segments.
-//
-// Computes a tensor such that
-// \\(output_i = \min_j(data_j)\\) where `min` is over `j` such
-// that `segment_ids[j] == i`.
-//
-// If the min is empty for a given segment ID `i`, `output[i] = 0`.
-//
-// <div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;">
-// <img style="width:100%" src="https://www.tensorflow.org/images/SegmentMin.png" alt>
-// </div>
-//
-// Arguments:
-//
-// segment_ids: A 1-D tensor whose rank is equal to the rank of `data`'s
-// first dimension. Values should be sorted and can be repeated.
+// TakeManySparseFromTensorsMapAttr is an optional argument to TakeManySparseFromTensorsMap.
+type TakeManySparseFromTensorsMapAttr func(optionalAttr)
+
+// TakeManySparseFromTensorsMapContainer sets the optional container attribute to value.
//
-// Returns Has same shape as data, except for dimension 0 which
-// has size `k`, the number of segments.
-func SegmentMin(scope *Scope, data tf.Output, segment_ids tf.Output) (output tf.Output) {
- if scope.Err() != nil {
- return
- }
- opspec := tf.OpSpec{
- Type: "SegmentMin",
- Input: []tf.Input{
- data, segment_ids,
- },
+// value: The container name for the `SparseTensorsMap` read by this op.
+// If not specified, defaults to ""
+func TakeManySparseFromTensorsMapContainer(value string) TakeManySparseFromTensorsMapAttr {
+ return func(m optionalAttr) {
+ m["container"] = value
}
- op := scope.AddOperation(opspec)
- return op.Output(0)
}
-// SdcaOptimizerAttr is an optional argument to SdcaOptimizer.
-type SdcaOptimizerAttr func(optionalAttr)
-
-// SdcaOptimizerAdaptative sets the optional adaptative attribute to value.
+// TakeManySparseFromTensorsMapSharedName sets the optional shared_name attribute to value.
//
-// value: Whether to use Adapative SDCA for the inner loop.
-// If not specified, defaults to false
-func SdcaOptimizerAdaptative(value bool) SdcaOptimizerAttr {
+// value: The shared name for the `SparseTensorsMap` read by this op.
+// It should not be blank; rather the `shared_name` or unique Operation name
+// of the Op that created the original `SparseTensorsMap` should be used.
+// If not specified, defaults to ""
+func TakeManySparseFromTensorsMapSharedName(value string) TakeManySparseFromTensorsMapAttr {
return func(m optionalAttr) {
- m["adaptative"] = value
+ m["shared_name"] = value
}
}
-// Distributed version of Stochastic Dual Coordinate Ascent (SDCA) optimizer for
+// Read `SparseTensors` from a `SparseTensorsMap` and concatenate them.
//
-// linear models with L1 + L2 regularization. As global optimization objective is
-// strongly-convex, the optimizer optimizes the dual objective at each step. The
-// optimizer applies each update one example at a time. Examples are sampled
-// uniformly, and the optimizer is learning rate free and enjoys linear convergence
-// rate.
+// The input `sparse_handles` must be an `int64` matrix of shape `[N, 1]` where
+// `N` is the minibatch size and the rows correspond to the output handles of
+// `AddSparseToTensorsMap` or `AddManySparseToTensorsMap`. The ranks of the
+// original `SparseTensor` objects that went into the given input ops must all
+// match. When the final `SparseTensor` is created, it has rank one
+// higher than the ranks of the incoming `SparseTensor` objects
+// (they have been concatenated along a new row dimension on the left).
//
-// [Proximal Stochastic Dual Coordinate Ascent](http://arxiv.org/pdf/1211.2717v1.pdf).<br>
-// Shai Shalev-Shwartz, Tong Zhang. 2012
+// The output `SparseTensor` object's shape values for all dimensions but the
+// first are the max across the input `SparseTensor` objects' shape values
+// for the corresponding dimensions. Its first shape value is `N`, the minibatch
+// size.
//
-// $$Loss Objective = \sum f_{i} (wx_{i}) + (l2 / 2) * |w|^2 + l1 * |w|$$
+// The input `SparseTensor` objects' indices are assumed ordered in
+// standard lexicographic order. If this is not the case, after this
+// step run `SparseReorder` to restore index ordering.
//
-// [Adding vs. Averaging in Distributed Primal-Dual Optimization](http://arxiv.org/abs/1502.03508).<br>
-// Chenxin Ma, Virginia Smith, Martin Jaggi, Michael I. Jordan,
-// Peter Richtarik, Martin Takac. 2015
+// For example, if the handles represent an input, which is a `[2, 3]` matrix
+// representing two original `SparseTensor` objects:
//
-// [Stochastic Dual Coordinate Ascent with Adaptive Probabilities](https://arxiv.org/abs/1502.08053).<br>
-// Dominik Csiba, Zheng Qu, Peter Richtarik. 2015
+// ```
+// index = [ 0]
+// [10]
+// [20]
+// values = [1, 2, 3]
+// shape = [50]
+// ```
+//
+// and
+//
+// ```
+// index = [ 2]
+// [10]
+// values = [4, 5]
+// shape = [30]
+// ```
+//
+// then the final `SparseTensor` will be:
+//
+// ```
+// index = [0 0]
+// [0 10]
+// [0 20]
+// [1 2]
+// [1 10]
+// values = [1, 2, 3, 4, 5]
+// shape = [2 50]
+// ```
//
// Arguments:
-// sparse_example_indices: a list of vectors which contain example indices.
-// sparse_feature_indices: a list of vectors which contain feature indices.
-// sparse_feature_values: a list of vectors which contains feature value
-// associated with each feature group.
-// dense_features: a list of matrices which contains the dense feature values.
-// example_weights: a vector which contains the weight associated with each
-// example.
-// example_labels: a vector which contains the label/target associated with each
-// example.
-// sparse_indices: a list of vectors where each value is the indices which has
-// corresponding weights in sparse_weights. This field maybe omitted for the
-// dense approach.
-// sparse_weights: a list of vectors where each value is the weight associated with
-// a sparse feature group.
-// dense_weights: a list of vectors where the values are the weights associated
-// with a dense feature group.
-// example_state_data: a list of vectors containing the example state data.
-// loss_type: Type of the primal loss. Currently SdcaSolver supports logistic,
-// squared and hinge losses.
-// l1: Symmetric l1 regularization strength.
-// l2: Symmetric l2 regularization strength.
-// num_loss_partitions: Number of partitions of the global loss function.
-// num_inner_iterations: Number of iterations per mini-batch.
+// sparse_handles: 1-D, The `N` serialized `SparseTensor` objects.
+// Shape: `[N]`.
+// dtype: The `dtype` of the `SparseTensor` objects stored in the
+// `SparseTensorsMap`.
//
-// Returns a list of vectors containing the updated example state
-// data.a list of vectors where each value is the delta
-// weights associated with a sparse feature group.a list of vectors where the values are the delta
-// weights associated with a dense feature group.
-func SdcaOptimizer(scope *Scope, sparse_example_indices []tf.Output, sparse_feature_indices []tf.Output, sparse_feature_values []tf.Output, dense_features []tf.Output, example_weights tf.Output, example_labels tf.Output, sparse_indices []tf.Output, sparse_weights []tf.Output, dense_weights []tf.Output, example_state_data tf.Output, loss_type string, l1 float32, l2 float32, num_loss_partitions int64, num_inner_iterations int64, optional ...SdcaOptimizerAttr) (out_example_state_data tf.Output, out_delta_sparse_weights []tf.Output, out_delta_dense_weights []tf.Output) {
+// Returns 2-D. The `indices` of the minibatch `SparseTensor`.1-D. The `values` of the minibatch `SparseTensor`.1-D. The `shape` of the minibatch `SparseTensor`.
+func TakeManySparseFromTensorsMap(scope *Scope, sparse_handles tf.Output, dtype tf.DataType, optional ...TakeManySparseFromTensorsMapAttr) (sparse_indices tf.Output, sparse_values tf.Output, sparse_shape tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{"loss_type": loss_type, "l1": l1, "l2": l2, "num_loss_partitions": num_loss_partitions, "num_inner_iterations": num_inner_iterations}
+ attrs := map[string]interface{}{"dtype": dtype}
for _, a := range optional {
a(attrs)
}
opspec := tf.OpSpec{
- Type: "SdcaOptimizer",
+ Type: "TakeManySparseFromTensorsMap",
Input: []tf.Input{
- tf.OutputList(sparse_example_indices), tf.OutputList(sparse_feature_indices), tf.OutputList(sparse_feature_values), tf.OutputList(dense_features), example_weights, example_labels, tf.OutputList(sparse_indices), tf.OutputList(sparse_weights), tf.OutputList(dense_weights), example_state_data,
+ sparse_handles,
},
Attrs: attrs,
}
op := scope.AddOperation(opspec)
- if scope.Err() != nil {
- return
- }
- var idx int
- var err error
- out_example_state_data = op.Output(idx)
- if out_delta_sparse_weights, idx, err = makeOutputList(op, idx, "out_delta_sparse_weights"); err != nil {
- scope.UpdateErr("SdcaOptimizer", err)
- return
- }
- if out_delta_dense_weights, idx, err = makeOutputList(op, idx, "out_delta_dense_weights"); err != nil {
- scope.UpdateErr("SdcaOptimizer", err)
- return
- }
- return out_example_state_data, out_delta_sparse_weights, out_delta_dense_weights
-}
-
-// SparseMatMulAttr is an optional argument to SparseMatMul.
-type SparseMatMulAttr func(optionalAttr)
-
-// SparseMatMulTransposeA sets the optional transpose_a attribute to value.
-// If not specified, defaults to false
-func SparseMatMulTransposeA(value bool) SparseMatMulAttr {
- return func(m optionalAttr) {
- m["transpose_a"] = value
- }
-}
-
-// SparseMatMulTransposeB sets the optional transpose_b attribute to value.
-// If not specified, defaults to false
-func SparseMatMulTransposeB(value bool) SparseMatMulAttr {
- return func(m optionalAttr) {
- m["transpose_b"] = value
- }
+ return op.Output(0), op.Output(1), op.Output(2)
}
-// SparseMatMulAIsSparse sets the optional a_is_sparse attribute to value.
-// If not specified, defaults to false
-func SparseMatMulAIsSparse(value bool) SparseMatMulAttr {
- return func(m optionalAttr) {
- m["a_is_sparse"] = value
- }
-}
+// MaxPoolAttr is an optional argument to MaxPool.
+type MaxPoolAttr func(optionalAttr)
-// SparseMatMulBIsSparse sets the optional b_is_sparse attribute to value.
-// If not specified, defaults to false
-func SparseMatMulBIsSparse(value bool) SparseMatMulAttr {
+// MaxPoolDataFormat sets the optional data_format attribute to value.
+//
+// value: Specify the data format of the input and output data. With the
+// default format "NHWC", the data is stored in the order of:
+// [batch, in_height, in_width, in_channels].
+// Alternatively, the format could be "NCHW", the data storage order of:
+// [batch, in_channels, in_height, in_width].
+// If not specified, defaults to "NHWC"
+func MaxPoolDataFormat(value string) MaxPoolAttr {
return func(m optionalAttr) {
- m["b_is_sparse"] = value
+ m["data_format"] = value
}
}
-// Multiply matrix "a" by matrix "b".
+// Performs max pooling on the input.
//
-// The inputs must be two-dimensional matrices and the inner dimension of "a" must
-// match the outer dimension of "b". This op is optimized for the case where at
-// least one of "a" or "b" is sparse. The breakeven for using this versus a dense
-// matrix multiply on one platform was 30% zero values in the sparse matrix.
+// Arguments:
+// input: 4-D input to pool over.
+// ksize: The size of the window for each dimension of the input tensor.
+// strides: The stride of the sliding window for each dimension of the
+// input tensor.
+// padding: The type of padding algorithm to use.
//
-// The gradient computation of this operation will only take advantage of sparsity
-// in the input gradient when that gradient comes from a Relu.
-func SparseMatMul(scope *Scope, a tf.Output, b tf.Output, optional ...SparseMatMulAttr) (product tf.Output) {
+// Returns The max pooled output tensor.
+func MaxPool(scope *Scope, input tf.Output, ksize []int64, strides []int64, padding string, optional ...MaxPoolAttr) (output tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{}
+ attrs := map[string]interface{}{"ksize": ksize, "strides": strides, "padding": padding}
for _, a := range optional {
a(attrs)
}
opspec := tf.OpSpec{
- Type: "SparseMatMul",
+ Type: "MaxPool",
Input: []tf.Input{
- a, b,
+ input,
},
Attrs: attrs,
}
return op.Output(0)
}
-// Computes the power of one value to another.
+// Says whether the targets are in the top `K` predictions.
//
-// Given a tensor `x` and a tensor `y`, this operation computes \\(x^y\\) for
-// corresponding elements in `x` and `y`. For example:
+// This outputs a `batch_size` bool array, an entry `out[i]` is `true` if the
+// prediction for the target class is among the top `k` predictions among
+// all predictions for example `i`. Note that the behavior of `InTopK` differs
+// from the `TopK` op in its handling of ties; if multiple classes have the
+// same prediction value and straddle the top-`k` boundary, all of those
+// classes are considered to be in the top `k`.
//
-// ```
-// # tensor 'x' is [[2, 2]], [3, 3]]
-// # tensor 'y' is [[8, 16], [2, 3]]
-// tf.pow(x, y) ==> [[256, 65536], [9, 27]]
-// ```
-func Pow(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) {
+// More formally, let
+//
+// \\(predictions_i\\) be the predictions for all classes for example `i`,
+// \\(targets_i\\) be the target class for example `i`,
+// \\(out_i\\) be the output for example `i`,
+//
+// $$out_i = predictions_{i, targets_i} \in TopKIncludingTies(predictions_i)$$
+//
+// Arguments:
+// predictions: A `batch_size` x `classes` tensor.
+// targets: A `batch_size` vector of class ids.
+// k: Number of top elements to look at for computing precision.
+//
+// Returns Computed precision at `k` as a `bool Tensor`.
+func InTopKV2(scope *Scope, predictions tf.Output, targets tf.Output, k tf.Output) (precision tf.Output) {
if scope.Err() != nil {
return
}
opspec := tf.OpSpec{
- Type: "Pow",
+ Type: "InTopKV2",
Input: []tf.Input{
- x, y,
+ predictions, targets, k,
},
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// ShapeAttr is an optional argument to Shape.
-type ShapeAttr func(optionalAttr)
-
-// ShapeOutType sets the optional out_type attribute to value.
-// If not specified, defaults to DT_INT32
-func ShapeOutType(value tf.DataType) ShapeAttr {
- return func(m optionalAttr) {
- m["out_type"] = value
- }
-}
-
-// Returns the shape of a tensor.
+// Assigns a new value to a variable.
//
-// This operation returns a 1-D integer tensor representing the shape of `input`.
+// Any ReadVariableOp with a control dependency on this op is guaranteed to return
+// this value or a subsequent newer value of the variable.
//
-// For example:
+// Arguments:
+// resource: handle to the resource in which to store the variable.
+// value: the value to set the new tensor to use.
//
-// ```
-// # 't' is [[[1, 1, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]]]
-// shape(t) ==> [2, 2, 3]
-// ```
-func Shape(scope *Scope, input tf.Output, optional ...ShapeAttr) (output tf.Output) {
+// Returns the created operation.
+func AssignVariableOp(scope *Scope, resource tf.Output, value tf.Output) (o *tf.Operation) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{}
- for _, a := range optional {
- a(attrs)
- }
opspec := tf.OpSpec{
- Type: "Shape",
+ Type: "AssignVariableOp",
Input: []tf.Input{
- input,
+ resource, value,
},
- Attrs: attrs,
}
- op := scope.AddOperation(opspec)
- return op.Output(0)
+ return scope.AddOperation(opspec)
}
-// Computes fingerprints of the input strings.
+// Returns a tensor of ones with the same shape and type as x.
//
// Arguments:
-// input: vector of strings to compute fingerprints on.
+// x: a tensor of type T.
//
-// Returns a (N,2) shaped matrix where N is the number of elements in the input
-// vector. Each row contains the low and high parts of the fingerprint.
-func SdcaFprint(scope *Scope, input tf.Output) (output tf.Output) {
+// Returns a tensor of the same shape and type as x but filled with ones.
+func OnesLike(scope *Scope, x tf.Output) (y tf.Output) {
if scope.Err() != nil {
return
}
opspec := tf.OpSpec{
- Type: "SdcaFprint",
+ Type: "OnesLike",
Input: []tf.Input{
- input,
+ x,
},
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// RandomPoissonV2Attr is an optional argument to RandomPoissonV2.
-type RandomPoissonV2Attr func(optionalAttr)
-
-// RandomPoissonV2Seed sets the optional seed attribute to value.
+// The gradient of SparseFillEmptyRows.
//
-// value: If either `seed` or `seed2` are set to be non-zero, the random number
-// generator is seeded by the given seed. Otherwise, it is seeded by a
-// random seed.
-// If not specified, defaults to 0
-func RandomPoissonV2Seed(value int64) RandomPoissonV2Attr {
- return func(m optionalAttr) {
- m["seed"] = value
- }
-}
-
-// RandomPoissonV2Seed2 sets the optional seed2 attribute to value.
+// Takes vectors reverse_index_map, shaped `[N]`, and grad_values,
+// shaped `[N_full]`, where `N_full >= N` and copies data into either
+// `d_values` or `d_default_value`. Here `d_values` is shaped `[N]` and
+// `d_default_value` is a scalar.
//
-// value: A second seed to avoid seed collision.
-// If not specified, defaults to 0
-func RandomPoissonV2Seed2(value int64) RandomPoissonV2Attr {
- return func(m optionalAttr) {
- m["seed2"] = value
+// d_values[j] = grad_values[reverse_index_map[j]]
+// d_default_value = sum_{k : 0 .. N_full - 1} (
+// grad_values[k] * 1{k not in reverse_index_map})
+//
+// Arguments:
+// reverse_index_map: 1-D. The reverse index map from SparseFillEmptyRows.
+// grad_values: 1-D. The gradients from backprop.
+//
+// Returns 1-D. The backprop into values.0-D. The backprop into default_value.
+func SparseFillEmptyRowsGrad(scope *Scope, reverse_index_map tf.Output, grad_values tf.Output) (d_values tf.Output, d_default_value tf.Output) {
+ if scope.Err() != nil {
+ return
}
-}
-
-// RandomPoissonV2Dtype sets the optional dtype attribute to value.
-// If not specified, defaults to DT_INT64
-func RandomPoissonV2Dtype(value tf.DataType) RandomPoissonV2Attr {
- return func(m optionalAttr) {
- m["dtype"] = value
+ opspec := tf.OpSpec{
+ Type: "SparseFillEmptyRowsGrad",
+ Input: []tf.Input{
+ reverse_index_map, grad_values,
+ },
}
+ op := scope.AddOperation(opspec)
+ return op.Output(0), op.Output(1)
}
-// Outputs random values from the Poisson distribution(s) described by rate.
-//
-// This op uses two algorithms, depending on rate. If rate >= 10, then
-// the algorithm by Hormann is used to acquire samples via
-// transformation-rejection.
-// See http://www.sciencedirect.com/science/article/pii/0167668793909974.
-//
-// Otherwise, Knuth's algorithm is used to acquire samples via multiplying uniform
-// random variables.
-// See Donald E. Knuth (1969). Seminumerical Algorithms. The Art of Computer
-// Programming, Volume 2. Addison Wesley
+// Computes scaled exponential linear: `scale * alpha * (exp(features) - 1)`
//
-// Arguments:
-// shape: 1-D integer tensor. Shape of independent samples to draw from each
-// distribution described by the shape parameters given in rate.
-// rate: A tensor in which each scalar is a "rate" parameter describing the
-// associated poisson distribution.
+// if < 0, `scale * features` otherwise.
//
-// Returns A tensor with shape `shape + shape(rate)`. Each slice
-// `[:, ..., :, i0, i1, ...iN]` contains the samples drawn for
-// `rate[i0, i1, ...iN]`.
-func RandomPoissonV2(scope *Scope, shape tf.Output, rate tf.Output, optional ...RandomPoissonV2Attr) (output tf.Output) {
+// See [Self-Normalizing Neural Networks](https://arxiv.org/abs/1706.02515)
+func Selu(scope *Scope, features tf.Output) (activations tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{}
- for _, a := range optional {
- a(attrs)
- }
opspec := tf.OpSpec{
- Type: "RandomPoissonV2",
+ Type: "Selu",
Input: []tf.Input{
- shape, rate,
+ features,
},
- Attrs: attrs,
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// MatrixTriangularSolveAttr is an optional argument to MatrixTriangularSolve.
-type MatrixTriangularSolveAttr func(optionalAttr)
+// SetSizeAttr is an optional argument to SetSize.
+type SetSizeAttr func(optionalAttr)
-// MatrixTriangularSolveLower sets the optional lower attribute to value.
-//
-// value: Boolean indicating whether the innermost matrices in `matrix` are
-// lower or upper triangular.
+// SetSizeValidateIndices sets the optional validate_indices attribute to value.
// If not specified, defaults to true
-func MatrixTriangularSolveLower(value bool) MatrixTriangularSolveAttr {
- return func(m optionalAttr) {
- m["lower"] = value
- }
-}
-
-// MatrixTriangularSolveAdjoint sets the optional adjoint attribute to value.
-//
-// value: Boolean indicating whether to solve with `matrix` or its (block-wise)
-// adjoint.
-//
-// @compatibility(numpy)
-// Equivalent to np.linalg.triangular_solve
-// @end_compatibility
-// If not specified, defaults to false
-func MatrixTriangularSolveAdjoint(value bool) MatrixTriangularSolveAttr {
+func SetSizeValidateIndices(value bool) SetSizeAttr {
return func(m optionalAttr) {
- m["adjoint"] = value
+ m["validate_indices"] = value
}
}
-// Solves systems of linear equations with upper or lower triangular matrices by
-//
-// backsubstitution.
+// Number of unique elements along last dimension of input `set`.
//
-// `matrix` is a tensor of shape `[..., M, M]` whose inner-most 2 dimensions form
-// square matrices. If `lower` is `True` then the strictly upper triangular part
-// of each inner-most matrix is assumed to be zero and not accessed.
-// If `lower` is False then the strictly lower triangular part of each inner-most
-// matrix is assumed to be zero and not accessed.
-// `rhs` is a tensor of shape `[..., M, K]`.
+// Input `set` is a `SparseTensor` represented by `set_indices`, `set_values`,
+// and `set_shape`. The last dimension contains values in a set, duplicates are
+// allowed but ignored.
//
-// The output is a tensor of shape `[..., M, K]`. If `adjoint` is
-// `True` then the innermost matrices in `output` satisfy matrix equations
-// `matrix[..., :, :] * output[..., :, :] = rhs[..., :, :]`.
-// If `adjoint` is `False` then the strictly then the innermost matrices in
-// `output` satisfy matrix equations
-// `adjoint(matrix[..., i, k]) * output[..., k, j] = rhs[..., i, j]`.
+// If `validate_indices` is `True`, this op validates the order and range of `set`
+// indices.
//
// Arguments:
-// matrix: Shape is `[..., M, M]`.
-// rhs: Shape is `[..., M, K]`.
+// set_indices: 2D `Tensor`, indices of a `SparseTensor`.
+// set_values: 1D `Tensor`, values of a `SparseTensor`.
+// set_shape: 1D `Tensor`, shape of a `SparseTensor`.
//
-// Returns Shape is `[..., M, K]`.
-func MatrixTriangularSolve(scope *Scope, matrix tf.Output, rhs tf.Output, optional ...MatrixTriangularSolveAttr) (output tf.Output) {
+// Returns For `set` ranked `n`, this is a `Tensor` with rank `n-1`, and the same 1st
+// `n-1` dimensions as `set`. Each value is the number of unique elements in
+// the corresponding `[0...n-1]` dimension of `set`.
+func SetSize(scope *Scope, set_indices tf.Output, set_values tf.Output, set_shape tf.Output, optional ...SetSizeAttr) (size tf.Output) {
if scope.Err() != nil {
return
}
a(attrs)
}
opspec := tf.OpSpec{
- Type: "MatrixTriangularSolve",
+ Type: "SetSize",
Input: []tf.Input{
- matrix, rhs,
+ set_indices, set_values, set_shape,
},
Attrs: attrs,
}
return op.Output(0)
}
-// Computes inverse hyperbolic sine of x element-wise.
-func Asinh(scope *Scope, x tf.Output) (y tf.Output) {
+// Computes the sign and the log of the absolute value of the determinant of
+//
+// one or more square matrices.
+//
+// The input is a tensor of shape `[N, M, M]` whose inner-most 2 dimensions
+// form square matrices. The outputs are two tensors containing the signs and
+// absolute values of the log determinants for all N input submatrices
+// `[..., :, :]` such that the determinant = sign*exp(log_abs_determinant).
+// The log_abs_determinant is computed as det(P)*sum(log(diag(LU))) where LU
+// is the LU decomposition of the input and P is the corresponding
+// permutation matrix.
+//
+// Arguments:
+// input: Shape is `[N, M, M]`.
+//
+// Returns The signs of the log determinants of the inputs. Shape is `[N]`.The logs of the absolute values of the determinants
+// of the N input matrices. Shape is `[N]`.
+func LogMatrixDeterminant(scope *Scope, input tf.Output) (sign tf.Output, log_abs_determinant tf.Output) {
if scope.Err() != nil {
return
}
opspec := tf.OpSpec{
- Type: "Asinh",
+ Type: "LogMatrixDeterminant",
Input: []tf.Input{
- x,
+ input,
},
}
op := scope.AddOperation(opspec)
- return op.Output(0)
-}
-
-// Creates a dataset with a range of values. Corresponds to python's xrange.
-//
-// Arguments:
-// start: corresponds to start in python's xrange().
-// stop: corresponds to stop in python's xrange().
-// step: corresponds to step in python's xrange().
-//
-//
-func RangeDataset(scope *Scope, start tf.Output, stop tf.Output, step tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) {
- if scope.Err() != nil {
- return
- }
- attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes}
- opspec := tf.OpSpec{
- Type: "RangeDataset",
- Input: []tf.Input{
- start, stop, step,
- },
- Attrs: attrs,
- }
- op := scope.AddOperation(opspec)
- return op.Output(0)
+ return op.Output(0), op.Output(1)
}
-// DepthwiseConv2dNativeBackpropInputAttr is an optional argument to DepthwiseConv2dNativeBackpropInput.
-type DepthwiseConv2dNativeBackpropInputAttr func(optionalAttr)
+// SumAttr is an optional argument to Sum.
+type SumAttr func(optionalAttr)
-// DepthwiseConv2dNativeBackpropInputDataFormat sets the optional data_format attribute to value.
+// SumKeepDims sets the optional keep_dims attribute to value.
//
-// value: Specify the data format of the input and output data. With the
-// default format "NHWC", the data is stored in the order of:
-// [batch, height, width, channels].
-// Alternatively, the format could be "NCHW", the data storage order of:
-// [batch, channels, height, width].
-// If not specified, defaults to "NHWC"
-func DepthwiseConv2dNativeBackpropInputDataFormat(value string) DepthwiseConv2dNativeBackpropInputAttr {
+// value: If true, retain reduced dimensions with length 1.
+// If not specified, defaults to false
+func SumKeepDims(value bool) SumAttr {
return func(m optionalAttr) {
- m["data_format"] = value
+ m["keep_dims"] = value
}
}
-// DepthwiseConv2dNativeBackpropInputDilations sets the optional dilations attribute to value.
+// Computes the sum of elements across dimensions of a tensor.
//
-// value: 1-D tensor of length 4. The dilation factor for each dimension of
-// `input`. If set to k > 1, there will be k-1 skipped cells between each filter
-// element on that dimension. The dimension order is determined by the value of
-// `data_format`, see above for details. Dilations in the batch and depth
-// dimensions must be 1.
-// If not specified, defaults to <i:1 i:1 i:1 i:1 >
-func DepthwiseConv2dNativeBackpropInputDilations(value []int64) DepthwiseConv2dNativeBackpropInputAttr {
- return func(m optionalAttr) {
- m["dilations"] = value
- }
-}
-
-// Computes the gradients of depthwise convolution with respect to the input.
+// Reduces `input` along the dimensions given in `axis`. Unless
+// `keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in
+// `axis`. If `keep_dims` is true, the reduced dimensions are
+// retained with length 1.
//
// Arguments:
-// input_sizes: An integer vector representing the shape of `input`, based
-// on `data_format`. For example, if `data_format` is 'NHWC' then
-// `input` is a 4-D `[batch, height, width, channels]` tensor.
-// filter: 4-D with shape
-// `[filter_height, filter_width, in_channels, depthwise_multiplier]`.
-// out_backprop: 4-D with shape based on `data_format`.
-// For example, if `data_format` is 'NHWC' then
-// out_backprop shape is `[batch, out_height, out_width, out_channels]`.
-// Gradients w.r.t. the output of the convolution.
-// strides: The stride of the sliding window for each dimension of the input
-// of the convolution.
-// padding: The type of padding algorithm to use.
+// input: The tensor to reduce.
+// axis: The dimensions to reduce. Must be in the range
+// `[-rank(input), rank(input))`.
//
-// Returns 4-D with shape according to `data_format`. For example, if
-// `data_format` is 'NHWC', output shape is `[batch, in_height,
-// in_width, in_channels]`. Gradient w.r.t. the input of the
-// convolution.
-func DepthwiseConv2dNativeBackpropInput(scope *Scope, input_sizes tf.Output, filter tf.Output, out_backprop tf.Output, strides []int64, padding string, optional ...DepthwiseConv2dNativeBackpropInputAttr) (output tf.Output) {
+// Returns The reduced tensor.
+func Sum(scope *Scope, input tf.Output, axis tf.Output, optional ...SumAttr) (output tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{"strides": strides, "padding": padding}
+ attrs := map[string]interface{}{}
for _, a := range optional {
a(attrs)
}
opspec := tf.OpSpec{
- Type: "DepthwiseConv2dNativeBackpropInput",
+ Type: "Sum",
Input: []tf.Input{
- input_sizes, filter, out_backprop,
+ input, axis,
},
Attrs: attrs,
}
return op.Output(0)
}
-// Adds sparse updates to the variable referenced by `resource`.
-//
-// This operation computes
-//
-// # Scalar indices
-// ref[indices, ...] += updates[...]
-//
-// # Vector indices (for each i)
-// ref[indices[i], ...] += updates[i, ...]
-//
-// # High rank indices (for each i, ..., j)
-// ref[indices[i, ..., j], ...] += updates[i, ..., j, ...]
-//
-// Duplicate entries are handled correctly: if multiple `indices` reference
-// the same location, their contributions add.
-//
-// Requires `updates.shape = indices.shape + ref.shape[1:]`.
-//
-// <div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;">
-// <img style="width:100%" src='https://www.tensorflow.org/images/ScatterAdd.png' alt>
-// </div>
+// Delete the tensor specified by its handle in the session.
//
// Arguments:
-// resource: Should be from a `Variable` node.
-// indices: A tensor of indices into the first dimension of `ref`.
-// updates: A tensor of updated values to add to `ref`.
+// handle: The handle for a tensor stored in the session state.
//
// Returns the created operation.
-func ResourceScatterAdd(scope *Scope, resource tf.Output, indices tf.Output, updates tf.Output) (o *tf.Operation) {
+func DeleteSessionTensor(scope *Scope, handle tf.Output) (o *tf.Operation) {
if scope.Err() != nil {
return
}
opspec := tf.OpSpec{
- Type: "ResourceScatterAdd",
+ Type: "DeleteSessionTensor",
Input: []tf.Input{
- resource, indices, updates,
+ handle,
},
}
return scope.AddOperation(opspec)
}
-// Says whether the targets are in the top `K` predictions.
-//
-// This outputs a `batch_size` bool array, an entry `out[i]` is `true` if the
-// prediction for the target class is among the top `k` predictions among
-// all predictions for example `i`. Note that the behavior of `InTopK` differs
-// from the `TopK` op in its handling of ties; if multiple classes have the
-// same prediction value and straddle the top-`k` boundary, all of those
-// classes are considered to be in the top `k`.
-//
-// More formally, let
+// L2 Loss.
//
-// \\(predictions_i\\) be the predictions for all classes for example `i`,
-// \\(targets_i\\) be the target class for example `i`,
-// \\(out_i\\) be the output for example `i`,
+// Computes half the L2 norm of a tensor without the `sqrt`:
//
-// $$out_i = predictions_{i, targets_i} \in TopKIncludingTies(predictions_i)$$
+// output = sum(t ** 2) / 2
//
// Arguments:
-// predictions: A `batch_size` x `classes` tensor.
-// targets: A `batch_size` vector of class ids.
-// k: Number of top elements to look at for computing precision.
+// t: Typically 2-D, but may have any dimensions.
//
-// Returns Computed Precision at `k` as a `bool Tensor`.
-func InTopK(scope *Scope, predictions tf.Output, targets tf.Output, k int64) (precision tf.Output) {
+// Returns 0-D.
+func L2Loss(scope *Scope, t tf.Output) (output tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{"k": k}
opspec := tf.OpSpec{
- Type: "InTopK",
+ Type: "L2Loss",
Input: []tf.Input{
- predictions, targets,
+ t,
},
- Attrs: attrs,
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// Computes the gradient for the inverse of `x` wrt its input.
-//
-// Specifically, `grad = -dy * y*y`, where `y = 1/x`, and `dy`
-// is the corresponding input gradient.
-func ReciprocalGrad(scope *Scope, y tf.Output, dy tf.Output) (z tf.Output) {
- if scope.Err() != nil {
- return
- }
- opspec := tf.OpSpec{
- Type: "ReciprocalGrad",
- Input: []tf.Input{
- y, dy,
- },
+// DenseToSparseSetOperationAttr is an optional argument to DenseToSparseSetOperation.
+type DenseToSparseSetOperationAttr func(optionalAttr)
+
+// DenseToSparseSetOperationValidateIndices sets the optional validate_indices attribute to value.
+// If not specified, defaults to true
+func DenseToSparseSetOperationValidateIndices(value bool) DenseToSparseSetOperationAttr {
+ return func(m optionalAttr) {
+ m["validate_indices"] = value
}
- op := scope.AddOperation(opspec)
- return op.Output(0)
}
-// Returns the min of x and y (i.e. x < y ? x : y) element-wise.
+// Applies set operation along last dimension of `Tensor` and `SparseTensor`.
//
-// *NOTE*: `Minimum` supports broadcasting. More about broadcasting
-// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
-func Minimum(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) {
+// See SetOperationOp::SetOperationFromContext for values of `set_operation`.
+//
+// Input `set2` is a `SparseTensor` represented by `set2_indices`, `set2_values`,
+// and `set2_shape`. For `set2` ranked `n`, 1st `n-1` dimensions must be the same
+// as `set1`. Dimension `n` contains values in a set, duplicates are allowed but
+// ignored.
+//
+// If `validate_indices` is `True`, this op validates the order and range of `set2`
+// indices.
+//
+// Output `result` is a `SparseTensor` represented by `result_indices`,
+// `result_values`, and `result_shape`. For `set1` and `set2` ranked `n`, this
+// has rank `n` and the same 1st `n-1` dimensions as `set1` and `set2`. The `nth`
+// dimension contains the result of `set_operation` applied to the corresponding
+// `[0...n-1]` dimension of `set`.
+//
+// Arguments:
+// set1: `Tensor` with rank `n`. 1st `n-1` dimensions must be the same as `set2`.
+// Dimension `n` contains values in a set, duplicates are allowed but ignored.
+// set2_indices: 2D `Tensor`, indices of a `SparseTensor`. Must be in row-major
+// order.
+// set2_values: 1D `Tensor`, values of a `SparseTensor`. Must be in row-major
+// order.
+// set2_shape: 1D `Tensor`, shape of a `SparseTensor`. `set2_shape[0...n-1]` must
+// be the same as the 1st `n-1` dimensions of `set1`, `result_shape[n]` is the
+// max set size across `n-1` dimensions.
+//
+//
+// Returns 2D indices of a `SparseTensor`.1D values of a `SparseTensor`.1D `Tensor` shape of a `SparseTensor`. `result_shape[0...n-1]` is
+// the same as the 1st `n-1` dimensions of `set1` and `set2`, `result_shape[n]`
+// is the max result set size across all `0...n-1` dimensions.
+func DenseToSparseSetOperation(scope *Scope, set1 tf.Output, set2_indices tf.Output, set2_values tf.Output, set2_shape tf.Output, set_operation string, optional ...DenseToSparseSetOperationAttr) (result_indices tf.Output, result_values tf.Output, result_shape tf.Output) {
if scope.Err() != nil {
return
}
+ attrs := map[string]interface{}{"set_operation": set_operation}
+ for _, a := range optional {
+ a(attrs)
+ }
opspec := tf.OpSpec{
- Type: "Minimum",
+ Type: "DenseToSparseSetOperation",
Input: []tf.Input{
- x, y,
+ set1, set2_indices, set2_values, set2_shape,
},
+ Attrs: attrs,
}
op := scope.AddOperation(opspec)
- return op.Output(0)
+ return op.Output(0), op.Output(1), op.Output(2)
}
-// Returns the element-wise sum of a list of tensors.
-//
-// `tf.accumulate_n_v2` performs the same operation as `tf.add_n`, but does not
-// wait for all of its inputs to be ready before beginning to sum. This can
-// save memory if inputs are ready at different times, since minimum temporary
-// storage is proportional to the output size rather than the inputs size.
+// Subtracts a value from the current value of a variable.
//
-// Unlike the original `accumulate_n`, `accumulate_n_v2` is differentiable.
+// Any ReadVariableOp which depends directly or indirectly on this assign is
+// guaranteed to see the incremented value or a subsequent newer one.
//
-// Returns a `Tensor` of same shape and type as the elements of `inputs`.
+// Outputs the incremented value, which can be used to totally order the
+// increments to this variable.
//
// Arguments:
-// inputs: A list of `Tensor` objects, each with same shape and type.
-// shape: Shape of elements of `inputs`.
-func AccumulateNV2(scope *Scope, inputs []tf.Output, shape tf.Shape) (sum tf.Output) {
+// resource: handle to the resource in which to store the variable.
+// value: the value by which the variable will be incremented.
+//
+// Returns the created operation.
+func AssignSubVariableOp(scope *Scope, resource tf.Output, value tf.Output) (o *tf.Operation) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{"shape": shape}
opspec := tf.OpSpec{
- Type: "AccumulateNV2",
+ Type: "AssignSubVariableOp",
Input: []tf.Input{
- tf.OutputList(inputs),
+ resource, value,
},
- Attrs: attrs,
}
- op := scope.AddOperation(opspec)
- return op.Output(0)
+ return scope.AddOperation(opspec)
}
-// Convert the quantized 'input' tensor into a lower-precision 'output', using the
-//
-// actual distribution of the values to maximize the usage of the lower bit depth
-// and adjusting the output min and max ranges accordingly.
-//
-// [input_min, input_max] are scalar floats that specify the range for the float
-// interpretation of the 'input' data. For example, if input_min is -1.0f and
-// input_max is 1.0f, and we are dealing with quint16 quantized data, then a 0
-// value in the 16-bit data should be interpreted as -1.0f, and a 65535 means 1.0f.
-//
-// This operator tries to squeeze as much precision as possible into an output with
-// a lower bit depth by calculating the actual min and max values found in the
-// data. For example, maybe that quint16 input has no values lower than 16,384 and
-// none higher than 49,152. That means only half the range is actually needed, all
-// the float interpretations are between -0.5f and 0.5f, so if we want to compress
-// the data into a quint8 output, we can use that range rather than the theoretical
-// -1.0f to 1.0f that is suggested by the input min and max.
-//
-// In practice, this is most useful for taking output from operations like
-// QuantizedMatMul that can produce higher bit-depth outputs than their inputs and
-// may have large potential output ranges, but in practice have a distribution of
-// input values that only uses a small fraction of the possible range. By feeding
-// that output into this operator, we can reduce it from 32 bits down to 8 with
-// minimal loss of accuracy.
-//
-// Arguments:
-//
-// input_min: The float value that the minimum quantized input value represents.
-// input_max: The float value that the maximum quantized input value represents.
-// out_type: The type of the output. Should be a lower bit depth than Tinput.
-//
-// Returns The float value that the minimum quantized output value represents.The float value that the maximum quantized output value represents.
-func QuantizeDownAndShrinkRange(scope *Scope, input tf.Output, input_min tf.Output, input_max tf.Output, out_type tf.DataType) (output tf.Output, output_min tf.Output, output_max tf.Output) {
- if scope.Err() != nil {
- return
- }
- attrs := map[string]interface{}{"out_type": out_type}
- opspec := tf.OpSpec{
- Type: "QuantizeDownAndShrinkRange",
- Input: []tf.Input{
- input, input_min, input_max,
- },
- Attrs: attrs,
- }
- op := scope.AddOperation(opspec)
- return op.Output(0), op.Output(1), op.Output(2)
-}
-
-// RandomGammaAttr is an optional argument to RandomGamma.
-type RandomGammaAttr func(optionalAttr)
+// RestoreAttr is an optional argument to Restore.
+type RestoreAttr func(optionalAttr)
-// RandomGammaSeed sets the optional seed attribute to value.
+// RestorePreferredShard sets the optional preferred_shard attribute to value.
//
-// value: If either `seed` or `seed2` are set to be non-zero, the random number
-// generator is seeded by the given seed. Otherwise, it is seeded by a
-// random seed.
-// If not specified, defaults to 0
-func RandomGammaSeed(value int64) RandomGammaAttr {
+// value: Index of file to open first if multiple files match
+// `file_pattern`.
+// If not specified, defaults to -1
+func RestorePreferredShard(value int64) RestoreAttr {
return func(m optionalAttr) {
- m["seed"] = value
+ m["preferred_shard"] = value
}
}
-// RandomGammaSeed2 sets the optional seed2 attribute to value.
+// Restores a tensor from checkpoint files.
//
-// value: A second seed to avoid seed collision.
-// If not specified, defaults to 0
-func RandomGammaSeed2(value int64) RandomGammaAttr {
- return func(m optionalAttr) {
- m["seed2"] = value
- }
-}
-
-// Outputs random values from the Gamma distribution(s) described by alpha.
+// Reads a tensor stored in one or several files. If there are several files (for
+// instance because a tensor was saved as slices), `file_pattern` may contain
+// wildcard symbols (`*` and `?`) in the filename portion only, not in the
+// directory portion.
//
-// This op uses the algorithm by Marsaglia et al. to acquire samples via
-// transformation-rejection from pairs of uniform and normal random variables.
-// See http://dl.acm.org/citation.cfm?id=358414
+// If a `file_pattern` matches several files, `preferred_shard` can be used to hint
+// in which file the requested tensor is likely to be found. This op will first
+// open the file at index `preferred_shard` in the list of matching files and try
+// to restore tensors from that file. Only if some tensors or tensor slices are
+// not found in that first file, then the Op opens all the files. Setting
+// `preferred_shard` to match the value passed as the `shard` input
+// of a matching `Save` Op may speed up Restore. This attribute only affects
+// performance, not correctness. The default value -1 means files are processed in
+// order.
+//
+// See also `RestoreSlice`.
//
// Arguments:
-// shape: 1-D integer tensor. Shape of independent samples to draw from each
-// distribution described by the shape parameters given in alpha.
-// alpha: A tensor in which each scalar is a "shape" parameter describing the
-// associated gamma distribution.
+// file_pattern: Must have a single element. The pattern of the files from
+// which we read the tensor.
+// tensor_name: Must have a single element. The name of the tensor to be
+// restored.
+// dt: The type of the tensor to be restored.
//
-// Returns A tensor with shape `shape + shape(alpha)`. Each slice
-// `[:, ..., :, i0, i1, ...iN]` contains the samples drawn for
-// `alpha[i0, i1, ...iN]`. The dtype of the output matches the dtype of alpha.
-func RandomGamma(scope *Scope, shape tf.Output, alpha tf.Output, optional ...RandomGammaAttr) (output tf.Output) {
+// Returns The restored tensor.
+func Restore(scope *Scope, file_pattern tf.Output, tensor_name tf.Output, dt tf.DataType, optional ...RestoreAttr) (tensor tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{}
+ attrs := map[string]interface{}{"dt": dt}
for _, a := range optional {
a(attrs)
}
opspec := tf.OpSpec{
- Type: "RandomGamma",
+ Type: "Restore",
Input: []tf.Input{
- shape, alpha,
+ file_pattern, tensor_name,
},
Attrs: attrs,
}
return op.Output(0)
}
-// RandomUniformIntAttr is an optional argument to RandomUniformInt.
-type RandomUniformIntAttr func(optionalAttr)
+// QuantizedResizeBilinearAttr is an optional argument to QuantizedResizeBilinear.
+type QuantizedResizeBilinearAttr func(optionalAttr)
-// RandomUniformIntSeed sets the optional seed attribute to value.
+// QuantizedResizeBilinearAlignCorners sets the optional align_corners attribute to value.
//
-// value: If either `seed` or `seed2` are set to be non-zero, the random number
-// generator is seeded by the given seed. Otherwise, it is seeded by a
-// random seed.
-// If not specified, defaults to 0
-func RandomUniformIntSeed(value int64) RandomUniformIntAttr {
+// value: If true, rescale input by (new_height - 1) / (height - 1), which
+// exactly aligns the 4 corners of images and resized images. If false, rescale
+// by new_height / height. Treat similarly the width dimension.
+// If not specified, defaults to false
+func QuantizedResizeBilinearAlignCorners(value bool) QuantizedResizeBilinearAttr {
return func(m optionalAttr) {
- m["seed"] = value
+ m["align_corners"] = value
}
}
-// RandomUniformIntSeed2 sets the optional seed2 attribute to value.
+// Resize quantized `images` to `size` using quantized bilinear interpolation.
//
-// value: A second seed to avoid seed collision.
-// If not specified, defaults to 0
-func RandomUniformIntSeed2(value int64) RandomUniformIntAttr {
- return func(m optionalAttr) {
- m["seed2"] = value
- }
-}
-
-// Outputs random integers from a uniform distribution.
+// Input images and output images must be quantized types.
//
-// The generated values are uniform integers in the range `[minval, maxval)`.
-// The lower bound `minval` is included in the range, while the upper bound
-// `maxval` is excluded.
+// Arguments:
+// images: 4-D with shape `[batch, height, width, channels]`.
+// size: = A 1-D int32 Tensor of 2 elements: `new_height, new_width`. The
+// new size for the images.
//
-// The random integers are slightly biased unless `maxval - minval` is an exact
-// power of two. The bias is small for values of `maxval - minval` significantly
-// smaller than the range of the output (either `2^32` or `2^64`).
//
-// Arguments:
-// shape: The shape of the output tensor.
-// minval: 0-D. Inclusive lower bound on the generated integers.
-// maxval: 0-D. Exclusive upper bound on the generated integers.
//
-// Returns A tensor of the specified shape filled with uniform random integers.
-func RandomUniformInt(scope *Scope, shape tf.Output, minval tf.Output, maxval tf.Output, optional ...RandomUniformIntAttr) (output tf.Output) {
+// Returns 4-D with shape
+// `[batch, new_height, new_width, channels]`.
+func QuantizedResizeBilinear(scope *Scope, images tf.Output, size tf.Output, min tf.Output, max tf.Output, optional ...QuantizedResizeBilinearAttr) (resized_images tf.Output, out_min tf.Output, out_max tf.Output) {
if scope.Err() != nil {
return
}
a(attrs)
}
opspec := tf.OpSpec{
- Type: "RandomUniformInt",
+ Type: "QuantizedResizeBilinear",
Input: []tf.Input{
- shape, minval, maxval,
+ images, size, min, max,
},
Attrs: attrs,
}
op := scope.AddOperation(opspec)
- return op.Output(0)
+ return op.Output(0), op.Output(1), op.Output(2)
}
-// SkipgramAttr is an optional argument to Skipgram.
-type SkipgramAttr func(optionalAttr)
-
-// SkipgramWindowSize sets the optional window_size attribute to value.
+// Computes the minimum along segments of a tensor.
//
-// value: The number of words to predict to the left and right of the target.
-// If not specified, defaults to 5
-func SkipgramWindowSize(value int64) SkipgramAttr {
- return func(m optionalAttr) {
- m["window_size"] = value
- }
-}
-
-// SkipgramMinCount sets the optional min_count attribute to value.
+// Read @{$math_ops#segmentation$the section on segmentation} for an explanation of
+// segments.
//
-// value: The minimum number of word occurrences for it to be included in the
-// vocabulary.
-// If not specified, defaults to 5
-func SkipgramMinCount(value int64) SkipgramAttr {
- return func(m optionalAttr) {
- m["min_count"] = value
- }
-}
-
-// SkipgramSubsample sets the optional subsample attribute to value.
+// Computes a tensor such that
+// \\(output_i = \min_j(data_j)\\) where `min` is over `j` such
+// that `segment_ids[j] == i`.
//
-// value: Threshold for word occurrence. Words that appear with higher
-// frequency will be randomly down-sampled. Set to 0 to disable.
-// If not specified, defaults to 0.001
-func SkipgramSubsample(value float32) SkipgramAttr {
- return func(m optionalAttr) {
- m["subsample"] = value
- }
-}
-
-// Parses a text file and creates a batch of examples.
+// If the min is empty for a given segment ID `i`, `output[i] = 0`.
//
-// DEPRECATED at GraphDef version 19: Moving word2vec into tensorflow_models/tutorials and deprecating its ops here as a result
+// <div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;">
+// <img style="width:100%" src="https://www.tensorflow.org/images/SegmentMin.png" alt>
+// </div>
//
// Arguments:
-// filename: The corpus's text file name.
-// batch_size: The size of produced batch.
//
-// Returns A vector of words in the corpus.Frequencies of words. Sorted in the non-ascending order.Number of words per epoch in the data file.The current epoch number.The total number of words processed so far.A vector of word ids.A vector of word ids.
-func Skipgram(scope *Scope, filename string, batch_size int64, optional ...SkipgramAttr) (vocab_word tf.Output, vocab_freq tf.Output, words_per_epoch tf.Output, current_epoch tf.Output, total_words_processed tf.Output, examples tf.Output, labels tf.Output) {
+// segment_ids: A 1-D tensor whose rank is equal to the rank of `data`'s
+// first dimension. Values should be sorted and can be repeated.
+//
+// Returns Has same shape as data, except for dimension 0 which
+// has size `k`, the number of segments.
+func SegmentMin(scope *Scope, data tf.Output, segment_ids tf.Output) (output tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{"filename": filename, "batch_size": batch_size}
- for _, a := range optional {
- a(attrs)
- }
opspec := tf.OpSpec{
- Type: "Skipgram",
-
- Attrs: attrs,
+ Type: "SegmentMin",
+ Input: []tf.Input{
+ data, segment_ids,
+ },
}
op := scope.AddOperation(opspec)
- return op.Output(0), op.Output(1), op.Output(2), op.Output(3), op.Output(4), op.Output(5), op.Output(6)
+ return op.Output(0)
}
-// StringToNumberAttr is an optional argument to StringToNumber.
-type StringToNumberAttr func(optionalAttr)
+// SdcaOptimizerAttr is an optional argument to SdcaOptimizer.
+type SdcaOptimizerAttr func(optionalAttr)
-// StringToNumberOutType sets the optional out_type attribute to value.
+// SdcaOptimizerAdaptative sets the optional adaptative attribute to value.
//
-// value: The numeric type to interpret each string in `string_tensor` as.
-// If not specified, defaults to DT_FLOAT
-func StringToNumberOutType(value tf.DataType) StringToNumberAttr {
+// value: Whether to use Adapative SDCA for the inner loop.
+// If not specified, defaults to false
+func SdcaOptimizerAdaptative(value bool) SdcaOptimizerAttr {
return func(m optionalAttr) {
- m["out_type"] = value
+ m["adaptative"] = value
}
}
-// Converts each string in the input Tensor to the specified numeric type.
-//
-// (Note that int32 overflow results in an error while float overflow
-// results in a rounded value.)
+// Distributed version of Stochastic Dual Coordinate Ascent (SDCA) optimizer for
//
-// Returns A Tensor of the same shape as the input `string_tensor`.
-func StringToNumber(scope *Scope, string_tensor tf.Output, optional ...StringToNumberAttr) (output tf.Output) {
+// linear models with L1 + L2 regularization. As global optimization objective is
+// strongly-convex, the optimizer optimizes the dual objective at each step. The
+// optimizer applies each update one example at a time. Examples are sampled
+// uniformly, and the optimizer is learning rate free and enjoys linear convergence
+// rate.
+//
+// [Proximal Stochastic Dual Coordinate Ascent](http://arxiv.org/pdf/1211.2717v1.pdf).<br>
+// Shai Shalev-Shwartz, Tong Zhang. 2012
+//
+// $$Loss Objective = \sum f_{i} (wx_{i}) + (l2 / 2) * |w|^2 + l1 * |w|$$
+//
+// [Adding vs. Averaging in Distributed Primal-Dual Optimization](http://arxiv.org/abs/1502.03508).<br>
+// Chenxin Ma, Virginia Smith, Martin Jaggi, Michael I. Jordan,
+// Peter Richtarik, Martin Takac. 2015
+//
+// [Stochastic Dual Coordinate Ascent with Adaptive Probabilities](https://arxiv.org/abs/1502.08053).<br>
+// Dominik Csiba, Zheng Qu, Peter Richtarik. 2015
+//
+// Arguments:
+// sparse_example_indices: a list of vectors which contain example indices.
+// sparse_feature_indices: a list of vectors which contain feature indices.
+// sparse_feature_values: a list of vectors which contains feature value
+// associated with each feature group.
+// dense_features: a list of matrices which contains the dense feature values.
+// example_weights: a vector which contains the weight associated with each
+// example.
+// example_labels: a vector which contains the label/target associated with each
+// example.
+// sparse_indices: a list of vectors where each value is the indices which has
+// corresponding weights in sparse_weights. This field maybe omitted for the
+// dense approach.
+// sparse_weights: a list of vectors where each value is the weight associated with
+// a sparse feature group.
+// dense_weights: a list of vectors where the values are the weights associated
+// with a dense feature group.
+// example_state_data: a list of vectors containing the example state data.
+// loss_type: Type of the primal loss. Currently SdcaSolver supports logistic,
+// squared and hinge losses.
+// l1: Symmetric l1 regularization strength.
+// l2: Symmetric l2 regularization strength.
+// num_loss_partitions: Number of partitions of the global loss function.
+// num_inner_iterations: Number of iterations per mini-batch.
+//
+// Returns a list of vectors containing the updated example state
+// data.a list of vectors where each value is the delta
+// weights associated with a sparse feature group.a list of vectors where the values are the delta
+// weights associated with a dense feature group.
+func SdcaOptimizer(scope *Scope, sparse_example_indices []tf.Output, sparse_feature_indices []tf.Output, sparse_feature_values []tf.Output, dense_features []tf.Output, example_weights tf.Output, example_labels tf.Output, sparse_indices []tf.Output, sparse_weights []tf.Output, dense_weights []tf.Output, example_state_data tf.Output, loss_type string, l1 float32, l2 float32, num_loss_partitions int64, num_inner_iterations int64, optional ...SdcaOptimizerAttr) (out_example_state_data tf.Output, out_delta_sparse_weights []tf.Output, out_delta_dense_weights []tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{}
+ attrs := map[string]interface{}{"loss_type": loss_type, "l1": l1, "l2": l2, "num_loss_partitions": num_loss_partitions, "num_inner_iterations": num_inner_iterations}
for _, a := range optional {
a(attrs)
}
opspec := tf.OpSpec{
- Type: "StringToNumber",
+ Type: "SdcaOptimizer",
Input: []tf.Input{
- string_tensor,
+ tf.OutputList(sparse_example_indices), tf.OutputList(sparse_feature_indices), tf.OutputList(sparse_feature_values), tf.OutputList(dense_features), example_weights, example_labels, tf.OutputList(sparse_indices), tf.OutputList(sparse_weights), tf.OutputList(dense_weights), example_state_data,
},
Attrs: attrs,
}
op := scope.AddOperation(opspec)
- return op.Output(0)
+ if scope.Err() != nil {
+ return
+ }
+ var idx int
+ var err error
+ out_example_state_data = op.Output(idx)
+ if out_delta_sparse_weights, idx, err = makeOutputList(op, idx, "out_delta_sparse_weights"); err != nil {
+ scope.UpdateErr("SdcaOptimizer", err)
+ return
+ }
+ if out_delta_dense_weights, idx, err = makeOutputList(op, idx, "out_delta_dense_weights"); err != nil {
+ scope.UpdateErr("SdcaOptimizer", err)
+ return
+ }
+ return out_example_state_data, out_delta_sparse_weights, out_delta_dense_weights
}
-// ResourceApplyFtrlV2Attr is an optional argument to ResourceApplyFtrlV2.
-type ResourceApplyFtrlV2Attr func(optionalAttr)
+// SparseMatMulAttr is an optional argument to SparseMatMul.
+type SparseMatMulAttr func(optionalAttr)
-// ResourceApplyFtrlV2UseLocking sets the optional use_locking attribute to value.
-//
-// value: If `True`, updating of the var and accum tensors will be protected
-// by a lock; otherwise the behavior is undefined, but may exhibit less
-// contention.
+// SparseMatMulTransposeA sets the optional transpose_a attribute to value.
// If not specified, defaults to false
-func ResourceApplyFtrlV2UseLocking(value bool) ResourceApplyFtrlV2Attr {
+func SparseMatMulTransposeA(value bool) SparseMatMulAttr {
return func(m optionalAttr) {
- m["use_locking"] = value
+ m["transpose_a"] = value
}
}
-// Update '*var' according to the Ftrl-proximal scheme.
-//
-// grad_with_shrinkage = grad + 2 * l2_shrinkage * var
-// accum_new = accum + grad_with_shrinkage * grad_with_shrinkage
-// linear += grad_with_shrinkage +
-// (accum_new^(-lr_power) - accum^(-lr_power)) / lr * var
-// quadratic = 1.0 / (accum_new^(lr_power) * lr) + 2 * l2
-// var = (sign(linear) * l1 - linear) / quadratic if |linear| > l1 else 0.0
-// accum = accum_new
-//
-// Arguments:
-// var_: Should be from a Variable().
-// accum: Should be from a Variable().
-// linear: Should be from a Variable().
-// grad: The gradient.
-// lr: Scaling factor. Must be a scalar.
-// l1: L1 regulariation. Must be a scalar.
-// l2: L2 shrinkage regulariation. Must be a scalar.
+// SparseMatMulTransposeB sets the optional transpose_b attribute to value.
+// If not specified, defaults to false
+func SparseMatMulTransposeB(value bool) SparseMatMulAttr {
+ return func(m optionalAttr) {
+ m["transpose_b"] = value
+ }
+}
+
+// SparseMatMulAIsSparse sets the optional a_is_sparse attribute to value.
+// If not specified, defaults to false
+func SparseMatMulAIsSparse(value bool) SparseMatMulAttr {
+ return func(m optionalAttr) {
+ m["a_is_sparse"] = value
+ }
+}
+
+// SparseMatMulBIsSparse sets the optional b_is_sparse attribute to value.
+// If not specified, defaults to false
+func SparseMatMulBIsSparse(value bool) SparseMatMulAttr {
+ return func(m optionalAttr) {
+ m["b_is_sparse"] = value
+ }
+}
+
+// Multiply matrix "a" by matrix "b".
//
-// lr_power: Scaling factor. Must be a scalar.
+// The inputs must be two-dimensional matrices and the inner dimension of "a" must
+// match the outer dimension of "b". This op is optimized for the case where at
+// least one of "a" or "b" is sparse. The breakeven for using this versus a dense
+// matrix multiply on one platform was 30% zero values in the sparse matrix.
//
-// Returns the created operation.
-func ResourceApplyFtrlV2(scope *Scope, var_ tf.Output, accum tf.Output, linear tf.Output, grad tf.Output, lr tf.Output, l1 tf.Output, l2 tf.Output, l2_shrinkage tf.Output, lr_power tf.Output, optional ...ResourceApplyFtrlV2Attr) (o *tf.Operation) {
+// The gradient computation of this operation will only take advantage of sparsity
+// in the input gradient when that gradient comes from a Relu.
+func SparseMatMul(scope *Scope, a tf.Output, b tf.Output, optional ...SparseMatMulAttr) (product tf.Output) {
if scope.Err() != nil {
return
}
a(attrs)
}
opspec := tf.OpSpec{
- Type: "ResourceApplyFtrlV2",
+ Type: "SparseMatMul",
Input: []tf.Input{
- var_, accum, linear, grad, lr, l1, l2, l2_shrinkage, lr_power,
+ a, b,
},
Attrs: attrs,
}
- return scope.AddOperation(opspec)
+ op := scope.AddOperation(opspec)
+ return op.Output(0)
}
-// TruncatedNormalAttr is an optional argument to TruncatedNormal.
-type TruncatedNormalAttr func(optionalAttr)
-
-// TruncatedNormalSeed sets the optional seed attribute to value.
+// Computes the power of one value to another.
//
-// value: If either `seed` or `seed2` are set to be non-zero, the random number
-// generator is seeded by the given seed. Otherwise, it is seeded by a
-// random seed.
-// If not specified, defaults to 0
-func TruncatedNormalSeed(value int64) TruncatedNormalAttr {
- return func(m optionalAttr) {
- m["seed"] = value
+// Given a tensor `x` and a tensor `y`, this operation computes \\(x^y\\) for
+// corresponding elements in `x` and `y`. For example:
+//
+// ```
+// # tensor 'x' is [[2, 2]], [3, 3]]
+// # tensor 'y' is [[8, 16], [2, 3]]
+// tf.pow(x, y) ==> [[256, 65536], [9, 27]]
+// ```
+func Pow(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) {
+ if scope.Err() != nil {
+ return
+ }
+ opspec := tf.OpSpec{
+ Type: "Pow",
+ Input: []tf.Input{
+ x, y,
+ },
}
+ op := scope.AddOperation(opspec)
+ return op.Output(0)
}
-// TruncatedNormalSeed2 sets the optional seed2 attribute to value.
-//
-// value: A second seed to avoid seed collision.
-// If not specified, defaults to 0
-func TruncatedNormalSeed2(value int64) TruncatedNormalAttr {
+// ShapeAttr is an optional argument to Shape.
+type ShapeAttr func(optionalAttr)
+
+// ShapeOutType sets the optional out_type attribute to value.
+// If not specified, defaults to DT_INT32
+func ShapeOutType(value tf.DataType) ShapeAttr {
return func(m optionalAttr) {
- m["seed2"] = value
+ m["out_type"] = value
}
}
-// Outputs random values from a truncated normal distribution.
+// Returns the shape of a tensor.
//
-// The generated values follow a normal distribution with mean 0 and standard
-// deviation 1, except that values whose magnitude is more than 2 standard
-// deviations from the mean are dropped and re-picked.
+// This operation returns a 1-D integer tensor representing the shape of `input`.
//
-// Arguments:
-// shape: The shape of the output tensor.
-// dtype: The type of the output.
+// For example:
//
-// Returns A tensor of the specified shape filled with random truncated normal
-// values.
-func TruncatedNormal(scope *Scope, shape tf.Output, dtype tf.DataType, optional ...TruncatedNormalAttr) (output tf.Output) {
+// ```
+// # 't' is [[[1, 1, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]]]
+// shape(t) ==> [2, 2, 3]
+// ```
+func Shape(scope *Scope, input tf.Output, optional ...ShapeAttr) (output tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{"dtype": dtype}
+ attrs := map[string]interface{}{}
for _, a := range optional {
a(attrs)
}
opspec := tf.OpSpec{
- Type: "TruncatedNormal",
+ Type: "Shape",
Input: []tf.Input{
- shape,
+ input,
},
Attrs: attrs,
}
return op.Output(0)
}
-// RandomShuffleAttr is an optional argument to RandomShuffle.
-type RandomShuffleAttr func(optionalAttr)
+// Computes fingerprints of the input strings.
+//
+// Arguments:
+// input: vector of strings to compute fingerprints on.
+//
+// Returns a (N,2) shaped matrix where N is the number of elements in the input
+// vector. Each row contains the low and high parts of the fingerprint.
+func SdcaFprint(scope *Scope, input tf.Output) (output tf.Output) {
+ if scope.Err() != nil {
+ return
+ }
+ opspec := tf.OpSpec{
+ Type: "SdcaFprint",
+ Input: []tf.Input{
+ input,
+ },
+ }
+ op := scope.AddOperation(opspec)
+ return op.Output(0)
+}
-// RandomShuffleSeed sets the optional seed attribute to value.
+// RandomPoissonV2Attr is an optional argument to RandomPoissonV2.
+type RandomPoissonV2Attr func(optionalAttr)
+
+// RandomPoissonV2Seed sets the optional seed attribute to value.
//
// value: If either `seed` or `seed2` are set to be non-zero, the random number
// generator is seeded by the given seed. Otherwise, it is seeded by a
// random seed.
// If not specified, defaults to 0
-func RandomShuffleSeed(value int64) RandomShuffleAttr {
+func RandomPoissonV2Seed(value int64) RandomPoissonV2Attr {
return func(m optionalAttr) {
m["seed"] = value
}
}
-// RandomShuffleSeed2 sets the optional seed2 attribute to value.
+// RandomPoissonV2Seed2 sets the optional seed2 attribute to value.
//
// value: A second seed to avoid seed collision.
// If not specified, defaults to 0
-func RandomShuffleSeed2(value int64) RandomShuffleAttr {
+func RandomPoissonV2Seed2(value int64) RandomPoissonV2Attr {
return func(m optionalAttr) {
m["seed2"] = value
}
}
-// Randomly shuffles a tensor along its first dimension.
+// RandomPoissonV2Dtype sets the optional dtype attribute to value.
+// If not specified, defaults to DT_INT64
+func RandomPoissonV2Dtype(value tf.DataType) RandomPoissonV2Attr {
+ return func(m optionalAttr) {
+ m["dtype"] = value
+ }
+}
+
+// Outputs random values from the Poisson distribution(s) described by rate.
//
-// The tensor is shuffled along dimension 0, such that each `value[j]` is mapped
-// to one and only one `output[i]`. For example, a mapping that might occur for a
-// 3x2 tensor is:
+// This op uses two algorithms, depending on rate. If rate >= 10, then
+// the algorithm by Hormann is used to acquire samples via
+// transformation-rejection.
+// See http://www.sciencedirect.com/science/article/pii/0167668793909974.
//
-// ```
-// [[1, 2], [[5, 6],
-// [3, 4], ==> [1, 2],
-// [5, 6]] [3, 4]]
-// ```
+// Otherwise, Knuth's algorithm is used to acquire samples via multiplying uniform
+// random variables.
+// See Donald E. Knuth (1969). Seminumerical Algorithms. The Art of Computer
+// Programming, Volume 2. Addison Wesley
//
// Arguments:
-// value: The tensor to be shuffled.
+// shape: 1-D integer tensor. Shape of independent samples to draw from each
+// distribution described by the shape parameters given in rate.
+// rate: A tensor in which each scalar is a "rate" parameter describing the
+// associated poisson distribution.
//
-// Returns A tensor of same shape and type as `value`, shuffled along its first
-// dimension.
-func RandomShuffle(scope *Scope, value tf.Output, optional ...RandomShuffleAttr) (output tf.Output) {
+// Returns A tensor with shape `shape + shape(rate)`. Each slice
+// `[:, ..., :, i0, i1, ...iN]` contains the samples drawn for
+// `rate[i0, i1, ...iN]`.
+func RandomPoissonV2(scope *Scope, shape tf.Output, rate tf.Output, optional ...RandomPoissonV2Attr) (output tf.Output) {
if scope.Err() != nil {
return
}
a(attrs)
}
opspec := tf.OpSpec{
- Type: "RandomShuffle",
+ Type: "RandomPoissonV2",
Input: []tf.Input{
- value,
+ shape, rate,
},
Attrs: attrs,
}
return op.Output(0)
}
-// OrderedMapIncompleteSizeAttr is an optional argument to OrderedMapIncompleteSize.
-type OrderedMapIncompleteSizeAttr func(optionalAttr)
+// MatrixTriangularSolveAttr is an optional argument to MatrixTriangularSolve.
+type MatrixTriangularSolveAttr func(optionalAttr)
-// OrderedMapIncompleteSizeCapacity sets the optional capacity attribute to value.
-// If not specified, defaults to 0
+// MatrixTriangularSolveLower sets the optional lower attribute to value.
//
-// REQUIRES: value >= 0
-func OrderedMapIncompleteSizeCapacity(value int64) OrderedMapIncompleteSizeAttr {
+// value: Boolean indicating whether the innermost matrices in `matrix` are
+// lower or upper triangular.
+// If not specified, defaults to true
+func MatrixTriangularSolveLower(value bool) MatrixTriangularSolveAttr {
return func(m optionalAttr) {
- m["capacity"] = value
+ m["lower"] = value
}
}
-// OrderedMapIncompleteSizeMemoryLimit sets the optional memory_limit attribute to value.
-// If not specified, defaults to 0
+// MatrixTriangularSolveAdjoint sets the optional adjoint attribute to value.
//
-// REQUIRES: value >= 0
-func OrderedMapIncompleteSizeMemoryLimit(value int64) OrderedMapIncompleteSizeAttr {
- return func(m optionalAttr) {
- m["memory_limit"] = value
- }
-}
-
-// OrderedMapIncompleteSizeContainer sets the optional container attribute to value.
-// If not specified, defaults to ""
-func OrderedMapIncompleteSizeContainer(value string) OrderedMapIncompleteSizeAttr {
- return func(m optionalAttr) {
- m["container"] = value
- }
-}
-
-// OrderedMapIncompleteSizeSharedName sets the optional shared_name attribute to value.
-// If not specified, defaults to ""
-func OrderedMapIncompleteSizeSharedName(value string) OrderedMapIncompleteSizeAttr {
+// value: Boolean indicating whether to solve with `matrix` or its (block-wise)
+// adjoint.
+//
+// @compatibility(numpy)
+// Equivalent to np.linalg.triangular_solve
+// @end_compatibility
+// If not specified, defaults to false
+func MatrixTriangularSolveAdjoint(value bool) MatrixTriangularSolveAttr {
return func(m optionalAttr) {
- m["shared_name"] = value
+ m["adjoint"] = value
}
}
-// Op returns the number of incomplete elements in the underlying container.
-func OrderedMapIncompleteSize(scope *Scope, dtypes []tf.DataType, optional ...OrderedMapIncompleteSizeAttr) (size tf.Output) {
+// Solves systems of linear equations with upper or lower triangular matrices by
+//
+// backsubstitution.
+//
+// `matrix` is a tensor of shape `[..., M, M]` whose inner-most 2 dimensions form
+// square matrices. If `lower` is `True` then the strictly upper triangular part
+// of each inner-most matrix is assumed to be zero and not accessed.
+// If `lower` is False then the strictly lower triangular part of each inner-most
+// matrix is assumed to be zero and not accessed.
+// `rhs` is a tensor of shape `[..., M, K]`.
+//
+// The output is a tensor of shape `[..., M, K]`. If `adjoint` is
+// `True` then the innermost matrices in `output` satisfy matrix equations
+// `matrix[..., :, :] * output[..., :, :] = rhs[..., :, :]`.
+// If `adjoint` is `False` then the strictly then the innermost matrices in
+// `output` satisfy matrix equations
+// `adjoint(matrix[..., i, k]) * output[..., k, j] = rhs[..., i, j]`.
+//
+// Arguments:
+// matrix: Shape is `[..., M, M]`.
+// rhs: Shape is `[..., M, K]`.
+//
+// Returns Shape is `[..., M, K]`.
+func MatrixTriangularSolve(scope *Scope, matrix tf.Output, rhs tf.Output, optional ...MatrixTriangularSolveAttr) (output tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{"dtypes": dtypes}
+ attrs := map[string]interface{}{}
for _, a := range optional {
a(attrs)
}
opspec := tf.OpSpec{
- Type: "OrderedMapIncompleteSize",
-
+ Type: "MatrixTriangularSolve",
+ Input: []tf.Input{
+ matrix, rhs,
+ },
Attrs: attrs,
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// DecodeRawAttr is an optional argument to DecodeRaw.
-type DecodeRawAttr func(optionalAttr)
-
-// DecodeRawLittleEndian sets the optional little_endian attribute to value.
-//
-// value: Whether the input `bytes` are in little-endian order.
-// Ignored for `out_type` values that are stored in a single byte like
-// `uint8`.
-// If not specified, defaults to true
-func DecodeRawLittleEndian(value bool) DecodeRawAttr {
- return func(m optionalAttr) {
- m["little_endian"] = value
- }
-}
-
-// Reinterpret the bytes of a string as a vector of numbers.
-//
-// Arguments:
-// bytes: All the elements must have the same length.
-//
-//
-// Returns A Tensor with one more dimension than the input `bytes`. The
-// added dimension will have size equal to the length of the elements
-// of `bytes` divided by the number of bytes to represent `out_type`.
-func DecodeRaw(scope *Scope, bytes tf.Output, out_type tf.DataType, optional ...DecodeRawAttr) (output tf.Output) {
+// Computes inverse hyperbolic sine of x element-wise.
+func Asinh(scope *Scope, x tf.Output) (y tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{"out_type": out_type}
- for _, a := range optional {
- a(attrs)
- }
opspec := tf.OpSpec{
- Type: "DecodeRaw",
+ Type: "Asinh",
Input: []tf.Input{
- bytes,
+ x,
},
- Attrs: attrs,
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// Copy a tensor setting everything outside a central band in each innermost matrix
-//
-// to zero.
-//
-// The `band` part is computed as follows:
-// Assume `input` has `k` dimensions `[I, J, K, ..., M, N]`, then the output is a
-// tensor with the same shape where
-//
-// `band[i, j, k, ..., m, n] = in_band(m, n) * input[i, j, k, ..., m, n]`.
-//
-// The indicator function
-//
-// `in_band(m, n) = (num_lower < 0 || (m-n) <= num_lower)) &&
-// (num_upper < 0 || (n-m) <= num_upper)`.
-//
-// For example:
-//
-// ```
-// # if 'input' is [[ 0, 1, 2, 3]
-// [-1, 0, 1, 2]
-// [-2, -1, 0, 1]
-// [-3, -2, -1, 0]],
-//
-// tf.matrix_band_part(input, 1, -1) ==> [[ 0, 1, 2, 3]
-// [-1, 0, 1, 2]
-// [ 0, -1, 0, 1]
-// [ 0, 0, -1, 0]],
-//
-// tf.matrix_band_part(input, 2, 1) ==> [[ 0, 1, 0, 0]
-// [-1, 0, 1, 0]
-// [-2, -1, 0, 1]
-// [ 0, -2, -1, 0]]
-// ```
-//
-// Useful special cases:
-//
-// ```
-// tf.matrix_band_part(input, 0, -1) ==> Upper triangular part.
-// tf.matrix_band_part(input, -1, 0) ==> Lower triangular part.
-// tf.matrix_band_part(input, 0, 0) ==> Diagonal.
-// ```
+// Creates a dataset with a range of values. Corresponds to python's xrange.
//
// Arguments:
-// input: Rank `k` tensor.
-// num_lower: 0-D tensor. Number of subdiagonals to keep. If negative, keep entire
-// lower triangle.
-// num_upper: 0-D tensor. Number of superdiagonals to keep. If negative, keep
-// entire upper triangle.
+// start: corresponds to start in python's xrange().
+// stop: corresponds to stop in python's xrange().
+// step: corresponds to step in python's xrange().
//
-// Returns Rank `k` tensor of the same shape as input. The extracted banded tensor.
-func MatrixBandPart(scope *Scope, input tf.Output, num_lower tf.Output, num_upper tf.Output) (band tf.Output) {
+//
+func RangeDataset(scope *Scope, start tf.Output, stop tf.Output, step tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) {
if scope.Err() != nil {
return
}
+ attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes}
opspec := tf.OpSpec{
- Type: "MatrixBandPart",
+ Type: "RangeDataset",
Input: []tf.Input{
- input, num_lower, num_upper,
+ start, stop, step,
},
+ Attrs: attrs,
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// QuantizedMatMulAttr is an optional argument to QuantizedMatMul.
-type QuantizedMatMulAttr func(optionalAttr)
-
-// QuantizedMatMulToutput sets the optional Toutput attribute to value.
-// If not specified, defaults to DT_QINT32
-func QuantizedMatMulToutput(value tf.DataType) QuantizedMatMulAttr {
- return func(m optionalAttr) {
- m["Toutput"] = value
- }
-}
+// DepthwiseConv2dNativeBackpropInputAttr is an optional argument to DepthwiseConv2dNativeBackpropInput.
+type DepthwiseConv2dNativeBackpropInputAttr func(optionalAttr)
-// QuantizedMatMulTransposeA sets the optional transpose_a attribute to value.
+// DepthwiseConv2dNativeBackpropInputDataFormat sets the optional data_format attribute to value.
//
-// value: If true, `a` is transposed before multiplication.
-// If not specified, defaults to false
-func QuantizedMatMulTransposeA(value bool) QuantizedMatMulAttr {
+// value: Specify the data format of the input and output data. With the
+// default format "NHWC", the data is stored in the order of:
+// [batch, height, width, channels].
+// Alternatively, the format could be "NCHW", the data storage order of:
+// [batch, channels, height, width].
+// If not specified, defaults to "NHWC"
+func DepthwiseConv2dNativeBackpropInputDataFormat(value string) DepthwiseConv2dNativeBackpropInputAttr {
return func(m optionalAttr) {
- m["transpose_a"] = value
+ m["data_format"] = value
}
}
-// QuantizedMatMulTransposeB sets the optional transpose_b attribute to value.
+// DepthwiseConv2dNativeBackpropInputDilations sets the optional dilations attribute to value.
//
-// value: If true, `b` is transposed before multiplication.
-// If not specified, defaults to false
-func QuantizedMatMulTransposeB(value bool) QuantizedMatMulAttr {
+// value: 1-D tensor of length 4. The dilation factor for each dimension of
+// `input`. If set to k > 1, there will be k-1 skipped cells between each filter
+// element on that dimension. The dimension order is determined by the value of
+// `data_format`, see above for details. Dilations in the batch and depth
+// dimensions must be 1.
+// If not specified, defaults to <i:1 i:1 i:1 i:1 >
+func DepthwiseConv2dNativeBackpropInputDilations(value []int64) DepthwiseConv2dNativeBackpropInputAttr {
return func(m optionalAttr) {
- m["transpose_b"] = value
+ m["dilations"] = value
}
}
-// QuantizedMatMulTactivation sets the optional Tactivation attribute to value.
-//
-// value: The type of output produced by activation function
-// following this operation.
-// If not specified, defaults to DT_QUINT8
-func QuantizedMatMulTactivation(value tf.DataType) QuantizedMatMulAttr {
- return func(m optionalAttr) {
- m["Tactivation"] = value
- }
-}
-
-// Perform a quantized matrix multiplication of `a` by the matrix `b`.
-//
-// The inputs must be two-dimensional matrices and the inner dimension of
-// `a` (after being transposed if `transpose_a` is non-zero) must match the
-// outer dimension of `b` (after being transposed if `transposed_b` is
-// non-zero).
+// Computes the gradients of depthwise convolution with respect to the input.
//
// Arguments:
-// a: Must be a two-dimensional tensor.
-// b: Must be a two-dimensional tensor.
-// min_a: The float value that the lowest quantized `a` value represents.
-// max_a: The float value that the highest quantized `a` value represents.
-// min_b: The float value that the lowest quantized `b` value represents.
-// max_b: The float value that the highest quantized `b` value represents.
+// input_sizes: An integer vector representing the shape of `input`, based
+// on `data_format`. For example, if `data_format` is 'NHWC' then
+// `input` is a 4-D `[batch, height, width, channels]` tensor.
+// filter: 4-D with shape
+// `[filter_height, filter_width, in_channels, depthwise_multiplier]`.
+// out_backprop: 4-D with shape based on `data_format`.
+// For example, if `data_format` is 'NHWC' then
+// out_backprop shape is `[batch, out_height, out_width, out_channels]`.
+// Gradients w.r.t. the output of the convolution.
+// strides: The stride of the sliding window for each dimension of the input
+// of the convolution.
+// padding: The type of padding algorithm to use.
//
-// Returns The float value that the lowest quantized output value represents.The float value that the highest quantized output value represents.
-func QuantizedMatMul(scope *Scope, a tf.Output, b tf.Output, min_a tf.Output, max_a tf.Output, min_b tf.Output, max_b tf.Output, optional ...QuantizedMatMulAttr) (out tf.Output, min_out tf.Output, max_out tf.Output) {
+// Returns 4-D with shape according to `data_format`. For example, if
+// `data_format` is 'NHWC', output shape is `[batch, in_height,
+// in_width, in_channels]`. Gradient w.r.t. the input of the
+// convolution.
+func DepthwiseConv2dNativeBackpropInput(scope *Scope, input_sizes tf.Output, filter tf.Output, out_backprop tf.Output, strides []int64, padding string, optional ...DepthwiseConv2dNativeBackpropInputAttr) (output tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{}
+ attrs := map[string]interface{}{"strides": strides, "padding": padding}
for _, a := range optional {
a(attrs)
}
opspec := tf.OpSpec{
- Type: "QuantizedMatMul",
+ Type: "DepthwiseConv2dNativeBackpropInput",
Input: []tf.Input{
- a, b, min_a, max_a, min_b, max_b,
+ input_sizes, filter, out_backprop,
},
Attrs: attrs,
}
op := scope.AddOperation(opspec)
- return op.Output(0), op.Output(1), op.Output(2)
+ return op.Output(0)
}
-// Does nothing. Serves as a control trigger for scheduling.
+// Adds sparse updates to the variable referenced by `resource`.
//
-// Only useful as a placeholder for control edges.
+// This operation computes
+//
+// # Scalar indices
+// ref[indices, ...] += updates[...]
+//
+// # Vector indices (for each i)
+// ref[indices[i], ...] += updates[i, ...]
+//
+// # High rank indices (for each i, ..., j)
+// ref[indices[i, ..., j], ...] += updates[i, ..., j, ...]
+//
+// Duplicate entries are handled correctly: if multiple `indices` reference
+// the same location, their contributions add.
+//
+// Requires `updates.shape = indices.shape + ref.shape[1:]`.
+//
+// <div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;">
+// <img style="width:100%" src='https://www.tensorflow.org/images/ScatterAdd.png' alt>
+// </div>
+//
+// Arguments:
+// resource: Should be from a `Variable` node.
+// indices: A tensor of indices into the first dimension of `ref`.
+// updates: A tensor of updated values to add to `ref`.
//
// Returns the created operation.
-func ControlTrigger(scope *Scope) (o *tf.Operation) {
+func ResourceScatterAdd(scope *Scope, resource tf.Output, indices tf.Output, updates tf.Output) (o *tf.Operation) {
if scope.Err() != nil {
return
}
opspec := tf.OpSpec{
- Type: "ControlTrigger",
+ Type: "ResourceScatterAdd",
+ Input: []tf.Input{
+ resource, indices, updates,
+ },
}
return scope.AddOperation(opspec)
}
-// Batch normalization.
+// Says whether the targets are in the top `K` predictions.
//
-// DEPRECATED at GraphDef version 9: Use tf.nn.batch_normalization()
+// This outputs a `batch_size` bool array, an entry `out[i]` is `true` if the
+// prediction for the target class is among the top `k` predictions among
+// all predictions for example `i`. Note that the behavior of `InTopK` differs
+// from the `TopK` op in its handling of ties; if multiple classes have the
+// same prediction value and straddle the top-`k` boundary, all of those
+// classes are considered to be in the top `k`.
//
-// This op is deprecated. Prefer `tf.nn.batch_normalization`.
+// More formally, let
+//
+// \\(predictions_i\\) be the predictions for all classes for example `i`,
+// \\(targets_i\\) be the target class for example `i`,
+// \\(out_i\\) be the output for example `i`,
+//
+// $$out_i = predictions_{i, targets_i} \in TopKIncludingTies(predictions_i)$$
//
// Arguments:
-// t: A 4D input Tensor.
-// m: A 1D mean Tensor with size matching the last dimension of t.
-// This is the first output from tf.nn.moments,
-// or a saved moving average thereof.
-// v: A 1D variance Tensor with size matching the last dimension of t.
-// This is the second output from tf.nn.moments,
-// or a saved moving average thereof.
-// beta: A 1D beta Tensor with size matching the last dimension of t.
-// An offset to be added to the normalized tensor.
-// gamma: A 1D gamma Tensor with size matching the last dimension of t.
-// If "scale_after_normalization" is true, this tensor will be multiplied
-// with the normalized tensor.
-// variance_epsilon: A small float number to avoid dividing by 0.
-// scale_after_normalization: A bool indicating whether the resulted tensor
-// needs to be multiplied with gamma.
-func BatchNormWithGlobalNormalization(scope *Scope, t tf.Output, m tf.Output, v tf.Output, beta tf.Output, gamma tf.Output, variance_epsilon float32, scale_after_normalization bool) (result tf.Output) {
+// predictions: A `batch_size` x `classes` tensor.
+// targets: A `batch_size` vector of class ids.
+// k: Number of top elements to look at for computing precision.
+//
+// Returns Computed Precision at `k` as a `bool Tensor`.
+func InTopK(scope *Scope, predictions tf.Output, targets tf.Output, k int64) (precision tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{"variance_epsilon": variance_epsilon, "scale_after_normalization": scale_after_normalization}
+ attrs := map[string]interface{}{"k": k}
opspec := tf.OpSpec{
- Type: "BatchNormWithGlobalNormalization",
+ Type: "InTopK",
Input: []tf.Input{
- t, m, v, beta, gamma,
+ predictions, targets,
},
Attrs: attrs,
}
return op.Output(0)
}
-// Deprecated. Use TensorArrayReadV3
+// Computes the gradient for the inverse of `x` wrt its input.
//
-// DEPRECATED at GraphDef version 26: Use TensorArrayReadV3
-func TensorArrayReadV2(scope *Scope, handle tf.Output, index tf.Output, flow_in tf.Output, dtype tf.DataType) (value tf.Output) {
+// Specifically, `grad = -dy * y*y`, where `y = 1/x`, and `dy`
+// is the corresponding input gradient.
+func ReciprocalGrad(scope *Scope, y tf.Output, dy tf.Output) (z tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{"dtype": dtype}
opspec := tf.OpSpec{
- Type: "TensorArrayReadV2",
+ Type: "ReciprocalGrad",
Input: []tf.Input{
- handle, index, flow_in,
+ y, dy,
},
- Attrs: attrs,
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// QuantizedMulAttr is an optional argument to QuantizedMul.
-type QuantizedMulAttr func(optionalAttr)
-
-// QuantizedMulToutput sets the optional Toutput attribute to value.
-// If not specified, defaults to DT_QINT32
-func QuantizedMulToutput(value tf.DataType) QuantizedMulAttr {
- return func(m optionalAttr) {
- m["Toutput"] = value
+// Returns the min of x and y (i.e. x < y ? x : y) element-wise.
+//
+// *NOTE*: `Minimum` supports broadcasting. More about broadcasting
+// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
+func Minimum(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) {
+ if scope.Err() != nil {
+ return
+ }
+ opspec := tf.OpSpec{
+ Type: "Minimum",
+ Input: []tf.Input{
+ x, y,
+ },
}
+ op := scope.AddOperation(opspec)
+ return op.Output(0)
}
-// Returns x * y element-wise, working on quantized buffers.
-//
-// Arguments:
+// Returns the element-wise sum of a list of tensors.
//
+// `tf.accumulate_n_v2` performs the same operation as `tf.add_n`, but does not
+// wait for all of its inputs to be ready before beginning to sum. This can
+// save memory if inputs are ready at different times, since minimum temporary
+// storage is proportional to the output size rather than the inputs size.
//
-// min_x: The float value that the lowest quantized `x` value represents.
-// max_x: The float value that the highest quantized `x` value represents.
-// min_y: The float value that the lowest quantized `y` value represents.
-// max_y: The float value that the highest quantized `y` value represents.
+// Unlike the original `accumulate_n`, `accumulate_n_v2` is differentiable.
//
-// Returns The float value that the lowest quantized output value represents.The float value that the highest quantized output value represents.
+// Returns a `Tensor` of same shape and type as the elements of `inputs`.
//
-// *NOTE*: `QuantizedMul` supports limited forms of broadcasting. More about
-// broadcasting [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
-func QuantizedMul(scope *Scope, x tf.Output, y tf.Output, min_x tf.Output, max_x tf.Output, min_y tf.Output, max_y tf.Output, optional ...QuantizedMulAttr) (z tf.Output, min_z tf.Output, max_z tf.Output) {
+// Arguments:
+// inputs: A list of `Tensor` objects, each with same shape and type.
+// shape: Shape of elements of `inputs`.
+func AccumulateNV2(scope *Scope, inputs []tf.Output, shape tf.Shape) (sum tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{}
- for _, a := range optional {
- a(attrs)
- }
+ attrs := map[string]interface{}{"shape": shape}
opspec := tf.OpSpec{
- Type: "QuantizedMul",
+ Type: "AccumulateNV2",
Input: []tf.Input{
- x, y, min_x, max_x, min_y, max_y,
+ tf.OutputList(inputs),
},
Attrs: attrs,
}
op := scope.AddOperation(opspec)
- return op.Output(0), op.Output(1), op.Output(2)
-}
-
-// QuantizedAddAttr is an optional argument to QuantizedAdd.
-type QuantizedAddAttr func(optionalAttr)
-
-// QuantizedAddToutput sets the optional Toutput attribute to value.
-// If not specified, defaults to DT_QINT32
-func QuantizedAddToutput(value tf.DataType) QuantizedAddAttr {
- return func(m optionalAttr) {
- m["Toutput"] = value
- }
+ return op.Output(0)
}
-// Returns x + y element-wise, working on quantized buffers.
+// Convert the quantized 'input' tensor into a lower-precision 'output', using the
//
-// Arguments:
+// actual distribution of the values to maximize the usage of the lower bit depth
+// and adjusting the output min and max ranges accordingly.
//
+// [input_min, input_max] are scalar floats that specify the range for the float
+// interpretation of the 'input' data. For example, if input_min is -1.0f and
+// input_max is 1.0f, and we are dealing with quint16 quantized data, then a 0
+// value in the 16-bit data should be interpreted as -1.0f, and a 65535 means 1.0f.
//
-// min_x: The float value that the lowest quantized `x` value represents.
-// max_x: The float value that the highest quantized `x` value represents.
-// min_y: The float value that the lowest quantized `y` value represents.
-// max_y: The float value that the highest quantized `y` value represents.
+// This operator tries to squeeze as much precision as possible into an output with
+// a lower bit depth by calculating the actual min and max values found in the
+// data. For example, maybe that quint16 input has no values lower than 16,384 and
+// none higher than 49,152. That means only half the range is actually needed, all
+// the float interpretations are between -0.5f and 0.5f, so if we want to compress
+// the data into a quint8 output, we can use that range rather than the theoretical
+// -1.0f to 1.0f that is suggested by the input min and max.
//
-// Returns The float value that the lowest quantized output value represents.The float value that the highest quantized output value represents.
+// In practice, this is most useful for taking output from operations like
+// QuantizedMatMul that can produce higher bit-depth outputs than their inputs and
+// may have large potential output ranges, but in practice have a distribution of
+// input values that only uses a small fraction of the possible range. By feeding
+// that output into this operator, we can reduce it from 32 bits down to 8 with
+// minimal loss of accuracy.
//
-// *NOTE*: `QuantizedAdd` supports limited forms of broadcasting. More about
-// broadcasting [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
-func QuantizedAdd(scope *Scope, x tf.Output, y tf.Output, min_x tf.Output, max_x tf.Output, min_y tf.Output, max_y tf.Output, optional ...QuantizedAddAttr) (z tf.Output, min_z tf.Output, max_z tf.Output) {
+// Arguments:
+//
+// input_min: The float value that the minimum quantized input value represents.
+// input_max: The float value that the maximum quantized input value represents.
+// out_type: The type of the output. Should be a lower bit depth than Tinput.
+//
+// Returns The float value that the minimum quantized output value represents.The float value that the maximum quantized output value represents.
+func QuantizeDownAndShrinkRange(scope *Scope, input tf.Output, input_min tf.Output, input_max tf.Output, out_type tf.DataType) (output tf.Output, output_min tf.Output, output_max tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{}
- for _, a := range optional {
- a(attrs)
- }
+ attrs := map[string]interface{}{"out_type": out_type}
opspec := tf.OpSpec{
- Type: "QuantizedAdd",
+ Type: "QuantizeDownAndShrinkRange",
Input: []tf.Input{
- x, y, min_x, max_x, min_y, max_y,
+ input, input_min, input_max,
},
Attrs: attrs,
}
return op.Output(0), op.Output(1), op.Output(2)
}
-// MfccAttr is an optional argument to Mfcc.
-type MfccAttr func(optionalAttr)
+// RandomGammaAttr is an optional argument to RandomGamma.
+type RandomGammaAttr func(optionalAttr)
-// MfccUpperFrequencyLimit sets the optional upper_frequency_limit attribute to value.
+// RandomGammaSeed sets the optional seed attribute to value.
//
-// value: The highest frequency to use when calculating the
-// ceptstrum.
-// If not specified, defaults to 4000
-func MfccUpperFrequencyLimit(value float32) MfccAttr {
- return func(m optionalAttr) {
- m["upper_frequency_limit"] = value
- }
-}
-
-// MfccLowerFrequencyLimit sets the optional lower_frequency_limit attribute to value.
-//
-// value: The lowest frequency to use when calculating the
-// ceptstrum.
-// If not specified, defaults to 20
-func MfccLowerFrequencyLimit(value float32) MfccAttr {
- return func(m optionalAttr) {
- m["lower_frequency_limit"] = value
- }
-}
-
-// MfccFilterbankChannelCount sets the optional filterbank_channel_count attribute to value.
-//
-// value: Resolution of the Mel bank used internally.
-// If not specified, defaults to 40
-func MfccFilterbankChannelCount(value int64) MfccAttr {
+// value: If either `seed` or `seed2` are set to be non-zero, the random number
+// generator is seeded by the given seed. Otherwise, it is seeded by a
+// random seed.
+// If not specified, defaults to 0
+func RandomGammaSeed(value int64) RandomGammaAttr {
return func(m optionalAttr) {
- m["filterbank_channel_count"] = value
+ m["seed"] = value
}
}
-// MfccDctCoefficientCount sets the optional dct_coefficient_count attribute to value.
+// RandomGammaSeed2 sets the optional seed2 attribute to value.
//
-// value: How many output channels to produce per time slice.
-// If not specified, defaults to 13
-func MfccDctCoefficientCount(value int64) MfccAttr {
+// value: A second seed to avoid seed collision.
+// If not specified, defaults to 0
+func RandomGammaSeed2(value int64) RandomGammaAttr {
return func(m optionalAttr) {
- m["dct_coefficient_count"] = value
+ m["seed2"] = value
}
}
-// Transforms a spectrogram into a form that's useful for speech recognition.
+// Outputs random values from the Gamma distribution(s) described by alpha.
//
-// Mel Frequency Cepstral Coefficients are a way of representing audio data that's
-// been effective as an input feature for machine learning. They are created by
-// taking the spectrum of a spectrogram (a 'cepstrum'), and discarding some of the
-// higher frequencies that are less significant to the human ear. They have a long
-// history in the speech recognition world, and https://en.wikipedia.org/wiki/Mel-frequency_cepstrum
-// is a good resource to learn more.
+// This op uses the algorithm by Marsaglia et al. to acquire samples via
+// transformation-rejection from pairs of uniform and normal random variables.
+// See http://dl.acm.org/citation.cfm?id=358414
//
// Arguments:
-// spectrogram: Typically produced by the Spectrogram op, with magnitude_squared
-// set to true.
-// sample_rate: How many samples per second the source audio used.
-func Mfcc(scope *Scope, spectrogram tf.Output, sample_rate tf.Output, optional ...MfccAttr) (output tf.Output) {
+// shape: 1-D integer tensor. Shape of independent samples to draw from each
+// distribution described by the shape parameters given in alpha.
+// alpha: A tensor in which each scalar is a "shape" parameter describing the
+// associated gamma distribution.
+//
+// Returns A tensor with shape `shape + shape(alpha)`. Each slice
+// `[:, ..., :, i0, i1, ...iN]` contains the samples drawn for
+// `alpha[i0, i1, ...iN]`. The dtype of the output matches the dtype of alpha.
+func RandomGamma(scope *Scope, shape tf.Output, alpha tf.Output, optional ...RandomGammaAttr) (output tf.Output) {
if scope.Err() != nil {
return
}
a(attrs)
}
opspec := tf.OpSpec{
- Type: "Mfcc",
+ Type: "RandomGamma",
Input: []tf.Input{
- spectrogram, sample_rate,
+ shape, alpha,
},
Attrs: attrs,
}
return op.Output(0)
}
-// Given a quantized tensor described by (input, input_min, input_max), outputs a
-//
-// range that covers the actual values present in that tensor. This op is
-// typically used to produce the requested_output_min and requested_output_max for
-// Requantize.
-//
-// Arguments:
-//
-// input_min: The float value that the minimum quantized input value represents.
-// input_max: The float value that the maximum quantized input value represents.
-//
-// Returns The computed min output.the computed max output.
-func RequantizationRange(scope *Scope, input tf.Output, input_min tf.Output, input_max tf.Output) (output_min tf.Output, output_max tf.Output) {
- if scope.Err() != nil {
- return
- }
- opspec := tf.OpSpec{
- Type: "RequantizationRange",
- Input: []tf.Input{
- input, input_min, input_max,
- },
+// QuantizeAndDequantizeAttr is an optional argument to QuantizeAndDequantize.
+type QuantizeAndDequantizeAttr func(optionalAttr)
+
+// QuantizeAndDequantizeSignedInput sets the optional signed_input attribute to value.
+// If not specified, defaults to true
+func QuantizeAndDequantizeSignedInput(value bool) QuantizeAndDequantizeAttr {
+ return func(m optionalAttr) {
+ m["signed_input"] = value
}
- op := scope.AddOperation(opspec)
- return op.Output(0), op.Output(1)
}
-// MapPeekAttr is an optional argument to MapPeek.
-type MapPeekAttr func(optionalAttr)
-
-// MapPeekCapacity sets the optional capacity attribute to value.
-// If not specified, defaults to 0
-//
-// REQUIRES: value >= 0
-func MapPeekCapacity(value int64) MapPeekAttr {
+// QuantizeAndDequantizeNumBits sets the optional num_bits attribute to value.
+// If not specified, defaults to 8
+func QuantizeAndDequantizeNumBits(value int64) QuantizeAndDequantizeAttr {
return func(m optionalAttr) {
- m["capacity"] = value
+ m["num_bits"] = value
}
}
-// MapPeekMemoryLimit sets the optional memory_limit attribute to value.
-// If not specified, defaults to 0
-//
-// REQUIRES: value >= 0
-func MapPeekMemoryLimit(value int64) MapPeekAttr {
+// QuantizeAndDequantizeRangeGiven sets the optional range_given attribute to value.
+// If not specified, defaults to false
+func QuantizeAndDequantizeRangeGiven(value bool) QuantizeAndDequantizeAttr {
return func(m optionalAttr) {
- m["memory_limit"] = value
+ m["range_given"] = value
}
}
-// MapPeekContainer sets the optional container attribute to value.
-// If not specified, defaults to ""
-func MapPeekContainer(value string) MapPeekAttr {
+// QuantizeAndDequantizeInputMin sets the optional input_min attribute to value.
+// If not specified, defaults to 0
+func QuantizeAndDequantizeInputMin(value float32) QuantizeAndDequantizeAttr {
return func(m optionalAttr) {
- m["container"] = value
+ m["input_min"] = value
}
}
-// MapPeekSharedName sets the optional shared_name attribute to value.
-// If not specified, defaults to ""
-func MapPeekSharedName(value string) MapPeekAttr {
+// QuantizeAndDequantizeInputMax sets the optional input_max attribute to value.
+// If not specified, defaults to 0
+func QuantizeAndDequantizeInputMax(value float32) QuantizeAndDequantizeAttr {
return func(m optionalAttr) {
- m["shared_name"] = value
+ m["input_max"] = value
}
}
-// Op peeks at the values at the specified key. If the
+// Use QuantizeAndDequantizeV2 instead.
//
-// underlying container does not contain this key
-// this op will block until it does.
-func MapPeek(scope *Scope, key tf.Output, indices tf.Output, dtypes []tf.DataType, optional ...MapPeekAttr) (values []tf.Output) {
+// DEPRECATED at GraphDef version 22: Replaced by QuantizeAndDequantizeV2
+func QuantizeAndDequantize(scope *Scope, input tf.Output, optional ...QuantizeAndDequantizeAttr) (output tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{"dtypes": dtypes}
+ attrs := map[string]interface{}{}
for _, a := range optional {
a(attrs)
}
opspec := tf.OpSpec{
- Type: "MapPeek",
+ Type: "QuantizeAndDequantize",
Input: []tf.Input{
- key, indices,
+ input,
},
Attrs: attrs,
}
op := scope.AddOperation(opspec)
- if scope.Err() != nil {
- return
- }
- var idx int
- var err error
- if values, idx, err = makeOutputList(op, idx, "values"); err != nil {
- scope.UpdateErr("MapPeek", err)
- return
- }
- return values
+ return op.Output(0)
}
-// Looks up keys in a table, outputs the corresponding values.
+// Returns locations of nonzero / true values in a tensor.
//
-// The tensor `keys` must of the same type as the keys of the table.
-// The output `values` is of the type of the table values.
+// This operation returns the coordinates of true elements in `condition`. The
+// coordinates are returned in a 2-D tensor where the first dimension (rows)
+// represents the number of true elements, and the second dimension (columns)
+// represents the coordinates of the true elements. Keep in mind, the shape of
+// the output tensor can vary depending on how many true values there are in
+// `condition`. Indices are output in row-major order.
//
-// The scalar `default_value` is the value output for keys not present in the
-// table. It must also be of the same type as the table values.
+// For example:
//
-// Arguments:
-// table_handle: Handle to the table.
-// keys: Any shape. Keys to look up.
+// ```
+// # 'input' tensor is [[True, False]
+// # [True, False]]
+// # 'input' has two true values, so output has two coordinates.
+// # 'input' has rank of 2, so coordinates have two indices.
+// where(input) ==> [[0, 0],
+// [1, 0]]
+//
+// # `condition` tensor is [[[True, False]
+// # [True, False]]
+// # [[False, True]
+// # [False, True]]
+// # [[False, False]
+// # [False, True]]]
+// # 'input' has 5 true values, so output has 5 coordinates.
+// # 'input' has rank of 3, so coordinates have three indices.
+// where(input) ==> [[0, 0, 0],
+// [0, 1, 0],
+// [1, 0, 1],
+// [1, 1, 1],
+// [2, 1, 1]]
//
+// # `condition` tensor is [[[1.5, 0.0]
+// # [-0.5, 0.0]]
+// # [[0.0, 0.25]
+// # [0.0, 0.75]]
+// # [[0.0, 0.0]
+// # [0.0, 0.01]]]
+// # 'input' has 5 nonzero values, so output has 5 coordinates.
+// # 'input' has rank of 3, so coordinates have three indices.
+// where(input) ==> [[0, 0, 0],
+// [0, 1, 0],
+// [1, 0, 1],
+// [1, 1, 1],
+// [2, 1, 1]]
//
-// Returns Same shape as `keys`. Values found in the table, or `default_values`
-// for missing keys.
-func LookupTableFindV2(scope *Scope, table_handle tf.Output, keys tf.Output, default_value tf.Output) (values tf.Output) {
+// # `condition` tensor is [[[1.5 + 0.0j, 0.0 + 0.0j]
+// # [0.0 + 0.5j, 0.0 + 0.0j]]
+// # [[0.0 + 0.0j, 0.25 + 1.5j]
+// # [0.0 + 0.0j, 0.75 + 0.0j]]
+// # [[0.0 + 0.0j, 0.0 + 0.0j]
+// # [0.0 + 0.0j, 0.01 + 0.0j]]]
+// # 'input' has 5 nonzero magnitude values, so output has 5 coordinates.
+// # 'input' has rank of 3, so coordinates have three indices.
+// where(input) ==> [[0, 0, 0],
+// [0, 1, 0],
+// [1, 0, 1],
+// [1, 1, 1],
+// [2, 1, 1]]
+// ```
+func Where(scope *Scope, condition tf.Output) (index tf.Output) {
if scope.Err() != nil {
return
}
opspec := tf.OpSpec{
- Type: "LookupTableFindV2",
+ Type: "Where",
Input: []tf.Input{
- table_handle, keys, default_value,
+ condition,
},
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// Bucketizes 'input' based on 'boundaries'.
+// QueueDequeueV2Attr is an optional argument to QueueDequeueV2.
+type QueueDequeueV2Attr func(optionalAttr)
+
+// QueueDequeueV2TimeoutMs sets the optional timeout_ms attribute to value.
//
-// For example, if the inputs are
-// boundaries = [0, 10, 100]
-// input = [[-5, 10000]
-// [150, 10]
-// [5, 100]]
+// value: If the queue is empty, this operation will block for up to
+// timeout_ms milliseconds.
+// Note: This option is not supported yet.
+// If not specified, defaults to -1
+func QueueDequeueV2TimeoutMs(value int64) QueueDequeueV2Attr {
+ return func(m optionalAttr) {
+ m["timeout_ms"] = value
+ }
+}
+
+// Dequeues a tuple of one or more tensors from the given queue.
//
-// then the output will be
-// output = [[0, 3]
-// [3, 2]
-// [1, 3]]
+// This operation has k outputs, where k is the number of components
+// in the tuples stored in the given queue, and output i is the ith
+// component of the dequeued tuple.
//
-// Arguments:
-// input: Any shape of Tensor contains with int or float type.
-// boundaries: A sorted list of floats gives the boundary of the buckets.
+// N.B. If the queue is empty, this operation will block until an element
+// has been dequeued (or 'timeout_ms' elapses, if specified).
//
-// Returns Same shape with 'input', each value of input replaced with bucket index.
+// Arguments:
+// handle: The handle to a queue.
+// component_types: The type of each component in a tuple.
//
-// @compatibility(numpy)
-// Equivalent to np.digitize.
-// @end_compatibility
-func Bucketize(scope *Scope, input tf.Output, boundaries []float32) (output tf.Output) {
+// Returns One or more tensors that were dequeued as a tuple.
+func QueueDequeueV2(scope *Scope, handle tf.Output, component_types []tf.DataType, optional ...QueueDequeueV2Attr) (components []tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{"boundaries": boundaries}
+ attrs := map[string]interface{}{"component_types": component_types}
+ for _, a := range optional {
+ a(attrs)
+ }
opspec := tf.OpSpec{
- Type: "Bucketize",
+ Type: "QueueDequeueV2",
Input: []tf.Input{
- input,
+ handle,
},
Attrs: attrs,
}
op := scope.AddOperation(opspec)
- return op.Output(0)
+ if scope.Err() != nil {
+ return
+ }
+ var idx int
+ var err error
+ if components, idx, err = makeOutputList(op, idx, "components"); err != nil {
+ scope.UpdateErr("QueueDequeueV2", err)
+ return
+ }
+ return components
}
-// EncodePngAttr is an optional argument to EncodePng.
-type EncodePngAttr func(optionalAttr)
+// RandomUniformIntAttr is an optional argument to RandomUniformInt.
+type RandomUniformIntAttr func(optionalAttr)
-// EncodePngCompression sets the optional compression attribute to value.
+// RandomUniformIntSeed sets the optional seed attribute to value.
//
-// value: Compression level.
-// If not specified, defaults to -1
-func EncodePngCompression(value int64) EncodePngAttr {
+// value: If either `seed` or `seed2` are set to be non-zero, the random number
+// generator is seeded by the given seed. Otherwise, it is seeded by a
+// random seed.
+// If not specified, defaults to 0
+func RandomUniformIntSeed(value int64) RandomUniformIntAttr {
return func(m optionalAttr) {
- m["compression"] = value
+ m["seed"] = value
}
}
-// PNG-encode an image.
+// RandomUniformIntSeed2 sets the optional seed2 attribute to value.
//
-// `image` is a 3-D uint8 or uint16 Tensor of shape `[height, width, channels]`
-// where `channels` is:
+// value: A second seed to avoid seed collision.
+// If not specified, defaults to 0
+func RandomUniformIntSeed2(value int64) RandomUniformIntAttr {
+ return func(m optionalAttr) {
+ m["seed2"] = value
+ }
+}
+
+// Outputs random integers from a uniform distribution.
//
-// * 1: for grayscale.
-// * 2: for grayscale + alpha.
-// * 3: for RGB.
-// * 4: for RGBA.
+// The generated values are uniform integers in the range `[minval, maxval)`.
+// The lower bound `minval` is included in the range, while the upper bound
+// `maxval` is excluded.
//
-// The ZLIB compression level, `compression`, can be -1 for the PNG-encoder
-// default or a value from 0 to 9. 9 is the highest compression level, generating
-// the smallest output, but is slower.
+// The random integers are slightly biased unless `maxval - minval` is an exact
+// power of two. The bias is small for values of `maxval - minval` significantly
+// smaller than the range of the output (either `2^32` or `2^64`).
//
// Arguments:
-// image: 3-D with shape `[height, width, channels]`.
+// shape: The shape of the output tensor.
+// minval: 0-D. Inclusive lower bound on the generated integers.
+// maxval: 0-D. Exclusive upper bound on the generated integers.
//
-// Returns 0-D. PNG-encoded image.
-func EncodePng(scope *Scope, image tf.Output, optional ...EncodePngAttr) (contents tf.Output) {
+// Returns A tensor of the specified shape filled with uniform random integers.
+func RandomUniformInt(scope *Scope, shape tf.Output, minval tf.Output, maxval tf.Output, optional ...RandomUniformIntAttr) (output tf.Output) {
if scope.Err() != nil {
return
}
a(attrs)
}
opspec := tf.OpSpec{
- Type: "EncodePng",
+ Type: "RandomUniformInt",
Input: []tf.Input{
- image,
+ shape, minval, maxval,
},
Attrs: attrs,
}
return op.Output(0)
}
-// Updates the table to associates keys with values.
-//
-// The tensor `keys` must be of the same type as the keys of the table.
-// The tensor `values` must be of the type of the table values.
+// SkipgramAttr is an optional argument to Skipgram.
+type SkipgramAttr func(optionalAttr)
+
+// SkipgramWindowSize sets the optional window_size attribute to value.
//
-// Arguments:
-// table_handle: Handle to the table.
-// keys: Any shape. Keys to look up.
-// values: Values to associate with keys.
+// value: The number of words to predict to the left and right of the target.
+// If not specified, defaults to 5
+func SkipgramWindowSize(value int64) SkipgramAttr {
+ return func(m optionalAttr) {
+ m["window_size"] = value
+ }
+}
+
+// SkipgramMinCount sets the optional min_count attribute to value.
//
-// Returns the created operation.
-func LookupTableInsertV2(scope *Scope, table_handle tf.Output, keys tf.Output, values tf.Output) (o *tf.Operation) {
- if scope.Err() != nil {
- return
+// value: The minimum number of word occurrences for it to be included in the
+// vocabulary.
+// If not specified, defaults to 5
+func SkipgramMinCount(value int64) SkipgramAttr {
+ return func(m optionalAttr) {
+ m["min_count"] = value
}
- opspec := tf.OpSpec{
- Type: "LookupTableInsertV2",
- Input: []tf.Input{
- table_handle, keys, values,
- },
+}
+
+// SkipgramSubsample sets the optional subsample attribute to value.
+//
+// value: Threshold for word occurrence. Words that appear with higher
+// frequency will be randomly down-sampled. Set to 0 to disable.
+// If not specified, defaults to 0.001
+func SkipgramSubsample(value float32) SkipgramAttr {
+ return func(m optionalAttr) {
+ m["subsample"] = value
}
- return scope.AddOperation(opspec)
}
-// Returns element-wise smallest integer in not less than x.
-func Ceil(scope *Scope, x tf.Output) (y tf.Output) {
+// Parses a text file and creates a batch of examples.
+//
+// DEPRECATED at GraphDef version 19: Moving word2vec into tensorflow_models/tutorials and deprecating its ops here as a result
+//
+// Arguments:
+// filename: The corpus's text file name.
+// batch_size: The size of produced batch.
+//
+// Returns A vector of words in the corpus.Frequencies of words. Sorted in the non-ascending order.Number of words per epoch in the data file.The current epoch number.The total number of words processed so far.A vector of word ids.A vector of word ids.
+func Skipgram(scope *Scope, filename string, batch_size int64, optional ...SkipgramAttr) (vocab_word tf.Output, vocab_freq tf.Output, words_per_epoch tf.Output, current_epoch tf.Output, total_words_processed tf.Output, examples tf.Output, labels tf.Output) {
if scope.Err() != nil {
return
}
+ attrs := map[string]interface{}{"filename": filename, "batch_size": batch_size}
+ for _, a := range optional {
+ a(attrs)
+ }
opspec := tf.OpSpec{
- Type: "Ceil",
- Input: []tf.Input{
- x,
- },
+ Type: "Skipgram",
+
+ Attrs: attrs,
}
op := scope.AddOperation(opspec)
- return op.Output(0)
+ return op.Output(0), op.Output(1), op.Output(2), op.Output(3), op.Output(4), op.Output(5), op.Output(6)
}
-// Computes the number of elements in the given table.
+// StringToNumberAttr is an optional argument to StringToNumber.
+type StringToNumberAttr func(optionalAttr)
+
+// StringToNumberOutType sets the optional out_type attribute to value.
//
-// Arguments:
-// table_handle: Handle to the table.
+// value: The numeric type to interpret each string in `string_tensor` as.
+// If not specified, defaults to DT_FLOAT
+func StringToNumberOutType(value tf.DataType) StringToNumberAttr {
+ return func(m optionalAttr) {
+ m["out_type"] = value
+ }
+}
+
+// Converts each string in the input Tensor to the specified numeric type.
//
-// Returns Scalar that contains number of elements in the table.
-func LookupTableSizeV2(scope *Scope, table_handle tf.Output) (size tf.Output) {
+// (Note that int32 overflow results in an error while float overflow
+// results in a rounded value.)
+//
+// Returns A Tensor of the same shape as the input `string_tensor`.
+func StringToNumber(scope *Scope, string_tensor tf.Output, optional ...StringToNumberAttr) (output tf.Output) {
if scope.Err() != nil {
return
}
+ attrs := map[string]interface{}{}
+ for _, a := range optional {
+ a(attrs)
+ }
opspec := tf.OpSpec{
- Type: "LookupTableSizeV2",
+ Type: "StringToNumber",
Input: []tf.Input{
- table_handle,
+ string_tensor,
},
+ Attrs: attrs,
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// ResizeBilinearGradAttr is an optional argument to ResizeBilinearGrad.
-type ResizeBilinearGradAttr func(optionalAttr)
+// ResourceApplyFtrlV2Attr is an optional argument to ResourceApplyFtrlV2.
+type ResourceApplyFtrlV2Attr func(optionalAttr)
-// ResizeBilinearGradAlignCorners sets the optional align_corners attribute to value.
+// ResourceApplyFtrlV2UseLocking sets the optional use_locking attribute to value.
//
-// value: If true, rescale grads by (orig_height - 1) / (height - 1), which
-// exactly aligns the 4 corners of grads and original_image. If false, rescale by
-// orig_height / height. Treat similarly the width dimension.
+// value: If `True`, updating of the var and accum tensors will be protected
+// by a lock; otherwise the behavior is undefined, but may exhibit less
+// contention.
// If not specified, defaults to false
-func ResizeBilinearGradAlignCorners(value bool) ResizeBilinearGradAttr {
+func ResourceApplyFtrlV2UseLocking(value bool) ResourceApplyFtrlV2Attr {
return func(m optionalAttr) {
- m["align_corners"] = value
+ m["use_locking"] = value
}
}
-// Computes the gradient of bilinear interpolation.
+// Update '*var' according to the Ftrl-proximal scheme.
+//
+// grad_with_shrinkage = grad + 2 * l2_shrinkage * var
+// accum_new = accum + grad_with_shrinkage * grad_with_shrinkage
+// linear += grad_with_shrinkage +
+// (accum_new^(-lr_power) - accum^(-lr_power)) / lr * var
+// quadratic = 1.0 / (accum_new^(lr_power) * lr) + 2 * l2
+// var = (sign(linear) * l1 - linear) / quadratic if |linear| > l1 else 0.0
+// accum = accum_new
//
// Arguments:
-// grads: 4-D with shape `[batch, height, width, channels]`.
-// original_image: 4-D with shape `[batch, orig_height, orig_width, channels]`,
-// The image tensor that was resized.
+// var_: Should be from a Variable().
+// accum: Should be from a Variable().
+// linear: Should be from a Variable().
+// grad: The gradient.
+// lr: Scaling factor. Must be a scalar.
+// l1: L1 regulariation. Must be a scalar.
+// l2: L2 shrinkage regulariation. Must be a scalar.
//
-// Returns 4-D with shape `[batch, orig_height, orig_width, channels]`.
-// Gradients with respect to the input image. Input image must have been
-// float or double.
-func ResizeBilinearGrad(scope *Scope, grads tf.Output, original_image tf.Output, optional ...ResizeBilinearGradAttr) (output tf.Output) {
+// lr_power: Scaling factor. Must be a scalar.
+//
+// Returns the created operation.
+func ResourceApplyFtrlV2(scope *Scope, var_ tf.Output, accum tf.Output, linear tf.Output, grad tf.Output, lr tf.Output, l1 tf.Output, l2 tf.Output, l2_shrinkage tf.Output, lr_power tf.Output, optional ...ResourceApplyFtrlV2Attr) (o *tf.Operation) {
if scope.Err() != nil {
return
}
a(attrs)
}
opspec := tf.OpSpec{
- Type: "ResizeBilinearGrad",
+ Type: "ResourceApplyFtrlV2",
Input: []tf.Input{
- grads, original_image,
+ var_, accum, linear, grad, lr, l1, l2, l2_shrinkage, lr_power,
},
Attrs: attrs,
}
- op := scope.AddOperation(opspec)
- return op.Output(0)
+ return scope.AddOperation(opspec)
}
-// Outputs all keys and values in the table.
+// TruncatedNormalAttr is an optional argument to TruncatedNormal.
+type TruncatedNormalAttr func(optionalAttr)
+
+// TruncatedNormalSeed sets the optional seed attribute to value.
//
-// Arguments:
-// table_handle: Handle to the table.
+// value: If either `seed` or `seed2` are set to be non-zero, the random number
+// generator is seeded by the given seed. Otherwise, it is seeded by a
+// random seed.
+// If not specified, defaults to 0
+func TruncatedNormalSeed(value int64) TruncatedNormalAttr {
+ return func(m optionalAttr) {
+ m["seed"] = value
+ }
+}
+
+// TruncatedNormalSeed2 sets the optional seed2 attribute to value.
+//
+// value: A second seed to avoid seed collision.
+// If not specified, defaults to 0
+func TruncatedNormalSeed2(value int64) TruncatedNormalAttr {
+ return func(m optionalAttr) {
+ m["seed2"] = value
+ }
+}
+
+// Outputs random values from a truncated normal distribution.
//
+// The generated values follow a normal distribution with mean 0 and standard
+// deviation 1, except that values whose magnitude is more than 2 standard
+// deviations from the mean are dropped and re-picked.
//
+// Arguments:
+// shape: The shape of the output tensor.
+// dtype: The type of the output.
//
-// Returns Vector of all keys present in the table.Tensor of all values in the table. Indexed in parallel with `keys`.
-func LookupTableExportV2(scope *Scope, table_handle tf.Output, Tkeys tf.DataType, Tvalues tf.DataType) (keys tf.Output, values tf.Output) {
+// Returns A tensor of the specified shape filled with random truncated normal
+// values.
+func TruncatedNormal(scope *Scope, shape tf.Output, dtype tf.DataType, optional ...TruncatedNormalAttr) (output tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{"Tkeys": Tkeys, "Tvalues": Tvalues}
+ attrs := map[string]interface{}{"dtype": dtype}
+ for _, a := range optional {
+ a(attrs)
+ }
opspec := tf.OpSpec{
- Type: "LookupTableExportV2",
+ Type: "TruncatedNormal",
Input: []tf.Input{
- table_handle,
+ shape,
},
Attrs: attrs,
}
op := scope.AddOperation(opspec)
- return op.Output(0), op.Output(1)
+ return op.Output(0)
}
-// Replaces the contents of the table with the specified keys and values.
+// RandomShuffleAttr is an optional argument to RandomShuffle.
+type RandomShuffleAttr func(optionalAttr)
+
+// RandomShuffleSeed sets the optional seed attribute to value.
//
-// The tensor `keys` must be of the same type as the keys of the table.
-// The tensor `values` must be of the type of the table values.
+// value: If either `seed` or `seed2` are set to be non-zero, the random number
+// generator is seeded by the given seed. Otherwise, it is seeded by a
+// random seed.
+// If not specified, defaults to 0
+func RandomShuffleSeed(value int64) RandomShuffleAttr {
+ return func(m optionalAttr) {
+ m["seed"] = value
+ }
+}
+
+// RandomShuffleSeed2 sets the optional seed2 attribute to value.
+//
+// value: A second seed to avoid seed collision.
+// If not specified, defaults to 0
+func RandomShuffleSeed2(value int64) RandomShuffleAttr {
+ return func(m optionalAttr) {
+ m["seed2"] = value
+ }
+}
+
+// Randomly shuffles a tensor along its first dimension.
+//
+// The tensor is shuffled along dimension 0, such that each `value[j]` is mapped
+// to one and only one `output[i]`. For example, a mapping that might occur for a
+// 3x2 tensor is:
+//
+// ```
+// [[1, 2], [[5, 6],
+// [3, 4], ==> [1, 2],
+// [5, 6]] [3, 4]]
+// ```
//
// Arguments:
-// table_handle: Handle to the table.
-// keys: Any shape. Keys to look up.
-// values: Values to associate with keys.
+// value: The tensor to be shuffled.
//
-// Returns the created operation.
-func LookupTableImportV2(scope *Scope, table_handle tf.Output, keys tf.Output, values tf.Output) (o *tf.Operation) {
+// Returns A tensor of same shape and type as `value`, shuffled along its first
+// dimension.
+func RandomShuffle(scope *Scope, value tf.Output, optional ...RandomShuffleAttr) (output tf.Output) {
if scope.Err() != nil {
return
}
+ attrs := map[string]interface{}{}
+ for _, a := range optional {
+ a(attrs)
+ }
opspec := tf.OpSpec{
- Type: "LookupTableImportV2",
+ Type: "RandomShuffle",
Input: []tf.Input{
- table_handle, keys, values,
+ value,
},
+ Attrs: attrs,
}
- return scope.AddOperation(opspec)
+ op := scope.AddOperation(opspec)
+ return op.Output(0)
}
-// MapUnstageNoKeyAttr is an optional argument to MapUnstageNoKey.
-type MapUnstageNoKeyAttr func(optionalAttr)
+// OrderedMapIncompleteSizeAttr is an optional argument to OrderedMapIncompleteSize.
+type OrderedMapIncompleteSizeAttr func(optionalAttr)
-// MapUnstageNoKeyCapacity sets the optional capacity attribute to value.
+// OrderedMapIncompleteSizeCapacity sets the optional capacity attribute to value.
// If not specified, defaults to 0
//
// REQUIRES: value >= 0
-func MapUnstageNoKeyCapacity(value int64) MapUnstageNoKeyAttr {
+func OrderedMapIncompleteSizeCapacity(value int64) OrderedMapIncompleteSizeAttr {
return func(m optionalAttr) {
m["capacity"] = value
}
}
-// MapUnstageNoKeyMemoryLimit sets the optional memory_limit attribute to value.
+// OrderedMapIncompleteSizeMemoryLimit sets the optional memory_limit attribute to value.
// If not specified, defaults to 0
//
// REQUIRES: value >= 0
-func MapUnstageNoKeyMemoryLimit(value int64) MapUnstageNoKeyAttr {
+func OrderedMapIncompleteSizeMemoryLimit(value int64) OrderedMapIncompleteSizeAttr {
return func(m optionalAttr) {
m["memory_limit"] = value
}
}
-// MapUnstageNoKeyContainer sets the optional container attribute to value.
+// OrderedMapIncompleteSizeContainer sets the optional container attribute to value.
// If not specified, defaults to ""
-func MapUnstageNoKeyContainer(value string) MapUnstageNoKeyAttr {
+func OrderedMapIncompleteSizeContainer(value string) OrderedMapIncompleteSizeAttr {
return func(m optionalAttr) {
m["container"] = value
}
}
-// MapUnstageNoKeySharedName sets the optional shared_name attribute to value.
+// OrderedMapIncompleteSizeSharedName sets the optional shared_name attribute to value.
// If not specified, defaults to ""
-func MapUnstageNoKeySharedName(value string) MapUnstageNoKeyAttr {
+func OrderedMapIncompleteSizeSharedName(value string) OrderedMapIncompleteSizeAttr {
return func(m optionalAttr) {
m["shared_name"] = value
}
}
-// Op removes and returns a random (key, value)
-//
-// from the underlying container. If the underlying container
-// does not contain elements, the op will block until it does.
-func MapUnstageNoKey(scope *Scope, indices tf.Output, dtypes []tf.DataType, optional ...MapUnstageNoKeyAttr) (key tf.Output, values []tf.Output) {
+// Op returns the number of incomplete elements in the underlying container.
+func OrderedMapIncompleteSize(scope *Scope, dtypes []tf.DataType, optional ...OrderedMapIncompleteSizeAttr) (size tf.Output) {
if scope.Err() != nil {
return
}
a(attrs)
}
opspec := tf.OpSpec{
- Type: "MapUnstageNoKey",
- Input: []tf.Input{
- indices,
- },
+ Type: "OrderedMapIncompleteSize",
+
Attrs: attrs,
}
op := scope.AddOperation(opspec)
- if scope.Err() != nil {
- return
- }
- var idx int
- var err error
- key = op.Output(idx)
- if values, idx, err = makeOutputList(op, idx, "values"); err != nil {
- scope.UpdateErr("MapUnstageNoKey", err)
- return
- }
- return key, values
-}
-
-// HashTableV2Attr is an optional argument to HashTableV2.
-type HashTableV2Attr func(optionalAttr)
-
-// HashTableV2Container sets the optional container attribute to value.
-//
-// value: If non-empty, this table is placed in the given container.
-// Otherwise, a default container is used.
-// If not specified, defaults to ""
-func HashTableV2Container(value string) HashTableV2Attr {
- return func(m optionalAttr) {
- m["container"] = value
- }
+ return op.Output(0)
}
-// HashTableV2SharedName sets the optional shared_name attribute to value.
-//
-// value: If non-empty, this table is shared under the given name across
-// multiple sessions.
-// If not specified, defaults to ""
-func HashTableV2SharedName(value string) HashTableV2Attr {
- return func(m optionalAttr) {
- m["shared_name"] = value
- }
-}
+// DecodeRawAttr is an optional argument to DecodeRaw.
+type DecodeRawAttr func(optionalAttr)
-// HashTableV2UseNodeNameSharing sets the optional use_node_name_sharing attribute to value.
+// DecodeRawLittleEndian sets the optional little_endian attribute to value.
//
-// value: If true and shared_name is empty, the table is shared
-// using the node name.
-// If not specified, defaults to false
-func HashTableV2UseNodeNameSharing(value bool) HashTableV2Attr {
+// value: Whether the input `bytes` are in little-endian order.
+// Ignored for `out_type` values that are stored in a single byte like
+// `uint8`.
+// If not specified, defaults to true
+func DecodeRawLittleEndian(value bool) DecodeRawAttr {
return func(m optionalAttr) {
- m["use_node_name_sharing"] = value
+ m["little_endian"] = value
}
}
-// Creates a non-initialized hash table.
-//
-// This op creates a hash table, specifying the type of its keys and values.
-// Before using the table you will have to initialize it. After initialization the
-// table will be immutable.
+// Reinterpret the bytes of a string as a vector of numbers.
//
// Arguments:
-// key_dtype: Type of the table keys.
-// value_dtype: Type of the table values.
+// bytes: All the elements must have the same length.
//
-// Returns Handle to a table.
-func HashTableV2(scope *Scope, key_dtype tf.DataType, value_dtype tf.DataType, optional ...HashTableV2Attr) (table_handle tf.Output) {
+//
+// Returns A Tensor with one more dimension than the input `bytes`. The
+// added dimension will have size equal to the length of the elements
+// of `bytes` divided by the number of bytes to represent `out_type`.
+func DecodeRaw(scope *Scope, bytes tf.Output, out_type tf.DataType, optional ...DecodeRawAttr) (output tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{"key_dtype": key_dtype, "value_dtype": value_dtype}
+ attrs := map[string]interface{}{"out_type": out_type}
for _, a := range optional {
a(attrs)
}
opspec := tf.OpSpec{
- Type: "HashTableV2",
-
+ Type: "DecodeRaw",
+ Input: []tf.Input{
+ bytes,
+ },
Attrs: attrs,
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// MutableHashTableV2Attr is an optional argument to MutableHashTableV2.
-type MutableHashTableV2Attr func(optionalAttr)
-
-// MutableHashTableV2Container sets the optional container attribute to value.
+// Copy a tensor setting everything outside a central band in each innermost matrix
//
-// value: If non-empty, this table is placed in the given container.
-// Otherwise, a default container is used.
-// If not specified, defaults to ""
-func MutableHashTableV2Container(value string) MutableHashTableV2Attr {
- return func(m optionalAttr) {
- m["container"] = value
- }
-}
-
-// MutableHashTableV2SharedName sets the optional shared_name attribute to value.
+// to zero.
//
-// value: If non-empty, this table is shared under the given name across
-// multiple sessions.
-// If not specified, defaults to ""
-func MutableHashTableV2SharedName(value string) MutableHashTableV2Attr {
- return func(m optionalAttr) {
- m["shared_name"] = value
- }
-}
-
-// MutableHashTableV2UseNodeNameSharing sets the optional use_node_name_sharing attribute to value.
+// The `band` part is computed as follows:
+// Assume `input` has `k` dimensions `[I, J, K, ..., M, N]`, then the output is a
+// tensor with the same shape where
//
-// value: If true and shared_name is empty, the table is shared
-// using the node name.
-// If not specified, defaults to false
-func MutableHashTableV2UseNodeNameSharing(value bool) MutableHashTableV2Attr {
- return func(m optionalAttr) {
- m["use_node_name_sharing"] = value
- }
-}
-
-// Creates an empty hash table.
+// `band[i, j, k, ..., m, n] = in_band(m, n) * input[i, j, k, ..., m, n]`.
//
-// This op creates a mutable hash table, specifying the type of its keys and
-// values. Each value must be a scalar. Data can be inserted into the table using
-// the insert operations. It does not support the initialization operation.
+// The indicator function
//
-// Arguments:
-// key_dtype: Type of the table keys.
-// value_dtype: Type of the table values.
+// `in_band(m, n) = (num_lower < 0 || (m-n) <= num_lower)) &&
+// (num_upper < 0 || (n-m) <= num_upper)`.
//
-// Returns Handle to a table.
-func MutableHashTableV2(scope *Scope, key_dtype tf.DataType, value_dtype tf.DataType, optional ...MutableHashTableV2Attr) (table_handle tf.Output) {
+// For example:
+//
+// ```
+// # if 'input' is [[ 0, 1, 2, 3]
+// [-1, 0, 1, 2]
+// [-2, -1, 0, 1]
+// [-3, -2, -1, 0]],
+//
+// tf.matrix_band_part(input, 1, -1) ==> [[ 0, 1, 2, 3]
+// [-1, 0, 1, 2]
+// [ 0, -1, 0, 1]
+// [ 0, 0, -1, 0]],
+//
+// tf.matrix_band_part(input, 2, 1) ==> [[ 0, 1, 0, 0]
+// [-1, 0, 1, 0]
+// [-2, -1, 0, 1]
+// [ 0, -2, -1, 0]]
+// ```
+//
+// Useful special cases:
+//
+// ```
+// tf.matrix_band_part(input, 0, -1) ==> Upper triangular part.
+// tf.matrix_band_part(input, -1, 0) ==> Lower triangular part.
+// tf.matrix_band_part(input, 0, 0) ==> Diagonal.
+// ```
+//
+// Arguments:
+// input: Rank `k` tensor.
+// num_lower: 0-D tensor. Number of subdiagonals to keep. If negative, keep entire
+// lower triangle.
+// num_upper: 0-D tensor. Number of superdiagonals to keep. If negative, keep
+// entire upper triangle.
+//
+// Returns Rank `k` tensor of the same shape as input. The extracted banded tensor.
+func MatrixBandPart(scope *Scope, input tf.Output, num_lower tf.Output, num_upper tf.Output) (band tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{"key_dtype": key_dtype, "value_dtype": value_dtype}
- for _, a := range optional {
- a(attrs)
- }
opspec := tf.OpSpec{
- Type: "MutableHashTableV2",
-
- Attrs: attrs,
+ Type: "MatrixBandPart",
+ Input: []tf.Input{
+ input, num_lower, num_upper,
+ },
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// DequantizeAttr is an optional argument to Dequantize.
-type DequantizeAttr func(optionalAttr)
+// QuantizedMatMulAttr is an optional argument to QuantizedMatMul.
+type QuantizedMatMulAttr func(optionalAttr)
-// DequantizeMode sets the optional mode attribute to value.
-// If not specified, defaults to "MIN_COMBINED"
-func DequantizeMode(value string) DequantizeAttr {
+// QuantizedMatMulToutput sets the optional Toutput attribute to value.
+// If not specified, defaults to DT_QINT32
+func QuantizedMatMulToutput(value tf.DataType) QuantizedMatMulAttr {
return func(m optionalAttr) {
- m["mode"] = value
+ m["Toutput"] = value
}
}
-// Dequantize the 'input' tensor into a float Tensor.
-//
-// [min_range, max_range] are scalar floats that specify the range for
-// the 'input' data. The 'mode' attribute controls exactly which calculations are
-// used to convert the float values to their quantized equivalents.
-//
-// In 'MIN_COMBINED' mode, each value of the tensor will undergo the following:
-//
-// ```
-// if T == qint8, in[i] += (range(T) + 1)/ 2.0
-// out[i] = min_range + (in[i]* (max_range - min_range) / range(T))
-// ```
-// here `range(T) = numeric_limits<T>::max() - numeric_limits<T>::min()`
-//
-// *MIN_COMBINED Mode Example*
-//
-// If the input comes from a QuantizedRelu6, the output type is
-// quint8 (range of 0-255) but the possible range of QuantizedRelu6 is
-// 0-6. The min_range and max_range values are therefore 0.0 and 6.0.
-// Dequantize on quint8 will take each value, cast to float, and multiply
-// by 6 / 255.
-// Note that if quantizedtype is qint8, the operation will additionally add
-// each value by 128 prior to casting.
-//
-// If the mode is 'MIN_FIRST', then this approach is used:
-//
-// ```c++
-// num_discrete_values = 1 << (# of bits in T)
-// range_adjust = num_discrete_values / (num_discrete_values - 1)
-// range = (range_max - range_min) * range_adjust
-// range_scale = range / num_discrete_values
-// const double offset_input = static_cast<double>(input) - lowest_quantized;
-// result = range_min + ((input - numeric_limits<T>::min()) * range_scale)
-// ```
-//
-// *SCALED mode Example*
-//
-// `SCALED` mode matches the quantization approach used in
-// `QuantizeAndDequantize{V2|V3}`.
-//
-// If the mode is `SCALED`, we do not use the full range of the output type,
-// choosing to elide the lowest possible value for symmetry (e.g., output range is
-// -127 to 127, not -128 to 127 for signed 8 bit quantization), so that 0.0 maps to
-// 0.
-//
-// We first find the range of values in our tensor. The
-// range we use is always centered on 0, so we find m such that
-// ```c++
-// m = max(abs(input_min), abs(input_max))
-// ```
-//
-// Our input tensor range is then `[-m, m]`.
+// QuantizedMatMulTransposeA sets the optional transpose_a attribute to value.
//
-// Next, we choose our fixed-point quantization buckets, `[min_fixed, max_fixed]`.
-// If T is signed, this is
-// ```
-// num_bits = sizeof(T) * 8
-// [min_fixed, max_fixed] =
-// [-(1 << (num_bits - 1) - 1), (1 << (num_bits - 1)) - 1]
-// ```
+// value: If true, `a` is transposed before multiplication.
+// If not specified, defaults to false
+func QuantizedMatMulTransposeA(value bool) QuantizedMatMulAttr {
+ return func(m optionalAttr) {
+ m["transpose_a"] = value
+ }
+}
+
+// QuantizedMatMulTransposeB sets the optional transpose_b attribute to value.
//
-// Otherwise, if T is unsigned, the fixed-point range is
-// ```
-// [min_fixed, max_fixed] = [0, (1 << num_bits) - 1]
-// ```
+// value: If true, `b` is transposed before multiplication.
+// If not specified, defaults to false
+func QuantizedMatMulTransposeB(value bool) QuantizedMatMulAttr {
+ return func(m optionalAttr) {
+ m["transpose_b"] = value
+ }
+}
+
+// QuantizedMatMulTactivation sets the optional Tactivation attribute to value.
//
-// From this we compute our scaling factor, s:
-// ```c++
-// s = (2 * m) / (max_fixed - min_fixed)
-// ```
+// value: The type of output produced by activation function
+// following this operation.
+// If not specified, defaults to DT_QUINT8
+func QuantizedMatMulTactivation(value tf.DataType) QuantizedMatMulAttr {
+ return func(m optionalAttr) {
+ m["Tactivation"] = value
+ }
+}
+
+// Perform a quantized matrix multiplication of `a` by the matrix `b`.
//
-// Now we can dequantize the elements of our tensor:
-// ```c++
-// result = input * s
-// ```
+// The inputs must be two-dimensional matrices and the inner dimension of
+// `a` (after being transposed if `transpose_a` is non-zero) must match the
+// outer dimension of `b` (after being transposed if `transposed_b` is
+// non-zero).
//
// Arguments:
+// a: Must be a two-dimensional tensor.
+// b: Must be a two-dimensional tensor.
+// min_a: The float value that the lowest quantized `a` value represents.
+// max_a: The float value that the highest quantized `a` value represents.
+// min_b: The float value that the lowest quantized `b` value represents.
+// max_b: The float value that the highest quantized `b` value represents.
//
-// min_range: The minimum scalar value possibly produced for the input.
-// max_range: The maximum scalar value possibly produced for the input.
-func Dequantize(scope *Scope, input tf.Output, min_range tf.Output, max_range tf.Output, optional ...DequantizeAttr) (output tf.Output) {
+// Returns The float value that the lowest quantized output value represents.The float value that the highest quantized output value represents.
+func QuantizedMatMul(scope *Scope, a tf.Output, b tf.Output, min_a tf.Output, max_a tf.Output, min_b tf.Output, max_b tf.Output, optional ...QuantizedMatMulAttr) (out tf.Output, min_out tf.Output, max_out tf.Output) {
if scope.Err() != nil {
return
}
a(attrs)
}
opspec := tf.OpSpec{
- Type: "Dequantize",
+ Type: "QuantizedMatMul",
Input: []tf.Input{
- input, min_range, max_range,
+ a, b, min_a, max_a, min_b, max_b,
},
Attrs: attrs,
}
op := scope.AddOperation(opspec)
- return op.Output(0)
+ return op.Output(0), op.Output(1), op.Output(2)
}
-// Flips all bits elementwise.
+// Does nothing. Serves as a control trigger for scheduling.
//
-// The result will have exactly those bits set, that are not set in `x`. The
-// computation is performed on the underlying representation of x.
-func Invert(scope *Scope, x tf.Output) (y tf.Output) {
+// Only useful as a placeholder for control edges.
+//
+// Returns the created operation.
+func ControlTrigger(scope *Scope) (o *tf.Operation) {
if scope.Err() != nil {
return
}
opspec := tf.OpSpec{
- Type: "Invert",
- Input: []tf.Input{
- x,
- },
+ Type: "ControlTrigger",
}
- op := scope.AddOperation(opspec)
- return op.Output(0)
+ return scope.AddOperation(opspec)
}
-// Deprecated. Disallowed in GraphDef version >= 2.
+// Batch normalization.
//
-// DEPRECATED at GraphDef version 2: Use AdjustContrastv2 instead
-func AdjustContrast(scope *Scope, images tf.Output, contrast_factor tf.Output, min_value tf.Output, max_value tf.Output) (output tf.Output) {
+// DEPRECATED at GraphDef version 9: Use tf.nn.batch_normalization()
+//
+// This op is deprecated. Prefer `tf.nn.batch_normalization`.
+//
+// Arguments:
+// t: A 4D input Tensor.
+// m: A 1D mean Tensor with size matching the last dimension of t.
+// This is the first output from tf.nn.moments,
+// or a saved moving average thereof.
+// v: A 1D variance Tensor with size matching the last dimension of t.
+// This is the second output from tf.nn.moments,
+// or a saved moving average thereof.
+// beta: A 1D beta Tensor with size matching the last dimension of t.
+// An offset to be added to the normalized tensor.
+// gamma: A 1D gamma Tensor with size matching the last dimension of t.
+// If "scale_after_normalization" is true, this tensor will be multiplied
+// with the normalized tensor.
+// variance_epsilon: A small float number to avoid dividing by 0.
+// scale_after_normalization: A bool indicating whether the resulted tensor
+// needs to be multiplied with gamma.
+func BatchNormWithGlobalNormalization(scope *Scope, t tf.Output, m tf.Output, v tf.Output, beta tf.Output, gamma tf.Output, variance_epsilon float32, scale_after_normalization bool) (result tf.Output) {
if scope.Err() != nil {
return
}
+ attrs := map[string]interface{}{"variance_epsilon": variance_epsilon, "scale_after_normalization": scale_after_normalization}
opspec := tf.OpSpec{
- Type: "AdjustContrast",
+ Type: "BatchNormWithGlobalNormalization",
Input: []tf.Input{
- images, contrast_factor, min_value, max_value,
+ t, m, v, beta, gamma,
},
+ Attrs: attrs,
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// Table initializer that takes two tensors for keys and values respectively.
-//
-// Arguments:
-// table_handle: Handle to a table which will be initialized.
-// keys: Keys of type Tkey.
-// values: Values of type Tval.
+// Deprecated. Use TensorArrayReadV3
//
-// Returns the created operation.
-func InitializeTableV2(scope *Scope, table_handle tf.Output, keys tf.Output, values tf.Output) (o *tf.Operation) {
+// DEPRECATED at GraphDef version 26: Use TensorArrayReadV3
+func TensorArrayReadV2(scope *Scope, handle tf.Output, index tf.Output, flow_in tf.Output, dtype tf.DataType) (value tf.Output) {
if scope.Err() != nil {
return
}
+ attrs := map[string]interface{}{"dtype": dtype}
opspec := tf.OpSpec{
- Type: "InitializeTableV2",
+ Type: "TensorArrayReadV2",
Input: []tf.Input{
- table_handle, keys, values,
+ handle, index, flow_in,
},
+ Attrs: attrs,
}
- return scope.AddOperation(opspec)
+ op := scope.AddOperation(opspec)
+ return op.Output(0)
}
-// PrintAttr is an optional argument to Print.
-type PrintAttr func(optionalAttr)
+// QuantizedMulAttr is an optional argument to QuantizedMul.
+type QuantizedMulAttr func(optionalAttr)
-// PrintMessage sets the optional message attribute to value.
-//
-// value: A string, prefix of the error message.
-// If not specified, defaults to ""
-func PrintMessage(value string) PrintAttr {
+// QuantizedMulToutput sets the optional Toutput attribute to value.
+// If not specified, defaults to DT_QINT32
+func QuantizedMulToutput(value tf.DataType) QuantizedMulAttr {
return func(m optionalAttr) {
- m["message"] = value
+ m["Toutput"] = value
}
}
-// PrintFirstN sets the optional first_n attribute to value.
+// Returns x * y element-wise, working on quantized buffers.
//
-// value: Only log `first_n` number of times. -1 disables logging.
-// If not specified, defaults to -1
-func PrintFirstN(value int64) PrintAttr {
- return func(m optionalAttr) {
- m["first_n"] = value
- }
-}
-
-// PrintSummarize sets the optional summarize attribute to value.
+// Arguments:
//
-// value: Only print this many entries of each tensor.
-// If not specified, defaults to 3
-func PrintSummarize(value int64) PrintAttr {
- return func(m optionalAttr) {
- m["summarize"] = value
- }
-}
-
-// Prints a list of tensors.
//
-// Passes `input` through to `output` and prints `data` when evaluating.
+// min_x: The float value that the lowest quantized `x` value represents.
+// max_x: The float value that the highest quantized `x` value represents.
+// min_y: The float value that the lowest quantized `y` value represents.
+// max_y: The float value that the highest quantized `y` value represents.
//
-// Arguments:
-// input: The tensor passed to `output`
-// data: A list of tensors to print out when op is evaluated.
+// Returns The float value that the lowest quantized output value represents.The float value that the highest quantized output value represents.
//
-// Returns = The unmodified `input` tensor
-func Print(scope *Scope, input tf.Output, data []tf.Output, optional ...PrintAttr) (output tf.Output) {
+// *NOTE*: `QuantizedMul` supports limited forms of broadcasting. More about
+// broadcasting [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
+func QuantizedMul(scope *Scope, x tf.Output, y tf.Output, min_x tf.Output, max_x tf.Output, min_y tf.Output, max_y tf.Output, optional ...QuantizedMulAttr) (z tf.Output, min_z tf.Output, max_z tf.Output) {
if scope.Err() != nil {
return
}
a(attrs)
}
opspec := tf.OpSpec{
- Type: "Print",
+ Type: "QuantizedMul",
Input: []tf.Input{
- input, tf.OutputList(data),
+ x, y, min_x, max_x, min_y, max_y,
},
Attrs: attrs,
}
op := scope.AddOperation(opspec)
- return op.Output(0)
+ return op.Output(0), op.Output(1), op.Output(2)
}
-// Outputs a `Summary` protocol buffer with a tensor and per-plugin data.
-//
-// Arguments:
-// tag: A string attached to this summary. Used for organization in TensorBoard.
-// tensor: A tensor to serialize.
-// serialized_summary_metadata: A serialized SummaryMetadata proto. Contains plugin
-// data.
-func TensorSummaryV2(scope *Scope, tag tf.Output, tensor tf.Output, serialized_summary_metadata tf.Output) (summary tf.Output) {
- if scope.Err() != nil {
- return
- }
- opspec := tf.OpSpec{
- Type: "TensorSummaryV2",
- Input: []tf.Input{
- tag, tensor, serialized_summary_metadata,
- },
+// QuantizedAddAttr is an optional argument to QuantizedAdd.
+type QuantizedAddAttr func(optionalAttr)
+
+// QuantizedAddToutput sets the optional Toutput attribute to value.
+// If not specified, defaults to DT_QINT32
+func QuantizedAddToutput(value tf.DataType) QuantizedAddAttr {
+ return func(m optionalAttr) {
+ m["Toutput"] = value
}
- op := scope.AddOperation(opspec)
- return op.Output(0)
}
-// Creates a dataset that asynchronously prefetches elements from `input_dataset`.
+// Returns x + y element-wise, working on quantized buffers.
//
// Arguments:
//
-// buffer_size: The maximum number of elements to buffer in an iterator over
-// this dataset.
//
+// min_x: The float value that the lowest quantized `x` value represents.
+// max_x: The float value that the highest quantized `x` value represents.
+// min_y: The float value that the lowest quantized `y` value represents.
+// max_y: The float value that the highest quantized `y` value represents.
//
-func PrefetchDataset(scope *Scope, input_dataset tf.Output, buffer_size tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) {
+// Returns The float value that the lowest quantized output value represents.The float value that the highest quantized output value represents.
+//
+// *NOTE*: `QuantizedAdd` supports limited forms of broadcasting. More about
+// broadcasting [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
+func QuantizedAdd(scope *Scope, x tf.Output, y tf.Output, min_x tf.Output, max_x tf.Output, min_y tf.Output, max_y tf.Output, optional ...QuantizedAddAttr) (z tf.Output, min_z tf.Output, max_z tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes}
+ attrs := map[string]interface{}{}
+ for _, a := range optional {
+ a(attrs)
+ }
opspec := tf.OpSpec{
- Type: "PrefetchDataset",
+ Type: "QuantizedAdd",
Input: []tf.Input{
- input_dataset, buffer_size,
+ x, y, min_x, max_x, min_y, max_y,
},
Attrs: attrs,
}
op := scope.AddOperation(opspec)
- return op.Output(0)
+ return op.Output(0), op.Output(1), op.Output(2)
}
-// TensorSummaryAttr is an optional argument to TensorSummary.
-type TensorSummaryAttr func(optionalAttr)
+// MfccAttr is an optional argument to Mfcc.
+type MfccAttr func(optionalAttr)
-// TensorSummaryDescription sets the optional description attribute to value.
+// MfccUpperFrequencyLimit sets the optional upper_frequency_limit attribute to value.
//
-// value: A json-encoded SummaryDescription proto.
-// If not specified, defaults to ""
-func TensorSummaryDescription(value string) TensorSummaryAttr {
+// value: The highest frequency to use when calculating the
+// ceptstrum.
+// If not specified, defaults to 4000
+func MfccUpperFrequencyLimit(value float32) MfccAttr {
return func(m optionalAttr) {
- m["description"] = value
+ m["upper_frequency_limit"] = value
}
}
-// TensorSummaryLabels sets the optional labels attribute to value.
+// MfccLowerFrequencyLimit sets the optional lower_frequency_limit attribute to value.
//
-// value: An unused list of strings.
-// If not specified, defaults to <>
-func TensorSummaryLabels(value []string) TensorSummaryAttr {
+// value: The lowest frequency to use when calculating the
+// ceptstrum.
+// If not specified, defaults to 20
+func MfccLowerFrequencyLimit(value float32) MfccAttr {
return func(m optionalAttr) {
- m["labels"] = value
+ m["lower_frequency_limit"] = value
}
}
-// TensorSummaryDisplayName sets the optional display_name attribute to value.
+// MfccFilterbankChannelCount sets the optional filterbank_channel_count attribute to value.
//
-// value: An unused string.
-// If not specified, defaults to ""
-func TensorSummaryDisplayName(value string) TensorSummaryAttr {
+// value: Resolution of the Mel bank used internally.
+// If not specified, defaults to 40
+func MfccFilterbankChannelCount(value int64) MfccAttr {
return func(m optionalAttr) {
- m["display_name"] = value
+ m["filterbank_channel_count"] = value
}
}
-// Outputs a `Summary` protocol buffer with a tensor.
+// MfccDctCoefficientCount sets the optional dct_coefficient_count attribute to value.
//
-// This op is being phased out in favor of TensorSummaryV2, which lets callers pass
-// a tag as well as a serialized SummaryMetadata proto string that contains
-// plugin-specific data. We will keep this op to maintain backwards compatibility.
+// value: How many output channels to produce per time slice.
+// If not specified, defaults to 13
+func MfccDctCoefficientCount(value int64) MfccAttr {
+ return func(m optionalAttr) {
+ m["dct_coefficient_count"] = value
+ }
+}
+
+// Transforms a spectrogram into a form that's useful for speech recognition.
+//
+// Mel Frequency Cepstral Coefficients are a way of representing audio data that's
+// been effective as an input feature for machine learning. They are created by
+// taking the spectrum of a spectrogram (a 'cepstrum'), and discarding some of the
+// higher frequencies that are less significant to the human ear. They have a long
+// history in the speech recognition world, and https://en.wikipedia.org/wiki/Mel-frequency_cepstrum
+// is a good resource to learn more.
//
// Arguments:
-// tensor: A tensor to serialize.
-func TensorSummary(scope *Scope, tensor tf.Output, optional ...TensorSummaryAttr) (summary tf.Output) {
+// spectrogram: Typically produced by the Spectrogram op, with magnitude_squared
+// set to true.
+// sample_rate: How many samples per second the source audio used.
+func Mfcc(scope *Scope, spectrogram tf.Output, sample_rate tf.Output, optional ...MfccAttr) (output tf.Output) {
if scope.Err() != nil {
return
}
a(attrs)
}
opspec := tf.OpSpec{
- Type: "TensorSummary",
+ Type: "Mfcc",
Input: []tf.Input{
- tensor,
+ spectrogram, sample_rate,
},
Attrs: attrs,
}
return op.Output(0)
}
-// Computes the gradient for the tanh of `x` wrt its input.
+// Given a quantized tensor described by (input, input_min, input_max), outputs a
//
-// Specifically, `grad = dy * (1 - y*y)`, where `y = tanh(x)`, and `dy`
-// is the corresponding input gradient.
-func TanhGrad(scope *Scope, y tf.Output, dy tf.Output) (z tf.Output) {
+// range that covers the actual values present in that tensor. This op is
+// typically used to produce the requested_output_min and requested_output_max for
+// Requantize.
+//
+// Arguments:
+//
+// input_min: The float value that the minimum quantized input value represents.
+// input_max: The float value that the maximum quantized input value represents.
+//
+// Returns The computed min output.the computed max output.
+func RequantizationRange(scope *Scope, input tf.Output, input_min tf.Output, input_max tf.Output) (output_min tf.Output, output_max tf.Output) {
if scope.Err() != nil {
return
}
opspec := tf.OpSpec{
- Type: "TanhGrad",
+ Type: "RequantizationRange",
Input: []tf.Input{
- y, dy,
+ input, input_min, input_max,
},
}
op := scope.AddOperation(opspec)
- return op.Output(0)
+ return op.Output(0), op.Output(1)
}
-// Outputs a `Summary` protocol buffer with scalar values.
+// MapPeekAttr is an optional argument to MapPeek.
+type MapPeekAttr func(optionalAttr)
+
+// MapPeekCapacity sets the optional capacity attribute to value.
+// If not specified, defaults to 0
//
-// The input `tags` and `values` must have the same shape. The generated summary
-// has a summary value for each tag-value pair in `tags` and `values`.
+// REQUIRES: value >= 0
+func MapPeekCapacity(value int64) MapPeekAttr {
+ return func(m optionalAttr) {
+ m["capacity"] = value
+ }
+}
+
+// MapPeekMemoryLimit sets the optional memory_limit attribute to value.
+// If not specified, defaults to 0
//
-// Arguments:
-// tags: Tags for the summary.
-// values: Same shape as `tags. Values for the summary.
+// REQUIRES: value >= 0
+func MapPeekMemoryLimit(value int64) MapPeekAttr {
+ return func(m optionalAttr) {
+ m["memory_limit"] = value
+ }
+}
+
+// MapPeekContainer sets the optional container attribute to value.
+// If not specified, defaults to ""
+func MapPeekContainer(value string) MapPeekAttr {
+ return func(m optionalAttr) {
+ m["container"] = value
+ }
+}
+
+// MapPeekSharedName sets the optional shared_name attribute to value.
+// If not specified, defaults to ""
+func MapPeekSharedName(value string) MapPeekAttr {
+ return func(m optionalAttr) {
+ m["shared_name"] = value
+ }
+}
+
+// Op peeks at the values at the specified key. If the
//
-// Returns Scalar. Serialized `Summary` protocol buffer.
-func ScalarSummary(scope *Scope, tags tf.Output, values tf.Output) (summary tf.Output) {
+// underlying container does not contain this key
+// this op will block until it does.
+func MapPeek(scope *Scope, key tf.Output, indices tf.Output, dtypes []tf.DataType, optional ...MapPeekAttr) (values []tf.Output) {
if scope.Err() != nil {
return
}
+ attrs := map[string]interface{}{"dtypes": dtypes}
+ for _, a := range optional {
+ a(attrs)
+ }
opspec := tf.OpSpec{
- Type: "ScalarSummary",
+ Type: "MapPeek",
Input: []tf.Input{
- tags, values,
+ key, indices,
},
+ Attrs: attrs,
}
op := scope.AddOperation(opspec)
- return op.Output(0)
-}
-
-// Outputs a `Summary` protocol buffer with a histogram.
-//
-// The generated
-// [`Summary`](https://www.tensorflow.org/code/tensorflow/core/framework/summary.proto)
-// has one summary value containing a histogram for `values`.
-//
-// This op reports an `InvalidArgument` error if any value is not finite.
-//
-// Arguments:
-// tag: Scalar. Tag to use for the `Summary.Value`.
-// values: Any shape. Values to use to build the histogram.
-//
-// Returns Scalar. Serialized `Summary` protocol buffer.
-func HistogramSummary(scope *Scope, tag tf.Output, values tf.Output) (summary tf.Output) {
if scope.Err() != nil {
return
}
- opspec := tf.OpSpec{
- Type: "HistogramSummary",
- Input: []tf.Input{
- tag, values,
- },
+ var idx int
+ var err error
+ if values, idx, err = makeOutputList(op, idx, "values"); err != nil {
+ scope.UpdateErr("MapPeek", err)
+ return
}
- op := scope.AddOperation(opspec)
- return op.Output(0)
+ return values
}
-// Computes the number of elements in the given queue.
+// Looks up keys in a table, outputs the corresponding values.
+//
+// The tensor `keys` must of the same type as the keys of the table.
+// The output `values` is of the type of the table values.
+//
+// The scalar `default_value` is the value output for keys not present in the
+// table. It must also be of the same type as the table values.
//
// Arguments:
-// handle: The handle to a queue.
+// table_handle: Handle to the table.
+// keys: Any shape. Keys to look up.
//
-// Returns The number of elements in the given queue.
-func QueueSizeV2(scope *Scope, handle tf.Output) (size tf.Output) {
+//
+// Returns Same shape as `keys`. Values found in the table, or `default_values`
+// for missing keys.
+func LookupTableFindV2(scope *Scope, table_handle tf.Output, keys tf.Output, default_value tf.Output) (values tf.Output) {
if scope.Err() != nil {
return
}
opspec := tf.OpSpec{
- Type: "QueueSizeV2",
+ Type: "LookupTableFindV2",
Input: []tf.Input{
- handle,
+ table_handle, keys, default_value,
},
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// ImageSummaryAttr is an optional argument to ImageSummary.
-type ImageSummaryAttr func(optionalAttr)
-
-// ImageSummaryMaxImages sets the optional max_images attribute to value.
-//
-// value: Max number of batch elements to generate images for.
-// If not specified, defaults to 3
-//
-// REQUIRES: value >= 1
-func ImageSummaryMaxImages(value int64) ImageSummaryAttr {
- return func(m optionalAttr) {
- m["max_images"] = value
- }
-}
-
-// ImageSummaryBadColor sets the optional bad_color attribute to value.
-//
-// value: Color to use for pixels with non-finite values.
-// If not specified, defaults to <dtype:DT_UINT8 tensor_shape:<dim:<size:4 > > int_val:255 int_val:0 int_val:0 int_val:255 >
-func ImageSummaryBadColor(value tf.Tensor) ImageSummaryAttr {
- return func(m optionalAttr) {
- m["bad_color"] = value
- }
-}
-
-// Outputs a `Summary` protocol buffer with images.
-//
-// The summary has up to `max_images` summary values containing images. The
-// images are built from `tensor` which must be 4-D with shape `[batch_size,
-// height, width, channels]` and where `channels` can be:
-//
-// * 1: `tensor` is interpreted as Grayscale.
-// * 3: `tensor` is interpreted as RGB.
-// * 4: `tensor` is interpreted as RGBA.
-//
-// The images have the same number of channels as the input tensor. For float
-// input, the values are normalized one image at a time to fit in the range
-// `[0, 255]`. `uint8` values are unchanged. The op uses two different
-// normalization algorithms:
-//
-// * If the input values are all positive, they are rescaled so the largest one
-// is 255.
-//
-// * If any input value is negative, the values are shifted so input value 0.0
-// is at 127. They are then rescaled so that either the smallest value is 0,
-// or the largest one is 255.
-//
-// The `tag` argument is a scalar `Tensor` of type `string`. It is used to
-// build the `tag` of the summary values:
+// Bucketizes 'input' based on 'boundaries'.
//
-// * If `max_images` is 1, the summary value tag is '*tag*/image'.
-// * If `max_images` is greater than 1, the summary value tags are
-// generated sequentially as '*tag*/image/0', '*tag*/image/1', etc.
+// For example, if the inputs are
+// boundaries = [0, 10, 100]
+// input = [[-5, 10000]
+// [150, 10]
+// [5, 100]]
//
-// The `bad_color` argument is the color to use in the generated images for
-// non-finite input values. It is a `unit8` 1-D tensor of length `channels`.
-// Each element must be in the range `[0, 255]` (It represents the value of a
-// pixel in the output image). Non-finite values in the input tensor are
-// replaced by this tensor in the output image. The default value is the color
-// red.
+// then the output will be
+// output = [[0, 3]
+// [3, 2]
+// [1, 3]]
//
// Arguments:
-// tag: Scalar. Used to build the `tag` attribute of the summary values.
-// tensor: 4-D of shape `[batch_size, height, width, channels]` where
-// `channels` is 1, 3, or 4.
+// input: Any shape of Tensor contains with int or float type.
+// boundaries: A sorted list of floats gives the boundary of the buckets.
//
-// Returns Scalar. Serialized `Summary` protocol buffer.
-func ImageSummary(scope *Scope, tag tf.Output, tensor tf.Output, optional ...ImageSummaryAttr) (summary tf.Output) {
+// Returns Same shape with 'input', each value of input replaced with bucket index.
+//
+// @compatibility(numpy)
+// Equivalent to np.digitize.
+// @end_compatibility
+func Bucketize(scope *Scope, input tf.Output, boundaries []float32) (output tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{}
- for _, a := range optional {
- a(attrs)
- }
+ attrs := map[string]interface{}{"boundaries": boundaries}
opspec := tf.OpSpec{
- Type: "ImageSummary",
+ Type: "Bucketize",
Input: []tf.Input{
- tag, tensor,
+ input,
},
Attrs: attrs,
}
return op.Output(0)
}
-// AudioSummaryV2Attr is an optional argument to AudioSummaryV2.
-type AudioSummaryV2Attr func(optionalAttr)
+// EncodePngAttr is an optional argument to EncodePng.
+type EncodePngAttr func(optionalAttr)
-// AudioSummaryV2MaxOutputs sets the optional max_outputs attribute to value.
-//
-// value: Max number of batch elements to generate audio for.
-// If not specified, defaults to 3
+// EncodePngCompression sets the optional compression attribute to value.
//
-// REQUIRES: value >= 1
-func AudioSummaryV2MaxOutputs(value int64) AudioSummaryV2Attr {
+// value: Compression level.
+// If not specified, defaults to -1
+func EncodePngCompression(value int64) EncodePngAttr {
return func(m optionalAttr) {
- m["max_outputs"] = value
+ m["compression"] = value
}
}
-// Outputs a `Summary` protocol buffer with audio.
+// PNG-encode an image.
//
-// The summary has up to `max_outputs` summary values containing audio. The
-// audio is built from `tensor` which must be 3-D with shape `[batch_size,
-// frames, channels]` or 2-D with shape `[batch_size, frames]`. The values are
-// assumed to be in the range of `[-1.0, 1.0]` with a sample rate of `sample_rate`.
+// `image` is a 3-D uint8 or uint16 Tensor of shape `[height, width, channels]`
+// where `channels` is:
//
-// The `tag` argument is a scalar `Tensor` of type `string`. It is used to
-// build the `tag` of the summary values:
+// * 1: for grayscale.
+// * 2: for grayscale + alpha.
+// * 3: for RGB.
+// * 4: for RGBA.
//
-// * If `max_outputs` is 1, the summary value tag is '*tag*/audio'.
-// * If `max_outputs` is greater than 1, the summary value tags are
-// generated sequentially as '*tag*/audio/0', '*tag*/audio/1', etc.
+// The ZLIB compression level, `compression`, can be -1 for the PNG-encoder
+// default or a value from 0 to 9. 9 is the highest compression level, generating
+// the smallest output, but is slower.
//
// Arguments:
-// tag: Scalar. Used to build the `tag` attribute of the summary values.
-// tensor: 2-D of shape `[batch_size, frames]`.
-// sample_rate: The sample rate of the signal in hertz.
+// image: 3-D with shape `[height, width, channels]`.
//
-// Returns Scalar. Serialized `Summary` protocol buffer.
-func AudioSummaryV2(scope *Scope, tag tf.Output, tensor tf.Output, sample_rate tf.Output, optional ...AudioSummaryV2Attr) (summary tf.Output) {
+// Returns 0-D. PNG-encoded image.
+func EncodePng(scope *Scope, image tf.Output, optional ...EncodePngAttr) (contents tf.Output) {
if scope.Err() != nil {
return
}
a(attrs)
}
opspec := tf.OpSpec{
- Type: "AudioSummaryV2",
+ Type: "EncodePng",
Input: []tf.Input{
- tag, tensor, sample_rate,
+ image,
},
Attrs: attrs,
}
return op.Output(0)
}
-// AvgPoolAttr is an optional argument to AvgPool.
-type AvgPoolAttr func(optionalAttr)
-
-// AvgPoolDataFormat sets the optional data_format attribute to value.
-//
-// value: Specify the data format of the input and output data. With the
-// default format "NHWC", the data is stored in the order of:
-// [batch, in_height, in_width, in_channels].
-// Alternatively, the format could be "NCHW", the data storage order of:
-// [batch, in_channels, in_height, in_width].
-// If not specified, defaults to "NHWC"
-func AvgPoolDataFormat(value string) AvgPoolAttr {
- return func(m optionalAttr) {
- m["data_format"] = value
- }
-}
-
-// Performs average pooling on the input.
+// Updates the table to associates keys with values.
//
-// Each entry in `output` is the mean of the corresponding size `ksize`
-// window in `value`.
+// The tensor `keys` must be of the same type as the keys of the table.
+// The tensor `values` must be of the type of the table values.
//
// Arguments:
-// value: 4-D with shape `[batch, height, width, channels]`.
-// ksize: The size of the sliding window for each dimension of `value`.
-// strides: The stride of the sliding window for each dimension of `value`.
-// padding: The type of padding algorithm to use.
+// table_handle: Handle to the table.
+// keys: Any shape. Keys to look up.
+// values: Values to associate with keys.
//
-// Returns The average pooled output tensor.
-func AvgPool(scope *Scope, value tf.Output, ksize []int64, strides []int64, padding string, optional ...AvgPoolAttr) (output tf.Output) {
+// Returns the created operation.
+func LookupTableInsertV2(scope *Scope, table_handle tf.Output, keys tf.Output, values tf.Output) (o *tf.Operation) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{"ksize": ksize, "strides": strides, "padding": padding}
- for _, a := range optional {
- a(attrs)
- }
opspec := tf.OpSpec{
- Type: "AvgPool",
+ Type: "LookupTableInsertV2",
Input: []tf.Input{
- value,
+ table_handle, keys, values,
},
- Attrs: attrs,
}
- op := scope.AddOperation(opspec)
- return op.Output(0)
+ return scope.AddOperation(opspec)
}
-// Merges summaries.
-//
-// This op creates a
-// [`Summary`](https://www.tensorflow.org/code/tensorflow/core/framework/summary.proto)
-// protocol buffer that contains the union of all the values in the input
-// summaries.
-//
-// When the Op is run, it reports an `InvalidArgument` error if multiple values
-// in the summaries to merge use the same tag.
-//
-// Arguments:
-// inputs: Can be of any shape. Each must contain serialized `Summary` protocol
-// buffers.
-//
-// Returns Scalar. Serialized `Summary` protocol buffer.
-func MergeSummary(scope *Scope, inputs []tf.Output) (summary tf.Output) {
+// Returns element-wise smallest integer in not less than x.
+func Ceil(scope *Scope, x tf.Output) (y tf.Output) {
if scope.Err() != nil {
return
}
opspec := tf.OpSpec{
- Type: "MergeSummary",
+ Type: "Ceil",
Input: []tf.Input{
- tf.OutputList(inputs),
+ x,
},
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// Computes the gradient of morphological 2-D dilation with respect to the filter.
+// Computes the number of elements in the given table.
//
// Arguments:
-// input: 4-D with shape `[batch, in_height, in_width, depth]`.
-// filter: 3-D with shape `[filter_height, filter_width, depth]`.
-// out_backprop: 4-D with shape `[batch, out_height, out_width, depth]`.
-// strides: 1-D of length 4. The stride of the sliding window for each dimension of
-// the input tensor. Must be: `[1, stride_height, stride_width, 1]`.
-// rates: 1-D of length 4. The input stride for atrous morphological dilation.
-// Must be: `[1, rate_height, rate_width, 1]`.
-// padding: The type of padding algorithm to use.
+// table_handle: Handle to the table.
//
-// Returns 3-D with shape `[filter_height, filter_width, depth]`.
-func Dilation2DBackpropFilter(scope *Scope, input tf.Output, filter tf.Output, out_backprop tf.Output, strides []int64, rates []int64, padding string) (filter_backprop tf.Output) {
+// Returns Scalar that contains number of elements in the table.
+func LookupTableSizeV2(scope *Scope, table_handle tf.Output) (size tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{"strides": strides, "rates": rates, "padding": padding}
opspec := tf.OpSpec{
- Type: "Dilation2DBackpropFilter",
+ Type: "LookupTableSizeV2",
Input: []tf.Input{
- input, filter, out_backprop,
+ table_handle,
},
- Attrs: attrs,
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// AddSparseToTensorsMapAttr is an optional argument to AddSparseToTensorsMap.
-type AddSparseToTensorsMapAttr func(optionalAttr)
-
-// AddSparseToTensorsMapContainer sets the optional container attribute to value.
-//
-// value: The container name for the `SparseTensorsMap` created by this op.
-// If not specified, defaults to ""
-func AddSparseToTensorsMapContainer(value string) AddSparseToTensorsMapAttr {
- return func(m optionalAttr) {
- m["container"] = value
- }
-}
+// ResizeBilinearGradAttr is an optional argument to ResizeBilinearGrad.
+type ResizeBilinearGradAttr func(optionalAttr)
-// AddSparseToTensorsMapSharedName sets the optional shared_name attribute to value.
+// ResizeBilinearGradAlignCorners sets the optional align_corners attribute to value.
//
-// value: The shared name for the `SparseTensorsMap` created by this op.
-// If blank, the new Operation's unique name is used.
-// If not specified, defaults to ""
-func AddSparseToTensorsMapSharedName(value string) AddSparseToTensorsMapAttr {
+// value: If true, rescale grads by (orig_height - 1) / (height - 1), which
+// exactly aligns the 4 corners of grads and original_image. If false, rescale by
+// orig_height / height. Treat similarly the width dimension.
+// If not specified, defaults to false
+func ResizeBilinearGradAlignCorners(value bool) ResizeBilinearGradAttr {
return func(m optionalAttr) {
- m["shared_name"] = value
+ m["align_corners"] = value
}
}
-// Add a `SparseTensor` to a `SparseTensorsMap` return its handle.
-//
-// A `SparseTensor` is represented by three tensors: `sparse_indices`,
-// `sparse_values`, and `sparse_shape`.
-//
-// This operator takes the given `SparseTensor` and adds it to a container
-// object (a `SparseTensorsMap`). A unique key within this container is generated
-// in the form of an `int64`, and this is the value that is returned.
-//
-// The `SparseTensor` can then be read out as part of a minibatch by passing
-// the key as a vector element to `TakeManySparseFromTensorsMap`. To ensure
-// the correct `SparseTensorsMap` is accessed, ensure that the same
-// `container` and `shared_name` are passed to that Op. If no `shared_name`
-// is provided here, instead use the *name* of the Operation created by calling
-// `AddSparseToTensorsMap` as the `shared_name` passed to
-// `TakeManySparseFromTensorsMap`. Ensure the Operations are colocated.
+// Computes the gradient of bilinear interpolation.
//
// Arguments:
-// sparse_indices: 2-D. The `indices` of the `SparseTensor`.
-// sparse_values: 1-D. The `values` of the `SparseTensor`.
-// sparse_shape: 1-D. The `shape` of the `SparseTensor`.
+// grads: 4-D with shape `[batch, height, width, channels]`.
+// original_image: 4-D with shape `[batch, orig_height, orig_width, channels]`,
+// The image tensor that was resized.
//
-// Returns 0-D. The handle of the `SparseTensor` now stored in the
-// `SparseTensorsMap`.
-func AddSparseToTensorsMap(scope *Scope, sparse_indices tf.Output, sparse_values tf.Output, sparse_shape tf.Output, optional ...AddSparseToTensorsMapAttr) (sparse_handle tf.Output) {
+// Returns 4-D with shape `[batch, orig_height, orig_width, channels]`.
+// Gradients with respect to the input image. Input image must have been
+// float or double.
+func ResizeBilinearGrad(scope *Scope, grads tf.Output, original_image tf.Output, optional ...ResizeBilinearGradAttr) (output tf.Output) {
if scope.Err() != nil {
return
}
a(attrs)
}
opspec := tf.OpSpec{
- Type: "AddSparseToTensorsMap",
+ Type: "ResizeBilinearGrad",
Input: []tf.Input{
- sparse_indices, sparse_values, sparse_shape,
+ grads, original_image,
},
Attrs: attrs,
}
return op.Output(0)
}
-// Computes the matrix exponential of one or more square matrices:
+// Outputs all keys and values in the table.
//
-// exp(A) = \sum_{n=0}^\infty A^n/n!
+// Arguments:
+// table_handle: Handle to the table.
//
-// The exponential is computed using a combination of the scaling and squaring
-// method and the Pade approximation. Details can be founds in:
-// Nicholas J. Higham, "The scaling and squaring method for the matrix exponential
-// revisited," SIAM J. Matrix Anal. Applic., 26:1179-1193, 2005.
//
-// The input is a tensor of shape `[..., M, M]` whose inner-most 2 dimensions
-// form square matrices. The output is a tensor of the same shape as the input
-// containing the exponential for all input submatrices `[..., :, :]`.
//
-// Arguments:
-// input: Shape is `[..., M, M]`.
+// Returns Vector of all keys present in the table.Tensor of all values in the table. Indexed in parallel with `keys`.
+func LookupTableExportV2(scope *Scope, table_handle tf.Output, Tkeys tf.DataType, Tvalues tf.DataType) (keys tf.Output, values tf.Output) {
+ if scope.Err() != nil {
+ return
+ }
+ attrs := map[string]interface{}{"Tkeys": Tkeys, "Tvalues": Tvalues}
+ opspec := tf.OpSpec{
+ Type: "LookupTableExportV2",
+ Input: []tf.Input{
+ table_handle,
+ },
+ Attrs: attrs,
+ }
+ op := scope.AddOperation(opspec)
+ return op.Output(0), op.Output(1)
+}
+
+// Replaces the contents of the table with the specified keys and values.
//
-// Returns Shape is `[..., M, M]`.
+// The tensor `keys` must be of the same type as the keys of the table.
+// The tensor `values` must be of the type of the table values.
//
-// @compatibility(scipy)
-// Equivalent to scipy.linalg.expm
-// @end_compatibility
-func MatrixExponential(scope *Scope, input tf.Output) (output tf.Output) {
+// Arguments:
+// table_handle: Handle to the table.
+// keys: Any shape. Keys to look up.
+// values: Values to associate with keys.
+//
+// Returns the created operation.
+func LookupTableImportV2(scope *Scope, table_handle tf.Output, keys tf.Output, values tf.Output) (o *tf.Operation) {
if scope.Err() != nil {
return
}
opspec := tf.OpSpec{
- Type: "MatrixExponential",
+ Type: "LookupTableImportV2",
Input: []tf.Input{
- input,
+ table_handle, keys, values,
},
}
- op := scope.AddOperation(opspec)
- return op.Output(0)
+ return scope.AddOperation(opspec)
}
-// QueueDequeueUpToV2Attr is an optional argument to QueueDequeueUpToV2.
-type QueueDequeueUpToV2Attr func(optionalAttr)
+// MapUnstageNoKeyAttr is an optional argument to MapUnstageNoKey.
+type MapUnstageNoKeyAttr func(optionalAttr)
-// QueueDequeueUpToV2TimeoutMs sets the optional timeout_ms attribute to value.
+// MapUnstageNoKeyCapacity sets the optional capacity attribute to value.
+// If not specified, defaults to 0
//
-// value: If the queue has fewer than n elements, this operation
-// will block for up to timeout_ms milliseconds.
-// Note: This option is not supported yet.
-// If not specified, defaults to -1
-func QueueDequeueUpToV2TimeoutMs(value int64) QueueDequeueUpToV2Attr {
+// REQUIRES: value >= 0
+func MapUnstageNoKeyCapacity(value int64) MapUnstageNoKeyAttr {
return func(m optionalAttr) {
- m["timeout_ms"] = value
+ m["capacity"] = value
}
}
-// Dequeues `n` tuples of one or more tensors from the given queue.
-//
-// This operation is not supported by all queues. If a queue does not support
-// DequeueUpTo, then an Unimplemented error is returned.
-//
-// If the queue is closed and there are more than 0 but less than `n`
-// elements remaining, then instead of returning an OutOfRange error like
-// QueueDequeueMany, less than `n` elements are returned immediately. If
-// the queue is closed and there are 0 elements left in the queue, then
-// an OutOfRange error is returned just like in QueueDequeueMany.
-// Otherwise the behavior is identical to QueueDequeueMany:
-//
-// This operation concatenates queue-element component tensors along the
-// 0th dimension to make a single component tensor. All of the components
-// in the dequeued tuple will have size n in the 0th dimension.
-//
-// This operation has `k` outputs, where `k` is the number of components in
-// the tuples stored in the given queue, and output `i` is the ith
-// component of the dequeued tuple.
+// MapUnstageNoKeyMemoryLimit sets the optional memory_limit attribute to value.
+// If not specified, defaults to 0
//
-// Arguments:
-// handle: The handle to a queue.
-// n: The number of tuples to dequeue.
-// component_types: The type of each component in a tuple.
+// REQUIRES: value >= 0
+func MapUnstageNoKeyMemoryLimit(value int64) MapUnstageNoKeyAttr {
+ return func(m optionalAttr) {
+ m["memory_limit"] = value
+ }
+}
+
+// MapUnstageNoKeyContainer sets the optional container attribute to value.
+// If not specified, defaults to ""
+func MapUnstageNoKeyContainer(value string) MapUnstageNoKeyAttr {
+ return func(m optionalAttr) {
+ m["container"] = value
+ }
+}
+
+// MapUnstageNoKeySharedName sets the optional shared_name attribute to value.
+// If not specified, defaults to ""
+func MapUnstageNoKeySharedName(value string) MapUnstageNoKeyAttr {
+ return func(m optionalAttr) {
+ m["shared_name"] = value
+ }
+}
+
+// Op removes and returns a random (key, value)
//
-// Returns One or more tensors that were dequeued as a tuple.
-func QueueDequeueUpToV2(scope *Scope, handle tf.Output, n tf.Output, component_types []tf.DataType, optional ...QueueDequeueUpToV2Attr) (components []tf.Output) {
+// from the underlying container. If the underlying container
+// does not contain elements, the op will block until it does.
+func MapUnstageNoKey(scope *Scope, indices tf.Output, dtypes []tf.DataType, optional ...MapUnstageNoKeyAttr) (key tf.Output, values []tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{"component_types": component_types}
+ attrs := map[string]interface{}{"dtypes": dtypes}
for _, a := range optional {
a(attrs)
}
opspec := tf.OpSpec{
- Type: "QueueDequeueUpToV2",
+ Type: "MapUnstageNoKey",
Input: []tf.Input{
- handle, n,
+ indices,
},
Attrs: attrs,
}
}
var idx int
var err error
- if components, idx, err = makeOutputList(op, idx, "components"); err != nil {
- scope.UpdateErr("QueueDequeueUpToV2", err)
+ key = op.Output(idx)
+ if values, idx, err = makeOutputList(op, idx, "values"); err != nil {
+ scope.UpdateErr("MapUnstageNoKey", err)
return
}
- return components
+ return key, values
}
-// Computes the Cholesky decomposition of one or more square matrices.
-//
-// The input is a tensor of shape `[..., M, M]` whose inner-most 2 dimensions
-// form square matrices.
-//
-// The input has to be symmetric and positive definite. Only the lower-triangular
-// part of the input will be used for this operation. The upper-triangular part
-// will not be read.
-//
-// The output is a tensor of the same shape as the input
-// containing the Cholesky decompositions for all input submatrices `[..., :, :]`.
-//
-// **Note**: The gradient computation on GPU is faster for large matrices but
-// not for large batch dimensions when the submatrices are small. In this
-// case it might be faster to use the CPU.
-//
-// Arguments:
-// input: Shape is `[..., M, M]`.
+// HashTableV2Attr is an optional argument to HashTableV2.
+type HashTableV2Attr func(optionalAttr)
+
+// HashTableV2Container sets the optional container attribute to value.
//
-// Returns Shape is `[..., M, M]`.
-func Cholesky(scope *Scope, input tf.Output) (output tf.Output) {
- if scope.Err() != nil {
- return
- }
- opspec := tf.OpSpec{
- Type: "Cholesky",
- Input: []tf.Input{
- input,
- },
+// value: If non-empty, this table is placed in the given container.
+// Otherwise, a default container is used.
+// If not specified, defaults to ""
+func HashTableV2Container(value string) HashTableV2Attr {
+ return func(m optionalAttr) {
+ m["container"] = value
}
- op := scope.AddOperation(opspec)
- return op.Output(0)
}
-// Writes contents to the file at input filename. Creates file and recursively
+// HashTableV2SharedName sets the optional shared_name attribute to value.
//
-// creates directory if not existing.
-//
-// Arguments:
-// filename: scalar. The name of the file to which we write the contents.
-// contents: scalar. The content to be written to the output file.
-//
-// Returns the created operation.
-func WriteFile(scope *Scope, filename tf.Output, contents tf.Output) (o *tf.Operation) {
- if scope.Err() != nil {
- return
- }
- opspec := tf.OpSpec{
- Type: "WriteFile",
- Input: []tf.Input{
- filename, contents,
- },
+// value: If non-empty, this table is shared under the given name across
+// multiple sessions.
+// If not specified, defaults to ""
+func HashTableV2SharedName(value string) HashTableV2Attr {
+ return func(m optionalAttr) {
+ m["shared_name"] = value
}
- return scope.AddOperation(opspec)
}
-// AllAttr is an optional argument to All.
-type AllAttr func(optionalAttr)
-
-// AllKeepDims sets the optional keep_dims attribute to value.
+// HashTableV2UseNodeNameSharing sets the optional use_node_name_sharing attribute to value.
//
-// value: If true, retain reduced dimensions with length 1.
+// value: If true and shared_name is empty, the table is shared
+// using the node name.
// If not specified, defaults to false
-func AllKeepDims(value bool) AllAttr {
+func HashTableV2UseNodeNameSharing(value bool) HashTableV2Attr {
return func(m optionalAttr) {
- m["keep_dims"] = value
+ m["use_node_name_sharing"] = value
}
}
-// Computes the "logical and" of elements across dimensions of a tensor.
+// Creates a non-initialized hash table.
//
-// Reduces `input` along the dimensions given in `axis`. Unless
-// `keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in
-// `axis`. If `keep_dims` is true, the reduced dimensions are
-// retained with length 1.
+// This op creates a hash table, specifying the type of its keys and values.
+// Before using the table you will have to initialize it. After initialization the
+// table will be immutable.
//
// Arguments:
-// input: The tensor to reduce.
-// axis: The dimensions to reduce. Must be in the range
-// `[-rank(input), rank(input))`.
+// key_dtype: Type of the table keys.
+// value_dtype: Type of the table values.
//
-// Returns The reduced tensor.
-func All(scope *Scope, input tf.Output, axis tf.Output, optional ...AllAttr) (output tf.Output) {
+// Returns Handle to a table.
+func HashTableV2(scope *Scope, key_dtype tf.DataType, value_dtype tf.DataType, optional ...HashTableV2Attr) (table_handle tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{}
+ attrs := map[string]interface{}{"key_dtype": key_dtype, "value_dtype": value_dtype}
for _, a := range optional {
a(attrs)
}
opspec := tf.OpSpec{
- Type: "All",
- Input: []tf.Input{
- input, axis,
- },
+ Type: "HashTableV2",
+
Attrs: attrs,
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// Computes the Eigen Decomposition of a batch of square self-adjoint matrices.
-//
-// DEPRECATED at GraphDef version 11: Use SelfAdjointEigV2 instead.
-//
-// The input is a tensor of shape `[..., M, M]` whose inner-most 2 dimensions
-// form square matrices, with the same constraints as the single matrix
-// SelfAdjointEig.
-//
-// The result is a [..., M+1, M] matrix with [..., 0,:] containing the
-// eigenvalues, and subsequent [...,1:, :] containing the eigenvectors.
+// MutableHashTableV2Attr is an optional argument to MutableHashTableV2.
+type MutableHashTableV2Attr func(optionalAttr)
+
+// MutableHashTableV2Container sets the optional container attribute to value.
//
-// Arguments:
-// input: Shape is `[..., M, M]`.
+// value: If non-empty, this table is placed in the given container.
+// Otherwise, a default container is used.
+// If not specified, defaults to ""
+func MutableHashTableV2Container(value string) MutableHashTableV2Attr {
+ return func(m optionalAttr) {
+ m["container"] = value
+ }
+}
+
+// MutableHashTableV2SharedName sets the optional shared_name attribute to value.
//
-// Returns Shape is `[..., M+1, M]`.
-func SelfAdjointEig(scope *Scope, input tf.Output) (output tf.Output) {
- if scope.Err() != nil {
- return
+// value: If non-empty, this table is shared under the given name across
+// multiple sessions.
+// If not specified, defaults to ""
+func MutableHashTableV2SharedName(value string) MutableHashTableV2Attr {
+ return func(m optionalAttr) {
+ m["shared_name"] = value
}
- opspec := tf.OpSpec{
- Type: "SelfAdjointEig",
- Input: []tf.Input{
- input,
- },
+}
+
+// MutableHashTableV2UseNodeNameSharing sets the optional use_node_name_sharing attribute to value.
+//
+// value: If true and shared_name is empty, the table is shared
+// using the node name.
+// If not specified, defaults to false
+func MutableHashTableV2UseNodeNameSharing(value bool) MutableHashTableV2Attr {
+ return func(m optionalAttr) {
+ m["use_node_name_sharing"] = value
}
- op := scope.AddOperation(opspec)
- return op.Output(0)
}
-// Computes softplus gradients for a softplus operation.
+// Creates an empty hash table.
+//
+// This op creates a mutable hash table, specifying the type of its keys and
+// values. Each value must be a scalar. Data can be inserted into the table using
+// the insert operations. It does not support the initialization operation.
//
// Arguments:
-// gradients: The backpropagated gradients to the corresponding softplus operation.
-// features: The features passed as input to the corresponding softplus operation.
+// key_dtype: Type of the table keys.
+// value_dtype: Type of the table values.
//
-// Returns The gradients: `gradients / (1 + exp(-features))`.
-func SoftplusGrad(scope *Scope, gradients tf.Output, features tf.Output) (backprops tf.Output) {
+// Returns Handle to a table.
+func MutableHashTableV2(scope *Scope, key_dtype tf.DataType, value_dtype tf.DataType, optional ...MutableHashTableV2Attr) (table_handle tf.Output) {
if scope.Err() != nil {
return
}
+ attrs := map[string]interface{}{"key_dtype": key_dtype, "value_dtype": value_dtype}
+ for _, a := range optional {
+ a(attrs)
+ }
opspec := tf.OpSpec{
- Type: "SoftplusGrad",
- Input: []tf.Input{
- gradients, features,
- },
+ Type: "MutableHashTableV2",
+
+ Attrs: attrs,
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// SelfAdjointEigV2Attr is an optional argument to SelfAdjointEigV2.
-type SelfAdjointEigV2Attr func(optionalAttr)
+// DequantizeAttr is an optional argument to Dequantize.
+type DequantizeAttr func(optionalAttr)
-// SelfAdjointEigV2ComputeV sets the optional compute_v attribute to value.
-//
-// value: If `True` then eigenvectors will be computed and returned in `v`.
-// Otherwise, only the eigenvalues will be computed.
-// If not specified, defaults to true
-func SelfAdjointEigV2ComputeV(value bool) SelfAdjointEigV2Attr {
+// DequantizeMode sets the optional mode attribute to value.
+// If not specified, defaults to "MIN_COMBINED"
+func DequantizeMode(value string) DequantizeAttr {
return func(m optionalAttr) {
- m["compute_v"] = value
+ m["mode"] = value
}
}
-// Computes the eigen decomposition of one or more square self-adjoint matrices.
+// Dequantize the 'input' tensor into a float Tensor.
//
-// Computes the eigenvalues and (optionally) eigenvectors of each inner matrix in
-// `input` such that `input[..., :, :] = v[..., :, :] * diag(e[..., :])`.
+// [min_range, max_range] are scalar floats that specify the range for
+// the 'input' data. The 'mode' attribute controls exactly which calculations are
+// used to convert the float values to their quantized equivalents.
//
-// ```python
-// # a is a tensor.
-// # e is a tensor of eigenvalues.
-// # v is a tensor of eigenvectors.
-// e, v = self_adjoint_eig(a)
-// e = self_adjoint_eig(a, compute_v=False)
+// In 'MIN_COMBINED' mode, each value of the tensor will undergo the following:
+//
+// ```
+// if T == qint8, in[i] += (range(T) + 1)/ 2.0
+// out[i] = min_range + (in[i]* (max_range - min_range) / range(T))
+// ```
+// here `range(T) = numeric_limits<T>::max() - numeric_limits<T>::min()`
+//
+// *MIN_COMBINED Mode Example*
+//
+// If the input comes from a QuantizedRelu6, the output type is
+// quint8 (range of 0-255) but the possible range of QuantizedRelu6 is
+// 0-6. The min_range and max_range values are therefore 0.0 and 6.0.
+// Dequantize on quint8 will take each value, cast to float, and multiply
+// by 6 / 255.
+// Note that if quantizedtype is qint8, the operation will additionally add
+// each value by 128 prior to casting.
+//
+// If the mode is 'MIN_FIRST', then this approach is used:
+//
+// ```c++
+// num_discrete_values = 1 << (# of bits in T)
+// range_adjust = num_discrete_values / (num_discrete_values - 1)
+// range = (range_max - range_min) * range_adjust
+// range_scale = range / num_discrete_values
+// const double offset_input = static_cast<double>(input) - lowest_quantized;
+// result = range_min + ((input - numeric_limits<T>::min()) * range_scale)
+// ```
+//
+// *SCALED mode Example*
+//
+// `SCALED` mode matches the quantization approach used in
+// `QuantizeAndDequantize{V2|V3}`.
+//
+// If the mode is `SCALED`, we do not use the full range of the output type,
+// choosing to elide the lowest possible value for symmetry (e.g., output range is
+// -127 to 127, not -128 to 127 for signed 8 bit quantization), so that 0.0 maps to
+// 0.
+//
+// We first find the range of values in our tensor. The
+// range we use is always centered on 0, so we find m such that
+// ```c++
+// m = max(abs(input_min), abs(input_max))
+// ```
+//
+// Our input tensor range is then `[-m, m]`.
+//
+// Next, we choose our fixed-point quantization buckets, `[min_fixed, max_fixed]`.
+// If T is signed, this is
+// ```
+// num_bits = sizeof(T) * 8
+// [min_fixed, max_fixed] =
+// [-(1 << (num_bits - 1) - 1), (1 << (num_bits - 1)) - 1]
+// ```
+//
+// Otherwise, if T is unsigned, the fixed-point range is
+// ```
+// [min_fixed, max_fixed] = [0, (1 << num_bits) - 1]
+// ```
+//
+// From this we compute our scaling factor, s:
+// ```c++
+// s = (2 * m) / (max_fixed - min_fixed)
+// ```
+//
+// Now we can dequantize the elements of our tensor:
+// ```c++
+// result = input * s
// ```
//
// Arguments:
-// input: `Tensor` input of shape `[N, N]`.
//
-// Returns Eigenvalues. Shape is `[N]`.Eigenvectors. Shape is `[N, N]`.
-func SelfAdjointEigV2(scope *Scope, input tf.Output, optional ...SelfAdjointEigV2Attr) (e tf.Output, v tf.Output) {
+// min_range: The minimum scalar value possibly produced for the input.
+// max_range: The maximum scalar value possibly produced for the input.
+func Dequantize(scope *Scope, input tf.Output, min_range tf.Output, max_range tf.Output, optional ...DequantizeAttr) (output tf.Output) {
if scope.Err() != nil {
return
}
a(attrs)
}
opspec := tf.OpSpec{
- Type: "SelfAdjointEigV2",
+ Type: "Dequantize",
Input: []tf.Input{
- input,
+ input, min_range, max_range,
},
Attrs: attrs,
}
op := scope.AddOperation(opspec)
- return op.Output(0), op.Output(1)
+ return op.Output(0)
}
-// Adjust the saturation of one or more images.
-//
-// `images` is a tensor of at least 3 dimensions. The last dimension is
-// interpretted as channels, and must be three.
-//
-// The input image is considered in the RGB colorspace. Conceptually, the RGB
-// colors are first mapped into HSV. A scale is then applied all the saturation
-// values, and then remapped back to RGB colorspace.
+// Flips all bits elementwise.
//
-// Arguments:
-// images: Images to adjust. At least 3-D.
-// scale: A float scale to add to the saturation.
-//
-// Returns The hue-adjusted image or images.
-func AdjustSaturation(scope *Scope, images tf.Output, scale tf.Output) (output tf.Output) {
+// The result will have exactly those bits set, that are not set in `x`. The
+// computation is performed on the underlying representation of x.
+func Invert(scope *Scope, x tf.Output) (y tf.Output) {
if scope.Err() != nil {
return
}
opspec := tf.OpSpec{
- Type: "AdjustSaturation",
+ Type: "Invert",
Input: []tf.Input{
- images, scale,
+ x,
},
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// SvdAttr is an optional argument to Svd.
-type SvdAttr func(optionalAttr)
-
-// SvdComputeUv sets the optional compute_uv attribute to value.
+// Deprecated. Disallowed in GraphDef version >= 2.
//
-// value: If true, left and right singular vectors will be
-// computed and returned in `u` and `v`, respectively.
-// If false, `u` and `v` are not set and should never referenced.
-// If not specified, defaults to true
-func SvdComputeUv(value bool) SvdAttr {
- return func(m optionalAttr) {
- m["compute_uv"] = value
+// DEPRECATED at GraphDef version 2: Use AdjustContrastv2 instead
+func AdjustContrast(scope *Scope, images tf.Output, contrast_factor tf.Output, min_value tf.Output, max_value tf.Output) (output tf.Output) {
+ if scope.Err() != nil {
+ return
}
-}
-
-// SvdFullMatrices sets the optional full_matrices attribute to value.
-//
-// value: If true, compute full-sized `u` and `v`. If false
-// (the default), compute only the leading `P` singular vectors.
-// Ignored if `compute_uv` is `False`.
-// If not specified, defaults to false
-func SvdFullMatrices(value bool) SvdAttr {
- return func(m optionalAttr) {
- m["full_matrices"] = value
+ opspec := tf.OpSpec{
+ Type: "AdjustContrast",
+ Input: []tf.Input{
+ images, contrast_factor, min_value, max_value,
+ },
}
+ op := scope.AddOperation(opspec)
+ return op.Output(0)
}
-// Computes the singular value decompositions of one or more matrices.
-//
-// Computes the SVD of each inner matrix in `input` such that
-// `input[..., :, :] = u[..., :, :] * diag(s[..., :, :]) * transpose(v[..., :, :])`
-//
-// ```python
-// # a is a tensor containing a batch of matrices.
-// # s is a tensor of singular values for each matrix.
-// # u is the tensor containing of left singular vectors for each matrix.
-// # v is the tensor containing of right singular vectors for each matrix.
-// s, u, v = svd(a)
-// s, _, _ = svd(a, compute_uv=False)
-// ```
+// Table initializer that takes two tensors for keys and values respectively.
//
// Arguments:
-// input: A tensor of shape `[..., M, N]` whose inner-most 2 dimensions
-// form matrices of size `[M, N]`. Let `P` be the minimum of `M` and `N`.
+// table_handle: Handle to a table which will be initialized.
+// keys: Keys of type Tkey.
+// values: Values of type Tval.
//
-// Returns Singular values. Shape is `[..., P]`.Left singular vectors. If `full_matrices` is `False` then shape is
-// `[..., M, P]`; if `full_matrices` is `True` then shape is
-// `[..., M, M]`. Undefined if `compute_uv` is `False`.Left singular vectors. If `full_matrices` is `False` then shape is
-// `[..., N, P]`. If `full_matrices` is `True` then shape is `[..., N, N]`.
-// Undefined if `compute_uv` is false.
-func Svd(scope *Scope, input tf.Output, optional ...SvdAttr) (s tf.Output, u tf.Output, v tf.Output) {
+// Returns the created operation.
+func InitializeTableV2(scope *Scope, table_handle tf.Output, keys tf.Output, values tf.Output) (o *tf.Operation) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{}
- for _, a := range optional {
- a(attrs)
- }
opspec := tf.OpSpec{
- Type: "Svd",
+ Type: "InitializeTableV2",
Input: []tf.Input{
- input,
+ table_handle, keys, values,
},
- Attrs: attrs,
}
- op := scope.AddOperation(opspec)
- return op.Output(0), op.Output(1), op.Output(2)
+ return scope.AddOperation(opspec)
}
-// QueueEnqueueManyV2Attr is an optional argument to QueueEnqueueManyV2.
-type QueueEnqueueManyV2Attr func(optionalAttr)
+// PrintAttr is an optional argument to Print.
+type PrintAttr func(optionalAttr)
-// QueueEnqueueManyV2TimeoutMs sets the optional timeout_ms attribute to value.
+// PrintMessage sets the optional message attribute to value.
//
-// value: If the queue is too full, this operation will block for up
-// to timeout_ms milliseconds.
-// Note: This option is not supported yet.
-// If not specified, defaults to -1
-func QueueEnqueueManyV2TimeoutMs(value int64) QueueEnqueueManyV2Attr {
+// value: A string, prefix of the error message.
+// If not specified, defaults to ""
+func PrintMessage(value string) PrintAttr {
return func(m optionalAttr) {
- m["timeout_ms"] = value
+ m["message"] = value
}
}
-// Enqueues zero or more tuples of one or more tensors in the given queue.
+// PrintFirstN sets the optional first_n attribute to value.
//
-// This operation slices each component tensor along the 0th dimension to
-// make multiple queue elements. All of the tuple components must have the
-// same size in the 0th dimension.
+// value: Only log `first_n` number of times. -1 disables logging.
+// If not specified, defaults to -1
+func PrintFirstN(value int64) PrintAttr {
+ return func(m optionalAttr) {
+ m["first_n"] = value
+ }
+}
+
+// PrintSummarize sets the optional summarize attribute to value.
//
-// The components input has k elements, which correspond to the components of
-// tuples stored in the given queue.
+// value: Only print this many entries of each tensor.
+// If not specified, defaults to 3
+func PrintSummarize(value int64) PrintAttr {
+ return func(m optionalAttr) {
+ m["summarize"] = value
+ }
+}
+
+// Prints a list of tensors.
//
-// N.B. If the queue is full, this operation will block until the given
-// elements have been enqueued (or 'timeout_ms' elapses, if specified).
+// Passes `input` through to `output` and prints `data` when evaluating.
//
// Arguments:
-// handle: The handle to a queue.
-// components: One or more tensors from which the enqueued tensors should
-// be taken.
+// input: The tensor passed to `output`
+// data: A list of tensors to print out when op is evaluated.
//
-// Returns the created operation.
-func QueueEnqueueManyV2(scope *Scope, handle tf.Output, components []tf.Output, optional ...QueueEnqueueManyV2Attr) (o *tf.Operation) {
+// Returns = The unmodified `input` tensor
+func Print(scope *Scope, input tf.Output, data []tf.Output, optional ...PrintAttr) (output tf.Output) {
if scope.Err() != nil {
return
}
a(attrs)
}
opspec := tf.OpSpec{
- Type: "QueueEnqueueManyV2",
+ Type: "Print",
Input: []tf.Input{
- handle, tf.OutputList(components),
+ input, tf.OutputList(data),
},
Attrs: attrs,
}
- return scope.AddOperation(opspec)
+ op := scope.AddOperation(opspec)
+ return op.Output(0)
}
-// Computes the product along segments of a tensor.
-//
-// Read @{$math_ops#segmentation$the section on segmentation} for an explanation of
-// segments.
-//
-// Computes a tensor such that
-// \\(output_i = \prod_j data_j\\) where the product is over `j` such
-// that `segment_ids[j] == i`.
-//
-// If the product is empty for a given segment ID `i`, `output[i] = 1`.
-//
-// <div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;">
-// <img style="width:100%" src="https://www.tensorflow.org/images/SegmentProd.png" alt>
-// </div>
+// Outputs a `Summary` protocol buffer with a tensor and per-plugin data.
//
// Arguments:
-//
-// segment_ids: A 1-D tensor whose rank is equal to the rank of `data`'s
-// first dimension. Values should be sorted and can be repeated.
-//
-// Returns Has same shape as data, except for dimension 0 which
-// has size `k`, the number of segments.
-func SegmentProd(scope *Scope, data tf.Output, segment_ids tf.Output) (output tf.Output) {
+// tag: A string attached to this summary. Used for organization in TensorBoard.
+// tensor: A tensor to serialize.
+// serialized_summary_metadata: A serialized SummaryMetadata proto. Contains plugin
+// data.
+func TensorSummaryV2(scope *Scope, tag tf.Output, tensor tf.Output, serialized_summary_metadata tf.Output) (summary tf.Output) {
if scope.Err() != nil {
return
}
opspec := tf.OpSpec{
- Type: "SegmentProd",
+ Type: "TensorSummaryV2",
Input: []tf.Input{
- data, segment_ids,
+ tag, tensor, serialized_summary_metadata,
},
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// Converts one or more images from RGB to HSV.
+// Creates a dataset that asynchronously prefetches elements from `input_dataset`.
//
-// Outputs a tensor of the same shape as the `images` tensor, containing the HSV
-// value of the pixels. The output is only well defined if the value in `images`
-// are in `[0,1]`.
+// Arguments:
//
-// `output[..., 0]` contains hue, `output[..., 1]` contains saturation, and
-// `output[..., 2]` contains value. All HSV values are in `[0,1]`. A hue of 0
-// corresponds to pure red, hue 1/3 is pure green, and 2/3 is pure blue.
+// buffer_size: The maximum number of elements to buffer in an iterator over
+// this dataset.
//
-// Arguments:
-// images: 1-D or higher rank. RGB data to convert. Last dimension must be size 3.
//
-// Returns `images` converted to HSV.
-func RGBToHSV(scope *Scope, images tf.Output) (output tf.Output) {
+func PrefetchDataset(scope *Scope, input_dataset tf.Output, buffer_size tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) {
if scope.Err() != nil {
return
}
+ attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes}
opspec := tf.OpSpec{
- Type: "RGBToHSV",
+ Type: "PrefetchDataset",
Input: []tf.Input{
- images,
+ input_dataset, buffer_size,
},
+ Attrs: attrs,
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// Does nothing. Only useful as a placeholder for control edges.
+// TensorSummaryAttr is an optional argument to TensorSummary.
+type TensorSummaryAttr func(optionalAttr)
+
+// TensorSummaryDescription sets the optional description attribute to value.
//
-// Returns the created operation.
-func NoOp(scope *Scope) (o *tf.Operation) {
- if scope.Err() != nil {
- return
- }
- opspec := tf.OpSpec{
- Type: "NoOp",
+// value: A json-encoded SummaryDescription proto.
+// If not specified, defaults to ""
+func TensorSummaryDescription(value string) TensorSummaryAttr {
+ return func(m optionalAttr) {
+ m["description"] = value
}
- return scope.AddOperation(opspec)
}
-// MergeV2CheckpointsAttr is an optional argument to MergeV2Checkpoints.
-type MergeV2CheckpointsAttr func(optionalAttr)
-
-// MergeV2CheckpointsDeleteOldDirs sets the optional delete_old_dirs attribute to value.
+// TensorSummaryLabels sets the optional labels attribute to value.
//
-// value: see above.
-// If not specified, defaults to true
-func MergeV2CheckpointsDeleteOldDirs(value bool) MergeV2CheckpointsAttr {
+// value: An unused list of strings.
+// If not specified, defaults to <>
+func TensorSummaryLabels(value []string) TensorSummaryAttr {
return func(m optionalAttr) {
- m["delete_old_dirs"] = value
+ m["labels"] = value
}
}
-// V2 format specific: merges the metadata files of sharded checkpoints. The
-//
-// result is one logical checkpoint, with one physical metadata file and renamed
-// data files.
+// TensorSummaryDisplayName sets the optional display_name attribute to value.
//
-// Intended for "grouping" multiple checkpoints in a sharded checkpoint setup.
+// value: An unused string.
+// If not specified, defaults to ""
+func TensorSummaryDisplayName(value string) TensorSummaryAttr {
+ return func(m optionalAttr) {
+ m["display_name"] = value
+ }
+}
+
+// Outputs a `Summary` protocol buffer with a tensor.
//
-// If delete_old_dirs is true, attempts to delete recursively the dirname of each
-// path in the input checkpoint_prefixes. This is useful when those paths are non
-// user-facing temporary locations.
+// This op is being phased out in favor of TensorSummaryV2, which lets callers pass
+// a tag as well as a serialized SummaryMetadata proto string that contains
+// plugin-specific data. We will keep this op to maintain backwards compatibility.
//
// Arguments:
-// checkpoint_prefixes: prefixes of V2 checkpoints to merge.
-// destination_prefix: scalar. The desired final prefix. Allowed to be the same
-// as one of the checkpoint_prefixes.
-//
-// Returns the created operation.
-func MergeV2Checkpoints(scope *Scope, checkpoint_prefixes tf.Output, destination_prefix tf.Output, optional ...MergeV2CheckpointsAttr) (o *tf.Operation) {
+// tensor: A tensor to serialize.
+func TensorSummary(scope *Scope, tensor tf.Output, optional ...TensorSummaryAttr) (summary tf.Output) {
if scope.Err() != nil {
return
}
a(attrs)
}
opspec := tf.OpSpec{
- Type: "MergeV2Checkpoints",
+ Type: "TensorSummary",
Input: []tf.Input{
- checkpoint_prefixes, destination_prefix,
+ tensor,
},
Attrs: attrs,
}
- return scope.AddOperation(opspec)
+ op := scope.AddOperation(opspec)
+ return op.Output(0)
}
-// Saves input tensors slices to disk.
-//
-// This is like `Save` except that tensors can be listed in the saved file as being
-// a slice of a larger tensor. `shapes_and_slices` specifies the shape of the
-// larger tensor and the slice that this tensor covers. `shapes_and_slices` must
-// have as many elements as `tensor_names`.
-//
-// Elements of the `shapes_and_slices` input must either be:
-//
-// * The empty string, in which case the corresponding tensor is
-// saved normally.
-// * A string of the form `dim0 dim1 ... dimN-1 slice-spec` where the
-// `dimI` are the dimensions of the larger tensor and `slice-spec`
-// specifies what part is covered by the tensor to save.
-//
-// `slice-spec` itself is a `:`-separated list: `slice0:slice1:...:sliceN-1`
-// where each `sliceI` is either:
+// Computes the gradient for the tanh of `x` wrt its input.
//
-// * The string `-` meaning that the slice covers all indices of this dimension
-// * `start,length` where `start` and `length` are integers. In that
-// case the slice covers `length` indices starting at `start`.
+// Specifically, `grad = dy * (1 - y*y)`, where `y = tanh(x)`, and `dy`
+// is the corresponding input gradient.
+func TanhGrad(scope *Scope, y tf.Output, dy tf.Output) (z tf.Output) {
+ if scope.Err() != nil {
+ return
+ }
+ opspec := tf.OpSpec{
+ Type: "TanhGrad",
+ Input: []tf.Input{
+ y, dy,
+ },
+ }
+ op := scope.AddOperation(opspec)
+ return op.Output(0)
+}
+
+// Outputs a `Summary` protocol buffer with scalar values.
//
-// See also `Save`.
+// The input `tags` and `values` must have the same shape. The generated summary
+// has a summary value for each tag-value pair in `tags` and `values`.
//
// Arguments:
-// filename: Must have a single element. The name of the file to which we write the
-// tensor.
-// tensor_names: Shape `[N]`. The names of the tensors to be saved.
-// shapes_and_slices: Shape `[N]`. The shapes and slice specifications to use when
-// saving the tensors.
-// data: `N` tensors to save.
+// tags: Tags for the summary.
+// values: Same shape as `tags. Values for the summary.
//
-// Returns the created operation.
-func SaveSlices(scope *Scope, filename tf.Output, tensor_names tf.Output, shapes_and_slices tf.Output, data []tf.Output) (o *tf.Operation) {
+// Returns Scalar. Serialized `Summary` protocol buffer.
+func ScalarSummary(scope *Scope, tags tf.Output, values tf.Output) (summary tf.Output) {
if scope.Err() != nil {
return
}
opspec := tf.OpSpec{
- Type: "SaveSlices",
+ Type: "ScalarSummary",
Input: []tf.Input{
- filename, tensor_names, shapes_and_slices, tf.OutputList(data),
+ tags, values,
},
}
- return scope.AddOperation(opspec)
-}
-
-// DenseToDenseSetOperationAttr is an optional argument to DenseToDenseSetOperation.
-type DenseToDenseSetOperationAttr func(optionalAttr)
-
-// DenseToDenseSetOperationValidateIndices sets the optional validate_indices attribute to value.
-// If not specified, defaults to true
-func DenseToDenseSetOperationValidateIndices(value bool) DenseToDenseSetOperationAttr {
- return func(m optionalAttr) {
- m["validate_indices"] = value
- }
+ op := scope.AddOperation(opspec)
+ return op.Output(0)
}
-// Applies set operation along last dimension of 2 `Tensor` inputs.
+// Outputs a `Summary` protocol buffer with a histogram.
//
-// See SetOperationOp::SetOperationFromContext for values of `set_operation`.
+// The generated
+// [`Summary`](https://www.tensorflow.org/code/tensorflow/core/framework/summary.proto)
+// has one summary value containing a histogram for `values`.
//
-// Output `result` is a `SparseTensor` represented by `result_indices`,
-// `result_values`, and `result_shape`. For `set1` and `set2` ranked `n`, this
-// has rank `n` and the same 1st `n-1` dimensions as `set1` and `set2`. The `nth`
-// dimension contains the result of `set_operation` applied to the corresponding
-// `[0...n-1]` dimension of `set`.
+// This op reports an `InvalidArgument` error if any value is not finite.
//
// Arguments:
-// set1: `Tensor` with rank `n`. 1st `n-1` dimensions must be the same as `set2`.
-// Dimension `n` contains values in a set, duplicates are allowed but ignored.
-// set2: `Tensor` with rank `n`. 1st `n-1` dimensions must be the same as `set1`.
-// Dimension `n` contains values in a set, duplicates are allowed but ignored.
-//
+// tag: Scalar. Tag to use for the `Summary.Value`.
+// values: Any shape. Values to use to build the histogram.
//
-// Returns 2D indices of a `SparseTensor`.1D values of a `SparseTensor`.1D `Tensor` shape of a `SparseTensor`. `result_shape[0...n-1]` is
-// the same as the 1st `n-1` dimensions of `set1` and `set2`, `result_shape[n]`
-// is the max result set size across all `0...n-1` dimensions.
-func DenseToDenseSetOperation(scope *Scope, set1 tf.Output, set2 tf.Output, set_operation string, optional ...DenseToDenseSetOperationAttr) (result_indices tf.Output, result_values tf.Output, result_shape tf.Output) {
+// Returns Scalar. Serialized `Summary` protocol buffer.
+func HistogramSummary(scope *Scope, tag tf.Output, values tf.Output) (summary tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{"set_operation": set_operation}
- for _, a := range optional {
- a(attrs)
- }
opspec := tf.OpSpec{
- Type: "DenseToDenseSetOperation",
+ Type: "HistogramSummary",
Input: []tf.Input{
- set1, set2,
+ tag, values,
},
- Attrs: attrs,
}
op := scope.AddOperation(opspec)
- return op.Output(0), op.Output(1), op.Output(2)
+ return op.Output(0)
}
-// Generate a sharded filename. The filename is printf formatted as
+// Computes the number of elements in the given queue.
//
-// %s-%05d-of-%05d, basename, shard, num_shards.
-func ShardedFilename(scope *Scope, basename tf.Output, shard tf.Output, num_shards tf.Output) (filename tf.Output) {
+// Arguments:
+// handle: The handle to a queue.
+//
+// Returns The number of elements in the given queue.
+func QueueSizeV2(scope *Scope, handle tf.Output) (size tf.Output) {
if scope.Err() != nil {
return
}
opspec := tf.OpSpec{
- Type: "ShardedFilename",
+ Type: "QueueSizeV2",
Input: []tf.Input{
- basename, shard, num_shards,
+ handle,
},
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// BatchToSpace for N-D tensors of type T.
-//
-// This operation reshapes the "batch" dimension 0 into `M + 1` dimensions of shape
-// `block_shape + [batch]`, interleaves these blocks back into the grid defined by
-// the spatial dimensions `[1, ..., M]`, to obtain a result with the same rank as
-// the input. The spatial dimensions of this intermediate result are then
-// optionally cropped according to `crops` to produce the output. This is the
-// reverse of SpaceToBatch. See below for a precise description.
+// ImageSummaryAttr is an optional argument to ImageSummary.
+type ImageSummaryAttr func(optionalAttr)
+
+// ImageSummaryMaxImages sets the optional max_images attribute to value.
//
-// Arguments:
-// input: N-D with shape `input_shape = [batch] + spatial_shape + remaining_shape`,
-// where spatial_shape has M dimensions.
-// block_shape: 1-D with shape `[M]`, all values must be >= 1.
-// crops: 2-D with shape `[M, 2]`, all values must be >= 0.
-// `crops[i] = [crop_start, crop_end]` specifies the amount to crop from input
-// dimension `i + 1`, which corresponds to spatial dimension `i`. It is
-// required that
-// `crop_start[i] + crop_end[i] <= block_shape[i] * input_shape[i + 1]`.
+// value: Max number of batch elements to generate images for.
+// If not specified, defaults to 3
//
-// This operation is equivalent to the following steps:
+// REQUIRES: value >= 1
+func ImageSummaryMaxImages(value int64) ImageSummaryAttr {
+ return func(m optionalAttr) {
+ m["max_images"] = value
+ }
+}
+
+// ImageSummaryBadColor sets the optional bad_color attribute to value.
//
-// 1. Reshape `input` to `reshaped` of shape:
-// [block_shape[0], ..., block_shape[M-1],
-// batch / prod(block_shape),
-// input_shape[1], ..., input_shape[N-1]]
+// value: Color to use for pixels with non-finite values.
+// If not specified, defaults to <dtype:DT_UINT8 tensor_shape:<dim:<size:4 > > int_val:255 int_val:0 int_val:0 int_val:255 >
+func ImageSummaryBadColor(value tf.Tensor) ImageSummaryAttr {
+ return func(m optionalAttr) {
+ m["bad_color"] = value
+ }
+}
+
+// Outputs a `Summary` protocol buffer with images.
//
-// 2. Permute dimensions of `reshaped` to produce `permuted` of shape
-// [batch / prod(block_shape),
+// The summary has up to `max_images` summary values containing images. The
+// images are built from `tensor` which must be 4-D with shape `[batch_size,
+// height, width, channels]` and where `channels` can be:
//
-// input_shape[1], block_shape[0],
-// ...,
-// input_shape[M], block_shape[M-1],
+// * 1: `tensor` is interpreted as Grayscale.
+// * 3: `tensor` is interpreted as RGB.
+// * 4: `tensor` is interpreted as RGBA.
//
-// input_shape[M+1], ..., input_shape[N-1]]
+// The images have the same number of channels as the input tensor. For float
+// input, the values are normalized one image at a time to fit in the range
+// `[0, 255]`. `uint8` values are unchanged. The op uses two different
+// normalization algorithms:
//
-// 3. Reshape `permuted` to produce `reshaped_permuted` of shape
-// [batch / prod(block_shape),
+// * If the input values are all positive, they are rescaled so the largest one
+// is 255.
//
-// input_shape[1] * block_shape[0],
-// ...,
-// input_shape[M] * block_shape[M-1],
+// * If any input value is negative, the values are shifted so input value 0.0
+// is at 127. They are then rescaled so that either the smallest value is 0,
+// or the largest one is 255.
//
-// input_shape[M+1],
-// ...,
-// input_shape[N-1]]
+// The `tag` argument is a scalar `Tensor` of type `string`. It is used to
+// build the `tag` of the summary values:
//
-// 4. Crop the start and end of dimensions `[1, ..., M]` of
-// `reshaped_permuted` according to `crops` to produce the output of shape:
-// [batch / prod(block_shape),
+// * If `max_images` is 1, the summary value tag is '*tag*/image'.
+// * If `max_images` is greater than 1, the summary value tags are
+// generated sequentially as '*tag*/image/0', '*tag*/image/1', etc.
//
-// input_shape[1] * block_shape[0] - crops[0,0] - crops[0,1],
-// ...,
-// input_shape[M] * block_shape[M-1] - crops[M-1,0] - crops[M-1,1],
+// The `bad_color` argument is the color to use in the generated images for
+// non-finite input values. It is a `unit8` 1-D tensor of length `channels`.
+// Each element must be in the range `[0, 255]` (It represents the value of a
+// pixel in the output image). Non-finite values in the input tensor are
+// replaced by this tensor in the output image. The default value is the color
+// red.
//
-// input_shape[M+1], ..., input_shape[N-1]]
+// Arguments:
+// tag: Scalar. Used to build the `tag` attribute of the summary values.
+// tensor: 4-D of shape `[batch_size, height, width, channels]` where
+// `channels` is 1, 3, or 4.
//
-// Some examples:
+// Returns Scalar. Serialized `Summary` protocol buffer.
+func ImageSummary(scope *Scope, tag tf.Output, tensor tf.Output, optional ...ImageSummaryAttr) (summary tf.Output) {
+ if scope.Err() != nil {
+ return
+ }
+ attrs := map[string]interface{}{}
+ for _, a := range optional {
+ a(attrs)
+ }
+ opspec := tf.OpSpec{
+ Type: "ImageSummary",
+ Input: []tf.Input{
+ tag, tensor,
+ },
+ Attrs: attrs,
+ }
+ op := scope.AddOperation(opspec)
+ return op.Output(0)
+}
+
+// AudioSummaryV2Attr is an optional argument to AudioSummaryV2.
+type AudioSummaryV2Attr func(optionalAttr)
+
+// AudioSummaryV2MaxOutputs sets the optional max_outputs attribute to value.
//
-// (1) For the following input of shape `[4, 1, 1, 1]`, `block_shape = [2, 2]`, and
-// `crops = [[0, 0], [0, 0]]`:
+// value: Max number of batch elements to generate audio for.
+// If not specified, defaults to 3
//
-// ```
-// [[[[1]]], [[[2]]], [[[3]]], [[[4]]]]
-// ```
-//
-// The output tensor has shape `[1, 2, 2, 1]` and value:
-//
-// ```
-// x = [[[[1], [2]], [[3], [4]]]]
-// ```
-//
-// (2) For the following input of shape `[4, 1, 1, 3]`, `block_shape = [2, 2]`, and
-// `crops = [[0, 0], [0, 0]]`:
-//
-// ```
-// [[[1, 2, 3]], [[4, 5, 6]], [[7, 8, 9]], [[10, 11, 12]]]
-// ```
-//
-// The output tensor has shape `[1, 2, 2, 3]` and value:
-//
-// ```
-// x = [[[[1, 2, 3], [4, 5, 6]],
-// [[7, 8, 9], [10, 11, 12]]]]
-// ```
-//
-// (3) For the following input of shape `[4, 2, 2, 1]`, `block_shape = [2, 2]`, and
-// `crops = [[0, 0], [0, 0]]`:
-//
-// ```
-// x = [[[[1], [3]], [[9], [11]]],
-// [[[2], [4]], [[10], [12]]],
-// [[[5], [7]], [[13], [15]]],
-// [[[6], [8]], [[14], [16]]]]
-// ```
-//
-// The output tensor has shape `[1, 4, 4, 1]` and value:
+// REQUIRES: value >= 1
+func AudioSummaryV2MaxOutputs(value int64) AudioSummaryV2Attr {
+ return func(m optionalAttr) {
+ m["max_outputs"] = value
+ }
+}
+
+// Outputs a `Summary` protocol buffer with audio.
//
-// ```
-// x = [[[1], [2], [3], [4]],
-// [[5], [6], [7], [8]],
-// [[9], [10], [11], [12]],
-// [[13], [14], [15], [16]]]
-// ```
+// The summary has up to `max_outputs` summary values containing audio. The
+// audio is built from `tensor` which must be 3-D with shape `[batch_size,
+// frames, channels]` or 2-D with shape `[batch_size, frames]`. The values are
+// assumed to be in the range of `[-1.0, 1.0]` with a sample rate of `sample_rate`.
//
-// (4) For the following input of shape `[8, 1, 3, 1]`, `block_shape = [2, 2]`, and
-// `crops = [[0, 0], [2, 0]]`:
+// The `tag` argument is a scalar `Tensor` of type `string`. It is used to
+// build the `tag` of the summary values:
//
-// ```
-// x = [[[[0], [1], [3]]], [[[0], [9], [11]]],
-// [[[0], [2], [4]]], [[[0], [10], [12]]],
-// [[[0], [5], [7]]], [[[0], [13], [15]]],
-// [[[0], [6], [8]]], [[[0], [14], [16]]]]
-// ```
+// * If `max_outputs` is 1, the summary value tag is '*tag*/audio'.
+// * If `max_outputs` is greater than 1, the summary value tags are
+// generated sequentially as '*tag*/audio/0', '*tag*/audio/1', etc.
//
-// The output tensor has shape `[2, 2, 4, 1]` and value:
+// Arguments:
+// tag: Scalar. Used to build the `tag` attribute of the summary values.
+// tensor: 2-D of shape `[batch_size, frames]`.
+// sample_rate: The sample rate of the signal in hertz.
//
-// ```
-// x = [[[[1], [2], [3], [4]],
-// [[5], [6], [7], [8]]],
-// [[[9], [10], [11], [12]],
-// [[13], [14], [15], [16]]]]
-// ```
-func BatchToSpaceND(scope *Scope, input tf.Output, block_shape tf.Output, crops tf.Output) (output tf.Output) {
+// Returns Scalar. Serialized `Summary` protocol buffer.
+func AudioSummaryV2(scope *Scope, tag tf.Output, tensor tf.Output, sample_rate tf.Output, optional ...AudioSummaryV2Attr) (summary tf.Output) {
if scope.Err() != nil {
return
}
+ attrs := map[string]interface{}{}
+ for _, a := range optional {
+ a(attrs)
+ }
opspec := tf.OpSpec{
- Type: "BatchToSpaceND",
+ Type: "AudioSummaryV2",
Input: []tf.Input{
- input, block_shape, crops,
+ tag, tensor, sample_rate,
},
+ Attrs: attrs,
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// UnpackAttr is an optional argument to Unpack.
-type UnpackAttr func(optionalAttr)
+// AvgPoolAttr is an optional argument to AvgPool.
+type AvgPoolAttr func(optionalAttr)
-// UnpackAxis sets the optional axis attribute to value.
+// AvgPoolDataFormat sets the optional data_format attribute to value.
//
-// value: Dimension along which to unpack. Negative values wrap around, so the
-// valid range is `[-R, R)`.
-// If not specified, defaults to 0
-func UnpackAxis(value int64) UnpackAttr {
+// value: Specify the data format of the input and output data. With the
+// default format "NHWC", the data is stored in the order of:
+// [batch, in_height, in_width, in_channels].
+// Alternatively, the format could be "NCHW", the data storage order of:
+// [batch, in_channels, in_height, in_width].
+// If not specified, defaults to "NHWC"
+func AvgPoolDataFormat(value string) AvgPoolAttr {
return func(m optionalAttr) {
- m["axis"] = value
+ m["data_format"] = value
}
}
-// Unpacks a given dimension of a rank-`R` tensor into `num` rank-`(R-1)` tensors.
-//
-// Unpacks `num` tensors from `value` by chipping it along the `axis` dimension.
-// For example, given a tensor of shape `(A, B, C, D)`;
-//
-// If `axis == 0` then the i'th tensor in `output` is the slice `value[i, :, :, :]`
-// and each tensor in `output` will have shape `(B, C, D)`. (Note that the
-// dimension unpacked along is gone, unlike `split`).
-//
-// If `axis == 1` then the i'th tensor in `output` is the slice `value[:, i, :, :]`
-// and each tensor in `output` will have shape `(A, C, D)`.
-// Etc.
+// Performs average pooling on the input.
//
-// This is the opposite of `pack`.
+// Each entry in `output` is the mean of the corresponding size `ksize`
+// window in `value`.
//
// Arguments:
-// value: 1-D or higher, with `axis` dimension size equal to `num`.
-//
+// value: 4-D with shape `[batch, height, width, channels]`.
+// ksize: The size of the sliding window for each dimension of `value`.
+// strides: The stride of the sliding window for each dimension of `value`.
+// padding: The type of padding algorithm to use.
//
-// Returns The list of tensors unpacked from `value`.
-func Unpack(scope *Scope, value tf.Output, num int64, optional ...UnpackAttr) (output []tf.Output) {
+// Returns The average pooled output tensor.
+func AvgPool(scope *Scope, value tf.Output, ksize []int64, strides []int64, padding string, optional ...AvgPoolAttr) (output tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{"num": num}
+ attrs := map[string]interface{}{"ksize": ksize, "strides": strides, "padding": padding}
for _, a := range optional {
a(attrs)
}
opspec := tf.OpSpec{
- Type: "Unpack",
+ Type: "AvgPool",
Input: []tf.Input{
value,
},
Attrs: attrs,
}
op := scope.AddOperation(opspec)
- if scope.Err() != nil {
- return
- }
- var idx int
- var err error
- if output, idx, err = makeOutputList(op, idx, "output"); err != nil {
- scope.UpdateErr("Unpack", err)
- return
- }
- return output
+ return op.Output(0)
}
-// Increments variable pointed to by 'resource' until it reaches 'limit'.
+// Merges summaries.
//
-// Arguments:
-// resource: Should be from a scalar `Variable` node.
-// limit: If incrementing ref would bring it above limit, instead generates an
-// 'OutOfRange' error.
+// This op creates a
+// [`Summary`](https://www.tensorflow.org/code/tensorflow/core/framework/summary.proto)
+// protocol buffer that contains the union of all the values in the input
+// summaries.
+//
+// When the Op is run, it reports an `InvalidArgument` error if multiple values
+// in the summaries to merge use the same tag.
//
+// Arguments:
+// inputs: Can be of any shape. Each must contain serialized `Summary` protocol
+// buffers.
//
-// Returns A copy of the input before increment. If nothing else modifies the
-// input, the values produced will all be distinct.
-func ResourceCountUpTo(scope *Scope, resource tf.Output, limit int64, T tf.DataType) (output tf.Output) {
+// Returns Scalar. Serialized `Summary` protocol buffer.
+func MergeSummary(scope *Scope, inputs []tf.Output) (summary tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{"limit": limit, "T": T}
opspec := tf.OpSpec{
- Type: "ResourceCountUpTo",
+ Type: "MergeSummary",
Input: []tf.Input{
- resource,
+ tf.OutputList(inputs),
},
- Attrs: attrs,
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// Delete the stack from its resource container.
+// Computes the gradient of morphological 2-D dilation with respect to the filter.
//
// Arguments:
-// handle: The handle to a stack.
+// input: 4-D with shape `[batch, in_height, in_width, depth]`.
+// filter: 3-D with shape `[filter_height, filter_width, depth]`.
+// out_backprop: 4-D with shape `[batch, out_height, out_width, depth]`.
+// strides: 1-D of length 4. The stride of the sliding window for each dimension of
+// the input tensor. Must be: `[1, stride_height, stride_width, 1]`.
+// rates: 1-D of length 4. The input stride for atrous morphological dilation.
+// Must be: `[1, rate_height, rate_width, 1]`.
+// padding: The type of padding algorithm to use.
//
-// Returns the created operation.
-func StackCloseV2(scope *Scope, handle tf.Output) (o *tf.Operation) {
- if scope.Err() != nil {
- return
- }
- opspec := tf.OpSpec{
- Type: "StackCloseV2",
- Input: []tf.Input{
- handle,
- },
- }
- return scope.AddOperation(opspec)
-}
-
-// Generate a glob pattern matching all sharded file names.
-func ShardedFilespec(scope *Scope, basename tf.Output, num_shards tf.Output) (filename tf.Output) {
+// Returns 3-D with shape `[filter_height, filter_width, depth]`.
+func Dilation2DBackpropFilter(scope *Scope, input tf.Output, filter tf.Output, out_backprop tf.Output, strides []int64, rates []int64, padding string) (filter_backprop tf.Output) {
if scope.Err() != nil {
return
}
+ attrs := map[string]interface{}{"strides": strides, "rates": rates, "padding": padding}
opspec := tf.OpSpec{
- Type: "ShardedFilespec",
+ Type: "Dilation2DBackpropFilter",
Input: []tf.Input{
- basename, num_shards,
+ input, filter, out_backprop,
},
+ Attrs: attrs,
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// TextLineReaderV2Attr is an optional argument to TextLineReaderV2.
-type TextLineReaderV2Attr func(optionalAttr)
-
-// TextLineReaderV2SkipHeaderLines sets the optional skip_header_lines attribute to value.
-//
-// value: Number of lines to skip from the beginning of every file.
-// If not specified, defaults to 0
-func TextLineReaderV2SkipHeaderLines(value int64) TextLineReaderV2Attr {
- return func(m optionalAttr) {
- m["skip_header_lines"] = value
- }
-}
+// AddSparseToTensorsMapAttr is an optional argument to AddSparseToTensorsMap.
+type AddSparseToTensorsMapAttr func(optionalAttr)
-// TextLineReaderV2Container sets the optional container attribute to value.
+// AddSparseToTensorsMapContainer sets the optional container attribute to value.
//
-// value: If non-empty, this reader is placed in the given container.
-// Otherwise, a default container is used.
+// value: The container name for the `SparseTensorsMap` created by this op.
// If not specified, defaults to ""
-func TextLineReaderV2Container(value string) TextLineReaderV2Attr {
+func AddSparseToTensorsMapContainer(value string) AddSparseToTensorsMapAttr {
return func(m optionalAttr) {
m["container"] = value
}
}
-// TextLineReaderV2SharedName sets the optional shared_name attribute to value.
+// AddSparseToTensorsMapSharedName sets the optional shared_name attribute to value.
//
-// value: If non-empty, this reader is named in the given bucket
-// with this shared_name. Otherwise, the node name is used instead.
+// value: The shared name for the `SparseTensorsMap` created by this op.
+// If blank, the new Operation's unique name is used.
// If not specified, defaults to ""
-func TextLineReaderV2SharedName(value string) TextLineReaderV2Attr {
+func AddSparseToTensorsMapSharedName(value string) AddSparseToTensorsMapAttr {
return func(m optionalAttr) {
m["shared_name"] = value
}
}
-// A Reader that outputs the lines of a file delimited by '\n'.
+// Add a `SparseTensor` to a `SparseTensorsMap` return its handle.
//
-// Returns The handle to reference the Reader.
-func TextLineReaderV2(scope *Scope, optional ...TextLineReaderV2Attr) (reader_handle tf.Output) {
- if scope.Err() != nil {
- return
- }
- attrs := map[string]interface{}{}
- for _, a := range optional {
- a(attrs)
- }
- opspec := tf.OpSpec{
- Type: "TextLineReaderV2",
-
- Attrs: attrs,
- }
- op := scope.AddOperation(opspec)
- return op.Output(0)
-}
-
-// LoadAndRemapMatrixAttr is an optional argument to LoadAndRemapMatrix.
-type LoadAndRemapMatrixAttr func(optionalAttr)
-
-// LoadAndRemapMatrixMaxRowsInMemory sets the optional max_rows_in_memory attribute to value.
-//
-// value: The maximum number of rows to load from the checkpoint at
-// once. If less than or equal to 0, the entire matrix will be loaded into
-// memory. Setting this arg trades increased disk reads for lower memory usage.
-// If not specified, defaults to -1
-func LoadAndRemapMatrixMaxRowsInMemory(value int64) LoadAndRemapMatrixAttr {
- return func(m optionalAttr) {
- m["max_rows_in_memory"] = value
- }
-}
-
-// Loads a 2-D (matrix) `Tensor` with name `old_tensor_name` from the checkpoint
-//
-// at `ckpt_path` and potentially reorders its rows and columns using the
-// specified remappings.
-//
-// Most users should use one of the wrapper initializers (such as
-// `tf.contrib.framework.load_and_remap_matrix_initializer`) instead of this
-// function directly.
-//
-// The remappings are 1-D tensors with the following properties:
-//
-// * `row_remapping` must have exactly `num_rows` entries. Row `i` of the output
-// matrix will be initialized from the row corresponding to index
-// `row_remapping[i]` in the old `Tensor` from the checkpoint.
-// * `col_remapping` must have either 0 entries (indicating that no column
-// reordering is needed) or `num_cols` entries. If specified, column `j` of the
-// output matrix will be initialized from the column corresponding to index
-// `col_remapping[j]` in the old `Tensor` from the checkpoint.
-// * A value of -1 in either of the remappings signifies a "missing" entry. In that
-// case, values from the `initializing_values` tensor will be used to fill that
-// missing row or column. If `row_remapping` has `r` missing entries and
-// `col_remapping` has `c` missing entries, then the following condition must be
-// true:
-//
-// `(r * num_cols) + (c * num_rows) - (r * c) == len(initializing_values)`
-//
-// The remapping tensors can be generated using the GenerateVocabRemapping op.
+// A `SparseTensor` is represented by three tensors: `sparse_indices`,
+// `sparse_values`, and `sparse_shape`.
//
-// As an example, with row_remapping = [1, 0, -1], col_remapping = [0, 2, -1],
-// initializing_values = [0.5, -0.5, 0.25, -0.25, 42], and w(i, j) representing
-// the value from row i, column j of the old tensor in the checkpoint, the output
-// matrix will look like the following:
+// This operator takes the given `SparseTensor` and adds it to a container
+// object (a `SparseTensorsMap`). A unique key within this container is generated
+// in the form of an `int64`, and this is the value that is returned.
//
-// [[w(1, 0), w(1, 2), 0.5],
-// [w(0, 0), w(0, 2), -0.5],
-// [0.25, -0.25, 42]]
+// The `SparseTensor` can then be read out as part of a minibatch by passing
+// the key as a vector element to `TakeManySparseFromTensorsMap`. To ensure
+// the correct `SparseTensorsMap` is accessed, ensure that the same
+// `container` and `shared_name` are passed to that Op. If no `shared_name`
+// is provided here, instead use the *name* of the Operation created by calling
+// `AddSparseToTensorsMap` as the `shared_name` passed to
+// `TakeManySparseFromTensorsMap`. Ensure the Operations are colocated.
//
// Arguments:
-// ckpt_path: Path to the TensorFlow checkpoint (version 2, `TensorBundle`) from
-// which the old matrix `Tensor` will be loaded.
-// old_tensor_name: Name of the 2-D `Tensor` to load from checkpoint.
-// row_remapping: An int `Tensor` of row remappings (generally created by
-// `generate_vocab_remapping`). Even if no row remapping is needed, this must
-// still be an index-valued Tensor (e.g. [0, 1, 2, ...]), or a shifted
-// index-valued `Tensor` (e.g. [8, 9, 10, ...], for partitioned `Variables`).
-// col_remapping: An int `Tensor` of column remappings (generally created by
-// `generate_vocab_remapping`). May be a size-0 `Tensor` if only row remapping
-// is to be done (e.g. column ordering is the same).
-// initializing_values: A float `Tensor` containing values to fill in for cells
-// in the output matrix that are not loaded from the checkpoint. Length must be
-// exactly the same as the number of missing / new cells.
-// num_rows: Number of rows (length of the 1st dimension) in the output matrix.
-// num_cols: Number of columns (length of the 2nd dimension) in the output matrix.
+// sparse_indices: 2-D. The `indices` of the `SparseTensor`.
+// sparse_values: 1-D. The `values` of the `SparseTensor`.
+// sparse_shape: 1-D. The `shape` of the `SparseTensor`.
//
-// Returns Output matrix containing existing values loaded from the
-// checkpoint, and with any missing values filled in from initializing_values.
-func LoadAndRemapMatrix(scope *Scope, ckpt_path tf.Output, old_tensor_name tf.Output, row_remapping tf.Output, col_remapping tf.Output, initializing_values tf.Output, num_rows int64, num_cols int64, optional ...LoadAndRemapMatrixAttr) (output_matrix tf.Output) {
+// Returns 0-D. The handle of the `SparseTensor` now stored in the
+// `SparseTensorsMap`.
+func AddSparseToTensorsMap(scope *Scope, sparse_indices tf.Output, sparse_values tf.Output, sparse_shape tf.Output, optional ...AddSparseToTensorsMapAttr) (sparse_handle tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{"num_rows": num_rows, "num_cols": num_cols}
+ attrs := map[string]interface{}{}
for _, a := range optional {
a(attrs)
}
opspec := tf.OpSpec{
- Type: "LoadAndRemapMatrix",
+ Type: "AddSparseToTensorsMap",
Input: []tf.Input{
- ckpt_path, old_tensor_name, row_remapping, col_remapping, initializing_values,
+ sparse_indices, sparse_values, sparse_shape,
},
Attrs: attrs,
}
return op.Output(0)
}
-// TFRecordReaderV2Attr is an optional argument to TFRecordReaderV2.
-type TFRecordReaderV2Attr func(optionalAttr)
-
-// TFRecordReaderV2Container sets the optional container attribute to value.
+// Computes the matrix exponential of one or more square matrices:
//
-// value: If non-empty, this reader is placed in the given container.
-// Otherwise, a default container is used.
-// If not specified, defaults to ""
-func TFRecordReaderV2Container(value string) TFRecordReaderV2Attr {
- return func(m optionalAttr) {
- m["container"] = value
- }
-}
-
-// TFRecordReaderV2SharedName sets the optional shared_name attribute to value.
+// exp(A) = \sum_{n=0}^\infty A^n/n!
//
-// value: If non-empty, this reader is named in the given bucket
-// with this shared_name. Otherwise, the node name is used instead.
-// If not specified, defaults to ""
-func TFRecordReaderV2SharedName(value string) TFRecordReaderV2Attr {
- return func(m optionalAttr) {
- m["shared_name"] = value
- }
-}
-
-// TFRecordReaderV2CompressionType sets the optional compression_type attribute to value.
-// If not specified, defaults to ""
-func TFRecordReaderV2CompressionType(value string) TFRecordReaderV2Attr {
- return func(m optionalAttr) {
- m["compression_type"] = value
- }
-}
-
-// A Reader that outputs the records from a TensorFlow Records file.
+// The exponential is computed using a combination of the scaling and squaring
+// method and the Pade approximation. Details can be founds in:
+// Nicholas J. Higham, "The scaling and squaring method for the matrix exponential
+// revisited," SIAM J. Matrix Anal. Applic., 26:1179-1193, 2005.
//
-// Returns The handle to reference the Reader.
-func TFRecordReaderV2(scope *Scope, optional ...TFRecordReaderV2Attr) (reader_handle tf.Output) {
+// The input is a tensor of shape `[..., M, M]` whose inner-most 2 dimensions
+// form square matrices. The output is a tensor of the same shape as the input
+// containing the exponential for all input submatrices `[..., :, :]`.
+//
+// Arguments:
+// input: Shape is `[..., M, M]`.
+//
+// Returns Shape is `[..., M, M]`.
+//
+// @compatibility(scipy)
+// Equivalent to scipy.linalg.expm
+// @end_compatibility
+func MatrixExponential(scope *Scope, input tf.Output) (output tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{}
- for _, a := range optional {
- a(attrs)
- }
opspec := tf.OpSpec{
- Type: "TFRecordReaderV2",
-
- Attrs: attrs,
+ Type: "MatrixExponential",
+ Input: []tf.Input{
+ input,
+ },
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// QuantizeAndDequantizeV3Attr is an optional argument to QuantizeAndDequantizeV3.
-type QuantizeAndDequantizeV3Attr func(optionalAttr)
-
-// QuantizeAndDequantizeV3SignedInput sets the optional signed_input attribute to value.
-// If not specified, defaults to true
-func QuantizeAndDequantizeV3SignedInput(value bool) QuantizeAndDequantizeV3Attr {
- return func(m optionalAttr) {
- m["signed_input"] = value
- }
-}
+// QueueDequeueUpToV2Attr is an optional argument to QueueDequeueUpToV2.
+type QueueDequeueUpToV2Attr func(optionalAttr)
-// QuantizeAndDequantizeV3RangeGiven sets the optional range_given attribute to value.
-// If not specified, defaults to true
-func QuantizeAndDequantizeV3RangeGiven(value bool) QuantizeAndDequantizeV3Attr {
+// QueueDequeueUpToV2TimeoutMs sets the optional timeout_ms attribute to value.
+//
+// value: If the queue has fewer than n elements, this operation
+// will block for up to timeout_ms milliseconds.
+// Note: This option is not supported yet.
+// If not specified, defaults to -1
+func QueueDequeueUpToV2TimeoutMs(value int64) QueueDequeueUpToV2Attr {
return func(m optionalAttr) {
- m["range_given"] = value
+ m["timeout_ms"] = value
}
}
-// Quantizes then dequantizes a tensor.
+// Dequeues `n` tuples of one or more tensors from the given queue.
//
-// This is almost identical to QuantizeAndDequantizeV2, except that num_bits is a
-// tensor, so its value can change during training.
-func QuantizeAndDequantizeV3(scope *Scope, input tf.Output, input_min tf.Output, input_max tf.Output, num_bits tf.Output, optional ...QuantizeAndDequantizeV3Attr) (output tf.Output) {
+// This operation is not supported by all queues. If a queue does not support
+// DequeueUpTo, then an Unimplemented error is returned.
+//
+// If the queue is closed and there are more than 0 but less than `n`
+// elements remaining, then instead of returning an OutOfRange error like
+// QueueDequeueMany, less than `n` elements are returned immediately. If
+// the queue is closed and there are 0 elements left in the queue, then
+// an OutOfRange error is returned just like in QueueDequeueMany.
+// Otherwise the behavior is identical to QueueDequeueMany:
+//
+// This operation concatenates queue-element component tensors along the
+// 0th dimension to make a single component tensor. All of the components
+// in the dequeued tuple will have size n in the 0th dimension.
+//
+// This operation has `k` outputs, where `k` is the number of components in
+// the tuples stored in the given queue, and output `i` is the ith
+// component of the dequeued tuple.
+//
+// Arguments:
+// handle: The handle to a queue.
+// n: The number of tuples to dequeue.
+// component_types: The type of each component in a tuple.
+//
+// Returns One or more tensors that were dequeued as a tuple.
+func QueueDequeueUpToV2(scope *Scope, handle tf.Output, n tf.Output, component_types []tf.DataType, optional ...QueueDequeueUpToV2Attr) (components []tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{}
+ attrs := map[string]interface{}{"component_types": component_types}
for _, a := range optional {
a(attrs)
}
opspec := tf.OpSpec{
- Type: "QuantizeAndDequantizeV3",
+ Type: "QueueDequeueUpToV2",
Input: []tf.Input{
- input, input_min, input_max, num_bits,
+ handle, n,
},
Attrs: attrs,
}
op := scope.AddOperation(opspec)
- return op.Output(0)
-}
-
-// IdentityReaderV2Attr is an optional argument to IdentityReaderV2.
-type IdentityReaderV2Attr func(optionalAttr)
-
-// IdentityReaderV2Container sets the optional container attribute to value.
-//
-// value: If non-empty, this reader is placed in the given container.
-// Otherwise, a default container is used.
-// If not specified, defaults to ""
-func IdentityReaderV2Container(value string) IdentityReaderV2Attr {
- return func(m optionalAttr) {
- m["container"] = value
+ if scope.Err() != nil {
+ return
}
-}
-
-// IdentityReaderV2SharedName sets the optional shared_name attribute to value.
-//
-// value: If non-empty, this reader is named in the given bucket
-// with this shared_name. Otherwise, the node name is used instead.
-// If not specified, defaults to ""
-func IdentityReaderV2SharedName(value string) IdentityReaderV2Attr {
- return func(m optionalAttr) {
- m["shared_name"] = value
+ var idx int
+ var err error
+ if components, idx, err = makeOutputList(op, idx, "components"); err != nil {
+ scope.UpdateErr("QueueDequeueUpToV2", err)
+ return
}
+ return components
}
-// A Reader that outputs the queued work as both the key and value.
+// Computes the Cholesky decomposition of one or more square matrices.
//
-// To use, enqueue strings in a Queue. ReaderRead will take the front
-// work string and output (work, work).
+// The input is a tensor of shape `[..., M, M]` whose inner-most 2 dimensions
+// form square matrices.
//
-// Returns The handle to reference the Reader.
-func IdentityReaderV2(scope *Scope, optional ...IdentityReaderV2Attr) (reader_handle tf.Output) {
+// The input has to be symmetric and positive definite. Only the lower-triangular
+// part of the input will be used for this operation. The upper-triangular part
+// will not be read.
+//
+// The output is a tensor of the same shape as the input
+// containing the Cholesky decompositions for all input submatrices `[..., :, :]`.
+//
+// **Note**: The gradient computation on GPU is faster for large matrices but
+// not for large batch dimensions when the submatrices are small. In this
+// case it might be faster to use the CPU.
+//
+// Arguments:
+// input: Shape is `[..., M, M]`.
+//
+// Returns Shape is `[..., M, M]`.
+func Cholesky(scope *Scope, input tf.Output) (output tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{}
- for _, a := range optional {
- a(attrs)
- }
opspec := tf.OpSpec{
- Type: "IdentityReaderV2",
-
- Attrs: attrs,
+ Type: "Cholesky",
+ Input: []tf.Input{
+ input,
+ },
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// ResourceApplyGradientDescentAttr is an optional argument to ResourceApplyGradientDescent.
-type ResourceApplyGradientDescentAttr func(optionalAttr)
-
-// ResourceApplyGradientDescentUseLocking sets the optional use_locking attribute to value.
+// Writes contents to the file at input filename. Creates file and recursively
//
-// value: If `True`, the subtraction will be protected by a lock;
-// otherwise the behavior is undefined, but may exhibit less contention.
-// If not specified, defaults to false
-func ResourceApplyGradientDescentUseLocking(value bool) ResourceApplyGradientDescentAttr {
- return func(m optionalAttr) {
- m["use_locking"] = value
- }
-}
-
-// Update '*var' by subtracting 'alpha' * 'delta' from it.
+// creates directory if not existing.
//
// Arguments:
-// var_: Should be from a Variable().
-// alpha: Scaling factor. Must be a scalar.
-// delta: The change.
+// filename: scalar. The name of the file to which we write the contents.
+// contents: scalar. The content to be written to the output file.
//
// Returns the created operation.
-func ResourceApplyGradientDescent(scope *Scope, var_ tf.Output, alpha tf.Output, delta tf.Output, optional ...ResourceApplyGradientDescentAttr) (o *tf.Operation) {
+func WriteFile(scope *Scope, filename tf.Output, contents tf.Output) (o *tf.Operation) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{}
- for _, a := range optional {
- a(attrs)
- }
opspec := tf.OpSpec{
- Type: "ResourceApplyGradientDescent",
+ Type: "WriteFile",
Input: []tf.Input{
- var_, alpha, delta,
+ filename, contents,
},
- Attrs: attrs,
}
return scope.AddOperation(opspec)
}
-// Returns the next record (key, value pair) produced by a Reader.
+// AllAttr is an optional argument to All.
+type AllAttr func(optionalAttr)
+
+// AllKeepDims sets the optional keep_dims attribute to value.
//
-// Will dequeue from the input queue if necessary (e.g. when the
-// Reader needs to start reading from a new file since it has finished
-// with the previous file).
+// value: If true, retain reduced dimensions with length 1.
+// If not specified, defaults to false
+func AllKeepDims(value bool) AllAttr {
+ return func(m optionalAttr) {
+ m["keep_dims"] = value
+ }
+}
+
+// Computes the "logical and" of elements across dimensions of a tensor.
+//
+// Reduces `input` along the dimensions given in `axis`. Unless
+// `keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in
+// `axis`. If `keep_dims` is true, the reduced dimensions are
+// retained with length 1.
//
// Arguments:
-// reader_handle: Handle to a Reader.
-// queue_handle: Handle to a Queue, with string work items.
+// input: The tensor to reduce.
+// axis: The dimensions to reduce. Must be in the range
+// `[-rank(input), rank(input))`.
//
-// Returns A scalar.A scalar.
-func ReaderReadV2(scope *Scope, reader_handle tf.Output, queue_handle tf.Output) (key tf.Output, value tf.Output) {
+// Returns The reduced tensor.
+func All(scope *Scope, input tf.Output, axis tf.Output, optional ...AllAttr) (output tf.Output) {
if scope.Err() != nil {
return
}
+ attrs := map[string]interface{}{}
+ for _, a := range optional {
+ a(attrs)
+ }
opspec := tf.OpSpec{
- Type: "ReaderReadV2",
+ Type: "All",
Input: []tf.Input{
- reader_handle, queue_handle,
+ input, axis,
},
+ Attrs: attrs,
}
op := scope.AddOperation(opspec)
- return op.Output(0), op.Output(1)
+ return op.Output(0)
}
-// Returns up to `num_records` (key, value) pairs produced by a Reader.
+// Computes the Eigen Decomposition of a batch of square self-adjoint matrices.
//
-// Will dequeue from the input queue if necessary (e.g. when the
-// Reader needs to start reading from a new file since it has finished
-// with the previous file).
-// It may return less than `num_records` even before the last batch.
+// DEPRECATED at GraphDef version 11: Use SelfAdjointEigV2 instead.
+//
+// The input is a tensor of shape `[..., M, M]` whose inner-most 2 dimensions
+// form square matrices, with the same constraints as the single matrix
+// SelfAdjointEig.
+//
+// The result is a [..., M+1, M] matrix with [..., 0,:] containing the
+// eigenvalues, and subsequent [...,1:, :] containing the eigenvectors.
//
// Arguments:
-// reader_handle: Handle to a `Reader`.
-// queue_handle: Handle to a `Queue`, with string work items.
-// num_records: number of records to read from `Reader`.
+// input: Shape is `[..., M, M]`.
//
-// Returns A 1-D tensor.A 1-D tensor.
-func ReaderReadUpToV2(scope *Scope, reader_handle tf.Output, queue_handle tf.Output, num_records tf.Output) (keys tf.Output, values tf.Output) {
+// Returns Shape is `[..., M+1, M]`.
+func SelfAdjointEig(scope *Scope, input tf.Output) (output tf.Output) {
if scope.Err() != nil {
return
}
opspec := tf.OpSpec{
- Type: "ReaderReadUpToV2",
+ Type: "SelfAdjointEig",
Input: []tf.Input{
- reader_handle, queue_handle, num_records,
+ input,
},
}
op := scope.AddOperation(opspec)
- return op.Output(0), op.Output(1)
+ return op.Output(0)
}
-// Restore a Reader to its initial clean state.
+// Computes softplus gradients for a softplus operation.
//
// Arguments:
-// reader_handle: Handle to a Reader.
+// gradients: The backpropagated gradients to the corresponding softplus operation.
+// features: The features passed as input to the corresponding softplus operation.
//
-// Returns the created operation.
-func ReaderResetV2(scope *Scope, reader_handle tf.Output) (o *tf.Operation) {
+// Returns The gradients: `gradients / (1 + exp(-features))`.
+func SoftplusGrad(scope *Scope, gradients tf.Output, features tf.Output) (backprops tf.Output) {
if scope.Err() != nil {
return
}
opspec := tf.OpSpec{
- Type: "ReaderResetV2",
+ Type: "SoftplusGrad",
Input: []tf.Input{
- reader_handle,
+ gradients, features,
},
}
- return scope.AddOperation(opspec)
+ op := scope.AddOperation(opspec)
+ return op.Output(0)
}
-// ResourceApplyAdamAttr is an optional argument to ResourceApplyAdam.
-type ResourceApplyAdamAttr func(optionalAttr)
+// SelfAdjointEigV2Attr is an optional argument to SelfAdjointEigV2.
+type SelfAdjointEigV2Attr func(optionalAttr)
-// ResourceApplyAdamUseLocking sets the optional use_locking attribute to value.
+// SelfAdjointEigV2ComputeV sets the optional compute_v attribute to value.
//
-// value: If `True`, updating of the var, m, and v tensors will be protected
-// by a lock; otherwise the behavior is undefined, but may exhibit less
-// contention.
-// If not specified, defaults to false
-func ResourceApplyAdamUseLocking(value bool) ResourceApplyAdamAttr {
+// value: If `True` then eigenvectors will be computed and returned in `v`.
+// Otherwise, only the eigenvalues will be computed.
+// If not specified, defaults to true
+func SelfAdjointEigV2ComputeV(value bool) SelfAdjointEigV2Attr {
return func(m optionalAttr) {
- m["use_locking"] = value
+ m["compute_v"] = value
}
}
-// ResourceApplyAdamUseNesterov sets the optional use_nesterov attribute to value.
+// Computes the eigen decomposition of one or more square self-adjoint matrices.
//
-// value: If `True`, uses the nesterov update.
-// If not specified, defaults to false
-func ResourceApplyAdamUseNesterov(value bool) ResourceApplyAdamAttr {
- return func(m optionalAttr) {
- m["use_nesterov"] = value
- }
-}
-
-// Update '*var' according to the Adam algorithm.
+// Computes the eigenvalues and (optionally) eigenvectors of each inner matrix in
+// `input` such that `input[..., :, :] = v[..., :, :] * diag(e[..., :])`.
//
-// lr_t <- learning_rate * sqrt(1 - beta2^t) / (1 - beta1^t)
-// m_t <- beta1 * m_{t-1} + (1 - beta1) * g_t
-// v_t <- beta2 * v_{t-1} + (1 - beta2) * g_t * g_t
-// variable <- variable - lr_t * m_t / (sqrt(v_t) + epsilon)
+// ```python
+// # a is a tensor.
+// # e is a tensor of eigenvalues.
+// # v is a tensor of eigenvectors.
+// e, v = self_adjoint_eig(a)
+// e = self_adjoint_eig(a, compute_v=False)
+// ```
//
// Arguments:
-// var_: Should be from a Variable().
-// m: Should be from a Variable().
-// v: Should be from a Variable().
-// beta1_power: Must be a scalar.
-// beta2_power: Must be a scalar.
-// lr: Scaling factor. Must be a scalar.
-// beta1: Momentum factor. Must be a scalar.
-// beta2: Momentum factor. Must be a scalar.
-// epsilon: Ridge term. Must be a scalar.
-// grad: The gradient.
+// input: `Tensor` input of shape `[N, N]`.
//
-// Returns the created operation.
-func ResourceApplyAdam(scope *Scope, var_ tf.Output, m tf.Output, v tf.Output, beta1_power tf.Output, beta2_power tf.Output, lr tf.Output, beta1 tf.Output, beta2 tf.Output, epsilon tf.Output, grad tf.Output, optional ...ResourceApplyAdamAttr) (o *tf.Operation) {
+// Returns Eigenvalues. Shape is `[N]`.Eigenvectors. Shape is `[N, N]`.
+func SelfAdjointEigV2(scope *Scope, input tf.Output, optional ...SelfAdjointEigV2Attr) (e tf.Output, v tf.Output) {
if scope.Err() != nil {
return
}
a(attrs)
}
opspec := tf.OpSpec{
- Type: "ResourceApplyAdam",
+ Type: "SelfAdjointEigV2",
Input: []tf.Input{
- var_, m, v, beta1_power, beta2_power, lr, beta1, beta2, epsilon, grad,
+ input,
},
Attrs: attrs,
}
- return scope.AddOperation(opspec)
+ op := scope.AddOperation(opspec)
+ return op.Output(0), op.Output(1)
}
-// Store the input tensor in the state of the current session.
+// Adjust the saturation of one or more images.
+//
+// `images` is a tensor of at least 3 dimensions. The last dimension is
+// interpretted as channels, and must be three.
+//
+// The input image is considered in the RGB colorspace. Conceptually, the RGB
+// colors are first mapped into HSV. A scale is then applied all the saturation
+// values, and then remapped back to RGB colorspace.
//
// Arguments:
-// value: The tensor to be stored.
+// images: Images to adjust. At least 3-D.
+// scale: A float scale to add to the saturation.
//
-// Returns The handle for the tensor stored in the session state, represented
-// as a ResourceHandle object.
-func GetSessionHandleV2(scope *Scope, value tf.Output) (handle tf.Output) {
+// Returns The hue-adjusted image or images.
+func AdjustSaturation(scope *Scope, images tf.Output, scale tf.Output) (output tf.Output) {
if scope.Err() != nil {
return
}
opspec := tf.OpSpec{
- Type: "GetSessionHandleV2",
+ Type: "AdjustSaturation",
Input: []tf.Input{
- value,
+ images, scale,
},
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// ResizeBicubicGradAttr is an optional argument to ResizeBicubicGrad.
-type ResizeBicubicGradAttr func(optionalAttr)
+// SvdAttr is an optional argument to Svd.
+type SvdAttr func(optionalAttr)
-// ResizeBicubicGradAlignCorners sets the optional align_corners attribute to value.
+// SvdComputeUv sets the optional compute_uv attribute to value.
//
-// value: If true, rescale grads by (orig_height - 1) / (height - 1), which
-// exactly aligns the 4 corners of grads and original_image. If false, rescale by
-// orig_height / height. Treat similarly the width dimension.
-// If not specified, defaults to false
-func ResizeBicubicGradAlignCorners(value bool) ResizeBicubicGradAttr {
+// value: If true, left and right singular vectors will be
+// computed and returned in `u` and `v`, respectively.
+// If false, `u` and `v` are not set and should never referenced.
+// If not specified, defaults to true
+func SvdComputeUv(value bool) SvdAttr {
return func(m optionalAttr) {
- m["align_corners"] = value
+ m["compute_uv"] = value
}
}
-// Computes the gradient of bicubic interpolation.
-//
-// Arguments:
-// grads: 4-D with shape `[batch, height, width, channels]`.
-// original_image: 4-D with shape `[batch, orig_height, orig_width, channels]`,
-// The image tensor that was resized.
+// SvdFullMatrices sets the optional full_matrices attribute to value.
//
-// Returns 4-D with shape `[batch, orig_height, orig_width, channels]`.
-// Gradients with respect to the input image. Input image must have been
-// float or double.
-func ResizeBicubicGrad(scope *Scope, grads tf.Output, original_image tf.Output, optional ...ResizeBicubicGradAttr) (output tf.Output) {
+// value: If true, compute full-sized `u` and `v`. If false
+// (the default), compute only the leading `P` singular vectors.
+// Ignored if `compute_uv` is `False`.
+// If not specified, defaults to false
+func SvdFullMatrices(value bool) SvdAttr {
+ return func(m optionalAttr) {
+ m["full_matrices"] = value
+ }
+}
+
+// Computes the singular value decompositions of one or more matrices.
+//
+// Computes the SVD of each inner matrix in `input` such that
+// `input[..., :, :] = u[..., :, :] * diag(s[..., :, :]) * transpose(v[..., :, :])`
+//
+// ```python
+// # a is a tensor containing a batch of matrices.
+// # s is a tensor of singular values for each matrix.
+// # u is the tensor containing of left singular vectors for each matrix.
+// # v is the tensor containing of right singular vectors for each matrix.
+// s, u, v = svd(a)
+// s, _, _ = svd(a, compute_uv=False)
+// ```
+//
+// Arguments:
+// input: A tensor of shape `[..., M, N]` whose inner-most 2 dimensions
+// form matrices of size `[M, N]`. Let `P` be the minimum of `M` and `N`.
+//
+// Returns Singular values. Shape is `[..., P]`.Left singular vectors. If `full_matrices` is `False` then shape is
+// `[..., M, P]`; if `full_matrices` is `True` then shape is
+// `[..., M, M]`. Undefined if `compute_uv` is `False`.Left singular vectors. If `full_matrices` is `False` then shape is
+// `[..., N, P]`. If `full_matrices` is `True` then shape is `[..., N, N]`.
+// Undefined if `compute_uv` is false.
+func Svd(scope *Scope, input tf.Output, optional ...SvdAttr) (s tf.Output, u tf.Output, v tf.Output) {
if scope.Err() != nil {
return
}
a(attrs)
}
opspec := tf.OpSpec{
- Type: "ResizeBicubicGrad",
+ Type: "Svd",
Input: []tf.Input{
- grads, original_image,
+ input,
},
Attrs: attrs,
}
op := scope.AddOperation(opspec)
- return op.Output(0)
+ return op.Output(0), op.Output(1), op.Output(2)
}
-// ResizeNearestNeighborAttr is an optional argument to ResizeNearestNeighbor.
-type ResizeNearestNeighborAttr func(optionalAttr)
+// QueueEnqueueManyV2Attr is an optional argument to QueueEnqueueManyV2.
+type QueueEnqueueManyV2Attr func(optionalAttr)
-// ResizeNearestNeighborAlignCorners sets the optional align_corners attribute to value.
+// QueueEnqueueManyV2TimeoutMs sets the optional timeout_ms attribute to value.
//
-// value: If true, rescale input by (new_height - 1) / (height - 1), which
-// exactly aligns the 4 corners of images and resized images. If false, rescale
-// by new_height / height. Treat similarly the width dimension.
-// If not specified, defaults to false
-func ResizeNearestNeighborAlignCorners(value bool) ResizeNearestNeighborAttr {
+// value: If the queue is too full, this operation will block for up
+// to timeout_ms milliseconds.
+// Note: This option is not supported yet.
+// If not specified, defaults to -1
+func QueueEnqueueManyV2TimeoutMs(value int64) QueueEnqueueManyV2Attr {
return func(m optionalAttr) {
- m["align_corners"] = value
+ m["timeout_ms"] = value
}
}
-// Resize `images` to `size` using nearest neighbor interpolation.
+// Enqueues zero or more tuples of one or more tensors in the given queue.
+//
+// This operation slices each component tensor along the 0th dimension to
+// make multiple queue elements. All of the tuple components must have the
+// same size in the 0th dimension.
+//
+// The components input has k elements, which correspond to the components of
+// tuples stored in the given queue.
+//
+// N.B. If the queue is full, this operation will block until the given
+// elements have been enqueued (or 'timeout_ms' elapses, if specified).
//
// Arguments:
-// images: 4-D with shape `[batch, height, width, channels]`.
-// size: = A 1-D int32 Tensor of 2 elements: `new_height, new_width`. The
-// new size for the images.
+// handle: The handle to a queue.
+// components: One or more tensors from which the enqueued tensors should
+// be taken.
//
-// Returns 4-D with shape
-// `[batch, new_height, new_width, channels]`.
-func ResizeNearestNeighbor(scope *Scope, images tf.Output, size tf.Output, optional ...ResizeNearestNeighborAttr) (resized_images tf.Output) {
+// Returns the created operation.
+func QueueEnqueueManyV2(scope *Scope, handle tf.Output, components []tf.Output, optional ...QueueEnqueueManyV2Attr) (o *tf.Operation) {
if scope.Err() != nil {
return
}
a(attrs)
}
opspec := tf.OpSpec{
- Type: "ResizeNearestNeighbor",
+ Type: "QueueEnqueueManyV2",
Input: []tf.Input{
- images, size,
+ handle, tf.OutputList(components),
},
Attrs: attrs,
}
- op := scope.AddOperation(opspec)
- return op.Output(0)
+ return scope.AddOperation(opspec)
}
-// ResizeNearestNeighborGradAttr is an optional argument to ResizeNearestNeighborGrad.
-type ResizeNearestNeighborGradAttr func(optionalAttr)
-
-// ResizeNearestNeighborGradAlignCorners sets the optional align_corners attribute to value.
+// Computes the product along segments of a tensor.
//
-// value: If true, rescale grads by (orig_height - 1) / (height - 1), which
-// exactly aligns the 4 corners of grads and original_image. If false, rescale by
-// orig_height / height. Treat similarly the width dimension.
-// If not specified, defaults to false
-func ResizeNearestNeighborGradAlignCorners(value bool) ResizeNearestNeighborGradAttr {
- return func(m optionalAttr) {
- m["align_corners"] = value
- }
-}
-
-// Computes the gradient of nearest neighbor interpolation.
+// Read @{$math_ops#segmentation$the section on segmentation} for an explanation of
+// segments.
+//
+// Computes a tensor such that
+// \\(output_i = \prod_j data_j\\) where the product is over `j` such
+// that `segment_ids[j] == i`.
+//
+// If the product is empty for a given segment ID `i`, `output[i] = 1`.
+//
+// <div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;">
+// <img style="width:100%" src="https://www.tensorflow.org/images/SegmentProd.png" alt>
+// </div>
//
// Arguments:
-// grads: 4-D with shape `[batch, height, width, channels]`.
-// size: = A 1-D int32 Tensor of 2 elements: `orig_height, orig_width`. The
-// original input size.
//
-// Returns 4-D with shape `[batch, orig_height, orig_width, channels]`. Gradients
-// with respect to the input image.
-func ResizeNearestNeighborGrad(scope *Scope, grads tf.Output, size tf.Output, optional ...ResizeNearestNeighborGradAttr) (output tf.Output) {
+// segment_ids: A 1-D tensor whose rank is equal to the rank of `data`'s
+// first dimension. Values should be sorted and can be repeated.
+//
+// Returns Has same shape as data, except for dimension 0 which
+// has size `k`, the number of segments.
+func SegmentProd(scope *Scope, data tf.Output, segment_ids tf.Output) (output tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{}
- for _, a := range optional {
- a(attrs)
- }
opspec := tf.OpSpec{
- Type: "ResizeNearestNeighborGrad",
+ Type: "SegmentProd",
Input: []tf.Input{
- grads, size,
+ data, segment_ids,
},
- Attrs: attrs,
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// ExtractJpegShapeAttr is an optional argument to ExtractJpegShape.
-type ExtractJpegShapeAttr func(optionalAttr)
-
-// ExtractJpegShapeOutputType sets the optional output_type attribute to value.
+// Converts one or more images from RGB to HSV.
//
-// value: (Optional) The output type of the operation (int32 or int64).
-// Defaults to int32.
-// If not specified, defaults to DT_INT32
-func ExtractJpegShapeOutputType(value tf.DataType) ExtractJpegShapeAttr {
- return func(m optionalAttr) {
- m["output_type"] = value
- }
-}
-
-// Extract the shape information of a JPEG-encoded image.
+// Outputs a tensor of the same shape as the `images` tensor, containing the HSV
+// value of the pixels. The output is only well defined if the value in `images`
+// are in `[0,1]`.
//
-// This op only parses the image header, so it is much faster than DecodeJpeg.
+// `output[..., 0]` contains hue, `output[..., 1]` contains saturation, and
+// `output[..., 2]` contains value. All HSV values are in `[0,1]`. A hue of 0
+// corresponds to pure red, hue 1/3 is pure green, and 2/3 is pure blue.
//
// Arguments:
-// contents: 0-D. The JPEG-encoded image.
+// images: 1-D or higher rank. RGB data to convert. Last dimension must be size 3.
//
-// Returns 1-D. The image shape with format [height, width, channels].
-func ExtractJpegShape(scope *Scope, contents tf.Output, optional ...ExtractJpegShapeAttr) (image_shape tf.Output) {
+// Returns `images` converted to HSV.
+func RGBToHSV(scope *Scope, images tf.Output) (output tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{}
- for _, a := range optional {
- a(attrs)
- }
opspec := tf.OpSpec{
- Type: "ExtractJpegShape",
+ Type: "RGBToHSV",
Input: []tf.Input{
- contents,
+ images,
},
- Attrs: attrs,
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// PaddingFIFOQueueV2Attr is an optional argument to PaddingFIFOQueueV2.
-type PaddingFIFOQueueV2Attr func(optionalAttr)
-
-// PaddingFIFOQueueV2Shapes sets the optional shapes attribute to value.
-//
-// value: The shape of each component in a value. The length of this attr must
-// be either 0 or the same as the length of component_types.
-// Shapes of fixed rank but variable size are allowed by setting
-// any shape dimension to -1. In this case, the inputs' shape may vary along
-// the given dimension, and DequeueMany will pad the given dimension with
-// zeros up to the maximum shape of all elements in the given batch.
-// If the length of this attr is 0, different queue elements may have
-// different ranks and shapes, but only one element may be dequeued at a time.
-// If not specified, defaults to <>
+// Does nothing. Only useful as a placeholder for control edges.
//
-// REQUIRES: len(value) >= 0
-func PaddingFIFOQueueV2Shapes(value []tf.Shape) PaddingFIFOQueueV2Attr {
- return func(m optionalAttr) {
- m["shapes"] = value
+// Returns the created operation.
+func NoOp(scope *Scope) (o *tf.Operation) {
+ if scope.Err() != nil {
+ return
}
-}
-
-// PaddingFIFOQueueV2Capacity sets the optional capacity attribute to value.
-//
-// value: The upper bound on the number of elements in this queue.
-// Negative numbers mean no limit.
-// If not specified, defaults to -1
-func PaddingFIFOQueueV2Capacity(value int64) PaddingFIFOQueueV2Attr {
- return func(m optionalAttr) {
- m["capacity"] = value
+ opspec := tf.OpSpec{
+ Type: "NoOp",
}
+ return scope.AddOperation(opspec)
}
-// PaddingFIFOQueueV2Container sets the optional container attribute to value.
-//
-// value: If non-empty, this queue is placed in the given container.
-// Otherwise, a default container is used.
-// If not specified, defaults to ""
-func PaddingFIFOQueueV2Container(value string) PaddingFIFOQueueV2Attr {
- return func(m optionalAttr) {
- m["container"] = value
- }
-}
+// MergeV2CheckpointsAttr is an optional argument to MergeV2Checkpoints.
+type MergeV2CheckpointsAttr func(optionalAttr)
-// PaddingFIFOQueueV2SharedName sets the optional shared_name attribute to value.
+// MergeV2CheckpointsDeleteOldDirs sets the optional delete_old_dirs attribute to value.
//
-// value: If non-empty, this queue will be shared under the given name
-// across multiple sessions.
-// If not specified, defaults to ""
-func PaddingFIFOQueueV2SharedName(value string) PaddingFIFOQueueV2Attr {
+// value: see above.
+// If not specified, defaults to true
+func MergeV2CheckpointsDeleteOldDirs(value bool) MergeV2CheckpointsAttr {
return func(m optionalAttr) {
- m["shared_name"] = value
+ m["delete_old_dirs"] = value
}
}
-// A queue that produces elements in first-in first-out order.
+// V2 format specific: merges the metadata files of sharded checkpoints. The
//
-// Variable-size shapes are allowed by setting the corresponding shape dimensions
-// to 0 in the shape attr. In this case DequeueMany will pad up to the maximum
-// size of any given element in the minibatch. See below for details.
+// result is one logical checkpoint, with one physical metadata file and renamed
+// data files.
+//
+// Intended for "grouping" multiple checkpoints in a sharded checkpoint setup.
+//
+// If delete_old_dirs is true, attempts to delete recursively the dirname of each
+// path in the input checkpoint_prefixes. This is useful when those paths are non
+// user-facing temporary locations.
//
// Arguments:
-// component_types: The type of each component in a value.
+// checkpoint_prefixes: prefixes of V2 checkpoints to merge.
+// destination_prefix: scalar. The desired final prefix. Allowed to be the same
+// as one of the checkpoint_prefixes.
//
-// Returns The handle to the queue.
-func PaddingFIFOQueueV2(scope *Scope, component_types []tf.DataType, optional ...PaddingFIFOQueueV2Attr) (handle tf.Output) {
- if scope.Err() != nil {
- return
- }
- attrs := map[string]interface{}{"component_types": component_types}
- for _, a := range optional {
- a(attrs)
- }
- opspec := tf.OpSpec{
- Type: "PaddingFIFOQueueV2",
-
- Attrs: attrs,
- }
- op := scope.AddOperation(opspec)
- return op.Output(0)
-}
-
-// DecodePngAttr is an optional argument to DecodePng.
-type DecodePngAttr func(optionalAttr)
-
-// DecodePngChannels sets the optional channels attribute to value.
-//
-// value: Number of color channels for the decoded image.
-// If not specified, defaults to 0
-func DecodePngChannels(value int64) DecodePngAttr {
- return func(m optionalAttr) {
- m["channels"] = value
- }
-}
-
-// DecodePngDtype sets the optional dtype attribute to value.
-// If not specified, defaults to DT_UINT8
-func DecodePngDtype(value tf.DataType) DecodePngAttr {
- return func(m optionalAttr) {
- m["dtype"] = value
- }
-}
-
-// Decode a PNG-encoded image to a uint8 or uint16 tensor.
-//
-// The attr `channels` indicates the desired number of color channels for the
-// decoded image.
-//
-// Accepted values are:
-//
-// * 0: Use the number of channels in the PNG-encoded image.
-// * 1: output a grayscale image.
-// * 3: output an RGB image.
-// * 4: output an RGBA image.
-//
-// If needed, the PNG-encoded image is transformed to match the requested number
-// of color channels.
-//
-// This op also supports decoding JPEGs and non-animated GIFs since the interface
-// is the same, though it is cleaner to use `tf.image.decode_image`.
-//
-// Arguments:
-// contents: 0-D. The PNG-encoded image.
-//
-// Returns 3-D with shape `[height, width, channels]`.
-func DecodePng(scope *Scope, contents tf.Output, optional ...DecodePngAttr) (image tf.Output) {
+// Returns the created operation.
+func MergeV2Checkpoints(scope *Scope, checkpoint_prefixes tf.Output, destination_prefix tf.Output, optional ...MergeV2CheckpointsAttr) (o *tf.Operation) {
if scope.Err() != nil {
return
}
a(attrs)
}
opspec := tf.OpSpec{
- Type: "DecodePng",
+ Type: "MergeV2Checkpoints",
Input: []tf.Input{
- contents,
+ checkpoint_prefixes, destination_prefix,
},
Attrs: attrs,
}
- op := scope.AddOperation(opspec)
- return op.Output(0)
+ return scope.AddOperation(opspec)
}
-// Decode the first frame of a GIF-encoded image to a uint8 tensor.
+// Saves input tensors slices to disk.
//
-// GIF with frame or transparency compression are not supported
-// convert animated GIF from compressed to uncompressed by:
+// This is like `Save` except that tensors can be listed in the saved file as being
+// a slice of a larger tensor. `shapes_and_slices` specifies the shape of the
+// larger tensor and the slice that this tensor covers. `shapes_and_slices` must
+// have as many elements as `tensor_names`.
//
-// convert $src.gif -coalesce $dst.gif
+// Elements of the `shapes_and_slices` input must either be:
//
-// This op also supports decoding JPEGs and PNGs, though it is cleaner to use
-// `tf.image.decode_image`.
+// * The empty string, in which case the corresponding tensor is
+// saved normally.
+// * A string of the form `dim0 dim1 ... dimN-1 slice-spec` where the
+// `dimI` are the dimensions of the larger tensor and `slice-spec`
+// specifies what part is covered by the tensor to save.
+//
+// `slice-spec` itself is a `:`-separated list: `slice0:slice1:...:sliceN-1`
+// where each `sliceI` is either:
+//
+// * The string `-` meaning that the slice covers all indices of this dimension
+// * `start,length` where `start` and `length` are integers. In that
+// case the slice covers `length` indices starting at `start`.
+//
+// See also `Save`.
//
// Arguments:
-// contents: 0-D. The GIF-encoded image.
+// filename: Must have a single element. The name of the file to which we write the
+// tensor.
+// tensor_names: Shape `[N]`. The names of the tensors to be saved.
+// shapes_and_slices: Shape `[N]`. The shapes and slice specifications to use when
+// saving the tensors.
+// data: `N` tensors to save.
//
-// Returns 4-D with shape `[num_frames, height, width, 3]`. RGB order
-func DecodeGif(scope *Scope, contents tf.Output) (image tf.Output) {
+// Returns the created operation.
+func SaveSlices(scope *Scope, filename tf.Output, tensor_names tf.Output, shapes_and_slices tf.Output, data []tf.Output) (o *tf.Operation) {
if scope.Err() != nil {
return
}
opspec := tf.OpSpec{
- Type: "DecodeGif",
+ Type: "SaveSlices",
Input: []tf.Input{
- contents,
+ filename, tensor_names, shapes_and_slices, tf.OutputList(data),
},
}
- op := scope.AddOperation(opspec)
- return op.Output(0)
+ return scope.AddOperation(opspec)
}
-// ResourceApplyCenteredRMSPropAttr is an optional argument to ResourceApplyCenteredRMSProp.
-type ResourceApplyCenteredRMSPropAttr func(optionalAttr)
+// DenseToDenseSetOperationAttr is an optional argument to DenseToDenseSetOperation.
+type DenseToDenseSetOperationAttr func(optionalAttr)
-// ResourceApplyCenteredRMSPropUseLocking sets the optional use_locking attribute to value.
-//
-// value: If `True`, updating of the var, mg, ms, and mom tensors is
-// protected by a lock; otherwise the behavior is undefined, but may exhibit less
-// contention.
-// If not specified, defaults to false
-func ResourceApplyCenteredRMSPropUseLocking(value bool) ResourceApplyCenteredRMSPropAttr {
+// DenseToDenseSetOperationValidateIndices sets the optional validate_indices attribute to value.
+// If not specified, defaults to true
+func DenseToDenseSetOperationValidateIndices(value bool) DenseToDenseSetOperationAttr {
return func(m optionalAttr) {
- m["use_locking"] = value
+ m["validate_indices"] = value
}
}
-// Update '*var' according to the centered RMSProp algorithm.
-//
-// The centered RMSProp algorithm uses an estimate of the centered second moment
-// (i.e., the variance) for normalization, as opposed to regular RMSProp, which
-// uses the (uncentered) second moment. This often helps with training, but is
-// slightly more expensive in terms of computation and memory.
-//
-// Note that in dense implementation of this algorithm, mg, ms, and mom will
-// update even if the grad is zero, but in this sparse implementation, mg, ms,
-// and mom will not update in iterations during which the grad is zero.
-//
-// mean_square = decay * mean_square + (1-decay) * gradient ** 2
-// mean_grad = decay * mean_grad + (1-decay) * gradient
+// Applies set operation along last dimension of 2 `Tensor` inputs.
//
-// Delta = learning_rate * gradient / sqrt(mean_square + epsilon - mean_grad ** 2)
+// See SetOperationOp::SetOperationFromContext for values of `set_operation`.
//
-// mg <- rho * mg_{t-1} + (1-rho) * grad
-// ms <- rho * ms_{t-1} + (1-rho) * grad * grad
-// mom <- momentum * mom_{t-1} + lr * grad / sqrt(ms - mg * mg + epsilon)
-// var <- var - mom
+// Output `result` is a `SparseTensor` represented by `result_indices`,
+// `result_values`, and `result_shape`. For `set1` and `set2` ranked `n`, this
+// has rank `n` and the same 1st `n-1` dimensions as `set1` and `set2`. The `nth`
+// dimension contains the result of `set_operation` applied to the corresponding
+// `[0...n-1]` dimension of `set`.
//
// Arguments:
-// var_: Should be from a Variable().
-// mg: Should be from a Variable().
-// ms: Should be from a Variable().
-// mom: Should be from a Variable().
-// lr: Scaling factor. Must be a scalar.
-// rho: Decay rate. Must be a scalar.
+// set1: `Tensor` with rank `n`. 1st `n-1` dimensions must be the same as `set2`.
+// Dimension `n` contains values in a set, duplicates are allowed but ignored.
+// set2: `Tensor` with rank `n`. 1st `n-1` dimensions must be the same as `set1`.
+// Dimension `n` contains values in a set, duplicates are allowed but ignored.
//
-// epsilon: Ridge term. Must be a scalar.
-// grad: The gradient.
//
-// Returns the created operation.
-func ResourceApplyCenteredRMSProp(scope *Scope, var_ tf.Output, mg tf.Output, ms tf.Output, mom tf.Output, lr tf.Output, rho tf.Output, momentum tf.Output, epsilon tf.Output, grad tf.Output, optional ...ResourceApplyCenteredRMSPropAttr) (o *tf.Operation) {
+// Returns 2D indices of a `SparseTensor`.1D values of a `SparseTensor`.1D `Tensor` shape of a `SparseTensor`. `result_shape[0...n-1]` is
+// the same as the 1st `n-1` dimensions of `set1` and `set2`, `result_shape[n]`
+// is the max result set size across all `0...n-1` dimensions.
+func DenseToDenseSetOperation(scope *Scope, set1 tf.Output, set2 tf.Output, set_operation string, optional ...DenseToDenseSetOperationAttr) (result_indices tf.Output, result_values tf.Output, result_shape tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{}
+ attrs := map[string]interface{}{"set_operation": set_operation}
for _, a := range optional {
a(attrs)
}
opspec := tf.OpSpec{
- Type: "ResourceApplyCenteredRMSProp",
+ Type: "DenseToDenseSetOperation",
Input: []tf.Input{
- var_, mg, ms, mom, lr, rho, momentum, epsilon, grad,
+ set1, set2,
},
Attrs: attrs,
}
- return scope.AddOperation(opspec)
-}
-
-// Returns a list of tensors with the same shapes and contents as the input
-//
-// tensors.
-//
-// This op can be used to override the gradient for complicated functions. For
-// example, suppose y = f(x) and we wish to apply a custom function g for backprop
-// such that dx = g(dy). In Python,
-//
-// ```python
-// with tf.get_default_graph().gradient_override_map(
-// {'IdentityN': 'OverrideGradientWithG'}):
-// y, _ = identity_n([f(x), x])
-//
-// @tf.RegisterGradient('OverrideGradientWithG')
-// def ApplyG(op, dy, _):
-// return [None, g(dy)] # Do not backprop to f(x).
-// ```
-func IdentityN(scope *Scope, input []tf.Output) (output []tf.Output) {
- if scope.Err() != nil {
- return
- }
- opspec := tf.OpSpec{
- Type: "IdentityN",
- Input: []tf.Input{
- tf.OutputList(input),
- },
- }
op := scope.AddOperation(opspec)
- if scope.Err() != nil {
- return
- }
- var idx int
- var err error
- if output, idx, err = makeOutputList(op, idx, "output"); err != nil {
- scope.UpdateErr("IdentityN", err)
- return
- }
- return output
+ return op.Output(0), op.Output(1), op.Output(2)
}
-// Computes the gradient of the sigmoid of `x` wrt its input.
+// Generate a sharded filename. The filename is printf formatted as
//
-// Specifically, `grad = dy * y * (1 - y)`, where `y = sigmoid(x)`, and
-// `dy` is the corresponding input gradient.
-func SigmoidGrad(scope *Scope, y tf.Output, dy tf.Output) (z tf.Output) {
+// %s-%05d-of-%05d, basename, shard, num_shards.
+func ShardedFilename(scope *Scope, basename tf.Output, shard tf.Output, num_shards tf.Output) (filename tf.Output) {
if scope.Err() != nil {
return
}
opspec := tf.OpSpec{
- Type: "SigmoidGrad",
+ Type: "ShardedFilename",
Input: []tf.Input{
- y, dy,
+ basename, shard, num_shards,
},
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// Convert one or more images from HSV to RGB.
-//
-// Outputs a tensor of the same shape as the `images` tensor, containing the RGB
-// value of the pixels. The output is only well defined if the value in `images`
-// are in `[0,1]`.
+// BatchToSpace for N-D tensors of type T.
//
-// See `rgb_to_hsv` for a description of the HSV encoding.
+// This operation reshapes the "batch" dimension 0 into `M + 1` dimensions of shape
+// `block_shape + [batch]`, interleaves these blocks back into the grid defined by
+// the spatial dimensions `[1, ..., M]`, to obtain a result with the same rank as
+// the input. The spatial dimensions of this intermediate result are then
+// optionally cropped according to `crops` to produce the output. This is the
+// reverse of SpaceToBatch. See below for a precise description.
//
// Arguments:
-// images: 1-D or higher rank. HSV data to convert. Last dimension must be size 3.
+// input: N-D with shape `input_shape = [batch] + spatial_shape + remaining_shape`,
+// where spatial_shape has M dimensions.
+// block_shape: 1-D with shape `[M]`, all values must be >= 1.
+// crops: 2-D with shape `[M, 2]`, all values must be >= 0.
+// `crops[i] = [crop_start, crop_end]` specifies the amount to crop from input
+// dimension `i + 1`, which corresponds to spatial dimension `i`. It is
+// required that
+// `crop_start[i] + crop_end[i] <= block_shape[i] * input_shape[i + 1]`.
//
-// Returns `images` converted to RGB.
-func HSVToRGB(scope *Scope, images tf.Output) (output tf.Output) {
- if scope.Err() != nil {
- return
+// This operation is equivalent to the following steps:
+//
+// 1. Reshape `input` to `reshaped` of shape:
+// [block_shape[0], ..., block_shape[M-1],
+// batch / prod(block_shape),
+// input_shape[1], ..., input_shape[N-1]]
+//
+// 2. Permute dimensions of `reshaped` to produce `permuted` of shape
+// [batch / prod(block_shape),
+//
+// input_shape[1], block_shape[0],
+// ...,
+// input_shape[M], block_shape[M-1],
+//
+// input_shape[M+1], ..., input_shape[N-1]]
+//
+// 3. Reshape `permuted` to produce `reshaped_permuted` of shape
+// [batch / prod(block_shape),
+//
+// input_shape[1] * block_shape[0],
+// ...,
+// input_shape[M] * block_shape[M-1],
+//
+// input_shape[M+1],
+// ...,
+// input_shape[N-1]]
+//
+// 4. Crop the start and end of dimensions `[1, ..., M]` of
+// `reshaped_permuted` according to `crops` to produce the output of shape:
+// [batch / prod(block_shape),
+//
+// input_shape[1] * block_shape[0] - crops[0,0] - crops[0,1],
+// ...,
+// input_shape[M] * block_shape[M-1] - crops[M-1,0] - crops[M-1,1],
+//
+// input_shape[M+1], ..., input_shape[N-1]]
+//
+// Some examples:
+//
+// (1) For the following input of shape `[4, 1, 1, 1]`, `block_shape = [2, 2]`, and
+// `crops = [[0, 0], [0, 0]]`:
+//
+// ```
+// [[[[1]]], [[[2]]], [[[3]]], [[[4]]]]
+// ```
+//
+// The output tensor has shape `[1, 2, 2, 1]` and value:
+//
+// ```
+// x = [[[[1], [2]], [[3], [4]]]]
+// ```
+//
+// (2) For the following input of shape `[4, 1, 1, 3]`, `block_shape = [2, 2]`, and
+// `crops = [[0, 0], [0, 0]]`:
+//
+// ```
+// [[[1, 2, 3]], [[4, 5, 6]], [[7, 8, 9]], [[10, 11, 12]]]
+// ```
+//
+// The output tensor has shape `[1, 2, 2, 3]` and value:
+//
+// ```
+// x = [[[[1, 2, 3], [4, 5, 6]],
+// [[7, 8, 9], [10, 11, 12]]]]
+// ```
+//
+// (3) For the following input of shape `[4, 2, 2, 1]`, `block_shape = [2, 2]`, and
+// `crops = [[0, 0], [0, 0]]`:
+//
+// ```
+// x = [[[[1], [3]], [[9], [11]]],
+// [[[2], [4]], [[10], [12]]],
+// [[[5], [7]], [[13], [15]]],
+// [[[6], [8]], [[14], [16]]]]
+// ```
+//
+// The output tensor has shape `[1, 4, 4, 1]` and value:
+//
+// ```
+// x = [[[1], [2], [3], [4]],
+// [[5], [6], [7], [8]],
+// [[9], [10], [11], [12]],
+// [[13], [14], [15], [16]]]
+// ```
+//
+// (4) For the following input of shape `[8, 1, 3, 1]`, `block_shape = [2, 2]`, and
+// `crops = [[0, 0], [2, 0]]`:
+//
+// ```
+// x = [[[[0], [1], [3]]], [[[0], [9], [11]]],
+// [[[0], [2], [4]]], [[[0], [10], [12]]],
+// [[[0], [5], [7]]], [[[0], [13], [15]]],
+// [[[0], [6], [8]]], [[[0], [14], [16]]]]
+// ```
+//
+// The output tensor has shape `[2, 2, 4, 1]` and value:
+//
+// ```
+// x = [[[[1], [2], [3], [4]],
+// [[5], [6], [7], [8]]],
+// [[[9], [10], [11], [12]],
+// [[13], [14], [15], [16]]]]
+// ```
+func BatchToSpaceND(scope *Scope, input tf.Output, block_shape tf.Output, crops tf.Output) (output tf.Output) {
+ if scope.Err() != nil {
+ return
+ }
+ opspec := tf.OpSpec{
+ Type: "BatchToSpaceND",
+ Input: []tf.Input{
+ input, block_shape, crops,
+ },
+ }
+ op := scope.AddOperation(opspec)
+ return op.Output(0)
+}
+
+// UnpackAttr is an optional argument to Unpack.
+type UnpackAttr func(optionalAttr)
+
+// UnpackAxis sets the optional axis attribute to value.
+//
+// value: Dimension along which to unpack. Negative values wrap around, so the
+// valid range is `[-R, R)`.
+// If not specified, defaults to 0
+func UnpackAxis(value int64) UnpackAttr {
+ return func(m optionalAttr) {
+ m["axis"] = value
+ }
+}
+
+// Unpacks a given dimension of a rank-`R` tensor into `num` rank-`(R-1)` tensors.
+//
+// Unpacks `num` tensors from `value` by chipping it along the `axis` dimension.
+// For example, given a tensor of shape `(A, B, C, D)`;
+//
+// If `axis == 0` then the i'th tensor in `output` is the slice `value[i, :, :, :]`
+// and each tensor in `output` will have shape `(B, C, D)`. (Note that the
+// dimension unpacked along is gone, unlike `split`).
+//
+// If `axis == 1` then the i'th tensor in `output` is the slice `value[:, i, :, :]`
+// and each tensor in `output` will have shape `(A, C, D)`.
+// Etc.
+//
+// This is the opposite of `pack`.
+//
+// Arguments:
+// value: 1-D or higher, with `axis` dimension size equal to `num`.
+//
+//
+// Returns The list of tensors unpacked from `value`.
+func Unpack(scope *Scope, value tf.Output, num int64, optional ...UnpackAttr) (output []tf.Output) {
+ if scope.Err() != nil {
+ return
+ }
+ attrs := map[string]interface{}{"num": num}
+ for _, a := range optional {
+ a(attrs)
+ }
+ opspec := tf.OpSpec{
+ Type: "Unpack",
+ Input: []tf.Input{
+ value,
+ },
+ Attrs: attrs,
+ }
+ op := scope.AddOperation(opspec)
+ if scope.Err() != nil {
+ return
+ }
+ var idx int
+ var err error
+ if output, idx, err = makeOutputList(op, idx, "output"); err != nil {
+ scope.UpdateErr("Unpack", err)
+ return
+ }
+ return output
+}
+
+// Increments variable pointed to by 'resource' until it reaches 'limit'.
+//
+// Arguments:
+// resource: Should be from a scalar `Variable` node.
+// limit: If incrementing ref would bring it above limit, instead generates an
+// 'OutOfRange' error.
+//
+//
+// Returns A copy of the input before increment. If nothing else modifies the
+// input, the values produced will all be distinct.
+func ResourceCountUpTo(scope *Scope, resource tf.Output, limit int64, T tf.DataType) (output tf.Output) {
+ if scope.Err() != nil {
+ return
+ }
+ attrs := map[string]interface{}{"limit": limit, "T": T}
+ opspec := tf.OpSpec{
+ Type: "ResourceCountUpTo",
+ Input: []tf.Input{
+ resource,
+ },
+ Attrs: attrs,
+ }
+ op := scope.AddOperation(opspec)
+ return op.Output(0)
+}
+
+// Delete the stack from its resource container.
+//
+// Arguments:
+// handle: The handle to a stack.
+//
+// Returns the created operation.
+func StackCloseV2(scope *Scope, handle tf.Output) (o *tf.Operation) {
+ if scope.Err() != nil {
+ return
+ }
+ opspec := tf.OpSpec{
+ Type: "StackCloseV2",
+ Input: []tf.Input{
+ handle,
+ },
+ }
+ return scope.AddOperation(opspec)
+}
+
+// Generate a glob pattern matching all sharded file names.
+func ShardedFilespec(scope *Scope, basename tf.Output, num_shards tf.Output) (filename tf.Output) {
+ if scope.Err() != nil {
+ return
+ }
+ opspec := tf.OpSpec{
+ Type: "ShardedFilespec",
+ Input: []tf.Input{
+ basename, num_shards,
+ },
+ }
+ op := scope.AddOperation(opspec)
+ return op.Output(0)
+}
+
+// TextLineReaderV2Attr is an optional argument to TextLineReaderV2.
+type TextLineReaderV2Attr func(optionalAttr)
+
+// TextLineReaderV2SkipHeaderLines sets the optional skip_header_lines attribute to value.
+//
+// value: Number of lines to skip from the beginning of every file.
+// If not specified, defaults to 0
+func TextLineReaderV2SkipHeaderLines(value int64) TextLineReaderV2Attr {
+ return func(m optionalAttr) {
+ m["skip_header_lines"] = value
+ }
+}
+
+// TextLineReaderV2Container sets the optional container attribute to value.
+//
+// value: If non-empty, this reader is placed in the given container.
+// Otherwise, a default container is used.
+// If not specified, defaults to ""
+func TextLineReaderV2Container(value string) TextLineReaderV2Attr {
+ return func(m optionalAttr) {
+ m["container"] = value
+ }
+}
+
+// TextLineReaderV2SharedName sets the optional shared_name attribute to value.
+//
+// value: If non-empty, this reader is named in the given bucket
+// with this shared_name. Otherwise, the node name is used instead.
+// If not specified, defaults to ""
+func TextLineReaderV2SharedName(value string) TextLineReaderV2Attr {
+ return func(m optionalAttr) {
+ m["shared_name"] = value
+ }
+}
+
+// A Reader that outputs the lines of a file delimited by '\n'.
+//
+// Returns The handle to reference the Reader.
+func TextLineReaderV2(scope *Scope, optional ...TextLineReaderV2Attr) (reader_handle tf.Output) {
+ if scope.Err() != nil {
+ return
+ }
+ attrs := map[string]interface{}{}
+ for _, a := range optional {
+ a(attrs)
+ }
+ opspec := tf.OpSpec{
+ Type: "TextLineReaderV2",
+
+ Attrs: attrs,
+ }
+ op := scope.AddOperation(opspec)
+ return op.Output(0)
+}
+
+// LoadAndRemapMatrixAttr is an optional argument to LoadAndRemapMatrix.
+type LoadAndRemapMatrixAttr func(optionalAttr)
+
+// LoadAndRemapMatrixMaxRowsInMemory sets the optional max_rows_in_memory attribute to value.
+//
+// value: The maximum number of rows to load from the checkpoint at
+// once. If less than or equal to 0, the entire matrix will be loaded into
+// memory. Setting this arg trades increased disk reads for lower memory usage.
+// If not specified, defaults to -1
+func LoadAndRemapMatrixMaxRowsInMemory(value int64) LoadAndRemapMatrixAttr {
+ return func(m optionalAttr) {
+ m["max_rows_in_memory"] = value
+ }
+}
+
+// Loads a 2-D (matrix) `Tensor` with name `old_tensor_name` from the checkpoint
+//
+// at `ckpt_path` and potentially reorders its rows and columns using the
+// specified remappings.
+//
+// Most users should use one of the wrapper initializers (such as
+// `tf.contrib.framework.load_and_remap_matrix_initializer`) instead of this
+// function directly.
+//
+// The remappings are 1-D tensors with the following properties:
+//
+// * `row_remapping` must have exactly `num_rows` entries. Row `i` of the output
+// matrix will be initialized from the row corresponding to index
+// `row_remapping[i]` in the old `Tensor` from the checkpoint.
+// * `col_remapping` must have either 0 entries (indicating that no column
+// reordering is needed) or `num_cols` entries. If specified, column `j` of the
+// output matrix will be initialized from the column corresponding to index
+// `col_remapping[j]` in the old `Tensor` from the checkpoint.
+// * A value of -1 in either of the remappings signifies a "missing" entry. In that
+// case, values from the `initializing_values` tensor will be used to fill that
+// missing row or column. If `row_remapping` has `r` missing entries and
+// `col_remapping` has `c` missing entries, then the following condition must be
+// true:
+//
+// `(r * num_cols) + (c * num_rows) - (r * c) == len(initializing_values)`
+//
+// The remapping tensors can be generated using the GenerateVocabRemapping op.
+//
+// As an example, with row_remapping = [1, 0, -1], col_remapping = [0, 2, -1],
+// initializing_values = [0.5, -0.5, 0.25, -0.25, 42], and w(i, j) representing
+// the value from row i, column j of the old tensor in the checkpoint, the output
+// matrix will look like the following:
+//
+// [[w(1, 0), w(1, 2), 0.5],
+// [w(0, 0), w(0, 2), -0.5],
+// [0.25, -0.25, 42]]
+//
+// Arguments:
+// ckpt_path: Path to the TensorFlow checkpoint (version 2, `TensorBundle`) from
+// which the old matrix `Tensor` will be loaded.
+// old_tensor_name: Name of the 2-D `Tensor` to load from checkpoint.
+// row_remapping: An int `Tensor` of row remappings (generally created by
+// `generate_vocab_remapping`). Even if no row remapping is needed, this must
+// still be an index-valued Tensor (e.g. [0, 1, 2, ...]), or a shifted
+// index-valued `Tensor` (e.g. [8, 9, 10, ...], for partitioned `Variables`).
+// col_remapping: An int `Tensor` of column remappings (generally created by
+// `generate_vocab_remapping`). May be a size-0 `Tensor` if only row remapping
+// is to be done (e.g. column ordering is the same).
+// initializing_values: A float `Tensor` containing values to fill in for cells
+// in the output matrix that are not loaded from the checkpoint. Length must be
+// exactly the same as the number of missing / new cells.
+// num_rows: Number of rows (length of the 1st dimension) in the output matrix.
+// num_cols: Number of columns (length of the 2nd dimension) in the output matrix.
+//
+// Returns Output matrix containing existing values loaded from the
+// checkpoint, and with any missing values filled in from initializing_values.
+func LoadAndRemapMatrix(scope *Scope, ckpt_path tf.Output, old_tensor_name tf.Output, row_remapping tf.Output, col_remapping tf.Output, initializing_values tf.Output, num_rows int64, num_cols int64, optional ...LoadAndRemapMatrixAttr) (output_matrix tf.Output) {
+ if scope.Err() != nil {
+ return
+ }
+ attrs := map[string]interface{}{"num_rows": num_rows, "num_cols": num_cols}
+ for _, a := range optional {
+ a(attrs)
+ }
+ opspec := tf.OpSpec{
+ Type: "LoadAndRemapMatrix",
+ Input: []tf.Input{
+ ckpt_path, old_tensor_name, row_remapping, col_remapping, initializing_values,
+ },
+ Attrs: attrs,
+ }
+ op := scope.AddOperation(opspec)
+ return op.Output(0)
+}
+
+// TFRecordReaderV2Attr is an optional argument to TFRecordReaderV2.
+type TFRecordReaderV2Attr func(optionalAttr)
+
+// TFRecordReaderV2Container sets the optional container attribute to value.
+//
+// value: If non-empty, this reader is placed in the given container.
+// Otherwise, a default container is used.
+// If not specified, defaults to ""
+func TFRecordReaderV2Container(value string) TFRecordReaderV2Attr {
+ return func(m optionalAttr) {
+ m["container"] = value
+ }
+}
+
+// TFRecordReaderV2SharedName sets the optional shared_name attribute to value.
+//
+// value: If non-empty, this reader is named in the given bucket
+// with this shared_name. Otherwise, the node name is used instead.
+// If not specified, defaults to ""
+func TFRecordReaderV2SharedName(value string) TFRecordReaderV2Attr {
+ return func(m optionalAttr) {
+ m["shared_name"] = value
+ }
+}
+
+// TFRecordReaderV2CompressionType sets the optional compression_type attribute to value.
+// If not specified, defaults to ""
+func TFRecordReaderV2CompressionType(value string) TFRecordReaderV2Attr {
+ return func(m optionalAttr) {
+ m["compression_type"] = value
+ }
+}
+
+// A Reader that outputs the records from a TensorFlow Records file.
+//
+// Returns The handle to reference the Reader.
+func TFRecordReaderV2(scope *Scope, optional ...TFRecordReaderV2Attr) (reader_handle tf.Output) {
+ if scope.Err() != nil {
+ return
+ }
+ attrs := map[string]interface{}{}
+ for _, a := range optional {
+ a(attrs)
+ }
+ opspec := tf.OpSpec{
+ Type: "TFRecordReaderV2",
+
+ Attrs: attrs,
+ }
+ op := scope.AddOperation(opspec)
+ return op.Output(0)
+}
+
+// QuantizeAndDequantizeV3Attr is an optional argument to QuantizeAndDequantizeV3.
+type QuantizeAndDequantizeV3Attr func(optionalAttr)
+
+// QuantizeAndDequantizeV3SignedInput sets the optional signed_input attribute to value.
+// If not specified, defaults to true
+func QuantizeAndDequantizeV3SignedInput(value bool) QuantizeAndDequantizeV3Attr {
+ return func(m optionalAttr) {
+ m["signed_input"] = value
+ }
+}
+
+// QuantizeAndDequantizeV3RangeGiven sets the optional range_given attribute to value.
+// If not specified, defaults to true
+func QuantizeAndDequantizeV3RangeGiven(value bool) QuantizeAndDequantizeV3Attr {
+ return func(m optionalAttr) {
+ m["range_given"] = value
+ }
+}
+
+// Quantizes then dequantizes a tensor.
+//
+// This is almost identical to QuantizeAndDequantizeV2, except that num_bits is a
+// tensor, so its value can change during training.
+func QuantizeAndDequantizeV3(scope *Scope, input tf.Output, input_min tf.Output, input_max tf.Output, num_bits tf.Output, optional ...QuantizeAndDequantizeV3Attr) (output tf.Output) {
+ if scope.Err() != nil {
+ return
+ }
+ attrs := map[string]interface{}{}
+ for _, a := range optional {
+ a(attrs)
+ }
+ opspec := tf.OpSpec{
+ Type: "QuantizeAndDequantizeV3",
+ Input: []tf.Input{
+ input, input_min, input_max, num_bits,
+ },
+ Attrs: attrs,
+ }
+ op := scope.AddOperation(opspec)
+ return op.Output(0)
+}
+
+// IdentityReaderV2Attr is an optional argument to IdentityReaderV2.
+type IdentityReaderV2Attr func(optionalAttr)
+
+// IdentityReaderV2Container sets the optional container attribute to value.
+//
+// value: If non-empty, this reader is placed in the given container.
+// Otherwise, a default container is used.
+// If not specified, defaults to ""
+func IdentityReaderV2Container(value string) IdentityReaderV2Attr {
+ return func(m optionalAttr) {
+ m["container"] = value
+ }
+}
+
+// IdentityReaderV2SharedName sets the optional shared_name attribute to value.
+//
+// value: If non-empty, this reader is named in the given bucket
+// with this shared_name. Otherwise, the node name is used instead.
+// If not specified, defaults to ""
+func IdentityReaderV2SharedName(value string) IdentityReaderV2Attr {
+ return func(m optionalAttr) {
+ m["shared_name"] = value
+ }
+}
+
+// A Reader that outputs the queued work as both the key and value.
+//
+// To use, enqueue strings in a Queue. ReaderRead will take the front
+// work string and output (work, work).
+//
+// Returns The handle to reference the Reader.
+func IdentityReaderV2(scope *Scope, optional ...IdentityReaderV2Attr) (reader_handle tf.Output) {
+ if scope.Err() != nil {
+ return
+ }
+ attrs := map[string]interface{}{}
+ for _, a := range optional {
+ a(attrs)
+ }
+ opspec := tf.OpSpec{
+ Type: "IdentityReaderV2",
+
+ Attrs: attrs,
+ }
+ op := scope.AddOperation(opspec)
+ return op.Output(0)
+}
+
+// ResourceApplyGradientDescentAttr is an optional argument to ResourceApplyGradientDescent.
+type ResourceApplyGradientDescentAttr func(optionalAttr)
+
+// ResourceApplyGradientDescentUseLocking sets the optional use_locking attribute to value.
+//
+// value: If `True`, the subtraction will be protected by a lock;
+// otherwise the behavior is undefined, but may exhibit less contention.
+// If not specified, defaults to false
+func ResourceApplyGradientDescentUseLocking(value bool) ResourceApplyGradientDescentAttr {
+ return func(m optionalAttr) {
+ m["use_locking"] = value
+ }
+}
+
+// Update '*var' by subtracting 'alpha' * 'delta' from it.
+//
+// Arguments:
+// var_: Should be from a Variable().
+// alpha: Scaling factor. Must be a scalar.
+// delta: The change.
+//
+// Returns the created operation.
+func ResourceApplyGradientDescent(scope *Scope, var_ tf.Output, alpha tf.Output, delta tf.Output, optional ...ResourceApplyGradientDescentAttr) (o *tf.Operation) {
+ if scope.Err() != nil {
+ return
+ }
+ attrs := map[string]interface{}{}
+ for _, a := range optional {
+ a(attrs)
+ }
+ opspec := tf.OpSpec{
+ Type: "ResourceApplyGradientDescent",
+ Input: []tf.Input{
+ var_, alpha, delta,
+ },
+ Attrs: attrs,
+ }
+ return scope.AddOperation(opspec)
+}
+
+// Returns the next record (key, value pair) produced by a Reader.
+//
+// Will dequeue from the input queue if necessary (e.g. when the
+// Reader needs to start reading from a new file since it has finished
+// with the previous file).
+//
+// Arguments:
+// reader_handle: Handle to a Reader.
+// queue_handle: Handle to a Queue, with string work items.
+//
+// Returns A scalar.A scalar.
+func ReaderReadV2(scope *Scope, reader_handle tf.Output, queue_handle tf.Output) (key tf.Output, value tf.Output) {
+ if scope.Err() != nil {
+ return
+ }
+ opspec := tf.OpSpec{
+ Type: "ReaderReadV2",
+ Input: []tf.Input{
+ reader_handle, queue_handle,
+ },
+ }
+ op := scope.AddOperation(opspec)
+ return op.Output(0), op.Output(1)
+}
+
+// Returns up to `num_records` (key, value) pairs produced by a Reader.
+//
+// Will dequeue from the input queue if necessary (e.g. when the
+// Reader needs to start reading from a new file since it has finished
+// with the previous file).
+// It may return less than `num_records` even before the last batch.
+//
+// Arguments:
+// reader_handle: Handle to a `Reader`.
+// queue_handle: Handle to a `Queue`, with string work items.
+// num_records: number of records to read from `Reader`.
+//
+// Returns A 1-D tensor.A 1-D tensor.
+func ReaderReadUpToV2(scope *Scope, reader_handle tf.Output, queue_handle tf.Output, num_records tf.Output) (keys tf.Output, values tf.Output) {
+ if scope.Err() != nil {
+ return
+ }
+ opspec := tf.OpSpec{
+ Type: "ReaderReadUpToV2",
+ Input: []tf.Input{
+ reader_handle, queue_handle, num_records,
+ },
+ }
+ op := scope.AddOperation(opspec)
+ return op.Output(0), op.Output(1)
+}
+
+// Restore a Reader to its initial clean state.
+//
+// Arguments:
+// reader_handle: Handle to a Reader.
+//
+// Returns the created operation.
+func ReaderResetV2(scope *Scope, reader_handle tf.Output) (o *tf.Operation) {
+ if scope.Err() != nil {
+ return
+ }
+ opspec := tf.OpSpec{
+ Type: "ReaderResetV2",
+ Input: []tf.Input{
+ reader_handle,
+ },
+ }
+ return scope.AddOperation(opspec)
+}
+
+// ResourceApplyAdamAttr is an optional argument to ResourceApplyAdam.
+type ResourceApplyAdamAttr func(optionalAttr)
+
+// ResourceApplyAdamUseLocking sets the optional use_locking attribute to value.
+//
+// value: If `True`, updating of the var, m, and v tensors will be protected
+// by a lock; otherwise the behavior is undefined, but may exhibit less
+// contention.
+// If not specified, defaults to false
+func ResourceApplyAdamUseLocking(value bool) ResourceApplyAdamAttr {
+ return func(m optionalAttr) {
+ m["use_locking"] = value
+ }
+}
+
+// ResourceApplyAdamUseNesterov sets the optional use_nesterov attribute to value.
+//
+// value: If `True`, uses the nesterov update.
+// If not specified, defaults to false
+func ResourceApplyAdamUseNesterov(value bool) ResourceApplyAdamAttr {
+ return func(m optionalAttr) {
+ m["use_nesterov"] = value
+ }
+}
+
+// Update '*var' according to the Adam algorithm.
+//
+// lr_t <- learning_rate * sqrt(1 - beta2^t) / (1 - beta1^t)
+// m_t <- beta1 * m_{t-1} + (1 - beta1) * g_t
+// v_t <- beta2 * v_{t-1} + (1 - beta2) * g_t * g_t
+// variable <- variable - lr_t * m_t / (sqrt(v_t) + epsilon)
+//
+// Arguments:
+// var_: Should be from a Variable().
+// m: Should be from a Variable().
+// v: Should be from a Variable().
+// beta1_power: Must be a scalar.
+// beta2_power: Must be a scalar.
+// lr: Scaling factor. Must be a scalar.
+// beta1: Momentum factor. Must be a scalar.
+// beta2: Momentum factor. Must be a scalar.
+// epsilon: Ridge term. Must be a scalar.
+// grad: The gradient.
+//
+// Returns the created operation.
+func ResourceApplyAdam(scope *Scope, var_ tf.Output, m tf.Output, v tf.Output, beta1_power tf.Output, beta2_power tf.Output, lr tf.Output, beta1 tf.Output, beta2 tf.Output, epsilon tf.Output, grad tf.Output, optional ...ResourceApplyAdamAttr) (o *tf.Operation) {
+ if scope.Err() != nil {
+ return
+ }
+ attrs := map[string]interface{}{}
+ for _, a := range optional {
+ a(attrs)
+ }
+ opspec := tf.OpSpec{
+ Type: "ResourceApplyAdam",
+ Input: []tf.Input{
+ var_, m, v, beta1_power, beta2_power, lr, beta1, beta2, epsilon, grad,
+ },
+ Attrs: attrs,
+ }
+ return scope.AddOperation(opspec)
+}
+
+// Store the input tensor in the state of the current session.
+//
+// Arguments:
+// value: The tensor to be stored.
+//
+// Returns The handle for the tensor stored in the session state, represented
+// as a ResourceHandle object.
+func GetSessionHandleV2(scope *Scope, value tf.Output) (handle tf.Output) {
+ if scope.Err() != nil {
+ return
+ }
+ opspec := tf.OpSpec{
+ Type: "GetSessionHandleV2",
+ Input: []tf.Input{
+ value,
+ },
+ }
+ op := scope.AddOperation(opspec)
+ return op.Output(0)
+}
+
+// ResizeBicubicGradAttr is an optional argument to ResizeBicubicGrad.
+type ResizeBicubicGradAttr func(optionalAttr)
+
+// ResizeBicubicGradAlignCorners sets the optional align_corners attribute to value.
+//
+// value: If true, rescale grads by (orig_height - 1) / (height - 1), which
+// exactly aligns the 4 corners of grads and original_image. If false, rescale by
+// orig_height / height. Treat similarly the width dimension.
+// If not specified, defaults to false
+func ResizeBicubicGradAlignCorners(value bool) ResizeBicubicGradAttr {
+ return func(m optionalAttr) {
+ m["align_corners"] = value
+ }
+}
+
+// Computes the gradient of bicubic interpolation.
+//
+// Arguments:
+// grads: 4-D with shape `[batch, height, width, channels]`.
+// original_image: 4-D with shape `[batch, orig_height, orig_width, channels]`,
+// The image tensor that was resized.
+//
+// Returns 4-D with shape `[batch, orig_height, orig_width, channels]`.
+// Gradients with respect to the input image. Input image must have been
+// float or double.
+func ResizeBicubicGrad(scope *Scope, grads tf.Output, original_image tf.Output, optional ...ResizeBicubicGradAttr) (output tf.Output) {
+ if scope.Err() != nil {
+ return
+ }
+ attrs := map[string]interface{}{}
+ for _, a := range optional {
+ a(attrs)
+ }
+ opspec := tf.OpSpec{
+ Type: "ResizeBicubicGrad",
+ Input: []tf.Input{
+ grads, original_image,
+ },
+ Attrs: attrs,
+ }
+ op := scope.AddOperation(opspec)
+ return op.Output(0)
+}
+
+// ResizeNearestNeighborAttr is an optional argument to ResizeNearestNeighbor.
+type ResizeNearestNeighborAttr func(optionalAttr)
+
+// ResizeNearestNeighborAlignCorners sets the optional align_corners attribute to value.
+//
+// value: If true, rescale input by (new_height - 1) / (height - 1), which
+// exactly aligns the 4 corners of images and resized images. If false, rescale
+// by new_height / height. Treat similarly the width dimension.
+// If not specified, defaults to false
+func ResizeNearestNeighborAlignCorners(value bool) ResizeNearestNeighborAttr {
+ return func(m optionalAttr) {
+ m["align_corners"] = value
+ }
+}
+
+// Resize `images` to `size` using nearest neighbor interpolation.
+//
+// Arguments:
+// images: 4-D with shape `[batch, height, width, channels]`.
+// size: = A 1-D int32 Tensor of 2 elements: `new_height, new_width`. The
+// new size for the images.
+//
+// Returns 4-D with shape
+// `[batch, new_height, new_width, channels]`.
+func ResizeNearestNeighbor(scope *Scope, images tf.Output, size tf.Output, optional ...ResizeNearestNeighborAttr) (resized_images tf.Output) {
+ if scope.Err() != nil {
+ return
+ }
+ attrs := map[string]interface{}{}
+ for _, a := range optional {
+ a(attrs)
+ }
+ opspec := tf.OpSpec{
+ Type: "ResizeNearestNeighbor",
+ Input: []tf.Input{
+ images, size,
+ },
+ Attrs: attrs,
+ }
+ op := scope.AddOperation(opspec)
+ return op.Output(0)
+}
+
+// ResizeNearestNeighborGradAttr is an optional argument to ResizeNearestNeighborGrad.
+type ResizeNearestNeighborGradAttr func(optionalAttr)
+
+// ResizeNearestNeighborGradAlignCorners sets the optional align_corners attribute to value.
+//
+// value: If true, rescale grads by (orig_height - 1) / (height - 1), which
+// exactly aligns the 4 corners of grads and original_image. If false, rescale by
+// orig_height / height. Treat similarly the width dimension.
+// If not specified, defaults to false
+func ResizeNearestNeighborGradAlignCorners(value bool) ResizeNearestNeighborGradAttr {
+ return func(m optionalAttr) {
+ m["align_corners"] = value
+ }
+}
+
+// Computes the gradient of nearest neighbor interpolation.
+//
+// Arguments:
+// grads: 4-D with shape `[batch, height, width, channels]`.
+// size: = A 1-D int32 Tensor of 2 elements: `orig_height, orig_width`. The
+// original input size.
+//
+// Returns 4-D with shape `[batch, orig_height, orig_width, channels]`. Gradients
+// with respect to the input image.
+func ResizeNearestNeighborGrad(scope *Scope, grads tf.Output, size tf.Output, optional ...ResizeNearestNeighborGradAttr) (output tf.Output) {
+ if scope.Err() != nil {
+ return
+ }
+ attrs := map[string]interface{}{}
+ for _, a := range optional {
+ a(attrs)
+ }
+ opspec := tf.OpSpec{
+ Type: "ResizeNearestNeighborGrad",
+ Input: []tf.Input{
+ grads, size,
+ },
+ Attrs: attrs,
+ }
+ op := scope.AddOperation(opspec)
+ return op.Output(0)
+}
+
+// ExtractJpegShapeAttr is an optional argument to ExtractJpegShape.
+type ExtractJpegShapeAttr func(optionalAttr)
+
+// ExtractJpegShapeOutputType sets the optional output_type attribute to value.
+//
+// value: (Optional) The output type of the operation (int32 or int64).
+// Defaults to int32.
+// If not specified, defaults to DT_INT32
+func ExtractJpegShapeOutputType(value tf.DataType) ExtractJpegShapeAttr {
+ return func(m optionalAttr) {
+ m["output_type"] = value
+ }
+}
+
+// Extract the shape information of a JPEG-encoded image.
+//
+// This op only parses the image header, so it is much faster than DecodeJpeg.
+//
+// Arguments:
+// contents: 0-D. The JPEG-encoded image.
+//
+// Returns 1-D. The image shape with format [height, width, channels].
+func ExtractJpegShape(scope *Scope, contents tf.Output, optional ...ExtractJpegShapeAttr) (image_shape tf.Output) {
+ if scope.Err() != nil {
+ return
+ }
+ attrs := map[string]interface{}{}
+ for _, a := range optional {
+ a(attrs)
+ }
+ opspec := tf.OpSpec{
+ Type: "ExtractJpegShape",
+ Input: []tf.Input{
+ contents,
+ },
+ Attrs: attrs,
+ }
+ op := scope.AddOperation(opspec)
+ return op.Output(0)
+}
+
+// PaddingFIFOQueueV2Attr is an optional argument to PaddingFIFOQueueV2.
+type PaddingFIFOQueueV2Attr func(optionalAttr)
+
+// PaddingFIFOQueueV2Shapes sets the optional shapes attribute to value.
+//
+// value: The shape of each component in a value. The length of this attr must
+// be either 0 or the same as the length of component_types.
+// Shapes of fixed rank but variable size are allowed by setting
+// any shape dimension to -1. In this case, the inputs' shape may vary along
+// the given dimension, and DequeueMany will pad the given dimension with
+// zeros up to the maximum shape of all elements in the given batch.
+// If the length of this attr is 0, different queue elements may have
+// different ranks and shapes, but only one element may be dequeued at a time.
+// If not specified, defaults to <>
+//
+// REQUIRES: len(value) >= 0
+func PaddingFIFOQueueV2Shapes(value []tf.Shape) PaddingFIFOQueueV2Attr {
+ return func(m optionalAttr) {
+ m["shapes"] = value
+ }
+}
+
+// PaddingFIFOQueueV2Capacity sets the optional capacity attribute to value.
+//
+// value: The upper bound on the number of elements in this queue.
+// Negative numbers mean no limit.
+// If not specified, defaults to -1
+func PaddingFIFOQueueV2Capacity(value int64) PaddingFIFOQueueV2Attr {
+ return func(m optionalAttr) {
+ m["capacity"] = value
+ }
+}
+
+// PaddingFIFOQueueV2Container sets the optional container attribute to value.
+//
+// value: If non-empty, this queue is placed in the given container.
+// Otherwise, a default container is used.
+// If not specified, defaults to ""
+func PaddingFIFOQueueV2Container(value string) PaddingFIFOQueueV2Attr {
+ return func(m optionalAttr) {
+ m["container"] = value
+ }
+}
+
+// PaddingFIFOQueueV2SharedName sets the optional shared_name attribute to value.
+//
+// value: If non-empty, this queue will be shared under the given name
+// across multiple sessions.
+// If not specified, defaults to ""
+func PaddingFIFOQueueV2SharedName(value string) PaddingFIFOQueueV2Attr {
+ return func(m optionalAttr) {
+ m["shared_name"] = value
+ }
+}
+
+// A queue that produces elements in first-in first-out order.
+//
+// Variable-size shapes are allowed by setting the corresponding shape dimensions
+// to 0 in the shape attr. In this case DequeueMany will pad up to the maximum
+// size of any given element in the minibatch. See below for details.
+//
+// Arguments:
+// component_types: The type of each component in a value.
+//
+// Returns The handle to the queue.
+func PaddingFIFOQueueV2(scope *Scope, component_types []tf.DataType, optional ...PaddingFIFOQueueV2Attr) (handle tf.Output) {
+ if scope.Err() != nil {
+ return
+ }
+ attrs := map[string]interface{}{"component_types": component_types}
+ for _, a := range optional {
+ a(attrs)
+ }
+ opspec := tf.OpSpec{
+ Type: "PaddingFIFOQueueV2",
+
+ Attrs: attrs,
+ }
+ op := scope.AddOperation(opspec)
+ return op.Output(0)
+}
+
+// DecodePngAttr is an optional argument to DecodePng.
+type DecodePngAttr func(optionalAttr)
+
+// DecodePngChannels sets the optional channels attribute to value.
+//
+// value: Number of color channels for the decoded image.
+// If not specified, defaults to 0
+func DecodePngChannels(value int64) DecodePngAttr {
+ return func(m optionalAttr) {
+ m["channels"] = value
+ }
+}
+
+// DecodePngDtype sets the optional dtype attribute to value.
+// If not specified, defaults to DT_UINT8
+func DecodePngDtype(value tf.DataType) DecodePngAttr {
+ return func(m optionalAttr) {
+ m["dtype"] = value
+ }
+}
+
+// Decode a PNG-encoded image to a uint8 or uint16 tensor.
+//
+// The attr `channels` indicates the desired number of color channels for the
+// decoded image.
+//
+// Accepted values are:
+//
+// * 0: Use the number of channels in the PNG-encoded image.
+// * 1: output a grayscale image.
+// * 3: output an RGB image.
+// * 4: output an RGBA image.
+//
+// If needed, the PNG-encoded image is transformed to match the requested number
+// of color channels.
+//
+// This op also supports decoding JPEGs and non-animated GIFs since the interface
+// is the same, though it is cleaner to use `tf.image.decode_image`.
+//
+// Arguments:
+// contents: 0-D. The PNG-encoded image.
+//
+// Returns 3-D with shape `[height, width, channels]`.
+func DecodePng(scope *Scope, contents tf.Output, optional ...DecodePngAttr) (image tf.Output) {
+ if scope.Err() != nil {
+ return
+ }
+ attrs := map[string]interface{}{}
+ for _, a := range optional {
+ a(attrs)
+ }
+ opspec := tf.OpSpec{
+ Type: "DecodePng",
+ Input: []tf.Input{
+ contents,
+ },
+ Attrs: attrs,
+ }
+ op := scope.AddOperation(opspec)
+ return op.Output(0)
+}
+
+// Decode the first frame of a GIF-encoded image to a uint8 tensor.
+//
+// GIF with frame or transparency compression are not supported
+// convert animated GIF from compressed to uncompressed by:
+//
+// convert $src.gif -coalesce $dst.gif
+//
+// This op also supports decoding JPEGs and PNGs, though it is cleaner to use
+// `tf.image.decode_image`.
+//
+// Arguments:
+// contents: 0-D. The GIF-encoded image.
+//
+// Returns 4-D with shape `[num_frames, height, width, 3]`. RGB order
+func DecodeGif(scope *Scope, contents tf.Output) (image tf.Output) {
+ if scope.Err() != nil {
+ return
+ }
+ opspec := tf.OpSpec{
+ Type: "DecodeGif",
+ Input: []tf.Input{
+ contents,
+ },
+ }
+ op := scope.AddOperation(opspec)
+ return op.Output(0)
+}
+
+// ResourceApplyCenteredRMSPropAttr is an optional argument to ResourceApplyCenteredRMSProp.
+type ResourceApplyCenteredRMSPropAttr func(optionalAttr)
+
+// ResourceApplyCenteredRMSPropUseLocking sets the optional use_locking attribute to value.
+//
+// value: If `True`, updating of the var, mg, ms, and mom tensors is
+// protected by a lock; otherwise the behavior is undefined, but may exhibit less
+// contention.
+// If not specified, defaults to false
+func ResourceApplyCenteredRMSPropUseLocking(value bool) ResourceApplyCenteredRMSPropAttr {
+ return func(m optionalAttr) {
+ m["use_locking"] = value
+ }
+}
+
+// Update '*var' according to the centered RMSProp algorithm.
+//
+// The centered RMSProp algorithm uses an estimate of the centered second moment
+// (i.e., the variance) for normalization, as opposed to regular RMSProp, which
+// uses the (uncentered) second moment. This often helps with training, but is
+// slightly more expensive in terms of computation and memory.
+//
+// Note that in dense implementation of this algorithm, mg, ms, and mom will
+// update even if the grad is zero, but in this sparse implementation, mg, ms,
+// and mom will not update in iterations during which the grad is zero.
+//
+// mean_square = decay * mean_square + (1-decay) * gradient ** 2
+// mean_grad = decay * mean_grad + (1-decay) * gradient
+//
+// Delta = learning_rate * gradient / sqrt(mean_square + epsilon - mean_grad ** 2)
+//
+// mg <- rho * mg_{t-1} + (1-rho) * grad
+// ms <- rho * ms_{t-1} + (1-rho) * grad * grad
+// mom <- momentum * mom_{t-1} + lr * grad / sqrt(ms - mg * mg + epsilon)
+// var <- var - mom
+//
+// Arguments:
+// var_: Should be from a Variable().
+// mg: Should be from a Variable().
+// ms: Should be from a Variable().
+// mom: Should be from a Variable().
+// lr: Scaling factor. Must be a scalar.
+// rho: Decay rate. Must be a scalar.
+//
+// epsilon: Ridge term. Must be a scalar.
+// grad: The gradient.
+//
+// Returns the created operation.
+func ResourceApplyCenteredRMSProp(scope *Scope, var_ tf.Output, mg tf.Output, ms tf.Output, mom tf.Output, lr tf.Output, rho tf.Output, momentum tf.Output, epsilon tf.Output, grad tf.Output, optional ...ResourceApplyCenteredRMSPropAttr) (o *tf.Operation) {
+ if scope.Err() != nil {
+ return
+ }
+ attrs := map[string]interface{}{}
+ for _, a := range optional {
+ a(attrs)
+ }
+ opspec := tf.OpSpec{
+ Type: "ResourceApplyCenteredRMSProp",
+ Input: []tf.Input{
+ var_, mg, ms, mom, lr, rho, momentum, epsilon, grad,
+ },
+ Attrs: attrs,
+ }
+ return scope.AddOperation(opspec)
+}
+
+// Returns a list of tensors with the same shapes and contents as the input
+//
+// tensors.
+//
+// This op can be used to override the gradient for complicated functions. For
+// example, suppose y = f(x) and we wish to apply a custom function g for backprop
+// such that dx = g(dy). In Python,
+//
+// ```python
+// with tf.get_default_graph().gradient_override_map(
+// {'IdentityN': 'OverrideGradientWithG'}):
+// y, _ = identity_n([f(x), x])
+//
+// @tf.RegisterGradient('OverrideGradientWithG')
+// def ApplyG(op, dy, _):
+// return [None, g(dy)] # Do not backprop to f(x).
+// ```
+func IdentityN(scope *Scope, input []tf.Output) (output []tf.Output) {
+ if scope.Err() != nil {
+ return
+ }
+ opspec := tf.OpSpec{
+ Type: "IdentityN",
+ Input: []tf.Input{
+ tf.OutputList(input),
+ },
+ }
+ op := scope.AddOperation(opspec)
+ if scope.Err() != nil {
+ return
+ }
+ var idx int
+ var err error
+ if output, idx, err = makeOutputList(op, idx, "output"); err != nil {
+ scope.UpdateErr("IdentityN", err)
+ return
+ }
+ return output
+}
+
+// Computes the gradient of the sigmoid of `x` wrt its input.
+//
+// Specifically, `grad = dy * y * (1 - y)`, where `y = sigmoid(x)`, and
+// `dy` is the corresponding input gradient.
+func SigmoidGrad(scope *Scope, y tf.Output, dy tf.Output) (z tf.Output) {
+ if scope.Err() != nil {
+ return
+ }
+ opspec := tf.OpSpec{
+ Type: "SigmoidGrad",
+ Input: []tf.Input{
+ y, dy,
+ },
+ }
+ op := scope.AddOperation(opspec)
+ return op.Output(0)
+}
+
+// Convert one or more images from HSV to RGB.
+//
+// Outputs a tensor of the same shape as the `images` tensor, containing the RGB
+// value of the pixels. The output is only well defined if the value in `images`
+// are in `[0,1]`.
+//
+// See `rgb_to_hsv` for a description of the HSV encoding.
+//
+// Arguments:
+// images: 1-D or higher rank. HSV data to convert. Last dimension must be size 3.
+//
+// Returns `images` converted to RGB.
+func HSVToRGB(scope *Scope, images tf.Output) (output tf.Output) {
+ if scope.Err() != nil {
+ return
}
opspec := tf.OpSpec{
Type: "HSVToRGB",
func TensorArrayGatherV2(scope *Scope, handle tf.Output, indices tf.Output, flow_in tf.Output, dtype tf.DataType, optional ...TensorArrayGatherV2Attr) (value tf.Output) {
if scope.Err() != nil {
return
- }
- attrs := map[string]interface{}{"dtype": dtype}
- for _, a := range optional {
- a(attrs)
- }
- opspec := tf.OpSpec{
- Type: "TensorArrayGatherV2",
- Input: []tf.Input{
- handle, indices, flow_in,
- },
- Attrs: attrs,
- }
- op := scope.AddOperation(opspec)
- return op.Output(0)
-}
-
-// Interleave the values from the `data` tensors into a single tensor.
-//
-// Builds a merged tensor such that
-//
-// ```python
-// merged[indices[m][i, ..., j], ...] = data[m][i, ..., j, ...]
-// ```
-//
-// For example, if each `indices[m]` is scalar or vector, we have
-//
-// ```python
-// # Scalar indices:
-// merged[indices[m], ...] = data[m][...]
-//
-// # Vector indices:
-// merged[indices[m][i], ...] = data[m][i, ...]
-// ```
-//
-// Each `data[i].shape` must start with the corresponding `indices[i].shape`,
-// and the rest of `data[i].shape` must be constant w.r.t. `i`. That is, we
-// must have `data[i].shape = indices[i].shape + constant`. In terms of this
-// `constant`, the output shape is
-//
-// merged.shape = [max(indices)] + constant
-//
-// Values may be merged in parallel, so if an index appears in both `indices[m][i]`
-// and `indices[n][j]`, the result may be invalid. This differs from the normal
-// DynamicStitch operator that defines the behavior in that case.
-//
-// For example:
-//
-// ```python
-// indices[0] = 6
-// indices[1] = [4, 1]
-// indices[2] = [[5, 2], [0, 3]]
-// data[0] = [61, 62]
-// data[1] = [[41, 42], [11, 12]]
-// data[2] = [[[51, 52], [21, 22]], [[1, 2], [31, 32]]]
-// merged = [[1, 2], [11, 12], [21, 22], [31, 32], [41, 42],
-// [51, 52], [61, 62]]
-// ```
-//
-// This method can be used to merge partitions created by `dynamic_partition`
-// as illustrated on the following example:
-//
-// ```python
-// # Apply function (increments x_i) on elements for which a certain condition
-// # apply (x_i != -1 in this example).
-// x=tf.constant([0.1, -1., 5.2, 4.3, -1., 7.4])
-// condition_mask=tf.not_equal(x,tf.constant(-1.))
-// partitioned_data = tf.dynamic_partition(
-// x, tf.cast(condition_mask, tf.int32) , 2)
-// partitioned_data[1] = partitioned_data[1] + 1.0
-// condition_indices = tf.dynamic_partition(
-// tf.range(tf.shape(x)[0]), tf.cast(condition_mask, tf.int32) , 2)
-// x = tf.dynamic_stitch(condition_indices, partitioned_data)
-// # Here x=[1.1, -1., 6.2, 5.3, -1, 8.4], the -1. values remain
-// # unchanged.
-// ```
-//
-// <div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;">
-// <img style="width:100%" src="https://www.tensorflow.org/images/DynamicStitch.png" alt>
-// </div>
-func ParallelDynamicStitch(scope *Scope, indices []tf.Output, data []tf.Output) (merged tf.Output) {
- if scope.Err() != nil {
- return
- }
- opspec := tf.OpSpec{
- Type: "ParallelDynamicStitch",
- Input: []tf.Input{
- tf.OutputList(indices), tf.OutputList(data),
- },
- }
- op := scope.AddOperation(opspec)
- return op.Output(0)
-}
-
-// Computes the gradient for the inverse of `x` wrt its input.
-//
-// Specifically, `grad = -dy * y*y`, where `y = 1/x`, and `dy`
-// is the corresponding input gradient.
-func InvGrad(scope *Scope, y tf.Output, dy tf.Output) (z tf.Output) {
- if scope.Err() != nil {
- return
- }
- opspec := tf.OpSpec{
- Type: "InvGrad",
- Input: []tf.Input{
- y, dy,
- },
- }
- op := scope.AddOperation(opspec)
- return op.Output(0)
-}
-
-// StridedSliceAttr is an optional argument to StridedSlice.
-type StridedSliceAttr func(optionalAttr)
-
-// StridedSliceBeginMask sets the optional begin_mask attribute to value.
-//
-// value: a bitmask where a bit i being 1 means to ignore the begin
-// value and instead use the largest interval possible. At runtime
-// begin[i] will be replaced with `[0, n-1) if `stride[i] > 0` or
-// `[-1, n-1]` if `stride[i] < 0`
-// If not specified, defaults to 0
-func StridedSliceBeginMask(value int64) StridedSliceAttr {
- return func(m optionalAttr) {
- m["begin_mask"] = value
- }
-}
-
-// StridedSliceEndMask sets the optional end_mask attribute to value.
-//
-// value: analogous to `begin_mask`
-// If not specified, defaults to 0
-func StridedSliceEndMask(value int64) StridedSliceAttr {
- return func(m optionalAttr) {
- m["end_mask"] = value
- }
-}
-
-// StridedSliceEllipsisMask sets the optional ellipsis_mask attribute to value.
-//
-// value: a bitmask where bit `i` being 1 means the `i`th
-// position is actually an ellipsis. One bit at most can be 1.
-// If `ellipsis_mask == 0`, then an implicit ellipsis mask of `1 << (m+1)`
-// is provided. This means that `foo[3:5] == foo[3:5, ...]`. An ellipsis
-// implicitly creates as many range specifications as necessary to fully
-// specify the sliced range for every dimension. For example for a 4-dimensional
-// tensor `foo` the slice `foo[2, ..., 5:8]` implies `foo[2, :, :, 5:8]`.
-// If not specified, defaults to 0
-func StridedSliceEllipsisMask(value int64) StridedSliceAttr {
- return func(m optionalAttr) {
- m["ellipsis_mask"] = value
- }
-}
-
-// StridedSliceNewAxisMask sets the optional new_axis_mask attribute to value.
-//
-// value: a bitmask where bit `i` being 1 means the `i`th
-// specification creates a new shape 1 dimension. For example
-// `foo[:4, tf.newaxis, :2]` would produce a shape `(4, 1, 2)` tensor.
-// If not specified, defaults to 0
-func StridedSliceNewAxisMask(value int64) StridedSliceAttr {
- return func(m optionalAttr) {
- m["new_axis_mask"] = value
- }
-}
-
-// StridedSliceShrinkAxisMask sets the optional shrink_axis_mask attribute to value.
-//
-// value: a bitmask where bit `i` implies that the `i`th
-// specification should shrink the dimensionality. begin and end
-// must imply a slice of size 1 in the dimension. For example in
-// python one might do `foo[:, 3, :]` which would result in
-// `shrink_axis_mask` being 2.
-// If not specified, defaults to 0
-func StridedSliceShrinkAxisMask(value int64) StridedSliceAttr {
- return func(m optionalAttr) {
- m["shrink_axis_mask"] = value
- }
-}
-
-// Return a strided slice from `input`.
-//
-// Note, most python users will want to use the Python `Tensor.__getitem__`
-// or `Variable.__getitem__` rather than this op directly.
-//
-// The goal of this op is to produce a new tensor with a subset of
-// the elements from the `n` dimensional `input` tensor. The subset is chosen using
-// a sequence of `m` sparse range specifications encoded into the arguments
-// of this function. Note, in some cases
-// `m` could be equal to `n`, but this need not be the case. Each
-// range specification entry can be one of the following:
-//
-// - An ellipsis (...). Ellipses are used to imply zero or more
-// dimensions of full-dimension selection and are produced using
-// `ellipsis_mask`. For example, `foo[...]` is the identity slice.
-//
-// - A new axis. This is used to insert a new shape=1 dimension and is
-// produced using `new_axis_mask`. For example, `foo[:, ...]` where
-// `foo` is shape `(3, 4)` produces a `(1, 3, 4)` tensor.
-//
-//
-// - A range `begin:end:stride`. This is used to specify how much to choose from
-// a given dimension. `stride` can be any integer but 0. `begin` is an integer
-// which represents the index of the first value to select while `end` represents
-// the index of the last value to select. The number of values selected in each
-// dimension is `end - begin` if `stride > 0` and `begin - end` if `stride < 0`.
-// `begin` and `end` can be negative where `-1` is the last element, `-2` is
-// the second to last. `begin_mask` controls whether to replace the explicitly
-// given `begin` with an implicit effective value of `0` if `stride > 0` and
-// `-1` if `stride < 0`. `end_mask` is analogous but produces the number
-// required to create the largest open interval. For example, given a shape
-// `(3,)` tensor `foo[:]`, the effective `begin` and `end` are `0` and `3`. Do
-// not assume this is equivalent to `foo[0:-1]` which has an effective `begin`
-// and `end` of `0` and `2`. Another example is `foo[-2::-1]` which reverses the
-// first dimension of a tensor while dropping the last two (in the original
-// order elements). For example `foo = [1,2,3,4]; foo[-2::-1]` is `[4,3]`.
-//
-// - A single index. This is used to keep only elements that have a given
-// index. For example (`foo[2, :]` on a shape `(5,6)` tensor produces a
-// shape `(6,)` tensor. This is encoded in `begin` and `end` and
-// `shrink_axis_mask`.
+ }
+ attrs := map[string]interface{}{"dtype": dtype}
+ for _, a := range optional {
+ a(attrs)
+ }
+ opspec := tf.OpSpec{
+ Type: "TensorArrayGatherV2",
+ Input: []tf.Input{
+ handle, indices, flow_in,
+ },
+ Attrs: attrs,
+ }
+ op := scope.AddOperation(opspec)
+ return op.Output(0)
+}
+
+// Interleave the values from the `data` tensors into a single tensor.
//
-// Each conceptual range specification is encoded in the op's argument. This
-// encoding is best understand by considering a non-trivial example. In
-// particular,
-// `foo[1, 2:4, None, ..., :-3:-1, :]` will be encoded as
+// Builds a merged tensor such that
//
-// ```
-// begin = [1, 2, x, x, 0, x] # x denotes don't care (usually 0)
-// end = [2, 4, x, x, -3, x]
-// strides = [1, 1, x, x, -1, 1]
-// begin_mask = 1<<4 | 1 << 5 = 48
-// end_mask = 1<<5 = 32
-// ellipsis_mask = 1<<3 = 8
-// new_axis_mask = 1<<2 4
-// shrink_axis_mask = 1<<0
+// ```python
+// merged[indices[m][i, ..., j], ...] = data[m][i, ..., j, ...]
// ```
//
-// In this case if `foo.shape` is (5, 5, 5, 5, 5, 5) the final shape of
-// the slice becomes (2, 1, 5, 5, 2, 5).
-// Let us walk step by step through each argument specification.
+// For example, if each `indices[m]` is scalar or vector, we have
//
-// 1. The first argument in the example slice is turned into `begin = 1` and
-// `end = begin + 1 = 2`. To disambiguate from the original spec `2:4` we
-// also set the appropriate bit in `shrink_axis_mask`.
+// ```python
+// # Scalar indices:
+// merged[indices[m], ...] = data[m][...]
//
-// 2. `2:4` is contributes 2, 4, 1 to begin, end, and stride. All masks have
-// zero bits contributed.
+// # Vector indices:
+// merged[indices[m][i], ...] = data[m][i, ...]
+// ```
//
-// 3. None is a synonym for `tf.newaxis`. This means insert a dimension of size 1
-// dimension in the final shape. Dummy values are contributed to begin,
-// end and stride, while the new_axis_mask bit is set.
+// Each `data[i].shape` must start with the corresponding `indices[i].shape`,
+// and the rest of `data[i].shape` must be constant w.r.t. `i`. That is, we
+// must have `data[i].shape = indices[i].shape + constant`. In terms of this
+// `constant`, the output shape is
//
-// 4. `...` grab the full ranges from as many dimensions as needed to
-// fully specify a slice for every dimension of the input shape.
+// merged.shape = [max(indices)] + constant
//
-// 5. `:-3:-1` shows the use of negative indices. A negative index `i` associated
-// with a dimension that has shape `s` is converted to a positive index
-// `s + i`. So `-1` becomes `s-1` (i.e. the last element). This conversion
-// is done internally so begin, end and strides receive x, -3, and -1.
-// The appropriate begin_mask bit is set to indicate the start range is the
-// full range (ignoring the x).
+// Values may be merged in parallel, so if an index appears in both `indices[m][i]`
+// and `indices[n][j]`, the result may be invalid. This differs from the normal
+// DynamicStitch operator that defines the behavior in that case.
//
-// 6. `:` indicates that the entire contents of the corresponding dimension
-// is selected. This is equivalent to `::` or `0::1`. begin, end, and strides
-// receive 0, 0, and 1, respectively. The appropriate bits in `begin_mask` and
-// `end_mask` are also set.
+// For example:
//
-// *Requirements*:
-// `0 != strides[i] for i in [0, m)`
-// `ellipsis_mask must be a power of two (only one ellipsis)`
+// ```python
+// indices[0] = 6
+// indices[1] = [4, 1]
+// indices[2] = [[5, 2], [0, 3]]
+// data[0] = [61, 62]
+// data[1] = [[41, 42], [11, 12]]
+// data[2] = [[[51, 52], [21, 22]], [[1, 2], [31, 32]]]
+// merged = [[1, 2], [11, 12], [21, 22], [31, 32], [41, 42],
+// [51, 52], [61, 62]]
+// ```
//
-// Arguments:
+// This method can be used to merge partitions created by `dynamic_partition`
+// as illustrated on the following example:
//
-// begin: `begin[k]` specifies the offset into the `k`th range specification.
-// The exact dimension this corresponds to will be determined by context.
-// Out-of-bounds values will be silently clamped. If the `k`th bit of
-// `begin_mask` then `begin[k]` is ignored and the full range of the
-// appropriate dimension is used instead. Negative values causes indexing
-// to start from the highest element e.g. If `foo==[1,2,3]` then `foo[-1]==3`.
-// end: `end[i]` is like `begin` with the exception that `end_mask` is
-// used to determine full ranges.
-// strides: `strides[i]` specifies the increment in the `i`th specification
-// after extracting a given element. Negative indices will reverse
-// the original order. Out or range values are
-// clamped to `[0,dim[i]) if slice[i]>0` or `[-1,dim[i]-1] if slice[i] < 0`
-func StridedSlice(scope *Scope, input tf.Output, begin tf.Output, end tf.Output, strides tf.Output, optional ...StridedSliceAttr) (output tf.Output) {
+// ```python
+// # Apply function (increments x_i) on elements for which a certain condition
+// # apply (x_i != -1 in this example).
+// x=tf.constant([0.1, -1., 5.2, 4.3, -1., 7.4])
+// condition_mask=tf.not_equal(x,tf.constant(-1.))
+// partitioned_data = tf.dynamic_partition(
+// x, tf.cast(condition_mask, tf.int32) , 2)
+// partitioned_data[1] = partitioned_data[1] + 1.0
+// condition_indices = tf.dynamic_partition(
+// tf.range(tf.shape(x)[0]), tf.cast(condition_mask, tf.int32) , 2)
+// x = tf.dynamic_stitch(condition_indices, partitioned_data)
+// # Here x=[1.1, -1., 6.2, 5.3, -1, 8.4], the -1. values remain
+// # unchanged.
+// ```
+//
+// <div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;">
+// <img style="width:100%" src="https://www.tensorflow.org/images/DynamicStitch.png" alt>
+// </div>
+func ParallelDynamicStitch(scope *Scope, indices []tf.Output, data []tf.Output) (merged tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{}
- for _, a := range optional {
- a(attrs)
+ opspec := tf.OpSpec{
+ Type: "ParallelDynamicStitch",
+ Input: []tf.Input{
+ tf.OutputList(indices), tf.OutputList(data),
+ },
+ }
+ op := scope.AddOperation(opspec)
+ return op.Output(0)
+}
+
+// Computes the gradient for the inverse of `x` wrt its input.
+//
+// Specifically, `grad = -dy * y*y`, where `y = 1/x`, and `dy`
+// is the corresponding input gradient.
+func InvGrad(scope *Scope, y tf.Output, dy tf.Output) (z tf.Output) {
+ if scope.Err() != nil {
+ return
}
opspec := tf.OpSpec{
- Type: "StridedSlice",
+ Type: "InvGrad",
Input: []tf.Input{
- input, begin, end, strides,
+ y, dy,
},
- Attrs: attrs,
}
op := scope.AddOperation(opspec)
return op.Output(0)
scope.UpdateErr("Unstage", err)
return
}
- return values
-}
-
-// ArgMaxAttr is an optional argument to ArgMax.
-type ArgMaxAttr func(optionalAttr)
-
-// ArgMaxOutputType sets the optional output_type attribute to value.
-// If not specified, defaults to DT_INT64
-func ArgMaxOutputType(value tf.DataType) ArgMaxAttr {
- return func(m optionalAttr) {
- m["output_type"] = value
- }
-}
-
-// Returns the index with the largest value across dimensions of a tensor.
-//
-// Note that in case of ties the identity of the return value is not guaranteed.
-//
-// Arguments:
-//
-// dimension: int32 or int64, must be in the range `[-rank(input), rank(input))`.
-// Describes which dimension of the input Tensor to reduce across. For vectors,
-// use dimension = 0.
-func ArgMax(scope *Scope, input tf.Output, dimension tf.Output, optional ...ArgMaxAttr) (output tf.Output) {
- if scope.Err() != nil {
- return
- }
- attrs := map[string]interface{}{}
- for _, a := range optional {
- a(attrs)
- }
- opspec := tf.OpSpec{
- Type: "ArgMax",
- Input: []tf.Input{
- input, dimension,
- },
- Attrs: attrs,
- }
- op := scope.AddOperation(opspec)
- return op.Output(0)
-}
-
-// ResourceStridedSliceAssignAttr is an optional argument to ResourceStridedSliceAssign.
-type ResourceStridedSliceAssignAttr func(optionalAttr)
-
-// ResourceStridedSliceAssignBeginMask sets the optional begin_mask attribute to value.
-// If not specified, defaults to 0
-func ResourceStridedSliceAssignBeginMask(value int64) ResourceStridedSliceAssignAttr {
- return func(m optionalAttr) {
- m["begin_mask"] = value
- }
-}
-
-// ResourceStridedSliceAssignEndMask sets the optional end_mask attribute to value.
-// If not specified, defaults to 0
-func ResourceStridedSliceAssignEndMask(value int64) ResourceStridedSliceAssignAttr {
- return func(m optionalAttr) {
- m["end_mask"] = value
- }
-}
-
-// ResourceStridedSliceAssignEllipsisMask sets the optional ellipsis_mask attribute to value.
-// If not specified, defaults to 0
-func ResourceStridedSliceAssignEllipsisMask(value int64) ResourceStridedSliceAssignAttr {
- return func(m optionalAttr) {
- m["ellipsis_mask"] = value
- }
-}
-
-// ResourceStridedSliceAssignNewAxisMask sets the optional new_axis_mask attribute to value.
-// If not specified, defaults to 0
-func ResourceStridedSliceAssignNewAxisMask(value int64) ResourceStridedSliceAssignAttr {
- return func(m optionalAttr) {
- m["new_axis_mask"] = value
- }
-}
-
-// ResourceStridedSliceAssignShrinkAxisMask sets the optional shrink_axis_mask attribute to value.
-// If not specified, defaults to 0
-func ResourceStridedSliceAssignShrinkAxisMask(value int64) ResourceStridedSliceAssignAttr {
- return func(m optionalAttr) {
- m["shrink_axis_mask"] = value
- }
-}
-
-// Assign `value` to the sliced l-value reference of `ref`.
-//
-// The values of `value` are assigned to the positions in the variable
-// `ref` that are selected by the slice parameters. The slice parameters
-// `begin, `end`, `strides`, etc. work exactly as in `StridedSlice`.
-//
-// NOTE this op currently does not support broadcasting and so `value`'s
-// shape must be exactly the shape produced by the slice of `ref`.
-//
-// Returns the created operation.
-func ResourceStridedSliceAssign(scope *Scope, ref tf.Output, begin tf.Output, end tf.Output, strides tf.Output, value tf.Output, optional ...ResourceStridedSliceAssignAttr) (o *tf.Operation) {
- if scope.Err() != nil {
- return
- }
- attrs := map[string]interface{}{}
- for _, a := range optional {
- a(attrs)
- }
- opspec := tf.OpSpec{
- Type: "ResourceStridedSliceAssign",
- Input: []tf.Input{
- ref, begin, end, strides, value,
- },
- Attrs: attrs,
- }
- return scope.AddOperation(opspec)
+ return values
}
// QueueEnqueueV2Attr is an optional argument to QueueEnqueueV2.
return op.Output(0), op.Output(1), op.Output(2)
}
-// Elementwise computes the bitwise AND of `x` and `y`.
-//
-// The result will have those bits set, that are set in both `x` and `y`. The
-// computation is performed on the underlying representations of `x` and `y`.
-func BitwiseAnd(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) {
- if scope.Err() != nil {
- return
- }
- opspec := tf.OpSpec{
- Type: "BitwiseAnd",
- Input: []tf.Input{
- x, y,
- },
- }
- op := scope.AddOperation(opspec)
- return op.Output(0)
-}
-
-// Elementwise computes the bitwise left-shift of `x` and `y`.
-//
-// If `y` is negative, or greater than or equal to the width of `x` in bits the
-// result is implementation defined.
-func LeftShift(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) {
- if scope.Err() != nil {
- return
- }
- opspec := tf.OpSpec{
- Type: "LeftShift",
- Input: []tf.Input{
- x, y,
- },
- }
- op := scope.AddOperation(opspec)
- return op.Output(0)
-}
-
-// Elementwise computes the bitwise right-shift of `x` and `y`.
-//
-// Performs a logical shift for unsigned integer types, and an arithmetic shift
-// for signed integer types.
-//
-// If `y` is negative, or greater than or equal to than the width of `x` in bits
-// the result is implementation defined.
-func RightShift(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) {
- if scope.Err() != nil {
- return
- }
- opspec := tf.OpSpec{
- Type: "RightShift",
- Input: []tf.Input{
- x, y,
- },
- }
- op := scope.AddOperation(opspec)
- return op.Output(0)
-}
+// WholeFileReaderV2Attr is an optional argument to WholeFileReaderV2.
+type WholeFileReaderV2Attr func(optionalAttr)
-// Adjust the hue of one or more images.
-//
-// `images` is a tensor of at least 3 dimensions. The last dimension is
-// interpretted as channels, and must be three.
-//
-// The input image is considered in the RGB colorspace. Conceptually, the RGB
-// colors are first mapped into HSV. A delta is then applied all the hue values,
-// and then remapped back to RGB colorspace.
-//
-// Arguments:
-// images: Images to adjust. At least 3-D.
-// delta: A float delta to add to the hue.
+// WholeFileReaderV2Container sets the optional container attribute to value.
//
-// Returns The hue-adjusted image or images.
-func AdjustHue(scope *Scope, images tf.Output, delta tf.Output) (output tf.Output) {
- if scope.Err() != nil {
- return
- }
- opspec := tf.OpSpec{
- Type: "AdjustHue",
- Input: []tf.Input{
- images, delta,
- },
+// value: If non-empty, this reader is placed in the given container.
+// Otherwise, a default container is used.
+// If not specified, defaults to ""
+func WholeFileReaderV2Container(value string) WholeFileReaderV2Attr {
+ return func(m optionalAttr) {
+ m["container"] = value
}
- op := scope.AddOperation(opspec)
- return op.Output(0)
}
-// AvgPool3DGradAttr is an optional argument to AvgPool3DGrad.
-type AvgPool3DGradAttr func(optionalAttr)
-
-// AvgPool3DGradDataFormat sets the optional data_format attribute to value.
+// WholeFileReaderV2SharedName sets the optional shared_name attribute to value.
//
-// value: The data format of the input and output data. With the
-// default format "NDHWC", the data is stored in the order of:
-// [batch, in_depth, in_height, in_width, in_channels].
-// Alternatively, the format could be "NCDHW", the data storage order is:
-// [batch, in_channels, in_depth, in_height, in_width].
-// If not specified, defaults to "NDHWC"
-func AvgPool3DGradDataFormat(value string) AvgPool3DGradAttr {
+// value: If non-empty, this reader is named in the given bucket
+// with this shared_name. Otherwise, the node name is used instead.
+// If not specified, defaults to ""
+func WholeFileReaderV2SharedName(value string) WholeFileReaderV2Attr {
return func(m optionalAttr) {
- m["data_format"] = value
+ m["shared_name"] = value
}
}
-// Computes gradients of average pooling function.
+// A Reader that outputs the entire contents of a file as a value.
//
-// Arguments:
-// orig_input_shape: The original input dimensions.
-// grad: Output backprop of shape `[batch, depth, rows, cols, channels]`.
-// ksize: 1-D tensor of length 5. The size of the window for each dimension of
-// the input tensor. Must have `ksize[0] = ksize[4] = 1`.
-// strides: 1-D tensor of length 5. The stride of the sliding window for each
-// dimension of `input`. Must have `strides[0] = strides[4] = 1`.
-// padding: The type of padding algorithm to use.
+// To use, enqueue filenames in a Queue. The output of ReaderRead will
+// be a filename (key) and the contents of that file (value).
//
-// Returns The backprop for input.
-func AvgPool3DGrad(scope *Scope, orig_input_shape tf.Output, grad tf.Output, ksize []int64, strides []int64, padding string, optional ...AvgPool3DGradAttr) (output tf.Output) {
+// Returns The handle to reference the Reader.
+func WholeFileReaderV2(scope *Scope, optional ...WholeFileReaderV2Attr) (reader_handle tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{"ksize": ksize, "strides": strides, "padding": padding}
+ attrs := map[string]interface{}{}
for _, a := range optional {
a(attrs)
}
opspec := tf.OpSpec{
- Type: "AvgPool3DGrad",
- Input: []tf.Input{
- orig_input_shape, grad,
- },
+ Type: "WholeFileReaderV2",
+
Attrs: attrs,
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// ParseSingleSequenceExampleAttr is an optional argument to ParseSingleSequenceExample.
-type ParseSingleSequenceExampleAttr func(optionalAttr)
-
-// ParseSingleSequenceExampleContextSparseTypes sets the optional context_sparse_types attribute to value.
-//
-// value: A list of Ncontext_sparse types; the data types of data in
-// each context Feature given in context_sparse_keys.
-// Currently the ParseSingleSequenceExample supports DT_FLOAT (FloatList),
-// DT_INT64 (Int64List), and DT_STRING (BytesList).
-// If not specified, defaults to <>
-//
-// REQUIRES: len(value) >= 0
-func ParseSingleSequenceExampleContextSparseTypes(value []tf.DataType) ParseSingleSequenceExampleAttr {
- return func(m optionalAttr) {
- m["context_sparse_types"] = value
- }
-}
-
-// ParseSingleSequenceExampleFeatureListDenseTypes sets the optional feature_list_dense_types attribute to value.
-// If not specified, defaults to <>
-//
-// REQUIRES: len(value) >= 0
-func ParseSingleSequenceExampleFeatureListDenseTypes(value []tf.DataType) ParseSingleSequenceExampleAttr {
- return func(m optionalAttr) {
- m["feature_list_dense_types"] = value
- }
-}
-
-// ParseSingleSequenceExampleContextDenseShapes sets the optional context_dense_shapes attribute to value.
-//
-// value: A list of Ncontext_dense shapes; the shapes of data in
-// each context Feature given in context_dense_keys.
-// The number of elements in the Feature corresponding to context_dense_key[j]
-// must always equal context_dense_shapes[j].NumEntries().
-// The shape of context_dense_values[j] will match context_dense_shapes[j].
-// If not specified, defaults to <>
-//
-// REQUIRES: len(value) >= 0
-func ParseSingleSequenceExampleContextDenseShapes(value []tf.Shape) ParseSingleSequenceExampleAttr {
- return func(m optionalAttr) {
- m["context_dense_shapes"] = value
- }
-}
-
-// ParseSingleSequenceExampleFeatureListSparseTypes sets the optional feature_list_sparse_types attribute to value.
-//
-// value: A list of Nfeature_list_sparse types; the data types
-// of data in each FeatureList given in feature_list_sparse_keys.
-// Currently the ParseSingleSequenceExample supports DT_FLOAT (FloatList),
-// DT_INT64 (Int64List), and DT_STRING (BytesList).
-// If not specified, defaults to <>
-//
-// REQUIRES: len(value) >= 0
-func ParseSingleSequenceExampleFeatureListSparseTypes(value []tf.DataType) ParseSingleSequenceExampleAttr {
- return func(m optionalAttr) {
- m["feature_list_sparse_types"] = value
- }
-}
-
-// ParseSingleSequenceExampleFeatureListDenseShapes sets the optional feature_list_dense_shapes attribute to value.
-//
-// value: A list of Nfeature_list_dense shapes; the shapes of
-// data in each FeatureList given in feature_list_dense_keys.
-// The shape of each Feature in the FeatureList corresponding to
-// feature_list_dense_key[j] must always equal
-// feature_list_dense_shapes[j].NumEntries().
-// If not specified, defaults to <>
-//
-// REQUIRES: len(value) >= 0
-func ParseSingleSequenceExampleFeatureListDenseShapes(value []tf.Shape) ParseSingleSequenceExampleAttr {
- return func(m optionalAttr) {
- m["feature_list_dense_shapes"] = value
- }
-}
-
-// Transforms a scalar brain.SequenceExample proto (as strings) into typed tensors.
+// Transforms a tf.Example proto (as a string) into typed tensors.
//
// Arguments:
-// serialized: A scalar containing a binary serialized SequenceExample proto.
-// feature_list_dense_missing_assumed_empty: A vector listing the
-// FeatureList keys which may be missing from the SequenceExample. If the
-// associated FeatureList is missing, it is treated as empty. By default,
-// any FeatureList not listed in this vector must exist in the SequenceExample.
-// context_sparse_keys: A list of Ncontext_sparse string Tensors (scalars).
-// The keys expected in the Examples' features associated with context_sparse
-// values.
-// context_dense_keys: A list of Ncontext_dense string Tensors (scalars).
-// The keys expected in the SequenceExamples' context features associated with
-// dense values.
-// feature_list_sparse_keys: A list of Nfeature_list_sparse string Tensors
-// (scalars). The keys expected in the FeatureLists associated with sparse
+// serialized: A vector containing a batch of binary serialized Example protos.
+// dense_defaults: A list of Tensors (some may be empty), whose length matches
+// the length of `dense_keys`. dense_defaults[j] provides default values
+// when the example's feature_map lacks dense_key[j]. If an empty Tensor is
+// provided for dense_defaults[j], then the Feature dense_keys[j] is required.
+// The input type is inferred from dense_defaults[j], even when it's empty.
+// If dense_defaults[j] is not empty, and dense_shapes[j] is fully defined,
+// then the shape of dense_defaults[j] must match that of dense_shapes[j].
+// If dense_shapes[j] has an undefined major dimension (variable strides dense
+// feature), dense_defaults[j] must contain a single element:
+// the padding element.
+// num_sparse: The number of sparse features to be parsed from the example. This
+// must match the lengths of `sparse_keys` and `sparse_types`.
+// sparse_keys: A list of `num_sparse` strings.
+// The keys expected in the Examples' features associated with sparse values.
+// dense_keys: The keys expected in the Examples' features associated with dense
// values.
-// feature_list_dense_keys: A list of Nfeature_list_dense string Tensors (scalars).
-// The keys expected in the SequenceExamples' feature_lists associated
-// with lists of dense values.
-// context_dense_defaults: A list of Ncontext_dense Tensors (some may be empty).
-// context_dense_defaults[j] provides default values
-// when the SequenceExample's context map lacks context_dense_key[j].
-// If an empty Tensor is provided for context_dense_defaults[j],
-// then the Feature context_dense_keys[j] is required.
-// The input type is inferred from context_dense_defaults[j], even when it's
-// empty. If context_dense_defaults[j] is not empty, its shape must match
-// context_dense_shapes[j].
-// debug_name: A scalar containing the name of the serialized proto.
-// May contain, for example, table key (descriptive) name for the
-// corresponding serialized proto. This is purely useful for debugging
-// purposes, and the presence of values here has no effect on the output.
-// May also be an empty scalar if no name is available.
-func ParseSingleSequenceExample(scope *Scope, serialized tf.Output, feature_list_dense_missing_assumed_empty tf.Output, context_sparse_keys []tf.Output, context_dense_keys []tf.Output, feature_list_sparse_keys []tf.Output, feature_list_dense_keys []tf.Output, context_dense_defaults []tf.Output, debug_name tf.Output, optional ...ParseSingleSequenceExampleAttr) (context_sparse_indices []tf.Output, context_sparse_values []tf.Output, context_sparse_shapes []tf.Output, context_dense_values []tf.Output, feature_list_sparse_indices []tf.Output, feature_list_sparse_values []tf.Output, feature_list_sparse_shapes []tf.Output, feature_list_dense_values []tf.Output) {
+// sparse_types: A list of `num_sparse` types; the data types of data in each
+// Feature given in sparse_keys.
+// Currently the ParseSingleExample op supports DT_FLOAT (FloatList),
+// DT_INT64 (Int64List), and DT_STRING (BytesList).
+// dense_shapes: The shapes of data in each Feature given in dense_keys.
+// The length of this list must match the length of `dense_keys`. The
+// number of elements in the Feature corresponding to dense_key[j] must
+// always equal dense_shapes[j].NumEntries(). If dense_shapes[j] ==
+// (D0, D1, ..., DN) then the shape of output Tensor dense_values[j]
+// will be (D0, D1, ..., DN): In the case dense_shapes[j] = (-1, D1,
+// ..., DN), the shape of the output Tensor dense_values[j] will be (M,
+// D1, .., DN), where M is the number of blocks of elements of length
+// D1 * .... * DN, in the input.
+func ParseSingleExample(scope *Scope, serialized tf.Output, dense_defaults []tf.Output, num_sparse int64, sparse_keys []string, dense_keys []string, sparse_types []tf.DataType, dense_shapes []tf.Shape) (sparse_indices []tf.Output, sparse_values []tf.Output, sparse_shapes []tf.Output, dense_values []tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{}
- for _, a := range optional {
- a(attrs)
- }
+ attrs := map[string]interface{}{"num_sparse": num_sparse, "sparse_keys": sparse_keys, "dense_keys": dense_keys, "sparse_types": sparse_types, "dense_shapes": dense_shapes}
opspec := tf.OpSpec{
- Type: "ParseSingleSequenceExample",
+ Type: "ParseSingleExample",
Input: []tf.Input{
- serialized, feature_list_dense_missing_assumed_empty, tf.OutputList(context_sparse_keys), tf.OutputList(context_dense_keys), tf.OutputList(feature_list_sparse_keys), tf.OutputList(feature_list_dense_keys), tf.OutputList(context_dense_defaults), debug_name,
+ serialized, tf.OutputList(dense_defaults),
},
Attrs: attrs,
}
}
var idx int
var err error
- if context_sparse_indices, idx, err = makeOutputList(op, idx, "context_sparse_indices"); err != nil {
- scope.UpdateErr("ParseSingleSequenceExample", err)
- return
- }
- if context_sparse_values, idx, err = makeOutputList(op, idx, "context_sparse_values"); err != nil {
- scope.UpdateErr("ParseSingleSequenceExample", err)
- return
- }
- if context_sparse_shapes, idx, err = makeOutputList(op, idx, "context_sparse_shapes"); err != nil {
- scope.UpdateErr("ParseSingleSequenceExample", err)
- return
- }
- if context_dense_values, idx, err = makeOutputList(op, idx, "context_dense_values"); err != nil {
- scope.UpdateErr("ParseSingleSequenceExample", err)
- return
- }
- if feature_list_sparse_indices, idx, err = makeOutputList(op, idx, "feature_list_sparse_indices"); err != nil {
- scope.UpdateErr("ParseSingleSequenceExample", err)
+ if sparse_indices, idx, err = makeOutputList(op, idx, "sparse_indices"); err != nil {
+ scope.UpdateErr("ParseSingleExample", err)
return
}
- if feature_list_sparse_values, idx, err = makeOutputList(op, idx, "feature_list_sparse_values"); err != nil {
- scope.UpdateErr("ParseSingleSequenceExample", err)
+ if sparse_values, idx, err = makeOutputList(op, idx, "sparse_values"); err != nil {
+ scope.UpdateErr("ParseSingleExample", err)
return
}
- if feature_list_sparse_shapes, idx, err = makeOutputList(op, idx, "feature_list_sparse_shapes"); err != nil {
- scope.UpdateErr("ParseSingleSequenceExample", err)
+ if sparse_shapes, idx, err = makeOutputList(op, idx, "sparse_shapes"); err != nil {
+ scope.UpdateErr("ParseSingleExample", err)
return
}
- if feature_list_dense_values, idx, err = makeOutputList(op, idx, "feature_list_dense_values"); err != nil {
- scope.UpdateErr("ParseSingleSequenceExample", err)
+ if dense_values, idx, err = makeOutputList(op, idx, "dense_values"); err != nil {
+ scope.UpdateErr("ParseSingleExample", err)
return
}
- return context_sparse_indices, context_sparse_values, context_sparse_shapes, context_dense_values, feature_list_sparse_indices, feature_list_sparse_values, feature_list_sparse_shapes, feature_list_dense_values
+ return sparse_indices, sparse_values, sparse_shapes, dense_values
}
-// DecodeWavAttr is an optional argument to DecodeWav.
-type DecodeWavAttr func(optionalAttr)
-
-// DecodeWavDesiredChannels sets the optional desired_channels attribute to value.
+// Elementwise computes the bitwise AND of `x` and `y`.
//
-// value: Number of sample channels wanted.
-// If not specified, defaults to -1
-func DecodeWavDesiredChannels(value int64) DecodeWavAttr {
- return func(m optionalAttr) {
- m["desired_channels"] = value
+// The result will have those bits set, that are set in both `x` and `y`. The
+// computation is performed on the underlying representations of `x` and `y`.
+func BitwiseAnd(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) {
+ if scope.Err() != nil {
+ return
}
-}
-
-// DecodeWavDesiredSamples sets the optional desired_samples attribute to value.
-//
-// value: Length of audio requested.
-// If not specified, defaults to -1
-func DecodeWavDesiredSamples(value int64) DecodeWavAttr {
- return func(m optionalAttr) {
- m["desired_samples"] = value
+ opspec := tf.OpSpec{
+ Type: "BitwiseAnd",
+ Input: []tf.Input{
+ x, y,
+ },
}
+ op := scope.AddOperation(opspec)
+ return op.Output(0)
}
-// Decode a 16-bit PCM WAV file to a float tensor.
-//
-// The -32768 to 32767 signed 16-bit values will be scaled to -1.0 to 1.0 in float.
-//
-// When desired_channels is set, if the input contains fewer channels than this
-// then the last channel will be duplicated to give the requested number, else if
-// the input has more channels than requested then the additional channels will be
-// ignored.
-//
-// If desired_samples is set, then the audio will be cropped or padded with zeroes
-// to the requested length.
-//
-// The first output contains a Tensor with the content of the audio samples. The
-// lowest dimension will be the number of channels, and the second will be the
-// number of samples. For example, a ten-sample-long stereo WAV file should give an
-// output shape of [10, 2].
-//
-// Arguments:
-// contents: The WAV-encoded audio, usually from a file.
+// Elementwise computes the bitwise left-shift of `x` and `y`.
//
-// Returns 2-D with shape `[length, channels]`.Scalar holding the sample rate found in the WAV header.
-func DecodeWav(scope *Scope, contents tf.Output, optional ...DecodeWavAttr) (audio tf.Output, sample_rate tf.Output) {
+// If `y` is negative, or greater than or equal to the width of `x` in bits the
+// result is implementation defined.
+func LeftShift(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{}
- for _, a := range optional {
- a(attrs)
- }
opspec := tf.OpSpec{
- Type: "DecodeWav",
+ Type: "LeftShift",
Input: []tf.Input{
- contents,
+ x, y,
},
- Attrs: attrs,
}
op := scope.AddOperation(opspec)
- return op.Output(0), op.Output(1)
-}
-
-// UniqueAttr is an optional argument to Unique.
-type UniqueAttr func(optionalAttr)
-
-// UniqueOutIdx sets the optional out_idx attribute to value.
-// If not specified, defaults to DT_INT32
-func UniqueOutIdx(value tf.DataType) UniqueAttr {
- return func(m optionalAttr) {
- m["out_idx"] = value
- }
+ return op.Output(0)
}
-// Finds unique elements in a 1-D tensor.
-//
-// This operation returns a tensor `y` containing all of the unique elements of `x`
-// sorted in the same order that they occur in `x`. This operation also returns a
-// tensor `idx` the same size as `x` that contains the index of each value of `x`
-// in the unique output `y`. In other words:
-//
-// `y[idx[i]] = x[i] for i in [0, 1,...,rank(x) - 1]`
-//
-// For example:
-//
-// ```
-// # tensor 'x' is [1, 1, 2, 4, 4, 4, 7, 8, 8]
-// y, idx = unique(x)
-// y ==> [1, 2, 4, 7, 8]
-// idx ==> [0, 0, 1, 2, 2, 2, 3, 4, 4]
-// ```
+// Elementwise computes the bitwise right-shift of `x` and `y`.
//
-// Arguments:
-// x: 1-D.
+// Performs a logical shift for unsigned integer types, and an arithmetic shift
+// for signed integer types.
//
-// Returns 1-D.1-D.
-func Unique(scope *Scope, x tf.Output, optional ...UniqueAttr) (y tf.Output, idx tf.Output) {
+// If `y` is negative, or greater than or equal to than the width of `x` in bits
+// the result is implementation defined.
+func RightShift(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{}
- for _, a := range optional {
- a(attrs)
- }
opspec := tf.OpSpec{
- Type: "Unique",
+ Type: "RightShift",
Input: []tf.Input{
- x,
+ x, y,
},
- Attrs: attrs,
}
op := scope.AddOperation(opspec)
- return op.Output(0), op.Output(1)
+ return op.Output(0)
}
-// Concatenates a list of `N` tensors along the first dimension.
-//
-// The input tensors are all required to have size 1 in the first dimension.
-//
-// For example:
+// Adjust the hue of one or more images.
//
-// ```
-// # 'x' is [[1, 4]]
-// # 'y' is [[2, 5]]
-// # 'z' is [[3, 6]]
-// parallel_concat([x, y, z]) => [[1, 4], [2, 5], [3, 6]] # Pack along first dim.
-// ```
+// `images` is a tensor of at least 3 dimensions. The last dimension is
+// interpretted as channels, and must be three.
//
-// The difference between concat and parallel_concat is that concat requires all
-// of the inputs be computed before the operation will begin but doesn't require
-// that the input shapes be known during graph construction. Parallel concat
-// will copy pieces of the input into the output as they become available, in
-// some situations this can provide a performance benefit.
+// The input image is considered in the RGB colorspace. Conceptually, the RGB
+// colors are first mapped into HSV. A delta is then applied all the hue values,
+// and then remapped back to RGB colorspace.
//
// Arguments:
-// values: Tensors to be concatenated. All must have size 1 in the first dimension
-// and same shape.
-// shape: the final shape of the result; should be equal to the shapes of any input
-// but with the number of input values in the first dimension.
+// images: Images to adjust. At least 3-D.
+// delta: A float delta to add to the hue.
//
-// Returns The concatenated tensor.
-func ParallelConcat(scope *Scope, values []tf.Output, shape tf.Shape) (output tf.Output) {
+// Returns The hue-adjusted image or images.
+func AdjustHue(scope *Scope, images tf.Output, delta tf.Output) (output tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{"shape": shape}
opspec := tf.OpSpec{
- Type: "ParallelConcat",
+ Type: "AdjustHue",
Input: []tf.Input{
- tf.OutputList(values),
+ images, delta,
},
- Attrs: attrs,
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// Compute the lower regularized incomplete Gamma function `Q(a, x)`.
-//
-// The lower regularized incomplete Gamma function is defined as:
-//
-//
-// \\(P(a, x) = gamma(a, x) / Gamma(a) = 1 - Q(a, x)\\)
-//
-// where
+// AvgPool3DGradAttr is an optional argument to AvgPool3DGrad.
+type AvgPool3DGradAttr func(optionalAttr)
+
+// AvgPool3DGradDataFormat sets the optional data_format attribute to value.
//
-// \\(gamma(a, x) = int_{0}^{x} t^{a-1} exp(-t) dt\\)
+// value: The data format of the input and output data. With the
+// default format "NDHWC", the data is stored in the order of:
+// [batch, in_depth, in_height, in_width, in_channels].
+// Alternatively, the format could be "NCDHW", the data storage order is:
+// [batch, in_channels, in_depth, in_height, in_width].
+// If not specified, defaults to "NDHWC"
+func AvgPool3DGradDataFormat(value string) AvgPool3DGradAttr {
+ return func(m optionalAttr) {
+ m["data_format"] = value
+ }
+}
+
+// Computes gradients of average pooling function.
//
-// is the lower incomplete Gamma function.
+// Arguments:
+// orig_input_shape: The original input dimensions.
+// grad: Output backprop of shape `[batch, depth, rows, cols, channels]`.
+// ksize: 1-D tensor of length 5. The size of the window for each dimension of
+// the input tensor. Must have `ksize[0] = ksize[4] = 1`.
+// strides: 1-D tensor of length 5. The stride of the sliding window for each
+// dimension of `input`. Must have `strides[0] = strides[4] = 1`.
+// padding: The type of padding algorithm to use.
//
-// Note, above `Q(a, x)` (`Igammac`) is the upper regularized complete
-// Gamma function.
-func Igamma(scope *Scope, a tf.Output, x tf.Output) (z tf.Output) {
+// Returns The backprop for input.
+func AvgPool3DGrad(scope *Scope, orig_input_shape tf.Output, grad tf.Output, ksize []int64, strides []int64, padding string, optional ...AvgPool3DGradAttr) (output tf.Output) {
if scope.Err() != nil {
return
}
+ attrs := map[string]interface{}{"ksize": ksize, "strides": strides, "padding": padding}
+ for _, a := range optional {
+ a(attrs)
+ }
opspec := tf.OpSpec{
- Type: "Igamma",
+ Type: "AvgPool3DGrad",
Input: []tf.Input{
- a, x,
+ orig_input_shape, grad,
},
+ Attrs: attrs,
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// Computes offsets of concat inputs within its output.
+// ParseSingleSequenceExampleAttr is an optional argument to ParseSingleSequenceExample.
+type ParseSingleSequenceExampleAttr func(optionalAttr)
+
+// ParseSingleSequenceExampleContextSparseTypes sets the optional context_sparse_types attribute to value.
//
-// For example:
+// value: A list of Ncontext_sparse types; the data types of data in
+// each context Feature given in context_sparse_keys.
+// Currently the ParseSingleSequenceExample supports DT_FLOAT (FloatList),
+// DT_INT64 (Int64List), and DT_STRING (BytesList).
+// If not specified, defaults to <>
//
-// ```
-// # 'x' is [2, 2, 7]
-// # 'y' is [2, 3, 7]
-// # 'z' is [2, 5, 7]
-// concat_offset(2, [x, y, z]) => [0, 0, 0], [0, 2, 0], [0, 5, 0]
-// ```
+// REQUIRES: len(value) >= 0
+func ParseSingleSequenceExampleContextSparseTypes(value []tf.DataType) ParseSingleSequenceExampleAttr {
+ return func(m optionalAttr) {
+ m["context_sparse_types"] = value
+ }
+}
+
+// ParseSingleSequenceExampleFeatureListDenseTypes sets the optional feature_list_dense_types attribute to value.
+// If not specified, defaults to <>
//
-// This is typically used by gradient computations for a concat operation.
+// REQUIRES: len(value) >= 0
+func ParseSingleSequenceExampleFeatureListDenseTypes(value []tf.DataType) ParseSingleSequenceExampleAttr {
+ return func(m optionalAttr) {
+ m["feature_list_dense_types"] = value
+ }
+}
+
+// ParseSingleSequenceExampleContextDenseShapes sets the optional context_dense_shapes attribute to value.
//
-// Arguments:
-// concat_dim: The dimension along which to concatenate.
-// shape: The `N` int32 vectors representing shape of tensors being concatenated.
+// value: A list of Ncontext_dense shapes; the shapes of data in
+// each context Feature given in context_dense_keys.
+// The number of elements in the Feature corresponding to context_dense_key[j]
+// must always equal context_dense_shapes[j].NumEntries().
+// The shape of context_dense_values[j] will match context_dense_shapes[j].
+// If not specified, defaults to <>
//
-// Returns The `N` int32 vectors representing the starting offset
-// of input tensors within the concatenated output.
-func ConcatOffset(scope *Scope, concat_dim tf.Output, shape []tf.Output) (offset []tf.Output) {
- if scope.Err() != nil {
- return
- }
- opspec := tf.OpSpec{
- Type: "ConcatOffset",
- Input: []tf.Input{
- concat_dim, tf.OutputList(shape),
- },
+// REQUIRES: len(value) >= 0
+func ParseSingleSequenceExampleContextDenseShapes(value []tf.Shape) ParseSingleSequenceExampleAttr {
+ return func(m optionalAttr) {
+ m["context_dense_shapes"] = value
}
- op := scope.AddOperation(opspec)
- if scope.Err() != nil {
- return
+}
+
+// ParseSingleSequenceExampleFeatureListSparseTypes sets the optional feature_list_sparse_types attribute to value.
+//
+// value: A list of Nfeature_list_sparse types; the data types
+// of data in each FeatureList given in feature_list_sparse_keys.
+// Currently the ParseSingleSequenceExample supports DT_FLOAT (FloatList),
+// DT_INT64 (Int64List), and DT_STRING (BytesList).
+// If not specified, defaults to <>
+//
+// REQUIRES: len(value) >= 0
+func ParseSingleSequenceExampleFeatureListSparseTypes(value []tf.DataType) ParseSingleSequenceExampleAttr {
+ return func(m optionalAttr) {
+ m["feature_list_sparse_types"] = value
}
- var idx int
- var err error
- if offset, idx, err = makeOutputList(op, idx, "offset"); err != nil {
- scope.UpdateErr("ConcatOffset", err)
- return
+}
+
+// ParseSingleSequenceExampleFeatureListDenseShapes sets the optional feature_list_dense_shapes attribute to value.
+//
+// value: A list of Nfeature_list_dense shapes; the shapes of
+// data in each FeatureList given in feature_list_dense_keys.
+// The shape of each Feature in the FeatureList corresponding to
+// feature_list_dense_key[j] must always equal
+// feature_list_dense_shapes[j].NumEntries().
+// If not specified, defaults to <>
+//
+// REQUIRES: len(value) >= 0
+func ParseSingleSequenceExampleFeatureListDenseShapes(value []tf.Shape) ParseSingleSequenceExampleAttr {
+ return func(m optionalAttr) {
+ m["feature_list_dense_shapes"] = value
}
- return offset
}
-// Splits a tensor into `num_split` tensors along one dimension.
+// Transforms a scalar brain.SequenceExample proto (as strings) into typed tensors.
//
// Arguments:
-// axis: 0-D. The dimension along which to split. Must be in the range
-// `[-rank(value), rank(value))`.
-// value: The tensor to split.
-// num_split: The number of ways to split. Must evenly divide
-// `value.shape[split_dim]`.
-//
-// Returns They are identically shaped tensors, whose shape matches that of `value`
-// except along `axis`, where their sizes are
-// `values.shape[split_dim] / num_split`.
-func Split(scope *Scope, axis tf.Output, value tf.Output, num_split int64) (output []tf.Output) {
+// serialized: A scalar containing a binary serialized SequenceExample proto.
+// feature_list_dense_missing_assumed_empty: A vector listing the
+// FeatureList keys which may be missing from the SequenceExample. If the
+// associated FeatureList is missing, it is treated as empty. By default,
+// any FeatureList not listed in this vector must exist in the SequenceExample.
+// context_sparse_keys: A list of Ncontext_sparse string Tensors (scalars).
+// The keys expected in the Examples' features associated with context_sparse
+// values.
+// context_dense_keys: A list of Ncontext_dense string Tensors (scalars).
+// The keys expected in the SequenceExamples' context features associated with
+// dense values.
+// feature_list_sparse_keys: A list of Nfeature_list_sparse string Tensors
+// (scalars). The keys expected in the FeatureLists associated with sparse
+// values.
+// feature_list_dense_keys: A list of Nfeature_list_dense string Tensors (scalars).
+// The keys expected in the SequenceExamples' feature_lists associated
+// with lists of dense values.
+// context_dense_defaults: A list of Ncontext_dense Tensors (some may be empty).
+// context_dense_defaults[j] provides default values
+// when the SequenceExample's context map lacks context_dense_key[j].
+// If an empty Tensor is provided for context_dense_defaults[j],
+// then the Feature context_dense_keys[j] is required.
+// The input type is inferred from context_dense_defaults[j], even when it's
+// empty. If context_dense_defaults[j] is not empty, its shape must match
+// context_dense_shapes[j].
+// debug_name: A scalar containing the name of the serialized proto.
+// May contain, for example, table key (descriptive) name for the
+// corresponding serialized proto. This is purely useful for debugging
+// purposes, and the presence of values here has no effect on the output.
+// May also be an empty scalar if no name is available.
+func ParseSingleSequenceExample(scope *Scope, serialized tf.Output, feature_list_dense_missing_assumed_empty tf.Output, context_sparse_keys []tf.Output, context_dense_keys []tf.Output, feature_list_sparse_keys []tf.Output, feature_list_dense_keys []tf.Output, context_dense_defaults []tf.Output, debug_name tf.Output, optional ...ParseSingleSequenceExampleAttr) (context_sparse_indices []tf.Output, context_sparse_values []tf.Output, context_sparse_shapes []tf.Output, context_dense_values []tf.Output, feature_list_sparse_indices []tf.Output, feature_list_sparse_values []tf.Output, feature_list_sparse_shapes []tf.Output, feature_list_dense_values []tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{"num_split": num_split}
+ attrs := map[string]interface{}{}
+ for _, a := range optional {
+ a(attrs)
+ }
opspec := tf.OpSpec{
- Type: "Split",
+ Type: "ParseSingleSequenceExample",
Input: []tf.Input{
- axis, value,
+ serialized, feature_list_dense_missing_assumed_empty, tf.OutputList(context_sparse_keys), tf.OutputList(context_dense_keys), tf.OutputList(feature_list_sparse_keys), tf.OutputList(feature_list_dense_keys), tf.OutputList(context_dense_defaults), debug_name,
},
Attrs: attrs,
}
}
var idx int
var err error
- if output, idx, err = makeOutputList(op, idx, "output"); err != nil {
- scope.UpdateErr("Split", err)
+ if context_sparse_indices, idx, err = makeOutputList(op, idx, "context_sparse_indices"); err != nil {
+ scope.UpdateErr("ParseSingleSequenceExample", err)
return
}
- return output
-}
-
-// Splits a tensor into `num_split` tensors along one dimension.
-//
-// Arguments:
-// value: The tensor to split.
-// size_splits: list containing the sizes of each output tensor along the split
-// dimension. Must sum to the dimension of value along split_dim.
-// Can contain one -1 indicating that dimension is to be inferred.
-// axis: 0-D. The dimension along which to split. Must be in the range
-// `[-rank(value), rank(value))`.
-//
-//
-// Returns Tensors whose shape matches that of `value`
-// except along `axis`, where their sizes are
-// `size_splits[i]`.
-func SplitV(scope *Scope, value tf.Output, size_splits tf.Output, axis tf.Output, num_split int64) (output []tf.Output) {
- if scope.Err() != nil {
+ if context_sparse_values, idx, err = makeOutputList(op, idx, "context_sparse_values"); err != nil {
+ scope.UpdateErr("ParseSingleSequenceExample", err)
return
}
- attrs := map[string]interface{}{"num_split": num_split}
- opspec := tf.OpSpec{
- Type: "SplitV",
- Input: []tf.Input{
- value, size_splits, axis,
- },
- Attrs: attrs,
- }
- op := scope.AddOperation(opspec)
- if scope.Err() != nil {
+ if context_sparse_shapes, idx, err = makeOutputList(op, idx, "context_sparse_shapes"); err != nil {
+ scope.UpdateErr("ParseSingleSequenceExample", err)
return
}
- var idx int
- var err error
- if output, idx, err = makeOutputList(op, idx, "output"); err != nil {
- scope.UpdateErr("SplitV", err)
+ if context_dense_values, idx, err = makeOutputList(op, idx, "context_dense_values"); err != nil {
+ scope.UpdateErr("ParseSingleSequenceExample", err)
return
}
- return output
-}
-
-// Gives a guarantee to the TF runtime that the input tensor is a constant.
-//
-// The runtime is then free to make optimizations based on this.
-//
-// Only accepts value typed tensors as inputs and rejects resource variable handles
-// as input.
-//
-// Returns the input tensor without modification.
-func GuaranteeConst(scope *Scope, input tf.Output) (output tf.Output) {
- if scope.Err() != nil {
+ if feature_list_sparse_indices, idx, err = makeOutputList(op, idx, "feature_list_sparse_indices"); err != nil {
+ scope.UpdateErr("ParseSingleSequenceExample", err)
return
}
- opspec := tf.OpSpec{
- Type: "GuaranteeConst",
- Input: []tf.Input{
- input,
- },
- }
- op := scope.AddOperation(opspec)
- return op.Output(0)
-}
-
-// Returns a tensor of zeros with the same shape and type as x.
-//
-// Arguments:
-// x: a tensor of type T.
-//
-// Returns a tensor of the same shape and type as x but filled with zeros.
-func ZerosLike(scope *Scope, x tf.Output) (y tf.Output) {
- if scope.Err() != nil {
+ if feature_list_sparse_values, idx, err = makeOutputList(op, idx, "feature_list_sparse_values"); err != nil {
+ scope.UpdateErr("ParseSingleSequenceExample", err)
return
}
- opspec := tf.OpSpec{
- Type: "ZerosLike",
- Input: []tf.Input{
- x,
- },
+ if feature_list_sparse_shapes, idx, err = makeOutputList(op, idx, "feature_list_sparse_shapes"); err != nil {
+ scope.UpdateErr("ParseSingleSequenceExample", err)
+ return
}
- op := scope.AddOperation(opspec)
- return op.Output(0)
-}
-
-// QuantizedInstanceNormAttr is an optional argument to QuantizedInstanceNorm.
-type QuantizedInstanceNormAttr func(optionalAttr)
-
-// QuantizedInstanceNormOutputRangeGiven sets the optional output_range_given attribute to value.
-//
-// value: If True, `given_y_min` and `given_y_min`
-// and `given_y_max` are used as the output range. Otherwise,
-// the implementation computes the output range.
-// If not specified, defaults to false
-func QuantizedInstanceNormOutputRangeGiven(value bool) QuantizedInstanceNormAttr {
- return func(m optionalAttr) {
- m["output_range_given"] = value
+ if feature_list_dense_values, idx, err = makeOutputList(op, idx, "feature_list_dense_values"); err != nil {
+ scope.UpdateErr("ParseSingleSequenceExample", err)
+ return
}
+ return context_sparse_indices, context_sparse_values, context_sparse_shapes, context_dense_values, feature_list_sparse_indices, feature_list_sparse_values, feature_list_sparse_shapes, feature_list_dense_values
}
-// QuantizedInstanceNormGivenYMin sets the optional given_y_min attribute to value.
-//
-// value: Output in `y_min` if `output_range_given` is True.
-// If not specified, defaults to 0
-func QuantizedInstanceNormGivenYMin(value float32) QuantizedInstanceNormAttr {
- return func(m optionalAttr) {
- m["given_y_min"] = value
- }
-}
+// DecodeWavAttr is an optional argument to DecodeWav.
+type DecodeWavAttr func(optionalAttr)
-// QuantizedInstanceNormGivenYMax sets the optional given_y_max attribute to value.
+// DecodeWavDesiredChannels sets the optional desired_channels attribute to value.
//
-// value: Output in `y_max` if `output_range_given` is True.
-// If not specified, defaults to 0
-func QuantizedInstanceNormGivenYMax(value float32) QuantizedInstanceNormAttr {
+// value: Number of sample channels wanted.
+// If not specified, defaults to -1
+func DecodeWavDesiredChannels(value int64) DecodeWavAttr {
return func(m optionalAttr) {
- m["given_y_max"] = value
+ m["desired_channels"] = value
}
}
-// QuantizedInstanceNormVarianceEpsilon sets the optional variance_epsilon attribute to value.
+// DecodeWavDesiredSamples sets the optional desired_samples attribute to value.
//
-// value: A small float number to avoid dividing by 0.
-// If not specified, defaults to 1e-05
-func QuantizedInstanceNormVarianceEpsilon(value float32) QuantizedInstanceNormAttr {
+// value: Length of audio requested.
+// If not specified, defaults to -1
+func DecodeWavDesiredSamples(value int64) DecodeWavAttr {
return func(m optionalAttr) {
- m["variance_epsilon"] = value
+ m["desired_samples"] = value
}
}
-// QuantizedInstanceNormMinSeparation sets the optional min_separation attribute to value.
+// Decode a 16-bit PCM WAV file to a float tensor.
//
-// value: Minimum value of `y_max - y_min`
-// If not specified, defaults to 0.001
-func QuantizedInstanceNormMinSeparation(value float32) QuantizedInstanceNormAttr {
- return func(m optionalAttr) {
- m["min_separation"] = value
- }
-}
-
-// Quantized Instance normalization.
+// The -32768 to 32767 signed 16-bit values will be scaled to -1.0 to 1.0 in float.
+//
+// When desired_channels is set, if the input contains fewer channels than this
+// then the last channel will be duplicated to give the requested number, else if
+// the input has more channels than requested then the additional channels will be
+// ignored.
+//
+// If desired_samples is set, then the audio will be cropped or padded with zeroes
+// to the requested length.
+//
+// The first output contains a Tensor with the content of the audio samples. The
+// lowest dimension will be the number of channels, and the second will be the
+// number of samples. For example, a ten-sample-long stereo WAV file should give an
+// output shape of [10, 2].
//
// Arguments:
-// x: A 4D input Tensor.
-// x_min: The value represented by the lowest quantized input.
-// x_max: The value represented by the highest quantized input.
+// contents: The WAV-encoded audio, usually from a file.
//
-// Returns A 4D Tensor.The value represented by the lowest quantized output.The value represented by the highest quantized output.
-func QuantizedInstanceNorm(scope *Scope, x tf.Output, x_min tf.Output, x_max tf.Output, optional ...QuantizedInstanceNormAttr) (y tf.Output, y_min tf.Output, y_max tf.Output) {
+// Returns 2-D with shape `[length, channels]`.Scalar holding the sample rate found in the WAV header.
+func DecodeWav(scope *Scope, contents tf.Output, optional ...DecodeWavAttr) (audio tf.Output, sample_rate tf.Output) {
if scope.Err() != nil {
return
}
a(attrs)
}
opspec := tf.OpSpec{
- Type: "QuantizedInstanceNorm",
+ Type: "DecodeWav",
Input: []tf.Input{
- x, x_min, x_max,
+ contents,
},
Attrs: attrs,
}
op := scope.AddOperation(opspec)
- return op.Output(0), op.Output(1), op.Output(2)
+ return op.Output(0), op.Output(1)
}
-// Returns the diagonal part of the tensor.
-//
-// This operation returns a tensor with the `diagonal` part
-// of the `input`. The `diagonal` part is computed as follows:
+// UniqueAttr is an optional argument to Unique.
+type UniqueAttr func(optionalAttr)
+
+// UniqueOutIdx sets the optional out_idx attribute to value.
+// If not specified, defaults to DT_INT32
+func UniqueOutIdx(value tf.DataType) UniqueAttr {
+ return func(m optionalAttr) {
+ m["out_idx"] = value
+ }
+}
+
+// Finds unique elements in a 1-D tensor.
//
-// Assume `input` has dimensions `[D1,..., Dk, D1,..., Dk]`, then the output is a
-// tensor of rank `k` with dimensions `[D1,..., Dk]` where:
+// This operation returns a tensor `y` containing all of the unique elements of `x`
+// sorted in the same order that they occur in `x`. This operation also returns a
+// tensor `idx` the same size as `x` that contains the index of each value of `x`
+// in the unique output `y`. In other words:
//
-// `diagonal[i1,..., ik] = input[i1, ..., ik, i1,..., ik]`.
+// `y[idx[i]] = x[i] for i in [0, 1,...,rank(x) - 1]`
//
// For example:
//
// ```
-// # 'input' is [[1, 0, 0, 0]
-// [0, 2, 0, 0]
-// [0, 0, 3, 0]
-// [0, 0, 0, 4]]
-//
-// tf.diag_part(input) ==> [1, 2, 3, 4]
+// # tensor 'x' is [1, 1, 2, 4, 4, 4, 7, 8, 8]
+// y, idx = unique(x)
+// y ==> [1, 2, 4, 7, 8]
+// idx ==> [0, 0, 1, 2, 2, 2, 3, 4, 4]
// ```
//
// Arguments:
-// input: Rank k tensor where k is even and not zero.
+// x: 1-D.
//
-// Returns The extracted diagonal.
-func DiagPart(scope *Scope, input tf.Output) (diagonal tf.Output) {
+// Returns 1-D.1-D.
+func Unique(scope *Scope, x tf.Output, optional ...UniqueAttr) (y tf.Output, idx tf.Output) {
if scope.Err() != nil {
return
}
- opspec := tf.OpSpec{
- Type: "DiagPart",
- Input: []tf.Input{
- input,
- },
- }
- op := scope.AddOperation(opspec)
- return op.Output(0)
-}
-
-// Returns the element-wise max of two SparseTensors.
-//
-// Assumes the two SparseTensors have the same shape, i.e., no broadcasting.
-//
-// Arguments:
-// a_indices: 2-D. `N x R` matrix with the indices of non-empty values in a
-// SparseTensor, in the canonical lexicographic ordering.
-// a_values: 1-D. `N` non-empty values corresponding to `a_indices`.
-// a_shape: 1-D. Shape of the input SparseTensor.
-// b_indices: counterpart to `a_indices` for the other operand.
-// b_values: counterpart to `a_values` for the other operand; must be of the same dtype.
-// b_shape: counterpart to `a_shape` for the other operand; the two shapes must be equal.
-//
-// Returns 2-D. The indices of the output SparseTensor.1-D. The values of the output SparseTensor.
-func SparseSparseMaximum(scope *Scope, a_indices tf.Output, a_values tf.Output, a_shape tf.Output, b_indices tf.Output, b_values tf.Output, b_shape tf.Output) (output_indices tf.Output, output_values tf.Output) {
- if scope.Err() != nil {
- return
+ attrs := map[string]interface{}{}
+ for _, a := range optional {
+ a(attrs)
}
opspec := tf.OpSpec{
- Type: "SparseSparseMaximum",
+ Type: "Unique",
Input: []tf.Input{
- a_indices, a_values, a_shape, b_indices, b_values, b_shape,
+ x,
},
+ Attrs: attrs,
}
op := scope.AddOperation(opspec)
return op.Output(0), op.Output(1)
}
-// Returns a batched matrix tensor with new batched diagonal values.
-//
-// Given `input` and `diagonal`, this operation returns a tensor with the
-// same shape and values as `input`, except for the main diagonal of the
-// innermost matrices. These will be overwritten by the values in `diagonal`.
-//
-// The output is computed as follows:
-//
-// Assume `input` has `k+1` dimensions `[I, J, K, ..., M, N]` and `diagonal` has
-// `k` dimensions `[I, J, K, ..., min(M, N)]`. Then the output is a
-// tensor of rank `k+1` with dimensions `[I, J, K, ..., M, N]` where:
-//
-// * `output[i, j, k, ..., m, n] = diagonal[i, j, k, ..., n]` for `m == n`.
-// * `output[i, j, k, ..., m, n] = input[i, j, k, ..., m, n]` for `m != n`.
-//
-// Arguments:
-// input: Rank `k+1`, where `k >= 1`.
-// diagonal: Rank `k`, where `k >= 1`.
-//
-// Returns Rank `k+1`, with `output.shape = input.shape`.
-func MatrixSetDiag(scope *Scope, input tf.Output, diagonal tf.Output) (output tf.Output) {
- if scope.Err() != nil {
- return
- }
- opspec := tf.OpSpec{
- Type: "MatrixSetDiag",
- Input: []tf.Input{
- input, diagonal,
- },
- }
- op := scope.AddOperation(opspec)
- return op.Output(0)
-}
-
-// EditDistanceAttr is an optional argument to EditDistance.
-type EditDistanceAttr func(optionalAttr)
-
-// EditDistanceNormalize sets the optional normalize attribute to value.
+// Concatenates a list of `N` tensors along the first dimension.
//
-// value: boolean (if true, edit distances are normalized by length of truth).
+// The input tensors are all required to have size 1 in the first dimension.
//
-// The output is:
-// If not specified, defaults to true
-func EditDistanceNormalize(value bool) EditDistanceAttr {
- return func(m optionalAttr) {
- m["normalize"] = value
- }
-}
-
-// Computes the (possibly normalized) Levenshtein Edit Distance.
+// For example:
//
-// The inputs are variable-length sequences provided by SparseTensors
-// (hypothesis_indices, hypothesis_values, hypothesis_shape)
-// and
-// (truth_indices, truth_values, truth_shape).
+// ```
+// # 'x' is [[1, 4]]
+// # 'y' is [[2, 5]]
+// # 'z' is [[3, 6]]
+// parallel_concat([x, y, z]) => [[1, 4], [2, 5], [3, 6]] # Pack along first dim.
+// ```
//
-// The inputs are:
+// The difference between concat and parallel_concat is that concat requires all
+// of the inputs be computed before the operation will begin but doesn't require
+// that the input shapes be known during graph construction. Parallel concat
+// will copy pieces of the input into the output as they become available, in
+// some situations this can provide a performance benefit.
//
// Arguments:
-// hypothesis_indices: The indices of the hypothesis list SparseTensor.
-// This is an N x R int64 matrix.
-// hypothesis_values: The values of the hypothesis list SparseTensor.
-// This is an N-length vector.
-// hypothesis_shape: The shape of the hypothesis list SparseTensor.
-// This is an R-length vector.
-// truth_indices: The indices of the truth list SparseTensor.
-// This is an M x R int64 matrix.
-// truth_values: The values of the truth list SparseTensor.
-// This is an M-length vector.
-// truth_shape: truth indices, vector.
-//
-// Returns A dense float tensor with rank R - 1.
-//
-// For the example input:
-//
-// // hypothesis represents a 2x1 matrix with variable-length values:
-// // (0,0) = ["a"]
-// // (1,0) = ["b"]
-// hypothesis_indices = [[0, 0, 0],
-// [1, 0, 0]]
-// hypothesis_values = ["a", "b"]
-// hypothesis_shape = [2, 1, 1]
-//
-// // truth represents a 2x2 matrix with variable-length values:
-// // (0,0) = []
-// // (0,1) = ["a"]
-// // (1,0) = ["b", "c"]
-// // (1,1) = ["a"]
-// truth_indices = [[0, 1, 0],
-// [1, 0, 0],
-// [1, 0, 1],
-// [1, 1, 0]]
-// truth_values = ["a", "b", "c", "a"]
-// truth_shape = [2, 2, 2]
-// normalize = true
-//
-// The output will be:
+// values: Tensors to be concatenated. All must have size 1 in the first dimension
+// and same shape.
+// shape: the final shape of the result; should be equal to the shapes of any input
+// but with the number of input values in the first dimension.
//
-// // output is a 2x2 matrix with edit distances normalized by truth lengths.
-// output = [[inf, 1.0], // (0,0): no truth, (0,1): no hypothesis
-// [0.5, 1.0]] // (1,0): addition, (1,1): no hypothesis
-func EditDistance(scope *Scope, hypothesis_indices tf.Output, hypothesis_values tf.Output, hypothesis_shape tf.Output, truth_indices tf.Output, truth_values tf.Output, truth_shape tf.Output, optional ...EditDistanceAttr) (output tf.Output) {
+// Returns The concatenated tensor.
+func ParallelConcat(scope *Scope, values []tf.Output, shape tf.Shape) (output tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{}
- for _, a := range optional {
- a(attrs)
- }
+ attrs := map[string]interface{}{"shape": shape}
opspec := tf.OpSpec{
- Type: "EditDistance",
+ Type: "ParallelConcat",
Input: []tf.Input{
- hypothesis_indices, hypothesis_values, hypothesis_shape, truth_indices, truth_values, truth_shape,
+ tf.OutputList(values),
},
Attrs: attrs,
}
return op.Output(0)
}
-// Gather slices from `params` into a Tensor with shape specified by `indices`.
-//
-// `indices` is an K-dimensional integer tensor, best thought of as a
-// (K-1)-dimensional tensor of indices into `params`, where each element defines a
-// slice of `params`:
-//
-// output[i_0, ..., i_{K-2}] = params[indices[i0, ..., i_{K-2}]]
-//
-// Whereas in @{tf.gather} `indices` defines slices into the first
-// dimension of `params`, in `tf.gather_nd`, `indices` defines slices into the
-// first `N` dimensions of `params`, where `N = indices.shape[-1]`.
-//
-// The last dimension of `indices` can be at most the rank of
-// `params`:
-//
-// indices.shape[-1] <= params.rank
-//
-// The last dimension of `indices` corresponds to elements
-// (if `indices.shape[-1] == params.rank`) or slices
-// (if `indices.shape[-1] < params.rank`) along dimension `indices.shape[-1]`
-// of `params`. The output tensor has shape
-//
-// indices.shape[:-1] + params.shape[indices.shape[-1]:]
-//
-// Some examples below.
-//
-// Simple indexing into a matrix:
-//
-// ```python
-// indices = [[0, 0], [1, 1]]
-// params = [['a', 'b'], ['c', 'd']]
-// output = ['a', 'd']
-// ```
-//
-// Slice indexing into a matrix:
-//
-// ```python
-// indices = [[1], [0]]
-// params = [['a', 'b'], ['c', 'd']]
-// output = [['c', 'd'], ['a', 'b']]
-// ```
-//
-// Indexing into a 3-tensor:
-//
-// ```python
-// indices = [[1]]
-// params = [[['a0', 'b0'], ['c0', 'd0']],
-// [['a1', 'b1'], ['c1', 'd1']]]
-// output = [[['a1', 'b1'], ['c1', 'd1']]]
-//
-//
-// indices = [[0, 1], [1, 0]]
-// params = [[['a0', 'b0'], ['c0', 'd0']],
-// [['a1', 'b1'], ['c1', 'd1']]]
-// output = [['c0', 'd0'], ['a1', 'b1']]
-//
-//
-// indices = [[0, 0, 1], [1, 0, 1]]
-// params = [[['a0', 'b0'], ['c0', 'd0']],
-// [['a1', 'b1'], ['c1', 'd1']]]
-// output = ['b0', 'b1']
-// ```
-//
-// Batched indexing into a matrix:
-//
-// ```python
-// indices = [[[0, 0]], [[0, 1]]]
-// params = [['a', 'b'], ['c', 'd']]
-// output = [['a'], ['b']]
-// ```
-//
-// Batched slice indexing into a matrix:
-//
-// ```python
-// indices = [[[1]], [[0]]]
-// params = [['a', 'b'], ['c', 'd']]
-// output = [[['c', 'd']], [['a', 'b']]]
-// ```
+// Compute the lower regularized incomplete Gamma function `Q(a, x)`.
//
-// Batched indexing into a 3-tensor:
+// The lower regularized incomplete Gamma function is defined as:
//
-// ```python
-// indices = [[[1]], [[0]]]
-// params = [[['a0', 'b0'], ['c0', 'd0']],
-// [['a1', 'b1'], ['c1', 'd1']]]
-// output = [[[['a1', 'b1'], ['c1', 'd1']]],
-// [[['a0', 'b0'], ['c0', 'd0']]]]
//
-// indices = [[[0, 1], [1, 0]], [[0, 0], [1, 1]]]
-// params = [[['a0', 'b0'], ['c0', 'd0']],
-// [['a1', 'b1'], ['c1', 'd1']]]
-// output = [[['c0', 'd0'], ['a1', 'b1']],
-// [['a0', 'b0'], ['c1', 'd1']]]
+// \\(P(a, x) = gamma(a, x) / Gamma(a) = 1 - Q(a, x)\\)
//
+// where
//
-// indices = [[[0, 0, 1], [1, 0, 1]], [[0, 1, 1], [1, 1, 0]]]
-// params = [[['a0', 'b0'], ['c0', 'd0']],
-// [['a1', 'b1'], ['c1', 'd1']]]
-// output = [['b0', 'b1'], ['d0', 'c1']]
-// ```
+// \\(gamma(a, x) = int_{0}^{x} t^{a-1} exp(-t) dt\\)
//
-// Arguments:
-// params: The tensor from which to gather values.
-// indices: Index tensor.
+// is the lower incomplete Gamma function.
//
-// Returns Values from `params` gathered from indices given by `indices`, with
-// shape `indices.shape[:-1] + params.shape[indices.shape[-1]:]`.
-func GatherNd(scope *Scope, params tf.Output, indices tf.Output) (output tf.Output) {
+// Note, above `Q(a, x)` (`Igammac`) is the upper regularized complete
+// Gamma function.
+func Igamma(scope *Scope, a tf.Output, x tf.Output) (z tf.Output) {
if scope.Err() != nil {
return
}
opspec := tf.OpSpec{
- Type: "GatherNd",
+ Type: "Igamma",
Input: []tf.Input{
- params, indices,
+ a, x,
},
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// Eagerly executes a python function to compute func(input)->output. The
+// Computes offsets of concat inputs within its output.
//
-// semantics of the input, output, and attributes are the same as those for
-// PyFunc.
-func EagerPyFunc(scope *Scope, input []tf.Output, token string, Tout []tf.DataType) (output []tf.Output) {
+// For example:
+//
+// ```
+// # 'x' is [2, 2, 7]
+// # 'y' is [2, 3, 7]
+// # 'z' is [2, 5, 7]
+// concat_offset(2, [x, y, z]) => [0, 0, 0], [0, 2, 0], [0, 5, 0]
+// ```
+//
+// This is typically used by gradient computations for a concat operation.
+//
+// Arguments:
+// concat_dim: The dimension along which to concatenate.
+// shape: The `N` int32 vectors representing shape of tensors being concatenated.
+//
+// Returns The `N` int32 vectors representing the starting offset
+// of input tensors within the concatenated output.
+func ConcatOffset(scope *Scope, concat_dim tf.Output, shape []tf.Output) (offset []tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{"token": token, "Tout": Tout}
opspec := tf.OpSpec{
- Type: "EagerPyFunc",
+ Type: "ConcatOffset",
Input: []tf.Input{
- tf.OutputList(input),
+ concat_dim, tf.OutputList(shape),
},
- Attrs: attrs,
}
op := scope.AddOperation(opspec)
if scope.Err() != nil {
}
var idx int
var err error
- if output, idx, err = makeOutputList(op, idx, "output"); err != nil {
- scope.UpdateErr("EagerPyFunc", err)
+ if offset, idx, err = makeOutputList(op, idx, "offset"); err != nil {
+ scope.UpdateErr("ConcatOffset", err)
return
}
- return output
+ return offset
}
-// Stops gradient computation.
-//
-// When executed in a graph, this op outputs its input tensor as-is.
-//
-// When building ops to compute gradients, this op prevents the contribution of
-// its inputs to be taken into account. Normally, the gradient generator adds ops
-// to a graph to compute the derivatives of a specified 'loss' by recursively
-// finding out inputs that contributed to its computation. If you insert this op
-// in the graph it inputs are masked from the gradient generator. They are not
-// taken into account for computing gradients.
+// Splits a tensor into `num_split` tensors along one dimension.
//
-// This is useful any time you want to compute a value with TensorFlow but need
-// to pretend that the value was a constant. Some examples include:
+// Arguments:
+// axis: 0-D. The dimension along which to split. Must be in the range
+// `[-rank(value), rank(value))`.
+// value: The tensor to split.
+// num_split: The number of ways to split. Must evenly divide
+// `value.shape[split_dim]`.
//
-// * The *EM* algorithm where the *M-step* should not involve backpropagation
-// through the output of the *E-step*.
-// * Contrastive divergence training of Boltzmann machines where, when
-// differentiating the energy function, the training must not backpropagate
-// through the graph that generated the samples from the model.
-// * Adversarial training, where no backprop should happen through the adversarial
-// example generation process.
-func StopGradient(scope *Scope, input tf.Output) (output tf.Output) {
+// Returns They are identically shaped tensors, whose shape matches that of `value`
+// except along `axis`, where their sizes are
+// `values.shape[split_dim] / num_split`.
+func Split(scope *Scope, axis tf.Output, value tf.Output, num_split int64) (output []tf.Output) {
if scope.Err() != nil {
return
}
+ attrs := map[string]interface{}{"num_split": num_split}
opspec := tf.OpSpec{
- Type: "StopGradient",
+ Type: "Split",
Input: []tf.Input{
- input,
+ axis, value,
},
+ Attrs: attrs,
}
op := scope.AddOperation(opspec)
- return op.Output(0)
-}
-
-// Computes asin of x element-wise.
-func Asin(scope *Scope, x tf.Output) (y tf.Output) {
if scope.Err() != nil {
return
}
- opspec := tf.OpSpec{
- Type: "Asin",
- Input: []tf.Input{
- x,
- },
- }
- op := scope.AddOperation(opspec)
- return op.Output(0)
-}
-
-// PreventGradientAttr is an optional argument to PreventGradient.
-type PreventGradientAttr func(optionalAttr)
-
-// PreventGradientMessage sets the optional message attribute to value.
-//
-// value: Will be printed in the error when anyone tries to differentiate
-// this operation.
-// If not specified, defaults to ""
-func PreventGradientMessage(value string) PreventGradientAttr {
- return func(m optionalAttr) {
- m["message"] = value
+ var idx int
+ var err error
+ if output, idx, err = makeOutputList(op, idx, "output"); err != nil {
+ scope.UpdateErr("Split", err)
+ return
}
+ return output
}
-// An identity op that triggers an error if a gradient is requested.
-//
-// When executed in a graph, this op outputs its input tensor as-is.
-//
-// When building ops to compute gradients, the TensorFlow gradient system
-// will return an error when trying to lookup the gradient of this op,
-// because no gradient must ever be registered for this function. This
-// op exists to prevent subtle bugs from silently returning unimplemented
-// gradients in some corner cases.
+// Splits a tensor into `num_split` tensors along one dimension.
//
// Arguments:
-// input: any tensor.
+// value: The tensor to split.
+// size_splits: list containing the sizes of each output tensor along the split
+// dimension. Must sum to the dimension of value along split_dim.
+// Can contain one -1 indicating that dimension is to be inferred.
+// axis: 0-D. The dimension along which to split. Must be in the range
+// `[-rank(value), rank(value))`.
//
-// Returns the same input tensor.
-func PreventGradient(scope *Scope, input tf.Output, optional ...PreventGradientAttr) (output tf.Output) {
+//
+// Returns Tensors whose shape matches that of `value`
+// except along `axis`, where their sizes are
+// `size_splits[i]`.
+func SplitV(scope *Scope, value tf.Output, size_splits tf.Output, axis tf.Output, num_split int64) (output []tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{}
- for _, a := range optional {
- a(attrs)
- }
+ attrs := map[string]interface{}{"num_split": num_split}
opspec := tf.OpSpec{
- Type: "PreventGradient",
+ Type: "SplitV",
Input: []tf.Input{
- input,
+ value, size_splits, axis,
},
Attrs: attrs,
}
op := scope.AddOperation(opspec)
- return op.Output(0)
+ if scope.Err() != nil {
+ return
+ }
+ var idx int
+ var err error
+ if output, idx, err = makeOutputList(op, idx, "output"); err != nil {
+ scope.UpdateErr("SplitV", err)
+ return
+ }
+ return output
}
-// Checks a tensor for NaN and Inf values.
+// Gives a guarantee to the TF runtime that the input tensor is a constant.
//
-// When run, reports an `InvalidArgument` error if `tensor` has any values
-// that are not a number (NaN) or infinity (Inf). Otherwise, passes `tensor` as-is.
+// The runtime is then free to make optimizations based on this.
//
-// Arguments:
+// Only accepts value typed tensors as inputs and rejects resource variable handles
+// as input.
//
-// message: Prefix of the error message.
-func CheckNumerics(scope *Scope, tensor tf.Output, message string) (output tf.Output) {
+// Returns the input tensor without modification.
+func GuaranteeConst(scope *Scope, input tf.Output) (output tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{"message": message}
opspec := tf.OpSpec{
- Type: "CheckNumerics",
+ Type: "GuaranteeConst",
Input: []tf.Input{
- tensor,
+ input,
},
- Attrs: attrs,
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// Shuffle dimensions of x according to a permutation and conjugate the result.
+// Returns a tensor of zeros with the same shape and type as x.
//
-// The output `y` has the same rank as `x`. The shapes of `x` and `y` satisfy:
-// `y.shape[i] == x.shape[perm[i]] for i in [0, 1, ..., rank(x) - 1]`
-// `y[i,j,k,...,s,t,u] == conj(x[perm[i], perm[j], perm[k],...,perm[s], perm[t], perm[u]])`
-func ConjugateTranspose(scope *Scope, x tf.Output, perm tf.Output) (y tf.Output) {
+// Arguments:
+// x: a tensor of type T.
+//
+// Returns a tensor of the same shape and type as x but filled with zeros.
+func ZerosLike(scope *Scope, x tf.Output) (y tf.Output) {
if scope.Err() != nil {
return
}
opspec := tf.OpSpec{
- Type: "ConjugateTranspose",
+ Type: "ZerosLike",
Input: []tf.Input{
- x, perm,
+ x,
},
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// UniqueV2Attr is an optional argument to UniqueV2.
-type UniqueV2Attr func(optionalAttr)
+// QuantizedInstanceNormAttr is an optional argument to QuantizedInstanceNorm.
+type QuantizedInstanceNormAttr func(optionalAttr)
-// UniqueV2OutIdx sets the optional out_idx attribute to value.
-// If not specified, defaults to DT_INT32
-func UniqueV2OutIdx(value tf.DataType) UniqueV2Attr {
+// QuantizedInstanceNormOutputRangeGiven sets the optional output_range_given attribute to value.
+//
+// value: If True, `given_y_min` and `given_y_min`
+// and `given_y_max` are used as the output range. Otherwise,
+// the implementation computes the output range.
+// If not specified, defaults to false
+func QuantizedInstanceNormOutputRangeGiven(value bool) QuantizedInstanceNormAttr {
return func(m optionalAttr) {
- m["out_idx"] = value
+ m["output_range_given"] = value
}
}
-// Finds unique elements in a 1-D tensor.
+// QuantizedInstanceNormGivenYMin sets the optional given_y_min attribute to value.
//
-// This operation returns a tensor `y` containing all of the unique elements of `x`
-// sorted in the same order that they occur in `x`. This operation also returns a
-// tensor `idx` the same size as `x` that contains the index of each value of `x`
-// in the unique output `y`. In other words:
+// value: Output in `y_min` if `output_range_given` is True.
+// If not specified, defaults to 0
+func QuantizedInstanceNormGivenYMin(value float32) QuantizedInstanceNormAttr {
+ return func(m optionalAttr) {
+ m["given_y_min"] = value
+ }
+}
+
+// QuantizedInstanceNormGivenYMax sets the optional given_y_max attribute to value.
//
-// `y[idx[i]] = x[i] for i in [0, 1,...,rank(x) - 1]`
+// value: Output in `y_max` if `output_range_given` is True.
+// If not specified, defaults to 0
+func QuantizedInstanceNormGivenYMax(value float32) QuantizedInstanceNormAttr {
+ return func(m optionalAttr) {
+ m["given_y_max"] = value
+ }
+}
+
+// QuantizedInstanceNormVarianceEpsilon sets the optional variance_epsilon attribute to value.
//
-// For example:
+// value: A small float number to avoid dividing by 0.
+// If not specified, defaults to 1e-05
+func QuantizedInstanceNormVarianceEpsilon(value float32) QuantizedInstanceNormAttr {
+ return func(m optionalAttr) {
+ m["variance_epsilon"] = value
+ }
+}
+
+// QuantizedInstanceNormMinSeparation sets the optional min_separation attribute to value.
//
-// ```
-// # tensor 'x' is [1, 1, 2, 4, 4, 4, 7, 8, 8]
-// y, idx = unique(x)
-// y ==> [1, 2, 4, 7, 8]
-// idx ==> [0, 0, 1, 2, 2, 2, 3, 4, 4]
-// ```
+// value: Minimum value of `y_max - y_min`
+// If not specified, defaults to 0.001
+func QuantizedInstanceNormMinSeparation(value float32) QuantizedInstanceNormAttr {
+ return func(m optionalAttr) {
+ m["min_separation"] = value
+ }
+}
+
+// Quantized Instance normalization.
//
// Arguments:
-// x: A `Tensor`.
-// axis: A `Tensor` of type `int64` (default: 0). The axis of the Tensor to
-// find the unique elements.
+// x: A 4D input Tensor.
+// x_min: The value represented by the lowest quantized input.
+// x_max: The value represented by the highest quantized input.
//
-// Returns A `Tensor`. Unique elements along the `axis` of `Tensor` x.A 1-D Tensor. Has the same type as x that contains the index of each
-// value of x in the output y.
-func UniqueV2(scope *Scope, x tf.Output, axis tf.Output, optional ...UniqueV2Attr) (y tf.Output, idx tf.Output) {
+// Returns A 4D Tensor.The value represented by the lowest quantized output.The value represented by the highest quantized output.
+func QuantizedInstanceNorm(scope *Scope, x tf.Output, x_min tf.Output, x_max tf.Output, optional ...QuantizedInstanceNormAttr) (y tf.Output, y_min tf.Output, y_max tf.Output) {
if scope.Err() != nil {
return
}
a(attrs)
}
opspec := tf.OpSpec{
- Type: "UniqueV2",
+ Type: "QuantizedInstanceNorm",
Input: []tf.Input{
- x, axis,
+ x, x_min, x_max,
},
Attrs: attrs,
}
op := scope.AddOperation(opspec)
- return op.Output(0), op.Output(1)
+ return op.Output(0), op.Output(1), op.Output(2)
}
-// Return a slice from 'input'.
+// Returns the diagonal part of the tensor.
//
-// The output tensor is a tensor with dimensions described by 'size'
-// whose values are extracted from 'input' starting at the offsets in
-// 'begin'.
+// This operation returns a tensor with the `diagonal` part
+// of the `input`. The `diagonal` part is computed as follows:
//
-// *Requirements*:
-// 0 <= begin[i] <= begin[i] + size[i] <= Di for i in [0, n)
+// Assume `input` has dimensions `[D1,..., Dk, D1,..., Dk]`, then the output is a
+// tensor of rank `k` with dimensions `[D1,..., Dk]` where:
+//
+// `diagonal[i1,..., ik] = input[i1, ..., ik, i1,..., ik]`.
+//
+// For example:
+//
+// ```
+// # 'input' is [[1, 0, 0, 0]
+// [0, 2, 0, 0]
+// [0, 0, 3, 0]
+// [0, 0, 0, 4]]
+//
+// tf.diag_part(input) ==> [1, 2, 3, 4]
+// ```
//
// Arguments:
+// input: Rank k tensor where k is even and not zero.
//
-// begin: begin[i] specifies the offset into the 'i'th dimension of
-// 'input' to slice from.
-// size: size[i] specifies the number of elements of the 'i'th dimension
-// of 'input' to slice. If size[i] is -1, all remaining elements in dimension
-// i are included in the slice (i.e. this is equivalent to setting
-// size[i] = input.dim_size(i) - begin[i]).
-func Slice(scope *Scope, input tf.Output, begin tf.Output, size tf.Output) (output tf.Output) {
+// Returns The extracted diagonal.
+func DiagPart(scope *Scope, input tf.Output) (diagonal tf.Output) {
if scope.Err() != nil {
return
}
opspec := tf.OpSpec{
- Type: "Slice",
+ Type: "DiagPart",
Input: []tf.Input{
- input, begin, size,
+ input,
},
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// StridedSliceGradAttr is an optional argument to StridedSliceGrad.
-type StridedSliceGradAttr func(optionalAttr)
-
-// StridedSliceGradBeginMask sets the optional begin_mask attribute to value.
-// If not specified, defaults to 0
-func StridedSliceGradBeginMask(value int64) StridedSliceGradAttr {
- return func(m optionalAttr) {
- m["begin_mask"] = value
- }
-}
-
-// StridedSliceGradEndMask sets the optional end_mask attribute to value.
-// If not specified, defaults to 0
-func StridedSliceGradEndMask(value int64) StridedSliceGradAttr {
- return func(m optionalAttr) {
- m["end_mask"] = value
- }
-}
-
-// StridedSliceGradEllipsisMask sets the optional ellipsis_mask attribute to value.
-// If not specified, defaults to 0
-func StridedSliceGradEllipsisMask(value int64) StridedSliceGradAttr {
- return func(m optionalAttr) {
- m["ellipsis_mask"] = value
- }
-}
-
-// StridedSliceGradNewAxisMask sets the optional new_axis_mask attribute to value.
-// If not specified, defaults to 0
-func StridedSliceGradNewAxisMask(value int64) StridedSliceGradAttr {
- return func(m optionalAttr) {
- m["new_axis_mask"] = value
- }
-}
-
-// StridedSliceGradShrinkAxisMask sets the optional shrink_axis_mask attribute to value.
-// If not specified, defaults to 0
-func StridedSliceGradShrinkAxisMask(value int64) StridedSliceGradAttr {
- return func(m optionalAttr) {
- m["shrink_axis_mask"] = value
- }
-}
-
-// Returns the gradient of `StridedSlice`.
+// Returns the element-wise max of two SparseTensors.
//
-// Since `StridedSlice` cuts out pieces of its `input` which is size
-// `shape`, its gradient will have the same shape (which is passed here
-// as `shape`). The gradient will be zero in any element that the slice
-// does not select.
+// Assumes the two SparseTensors have the same shape, i.e., no broadcasting.
//
-// Arguments are the same as StridedSliceGrad with the exception that
-// `dy` is the input gradient to be propagated and `shape` is the
-// shape of `StridedSlice`'s `input`.
-func StridedSliceGrad(scope *Scope, shape tf.Output, begin tf.Output, end tf.Output, strides tf.Output, dy tf.Output, optional ...StridedSliceGradAttr) (output tf.Output) {
+// Arguments:
+// a_indices: 2-D. `N x R` matrix with the indices of non-empty values in a
+// SparseTensor, in the canonical lexicographic ordering.
+// a_values: 1-D. `N` non-empty values corresponding to `a_indices`.
+// a_shape: 1-D. Shape of the input SparseTensor.
+// b_indices: counterpart to `a_indices` for the other operand.
+// b_values: counterpart to `a_values` for the other operand; must be of the same dtype.
+// b_shape: counterpart to `a_shape` for the other operand; the two shapes must be equal.
+//
+// Returns 2-D. The indices of the output SparseTensor.1-D. The values of the output SparseTensor.
+func SparseSparseMaximum(scope *Scope, a_indices tf.Output, a_values tf.Output, a_shape tf.Output, b_indices tf.Output, b_values tf.Output, b_shape tf.Output) (output_indices tf.Output, output_values tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{}
- for _, a := range optional {
- a(attrs)
- }
opspec := tf.OpSpec{
- Type: "StridedSliceGrad",
+ Type: "SparseSparseMaximum",
Input: []tf.Input{
- shape, begin, end, strides, dy,
+ a_indices, a_values, a_shape, b_indices, b_values, b_shape,
},
- Attrs: attrs,
}
op := scope.AddOperation(opspec)
- return op.Output(0)
+ return op.Output(0), op.Output(1)
}
-// Returns the gradient of `Tile`.
+// Returns a batched matrix tensor with new batched diagonal values.
+//
+// Given `input` and `diagonal`, this operation returns a tensor with the
+// same shape and values as `input`, except for the main diagonal of the
+// innermost matrices. These will be overwritten by the values in `diagonal`.
//
-// DEPRECATED at GraphDef version 3: TileGrad has been replaced with reduce_sum
+// The output is computed as follows:
//
-// Since `Tile` takes an input and repeats the input `multiples` times
-// along each dimension, `TileGrad` takes in `multiples` and aggregates
-// each repeated tile of `input` into `output`.
-func TileGrad(scope *Scope, input tf.Output, multiples tf.Output) (output tf.Output) {
+// Assume `input` has `k+1` dimensions `[I, J, K, ..., M, N]` and `diagonal` has
+// `k` dimensions `[I, J, K, ..., min(M, N)]`. Then the output is a
+// tensor of rank `k+1` with dimensions `[I, J, K, ..., M, N]` where:
+//
+// * `output[i, j, k, ..., m, n] = diagonal[i, j, k, ..., n]` for `m == n`.
+// * `output[i, j, k, ..., m, n] = input[i, j, k, ..., m, n]` for `m != n`.
+//
+// Arguments:
+// input: Rank `k+1`, where `k >= 1`.
+// diagonal: Rank `k`, where `k >= 1`.
+//
+// Returns Rank `k+1`, with `output.shape = input.shape`.
+func MatrixSetDiag(scope *Scope, input tf.Output, diagonal tf.Output) (output tf.Output) {
if scope.Err() != nil {
return
}
opspec := tf.OpSpec{
- Type: "TileGrad",
+ Type: "MatrixSetDiag",
Input: []tf.Input{
- input, multiples,
+ input, diagonal,
},
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// QuantizeAndDequantizeAttr is an optional argument to QuantizeAndDequantize.
-type QuantizeAndDequantizeAttr func(optionalAttr)
+// EditDistanceAttr is an optional argument to EditDistance.
+type EditDistanceAttr func(optionalAttr)
-// QuantizeAndDequantizeSignedInput sets the optional signed_input attribute to value.
+// EditDistanceNormalize sets the optional normalize attribute to value.
+//
+// value: boolean (if true, edit distances are normalized by length of truth).
+//
+// The output is:
// If not specified, defaults to true
-func QuantizeAndDequantizeSignedInput(value bool) QuantizeAndDequantizeAttr {
- return func(m optionalAttr) {
- m["signed_input"] = value
- }
-}
-
-// QuantizeAndDequantizeNumBits sets the optional num_bits attribute to value.
-// If not specified, defaults to 8
-func QuantizeAndDequantizeNumBits(value int64) QuantizeAndDequantizeAttr {
- return func(m optionalAttr) {
- m["num_bits"] = value
- }
-}
-
-// QuantizeAndDequantizeRangeGiven sets the optional range_given attribute to value.
-// If not specified, defaults to false
-func QuantizeAndDequantizeRangeGiven(value bool) QuantizeAndDequantizeAttr {
- return func(m optionalAttr) {
- m["range_given"] = value
- }
-}
-
-// QuantizeAndDequantizeInputMin sets the optional input_min attribute to value.
-// If not specified, defaults to 0
-func QuantizeAndDequantizeInputMin(value float32) QuantizeAndDequantizeAttr {
- return func(m optionalAttr) {
- m["input_min"] = value
- }
-}
-
-// QuantizeAndDequantizeInputMax sets the optional input_max attribute to value.
-// If not specified, defaults to 0
-func QuantizeAndDequantizeInputMax(value float32) QuantizeAndDequantizeAttr {
+func EditDistanceNormalize(value bool) EditDistanceAttr {
return func(m optionalAttr) {
- m["input_max"] = value
+ m["normalize"] = value
}
}
-// Use QuantizeAndDequantizeV2 instead.
+// Computes the (possibly normalized) Levenshtein Edit Distance.
//
-// DEPRECATED at GraphDef version 22: Replaced by QuantizeAndDequantizeV2
-func QuantizeAndDequantize(scope *Scope, input tf.Output, optional ...QuantizeAndDequantizeAttr) (output tf.Output) {
+// The inputs are variable-length sequences provided by SparseTensors
+// (hypothesis_indices, hypothesis_values, hypothesis_shape)
+// and
+// (truth_indices, truth_values, truth_shape).
+//
+// The inputs are:
+//
+// Arguments:
+// hypothesis_indices: The indices of the hypothesis list SparseTensor.
+// This is an N x R int64 matrix.
+// hypothesis_values: The values of the hypothesis list SparseTensor.
+// This is an N-length vector.
+// hypothesis_shape: The shape of the hypothesis list SparseTensor.
+// This is an R-length vector.
+// truth_indices: The indices of the truth list SparseTensor.
+// This is an M x R int64 matrix.
+// truth_values: The values of the truth list SparseTensor.
+// This is an M-length vector.
+// truth_shape: truth indices, vector.
+//
+// Returns A dense float tensor with rank R - 1.
+//
+// For the example input:
+//
+// // hypothesis represents a 2x1 matrix with variable-length values:
+// // (0,0) = ["a"]
+// // (1,0) = ["b"]
+// hypothesis_indices = [[0, 0, 0],
+// [1, 0, 0]]
+// hypothesis_values = ["a", "b"]
+// hypothesis_shape = [2, 1, 1]
+//
+// // truth represents a 2x2 matrix with variable-length values:
+// // (0,0) = []
+// // (0,1) = ["a"]
+// // (1,0) = ["b", "c"]
+// // (1,1) = ["a"]
+// truth_indices = [[0, 1, 0],
+// [1, 0, 0],
+// [1, 0, 1],
+// [1, 1, 0]]
+// truth_values = ["a", "b", "c", "a"]
+// truth_shape = [2, 2, 2]
+// normalize = true
+//
+// The output will be:
+//
+// // output is a 2x2 matrix with edit distances normalized by truth lengths.
+// output = [[inf, 1.0], // (0,0): no truth, (0,1): no hypothesis
+// [0.5, 1.0]] // (1,0): addition, (1,1): no hypothesis
+func EditDistance(scope *Scope, hypothesis_indices tf.Output, hypothesis_values tf.Output, hypothesis_shape tf.Output, truth_indices tf.Output, truth_values tf.Output, truth_shape tf.Output, optional ...EditDistanceAttr) (output tf.Output) {
if scope.Err() != nil {
return
}
a(attrs)
}
opspec := tf.OpSpec{
- Type: "QuantizeAndDequantize",
+ Type: "EditDistance",
Input: []tf.Input{
- input,
+ hypothesis_indices, hypothesis_values, hypothesis_shape, truth_indices, truth_values, truth_shape,
},
Attrs: attrs,
}
return op.Output(0)
}
-// QueueDequeueV2Attr is an optional argument to QueueDequeueV2.
-type QueueDequeueV2Attr func(optionalAttr)
-
-// QueueDequeueV2TimeoutMs sets the optional timeout_ms attribute to value.
+// Gather slices from `params` into a Tensor with shape specified by `indices`.
//
-// value: If the queue is empty, this operation will block for up to
-// timeout_ms milliseconds.
-// Note: This option is not supported yet.
-// If not specified, defaults to -1
-func QueueDequeueV2TimeoutMs(value int64) QueueDequeueV2Attr {
- return func(m optionalAttr) {
- m["timeout_ms"] = value
+// `indices` is an K-dimensional integer tensor, best thought of as a
+// (K-1)-dimensional tensor of indices into `params`, where each element defines a
+// slice of `params`:
+//
+// output[i_0, ..., i_{K-2}] = params[indices[i0, ..., i_{K-2}]]
+//
+// Whereas in @{tf.gather} `indices` defines slices into the first
+// dimension of `params`, in `tf.gather_nd`, `indices` defines slices into the
+// first `N` dimensions of `params`, where `N = indices.shape[-1]`.
+//
+// The last dimension of `indices` can be at most the rank of
+// `params`:
+//
+// indices.shape[-1] <= params.rank
+//
+// The last dimension of `indices` corresponds to elements
+// (if `indices.shape[-1] == params.rank`) or slices
+// (if `indices.shape[-1] < params.rank`) along dimension `indices.shape[-1]`
+// of `params`. The output tensor has shape
+//
+// indices.shape[:-1] + params.shape[indices.shape[-1]:]
+//
+// Some examples below.
+//
+// Simple indexing into a matrix:
+//
+// ```python
+// indices = [[0, 0], [1, 1]]
+// params = [['a', 'b'], ['c', 'd']]
+// output = ['a', 'd']
+// ```
+//
+// Slice indexing into a matrix:
+//
+// ```python
+// indices = [[1], [0]]
+// params = [['a', 'b'], ['c', 'd']]
+// output = [['c', 'd'], ['a', 'b']]
+// ```
+//
+// Indexing into a 3-tensor:
+//
+// ```python
+// indices = [[1]]
+// params = [[['a0', 'b0'], ['c0', 'd0']],
+// [['a1', 'b1'], ['c1', 'd1']]]
+// output = [[['a1', 'b1'], ['c1', 'd1']]]
+//
+//
+// indices = [[0, 1], [1, 0]]
+// params = [[['a0', 'b0'], ['c0', 'd0']],
+// [['a1', 'b1'], ['c1', 'd1']]]
+// output = [['c0', 'd0'], ['a1', 'b1']]
+//
+//
+// indices = [[0, 0, 1], [1, 0, 1]]
+// params = [[['a0', 'b0'], ['c0', 'd0']],
+// [['a1', 'b1'], ['c1', 'd1']]]
+// output = ['b0', 'b1']
+// ```
+//
+// Batched indexing into a matrix:
+//
+// ```python
+// indices = [[[0, 0]], [[0, 1]]]
+// params = [['a', 'b'], ['c', 'd']]
+// output = [['a'], ['b']]
+// ```
+//
+// Batched slice indexing into a matrix:
+//
+// ```python
+// indices = [[[1]], [[0]]]
+// params = [['a', 'b'], ['c', 'd']]
+// output = [[['c', 'd']], [['a', 'b']]]
+// ```
+//
+// Batched indexing into a 3-tensor:
+//
+// ```python
+// indices = [[[1]], [[0]]]
+// params = [[['a0', 'b0'], ['c0', 'd0']],
+// [['a1', 'b1'], ['c1', 'd1']]]
+// output = [[[['a1', 'b1'], ['c1', 'd1']]],
+// [[['a0', 'b0'], ['c0', 'd0']]]]
+//
+// indices = [[[0, 1], [1, 0]], [[0, 0], [1, 1]]]
+// params = [[['a0', 'b0'], ['c0', 'd0']],
+// [['a1', 'b1'], ['c1', 'd1']]]
+// output = [[['c0', 'd0'], ['a1', 'b1']],
+// [['a0', 'b0'], ['c1', 'd1']]]
+//
+//
+// indices = [[[0, 0, 1], [1, 0, 1]], [[0, 1, 1], [1, 1, 0]]]
+// params = [[['a0', 'b0'], ['c0', 'd0']],
+// [['a1', 'b1'], ['c1', 'd1']]]
+// output = [['b0', 'b1'], ['d0', 'c1']]
+// ```
+//
+// Arguments:
+// params: The tensor from which to gather values.
+// indices: Index tensor.
+//
+// Returns Values from `params` gathered from indices given by `indices`, with
+// shape `indices.shape[:-1] + params.shape[indices.shape[-1]:]`.
+func GatherNd(scope *Scope, params tf.Output, indices tf.Output) (output tf.Output) {
+ if scope.Err() != nil {
+ return
+ }
+ opspec := tf.OpSpec{
+ Type: "GatherNd",
+ Input: []tf.Input{
+ params, indices,
+ },
}
+ op := scope.AddOperation(opspec)
+ return op.Output(0)
}
-// Dequeues a tuple of one or more tensors from the given queue.
-//
-// This operation has k outputs, where k is the number of components
-// in the tuples stored in the given queue, and output i is the ith
-// component of the dequeued tuple.
-//
-// N.B. If the queue is empty, this operation will block until an element
-// has been dequeued (or 'timeout_ms' elapses, if specified).
-//
-// Arguments:
-// handle: The handle to a queue.
-// component_types: The type of each component in a tuple.
+// Eagerly executes a python function to compute func(input)->output. The
//
-// Returns One or more tensors that were dequeued as a tuple.
-func QueueDequeueV2(scope *Scope, handle tf.Output, component_types []tf.DataType, optional ...QueueDequeueV2Attr) (components []tf.Output) {
+// semantics of the input, output, and attributes are the same as those for
+// PyFunc.
+func EagerPyFunc(scope *Scope, input []tf.Output, token string, Tout []tf.DataType) (output []tf.Output) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{"component_types": component_types}
- for _, a := range optional {
- a(attrs)
- }
+ attrs := map[string]interface{}{"token": token, "Tout": Tout}
opspec := tf.OpSpec{
- Type: "QueueDequeueV2",
+ Type: "EagerPyFunc",
Input: []tf.Input{
- handle,
+ tf.OutputList(input),
},
Attrs: attrs,
}
}
var idx int
var err error
- if components, idx, err = makeOutputList(op, idx, "components"); err != nil {
- scope.UpdateErr("QueueDequeueV2", err)
+ if output, idx, err = makeOutputList(op, idx, "output"); err != nil {
+ scope.UpdateErr("EagerPyFunc", err)
return
}
- return components
+ return output
}
-// Returns locations of nonzero / true values in a tensor.
-//
-// This operation returns the coordinates of true elements in `condition`. The
-// coordinates are returned in a 2-D tensor where the first dimension (rows)
-// represents the number of true elements, and the second dimension (columns)
-// represents the coordinates of the true elements. Keep in mind, the shape of
-// the output tensor can vary depending on how many true values there are in
-// `condition`. Indices are output in row-major order.
-//
-// For example:
+// Stops gradient computation.
//
-// ```
-// # 'input' tensor is [[True, False]
-// # [True, False]]
-// # 'input' has two true values, so output has two coordinates.
-// # 'input' has rank of 2, so coordinates have two indices.
-// where(input) ==> [[0, 0],
-// [1, 0]]
+// When executed in a graph, this op outputs its input tensor as-is.
//
-// # `condition` tensor is [[[True, False]
-// # [True, False]]
-// # [[False, True]
-// # [False, True]]
-// # [[False, False]
-// # [False, True]]]
-// # 'input' has 5 true values, so output has 5 coordinates.
-// # 'input' has rank of 3, so coordinates have three indices.
-// where(input) ==> [[0, 0, 0],
-// [0, 1, 0],
-// [1, 0, 1],
-// [1, 1, 1],
-// [2, 1, 1]]
+// When building ops to compute gradients, this op prevents the contribution of
+// its inputs to be taken into account. Normally, the gradient generator adds ops
+// to a graph to compute the derivatives of a specified 'loss' by recursively
+// finding out inputs that contributed to its computation. If you insert this op
+// in the graph it inputs are masked from the gradient generator. They are not
+// taken into account for computing gradients.
//
-// # `condition` tensor is [[[1.5, 0.0]
-// # [-0.5, 0.0]]
-// # [[0.0, 0.25]
-// # [0.0, 0.75]]
-// # [[0.0, 0.0]
-// # [0.0, 0.01]]]
-// # 'input' has 5 nonzero values, so output has 5 coordinates.
-// # 'input' has rank of 3, so coordinates have three indices.
-// where(input) ==> [[0, 0, 0],
-// [0, 1, 0],
-// [1, 0, 1],
-// [1, 1, 1],
-// [2, 1, 1]]
+// This is useful any time you want to compute a value with TensorFlow but need
+// to pretend that the value was a constant. Some examples include:
//
-// # `condition` tensor is [[[1.5 + 0.0j, 0.0 + 0.0j]
-// # [0.0 + 0.5j, 0.0 + 0.0j]]
-// # [[0.0 + 0.0j, 0.25 + 1.5j]
-// # [0.0 + 0.0j, 0.75 + 0.0j]]
-// # [[0.0 + 0.0j, 0.0 + 0.0j]
-// # [0.0 + 0.0j, 0.01 + 0.0j]]]
-// # 'input' has 5 nonzero magnitude values, so output has 5 coordinates.
-// # 'input' has rank of 3, so coordinates have three indices.
-// where(input) ==> [[0, 0, 0],
-// [0, 1, 0],
-// [1, 0, 1],
-// [1, 1, 1],
-// [2, 1, 1]]
-// ```
-func Where(scope *Scope, condition tf.Output) (index tf.Output) {
+// * The *EM* algorithm where the *M-step* should not involve backpropagation
+// through the output of the *E-step*.
+// * Contrastive divergence training of Boltzmann machines where, when
+// differentiating the energy function, the training must not backpropagate
+// through the graph that generated the samples from the model.
+// * Adversarial training, where no backprop should happen through the adversarial
+// example generation process.
+func StopGradient(scope *Scope, input tf.Output) (output tf.Output) {
if scope.Err() != nil {
return
}
opspec := tf.OpSpec{
- Type: "Where",
+ Type: "StopGradient",
Input: []tf.Input{
- condition,
+ input,
},
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// DataFormatDimMapAttr is an optional argument to DataFormatDimMap.
-type DataFormatDimMapAttr func(optionalAttr)
-
-// DataFormatDimMapSrcFormat sets the optional src_format attribute to value.
-//
-// value: source data format.
-// If not specified, defaults to "NHWC"
-func DataFormatDimMapSrcFormat(value string) DataFormatDimMapAttr {
- return func(m optionalAttr) {
- m["src_format"] = value
+// Computes asin of x element-wise.
+func Asin(scope *Scope, x tf.Output) (y tf.Output) {
+ if scope.Err() != nil {
+ return
}
+ opspec := tf.OpSpec{
+ Type: "Asin",
+ Input: []tf.Input{
+ x,
+ },
+ }
+ op := scope.AddOperation(opspec)
+ return op.Output(0)
}
-// DataFormatDimMapDstFormat sets the optional dst_format attribute to value.
+// PreventGradientAttr is an optional argument to PreventGradient.
+type PreventGradientAttr func(optionalAttr)
+
+// PreventGradientMessage sets the optional message attribute to value.
//
-// value: destination data format.
-// If not specified, defaults to "NCHW"
-func DataFormatDimMapDstFormat(value string) DataFormatDimMapAttr {
+// value: Will be printed in the error when anyone tries to differentiate
+// this operation.
+// If not specified, defaults to ""
+func PreventGradientMessage(value string) PreventGradientAttr {
return func(m optionalAttr) {
- m["dst_format"] = value
+ m["message"] = value
}
}
-// Returns the dimension index in the destination data format given the one in
+// An identity op that triggers an error if a gradient is requested.
//
-// the source data format.
+// When executed in a graph, this op outputs its input tensor as-is.
+//
+// When building ops to compute gradients, the TensorFlow gradient system
+// will return an error when trying to lookup the gradient of this op,
+// because no gradient must ever be registered for this function. This
+// op exists to prevent subtle bugs from silently returning unimplemented
+// gradients in some corner cases.
//
// Arguments:
-// x: A Tensor with each element as a dimension index in source data format.
-// Must be in the range [-4, 4).
+// input: any tensor.
//
-// Returns A Tensor with each element as a dimension index in destination data format.
-func DataFormatDimMap(scope *Scope, x tf.Output, optional ...DataFormatDimMapAttr) (y tf.Output) {
+// Returns the same input tensor.
+func PreventGradient(scope *Scope, input tf.Output, optional ...PreventGradientAttr) (output tf.Output) {
if scope.Err() != nil {
return
}
a(attrs)
}
opspec := tf.OpSpec{
- Type: "DataFormatDimMap",
+ Type: "PreventGradient",
Input: []tf.Input{
- x,
+ input,
},
Attrs: attrs,
}
return op.Output(0)
}
-// Return the shape of s0 op s1 with broadcast.
+// Checks a tensor for NaN and Inf values.
//
-// Given `s0` and `s1`, tensors that represent shapes, compute `r0`, the
-// broadcasted shape. `s0`, `s1` and `r0` are all integer vectors.
-func BroadcastArgs(scope *Scope, s0 tf.Output, s1 tf.Output) (r0 tf.Output) {
+// When run, reports an `InvalidArgument` error if `tensor` has any values
+// that are not a number (NaN) or infinity (Inf). Otherwise, passes `tensor` as-is.
+//
+// Arguments:
+//
+// message: Prefix of the error message.
+func CheckNumerics(scope *Scope, tensor tf.Output, message string) (output tf.Output) {
if scope.Err() != nil {
return
}
+ attrs := map[string]interface{}{"message": message}
opspec := tf.OpSpec{
- Type: "BroadcastArgs",
+ Type: "CheckNumerics",
Input: []tf.Input{
- s0, s1,
+ tensor,
},
+ Attrs: attrs,
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
-// Return the reduction indices for computing gradients of s0 op s1 with broadcast.
+// Shuffle dimensions of x according to a permutation and conjugate the result.
//
-// This is typically used by gradient computations for a broadcasting operation.
-func BroadcastGradientArgs(scope *Scope, s0 tf.Output, s1 tf.Output) (r0 tf.Output, r1 tf.Output) {
+// The output `y` has the same rank as `x`. The shapes of `x` and `y` satisfy:
+// `y.shape[i] == x.shape[perm[i]] for i in [0, 1, ..., rank(x) - 1]`
+// `y[i,j,k,...,s,t,u] == conj(x[perm[i], perm[j], perm[k],...,perm[s], perm[t], perm[u]])`
+func ConjugateTranspose(scope *Scope, x tf.Output, perm tf.Output) (y tf.Output) {
if scope.Err() != nil {
return
}
opspec := tf.OpSpec{
- Type: "BroadcastGradientArgs",
+ Type: "ConjugateTranspose",
Input: []tf.Input{
- s0, s1,
+ x, perm,
+ },
+ }
+ op := scope.AddOperation(opspec)
+ return op.Output(0)
+}
+
+// UniqueV2Attr is an optional argument to UniqueV2.
+type UniqueV2Attr func(optionalAttr)
+
+// UniqueV2OutIdx sets the optional out_idx attribute to value.
+// If not specified, defaults to DT_INT32
+func UniqueV2OutIdx(value tf.DataType) UniqueV2Attr {
+ return func(m optionalAttr) {
+ m["out_idx"] = value
+ }
+}
+
+// Finds unique elements in a 1-D tensor.
+//
+// This operation returns a tensor `y` containing all of the unique elements of `x`
+// sorted in the same order that they occur in `x`. This operation also returns a
+// tensor `idx` the same size as `x` that contains the index of each value of `x`
+// in the unique output `y`. In other words:
+//
+// `y[idx[i]] = x[i] for i in [0, 1,...,rank(x) - 1]`
+//
+// For example:
+//
+// ```
+// # tensor 'x' is [1, 1, 2, 4, 4, 4, 7, 8, 8]
+// y, idx = unique(x)
+// y ==> [1, 2, 4, 7, 8]
+// idx ==> [0, 0, 1, 2, 2, 2, 3, 4, 4]
+// ```
+//
+// Arguments:
+// x: A `Tensor`.
+// axis: A `Tensor` of type `int64` (default: 0). The axis of the Tensor to
+// find the unique elements.
+//
+// Returns A `Tensor`. Unique elements along the `axis` of `Tensor` x.A 1-D Tensor. Has the same type as x that contains the index of each
+// value of x in the output y.
+func UniqueV2(scope *Scope, x tf.Output, axis tf.Output, optional ...UniqueV2Attr) (y tf.Output, idx tf.Output) {
+ if scope.Err() != nil {
+ return
+ }
+ attrs := map[string]interface{}{}
+ for _, a := range optional {
+ a(attrs)
+ }
+ opspec := tf.OpSpec{
+ Type: "UniqueV2",
+ Input: []tf.Input{
+ x, axis,
},
+ Attrs: attrs,
}
op := scope.AddOperation(opspec)
return op.Output(0), op.Output(1)
projects / platform / upstream / tensorflow.git / commitdiff