diff options
author | A. Unique TensorFlower <gardener@tensorflow.org> | 2018-05-04 17:46:13 -0700 |
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committer | TensorFlower Gardener <gardener@tensorflow.org> | 2018-05-05 08:38:38 -0700 |
commit | b81f2dcaaa2559880c7705fb0ea91d0d825fd59c (patch) | |
tree | 9fb8da3508131c81b4d5cd9e2d33e0da9adf8b2a /tensorflow/go | |
parent | dedcf1dc3f59416fb2eaebb4fc2e5470fc2ae647 (diff) |
Go: Update generated wrapper functions for TensorFlow ops.
PiperOrigin-RevId: 195499636
Diffstat (limited to 'tensorflow/go')
-rw-r--r-- | tensorflow/go/op/wrappers.go | 262 |
1 files changed, 131 insertions, 131 deletions
diff --git a/tensorflow/go/op/wrappers.go b/tensorflow/go/op/wrappers.go index c12ea51563..eed6dac071 100644 --- a/tensorflow/go/op/wrappers.go +++ b/tensorflow/go/op/wrappers.go @@ -2544,6 +2544,72 @@ func EditDistance(scope *Scope, hypothesis_indices tf.Output, hypothesis_values return op.Output(0) } +// Reverses specific dimensions of a tensor. +// +// Given a `tensor`, and a `bool` tensor `dims` representing the dimensions +// of `tensor`, this operation reverses each dimension i of `tensor` where +// `dims[i]` is `True`. +// +// `tensor` can have up to 8 dimensions. The number of dimensions +// of `tensor` must equal the number of elements in `dims`. In other words: +// +// `rank(tensor) = size(dims)` +// +// For example: +// +// ``` +// # tensor 't' is [[[[ 0, 1, 2, 3], +// # [ 4, 5, 6, 7], +// # [ 8, 9, 10, 11]], +// # [[12, 13, 14, 15], +// # [16, 17, 18, 19], +// # [20, 21, 22, 23]]]] +// # tensor 't' shape is [1, 2, 3, 4] +// +// # 'dims' is [False, False, False, True] +// reverse(t, dims) ==> [[[[ 3, 2, 1, 0], +// [ 7, 6, 5, 4], +// [ 11, 10, 9, 8]], +// [[15, 14, 13, 12], +// [19, 18, 17, 16], +// [23, 22, 21, 20]]]] +// +// # 'dims' is [False, True, False, False] +// reverse(t, dims) ==> [[[[12, 13, 14, 15], +// [16, 17, 18, 19], +// [20, 21, 22, 23] +// [[ 0, 1, 2, 3], +// [ 4, 5, 6, 7], +// [ 8, 9, 10, 11]]]] +// +// # 'dims' is [False, False, True, False] +// reverse(t, dims) ==> [[[[8, 9, 10, 11], +// [4, 5, 6, 7], +// [0, 1, 2, 3]] +// [[20, 21, 22, 23], +// [16, 17, 18, 19], +// [12, 13, 14, 15]]]] +// ``` +// +// Arguments: +// tensor: Up to 8-D. +// dims: 1-D. The dimensions to reverse. +// +// Returns The same shape as `tensor`. +func Reverse(scope *Scope, tensor tf.Output, dims tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Reverse", + Input: []tf.Input{ + tensor, dims, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + // Clips tensor values to a specified min and max. // // Given a tensor `t`, this operation returns a tensor of the same type and @@ -2796,71 +2862,6 @@ func Asin(scope *Scope, x tf.Output) (y tf.Output) { return op.Output(0) } -// SparseToDenseAttr is an optional argument to SparseToDense. -type SparseToDenseAttr func(optionalAttr) - -// SparseToDenseValidateIndices sets the optional validate_indices attribute to value. -// -// value: If true, indices are checked to make sure they are sorted in -// lexicographic order and that there are no repeats. -// If not specified, defaults to true -func SparseToDenseValidateIndices(value bool) SparseToDenseAttr { - return func(m optionalAttr) { - m["validate_indices"] = value - } -} - -// Converts a sparse representation into a dense tensor. -// -// Builds an array `dense` with shape `output_shape` such that -// -// ``` -// # If sparse_indices is scalar -// dense[i] = (i == sparse_indices ? sparse_values : default_value) -// -// # If sparse_indices is a vector, then for each i -// dense[sparse_indices[i]] = sparse_values[i] -// -// # If sparse_indices is an n by d matrix, then for each i in [0, n) -// dense[sparse_indices[i][0], ..., sparse_indices[i][d-1]] = sparse_values[i] -// ``` -// -// All other values in `dense` are set to `default_value`. If `sparse_values` is a -// scalar, all sparse indices are set to this single value. -// -// Indices should be sorted in lexicographic order, and indices must not -// contain any repeats. If `validate_indices` is true, these properties -// are checked during execution. -// -// Arguments: -// sparse_indices: 0-D, 1-D, or 2-D. `sparse_indices[i]` contains the complete -// index where `sparse_values[i]` will be placed. -// output_shape: 1-D. Shape of the dense output tensor. -// sparse_values: 1-D. Values corresponding to each row of `sparse_indices`, -// or a scalar value to be used for all sparse indices. -// default_value: Scalar value to set for indices not specified in -// `sparse_indices`. -// -// Returns Dense output tensor of shape `output_shape`. -func SparseToDense(scope *Scope, sparse_indices tf.Output, output_shape tf.Output, sparse_values tf.Output, default_value tf.Output, optional ...SparseToDenseAttr) (dense tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "SparseToDense", - Input: []tf.Input{ - sparse_indices, output_shape, sparse_values, default_value, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - // Computes the sum along sparse segments of a tensor. // // Read @{$math_ops#Segmentation$the section on segmentation} for an explanation of @@ -6469,72 +6470,6 @@ func SparseFillEmptyRows(scope *Scope, indices tf.Output, values tf.Output, dens return op.Output(0), op.Output(1), op.Output(2), op.Output(3) } -// Reverses specific dimensions of a tensor. -// -// Given a `tensor`, and a `bool` tensor `dims` representing the dimensions -// of `tensor`, this operation reverses each dimension i of `tensor` where -// `dims[i]` is `True`. -// -// `tensor` can have up to 8 dimensions. The number of dimensions -// of `tensor` must equal the number of elements in `dims`. In other words: -// -// `rank(tensor) = size(dims)` -// -// For example: -// -// ``` -// # tensor 't' is [[[[ 0, 1, 2, 3], -// # [ 4, 5, 6, 7], -// # [ 8, 9, 10, 11]], -// # [[12, 13, 14, 15], -// # [16, 17, 18, 19], -// # [20, 21, 22, 23]]]] -// # tensor 't' shape is [1, 2, 3, 4] -// -// # 'dims' is [False, False, False, True] -// reverse(t, dims) ==> [[[[ 3, 2, 1, 0], -// [ 7, 6, 5, 4], -// [ 11, 10, 9, 8]], -// [[15, 14, 13, 12], -// [19, 18, 17, 16], -// [23, 22, 21, 20]]]] -// -// # 'dims' is [False, True, False, False] -// reverse(t, dims) ==> [[[[12, 13, 14, 15], -// [16, 17, 18, 19], -// [20, 21, 22, 23] -// [[ 0, 1, 2, 3], -// [ 4, 5, 6, 7], -// [ 8, 9, 10, 11]]]] -// -// # 'dims' is [False, False, True, False] -// reverse(t, dims) ==> [[[[8, 9, 10, 11], -// [4, 5, 6, 7], -// [0, 1, 2, 3]] -// [[20, 21, 22, 23], -// [16, 17, 18, 19], -// [12, 13, 14, 15]]]] -// ``` -// -// Arguments: -// tensor: Up to 8-D. -// dims: 1-D. The dimensions to reverse. -// -// Returns The same shape as `tensor`. -func Reverse(scope *Scope, tensor tf.Output, dims tf.Output) (output tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "Reverse", - Input: []tf.Input{ - tensor, dims, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - // BiasAddGradAttr is an optional argument to BiasAddGrad. type BiasAddGradAttr func(optionalAttr) @@ -24884,6 +24819,71 @@ func DecodeJSONExample(scope *Scope, json_examples tf.Output) (binary_examples t return op.Output(0) } +// SparseToDenseAttr is an optional argument to SparseToDense. +type SparseToDenseAttr func(optionalAttr) + +// SparseToDenseValidateIndices sets the optional validate_indices attribute to value. +// +// value: If true, indices are checked to make sure they are sorted in +// lexicographic order and that there are no repeats. +// If not specified, defaults to true +func SparseToDenseValidateIndices(value bool) SparseToDenseAttr { + return func(m optionalAttr) { + m["validate_indices"] = value + } +} + +// Converts a sparse representation into a dense tensor. +// +// Builds an array `dense` with shape `output_shape` such that +// +// ``` +// # If sparse_indices is scalar +// dense[i] = (i == sparse_indices ? sparse_values : default_value) +// +// # If sparse_indices is a vector, then for each i +// dense[sparse_indices[i]] = sparse_values[i] +// +// # If sparse_indices is an n by d matrix, then for each i in [0, n) +// dense[sparse_indices[i][0], ..., sparse_indices[i][d-1]] = sparse_values[i] +// ``` +// +// All other values in `dense` are set to `default_value`. If `sparse_values` is a +// scalar, all sparse indices are set to this single value. +// +// Indices should be sorted in lexicographic order, and indices must not +// contain any repeats. If `validate_indices` is true, these properties +// are checked during execution. +// +// Arguments: +// sparse_indices: 0-D, 1-D, or 2-D. `sparse_indices[i]` contains the complete +// index where `sparse_values[i]` will be placed. +// output_shape: 1-D. Shape of the dense output tensor. +// sparse_values: 1-D. Values corresponding to each row of `sparse_indices`, +// or a scalar value to be used for all sparse indices. +// default_value: Scalar value to set for indices not specified in +// `sparse_indices`. +// +// Returns Dense output tensor of shape `output_shape`. +func SparseToDense(scope *Scope, sparse_indices tf.Output, output_shape tf.Output, sparse_values tf.Output, default_value tf.Output, optional ...SparseToDenseAttr) (dense tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "SparseToDense", + Input: []tf.Input{ + sparse_indices, output_shape, sparse_values, default_value, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + // Computes the grayscale dilation of 4-D `input` and 3-D `filter` tensors. // // The `input` tensor has shape `[batch, in_height, in_width, depth]` and the |