diff options
author | A. Unique TensorFlower <gardener@tensorflow.org> | 2018-03-15 17:46:39 -0700 |
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committer | TensorFlower Gardener <gardener@tensorflow.org> | 2018-03-15 17:51:01 -0700 |
commit | 2dd9a59d292c1226d03a1fa5bd551e5ccc5e6f9c (patch) | |
tree | c6665ae5966e481b765d7866f76a79732cbdc1c8 /tensorflow/go | |
parent | f4719f33c0507e0e216f8370898bf56397654d1e (diff) |
Go: Update generated wrapper functions for TensorFlow ops.
PiperOrigin-RevId: 189274859
Diffstat (limited to 'tensorflow/go')
-rw-r--r-- | tensorflow/go/op/wrappers.go | 190 |
1 files changed, 95 insertions, 95 deletions
diff --git a/tensorflow/go/op/wrappers.go b/tensorflow/go/op/wrappers.go index 0424c12fd9..469d1e9adb 100644 --- a/tensorflow/go/op/wrappers.go +++ b/tensorflow/go/op/wrappers.go @@ -509,101 +509,6 @@ func FakeQuantWithMinMaxArgs(scope *Scope, inputs tf.Output, optional ...FakeQua return op.Output(0) } -// Scatter `updates` into a new (initially zero) tensor according to `indices`. -// -// Creates a new tensor by applying sparse `updates` to individual -// values or slices within a zero tensor of the given `shape` according to -// indices. This operator is the inverse of the @{tf.gather_nd} operator which -// extracts values or slices from a given tensor. -// -// **WARNING**: The order in which updates are applied is nondeterministic, so the -// output will be nondeterministic if `indices` contains duplicates. -// -// `indices` is an integer tensor containing indices into a new tensor of shape -// `shape`. The last dimension of `indices` can be at most the rank of `shape`: -// -// indices.shape[-1] <= shape.rank -// -// The last dimension of `indices` corresponds to indices into elements -// (if `indices.shape[-1] = shape.rank`) or slices -// (if `indices.shape[-1] < shape.rank`) along dimension `indices.shape[-1]` of -// `shape`. `updates` is a tensor with shape -// -// indices.shape[:-1] + shape[indices.shape[-1]:] -// -// The simplest form of scatter is to insert individual elements in a tensor by -// index. For example, say we want to insert 4 scattered elements in a rank-1 -// tensor with 8 elements. -// -// <div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;"> -// <img style="width:100%" src="https://www.tensorflow.org/images/ScatterNd1.png" alt> -// </div> -// -// In Python, this scatter operation would look like this: -// -// ```python -// indices = tf.constant([[4], [3], [1], [7]]) -// updates = tf.constant([9, 10, 11, 12]) -// shape = tf.constant([8]) -// scatter = tf.scatter_nd(indices, updates, shape) -// with tf.Session() as sess: -// print(sess.run(scatter)) -// ``` -// -// The resulting tensor would look like this: -// -// [0, 11, 0, 10, 9, 0, 0, 12] -// -// We can also, insert entire slices of a higher rank tensor all at once. For -// example, if we wanted to insert two slices in the first dimension of a -// rank-3 tensor with two matrices of new values. -// -// <div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;"> -// <img style="width:100%" src="https://www.tensorflow.org/images/ScatterNd2.png" alt> -// </div> -// -// In Python, this scatter operation would look like this: -// -// ```python -// indices = tf.constant([[0], [2]]) -// updates = tf.constant([[[5, 5, 5, 5], [6, 6, 6, 6], -// [7, 7, 7, 7], [8, 8, 8, 8]], -// [[5, 5, 5, 5], [6, 6, 6, 6], -// [7, 7, 7, 7], [8, 8, 8, 8]]]) -// shape = tf.constant([4, 4, 4]) -// scatter = tf.scatter_nd(indices, updates, shape) -// with tf.Session() as sess: -// print(sess.run(scatter)) -// ``` -// -// The resulting tensor would look like this: -// -// [[[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]], -// [[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]]] -// -// Arguments: -// indices: Index tensor. -// updates: Updates to scatter into output. -// shape: 1-D. The shape of the resulting tensor. -// -// Returns A new tensor with the given shape and updates applied according -// to the indices. -func ScatterNd(scope *Scope, indices tf.Output, updates tf.Output, shape tf.Output) (output tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "ScatterNd", - Input: []tf.Input{ - indices, updates, shape, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - // Replaces the contents of the table with the specified keys and values. // // The tensor `keys` must be of the same type as the keys of the table. @@ -25067,6 +24972,101 @@ func Snapshot(scope *Scope, input tf.Output) (output tf.Output) { return op.Output(0) } +// Scatter `updates` into a new (initially zero) tensor according to `indices`. +// +// Creates a new tensor by applying sparse `updates` to individual +// values or slices within a zero tensor of the given `shape` according to +// indices. This operator is the inverse of the @{tf.gather_nd} operator which +// extracts values or slices from a given tensor. +// +// **WARNING**: The order in which updates are applied is nondeterministic, so the +// output will be nondeterministic if `indices` contains duplicates. +// +// `indices` is an integer tensor containing indices into a new tensor of shape +// `shape`. The last dimension of `indices` can be at most the rank of `shape`: +// +// indices.shape[-1] <= shape.rank +// +// The last dimension of `indices` corresponds to indices into elements +// (if `indices.shape[-1] = shape.rank`) or slices +// (if `indices.shape[-1] < shape.rank`) along dimension `indices.shape[-1]` of +// `shape`. `updates` is a tensor with shape +// +// indices.shape[:-1] + shape[indices.shape[-1]:] +// +// The simplest form of scatter is to insert individual elements in a tensor by +// index. For example, say we want to insert 4 scattered elements in a rank-1 +// tensor with 8 elements. +// +// <div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;"> +// <img style="width:100%" src="https://www.tensorflow.org/images/ScatterNd1.png" alt> +// </div> +// +// In Python, this scatter operation would look like this: +// +// ```python +// indices = tf.constant([[4], [3], [1], [7]]) +// updates = tf.constant([9, 10, 11, 12]) +// shape = tf.constant([8]) +// scatter = tf.scatter_nd(indices, updates, shape) +// with tf.Session() as sess: +// print(sess.run(scatter)) +// ``` +// +// The resulting tensor would look like this: +// +// [0, 11, 0, 10, 9, 0, 0, 12] +// +// We can also, insert entire slices of a higher rank tensor all at once. For +// example, if we wanted to insert two slices in the first dimension of a +// rank-3 tensor with two matrices of new values. +// +// <div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;"> +// <img style="width:100%" src="https://www.tensorflow.org/images/ScatterNd2.png" alt> +// </div> +// +// In Python, this scatter operation would look like this: +// +// ```python +// indices = tf.constant([[0], [2]]) +// updates = tf.constant([[[5, 5, 5, 5], [6, 6, 6, 6], +// [7, 7, 7, 7], [8, 8, 8, 8]], +// [[5, 5, 5, 5], [6, 6, 6, 6], +// [7, 7, 7, 7], [8, 8, 8, 8]]]) +// shape = tf.constant([4, 4, 4]) +// scatter = tf.scatter_nd(indices, updates, shape) +// with tf.Session() as sess: +// print(sess.run(scatter)) +// ``` +// +// The resulting tensor would look like this: +// +// [[[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]], +// [[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]]] +// +// Arguments: +// indices: Index tensor. +// updates: Updates to scatter into output. +// shape: 1-D. The shape of the resulting tensor. +// +// Returns A new tensor with the given shape and updates applied according +// to the indices. +func ScatterNd(scope *Scope, indices tf.Output, updates tf.Output, shape tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "ScatterNd", + Input: []tf.Input{ + indices, updates, shape, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + // SpaceToDepthAttr is an optional argument to SpaceToDepth. type SpaceToDepthAttr func(optionalAttr) |