return list, start + size, nil
}
-// Writes a `Summary` protocol buffer with scalar values.
-//
-// The input `tag` and `value` must have the scalars.
-//
-// Arguments:
-// writer: A handle to a summary writer.
-// step: The step to write the summary for.
-// tag: Tag for the summary.
-// value: Value for the summary.
-//
-// Returns the created operation.
-func WriteScalarSummary(scope *Scope, writer tf.Output, step tf.Output, tag tf.Output, value tf.Output) (o *tf.Operation) {
- if scope.Err() != nil {
- return
- }
- opspec := tf.OpSpec{
- Type: "WriteScalarSummary",
- Input: []tf.Input{
- writer, step, tag, value,
- },
- }
- return scope.AddOperation(opspec)
-}
-
// Outputs a `tf.Event` protocol buffer.
//
// When CreateSummaryDbWriter is being used, this op can be useful for
return op.Output(0)
}
+// Writes a `Summary` protocol buffer with scalar values.
+//
+// The input `tag` and `value` must have the scalars.
+//
+// Arguments:
+// writer: A handle to a summary writer.
+// step: The step to write the summary for.
+// tag: Tag for the summary.
+// value: Value for the summary.
+//
+// Returns the created operation.
+func WriteScalarSummary(scope *Scope, writer tf.Output, step tf.Output, tag tf.Output, value tf.Output) (o *tf.Operation) {
+ if scope.Err() != nil {
+ return
+ }
+ opspec := tf.OpSpec{
+ Type: "WriteScalarSummary",
+ Input: []tf.Input{
+ writer, step, tag, value,
+ },
+ }
+ return scope.AddOperation(opspec)
+}
+
+// Transforms a tf.Example proto (as a string) into typed tensors.
+//
+// Arguments:
+// serialized: A vector containing a batch of binary serialized Example protos.
+// dense_defaults: A list of Tensors (some may be empty), whose length matches
+// the length of `dense_keys`. dense_defaults[j] provides default values
+// when the example's feature_map lacks dense_key[j]. If an empty Tensor is
+// provided for dense_defaults[j], then the Feature dense_keys[j] is required.
+// The input type is inferred from dense_defaults[j], even when it's empty.
+// If dense_defaults[j] is not empty, and dense_shapes[j] is fully defined,
+// then the shape of dense_defaults[j] must match that of dense_shapes[j].
+// If dense_shapes[j] has an undefined major dimension (variable strides dense
+// feature), dense_defaults[j] must contain a single element:
+// the padding element.
+// num_sparse: The number of sparse features to be parsed from the example. This
+// must match the lengths of `sparse_keys` and `sparse_types`.
+// sparse_keys: A list of `num_sparse` strings.
+// The keys expected in the Examples' features associated with sparse values.
+// dense_keys: The keys expected in the Examples' features associated with dense
+// values.
+// sparse_types: A list of `num_sparse` types; the data types of data in each
+// Feature given in sparse_keys.
+// Currently the ParseSingleExample op supports DT_FLOAT (FloatList),
+// DT_INT64 (Int64List), and DT_STRING (BytesList).
+// dense_shapes: The shapes of data in each Feature given in dense_keys.
+// The length of this list must match the length of `dense_keys`. The
+// number of elements in the Feature corresponding to dense_key[j] must
+// always equal dense_shapes[j].NumEntries(). If dense_shapes[j] ==
+// (D0, D1, ..., DN) then the shape of output Tensor dense_values[j]
+// will be (D0, D1, ..., DN): In the case dense_shapes[j] = (-1, D1,
+// ..., DN), the shape of the output Tensor dense_values[j] will be (M,
+// D1, .., DN), where M is the number of blocks of elements of length
+// D1 * .... * DN, in the input.
+func ParseSingleExample(scope *Scope, serialized tf.Output, dense_defaults []tf.Output, num_sparse int64, sparse_keys []string, dense_keys []string, sparse_types []tf.DataType, dense_shapes []tf.Shape) (sparse_indices []tf.Output, sparse_values []tf.Output, sparse_shapes []tf.Output, dense_values []tf.Output) {
+ if scope.Err() != nil {
+ return
+ }
+ attrs := map[string]interface{}{"num_sparse": num_sparse, "sparse_keys": sparse_keys, "dense_keys": dense_keys, "sparse_types": sparse_types, "dense_shapes": dense_shapes}
+ opspec := tf.OpSpec{
+ Type: "ParseSingleExample",
+ Input: []tf.Input{
+ serialized, tf.OutputList(dense_defaults),
+ },
+ Attrs: attrs,
+ }
+ op := scope.AddOperation(opspec)
+ if scope.Err() != nil {
+ return
+ }
+ var idx int
+ var err error
+ if sparse_indices, idx, err = makeOutputList(op, idx, "sparse_indices"); err != nil {
+ scope.UpdateErr("ParseSingleExample", err)
+ return
+ }
+ if sparse_values, idx, err = makeOutputList(op, idx, "sparse_values"); err != nil {
+ scope.UpdateErr("ParseSingleExample", err)
+ return
+ }
+ if sparse_shapes, idx, err = makeOutputList(op, idx, "sparse_shapes"); err != nil {
+ scope.UpdateErr("ParseSingleExample", err)
+ return
+ }
+ if dense_values, idx, err = makeOutputList(op, idx, "dense_values"); err != nil {
+ scope.UpdateErr("ParseSingleExample", err)
+ return
+ }
+ return sparse_indices, sparse_values, sparse_shapes, dense_values
+}
+
// Copy a tensor setting everything outside a central band in each innermost matrix
//
// to zero.
return op.Output(0), op.Output(1), op.Output(2)
}
+// InitializeTableFromTextFileV2Attr is an optional argument to InitializeTableFromTextFileV2.
+type InitializeTableFromTextFileV2Attr func(optionalAttr)
+
+// InitializeTableFromTextFileV2VocabSize sets the optional vocab_size attribute to value.
+//
+// value: Number of elements of the file, use -1 if unknown.
+// If not specified, defaults to -1
+//
+// REQUIRES: value >= -1
+func InitializeTableFromTextFileV2VocabSize(value int64) InitializeTableFromTextFileV2Attr {
+ return func(m optionalAttr) {
+ m["vocab_size"] = value
+ }
+}
+
+// InitializeTableFromTextFileV2Delimiter sets the optional delimiter attribute to value.
+//
+// value: Delimiter to separate fields in a line.
+// If not specified, defaults to "\t"
+func InitializeTableFromTextFileV2Delimiter(value string) InitializeTableFromTextFileV2Attr {
+ return func(m optionalAttr) {
+ m["delimiter"] = value
+ }
+}
+
+// Initializes a table from a text file.
+//
+// It inserts one key-value pair into the table for each line of the file.
+// The key and value is extracted from the whole line content, elements from the
+// split line based on `delimiter` or the line number (starting from zero).
+// Where to extract the key and value from a line is specified by `key_index` and
+// `value_index`.
+//
+// - A value of -1 means use the line number(starting from zero), expects `int64`.
+// - A value of -2 means use the whole line content, expects `string`.
+// - A value >= 0 means use the index (starting at zero) of the split line based
+// on `delimiter`.
+//
+// Arguments:
+// table_handle: Handle to a table which will be initialized.
+// filename: Filename of a vocabulary text file.
+// key_index: Column index in a line to get the table `key` values from.
+// value_index: Column index that represents information of a line to get the table
+// `value` values from.
+//
+// Returns the created operation.
+func InitializeTableFromTextFileV2(scope *Scope, table_handle tf.Output, filename tf.Output, key_index int64, value_index int64, optional ...InitializeTableFromTextFileV2Attr) (o *tf.Operation) {
+ if scope.Err() != nil {
+ return
+ }
+ attrs := map[string]interface{}{"key_index": key_index, "value_index": value_index}
+ for _, a := range optional {
+ a(attrs)
+ }
+ opspec := tf.OpSpec{
+ Type: "InitializeTableFromTextFileV2",
+ Input: []tf.Input{
+ table_handle, filename,
+ },
+ Attrs: attrs,
+ }
+ return scope.AddOperation(opspec)
+}
+
+// ResourceSparseApplyProximalGradientDescentAttr is an optional argument to ResourceSparseApplyProximalGradientDescent.
+type ResourceSparseApplyProximalGradientDescentAttr func(optionalAttr)
+
+// ResourceSparseApplyProximalGradientDescentUseLocking sets the optional use_locking attribute to value.
+//
+// value: If True, the subtraction will be protected by a lock;
+// otherwise the behavior is undefined, but may exhibit less contention.
+// If not specified, defaults to false
+func ResourceSparseApplyProximalGradientDescentUseLocking(value bool) ResourceSparseApplyProximalGradientDescentAttr {
+ return func(m optionalAttr) {
+ m["use_locking"] = value
+ }
+}
+
+// Sparse update '*var' as FOBOS algorithm with fixed learning rate.
+//
+// That is for rows we have grad for, we update var as follows:
+// prox_v = var - alpha * grad
+// var = sign(prox_v)/(1+alpha*l2) * max{|prox_v|-alpha*l1,0}
+//
+// Arguments:
+// var_: Should be from a Variable().
+// alpha: Scaling factor. Must be a scalar.
+// l1: L1 regularization. Must be a scalar.
+// l2: L2 regularization. Must be a scalar.
+// grad: The gradient.
+// indices: A vector of indices into the first dimension of var and accum.
+//
+// Returns the created operation.
+func ResourceSparseApplyProximalGradientDescent(scope *Scope, var_ tf.Output, alpha tf.Output, l1 tf.Output, l2 tf.Output, grad tf.Output, indices tf.Output, optional ...ResourceSparseApplyProximalGradientDescentAttr) (o *tf.Operation) {
+ if scope.Err() != nil {
+ return
+ }
+ attrs := map[string]interface{}{}
+ for _, a := range optional {
+ a(attrs)
+ }
+ opspec := tf.OpSpec{
+ Type: "ResourceSparseApplyProximalGradientDescent",
+ Input: []tf.Input{
+ var_, alpha, l1, l2, grad, indices,
+ },
+ Attrs: attrs,
+ }
+ return scope.AddOperation(opspec)
+}
+
// Records the bytes size of each element of `input_dataset` in a StatsAggregator.
func BytesProducedStatsDataset(scope *Scope, input_dataset tf.Output, tag tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) {
if scope.Err() != nil {
return op.Output(0)
}
+// Computes the Gauss error function of `x` element-wise.
+func Erf(scope *Scope, x tf.Output) (y tf.Output) {
+ if scope.Err() != nil {
+ return
+ }
+ opspec := tf.OpSpec{
+ Type: "Erf",
+ Input: []tf.Input{
+ x,
+ },
+ }
+ op := scope.AddOperation(opspec)
+ return op.Output(0)
+}
+
+// Returns element-wise largest integer not greater than x.
+func Floor(scope *Scope, x tf.Output) (y tf.Output) {
+ if scope.Err() != nil {
+ return
+ }
+ opspec := tf.OpSpec{
+ Type: "Floor",
+ Input: []tf.Input{
+ x,
+ },
+ }
+ op := scope.AddOperation(opspec)
+ return op.Output(0)
+}
+
// ThreadUnsafeUnigramCandidateSamplerAttr is an optional argument to ThreadUnsafeUnigramCandidateSampler.
type ThreadUnsafeUnigramCandidateSamplerAttr func(optionalAttr)
return op.Output(0)
}
-// Returns element-wise largest integer not greater than x.
-func Floor(scope *Scope, x tf.Output) (y tf.Output) {
- if scope.Err() != nil {
- return
- }
- opspec := tf.OpSpec{
- Type: "Floor",
- Input: []tf.Input{
- x,
- },
- }
- op := scope.AddOperation(opspec)
- return op.Output(0)
-}
-
-// Computes the Gauss error function of `x` element-wise.
-func Erf(scope *Scope, x tf.Output) (y tf.Output) {
- if scope.Err() != nil {
- return
- }
- opspec := tf.OpSpec{
- Type: "Erf",
- Input: []tf.Input{
- x,
- },
- }
- op := scope.AddOperation(opspec)
- return op.Output(0)
-}
-
// Gather slices from `params` axis `axis` according to `indices`.
//
// `indices` must be an integer tensor of any dimension (usually 0-D or 1-D).
return op.Output(0)
}
-// ResourceSparseApplyProximalGradientDescentAttr is an optional argument to ResourceSparseApplyProximalGradientDescent.
-type ResourceSparseApplyProximalGradientDescentAttr func(optionalAttr)
-
-// ResourceSparseApplyProximalGradientDescentUseLocking sets the optional use_locking attribute to value.
-//
-// value: If True, the subtraction will be protected by a lock;
-// otherwise the behavior is undefined, but may exhibit less contention.
-// If not specified, defaults to false
-func ResourceSparseApplyProximalGradientDescentUseLocking(value bool) ResourceSparseApplyProximalGradientDescentAttr {
- return func(m optionalAttr) {
- m["use_locking"] = value
- }
-}
-
-// Sparse update '*var' as FOBOS algorithm with fixed learning rate.
-//
-// That is for rows we have grad for, we update var as follows:
-// prox_v = var - alpha * grad
-// var = sign(prox_v)/(1+alpha*l2) * max{|prox_v|-alpha*l1,0}
-//
-// Arguments:
-// var_: Should be from a Variable().
-// alpha: Scaling factor. Must be a scalar.
-// l1: L1 regularization. Must be a scalar.
-// l2: L2 regularization. Must be a scalar.
-// grad: The gradient.
-// indices: A vector of indices into the first dimension of var and accum.
-//
-// Returns the created operation.
-func ResourceSparseApplyProximalGradientDescent(scope *Scope, var_ tf.Output, alpha tf.Output, l1 tf.Output, l2 tf.Output, grad tf.Output, indices tf.Output, optional ...ResourceSparseApplyProximalGradientDescentAttr) (o *tf.Operation) {
- if scope.Err() != nil {
- return
- }
- attrs := map[string]interface{}{}
- for _, a := range optional {
- a(attrs)
- }
- opspec := tf.OpSpec{
- Type: "ResourceSparseApplyProximalGradientDescent",
- Input: []tf.Input{
- var_, alpha, l1, l2, grad, indices,
- },
- Attrs: attrs,
- }
- return scope.AddOperation(opspec)
-}
-
-// InitializeTableFromTextFileV2Attr is an optional argument to InitializeTableFromTextFileV2.
-type InitializeTableFromTextFileV2Attr func(optionalAttr)
-
-// InitializeTableFromTextFileV2VocabSize sets the optional vocab_size attribute to value.
-//
-// value: Number of elements of the file, use -1 if unknown.
-// If not specified, defaults to -1
-//
-// REQUIRES: value >= -1
-func InitializeTableFromTextFileV2VocabSize(value int64) InitializeTableFromTextFileV2Attr {
- return func(m optionalAttr) {
- m["vocab_size"] = value
- }
-}
-
-// InitializeTableFromTextFileV2Delimiter sets the optional delimiter attribute to value.
-//
-// value: Delimiter to separate fields in a line.
-// If not specified, defaults to "\t"
-func InitializeTableFromTextFileV2Delimiter(value string) InitializeTableFromTextFileV2Attr {
- return func(m optionalAttr) {
- m["delimiter"] = value
- }
-}
-
-// Initializes a table from a text file.
-//
-// It inserts one key-value pair into the table for each line of the file.
-// The key and value is extracted from the whole line content, elements from the
-// split line based on `delimiter` or the line number (starting from zero).
-// Where to extract the key and value from a line is specified by `key_index` and
-// `value_index`.
-//
-// - A value of -1 means use the line number(starting from zero), expects `int64`.
-// - A value of -2 means use the whole line content, expects `string`.
-// - A value >= 0 means use the index (starting at zero) of the split line based
-// on `delimiter`.
-//
-// Arguments:
-// table_handle: Handle to a table which will be initialized.
-// filename: Filename of a vocabulary text file.
-// key_index: Column index in a line to get the table `key` values from.
-// value_index: Column index that represents information of a line to get the table
-// `value` values from.
-//
-// Returns the created operation.
-func InitializeTableFromTextFileV2(scope *Scope, table_handle tf.Output, filename tf.Output, key_index int64, value_index int64, optional ...InitializeTableFromTextFileV2Attr) (o *tf.Operation) {
- if scope.Err() != nil {
- return
- }
- attrs := map[string]interface{}{"key_index": key_index, "value_index": value_index}
- for _, a := range optional {
- a(attrs)
- }
- opspec := tf.OpSpec{
- Type: "InitializeTableFromTextFileV2",
- Input: []tf.Input{
- table_handle, filename,
- },
- Attrs: attrs,
- }
- return scope.AddOperation(opspec)
-}
-
// Computes atan of x element-wise.
func Atan(scope *Scope, x tf.Output) (y tf.Output) {
if scope.Err() != nil {
projects / platform / upstream / tensorflow.git / commitdiff