// FakeQuantWithMinMaxVarsPerChannelGradientNumBits sets the optional num_bits attribute to value.
//
-// value: The bitwidth of the quantization; between 2 and 8, inclusive.
+// value: The bitwidth of the quantization; between 2 and 16, inclusive.
// If not specified, defaults to 8
func FakeQuantWithMinMaxVarsPerChannelGradientNumBits(value int64) FakeQuantWithMinMaxVarsPerChannelGradientAttr {
return func(m optionalAttr) {
// `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.
+// `num_bits` is the bitwidth of the quantization; between 2 and 16, inclusive.
//
// This operation has a gradient and thus allows for training `min` and `max`
// values.
// `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.
+// `num_bits` is the bitwidth of the quantization; between 2 and 16, inclusive.
//
// Quantization is called fake since the output is still in floating point.
func FakeQuantWithMinMaxArgs(scope *Scope, inputs tf.Output, optional ...FakeQuantWithMinMaxArgsAttr) (outputs tf.Output) {
// [[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]],
// [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]]]
//
+// Note that on CPU, if an out of bound index is found, an error is returned.
+// On GPU, if an out of bound index is found, the index is ignored.
+//
// Arguments:
// indices: Index tensor.
// updates: Updates to scatter into output.
return op.Output(0)
}
+// UniqueWithCountsV2Attr is an optional argument to UniqueWithCountsV2.
+type UniqueWithCountsV2Attr func(optionalAttr)
+
+// UniqueWithCountsV2OutIdx sets the optional out_idx attribute to value.
+// If not specified, defaults to DT_INT32
+func UniqueWithCountsV2OutIdx(value tf.DataType) UniqueWithCountsV2Attr {
+ return func(m optionalAttr) {
+ m["out_idx"] = value
+ }
+}
+
+// Finds unique elements along an axis of a tensor.
+//
+// This operation either returns a tensor `y` containing unique elements
+// along the `axis` of a tensor. The returned unique elements is sorted
+// in the same order as they occur along `axis` in `x`.
+// This operation also returns a tensor `idx` and a tensor `count`
+// that are the same size as the number of the elements in `x` along the
+// `axis` dimension. The `idx` contains the index in the unique output `y`
+// and the `count` contains the count in the unique output `y`.
+// In other words, for an `1-D` tensor `x` with `axis = None:
+//
+// `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]
+// ```
+//
+// For an `2-D` tensor `x` with `axis = 0`:
+//
+// ```
+// # tensor 'x' is [[1, 0, 0],
+// # [1, 0, 0],
+// # [2, 0, 0]]
+// y, idx, count = unique_with_counts(x, axis=0)
+// y ==> [[1, 0, 0],
+// [2, 0, 0]]
+// idx ==> [0, 0, 1]
+// count ==> [2, 1]
+// ```
+//
+// For an `2-D` tensor `x` with `axis = 1`:
+//
+// ```
+// # tensor 'x' is [[1, 0, 0],
+// # [1, 0, 0],
+// # [2, 0, 0]]
+// y, idx, count = unique_with_counts(x, axis=1)
+// y ==> [[1, 0],
+// [1, 0],
+// [2, 0]]
+// idx ==> [0, 1, 1]
+// count ==> [1, 2]
+// ```
+//
+// Arguments:
+// x: A `Tensor`.
+// axis: A `Tensor` of type `int32` (default: None). 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.A 1-D Tensor. The count of each value of x in the output y.
+func UniqueWithCountsV2(scope *Scope, x tf.Output, axis tf.Output, optional ...UniqueWithCountsV2Attr) (y tf.Output, idx tf.Output, count tf.Output) {
+ if scope.Err() != nil {
+ return
+ }
+ attrs := map[string]interface{}{}
+ for _, a := range optional {
+ a(attrs)
+ }
+ opspec := tf.OpSpec{
+ Type: "UniqueWithCountsV2",
+ Input: []tf.Input{
+ x, axis,
+ },
+ Attrs: attrs,
+ }
+ op := scope.AddOperation(opspec)
+ return op.Output(0), op.Output(1), op.Output(2)
+}
+
// UniqueWithCountsAttr is an optional argument to UniqueWithCounts.
type UniqueWithCountsAttr func(optionalAttr)
}
}
-// Finds unique elements in a 1-D tensor.
+// Finds unique elements along an axis of a 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:
+// This operation either returns a tensor `y` containing unique elements
+// along the `axis` of a tensor. The returned unique elements is sorted
+// in the same order as they occur along `axis` in `x`.
+// This operation also returns a tensor `idx` that is the same size as
+// the number of the elements in `x` along the `axis` dimension. It
+// contains the index in the unique output `y`.
+// In other words, for an `1-D` tensor `x` with `axis = None:
//
// `y[idx[i]] = x[i] for i in [0, 1,...,rank(x) - 1]`
//
// idx ==> [0, 0, 1, 2, 2, 2, 3, 4, 4]
// ```
//
+// For an `2-D` tensor `x` with `axis = 0`:
+//
+// ```
+// # tensor 'x' is [[1, 0, 0],
+// # [1, 0, 0],
+// # [2, 0, 0]]
+// y, idx = unique(x, axis=0)
+// y ==> [[1, 0, 0],
+// [2, 0, 0]]
+// idx ==> [0, 0, 1]
+// ```
+//
+// For an `2-D` tensor `x` with `axis = 1`:
+//
+// ```
+// # tensor 'x' is [[1, 0, 0],
+// # [1, 0, 0],
+// # [2, 0, 0]]
+// y, idx = unique(x, axis=1)
+// y ==> [[1, 0],
+// [1, 0],
+// [2, 0]]
+// idx ==> [0, 1, 1]
+// ```
+//
// Arguments:
// x: A `Tensor`.
-// axis: A `Tensor` of type `int64` (default: 0). The axis of the Tensor to
+// axis: A `Tensor` of type `int32` (default: None). 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
return scope.AddOperation(opspec)
}
+// Clips tensor values to a specified min and max.
+//
+// Given a tensor `t`, this operation returns a tensor of the same type and
+// shape as `t` with its values clipped to `clip_value_min` and `clip_value_max`.
+// Any values less than `clip_value_min` are set to `clip_value_min`. Any values
+// greater than `clip_value_max` are set to `clip_value_max`.
+//
+// Arguments:
+// t: A `Tensor`.
+// clip_value_min: A 0-D (scalar) `Tensor`, or a `Tensor` with the same shape
+// as `t`. The minimum value to clip by.
+// clip_value_max: A 0-D (scalar) `Tensor`, or a `Tensor` with the same shape
+// as `t`. The maximum value to clip by.
+//
+// Returns A clipped `Tensor` with the same shape as input 't'.
+func ClipByValue(scope *Scope, t tf.Output, clip_value_min tf.Output, clip_value_max tf.Output) (output tf.Output) {
+ if scope.Err() != nil {
+ return
+ }
+ opspec := tf.OpSpec{
+ Type: "ClipByValue",
+ Input: []tf.Input{
+ t, clip_value_min, clip_value_max,
+ },
+ }
+ 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
// Computes the mean along sparse segments of a tensor.
//
-// Read @{$math_ops#segmentation$the section on segmentation} for an explanation of
+// 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
// 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
+// Read @{$math_ops#Segmentation$the section on segmentation} for an explanation of
// segments.
//
// For example:
// Computes the sum along sparse segments of a tensor.
//
-// Read @{$math_ops#segmentation$the section on segmentation} for an explanation of
+// 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
return op.Output(0)
}
+// Computes the minimum 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 found
+// [(here)](../../../api_docs/python/math_ops.md#UnsortedSegmentSum).
+// Instead of computing the sum over segments, it computes the minimum such that:
+//
+// \\(output_i = \min_j data_j\\) where min is over `j` such
+// that `segment_ids[j] == i`.
+//
+// If the minimum is empty for a given segment ID `i`, it outputs the largest
+// possible value for the specific numeric type,
+// `output[i] = numeric_limits<T>::max()`.
+//
+// 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 UnsortedSegmentMin(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: "UnsortedSegmentMin",
+ Input: []tf.Input{
+ data, segment_ids, num_segments,
+ },
+ }
+ 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) {
if scope.Err() != nil {
// Computes the sum along segments of a tensor.
//
-// Read @{$math_ops#segmentation$the section on segmentation} for an explanation of
+// Read @{$math_ops#Segmentation$the section on segmentation} for an explanation of
// segments.
//
// Computes a tensor such that
return op.Output(0)
}
+// Creates a dataset that passes a sliding window over `input_dataset`.
+//
+// Arguments:
+//
+// window_size: A scalar representing the number of elements in the
+// sliding window.
+// stride: A scalar representing the steps moving the sliding window
+// forward in one iteration. It must be in `[1, window_size)`.
+//
+//
+func SlideDataset(scope *Scope, input_dataset tf.Output, window_size tf.Output, stride 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: "SlideDataset",
+ Input: []tf.Input{
+ input_dataset, window_size, stride,
+ },
+ Attrs: attrs,
+ }
+ 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.
//
-// Read @{$math_ops#segmentation$the section on segmentation} for an explanation of
+// Read @{$math_ops#Segmentation$the section on segmentation} for an explanation of
// segments.
//
// Arguments:
return op.Output(0)
}
-// Computes the Max along segments of a tensor.
+// Computes the maximum along segments of a tensor.
//
-// Read @{$math_ops#segmentation$the section on segmentation} for an explanation of
+// 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:
+// This operator is similar to the unsorted segment sum operator found
+// [(here)](../../../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()`.
+// If the maximum is empty for a given segment ID `i`, it outputs the smallest
+// possible value for the specific numeric type,
+// `output[i] = numeric_limits<T>::lowest()`.
//
// <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>
// 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
+// Read @{$math_ops#Segmentation$the section on segmentation} for an explanation of
// segments.
//
// Arguments:
// 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.
+// value: If true, the centers of the 4 corner pixels of the input and output tensors are
+// aligned, preserving the values at the corner pixels. Defaults to false.
// If not specified, defaults to false
func ResizeBicubicAlignCorners(value bool) ResizeBicubicAttr {
return func(m optionalAttr) {
return op.Output(0)
}
+// Checks whether a tree ensemble has been initialized.
+//
+// Arguments:
+// tree_ensemble_handle: Handle to the tree ensemble resouce.
+//
+// Returns output boolean on whether it is initialized or not.
+func IsBoostedTreesEnsembleInitialized(scope *Scope, tree_ensemble_handle tf.Output) (is_initialized tf.Output) {
+ if scope.Err() != nil {
+ return
+ }
+ opspec := tf.OpSpec{
+ Type: "IsBoostedTreesEnsembleInitialized",
+ Input: []tf.Input{
+ tree_ensemble_handle,
+ },
+ }
+ 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 op.Output(0)
}
+// Provides the time since epoch in seconds.
+//
+// Returns the timestamp as a `float64` for seconds since the Unix epoch.
+//
+// Note: the timestamp is computed when the op is executed, not when it is added
+// to the graph.
+func Timestamp(scope *Scope) (ts tf.Output) {
+ if scope.Err() != nil {
+ return
+ }
+ opspec := tf.OpSpec{
+ Type: "Timestamp",
+ }
+ op := scope.AddOperation(opspec)
+ return op.Output(0)
+}
+
// BatchMatMulAttr is an optional argument to BatchMatMul.
type BatchMatMulAttr func(optionalAttr)
return op.Output(0)
}
+// MutexV2Attr is an optional argument to MutexV2.
+type MutexV2Attr func(optionalAttr)
+
+// MutexV2Container sets the optional container attribute to value.
+//
+// value: If non-empty, this variable is placed in the given container.
+// Otherwise, a default container is used.
+// If not specified, defaults to ""
+func MutexV2Container(value string) MutexV2Attr {
+ return func(m optionalAttr) {
+ m["container"] = value
+ }
+}
+
+// MutexV2SharedName sets the optional shared_name attribute to value.
+//
+// value: If non-empty, this variable is named in the given bucket
+// with this shared_name. Otherwise, the node name is used instead.
+// If not specified, defaults to ""
+func MutexV2SharedName(value string) MutexV2Attr {
+ return func(m optionalAttr) {
+ m["shared_name"] = value
+ }
+}
+
+// Creates a Mutex resource that can be locked by `MutexLock`.
+//
+// Returns The mutex resource.
+func MutexV2(scope *Scope, optional ...MutexV2Attr) (resource tf.Output) {
+ if scope.Err() != nil {
+ return
+ }
+ attrs := map[string]interface{}{}
+ for _, a := range optional {
+ a(attrs)
+ }
+ opspec := tf.OpSpec{
+ Type: "MutexV2",
+
+ Attrs: attrs,
+ }
+ op := scope.AddOperation(opspec)
+ return op.Output(0)
+}
+
// AvgPool3DAttr is an optional argument to AvgPool3D.
type AvgPool3DAttr func(optionalAttr)
return op.Output(0)
}
+// Broadcasts a tensor value to one or more other devices.
+func CollectiveBcastSend(scope *Scope, input tf.Output, group_size int64, group_key int64, instance_key int64, shape tf.Shape) (data tf.Output) {
+ if scope.Err() != nil {
+ return
+ }
+ attrs := map[string]interface{}{"group_size": group_size, "group_key": group_key, "instance_key": instance_key, "shape": shape}
+ opspec := tf.OpSpec{
+ Type: "CollectiveBcastSend",
+ Input: []tf.Input{
+ input,
+ },
+ Attrs: attrs,
+ }
+ op := scope.AddOperation(opspec)
+ return op.Output(0)
+}
+
+// Makes a copy of `x`.
+//
+// Arguments:
+// x: The source tensor of type `T`.
+//
+// Returns y: A `Tensor` of type `T`. A copy of `x`. Guaranteed that `y`
+// is not an alias of `x`.
+func DeepCopy(scope *Scope, x tf.Output) (y tf.Output) {
+ if scope.Err() != nil {
+ return
+ }
+ opspec := tf.OpSpec{
+ Type: "DeepCopy",
+ Input: []tf.Input{
+ x,
+ },
+ }
+ op := scope.AddOperation(opspec)
+ return op.Output(0)
+}
+
// Split a `SparseTensor` into `num_split` tensors along one dimension.
//
// If the `shape[split_dim]` is not an integer multiple of `num_split`. Slices
return op.Output(0)
}
+// Updates the tree ensemble by either adding a layer to the last tree being grown
+//
+// or by starting a new tree.
+//
+// Arguments:
+// tree_ensemble_handle: Handle to the ensemble variable.
+// feature_ids: Rank 1 tensor with ids for each feature. This is the real id of
+// the feature that will be used in the split.
+// node_ids: List of rank 1 tensors representing the nodes for which this feature
+// has a split.
+// gains: List of rank 1 tensors representing the gains for each of the feature's
+// split.
+// thresholds: List of rank 1 tensors representing the thesholds for each of the
+// feature's split.
+// left_node_contribs: List of rank 2 tensors with left leaf contribs for each of
+// the feature's splits. Will be added to the previous node values to constitute
+// the values of the left nodes.
+// right_node_contribs: List of rank 2 tensors with right leaf contribs for each
+// of the feature's splits. Will be added to the previous node values to constitute
+// the values of the right nodes.
+// max_depth: Max depth of the tree to build.
+// learning_rate: shrinkage const for each new tree.
+// pruning_mode: 0-No pruning, 1-Pre-pruning, 2-Post-pruning.
+//
+// Returns the created operation.
+func BoostedTreesUpdateEnsemble(scope *Scope, tree_ensemble_handle tf.Output, feature_ids tf.Output, node_ids []tf.Output, gains []tf.Output, thresholds []tf.Output, left_node_contribs []tf.Output, right_node_contribs []tf.Output, max_depth tf.Output, learning_rate tf.Output, pruning_mode int64) (o *tf.Operation) {
+ if scope.Err() != nil {
+ return
+ }
+ attrs := map[string]interface{}{"pruning_mode": pruning_mode}
+ opspec := tf.OpSpec{
+ Type: "BoostedTreesUpdateEnsemble",
+ Input: []tf.Input{
+ tree_ensemble_handle, feature_ids, tf.OutputList(node_ids), tf.OutputList(gains), tf.OutputList(thresholds), tf.OutputList(left_node_contribs), tf.OutputList(right_node_contribs), max_depth, learning_rate,
+ },
+ Attrs: attrs,
+ }
+ return scope.AddOperation(opspec)
+}
+
// ResourceSparseApplyFtrlAttr is an optional argument to ResourceSparseApplyFtrl.
type ResourceSparseApplyFtrlAttr func(optionalAttr)
//
// N is the size of the segment being reduced.
//
-// Read @{$math_ops#segmentation$the section on segmentation} for an explanation of
+// Read @{$math_ops#Segmentation$the section on segmentation} for an explanation of
// segments.
//
// Arguments:
return op.Output(0)
}
-// StatelessRandomNormalAttr is an optional argument to StatelessRandomNormal.
-type StatelessRandomNormalAttr func(optionalAttr)
-
-// StatelessRandomNormalDtype sets the optional dtype attribute to value.
+// Divides sparse updates into the variable referenced by `resource`.
//
-// value: The type of the output.
-// If not specified, defaults to DT_FLOAT
-func StatelessRandomNormalDtype(value tf.DataType) StatelessRandomNormalAttr {
- return func(m optionalAttr) {
- m["dtype"] = value
- }
-}
-
-// Outputs deterministic pseudorandom values from a normal distribution.
+// This operation computes
//
-// The generated values will have mean 0 and standard deviation 1.
+// # Scalar indices
+// ref[indices, ...] /= updates[...]
//
-// The outputs are a deterministic function of `shape` and `seed`.
+// # 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 multiply.
+//
+// Requires `updates.shape = indices.shape + ref.shape[1:]` or `updates.shape = []`.
+//
+// <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 ResourceScatterDiv(scope *Scope, resource tf.Output, indices tf.Output, updates tf.Output) (o *tf.Operation) {
+ if scope.Err() != nil {
+ return
+ }
+ opspec := tf.OpSpec{
+ Type: "ResourceScatterDiv",
+ Input: []tf.Input{
+ resource, indices, updates,
+ },
+ }
+ return scope.AddOperation(opspec)
+}
+
+// StatelessRandomNormalAttr is an optional argument to StatelessRandomNormal.
+type StatelessRandomNormalAttr func(optionalAttr)
+
+// StatelessRandomNormalDtype sets the optional dtype attribute to value.
+//
+// value: The type of the output.
+// If not specified, defaults to DT_FLOAT
+func StatelessRandomNormalDtype(value tf.DataType) StatelessRandomNormalAttr {
+ return func(m optionalAttr) {
+ m["dtype"] = value
+ }
+}
+
+// Outputs deterministic pseudorandom values from a normal distribution.
+//
+// The generated values will have mean 0 and standard deviation 1.
+//
+// The outputs are a deterministic function of `shape` and `seed`.
//
// Arguments:
// shape: The shape of the output tensor.
return op.Output(0)
}
+// Reduces sparse updates into the variable referenced by `resource` using the `min` operation.
+//
+// This operation computes
+//
+// # Scalar indices
+// ref[indices, ...] = min(ref[indices, ...], updates[...])
+//
+// # Vector indices (for each i)
+// ref[indices[i], ...] = min(ref[indices[i], ...], updates[i, ...])
+//
+// # High rank indices (for each i, ..., j)
+// ref[indices[i, ..., j], ...] = min(ref[indices[i, ..., j], ...], updates[i, ..., j, ...])
+//
+// Duplicate entries are handled correctly: if multiple `indices` reference
+// the same location, their contributions are combined.
+//
+// Requires `updates.shape = indices.shape + ref.shape[1:]` or `updates.shape = []`.
+//
+// <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 ResourceScatterMin(scope *Scope, resource tf.Output, indices tf.Output, updates tf.Output) (o *tf.Operation) {
+ if scope.Err() != nil {
+ return
+ }
+ opspec := tf.OpSpec{
+ Type: "ResourceScatterMin",
+ Input: []tf.Input{
+ resource, indices, updates,
+ },
+ }
+ return scope.AddOperation(opspec)
+}
+
// Reshapes a quantized tensor as per the Reshape op.
//
// ```
// ResizeBilinearAlignCorners 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.
+// value: If true, the centers of the 4 corner pixels of the input and output tensors are
+// aligned, preserving the values at the corner pixels. Defaults to false.
// If not specified, defaults to false
func ResizeBilinearAlignCorners(value bool) ResizeBilinearAttr {
return func(m optionalAttr) {
return op.Output(0)
}
+// Creates a TensorList which, when stacked, has the value of `tensor`.
+//
+// Each tensor in the result list corresponds to one row of the input tensor.
+//
+// tensor: The input tensor.
+// output_handle: The list.
+func TensorListFromTensor(scope *Scope, tensor tf.Output, element_shape tf.Output) (output_handle tf.Output) {
+ if scope.Err() != nil {
+ return
+ }
+ opspec := tf.OpSpec{
+ Type: "TensorListFromTensor",
+ Input: []tf.Input{
+ tensor, element_shape,
+ },
+ }
+ op := scope.AddOperation(opspec)
+ return op.Output(0)
+}
+
// GenerateVocabRemappingAttr is an optional argument to GenerateVocabRemapping.
type GenerateVocabRemappingAttr func(optionalAttr)
return scope.AddOperation(opspec)
}
+// Creates and returns an empty tensor list.
+//
+// All list elements must be tensors of dtype element_dtype and shape compatible
+// with element_shape.
+//
+// handle: an empty tensor list.
+// element_dtype: the type of elements in the list.
+// element_shape: a shape compatible with that of elements in the list.
+func EmptyTensorList(scope *Scope, element_shape tf.Output, element_dtype tf.DataType) (handle tf.Output) {
+ if scope.Err() != nil {
+ return
+ }
+ attrs := map[string]interface{}{"element_dtype": element_dtype}
+ opspec := tf.OpSpec{
+ Type: "EmptyTensorList",
+ Input: []tf.Input{
+ element_shape,
+ },
+ Attrs: attrs,
+ }
+ op := scope.AddOperation(opspec)
+ return op.Output(0)
+}
+
// AvgPoolGradAttr is an optional argument to AvgPoolGrad.
type AvgPoolGradAttr func(optionalAttr)
return op.Output(0)
}
+// RegexReplaceAttr is an optional argument to RegexReplace.
+type RegexReplaceAttr func(optionalAttr)
+
+// RegexReplaceReplaceGlobal sets the optional replace_global attribute to value.
+//
+// value: If True, the replacement is global, otherwise the replacement
+// is done only on the first match.
+// If not specified, defaults to true
+func RegexReplaceReplaceGlobal(value bool) RegexReplaceAttr {
+ return func(m optionalAttr) {
+ m["replace_global"] = value
+ }
+}
+
+// Replaces the match of pattern in input with rewrite.
+//
+// It follows the re2 syntax (https://github.com/google/re2/wiki/Syntax)
+//
+// Arguments:
+// input: The text to be processed.
+// pattern: The regular expression to match the input.
+// rewrite: The rewrite to be applied to the matched expresion.
+//
+// Returns The text after applying pattern and rewrite.
+func RegexReplace(scope *Scope, input tf.Output, pattern tf.Output, rewrite tf.Output, optional ...RegexReplaceAttr) (output tf.Output) {
+ if scope.Err() != nil {
+ return
+ }
+ attrs := map[string]interface{}{}
+ for _, a := range optional {
+ a(attrs)
+ }
+ opspec := tf.OpSpec{
+ Type: "RegexReplace",
+ Input: []tf.Input{
+ input, pattern, rewrite,
+ },
+ Attrs: attrs,
+ }
+ op := scope.AddOperation(opspec)
+ return op.Output(0)
+}
+
// Computes numerical negative value element-wise.
//
// I.e., \\(y = -x\\).
// `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.
+// `num_bits` is the bitwidth of the quantization; between 2 and 16, inclusive.
//
// This operation has a gradient and thus allows for training `min` and `max`
// values.
return scope.AddOperation(opspec)
}
+// Locks a mutex resource. The output is the lock. So long as the lock tensor
+//
+// is alive, any other request to use `MutexLock` with this mutex will wait.
+//
+// This is particularly useful for creating a critical section when used in
+// conjunction with `MutexLockIdentity`:
+//
+// ```python
+//
+// mutex = mutex_v2(
+// shared_name=handle_name, container=container, name=name)
+//
+// def execute_in_critical_section(fn, *args, **kwargs):
+// lock = gen_resource_variable_ops.mutex_lock(mutex)
+//
+// with ops.control_dependencies([lock]):
+// r = fn(*args, **kwargs)
+//
+// with ops.control_dependencies(nest.flatten(r)):
+// with ops.colocate_with(mutex):
+// ensure_lock_exists = mutex_lock_identity(lock)
+//
+// # Make sure that if any element of r is accessed, all of
+// # them are executed together.
+// r = nest.map_structure(tf.identity, r)
+//
+// with ops.control_dependencies([ensure_lock_exists]):
+// return nest.map_structure(tf.identity, r)
+// ```
+//
+// While `fn` is running in the critical section, no other functions which wish to
+// use this critical section may run.
+//
+// Often the use case is that two executions of the same graph, in parallel,
+// wish to run `fn`; and we wish to ensure that only one of them executes
+// at a time. This is especially important if `fn` modifies one or more
+// variables at a time.
+//
+// It is also useful if two separate functions must share a resource, but we
+// wish to ensure the usage is exclusive.
+//
+// Arguments:
+// mutex: The mutex resource to lock.
+//
+// Returns A tensor that keeps a shared pointer to a lock on the mutex;
+// when the Tensor is destroyed, the use count on the shared pointer is decreased
+// by 1. When it reaches 0, the lock is released.
+func MutexLock(scope *Scope, mutex tf.Output) (mutex_lock tf.Output) {
+ if scope.Err() != nil {
+ return
+ }
+ opspec := tf.OpSpec{
+ Type: "MutexLock",
+ Input: []tf.Input{
+ mutex,
+ },
+ }
+ op := scope.AddOperation(opspec)
+ return op.Output(0)
+}
+
// Computes the mean along segments of a tensor.
//
-// Read @{$math_ops#segmentation$the section on segmentation} for an explanation of
+// Read @{$math_ops#Segmentation$the section on segmentation} for an explanation of
// segments.
//
// Computes a tensor such that
// ResizeAreaAlignCorners 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.
+// value: If true, the centers of the 4 corner pixels of the input and output tensors are
+// aligned, preserving the values at the corner pixels. Defaults to false.
// If not specified, defaults to false
func ResizeAreaAlignCorners(value bool) ResizeAreaAttr {
return func(m optionalAttr) {
//
// Input images can be of different types but output images are always float.
//
+// The range of pixel values for the output image might be slightly different
+// from the range for the input image because of limited numerical precision.
+// To guarantee an output range, for example `[0.0, 1.0]`, apply
+// `tf.clip_by_value` to the output.
+//
// 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
return op.Output(0)
}
+// Mutually reduces multiple tensors of identical type and shape.
+func CollectiveReduce(scope *Scope, input tf.Output, group_size int64, group_key int64, instance_key int64, merge_op string, final_op string, subdiv_offsets []int64) (data tf.Output) {
+ if scope.Err() != nil {
+ return
+ }
+ attrs := map[string]interface{}{"group_size": group_size, "group_key": group_key, "instance_key": instance_key, "merge_op": merge_op, "final_op": final_op, "subdiv_offsets": subdiv_offsets}
+ opspec := tf.OpSpec{
+ Type: "CollectiveReduce",
+ Input: []tf.Input{
+ input,
+ },
+ Attrs: attrs,
+ }
+ op := scope.AddOperation(opspec)
+ return op.Output(0)
+}
+
+// This op consumes a lock created by `MutexLock`.
+//
+// This op exists to consume a tensor created by `MutexLock` (other than
+// direct control dependencies). It should be the only that consumes the tensor,
+// and will raise an error if it is not. Its only purpose is to keep the
+// mutex lock tensor alive until it is consumed by this op.
+//
+// **NOTE**: This operation must run on the same device as its input. This may
+// be enforced via the `colocate_with` mechanism.
+//
+// Arguments:
+// mutex_lock: A tensor returned by `MutexLock`.
+//
+// Returns the created operation.
+func ConsumeMutexLock(scope *Scope, mutex_lock tf.Output) (o *tf.Operation) {
+ if scope.Err() != nil {
+ return
+ }
+ opspec := tf.OpSpec{
+ Type: "ConsumeMutexLock",
+ Input: []tf.Input{
+ mutex_lock,
+ },
+ }
+ return scope.AddOperation(opspec)
+}
+
// Returns x / y element-wise for integer types.
//
// Truncation designates that negative numbers will round fractional quantities
// Computes the maximum along segments of a tensor.
//
-// Read @{$math_ops#segmentation$the section on segmentation} for an explanation of
+// Read @{$math_ops#Segmentation$the section on segmentation} for an explanation of
// segments.
//
// Computes a tensor such that
return op.Output(0)
}
+// Receives a tensor value broadcast from another device.
+func CollectiveBcastRecv(scope *Scope, T tf.DataType, group_size int64, group_key int64, instance_key int64, shape tf.Shape) (data tf.Output) {
+ if scope.Err() != nil {
+ return
+ }
+ attrs := map[string]interface{}{"T": T, "group_size": group_size, "group_key": group_key, "instance_key": instance_key, "shape": shape}
+ opspec := tf.OpSpec{
+ Type: "CollectiveBcastRecv",
+
+ Attrs: attrs,
+ }
+ op := scope.AddOperation(opspec)
+ return op.Output(0)
+}
+
// Decode web-safe base64-encoded strings.
//
// Input may or may not have padding at the end. See EncodeBase64 for padding.
return op.Output(0), op.Output(1)
}
+// Runs multiple additive regression ensemble predictors on input instances and
+//
+// computes the logits. It is designed to be used during prediction.
+// It traverses all the trees and calculates the final score for each instance.
+//
+// Arguments:
+//
+// bucketized_features: A list of rank 1 Tensors containing bucket id for each
+// feature.
+// logits_dimension: scalar, dimension of the logits, to be used for partial logits
+// shape.
+//
+// Returns Output rank 2 Tensor containing logits for each example.
+func BoostedTreesPredict(scope *Scope, tree_ensemble_handle tf.Output, bucketized_features []tf.Output, logits_dimension int64) (logits tf.Output) {
+ if scope.Err() != nil {
+ return
+ }
+ attrs := map[string]interface{}{"logits_dimension": logits_dimension}
+ opspec := tf.OpSpec{
+ Type: "BoostedTreesPredict",
+ Input: []tf.Input{
+ tree_ensemble_handle, tf.OutputList(bucketized_features),
+ },
+ Attrs: attrs,
+ }
+ op := scope.AddOperation(opspec)
+ return op.Output(0)
+}
+
// MatrixSolveLsAttr is an optional argument to MatrixSolveLs.
type MatrixSolveLsAttr func(optionalAttr)
// 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
+// 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
+// \\(\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
return op.Output(0)
}
+// Subtracts sparse updates from 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:]` or `updates.shape = []`.
+//
+// <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 ResourceScatterSub(scope *Scope, resource tf.Output, indices tf.Output, updates tf.Output) (o *tf.Operation) {
+ if scope.Err() != nil {
+ return
+ }
+ opspec := tf.OpSpec{
+ Type: "ResourceScatterSub",
+ Input: []tf.Input{
+ resource, indices, updates,
+ },
+ }
+ return scope.AddOperation(opspec)
+}
+
// Inverse 2D fast Fourier transform.
//
// Computes the inverse 2-dimensional discrete Fourier transform over the
// FusedResizeAndPadConv2DResizeAlignCorners sets the optional resize_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.
+// value: If true, the centers of the 4 corner pixels of the input and output tensors are
+// aligned, preserving the values at the corner pixels. Defaults to false.
// If not specified, defaults to false
func FusedResizeAndPadConv2DResizeAlignCorners(value bool) FusedResizeAndPadConv2DAttr {
return func(m optionalAttr) {
return scope.AddOperation(opspec)
}
+// CudnnRNNBackpropAttr is an optional argument to CudnnRNNBackprop.
+type CudnnRNNBackpropAttr func(optionalAttr)
+
+// CudnnRNNBackpropRnnMode sets the optional rnn_mode attribute to value.
+// If not specified, defaults to "lstm"
+func CudnnRNNBackpropRnnMode(value string) CudnnRNNBackpropAttr {
+ return func(m optionalAttr) {
+ m["rnn_mode"] = value
+ }
+}
+
+// CudnnRNNBackpropInputMode sets the optional input_mode attribute to value.
+// If not specified, defaults to "linear_input"
+func CudnnRNNBackpropInputMode(value string) CudnnRNNBackpropAttr {
+ return func(m optionalAttr) {
+ m["input_mode"] = value
+ }
+}
+
+// CudnnRNNBackpropDirection sets the optional direction attribute to value.
+// If not specified, defaults to "unidirectional"
+func CudnnRNNBackpropDirection(value string) CudnnRNNBackpropAttr {
+ return func(m optionalAttr) {
+ m["direction"] = value
+ }
+}
+
+// CudnnRNNBackpropDropout sets the optional dropout attribute to value.
+// If not specified, defaults to 0
+func CudnnRNNBackpropDropout(value float32) CudnnRNNBackpropAttr {
+ return func(m optionalAttr) {
+ m["dropout"] = value
+ }
+}
+
+// CudnnRNNBackpropSeed sets the optional seed attribute to value.
+// If not specified, defaults to 0
+func CudnnRNNBackpropSeed(value int64) CudnnRNNBackpropAttr {
+ return func(m optionalAttr) {
+ m["seed"] = value
+ }
+}
+
+// CudnnRNNBackpropSeed2 sets the optional seed2 attribute to value.
+// If not specified, defaults to 0
+func CudnnRNNBackpropSeed2(value int64) CudnnRNNBackpropAttr {
+ return func(m optionalAttr) {
+ m["seed2"] = value
+ }
+}
+
+// Backprop step of CudnnRNN.
+//
+// Compute the backprop of both data and weights in a RNN.
+//
+// rnn_mode: Indicates the type of the RNN model.
+// input_mode: Indicate whether there is a linear projection between the input and
+// The actual computation before the first layer. 'skip_input' is only allowed
+// when input_size == num_units; 'auto_select' implies 'skip_input' when
+// input_size == num_units; otherwise, it implies 'linear_input'.
+// direction: Indicates whether a bidirectional model will be used.
+// dir = (direction == bidirectional) ? 2 : 1
+// dropout: dropout probability. When set to 0., dropout is disabled.
+// seed: the 1st part of a seed to initialize dropout.
+// seed2: the 2nd part of a seed to initialize dropout.
+// input: a 3-D tensor with the shape of [seq_length, batch_size, input_size].
+// input_h: a 3-D tensor with the shape of [num_layer * dir, batch_size,
+// num_units].
+// input_c: For LSTM, a 3-D tensor with the shape of
+// [num_layer * dir, batch, num_units]. For other models, it is ignored.
+// params: a 1-D tensor that contains the weights and biases in an opaque layout.
+// The size must be created through CudnnRNNParamsSize, and initialized
+// separately. Note that they might not be compatible across different
+// generations. So it is a good idea to save and restore
+// output: a 3-D tensor with the shape of [seq_length, batch_size,
+// dir * num_units].
+// output_h: the same shape has input_h.
+// output_c: the same shape as input_c for LSTM. An empty tensor for other models.
+// output_backprop: A 3-D tensor with the same shape as output in the forward pass.
+// output_h_backprop: A 3-D tensor with the same shape as output_h in the forward
+// pass.
+// output_c_backprop: A 3-D tensor with the same shape as output_c in the forward
+// pass.
+// reserve_space: The same reserve_space produced in for forward operation.
+// input_backprop: The backprop to input in the forward pass. Has the same shape
+// as input.
+// input_h_backprop: The backprop to input_h in the forward pass. Has the same
+// shape as input_h.
+// input_c_backprop: The backprop to input_c in the forward pass. Has the same
+// shape as input_c.
+// params_backprop: The backprop to the params buffer in the forward pass. Has the
+// same shape as params.
+func CudnnRNNBackprop(scope *Scope, input tf.Output, input_h tf.Output, input_c tf.Output, params tf.Output, output tf.Output, output_h tf.Output, output_c tf.Output, output_backprop tf.Output, output_h_backprop tf.Output, output_c_backprop tf.Output, reserve_space tf.Output, optional ...CudnnRNNBackpropAttr) (input_backprop tf.Output, input_h_backprop tf.Output, input_c_backprop tf.Output, params_backprop tf.Output) {
+ if scope.Err() != nil {
+ return
+ }
+ attrs := map[string]interface{}{}
+ for _, a := range optional {
+ a(attrs)
+ }
+ opspec := tf.OpSpec{
+ Type: "CudnnRNNBackprop",
+ Input: []tf.Input{
+ input, input_h, input_c, params, output, output_h, output_c, output_backprop, output_h_backprop, output_c_backprop, reserve_space,
+ },
+ Attrs: attrs,
+ }
+ op := scope.AddOperation(opspec)
+ return op.Output(0), op.Output(1), op.Output(2), op.Output(3)
+}
+
// FractionalMaxPoolGradAttr is an optional argument to FractionalMaxPoolGrad.
type FractionalMaxPoolGradAttr func(optionalAttr)
return scope.AddOperation(opspec)
}
+// CudnnRNNCanonicalToParamsAttr is an optional argument to CudnnRNNCanonicalToParams.
+type CudnnRNNCanonicalToParamsAttr func(optionalAttr)
+
+// CudnnRNNCanonicalToParamsRnnMode sets the optional rnn_mode attribute to value.
+// If not specified, defaults to "lstm"
+func CudnnRNNCanonicalToParamsRnnMode(value string) CudnnRNNCanonicalToParamsAttr {
+ return func(m optionalAttr) {
+ m["rnn_mode"] = value
+ }
+}
+
+// CudnnRNNCanonicalToParamsInputMode sets the optional input_mode attribute to value.
+// If not specified, defaults to "linear_input"
+func CudnnRNNCanonicalToParamsInputMode(value string) CudnnRNNCanonicalToParamsAttr {
+ return func(m optionalAttr) {
+ m["input_mode"] = value
+ }
+}
+
+// CudnnRNNCanonicalToParamsDirection sets the optional direction attribute to value.
+// If not specified, defaults to "unidirectional"
+func CudnnRNNCanonicalToParamsDirection(value string) CudnnRNNCanonicalToParamsAttr {
+ return func(m optionalAttr) {
+ m["direction"] = value
+ }
+}
+
+// CudnnRNNCanonicalToParamsDropout sets the optional dropout attribute to value.
+// If not specified, defaults to 0
+func CudnnRNNCanonicalToParamsDropout(value float32) CudnnRNNCanonicalToParamsAttr {
+ return func(m optionalAttr) {
+ m["dropout"] = value
+ }
+}
+
+// CudnnRNNCanonicalToParamsSeed sets the optional seed attribute to value.
+// If not specified, defaults to 0
+func CudnnRNNCanonicalToParamsSeed(value int64) CudnnRNNCanonicalToParamsAttr {
+ return func(m optionalAttr) {
+ m["seed"] = value
+ }
+}
+
+// CudnnRNNCanonicalToParamsSeed2 sets the optional seed2 attribute to value.
+// If not specified, defaults to 0
+func CudnnRNNCanonicalToParamsSeed2(value int64) CudnnRNNCanonicalToParamsAttr {
+ return func(m optionalAttr) {
+ m["seed2"] = value
+ }
+}
+
+// Converts CudnnRNN params from canonical form to usable form.
+//
+// Writes a set of weights into the opaque params buffer so they can be used in
+// upcoming training or inferences.
+//
+// Note that the params buffer may not be compatible across different GPUs. So any
+// save and restoration should be converted to and from the canonical weights and
+// biases.
+//
+// num_layers: Specifies the number of layers in the RNN model.
+// num_units: Specifies the size of the hidden state.
+// input_size: Specifies the size of the input state.
+// weights: the canonical form of weights that can be used for saving
+// and restoration. They are more likely to be compatible across different
+// generations.
+// biases: the canonical form of biases that can be used for saving
+// and restoration. They are more likely to be compatible across different
+// generations.
+// num_params: number of parameter sets for all layers.
+// Each layer may contain multiple parameter sets, with each set consisting of
+// a weight matrix and a bias vector.
+// rnn_mode: Indicates the type of the RNN model.
+// input_mode: Indicate whether there is a linear projection between the input and
+// The actual computation before the first layer. 'skip_input' is only allowed
+// when input_size == num_units; 'auto_select' implies 'skip_input' when
+// input_size == num_units; otherwise, it implies 'linear_input'.
+// direction: Indicates whether a bidirectional model will be used.
+// dir = (direction == bidirectional) ? 2 : 1
+// dropout: dropout probability. When set to 0., dropout is disabled.
+// seed: the 1st part of a seed to initialize dropout.
+// seed2: the 2nd part of a seed to initialize dropout.
+func CudnnRNNCanonicalToParams(scope *Scope, num_layers tf.Output, num_units tf.Output, input_size tf.Output, weights []tf.Output, biases []tf.Output, optional ...CudnnRNNCanonicalToParamsAttr) (params tf.Output) {
+ if scope.Err() != nil {
+ return
+ }
+ attrs := map[string]interface{}{}
+ for _, a := range optional {
+ a(attrs)
+ }
+ opspec := tf.OpSpec{
+ Type: "CudnnRNNCanonicalToParams",
+ Input: []tf.Input{
+ num_layers, num_units, input_size, tf.OutputList(weights), tf.OutputList(biases),
+ },
+ Attrs: attrs,
+ }
+ op := scope.AddOperation(opspec)
+ return op.Output(0)
+}
+
// SparseReduceMaxSparseAttr is an optional argument to SparseReduceMaxSparse.
type SparseReduceMaxSparseAttr func(optionalAttr)
return op.Output(0)
}
+// Multiplies sparse updates into 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 multiply.
+//
+// Requires `updates.shape = indices.shape + ref.shape[1:]` or `updates.shape = []`.
+//
+// <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 ResourceScatterMul(scope *Scope, resource tf.Output, indices tf.Output, updates tf.Output) (o *tf.Operation) {
+ if scope.Err() != nil {
+ return
+ }
+ opspec := tf.OpSpec{
+ Type: "ResourceScatterMul",
+ Input: []tf.Input{
+ resource, indices, updates,
+ },
+ }
+ return scope.AddOperation(opspec)
+}
+
// Computes sigmoid of `x` element-wise.
//
// Specifically, `y = 1 / (1 + exp(-x))`.
return op.Output(0)
}
+// Updates specified rows with values in `v`.
+//
+// Computes `x[i, :] = v; return x`.
+//
+// Arguments:
+// x: A tensor of type `T`.
+// i: A vector. Indices into the left-most dimension of `x`.
+// v: A `Tensor` of type T. Same dimension sizes as x except the first dimension, which must be the same as i's size.
+//
+// Returns A `Tensor` of type T. An alias of `x`. The content of `y` is undefined if there are duplicates in `i`.
+func InplaceUpdate(scope *Scope, x tf.Output, i tf.Output, v tf.Output) (y tf.Output) {
+ if scope.Err() != nil {
+ return
+ }
+ opspec := tf.OpSpec{
+ Type: "InplaceUpdate",
+ Input: []tf.Input{
+ x, i, v,
+ },
+ }
+ op := scope.AddOperation(opspec)
+ return op.Output(0)
+}
+
// FusedBatchNormAttr is an optional argument to FusedBatchNorm.
type FusedBatchNormAttr func(optionalAttr)
return op.Output(0)
}
+// BoostedTreesEnsembleResourceHandleOpAttr is an optional argument to BoostedTreesEnsembleResourceHandleOp.
+type BoostedTreesEnsembleResourceHandleOpAttr func(optionalAttr)
+
+// BoostedTreesEnsembleResourceHandleOpContainer sets the optional container attribute to value.
+// If not specified, defaults to ""
+func BoostedTreesEnsembleResourceHandleOpContainer(value string) BoostedTreesEnsembleResourceHandleOpAttr {
+ return func(m optionalAttr) {
+ m["container"] = value
+ }
+}
+
+// BoostedTreesEnsembleResourceHandleOpSharedName sets the optional shared_name attribute to value.
+// If not specified, defaults to ""
+func BoostedTreesEnsembleResourceHandleOpSharedName(value string) BoostedTreesEnsembleResourceHandleOpAttr {
+ return func(m optionalAttr) {
+ m["shared_name"] = value
+ }
+}
+
+// Creates a handle to a BoostedTreesEnsembleResource
+func BoostedTreesEnsembleResourceHandleOp(scope *Scope, optional ...BoostedTreesEnsembleResourceHandleOpAttr) (resource tf.Output) {
+ if scope.Err() != nil {
+ return
+ }
+ attrs := map[string]interface{}{}
+ for _, a := range optional {
+ a(attrs)
+ }
+ opspec := tf.OpSpec{
+ Type: "BoostedTreesEnsembleResourceHandleOp",
+
+ Attrs: attrs,
+ }
+ op := scope.AddOperation(opspec)
+ return op.Output(0)
+}
+
// Concatenates tensors along one dimension.
//
// Arguments:
return op.Output(0)
}
+// Returns the last element of the input list as well as a list with all but that element.
+//
+// Fails if the list is empty.
+//
+// input_handle: the input list
+// tensor: the withdrawn last element of the list
+// element_dtype: the type of elements in the list
+// element_shape: the shape of the output tensor
+func TensorListPopBack(scope *Scope, input_handle tf.Output, element_dtype tf.DataType) (output_handle tf.Output, tensor tf.Output) {
+ if scope.Err() != nil {
+ return
+ }
+ attrs := map[string]interface{}{"element_dtype": element_dtype}
+ opspec := tf.OpSpec{
+ Type: "TensorListPopBack",
+ Input: []tf.Input{
+ input_handle,
+ },
+ Attrs: attrs,
+ }
+ op := scope.AddOperation(opspec)
+ return op.Output(0), op.Output(1)
+}
+
// Returns element-wise integer closest to x.
//
// If the result is midway between two representable values,
return op.Output(0)
}
+// Serializes the tree ensemble to a proto.
+//
+// Arguments:
+// tree_ensemble_handle: Handle to the tree ensemble.
+//
+// Returns Stamp token of the tree ensemble resource.Serialized proto of the ensemble.
+func BoostedTreesSerializeEnsemble(scope *Scope, tree_ensemble_handle tf.Output) (stamp_token tf.Output, tree_ensemble_serialized tf.Output) {
+ if scope.Err() != nil {
+ return
+ }
+ opspec := tf.OpSpec{
+ Type: "BoostedTreesSerializeEnsemble",
+ Input: []tf.Input{
+ tree_ensemble_handle,
+ },
+ }
+ op := scope.AddOperation(opspec)
+ return op.Output(0), op.Output(1)
+}
+
// StageSizeAttr is an optional argument to StageSize.
type StageSizeAttr func(optionalAttr)
// Computes the sum along segments of a tensor.
//
-// Read @{$math_ops#segmentation$the section on segmentation} for an explanation of
+// Read @{$math_ops#Segmentation$the section on segmentation} for an explanation of
// segments.
//
// Computes a tensor such that
return op.Output(0)
}
+// CudnnRNNParamsSizeAttr is an optional argument to CudnnRNNParamsSize.
+type CudnnRNNParamsSizeAttr func(optionalAttr)
+
+// CudnnRNNParamsSizeRnnMode sets the optional rnn_mode attribute to value.
+// If not specified, defaults to "lstm"
+func CudnnRNNParamsSizeRnnMode(value string) CudnnRNNParamsSizeAttr {
+ return func(m optionalAttr) {
+ m["rnn_mode"] = value
+ }
+}
+
+// CudnnRNNParamsSizeInputMode sets the optional input_mode attribute to value.
+// If not specified, defaults to "linear_input"
+func CudnnRNNParamsSizeInputMode(value string) CudnnRNNParamsSizeAttr {
+ return func(m optionalAttr) {
+ m["input_mode"] = value
+ }
+}
+
+// CudnnRNNParamsSizeDirection sets the optional direction attribute to value.
+// If not specified, defaults to "unidirectional"
+func CudnnRNNParamsSizeDirection(value string) CudnnRNNParamsSizeAttr {
+ return func(m optionalAttr) {
+ m["direction"] = value
+ }
+}
+
+// CudnnRNNParamsSizeDropout sets the optional dropout attribute to value.
+// If not specified, defaults to 0
+func CudnnRNNParamsSizeDropout(value float32) CudnnRNNParamsSizeAttr {
+ return func(m optionalAttr) {
+ m["dropout"] = value
+ }
+}
+
+// CudnnRNNParamsSizeSeed sets the optional seed attribute to value.
+// If not specified, defaults to 0
+func CudnnRNNParamsSizeSeed(value int64) CudnnRNNParamsSizeAttr {
+ return func(m optionalAttr) {
+ m["seed"] = value
+ }
+}
+
+// CudnnRNNParamsSizeSeed2 sets the optional seed2 attribute to value.
+// If not specified, defaults to 0
+func CudnnRNNParamsSizeSeed2(value int64) CudnnRNNParamsSizeAttr {
+ return func(m optionalAttr) {
+ m["seed2"] = value
+ }
+}
+
+// Computes size of weights that can be used by a Cudnn RNN model.
+//
+// Return the params size that can be used by the Cudnn RNN model. Subsequent
+// weight allocation and initialization should use this size.
+//
+// num_layers: Specifies the number of layers in the RNN model.
+// num_units: Specifies the size of the hidden state.
+// input_size: Specifies the size of the input state.
+// rnn_mode: Indicates the type of the RNN model.
+// input_mode: Indicate whether there is a linear projection between the input and
+// The actual computation before the first layer. 'skip_input' is only allowed
+// when input_size == num_units; 'auto_select' implies 'skip_input' when
+// input_size == num_units; otherwise, it implies 'linear_input'.
+// direction: Indicates whether a bidirectional model will be used.
+// dir = (direction == bidirectional) ? 2 : 1
+// dropout: dropout probability. When set to 0., dropout is disabled.
+// seed: the 1st part of a seed to initialize dropout.
+// seed2: the 2nd part of a seed to initialize dropout.
+// params_size: The size of the params buffer that should be allocated and
+// initialized for this RNN model. Note that this params buffer may not be
+// compatible across GPUs. Please use CudnnRNNParamsWeights and
+// CudnnRNNParamsBiases to save and restore them in a way that is compatible
+// across different runs.
+func CudnnRNNParamsSize(scope *Scope, num_layers tf.Output, num_units tf.Output, input_size tf.Output, T tf.DataType, S tf.DataType, optional ...CudnnRNNParamsSizeAttr) (params_size tf.Output) {
+ if scope.Err() != nil {
+ return
+ }
+ attrs := map[string]interface{}{"T": T, "S": S}
+ for _, a := range optional {
+ a(attrs)
+ }
+ opspec := tf.OpSpec{
+ Type: "CudnnRNNParamsSize",
+ Input: []tf.Input{
+ num_layers, num_units, input_size,
+ },
+ Attrs: attrs,
+ }
+ 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
//
// Note that this routine only supports wildcard characters in the
// basename portion of the pattern, not in the directory portion.
+// Note also that the order of filenames returned can be non-deterministic.
//
// Arguments:
// pattern: Shell wildcard pattern(s). Scalar or vector of type string.
return op.Output(0), op.Output(1), op.Output(2)
}
-// ResourceGatherAttr is an optional argument to ResourceGather.
-type ResourceGatherAttr func(optionalAttr)
+// StatelessMultinomialAttr is an optional argument to StatelessMultinomial.
+type StatelessMultinomialAttr func(optionalAttr)
-// ResourceGatherValidateIndices sets the optional validate_indices attribute to value.
-// If not specified, defaults to true
-func ResourceGatherValidateIndices(value bool) ResourceGatherAttr {
+// StatelessMultinomialOutputDtype sets the optional output_dtype attribute to value.
+// If not specified, defaults to DT_INT64
+func StatelessMultinomialOutputDtype(value tf.DataType) StatelessMultinomialAttr {
return func(m optionalAttr) {
- m["validate_indices"] = value
+ m["output_dtype"] = value
}
}
-// Gather slices from the variable pointed to by `resource` according to `indices`.
+// Draws samples from a multinomial distribution.
+//
+// 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.
+// seed: 2 seeds (shape [2]).
+//
+// Returns 2-D Tensor with shape `[batch_size, num_samples]`. Each slice `[i, :]`
+// contains the drawn class labels with range `[0, num_classes)`.
+func StatelessMultinomial(scope *Scope, logits tf.Output, num_samples tf.Output, seed tf.Output, optional ...StatelessMultinomialAttr) (output tf.Output) {
+ if scope.Err() != nil {
+ return
+ }
+ attrs := map[string]interface{}{}
+ for _, a := range optional {
+ a(attrs)
+ }
+ opspec := tf.OpSpec{
+ Type: "StatelessMultinomial",
+ Input: []tf.Input{
+ logits, num_samples, seed,
+ },
+ Attrs: attrs,
+ }
+ op := scope.AddOperation(opspec)
+ return op.Output(0)
+}
+
+// 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`.
//
// `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:
// 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.
+// Any ReadVariableOp with a control dependency on this op is guaranteed to
+// see the decremented value or a subsequent newer one.
//
// Arguments:
// resource: handle to the resource in which to store the variable.
// 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.
+// value: If true, the centers of the 4 corner pixels of the input and output tensors are
+// aligned, preserving the values at the corner pixels. Defaults to false.
// If not specified, defaults to false
func QuantizedResizeBilinearAlignCorners(value bool) QuantizedResizeBilinearAttr {
return func(m optionalAttr) {
// Computes the minimum along segments of a tensor.
//
-// Read @{$math_ops#segmentation$the section on segmentation} for an explanation of
+// Read @{$math_ops#Segmentation$the section on segmentation} for an explanation of
// segments.
//
// Computes a tensor such that
// SdcaOptimizerAdaptative sets the optional adaptative attribute to value.
//
-// value: Whether to use Adapative SDCA for the inner loop.
-// If not specified, defaults to false
+// value: Whether to use Adaptive SDCA for the inner loop.
+// If not specified, defaults to true
func SdcaOptimizerAdaptative(value bool) SdcaOptimizerAttr {
return func(m optionalAttr) {
m["adaptative"] = value
// Duplicate entries are handled correctly: if multiple `indices` reference
// the same location, their contributions add.
//
-// Requires `updates.shape = indices.shape + ref.shape[1:]`.
+// Requires `updates.shape = indices.shape + ref.shape[1:]` or `updates.shape = []`.
//
// <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>
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.
+//
+// This operator is similar to the unsorted segment sum operator found
+// [(here)](../../../api_docs/python/math_ops.md#UnsortedSegmentSum).
+// Instead of computing the sum over segments, it computes the product of all
+// entries belonging to a segment such that:
+//
+// \\(output_i = \prod_j data_j\\) where the product is over `j` such
+// that `segment_ids[j] == i`.
+//
+// If there is no entry for a given segment ID `i`, it outputs 1.
+//
+// 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 UnsortedSegmentProd(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: "UnsortedSegmentProd",
+ Input: []tf.Input{
+ data, segment_ids, num_segments,
+ },
+ }
+ op := scope.AddOperation(opspec)
+ return op.Output(0)
+}
+
// RandomUniformIntAttr is an optional argument to RandomUniformInt.
type RandomUniformIntAttr func(optionalAttr)
return op.Output(0), op.Output(1)
}
+// Rolls the elements of a tensor along an axis.
+//
+// The elements are shifted positively (towards larger indices) by the offset of
+// `shift` along the dimension of `axis`. Negative `shift` values will shift
+// elements in the opposite direction. Elements that roll passed the last position
+// will wrap around to the first and vice versa. Multiple shifts along multiple
+// axes may be specified.
+//
+// For example:
+//
+// ```
+// # 't' is [0, 1, 2, 3, 4]
+// roll(t, shift=2, axis=0) ==> [3, 4, 0, 1, 2]
+//
+// # shifting along multiple dimensions
+// # 't' is [[0, 1, 2, 3, 4], [5, 6, 7, 8, 9]]
+// roll(t, shift=[1, -2], axis=[0, 1]) ==> [[7, 8, 9, 5, 6], [2, 3, 4, 0, 1]]
+//
+// # shifting along the same axis multiple times
+// # 't' is [[0, 1, 2, 3, 4], [5, 6, 7, 8, 9]]
+// roll(t, shift=[2, -3], axis=[1, 1]) ==> [[1, 2, 3, 4, 0], [6, 7, 8, 9, 5]]
+// ```
+//
+// Arguments:
+//
+// shift: Dimension must be 0-D or 1-D. `shift[i]` specifies the number of places by which
+// elements are shifted positively (towards larger indices) along the dimension
+// specified by `axis[i]`. Negative shifts will roll the elements in the opposite
+// direction.
+// axis: Dimension must be 0-D or 1-D. `axis[i]` specifies the dimension that the shift
+// `shift[i]` should occur. If the same axis is referenced more than once, the
+// total shift for that axis will be the sum of all the shifts that belong to that
+// axis.
+//
+// Returns Has the same shape and size as the input. The elements are shifted
+// positively (towards larger indices) by the offsets of `shift` along the
+// dimensions of `axis`.
+func Roll(scope *Scope, input tf.Output, shift tf.Output, axis tf.Output) (output tf.Output) {
+ if scope.Err() != nil {
+ return
+ }
+ opspec := tf.OpSpec{
+ Type: "Roll",
+ Input: []tf.Input{
+ input, shift, axis,
+ },
+ }
+ op := scope.AddOperation(opspec)
+ return op.Output(0)
+}
+
// MapPeekAttr is an optional argument to MapPeek.
type MapPeekAttr func(optionalAttr)
return op.Output(0)
}
+// Calculates gains for each feature and returns the best possible split information for the feature.
+//
+// The split information is the best threshold (bucket id), gains and left/right node contributions per node for each feature.
+//
+// It is possible that not all nodes can be split on each feature. Hence, the list of possible nodes can differ between the features. Therefore, we return `node_ids_list` for each feature, containing the list of nodes that this feature can be used to split.
+//
+// In this manner, the output is the best split per features and per node, so that it needs to be combined later to produce the best split for each node (among all possible features).
+//
+// The length of output lists are all of the same length, `num_features`.
+// The output shapes are compatible in a way that the first dimension of all tensors of all lists are the same and equal to the number of possible split nodes for each feature.
+//
+// Arguments:
+// node_id_range: A Rank 1 tensor (shape=[2]) to specify the range [first, last) of node ids to process within `stats_summary_list`. The nodes are iterated between the two nodes specified by the tensor, as like `for node_id in range(node_id_range[0], node_id_range[1])` (Note that the last index node_id_range[1] is exclusive).
+// stats_summary_list: A list of Rank 3 tensor (#shape=[max_splits, bucket, 2]) for accumulated stats summary (gradient/hessian) per node per buckets for each feature. The first dimension of the tensor is the maximum number of splits, and thus not all elements of it will be used, but only the indexes specified by node_ids will be used.
+// l1: l1 regularization factor on leaf weights, per instance based.
+// l2: l2 regularization factor on leaf weights, per instance based.
+// tree_complexity: adjustment to the gain, per leaf based.
+// min_node_weight: mininum avg of hessians in a node before required for the node to be considered for splitting.
+// max_splits: the number of nodes that can be split in the whole tree. Used as a dimension of output tensors.
+//
+// Returns An output list of Rank 1 tensors indicating possible split node ids for each feature. The length of the list is num_features, but each tensor has different size as each feature provides different possible nodes. See above for details like shapes and sizes.An output list of Rank 1 tensors indicating the best gains for each feature to split for certain nodes. See above for details like shapes and sizes.An output list of Rank 1 tensors indicating the bucket id to compare with (as a threshold) for split in each node. See above for details like shapes and sizes.A list of Rank 2 tensors indicating the contribution of the left nodes when branching from parent nodes (given by the tensor element in the output node_ids_list) to the left direction by the given threshold for each feature. This value will be used to make the left node value by adding to the parent node value. Second dimension size is 1 for 1-dimensional logits, but would be larger for multi-class problems. See above for details like shapes and sizes.A list of Rank 2 tensors, with the same shape/conditions as left_node_contribs_list, but just that the value is for the right node.
+func BoostedTreesCalculateBestGainsPerFeature(scope *Scope, node_id_range tf.Output, stats_summary_list []tf.Output, l1 tf.Output, l2 tf.Output, tree_complexity tf.Output, min_node_weight tf.Output, max_splits int64) (node_ids_list []tf.Output, gains_list []tf.Output, thresholds_list []tf.Output, left_node_contribs_list []tf.Output, right_node_contribs_list []tf.Output) {
+ if scope.Err() != nil {
+ return
+ }
+ attrs := map[string]interface{}{"max_splits": max_splits}
+ opspec := tf.OpSpec{
+ Type: "BoostedTreesCalculateBestGainsPerFeature",
+ Input: []tf.Input{
+ node_id_range, tf.OutputList(stats_summary_list), l1, l2, tree_complexity, min_node_weight,
+ },
+ Attrs: attrs,
+ }
+ op := scope.AddOperation(opspec)
+ if scope.Err() != nil {
+ return
+ }
+ var idx int
+ var err error
+ if node_ids_list, idx, err = makeOutputList(op, idx, "node_ids_list"); err != nil {
+ scope.UpdateErr("BoostedTreesCalculateBestGainsPerFeature", err)
+ return
+ }
+ if gains_list, idx, err = makeOutputList(op, idx, "gains_list"); err != nil {
+ scope.UpdateErr("BoostedTreesCalculateBestGainsPerFeature", err)
+ return
+ }
+ if thresholds_list, idx, err = makeOutputList(op, idx, "thresholds_list"); err != nil {
+ scope.UpdateErr("BoostedTreesCalculateBestGainsPerFeature", err)
+ return
+ }
+ if left_node_contribs_list, idx, err = makeOutputList(op, idx, "left_node_contribs_list"); err != nil {
+ scope.UpdateErr("BoostedTreesCalculateBestGainsPerFeature", err)
+ return
+ }
+ if right_node_contribs_list, idx, err = makeOutputList(op, idx, "right_node_contribs_list"); err != nil {
+ scope.UpdateErr("BoostedTreesCalculateBestGainsPerFeature", err)
+ return
+ }
+ return node_ids_list, gains_list, thresholds_list, left_node_contribs_list, right_node_contribs_list
+}
+
// EncodePngAttr is an optional argument to EncodePng.
type EncodePngAttr func(optionalAttr)
// ResizeBilinearGradAlignCorners 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.
+// value: If true, the centers of the 4 corner pixels of the input and grad tensors are
+// aligned. Defaults to false.
// If not specified, defaults to false
func ResizeBilinearGradAlignCorners(value bool) ResizeBilinearGradAttr {
return func(m optionalAttr) {
return op.Output(0)
}
+// Reduces sparse updates into the variable referenced by `resource` using the `max` operation.
+//
+// This operation computes
+//
+// # Scalar indices
+// ref[indices, ...] = max(ref[indices, ...], updates[...])
+//
+// # Vector indices (for each i)
+// ref[indices[i], ...] = max(ref[indices[i], ...], updates[i, ...])
+//
+// # High rank indices (for each i, ..., j)
+// ref[indices[i, ..., j], ...] = max(ref[indices[i, ..., j], ...], updates[i, ..., j, ...])
+//
+// Duplicate entries are handled correctly: if multiple `indices` reference
+// the same location, their contributions are combined.
+//
+// Requires `updates.shape = indices.shape + ref.shape[1:]` or `updates.shape = []`.
+//
+// <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 ResourceScatterMax(scope *Scope, resource tf.Output, indices tf.Output, updates tf.Output) (o *tf.Operation) {
+ if scope.Err() != nil {
+ return
+ }
+ opspec := tf.OpSpec{
+ Type: "ResourceScatterMax",
+ Input: []tf.Input{
+ resource, indices, updates,
+ },
+ }
+ return scope.AddOperation(opspec)
+}
+
// Outputs a `Summary` protocol buffer with scalar values.
//
// The input `tags` and `values` must have the same shape. The generated summary
return op.Output(0)
}
+// Returns a list list which has the passed-in `Tensor` as last element and the other elements of the given list in `input_handle`.
+//
+// tensor: The tensor to put on the list.
+// input_handle: The old list.
+// output_handle: A list with the elements of the old list followed by tensor.
+// element_dtype: the type of elements in the list.
+// element_shape: a shape compatible with that of elements in the list.
+func TensorListPushBack(scope *Scope, input_handle tf.Output, tensor tf.Output) (output_handle tf.Output) {
+ if scope.Err() != nil {
+ return
+ }
+ opspec := tf.OpSpec{
+ Type: "TensorListPushBack",
+ Input: []tf.Input{
+ input_handle, tensor,
+ },
+ }
+ op := scope.AddOperation(opspec)
+ return op.Output(0)
+}
+
+// Returns the number of tensors in the input tensor list.
+//
+// input_handle: the input list
+// length: the number of tensors in the list
+func TensorListLength(scope *Scope, input_handle tf.Output) (length tf.Output) {
+ if scope.Err() != nil {
+ return
+ }
+ opspec := tf.OpSpec{
+ Type: "TensorListLength",
+ Input: []tf.Input{
+ input_handle,
+ },
+ }
+ op := scope.AddOperation(opspec)
+ return op.Output(0)
+}
+
+// The shape of the elements of the given list, as a tensor.
+//
+// input_handle: the list
+// element_shape: the shape of elements of the list
+func TensorListElementShape(scope *Scope, input_handle tf.Output, shape_type tf.DataType) (element_shape tf.Output) {
+ if scope.Err() != nil {
+ return
+ }
+ attrs := map[string]interface{}{"shape_type": shape_type}
+ opspec := tf.OpSpec{
+ Type: "TensorListElementShape",
+ Input: []tf.Input{
+ input_handle,
+ },
+ Attrs: attrs,
+ }
+ op := scope.AddOperation(opspec)
+ return op.Output(0)
+}
+
+// Returns the item in the list with the given index.
+//
+// input_handle: the list
+// index: the position in the list from which an element will be retrieved
+// item: the element at that position
+//
+//
+func TensorListGetItem(scope *Scope, input_handle tf.Output, index tf.Output, element_dtype tf.DataType) (item tf.Output) {
+ if scope.Err() != nil {
+ return
+ }
+ attrs := map[string]interface{}{"element_dtype": element_dtype}
+ opspec := tf.OpSpec{
+ Type: "TensorListGetItem",
+ Input: []tf.Input{
+ input_handle, index,
+ },
+ Attrs: attrs,
+ }
+ op := scope.AddOperation(opspec)
+ return op.Output(0)
+}
+
// Computes the matrix exponential of one or more square matrices:
//
// exp(A) = \sum_{n=0}^\infty A^n/n!
return op.Output(0)
}
+// Computes the matrix logarithm of one or more square matrices:
+//
+//
+// log(exp(A)) = A
+//
+// This op is only defined for complex matrices. If A is positive-definite and
+// real, then casting to a complex matrix, taking the logarithm and casting back
+// to a real matrix will give the correct result.
+//
+// This function computes the matrix logarithm using the Schur-Parlett algorithm.
+// Details of the algorithm can be found in Section 11.6.2 of:
+// Nicholas J. Higham, Functions of Matrices: Theory and Computation, SIAM 2008.
+// ISBN 978-0-898716-46-7.
+//
+// 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.logm
+// @end_compatibility
+func MatrixLogarithm(scope *Scope, input tf.Output) (output tf.Output) {
+ if scope.Err() != nil {
+ return
+ }
+ opspec := tf.OpSpec{
+ Type: "MatrixLogarithm",
+ Input: []tf.Input{
+ input,
+ },
+ }
+ op := scope.AddOperation(opspec)
+ return op.Output(0)
+}
+
// QueueDequeueUpToV2Attr is an optional argument to QueueDequeueUpToV2.
type QueueDequeueUpToV2Attr func(optionalAttr)
// SelfAdjointEig.
//
// The result is a [..., M+1, M] matrix with [..., 0,:] containing the
-// eigenvalues, and subsequent [...,1:, :] containing the eigenvectors.
+// eigenvalues, and subsequent [...,1:, :] containing the eigenvectors. The eigenvalues
+// are sorted in non-decreasing order.
//
// Arguments:
// input: Shape is `[..., M, M]`.
// Computes the eigen decomposition of one or more square self-adjoint matrices.
//
// Computes the eigenvalues and (optionally) eigenvectors of each inner matrix in
-// `input` such that `input[..., :, :] = v[..., :, :] * diag(e[..., :])`.
+// `input` such that `input[..., :, :] = v[..., :, :] * diag(e[..., :])`. The eigenvalues
+// are sorted in non-decreasing order.
//
// ```python
// # a is a tensor.
// Computes the product along segments of a tensor.
//
-// Read @{$math_ops#segmentation$the section on segmentation} for an explanation of
+// Read @{$math_ops#Segmentation$the section on segmentation} for an explanation of
// segments.
//
// Computes a tensor such that
return op.Output(0), op.Output(1)
}
-// Restore a Reader to its initial clean state.
+// Adds v into specified rows of x.
+//
+// Computes y = x; y[i, :] += v; return y.
//
// Arguments:
-// reader_handle: Handle to a Reader.
+// x: A `Tensor` of type T.
+// i: A vector. Indices into the left-most dimension of `x`.
+// v: A `Tensor` of type T. Same dimension sizes as x except the first dimension, which must be the same as i's size.
//
-// Returns the created operation.
-func ReaderResetV2(scope *Scope, reader_handle tf.Output) (o *tf.Operation) {
+// Returns A `Tensor` of type T. An alias of `x`. The content of `y` is undefined if there are duplicates in `i`.
+func InplaceAdd(scope *Scope, x tf.Output, i tf.Output, v tf.Output) (y tf.Output) {
if scope.Err() != nil {
return
}
opspec := tf.OpSpec{
- Type: "ReaderResetV2",
+ Type: "InplaceAdd",
Input: []tf.Input{
- reader_handle,
+ x, i, v,
},
}
- return scope.AddOperation(opspec)
+ op := scope.AddOperation(opspec)
+ return op.Output(0)
}
-// ResourceApplyAdamAttr is an optional argument to ResourceApplyAdam.
-type ResourceApplyAdamAttr func(optionalAttr)
-
-// ResourceApplyAdamUseLocking sets the optional use_locking attribute to value.
+// 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
// 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.
+// value: If true, the centers of the 4 corner pixels of the input and grad tensors are
+// aligned. Defaults to false.
// If not specified, defaults to false
func ResizeBicubicGradAlignCorners(value bool) ResizeBicubicGradAttr {
return func(m 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.
+// value: If true, the centers of the 4 corner pixels of the input and output tensors are
+// aligned, preserving the values at the corner pixels. Defaults to false.
// If not specified, defaults to false
func ResizeNearestNeighborAlignCorners(value bool) ResizeNearestNeighborAttr {
return func(m 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.
+// value: If true, the centers of the 4 corner pixels of the input and grad tensors are
+// aligned. Defaults to false.
// If not specified, defaults to false
func ResizeNearestNeighborGradAlignCorners(value bool) ResizeNearestNeighborGradAttr {
return func(m optionalAttr) {
return op.Output(0)
}
+// Retrieves the tree ensemble resource stamp token, number of trees and growing statistics.
+//
+// Arguments:
+// tree_ensemble_handle: Handle to the tree ensemble.
+//
+// Returns Stamp token of the tree ensemble resource.The number of trees in the tree ensemble resource.The number of trees that were finished successfully.The number of layers we attempted to build (but not necessarily succeeded).Rank size 2 tensor that contains start and end ids of the nodes in the latest
+// layer.
+func BoostedTreesGetEnsembleStates(scope *Scope, tree_ensemble_handle tf.Output) (stamp_token tf.Output, num_trees tf.Output, num_finalized_trees tf.Output, num_attempted_layers tf.Output, last_layer_nodes_range tf.Output) {
+ if scope.Err() != nil {
+ return
+ }
+ opspec := tf.OpSpec{
+ Type: "BoostedTreesGetEnsembleStates",
+ Input: []tf.Input{
+ tree_ensemble_handle,
+ },
+ }
+ op := scope.AddOperation(opspec)
+ return op.Output(0), op.Output(1), op.Output(2), op.Output(3), op.Output(4)
+}
+
+// Gets the next output from the given iterator.
+//
+// This operation is a synchronous version IteratorGetNext. It should only be used
+// in situations where the iterator does not block the calling thread, or where
+// the calling thread is not a member of the thread pool used to execute parallel
+// operations (e.g. in eager mode).
+func IteratorGetNextSync(scope *Scope, iterator tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (components []tf.Output) {
+ if scope.Err() != nil {
+ return
+ }
+ attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes}
+ opspec := tf.OpSpec{
+ Type: "IteratorGetNextSync",
+ Input: []tf.Input{
+ iterator,
+ },
+ Attrs: attrs,
+ }
+ op := scope.AddOperation(opspec)
+ if scope.Err() != nil {
+ return
+ }
+ var idx int
+ var err error
+ if components, idx, err = makeOutputList(op, idx, "components"); err != nil {
+ scope.UpdateErr("IteratorGetNextSync", err)
+ return
+ }
+ return components
+}
+
// SampleDistortedBoundingBoxV2Attr is an optional argument to SampleDistortedBoundingBoxV2.
type SampleDistortedBoundingBoxV2Attr func(optionalAttr)
return op.Output(0)
}
+// EncodeProtoAttr is an optional argument to EncodeProto.
+type EncodeProtoAttr func(optionalAttr)
+
+// EncodeProtoDescriptorSource sets the optional descriptor_source attribute to value.
+// If not specified, defaults to "local://"
+func EncodeProtoDescriptorSource(value string) EncodeProtoAttr {
+ return func(m optionalAttr) {
+ m["descriptor_source"] = value
+ }
+}
+
+// The op serializes protobuf messages provided in the input tensors.
+//
+// The types of the tensors in `values` must match the schema for the
+// fields specified in `field_names`. All the tensors in `values` must
+// have a common shape prefix, *batch_shape*.
+//
+// The `sizes` tensor specifies repeat counts for each field. The repeat
+// count (last dimension) of a each tensor in `values` must be greater
+// than or equal to corresponding repeat count in `sizes`.
+//
+// A `message_type` name must be provided to give context for the field
+// names. The actual message descriptor can be looked up either in the
+// linked-in descriptor pool or a filename provided by the caller using
+// the `descriptor_source` attribute.
+//
+// The `descriptor_source` attribute selects a source of protocol
+// descriptors to consult when looking up `message_type`. This may be a
+// filename containing a serialized `FileDescriptorSet` message,
+// or the special value `local://`, in which case only descriptors linked
+// into the code will be searched; the filename can be on any filesystem
+// accessible to TensorFlow.
+//
+// You can build a `descriptor_source` file using the `--descriptor_set_out`
+// and `--include_imports` options to the protocol compiler `protoc`.
+//
+// The `local://` database only covers descriptors linked into the
+// code via C++ libraries, not Python imports. You can link in a proto descriptor
+// by creating a cc_library target with alwayslink=1.
+//
+// There are a few special cases in the value mapping:
+//
+// Submessage and group fields must be pre-serialized as TensorFlow strings.
+//
+// TensorFlow lacks support for unsigned int64s, so they must be
+// represented as `tf.int64` with the same twos-complement bit pattern
+// (the obvious way).
+//
+// Unsigned int32 values can be represented exactly with `tf.int64`, or
+// with sign wrapping if the input is of type `tf.int32`.
+//
+// Arguments:
+// sizes: Tensor of int32 with shape `[batch_shape, len(field_names)]`.
+// values: List of tensors containing values for the corresponding field.
+// field_names: List of strings containing proto field names.
+// message_type: Name of the proto message type to decode.
+//
+// Returns Tensor of serialized protos with shape `batch_shape`.
+func EncodeProto(scope *Scope, sizes tf.Output, values []tf.Output, field_names []string, message_type string, optional ...EncodeProtoAttr) (bytes tf.Output) {
+ if scope.Err() != nil {
+ return
+ }
+ attrs := map[string]interface{}{"field_names": field_names, "message_type": message_type}
+ for _, a := range optional {
+ a(attrs)
+ }
+ opspec := tf.OpSpec{
+ Type: "EncodeProto",
+ Input: []tf.Input{
+ sizes, tf.OutputList(values),
+ },
+ Attrs: attrs,
+ }
+ op := scope.AddOperation(opspec)
+ return op.Output(0)
+}
+
// Creates a TensorArray for storing the gradients of values in the given handle.
//
// If the given TensorArray gradient already exists, returns a reference to it.
return op.Output(0), op.Output(1)
}
+// DecodeProtoV2Attr is an optional argument to DecodeProtoV2.
+type DecodeProtoV2Attr func(optionalAttr)
+
+// DecodeProtoV2DescriptorSource sets the optional descriptor_source attribute to value.
+//
+// value: Either the special value `local://` or a path to a file containing
+// a serialized `FileDescriptorSet`.
+// If not specified, defaults to "local://"
+func DecodeProtoV2DescriptorSource(value string) DecodeProtoV2Attr {
+ return func(m optionalAttr) {
+ m["descriptor_source"] = value
+ }
+}
+
+// DecodeProtoV2MessageFormat sets the optional message_format attribute to value.
+//
+// value: Either `binary` or `text`.
+// If not specified, defaults to "binary"
+func DecodeProtoV2MessageFormat(value string) DecodeProtoV2Attr {
+ return func(m optionalAttr) {
+ m["message_format"] = value
+ }
+}
+
+// DecodeProtoV2Sanitize sets the optional sanitize attribute to value.
+//
+// value: Whether to sanitize the result or not.
+// If not specified, defaults to false
+func DecodeProtoV2Sanitize(value bool) DecodeProtoV2Attr {
+ return func(m optionalAttr) {
+ m["sanitize"] = value
+ }
+}
+
+// The op extracts fields from a serialized protocol buffers message into tensors.
+//
+// The `decode_proto` op extracts fields from a serialized protocol buffers
+// message into tensors. The fields in `field_names` are decoded and converted
+// to the corresponding `output_types` if possible.
+//
+// A `message_type` name must be provided to give context for the field
+// names. The actual message descriptor can be looked up either in the
+// linked-in descriptor pool or a filename provided by the caller using
+// the `descriptor_source` attribute.
+//
+// Each output tensor is a dense tensor. This means that it is padded to
+// hold the largest number of repeated elements seen in the input
+// minibatch. (The shape is also padded by one to prevent zero-sized
+// dimensions). The actual repeat counts for each example in the
+// minibatch can be found in the `sizes` output. In many cases the output
+// of `decode_proto` is fed immediately into tf.squeeze if missing values
+// are not a concern. When using tf.squeeze, always pass the squeeze
+// dimension explicitly to avoid surprises.
+//
+// For the most part, the mapping between Proto field types and
+// TensorFlow dtypes is straightforward. However, there are a few
+// special cases:
+//
+// - A proto field that contains a submessage or group can only be converted
+// to `DT_STRING` (the serialized submessage). This is to reduce the
+// complexity of the API. The resulting string can be used as input
+// to another instance of the decode_proto op.
+//
+// - TensorFlow lacks support for unsigned integers. The ops represent uint64
+// types as a `DT_INT64` with the same twos-complement bit pattern
+// (the obvious way). Unsigned int32 values can be represented exactly by
+// specifying type `DT_INT64`, or using twos-complement if the caller
+// specifies `DT_INT32` in the `output_types` attribute.
+//
+// The `descriptor_source` attribute selects a source of protocol
+// descriptors to consult when looking up `message_type`. This may be a
+// filename containing a serialized `FileDescriptorSet` message,
+// or the special value `local://`, in which case only descriptors linked
+// into the code will be searched; the filename can be on any filesystem
+// accessible to TensorFlow.
+//
+// You can build a `descriptor_source` file using the `--descriptor_set_out`
+// and `--include_imports` options to the protocol compiler `protoc`.
+//
+// The `local://` database only covers descriptors linked into the
+// code via C++ libraries, not Python imports. You can link in a proto descriptor
+// by creating a cc_library target with alwayslink=1.
+//
+// Both binary and text proto serializations are supported, and can be
+// chosen using the `format` attribute.
+//
+// Arguments:
+// bytes: Tensor of serialized protos with shape `batch_shape`.
+// message_type: Name of the proto message type to decode.
+// field_names: List of strings containing proto field names.
+// output_types: List of TF types to use for the respective field in field_names.
+//
+// Returns Tensor of int32 with shape `[batch_shape, len(field_names)]`.
+// Each entry is the number of values found for the corresponding field.
+// Optional fields may have 0 or 1 values.List of tensors containing values for the corresponding field.
+// `values[i]` has datatype `output_types[i]`
+// and shape `[batch_shape, max(sizes[...,i])]`.
+func DecodeProtoV2(scope *Scope, bytes tf.Output, message_type string, field_names []string, output_types []tf.DataType, optional ...DecodeProtoV2Attr) (sizes tf.Output, values []tf.Output) {
+ if scope.Err() != nil {
+ return
+ }
+ attrs := map[string]interface{}{"message_type": message_type, "field_names": field_names, "output_types": output_types}
+ for _, a := range optional {
+ a(attrs)
+ }
+ opspec := tf.OpSpec{
+ Type: "DecodeProtoV2",
+ Input: []tf.Input{
+ bytes,
+ },
+ Attrs: attrs,
+ }
+ op := scope.AddOperation(opspec)
+ if scope.Err() != nil {
+ return
+ }
+ var idx int
+ var err error
+ sizes = op.Output(idx)
+ if values, idx, err = makeOutputList(op, idx, "values"); err != nil {
+ scope.UpdateErr("DecodeProtoV2", err)
+ return
+ }
+ return sizes, values
+}
+
// Creates a dataset that splits a SparseTensor into elements row-wise.
func SparseTensorSliceDataset(scope *Scope, indices tf.Output, values tf.Output, dense_shape tf.Output) (handle tf.Output) {
if scope.Err() != nil {
// Adds a value to 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.
+// Any ReadVariableOp with a control dependency on this op is guaranteed to
+// see the incremented value or a subsequent newer one.
//
// Arguments:
// resource: handle to the resource in which to store the variable.
return scope.AddOperation(opspec)
}
+// Makes the summary of accumulated stats for the batch.
+//
+// The summary stats contains gradients and hessians accumulated into the corresponding node and bucket for each example.
+//
+// Arguments:
+// node_ids: int32 Rank 1 Tensor containing node ids, which each example falls into for the requested layer.
+// gradients: float32; Rank 2 Tensor (shape=[#examples, 1]) for gradients.
+// hessians: float32; Rank 2 Tensor (shape=[#examples, 1]) for hessians.
+// bucketized_features_list: int32 list of Rank 1 Tensors, each containing the bucketized feature (for each feature column).
+// max_splits: int; the maximum number of splits possible in the whole tree.
+// num_buckets: int; equals to the maximum possible value of bucketized feature.
+//
+// Returns output Rank 4 Tensor (shape=[#features, #splits, #buckets, 2]) containing accumulated stats put into the corresponding node and bucket. The first index of 4th dimension refers to gradients, and the second to hessians.
+func BoostedTreesMakeStatsSummary(scope *Scope, node_ids tf.Output, gradients tf.Output, hessians tf.Output, bucketized_features_list []tf.Output, max_splits int64, num_buckets int64) (stats_summary tf.Output) {
+ if scope.Err() != nil {
+ return
+ }
+ attrs := map[string]interface{}{"max_splits": max_splits, "num_buckets": num_buckets}
+ opspec := tf.OpSpec{
+ Type: "BoostedTreesMakeStatsSummary",
+ Input: []tf.Input{
+ node_ids, gradients, hessians, tf.OutputList(bucketized_features_list),
+ },
+ Attrs: attrs,
+ }
+ op := scope.AddOperation(opspec)
+ return op.Output(0)
+}
+
// Adjust the contrast of one or more images.
//
// `images` is a tensor of at least 3 dimensions. The last 3 dimensions are
// <img style="width:100%" src="https://www.tensorflow.org/images/Gather.png" alt>
// </div>
//
+// Note that on CPU, if an out of bound index is found, an error is returned.
+// On GPU, if an out of bound index is found, a 0 is stored in the
+// corresponding output value.
+//
// Arguments:
// params: The tensor from which to gather values. Must be at least rank
// `axis + 1`.
return op.Output(0)
}
+// List of the given size with empty elements.
+//
+// element_shape: the shape of the future elements of the list
+// num_elements: the number of elements to reserve
+// handle: the output list
+// element_dtype: the desired type of elements in the list.
+func TensorListReserve(scope *Scope, element_shape tf.Output, num_elements tf.Output, element_dtype tf.DataType) (handle tf.Output) {
+ if scope.Err() != nil {
+ return
+ }
+ attrs := map[string]interface{}{"element_dtype": element_dtype}
+ opspec := tf.OpSpec{
+ Type: "TensorListReserve",
+ Input: []tf.Input{
+ element_shape, num_elements,
+ },
+ Attrs: attrs,
+ }
+ op := scope.AddOperation(opspec)
+ return op.Output(0)
+}
+
// PriorityQueueV2Attr is an optional argument to PriorityQueueV2.
type PriorityQueueV2Attr func(optionalAttr)
return op.Output(0)
}
+// RpcAttr is an optional argument to Rpc.
+type RpcAttr func(optionalAttr)
+
+// RpcProtocol sets the optional protocol attribute to value.
+//
+// value: RPC protocol to use. Empty string means use the default protocol.
+// Options include 'grpc'.
+// If not specified, defaults to ""
+func RpcProtocol(value string) RpcAttr {
+ return func(m optionalAttr) {
+ m["protocol"] = value
+ }
+}
+
+// RpcFailFast sets the optional fail_fast attribute to value.
+//
+// value: `boolean`. If `true` (default), then failures to connect
+// (i.e., the server does not immediately respond) cause an RPC failure.
+// If not specified, defaults to true
+func RpcFailFast(value bool) RpcAttr {
+ return func(m optionalAttr) {
+ m["fail_fast"] = value
+ }
+}
+
+// RpcTimeoutInMs sets the optional timeout_in_ms attribute to value.
+//
+// value: `int`. If `0` (default), then the kernel will run the RPC
+// request and only time out if the RPC deadline passes or the session times out.
+// If this value is greater than `0`, then the op will raise an exception if
+// the RPC takes longer than `timeout_in_ms`.
+// If not specified, defaults to 0
+func RpcTimeoutInMs(value int64) RpcAttr {
+ return func(m optionalAttr) {
+ m["timeout_in_ms"] = value
+ }
+}
+
+// Perform batches of RPC requests.
+//
+// This op asynchronously performs either a single RPC request, or a batch
+// of requests. RPC requests are defined by three main parameters:
+//
+// - `address` (the host+port or BNS address of the request)
+// - `method` (the RPC method name for the request)
+// - `request` (the serialized proto string, or vector of strings,
+// of the RPC request argument).
+//
+// For example, if you have an RPC service running on port localhost:2345,
+// and its interface is configured with the following proto declaration:
+//
+// ```
+// service MyService {
+// rpc MyMethod(MyRequestProto) returns (MyResponseProto) {
+// }
+// };
+// ```
+//
+// then call this op with arguments:
+//
+// ```
+// address = "localhost:2345"
+// method = "MyService/MyMethod"
+// ```
+//
+// The `request` tensor is a string tensor representing serialized `MyRequestProto`
+// strings; and the output string tensor `response` will have the same shape
+// and contain (upon successful completion) corresponding serialized
+// `MyResponseProto` strings.
+//
+// For example, to send a single, empty, `MyRequestProto`, call
+// this op with `request = ""`. To send 5 **parallel** empty requests,
+// call this op with `request = ["", "", "", "", ""]`.
+//
+// More generally, one can create a batch of `MyRequestProto` serialized protos
+// from regular batched tensors using the `encode_proto` op, and convert
+// the response `MyResponseProto` serialized protos to batched tensors
+// using the `decode_proto` op.
+//
+// **NOTE** Working with serialized proto strings is faster than instantiating
+// actual proto objects in memory, so no performance degradation is expected
+// compared to writing custom kernels for this workflow.
+//
+// If the connection fails or the remote worker returns an error
+// status, the op reraises this exception locally.
+//
+// See the `TryRpc` op if you prefer to handle RPC failures manually in the graph.
+//
+// Arguments:
+// address: `0-D` or `1-D`. The address (i.e. host_name:port) of the RPC server.
+// If this tensor has more than 1 element, then multiple parallel rpc requests
+// are sent. This argument broadcasts with `method` and `request`.
+// method: `0-D` or `1-D`. The method address on the RPC server.
+// If this tensor has more than 1 element, then multiple parallel rpc requests
+// are sent. This argument broadcasts with `address` and `request`.
+// request: `0-D` or `1-D`. Serialized proto strings: the rpc request argument.
+// If this tensor has more than 1 element, then multiple parallel rpc requests
+// are sent. This argument broadcasts with `address` and `method`.
+//
+// Returns Same shape as `request`. Serialized proto strings: the rpc responses.
+func Rpc(scope *Scope, address tf.Output, method tf.Output, request tf.Output, optional ...RpcAttr) (response tf.Output) {
+ if scope.Err() != nil {
+ return
+ }
+ attrs := map[string]interface{}{}
+ for _, a := range optional {
+ a(attrs)
+ }
+ opspec := tf.OpSpec{
+ Type: "Rpc",
+ Input: []tf.Input{
+ address, method, request,
+ },
+ Attrs: attrs,
+ }
+ op := scope.AddOperation(opspec)
+ return op.Output(0)
+}
+
// OrderedMapStageAttr is an optional argument to OrderedMapStage.
type OrderedMapStageAttr func(optionalAttr)
return op.Output(0)
}
-// Compare values of `input` to `threshold` and pack resulting bits into a `uint8`.
-//
-// Each comparison returns a boolean `true` (if `input_value > threshold`)
-// or and `false` otherwise.
-//
-// This operation is useful for Locality-Sensitive-Hashing (LSH) and other
-// algorithms that use hashing approximations of cosine and `L2` distances;
-// codes can be generated from an input via:
-//
-// ```python
-// codebook_size = 50
-// codebook_bits = codebook_size * 32
-// codebook = tf.get_variable('codebook', [x.shape[-1].value, codebook_bits],
-// dtype=x.dtype,
-// initializer=tf.orthogonal_initializer())
-// codes = compare_and_threshold(tf.matmul(x, codebook), threshold=0.)
-// codes = tf.bitcast(codes, tf.int32) # go from uint8 to int32
-// # now codes has shape x.shape[:-1] + [codebook_size]
-// ```
-//
-// **NOTE**: Currently, the innermost dimension of the tensor must be divisible
-// by 8.
-//
-// Given an `input` shaped `[s0, s1, ..., s_n]`, the output is
+// CudnnRNNAttr is an optional argument to CudnnRNN.
+type CudnnRNNAttr func(optionalAttr)
+
+// CudnnRNNRnnMode sets the optional rnn_mode attribute to value.
+// If not specified, defaults to "lstm"
+func CudnnRNNRnnMode(value string) CudnnRNNAttr {
+ return func(m optionalAttr) {
+ m["rnn_mode"] = value
+ }
+}
+
+// CudnnRNNInputMode sets the optional input_mode attribute to value.
+// If not specified, defaults to "linear_input"
+func CudnnRNNInputMode(value string) CudnnRNNAttr {
+ return func(m optionalAttr) {
+ m["input_mode"] = value
+ }
+}
+
+// CudnnRNNDirection sets the optional direction attribute to value.
+// If not specified, defaults to "unidirectional"
+func CudnnRNNDirection(value string) CudnnRNNAttr {
+ return func(m optionalAttr) {
+ m["direction"] = value
+ }
+}
+
+// CudnnRNNDropout sets the optional dropout attribute to value.
+// If not specified, defaults to 0
+func CudnnRNNDropout(value float32) CudnnRNNAttr {
+ return func(m optionalAttr) {
+ m["dropout"] = value
+ }
+}
+
+// CudnnRNNSeed sets the optional seed attribute to value.
+// If not specified, defaults to 0
+func CudnnRNNSeed(value int64) CudnnRNNAttr {
+ return func(m optionalAttr) {
+ m["seed"] = value
+ }
+}
+
+// CudnnRNNSeed2 sets the optional seed2 attribute to value.
+// If not specified, defaults to 0
+func CudnnRNNSeed2(value int64) CudnnRNNAttr {
+ return func(m optionalAttr) {
+ m["seed2"] = value
+ }
+}
+
+// CudnnRNNIsTraining sets the optional is_training attribute to value.
+// If not specified, defaults to true
+func CudnnRNNIsTraining(value bool) CudnnRNNAttr {
+ return func(m optionalAttr) {
+ m["is_training"] = value
+ }
+}
+
+// A RNN backed by cuDNN.
+//
+// Computes the RNN from the input and initial states, with respect to the params
+// buffer.
+//
+// rnn_mode: Indicates the type of the RNN model.
+// input_mode: Indicate whether there is a linear projection between the input and
+// The actual computation before the first layer. 'skip_input' is only allowed
+// when input_size == num_units; 'auto_select' implies 'skip_input' when
+// input_size == num_units; otherwise, it implies 'linear_input'.
+// direction: Indicates whether a bidirectional model will be used.
+// dir = (direction == bidirectional) ? 2 : 1
+// dropout: dropout probability. When set to 0., dropout is disabled.
+// seed: the 1st part of a seed to initialize dropout.
+// seed2: the 2nd part of a seed to initialize dropout.
+// input: a 3-D tensor with the shape of [seq_length, batch_size, input_size].
+// input_h: a 3-D tensor with the shape of [num_layer * dir, batch_size,
+// num_units].
+// input_c: For LSTM, a 3-D tensor with the shape of
+// [num_layer * dir, batch, num_units]. For other models, it is ignored.
+// params: a 1-D tensor that contains the weights and biases in an opaque layout.
+// The size must be created through CudnnRNNParamsSize, and initialized
+// separately. Note that they might not be compatible across different
+// generations. So it is a good idea to save and restore
+// output: a 3-D tensor with the shape of [seq_length, batch_size,
+// dir * num_units].
+// output_h: the same shape has input_h.
+// output_c: the same shape as input_c for LSTM. An empty tensor for other models.
+// is_training: Indicates whether this operation is used for inferenece or
+// training.
+// reserve_space: an opaque tensor that can be used in backprop calculation. It
+// is only produced if is_training is false.
+func CudnnRNN(scope *Scope, input tf.Output, input_h tf.Output, input_c tf.Output, params tf.Output, optional ...CudnnRNNAttr) (output tf.Output, output_h tf.Output, output_c tf.Output, reserve_space tf.Output) {
+ if scope.Err() != nil {
+ return
+ }
+ attrs := map[string]interface{}{}
+ for _, a := range optional {
+ a(attrs)
+ }
+ opspec := tf.OpSpec{
+ Type: "CudnnRNN",
+ Input: []tf.Input{
+ input, input_h, input_c, params,
+ },
+ Attrs: attrs,
+ }
+ op := scope.AddOperation(opspec)
+ return op.Output(0), op.Output(1), op.Output(2), op.Output(3)
+}
+
+// Compare values of `input` to `threshold` and pack resulting bits into a `uint8`.
+//
+// Each comparison returns a boolean `true` (if `input_value > threshold`)
+// or and `false` otherwise.
+//
+// This operation is useful for Locality-Sensitive-Hashing (LSH) and other
+// algorithms that use hashing approximations of cosine and `L2` distances;
+// codes can be generated from an input via:
+//
+// ```python
+// codebook_size = 50
+// codebook_bits = codebook_size * 32
+// codebook = tf.get_variable('codebook', [x.shape[-1].value, codebook_bits],
+// dtype=x.dtype,
+// initializer=tf.orthogonal_initializer())
+// codes = compare_and_threshold(tf.matmul(x, codebook), threshold=0.)
+// codes = tf.bitcast(codes, tf.int32) # go from uint8 to int32
+// # now codes has shape x.shape[:-1] + [codebook_size]
+// ```
+//
+// **NOTE**: Currently, the innermost dimension of the tensor must be divisible
+// by 8.
+//
+// Given an `input` shaped `[s0, s1, ..., s_n]`, the output is
// a `uint8` tensor shaped `[s0, s1, ..., s_n / 8]`.
//
// Arguments:
return op.Output(0)
}
+// EmptyAttr is an optional argument to Empty.
+type EmptyAttr func(optionalAttr)
+
+// EmptyInit sets the optional init attribute to value.
+//
+// value: If True, initialize the returned tensor with the default value of dtype. Otherwise, the implementation is free not to initializethe tensor's content.
+// If not specified, defaults to false
+func EmptyInit(value bool) EmptyAttr {
+ return func(m optionalAttr) {
+ m["init"] = value
+ }
+}
+
+// Creates a tensor with the given shape.
+//
+// This operation creates a tensor of `shape` and `dtype`.
+//
+// Arguments:
+// shape: 1-D. Represents the shape of the output tensor.
+//
+//
+// Returns A `Tensor` of type `T`.
+func Empty(scope *Scope, shape tf.Output, dtype tf.DataType, optional ...EmptyAttr) (output tf.Output) {
+ if scope.Err() != nil {
+ return
+ }
+ attrs := map[string]interface{}{"dtype": dtype}
+ for _, a := range optional {
+ a(attrs)
+ }
+ opspec := tf.OpSpec{
+ Type: "Empty",
+ Input: []tf.Input{
+ shape,
+ },
+ Attrs: attrs,
+ }
+ op := scope.AddOperation(opspec)
+ return op.Output(0)
+}
+
// TensorArrayConcatV3Attr is an optional argument to TensorArrayConcatV3.
type TensorArrayConcatV3Attr func(optionalAttr)
return op.Output(0)
}
+// Sets the index-th position of the list to contain the given tensor.
+//
+// input_handle: the list
+// index: the position in the list to which the tensor will be assigned
+// item: the element to be assigned to that position
+// output_handle: the new list, with the element in the proper position
+//
+func TensorListSetItem(scope *Scope, input_handle tf.Output, index tf.Output, item tf.Output) (output_handle tf.Output) {
+ if scope.Err() != nil {
+ return
+ }
+ opspec := tf.OpSpec{
+ Type: "TensorListSetItem",
+ Input: []tf.Input{
+ input_handle, index, item,
+ },
+ }
+ op := scope.AddOperation(opspec)
+ return op.Output(0)
+}
+
// Returns a diagonal tensor with a given diagonal values.
//
// Given a `diagonal`, this operation returns a tensor with the `diagonal` and
return op.Output(0)
}
+// Creates a tree ensemble model and returns a handle to it.
+//
+// Arguments:
+// tree_ensemble_handle: Handle to the tree ensemble resource to be created.
+// stamp_token: Token to use as the initial value of the resource stamp.
+// tree_ensemble_serialized: Serialized proto of the tree ensemble.
+//
+// Returns the created operation.
+func BoostedTreesCreateEnsemble(scope *Scope, tree_ensemble_handle tf.Output, stamp_token tf.Output, tree_ensemble_serialized tf.Output) (o *tf.Operation) {
+ if scope.Err() != nil {
+ return
+ }
+ opspec := tf.OpSpec{
+ Type: "BoostedTreesCreateEnsemble",
+ Input: []tf.Input{
+ tree_ensemble_handle, stamp_token, tree_ensemble_serialized,
+ },
+ }
+ return scope.AddOperation(opspec)
+}
+
// Applies sparse addition to `input` using individual values or slices
//
// from `updates` according to indices `indices`. The updates are non-aliasing:
return output
}
+// CudnnRNNParamsToCanonicalAttr is an optional argument to CudnnRNNParamsToCanonical.
+type CudnnRNNParamsToCanonicalAttr func(optionalAttr)
+
+// CudnnRNNParamsToCanonicalRnnMode sets the optional rnn_mode attribute to value.
+// If not specified, defaults to "lstm"
+func CudnnRNNParamsToCanonicalRnnMode(value string) CudnnRNNParamsToCanonicalAttr {
+ return func(m optionalAttr) {
+ m["rnn_mode"] = value
+ }
+}
+
+// CudnnRNNParamsToCanonicalInputMode sets the optional input_mode attribute to value.
+// If not specified, defaults to "linear_input"
+func CudnnRNNParamsToCanonicalInputMode(value string) CudnnRNNParamsToCanonicalAttr {
+ return func(m optionalAttr) {
+ m["input_mode"] = value
+ }
+}
+
+// CudnnRNNParamsToCanonicalDirection sets the optional direction attribute to value.
+// If not specified, defaults to "unidirectional"
+func CudnnRNNParamsToCanonicalDirection(value string) CudnnRNNParamsToCanonicalAttr {
+ return func(m optionalAttr) {
+ m["direction"] = value
+ }
+}
+
+// CudnnRNNParamsToCanonicalDropout sets the optional dropout attribute to value.
+// If not specified, defaults to 0
+func CudnnRNNParamsToCanonicalDropout(value float32) CudnnRNNParamsToCanonicalAttr {
+ return func(m optionalAttr) {
+ m["dropout"] = value
+ }
+}
+
+// CudnnRNNParamsToCanonicalSeed sets the optional seed attribute to value.
+// If not specified, defaults to 0
+func CudnnRNNParamsToCanonicalSeed(value int64) CudnnRNNParamsToCanonicalAttr {
+ return func(m optionalAttr) {
+ m["seed"] = value
+ }
+}
+
+// CudnnRNNParamsToCanonicalSeed2 sets the optional seed2 attribute to value.
+// If not specified, defaults to 0
+func CudnnRNNParamsToCanonicalSeed2(value int64) CudnnRNNParamsToCanonicalAttr {
+ return func(m optionalAttr) {
+ m["seed2"] = value
+ }
+}
+
+// Retrieves CudnnRNN params in canonical form.
+//
+// Retrieves a set of weights from the opaque params buffer that can be saved and
+// restored in a way compatible with future runs.
+//
+// Note that the params buffer may not be compatible across different GPUs. So any
+// save and restoration should be converted to and from the canonical weights and
+// biases.
+//
+// num_layers: Specifies the number of layers in the RNN model.
+// num_units: Specifies the size of the hidden state.
+// input_size: Specifies the size of the input state.
+// num_params: number of parameter sets for all layers.
+// Each layer may contain multiple parameter sets, with each set consisting of
+// a weight matrix and a bias vector.
+// weights: the canonical form of weights that can be used for saving
+// and restoration. They are more likely to be compatible across different
+// generations.
+// biases: the canonical form of biases that can be used for saving
+// and restoration. They are more likely to be compatible across different
+// generations.
+// rnn_mode: Indicates the type of the RNN model.
+// input_mode: Indicate whether there is a linear projection between the input and
+// The actual computation before the first layer. 'skip_input' is only allowed
+// when input_size == num_units; 'auto_select' implies 'skip_input' when
+// input_size == num_units; otherwise, it implies 'linear_input'.
+// direction: Indicates whether a bidirectional model will be used.
+// dir = (direction == bidirectional) ? 2 : 1
+// dropout: dropout probability. When set to 0., dropout is disabled.
+// seed: the 1st part of a seed to initialize dropout.
+// seed2: the 2nd part of a seed to initialize dropout.
+func CudnnRNNParamsToCanonical(scope *Scope, num_layers tf.Output, num_units tf.Output, input_size tf.Output, params tf.Output, num_params int64, optional ...CudnnRNNParamsToCanonicalAttr) (weights []tf.Output, biases []tf.Output) {
+ if scope.Err() != nil {
+ return
+ }
+ attrs := map[string]interface{}{"num_params": num_params}
+ for _, a := range optional {
+ a(attrs)
+ }
+ opspec := tf.OpSpec{
+ Type: "CudnnRNNParamsToCanonical",
+ Input: []tf.Input{
+ num_layers, num_units, input_size, params,
+ },
+ Attrs: attrs,
+ }
+ op := scope.AddOperation(opspec)
+ if scope.Err() != nil {
+ return
+ }
+ var idx int
+ var err error
+ if weights, idx, err = makeOutputList(op, idx, "weights"); err != nil {
+ scope.UpdateErr("CudnnRNNParamsToCanonical", err)
+ return
+ }
+ if biases, idx, err = makeOutputList(op, idx, "biases"); err != nil {
+ scope.UpdateErr("CudnnRNNParamsToCanonical", err)
+ return
+ }
+ return weights, biases
+}
+
// UniformCandidateSamplerAttr is an optional argument to UniformCandidateSampler.
type UniformCandidateSamplerAttr func(optionalAttr)
return op.Output(0)
}
+// TryRpcAttr is an optional argument to TryRpc.
+type TryRpcAttr func(optionalAttr)
+
+// TryRpcProtocol sets the optional protocol attribute to value.
+//
+// value: RPC protocol to use. Empty string means use the default protocol.
+// Options include 'grpc'.
+// If not specified, defaults to ""
+func TryRpcProtocol(value string) TryRpcAttr {
+ return func(m optionalAttr) {
+ m["protocol"] = value
+ }
+}
+
+// TryRpcFailFast sets the optional fail_fast attribute to value.
+//
+// value: `boolean`. If `true` (default), then failures to connect
+// (i.e., the server does not immediately respond) cause an RPC failure.
+// If not specified, defaults to true
+func TryRpcFailFast(value bool) TryRpcAttr {
+ return func(m optionalAttr) {
+ m["fail_fast"] = value
+ }
+}
+
+// TryRpcTimeoutInMs sets the optional timeout_in_ms attribute to value.
+//
+// value: `int`. If `0` (default), then the kernel will run the RPC
+// request and only time out if the RPC deadline passes or the session times out.
+// If this value is greater than `0`, then the op will raise an exception if
+// the RPC takes longer than `timeout_in_ms`.
+// If not specified, defaults to 0
+func TryRpcTimeoutInMs(value int64) TryRpcAttr {
+ return func(m optionalAttr) {
+ m["timeout_in_ms"] = value
+ }
+}
+
+// Perform batches of RPC requests.
+//
+// This op asynchronously performs either a single RPC request, or a batch
+// of requests. RPC requests are defined by three main parameters:
+//
+// - `address` (the host+port or BNS address of the request)
+// - `method` (the method name for the request)
+// - `request` (the serialized proto string, or vector of strings,
+// of the RPC request argument).
+//
+// For example, if you have an RPC service running on port localhost:2345,
+// and its interface is configured with the following proto declaration:
+//
+// ```
+// service MyService {
+// rpc MyMethod(MyRequestProto) returns (MyResponseProto) {
+// }
+// };
+// ```
+//
+// then call this op with arguments:
+//
+// ```
+// address = "localhost:2345"
+// method = "MyService/MyMethod"
+// ```
+//
+// The `request` tensor is a string tensor representing serialized `MyRequestProto`
+// strings; and the output string tensor `response` will have the same shape
+// and contain (upon successful completion) corresponding serialized
+// `MyResponseProto` strings.
+//
+// For example, to send a single, empty, `MyRequestProto`, call
+// this op with `request = ""`. To send 5 **parallel** empty requests,
+// call this op with `request = ["", "", "", "", ""]`.
+//
+// More generally, one can create a batch of `MyRequestProto` serialized protos
+// from regular batched tensors using the `encode_proto` op, and convert
+// the response `MyResponseProto` serialized protos to batched tensors
+// using the `decode_proto` op.
+//
+// **NOTE** Working with serialized proto strings is faster than instantiating
+// actual proto objects in memory, so no performance degradation is expected
+// compared to writing custom kernels for this workflow.
+//
+// Unlike the standard `Rpc` op, if the connection fails or the remote worker
+// returns an error status, this op does **not** reraise the exception.
+// Instead, the `status_code` and `status_message` entry for the corresponding RPC
+// call is set with the error returned from the RPC call. The `response` tensor
+// will contain valid response values for those minibatch entries whose RPCs did
+// not fail; the rest of the entries will have empty strings.
+//
+// Arguments:
+// address: `0-D` or `1-D`. The address (i.e. host_name:port) of the RPC server.
+// If this tensor has more than 1 element, then multiple parallel rpc requests
+// are sent. This argument broadcasts with `method` and `request`.
+// method: `0-D` or `1-D`. The method address on the RPC server.
+// If this tensor has more than 1 element, then multiple parallel rpc requests
+// are sent. This argument broadcasts with `address` and `request`.
+// request: `0-D` or `1-D`. Serialized proto strings: the rpc request argument.
+// If this tensor has more than 1 element, then multiple parallel rpc requests
+// are sent. This argument broadcasts with `address` and `method`.
+//
+// Returns Same shape as `request`. Serialized proto strings: the rpc responses.Same shape as `request`. Values correspond to tensorflow Status enum codes.Same shape as `request`. Values correspond to Status messages
+// returned from the RPC calls.
+func TryRpc(scope *Scope, address tf.Output, method tf.Output, request tf.Output, optional ...TryRpcAttr) (response tf.Output, status_code tf.Output, status_message tf.Output) {
+ if scope.Err() != nil {
+ return
+ }
+ attrs := map[string]interface{}{}
+ for _, a := range optional {
+ a(attrs)
+ }
+ opspec := tf.OpSpec{
+ Type: "TryRpc",
+ Input: []tf.Input{
+ address, method, request,
+ },
+ Attrs: attrs,
+ }
+ op := scope.AddOperation(opspec)
+ return op.Output(0), op.Output(1), op.Output(2)
+}
+
// EnterAttr is an optional argument to Enter.
type EnterAttr func(optionalAttr)
return sparse_indices, sparse_values, sparse_shapes, dense_values
}
+// Deserializes a serialized tree ensemble config and replaces current tree
+//
+// ensemble.
+//
+// Arguments:
+// tree_ensemble_handle: Handle to the tree ensemble.
+// stamp_token: Token to use as the new value of the resource stamp.
+// tree_ensemble_serialized: Serialized proto of the ensemble.
+//
+// Returns the created operation.
+func BoostedTreesDeserializeEnsemble(scope *Scope, tree_ensemble_handle tf.Output, stamp_token tf.Output, tree_ensemble_serialized tf.Output) (o *tf.Operation) {
+ if scope.Err() != nil {
+ return
+ }
+ opspec := tf.OpSpec{
+ Type: "BoostedTreesDeserializeEnsemble",
+ Input: []tf.Input{
+ tree_ensemble_handle, stamp_token, tree_ensemble_serialized,
+ },
+ }
+ return scope.AddOperation(opspec)
+}
+
+// Runs multiple additive regression ensemble predictors on input instances and
+//
+// computes the update to cached logits. It is designed to be used during training.
+// It traverses the trees starting from cached tree id and cached node id and
+// calculates the updates to be pushed to the cache.
+//
+// Arguments:
+//
+// cached_tree_ids: Rank 1 Tensor containing cached tree ids which is the starting
+// tree of prediction.
+// cached_node_ids: Rank 1 Tensor containing cached node id which is the starting
+// node of prediction.
+// bucketized_features: A list of rank 1 Tensors containing bucket id for each
+// feature.
+// logits_dimension: scalar, dimension of the logits, to be used for partial logits
+// shape.
+//
+// Returns Rank 2 Tensor containing logits update (with respect to cached
+// values stored) for each example.Rank 1 Tensor containing new tree ids for each example.Rank 1 Tensor containing new node ids in the new tree_ids.
+func BoostedTreesTrainingPredict(scope *Scope, tree_ensemble_handle tf.Output, cached_tree_ids tf.Output, cached_node_ids tf.Output, bucketized_features []tf.Output, logits_dimension int64) (partial_logits tf.Output, tree_ids tf.Output, node_ids tf.Output) {
+ if scope.Err() != nil {
+ return
+ }
+ attrs := map[string]interface{}{"logits_dimension": logits_dimension}
+ opspec := tf.OpSpec{
+ Type: "BoostedTreesTrainingPredict",
+ Input: []tf.Input{
+ tree_ensemble_handle, cached_tree_ids, cached_node_ids, tf.OutputList(bucketized_features),
+ },
+ Attrs: attrs,
+ }
+ op := scope.AddOperation(opspec)
+ 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
return op.Output(0)
}
+// TensorListStackAttr is an optional argument to TensorListStack.
+type TensorListStackAttr func(optionalAttr)
+
+// TensorListStackNumElements sets the optional num_elements attribute to value.
+// If not specified, defaults to -1
+func TensorListStackNumElements(value int64) TensorListStackAttr {
+ return func(m optionalAttr) {
+ m["num_elements"] = value
+ }
+}
+
+// Stacks all tensors in the list.
+//
+// Requires that all tensors have the same shape.
+//
+// input_handle: the input list
+// tensor: the gathered result
+// num_elements: optional. If not -1, the number of elements in the list.
+//
+func TensorListStack(scope *Scope, input_handle tf.Output, element_dtype tf.DataType, optional ...TensorListStackAttr) (tensor tf.Output) {
+ if scope.Err() != nil {
+ return
+ }
+ attrs := map[string]interface{}{"element_dtype": element_dtype}
+ for _, a := range optional {
+ a(attrs)
+ }
+ opspec := tf.OpSpec{
+ Type: "TensorListStack",
+ Input: []tf.Input{
+ input_handle,
+ },
+ Attrs: attrs,
+ }
+ 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
return op.Output(0)
}
+// BatchAttr is an optional argument to Batch.
+type BatchAttr func(optionalAttr)
+
+// BatchMaxEnqueuedBatches sets the optional max_enqueued_batches attribute to value.
+// If not specified, defaults to 10
+func BatchMaxEnqueuedBatches(value int64) BatchAttr {
+ return func(m optionalAttr) {
+ m["max_enqueued_batches"] = value
+ }
+}
+
+// BatchAllowedBatchSizes sets the optional allowed_batch_sizes attribute to value.
+// If not specified, defaults to <>
+func BatchAllowedBatchSizes(value []int64) BatchAttr {
+ return func(m optionalAttr) {
+ m["allowed_batch_sizes"] = value
+ }
+}
+
+// BatchContainer sets the optional container attribute to value.
+// If not specified, defaults to ""
+func BatchContainer(value string) BatchAttr {
+ return func(m optionalAttr) {
+ m["container"] = value
+ }
+}
+
+// BatchSharedName sets the optional shared_name attribute to value.
+// If not specified, defaults to ""
+func BatchSharedName(value string) BatchAttr {
+ return func(m optionalAttr) {
+ m["shared_name"] = value
+ }
+}
+
+// BatchBatchingQueue sets the optional batching_queue attribute to value.
+// If not specified, defaults to ""
+func BatchBatchingQueue(value string) BatchAttr {
+ return func(m optionalAttr) {
+ m["batching_queue"] = value
+ }
+}
+
+// Batches all input tensors nondeterministically.
+//
+// When many instances of this Op are being run concurrently with the same
+// container/shared_name in the same device, some will output zero-shaped Tensors
+// and others will output Tensors of size up to max_batch_size.
+//
+// All Tensors in in_tensors are batched together (so, for example, labels and
+// features should be batched with a single instance of this operation.
+//
+// Each invocation of batch emits an `id` scalar which will be used to identify
+// this particular invocation when doing unbatch or its gradient.
+//
+// Each op which emits a non-empty batch will also emit a non-empty batch_index
+// Tensor, which, is a [K, 3] matrix where each row contains the invocation's id,
+// start, and length of elements of each set of Tensors present in batched_tensors.
+//
+// Batched tensors are concatenated along the first dimension, and all tensors in
+// in_tensors must have the first dimension of the same size.
+//
+// in_tensors: The tensors to be batched.
+// num_batch_threads: Number of scheduling threads for processing batches of work.
+// Determines the number of batches processed in parallel.
+// max_batch_size: Batch sizes will never be bigger than this.
+// batch_timeout_micros: Maximum number of microseconds to wait before outputting
+// an incomplete batch.
+// allowed_batch_sizes: Optional list of allowed batch sizes. If left empty, does
+// nothing. Otherwise, supplies a list of batch sizes, causing the op to pad
+// batches up to one of those sizes. The entries must increase monotonically, and
+// the final entry must equal max_batch_size.
+// grad_timeout_micros: The timeout to use for the gradient. See Unbatch.
+// batched_tensors: Either empty tensors or a batch of concatenated Tensors.
+// batch_index: If out_tensors is non-empty, has information to invert it.
+// container: Controls the scope of sharing of this batch.
+// id: always contains a scalar with a unique ID for this invocation of Batch.
+// shared_name: Concurrently running instances of batch in the same device with the
+// same container and shared_name will batch their elements together. If left
+// empty, the op name will be used as the shared name.
+// T: the types of tensors to be batched.
+func Batch(scope *Scope, in_tensors []tf.Output, num_batch_threads int64, max_batch_size int64, batch_timeout_micros int64, grad_timeout_micros int64, optional ...BatchAttr) (batched_tensors []tf.Output, batch_index tf.Output, id tf.Output) {
+ if scope.Err() != nil {
+ return
+ }
+ attrs := map[string]interface{}{"num_batch_threads": num_batch_threads, "max_batch_size": max_batch_size, "batch_timeout_micros": batch_timeout_micros, "grad_timeout_micros": grad_timeout_micros}
+ for _, a := range optional {
+ a(attrs)
+ }
+ opspec := tf.OpSpec{
+ Type: "Batch",
+ Input: []tf.Input{
+ tf.OutputList(in_tensors),
+ },
+ Attrs: attrs,
+ }
+ op := scope.AddOperation(opspec)
+ if scope.Err() != nil {
+ return
+ }
+ var idx int
+ var err error
+ if batched_tensors, idx, err = makeOutputList(op, idx, "batched_tensors"); err != nil {
+ scope.UpdateErr("Batch", err)
+ return
+ }
+ batch_index = op.Output(idx)
+ id = op.Output(idx)
+ return batched_tensors, batch_index, id
+}
+
+// UnbatchAttr is an optional argument to Unbatch.
+type UnbatchAttr func(optionalAttr)
+
+// UnbatchContainer sets the optional container attribute to value.
+// If not specified, defaults to ""
+func UnbatchContainer(value string) UnbatchAttr {
+ return func(m optionalAttr) {
+ m["container"] = value
+ }
+}
+
+// UnbatchSharedName sets the optional shared_name attribute to value.
+// If not specified, defaults to ""
+func UnbatchSharedName(value string) UnbatchAttr {
+ return func(m optionalAttr) {
+ m["shared_name"] = value
+ }
+}
+
+// Reverses the operation of Batch for a single output Tensor.
+//
+// An instance of Unbatch either receives an empty batched_tensor, in which case it
+// asynchronously waits until the values become available from a concurrently
+// running instance of Unbatch with the same container and shared_name, or receives
+// a non-empty batched_tensor in which case it finalizes all other concurrently
+// running instances and outputs its own element from the batch.
+//
+// batched_tensor: The possibly transformed output of Batch. The size of the first
+// dimension should remain unchanged by the transformations for the operation to
+// work.
+// batch_index: The matching batch_index obtained from Batch.
+// id: The id scalar emitted by Batch.
+// unbatched_tensor: The Tensor corresponding to this execution.
+// timeout_micros: Maximum amount of time (in microseconds) to wait to receive the
+// batched input tensor associated with a given invocation of the op.
+// container: Container to control resource sharing.
+// shared_name: Instances of Unbatch with the same container and shared_name are
+// assumed to possibly belong to the same batch. If left empty, the op name will
+// be used as the shared name.
+func Unbatch(scope *Scope, batched_tensor tf.Output, batch_index tf.Output, id tf.Output, timeout_micros int64, optional ...UnbatchAttr) (unbatched_tensor tf.Output) {
+ if scope.Err() != nil {
+ return
+ }
+ attrs := map[string]interface{}{"timeout_micros": timeout_micros}
+ for _, a := range optional {
+ a(attrs)
+ }
+ opspec := tf.OpSpec{
+ Type: "Unbatch",
+ Input: []tf.Input{
+ batched_tensor, batch_index, id,
+ },
+ Attrs: attrs,
+ }
+ op := scope.AddOperation(opspec)
+ return op.Output(0)
+}
+
// AvgPool3DGradAttr is an optional argument to AvgPool3DGrad.
type AvgPool3DGradAttr func(optionalAttr)
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
}
+// UnbatchGradAttr is an optional argument to UnbatchGrad.
+type UnbatchGradAttr func(optionalAttr)
+
+// UnbatchGradContainer sets the optional container attribute to value.
+// If not specified, defaults to ""
+func UnbatchGradContainer(value string) UnbatchGradAttr {
+ return func(m optionalAttr) {
+ m["container"] = value
+ }
+}
+
+// UnbatchGradSharedName sets the optional shared_name attribute to value.
+// If not specified, defaults to ""
+func UnbatchGradSharedName(value string) UnbatchGradAttr {
+ return func(m optionalAttr) {
+ m["shared_name"] = value
+ }
+}
+
+// Gradient of Unbatch.
+//
+// Acts like Batch but using the given batch_index index of batching things as they
+// become available. This ensures that the gradients are propagated back in the
+// same session which did the forward pass.
+//
+// original_input: The input to the Unbatch operation this is the gradient of.
+// batch_index: The batch_index given to the Unbatch operation this is the gradient
+// of.
+// grad: The downstream gradient.
+// id: The id scalar emitted by Batch.
+// batched_grad: The return value, either an empty tensor or the batched gradient.
+// container: Container to control resource sharing.
+// shared_name: Instances of UnbatchGrad with the same container and shared_name
+// are assumed to possibly belong to the same batch. If left empty, the op name
+// will be used as the shared name.
+func UnbatchGrad(scope *Scope, original_input tf.Output, batch_index tf.Output, grad tf.Output, id tf.Output, optional ...UnbatchGradAttr) (batched_grad tf.Output) {
+ if scope.Err() != nil {
+ return
+ }
+ attrs := map[string]interface{}{}
+ for _, a := range optional {
+ a(attrs)
+ }
+ opspec := tf.OpSpec{
+ Type: "UnbatchGrad",
+ Input: []tf.Input{
+ original_input, batch_index, grad, id,
+ },
+ Attrs: attrs,
+ }
+ op := scope.AddOperation(opspec)
+ return op.Output(0)
+}
+
// DecodeWavAttr is an optional argument to DecodeWav.
type DecodeWavAttr func(optionalAttr)
return op.Output(0)
}
+// Subtracts `v` into specified rows of `x`.
+//
+// Computes y = x; y[i, :] -= v; return y.
+//
+// Arguments:
+// x: A `Tensor` of type T.
+// i: A vector. Indices into the left-most dimension of `x`.
+// v: A `Tensor` of type T. Same dimension sizes as x except the first dimension, which must be the same as i's size.
+//
+// Returns A `Tensor` of type T. An alias of `x`. The content of `y` is undefined if there are duplicates in `i`.
+func InplaceSub(scope *Scope, x tf.Output, i tf.Output, v tf.Output) (y tf.Output) {
+ if scope.Err() != nil {
+ return
+ }
+ opspec := tf.OpSpec{
+ Type: "InplaceSub",
+ Input: []tf.Input{
+ x, i, v,
+ },
+ }
+ op := scope.AddOperation(opspec)
+ return op.Output(0)
+}
+
+// Converts a flat index or array of flat indices into a tuple of
+//
+// coordinate arrays.
+//
+// @compatibility(numpy)
+// Equivalent to np.unravel_index
+// @end_compatibility
+//
+// Arguments:
+// indices: An 0-D or 1-D `int` Tensor whose elements are indices into the
+// flattened version of an array of dimensions dims.
+// dims: An 1-D `int` Tensor. The shape of the array to use for unraveling
+// indices.
+//
+// Returns An 2-D (or 1-D if indices is 0-D) tensor where each row has the
+// same shape as the indices array.
+func UnravelIndex(scope *Scope, indices tf.Output, dims tf.Output) (output tf.Output) {
+ if scope.Err() != nil {
+ return
+ }
+ opspec := tf.OpSpec{
+ Type: "UnravelIndex",
+ Input: []tf.Input{
+ indices, dims,
+ },
+ }
+ 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:
//
// indices.shape[:-1] + params.shape[indices.shape[-1]:]
//
+// Note that on CPU, if an out of bound index is found, an error is returned.
+// On GPU, if an out of bound index is found, a 0 is stored in the
+// corresponding output value.
+//
// Some examples below.
//
// Simple indexing into a matrix:
projects / platform / upstream / tensorflow.git / commitdiff