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diff --git a/tensorflow/g3doc/api_docs/python/array_ops.md b/tensorflow/g3doc/api_docs/python/array_ops.md new file mode 100644 index 0000000000..eecb442f1c --- /dev/null +++ b/tensorflow/g3doc/api_docs/python/array_ops.md @@ -0,0 +1,1025 @@ +<!-- This file is machine generated: DO NOT EDIT! --> + +# Tensor Transformations +<!-- TOC-BEGIN This section is generated by neural network: DO NOT EDIT! --> +## Contents +* [Casting](#AUTOGENERATED-casting) + * [tf.string_to_number(string_tensor, out_type=None, name=None)](#string_to_number) + * [tf.to_double(x, name='ToDouble')](#to_double) + * [tf.to_float(x, name='ToFloat')](#to_float) + * [tf.to_bfloat16(x, name='ToBFloat16')](#to_bfloat16) + * [tf.to_int32(x, name='ToInt32')](#to_int32) + * [tf.to_int64(x, name='ToInt64')](#to_int64) + * [tf.cast(x, dtype, name=None)](#cast) +* [Shapes and Shaping](#AUTOGENERATED-shapes-and-shaping) + * [tf.shape(input, name=None)](#shape) + * [tf.size(input, name=None)](#size) + * [tf.rank(input, name=None)](#rank) + * [tf.reshape(tensor, shape, name=None)](#reshape) + * [tf.squeeze(input, squeeze_dims=None, name=None)](#squeeze) + * [tf.expand_dims(input, dim, name=None)](#expand_dims) +* [Slicing and Joining](#AUTOGENERATED-slicing-and-joining) + * [tf.slice(input_, begin, size, name=None)](#slice) + * [tf.split(split_dim, num_split, value, name='split')](#split) + * [tf.tile(input, multiples, name=None)](#tile) + * [tf.pad(input, paddings, name=None)](#pad) + * [tf.concat(concat_dim, values, name='concat')](#concat) + * [tf.pack(values, name='pack')](#pack) + * [tf.unpack(value, num=None, name='unpack')](#unpack) + * [tf.reverse_sequence(input, seq_lengths, seq_dim, name=None)](#reverse_sequence) + * [tf.reverse(tensor, dims, name=None)](#reverse) + * [tf.transpose(a, perm=None, name='transpose')](#transpose) + * [tf.gather(params, indices, name=None)](#gather) + * [tf.dynamic_partition(data, partitions, num_partitions, name=None)](#dynamic_partition) + * [tf.dynamic_stitch(indices, data, name=None)](#dynamic_stitch) + + +<!-- TOC-END This section was generated by neural network, THANKS FOR READING! --> + +## Casting <div class="md-anchor" id="AUTOGENERATED-casting">{#AUTOGENERATED-casting}</div> + +TensorFlow provides several operations that you can use to cast tensor data +types in your graph. + +- - - + +### tf.string_to_number(string_tensor, out_type=None, name=None) <div class="md-anchor" id="string_to_number">{#string_to_number}</div> + +Converts each string in the input Tensor to the specified numeric type. + +(Note that int32 overflow results in an error while float overflow +results in a rounded value.) + +##### Args: + + +* <b>string_tensor</b>: A `Tensor` of type `string`. +* <b>out_type</b>: An optional `tf.DType` from: `tf.float32, tf.int32`. Defaults to `tf.float32`. + The numeric type to interpret each string in string_tensor as. +* <b>name</b>: A name for the operation (optional). + +##### Returns: + + A `Tensor` of type `out_type`. + A Tensor of the same shape as the input string_tensor. + + +- - - + +### tf.to_double(x, name='ToDouble') <div class="md-anchor" id="to_double">{#to_double}</div> + +Casts a tensor to type `float64`. + +##### Args: + + +* <b>x</b>: A `Tensor` or `SparseTensor`. +* <b>name</b>: A name for the operation (optional). + +##### Returns: + + A `Tensor` or `SparseTensor` with same shape as `x` with type `float64`. + +##### Raises: + + +* <b>TypeError</b>: If `x` cannot be cast to the `float64`. + + +- - - + +### tf.to_float(x, name='ToFloat') <div class="md-anchor" id="to_float">{#to_float}</div> + +Casts a tensor to type `float32`. + +##### Args: + + +* <b>x</b>: A `Tensor` or `SparseTensor`. +* <b>name</b>: A name for the operation (optional). + +##### Returns: + + A `Tensor` or `SparseTensor` with same shape as `x` with type `float32`. + +##### Raises: + + +* <b>TypeError</b>: If `x` cannot be cast to the `float32`. + + +- - - + +### tf.to_bfloat16(x, name='ToBFloat16') <div class="md-anchor" id="to_bfloat16">{#to_bfloat16}</div> + +Casts a tensor to type `bfloat16`. + +##### Args: + + +* <b>x</b>: A `Tensor` or `SparseTensor`. +* <b>name</b>: A name for the operation (optional). + +##### Returns: + + A `Tensor` or `SparseTensor` with same shape as `x` with type `bfloat16`. + +##### Raises: + + +* <b>TypeError</b>: If `x` cannot be cast to the `bfloat16`. + + +- - - + +### tf.to_int32(x, name='ToInt32') <div class="md-anchor" id="to_int32">{#to_int32}</div> + +Casts a tensor to type `int32`. + +##### Args: + + +* <b>x</b>: A `Tensor` or `SparseTensor`. +* <b>name</b>: A name for the operation (optional). + +##### Returns: + + A `Tensor` or `SparseTensor` with same shape as `x` with type `int32`. + +##### Raises: + + +* <b>TypeError</b>: If `x` cannot be cast to the `int32`. + + +- - - + +### tf.to_int64(x, name='ToInt64') <div class="md-anchor" id="to_int64">{#to_int64}</div> + +Casts a tensor to type `int64`. + +##### Args: + + +* <b>x</b>: A `Tensor` or `SparseTensor`. +* <b>name</b>: A name for the operation (optional). + +##### Returns: + + A `Tensor` or `SparseTensor` with same shape as `x` with type `int64`. + +##### Raises: + + +* <b>TypeError</b>: If `x` cannot be cast to the `int64`. + + +- - - + +### tf.cast(x, dtype, name=None) <div class="md-anchor" id="cast">{#cast}</div> + +Casts a tensor to a new type. + +The operation casts `x` (in case of `Tensor`) or `x.values` +(in case of `SparseTensor`) to `dtype`. + +For example: + +```python +# tensor `a` is [1.8, 2.2], dtype=tf.float +tf.cast(a, tf.int32) ==> [1, 2] # dtype=tf.int32 +``` + +##### Args: + + +* <b>x</b>: A `Tensor` or `SparseTensor`. +* <b>dtype</b>: The destination type. +* <b>name</b>: A name for the operation (optional). + +##### Returns: + + A `Tensor` or `SparseTensor` with same shape as `x`. + +##### Raises: + + +* <b>TypeError</b>: If `x` cannot be cast to the `dtype`. + + + +## Shapes and Shaping <div class="md-anchor" id="AUTOGENERATED-shapes-and-shaping">{#AUTOGENERATED-shapes-and-shaping}</div> + +TensorFlow provides several operations that you can use to determine the shape +of a tensor and change the shape of a tensor. + +- - - + +### tf.shape(input, name=None) <div class="md-anchor" id="shape">{#shape}</div> + +Returns the shape of a tensor. + +This operation returns a 1-D integer tensor representing the shape of `input`. + +For example: + +```prettyprint +# 't' is [[[1, 1, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]]] +shape(t) ==> [2, 2, 3] +``` + +##### Args: + + +* <b>input</b>: A `Tensor`. +* <b>name</b>: A name for the operation (optional). + +##### Returns: + + A `Tensor` of type `int32`. + + +- - - + +### tf.size(input, name=None) <div class="md-anchor" id="size">{#size}</div> + +Returns the size of a tensor. + +This operation returns an integer representing the number of elements in +`input`. + +For example: + +```prettyprint +# 't' is [[[1, 1,, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]]]] +size(t) ==> 12 +``` + +##### Args: + + +* <b>input</b>: A `Tensor`. +* <b>name</b>: A name for the operation (optional). + +##### Returns: + + A `Tensor` of type `int32`. + + +- - - + +### tf.rank(input, name=None) <div class="md-anchor" id="rank">{#rank}</div> + +Returns the rank of a tensor. + +This operation returns an integer representing the rank of `input`. + +For example: + +```prettyprint +# 't' is [[[1, 1, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]]] +# shape of tensor 't' is [2, 2, 3] +rank(t) ==> 3 +``` + +**Note**: The rank of a tensor is not the same as the rank of a matrix. The rank +of a tensor is the number of indices required to uniquely select each element +of the tensor. Rank is also known as "order", "degree", or "ndims." + +##### Args: + + +* <b>input</b>: A `Tensor`. +* <b>name</b>: A name for the operation (optional). + +##### Returns: + + A `Tensor` of type `int32`. + + +- - - + +### tf.reshape(tensor, shape, name=None) <div class="md-anchor" id="reshape">{#reshape}</div> + +Reshapes a tensor. + +Given `tensor`, this operation returns a tensor that has the same values +as `tensor` with shape `shape`. + +If `shape` is the special value `[-1]`, then `tensor` is flattened and the +operation outputs a 1-D tensor with all elements of `tensor`. + +If `shape` is 1-D or higher, then the operation returns a tensor with shape +`shape` filled with the values of `tensor`. In this case, the number of elements +implied by `shape` must be the same as the number of elements in `tensor`. + +For example: + +```prettyprint +# tensor 't' is [1, 2, 3, 4, 5, 6, 7, 8, 9] +# tensor 't' has shape [9] +reshape(t, [3, 3]) ==> [[1, 2, 3] + [4, 5, 6] + [7, 8, 9]] + +# tensor 't' is [[[1, 1], [2, 2]] +# [[3, 3], [4, 4]]] +# tensor 't' has shape [2, 2] +reshape(t, [2, 4]) ==> [[1, 1, 2, 2] + [3, 3, 4, 4]] + +# tensor 't' is [[[1, 1, 1], +# [2, 2, 2]], +# [[3, 3, 3], +# [4, 4, 4]], +# [[5, 5, 5], +# [6, 6, 6]]] +# tensor 't' has shape [3, 2, 3] +# pass '[-1]' to flatten 't' +reshape(t, [-1]) ==> [1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6] +``` + +##### Args: + + +* <b>tensor</b>: A `Tensor`. +* <b>shape</b>: A `Tensor` of type `int32`. Defines the shape of the output tensor. +* <b>name</b>: A name for the operation (optional). + +##### Returns: + + A `Tensor`. Has the same type as `tensor`. + + +- - - + +### tf.squeeze(input, squeeze_dims=None, name=None) <div class="md-anchor" id="squeeze">{#squeeze}</div> + +Removes dimensions of size 1 from the shape of a tensor. + +Given a tensor `input`, this operation returns a tensor of the same type with +all dimensions of size 1 removed. If you don't want to remove all size 1 +dimensions, you can remove specific size 1 dimensions by specifying +`squeeze_dims`. + +For example: + +```prettyprint +# 't' is a tensor of shape [1, 2, 1, 3, 1, 1] +shape(squeeze(t)) ==> [2, 3] +``` + +Or, to remove specific size 1 dimensions: + +```prettyprint +# 't' is a tensor of shape [1, 2, 1, 3, 1, 1] +shape(squeeze(t, [2, 4])) ==> [1, 2, 3, 1] +``` + +##### Args: + + +* <b>input</b>: A `Tensor`. The `input` to squeeze. +* <b>squeeze_dims</b>: An optional list of `ints`. Defaults to `[]`. + If specified, only squeezes the dimensions listed. The dimension + index starts at 0. It is an error to squeeze a dimension that is not 1. +* <b>name</b>: A name for the operation (optional). + +##### Returns: + + A `Tensor`. Has the same type as `input`. + Contains the same data as `input`, but has one or more dimensions of + size 1 removed. + + +- - - + +### tf.expand_dims(input, dim, name=None) <div class="md-anchor" id="expand_dims">{#expand_dims}</div> + +Inserts a dimension of 1 into a tensor's shape. + +Given a tensor `input`, this operation inserts a dimension of 1 at the +dimension index `dim` of `input`'s shape. The dimension index `dim` starts at +zero; if you specify a negative number for `dim` it is counted backward from +the end. + +This operation is useful if you want to add a batch dimension to a single +element. For example, if you have a single image of shape `[height, width, +channels]`, you can make it a batch of 1 image with `expand_dims(image, 0)`, +which will make the shape `[1, height, width, channels]`. + +Other examples: + +```prettyprint +# 't' is a tensor of shape [2] +shape(expand_dims(t, 0)) ==> [1, 2] +shape(expand_dims(t, 1)) ==> [2, 1] +shape(expand_dims(t, -1)) ==> [2, 1] + +# 't2' is a tensor of shape [2, 3, 5] +shape(expand_dims(t2, 0)) ==> [1, 2, 3, 5] +shape(expand_dims(t2, 2)) ==> [2, 3, 1, 5] +shape(expand_dims(t2, 3)) ==> [2, 3, 5, 1] +``` + +This operation requires that: + +`-1-input.dims() <= dim <= input.dims()` + +This operation is related to `squeeze()`, which removes dimensions of +size 1. + +##### Args: + + +* <b>input</b>: A `Tensor`. +* <b>dim</b>: A `Tensor` of type `int32`. + 0-D (scalar). Specifies the dimension index at which to + expand the shape of `input`. +* <b>name</b>: A name for the operation (optional). + +##### Returns: + + A `Tensor`. Has the same type as `input`. + Contains the same data as `input`, but its shape has an additional + dimension of size 1 added. + + + +## Slicing and Joining <div class="md-anchor" id="AUTOGENERATED-slicing-and-joining">{#AUTOGENERATED-slicing-and-joining}</div> + +TensorFlow provides several operations to slice or extract parts of a tensor, +or join multiple tensors together. + +- - - + +### tf.slice(input_, begin, size, name=None) <div class="md-anchor" id="slice">{#slice}</div> + +Extracts a slice from a tensor. + +This operation extracts a slice of size `size` from a tensor `input` starting +at the location specified by `begin`. The slice `size` is represented as a +tensor shape, where `size[i]` is the number of elements of the 'i'th dimension +of `input` that you want to slice. The starting location (`begin`) for the +slice is represented as an offset in each dimension of `input`. In other +words, `begin[i]` is the offset into the 'i'th dimension of `input` that you +want to slice from. + +`begin` is zero-based; `size` is one-based. If `size[i]` is -1, +all remaining elements in dimension i are included in the +slice. In other words, this is equivalent to setting: + +`size[i] = input.dim_size(i) - begin[i]` + +This operation requires that: + +`0 <= begin[i] <= begin[i] + size[i] <= Di for i in [0, n]` + +For example: + +``` +# 'input' is [[[1, 1, 1], [2, 2, 2]], +# [[3, 3, 3], [4, 4, 4]], +# [[5, 5, 5], [6, 6, 6]]] +tf.slice(input, [1, 0, 0], [1, 1, 3]) ==> [[[3, 3, 3]]] +tf.slice(input, [1, 0, 0], [1, 2, 3]) ==> [[[3, 3, 3], + [4, 4, 4]]] +tf.slice(input, [1, 0, 0], [2, 1, 3]) ==> [[[3, 3, 3]], + [[5, 5, 5]]] +``` + +##### Args: + + +* <b>input_</b>: A `Tensor`. +* <b>begin</b>: An `int32` or `int64` `Tensor`. +* <b>size</b>: An `int32` or `int64` `Tensor`. +* <b>name</b>: A name for the operation (optional). + +##### Returns: + + A `Tensor` the same type as `input`. + + +- - - + +### tf.split(split_dim, num_split, value, name='split') <div class="md-anchor" id="split">{#split}</div> + +Splits a tensor into `num_split` tensors along one dimension. + +Splits `value` along dimension `split_dim` into `num_split` smaller tensors. +Requires that `num_split` evenly divide `value.shape[split_dim]`. + +For example: + +```python +# 'value' is a tensor with shape [5, 30] +# Split 'value' into 3 tensors along dimension 1 +split0, split1, split2 = tf.split(1, 3, value) +tf.shape(split0) ==> [5, 10] +``` + +##### Args: + + +* <b>split_dim</b>: A 0-D `int32` `Tensor`. The dimension along which to split. + Must be in the range `[0, rank(value))`. +* <b>num_split</b>: A 0-D `int32` `Tensor`. The number of ways to split. +* <b>value</b>: The `Tensor` to split. +* <b>name</b>: A name for the operation (optional). + +##### Returns: + + `num_split` `Tensor` objects resulting from splitting `value`. + + +- - - + +### tf.tile(input, multiples, name=None) <div class="md-anchor" id="tile">{#tile}</div> + +Constructs a tensor by tiling a given tensor. + +This operation creates a new tensor by replicating `input` `multiples` times. +The output tensor's i'th dimension has `input.dims(i) * multiples[i]` elements, +and the values of `input` are replicated `multiples[i]` times along the 'i'th +dimension. For example, tiling `[a b c d]` by `[2]` produces +`[a b c d a b c d]`. + +##### Args: + + +* <b>input</b>: A `Tensor`. 1-D or higher. +* <b>multiples</b>: A `Tensor` of type `int32`. + 1-D. Length must be the same as the number of dimensions in `input` +* <b>name</b>: A name for the operation (optional). + +##### Returns: + + A `Tensor`. Has the same type as `input`. + + +- - - + +### tf.pad(input, paddings, name=None) <div class="md-anchor" id="pad">{#pad}</div> + +Pads a tensor with zeros. + +This operation pads a `input` with zeros according to the `paddings` you +specify. `paddings` is an integer tensor with shape `[Dn, 2]`, where n is the +rank of `input`. For each dimension D of `input`, `paddings[D, 0]` indicates +how many zeros to add before the contents of `input` in that dimension, and +`paddings[D, 1]` indicates how many zeros to add after the contents of `input` +in that dimension. + +The padded size of each dimension D of the output is: + +`paddings(D, 0) + input.dim_size(D) + paddings(D, 1)` + +For example: + +```prettyprint +# 't' is [[1, 1], [2, 2]] +# 'paddings' is [[1, 1]], [2, 2]] +# rank of 't' is 2 +pad(t, paddings) ==> [[0, 0, 0, 0, 0] + [0, 0, 0, 0, 0] + [0, 1, 1, 0, 0] + [[0, 2, 2, 0, 0] + [0, 0, 0, 0, 0]] +``` + +##### Args: + + +* <b>input</b>: A `Tensor`. +* <b>paddings</b>: A `Tensor` of type `int32`. +* <b>name</b>: A name for the operation (optional). + +##### Returns: + + A `Tensor`. Has the same type as `input`. + + +- - - + +### tf.concat(concat_dim, values, name='concat') <div class="md-anchor" id="concat">{#concat}</div> + +Concatenates tensors along one dimension. + +Concatenates the list of tensors `values` along dimension `concat_dim`. If +`values[i].shape = [D0, D1, ... Dconcat_dim(i), ...Dn]`, the concatenated +result has shape + + [D0, D1, ... Rconcat_dim, ...Dn] + +where + + Rconcat_dim = sum(Dconcat_dim(i)) + +That is, the data from the input tensors is joined along the `concat_dim` +dimension. + +The number of dimensions of the input tensors must match, and all dimensions +except `concat_dim` must be equal. + +For example: + +```python +t1 = [[1, 2, 3], [4, 5, 6]] +t2 = [[7, 8, 9], [10, 11, 12]] +tf.concat(0, [t1, t2]) ==> [[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12]] +tf.concat(1, [t1, t2]) ==> [[1, 2, 3, 7, 8, 9], [4, 5, 6, 10, 11, 12]] + +# tensor t3 with shape [2, 3] +# tensor t4 with shape [2, 3] +tf.shape(tf.concat(0, [t3, t4])) ==> [4, 3] +tf.shape(tf.concat(1, [t3, t4])) ==> [2, 6] +``` + +##### Args: + + +* <b>concat_dim</b>: 0-D `int32` `Tensor`. Dimension along which to concatenate. +* <b>values</b>: A list of `Tensor` objects or a single `Tensor`. +* <b>name</b>: A name for the operation (optional). + +##### Returns: + + A `Tensor` resulting from concatenation of the input tensors. + + +- - - + +### tf.pack(values, name='pack') <div class="md-anchor" id="pack">{#pack}</div> + +Packs a list of rank-`R` tensors into one rank-`(R+1)` tensor. + +Packs tensors in `values` into a tensor with rank one higher than each tensor +in `values` and shape `[len(values)] + values[0].shape`. The output satisfies +`output[i, ...] = values[i][...]`. + +This is the opposite of unpack. The numpy equivalent is + + tf.pack([x, y, z]) = np.asarray([x, y, z]) + +##### Args: + + +* <b>values</b>: A list of `Tensor` objects with the same shape and type. +* <b>name</b>: A name for this operation (optional). + +##### Returns: + + +* <b>output</b>: A packed `Tensor` with the same type as `values`. + + +- - - + +### tf.unpack(value, num=None, name='unpack') <div class="md-anchor" id="unpack">{#unpack}</div> + +Unpacks the outer dimension of a rank-`R` tensor into rank-`(R-1)` tensors. + +Unpacks `num` tensors from `value` along the first dimension. +If `num` is not specified (the default), it is inferred from `value`'s shape. +If `value.shape[0]` is not known, `ValueError` is raised. + +The ith tensor in `output` is the slice `value[i, ...]`. Each tensor in +`output` has shape `value.shape[1:]`. + +This is the opposite of pack. The numpy equivalent is + + tf.unpack(x, n) = list(x) + +##### Args: + + +* <b>value</b>: A rank `R > 0` `Tensor` to be unpacked. +* <b>num</b>: An `int`. The first dimension of value. Automatically inferred if + `None` (the default). +* <b>name</b>: A name for the operation (optional). + +##### Returns: + + The list of `Tensor` objects unpacked from `value`. + +##### Raises: + + +* <b>ValueError</b>: If `num` is unspecified and cannot be inferred. + + +- - - + +### tf.reverse_sequence(input, seq_lengths, seq_dim, name=None) <div class="md-anchor" id="reverse_sequence">{#reverse_sequence}</div> + +Reverses variable length slices in dimension `seq_dim`. + +This op first slices `input` along the first dimension, and for each slice `i`, +reverses the first `seq_lengths[i]` elements along the dimension `seq_dim`. + +The elements of `seq_lengths` must obey `seq_lengths[i] < input.dims[seq_dim]`, +and `seq_lengths` must be a vector of length `input.dims(0)`. + +The output slice `i` along dimension 0 is then given by input slice `i`, with +the first `seq_lengths[i]` slices along dimension `seq_dim` reversed. + +For example: + +```prettyprint +# Given this: +seq_dim = 1 +input.dims = (4, ...) +seq_lengths = [7, 2, 3, 5] + +# then slices of input are reversed on seq_dim, but only up to seq_lengths: +output[0, 0:7, :, ...] = input[0, 7:0:-1, :, ...] +output[1, 0:2, :, ...] = input[1, 2:0:-1, :, ...] +output[2, 0:3, :, ...] = input[2, 3:0:-1, :, ...] +output[3, 0:5, :, ...] = input[3, 5:0:-1, :, ...] + +# while entries past seq_lens are copied through: +output[0, 7:, :, ...] = input[0, 7:, :, ...] +output[1, 2:, :, ...] = input[1, 2:, :, ...] +output[2, 3:, :, ...] = input[2, 3:, :, ...] +output[3, 2:, :, ...] = input[3, 2:, :, ...] +``` + +##### Args: + + +* <b>input</b>: A `Tensor`. The input to reverse. +* <b>seq_lengths</b>: A `Tensor` of type `int64`. + 1-D with length `input.dims(0)` and + `max(seq_lengths) < input.dims(seq_dim)` +* <b>seq_dim</b>: An `int`. The dimension which is partially reversed. +* <b>name</b>: A name for the operation (optional). + +##### Returns: + + A `Tensor`. Has the same type as `input`. + The partially reversed input. It has the same shape as `input`. + + +- - - + +### tf.reverse(tensor, dims, name=None) <div class="md-anchor" id="reverse">{#reverse}</div> + +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: + +```prettyprint +# 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]]]] +``` + +##### Args: + + +* <b>tensor</b>: A `Tensor`. Must be one of the following types: `uint8`, `int8`, `int32`, `bool`, `float32`, `float64`. + Up to 8-D. +* <b>dims</b>: A `Tensor` of type `bool`. 1-D. The dimensions to reverse. +* <b>name</b>: A name for the operation (optional). + +##### Returns: + + A `Tensor`. Has the same type as `tensor`. The same shape as `tensor`. + + +- - - + +### tf.transpose(a, perm=None, name='transpose') <div class="md-anchor" id="transpose">{#transpose}</div> + +Transposes `a`. Permutes the dimensions according to `perm`. + +The returned tensor's dimension i will correspond to the input dimension +`perm[i]`. If `perm` is not given, it is set to (n-1...0), where n is +the rank of the input tensor. Hence by default, this operation performs a +regular matrix transpose on 2-D input Tensors. + +For example: + +```python +# 'x' is [[1 2 3] +# [4 5 6]] +tf.transpose(x) ==> [[1 4] + [2 5] + [3 6]] + +# Equivalently +tf.transpose(x perm=[0, 1]) ==> [[1 4] + [2 5] + [3 6]] + +# 'perm' is more useful for n-dimensional tensors, for n > 2 +# 'x' is [[[1 2 3] +# [4 5 6]] +# [[7 8 9] +# [10 11 12]]] +# Take the transpose of the matrices in dimension-0 +tf.transpose(b, perm=[0, 2, 1]) ==> [[[1 4] + [2 5] + [3 6]] + + [[7 10] + [8 11] + [9 12]]] +``` + +##### Args: + + +* <b>a</b>: A `Tensor`. +* <b>perm</b>: A permutation of the dimensions of `a`. +* <b>name</b>: A name for the operation (optional). + +##### Returns: + + A transposed `Tensor`. + + +- - - + +### tf.gather(params, indices, name=None) <div class="md-anchor" id="gather">{#gather}</div> + +Gather slices from `params` 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: + + # Scalar indices + output[:, ..., :] = params[indices, :, ... :] + + # Vector indices + output[i, :, ..., :] = params[indices[i], :, ... :] + + # Higher rank indices + output[i, ..., j, :, ... :] = params[indices[i, ..., j], :, ..., :] + +If `indices` is a permutation and `len(indices) == params.shape[0]` then +this operation will permute `params` accordingly. + +<div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;"> +<img style="width:100%" src="../images/Gather.png" alt> +</div> + +##### Args: + + +* <b>params</b>: A `Tensor`. +* <b>indices</b>: A `Tensor`. Must be one of the following types: `int32`, `int64`. +* <b>name</b>: A name for the operation (optional). + +##### Returns: + + A `Tensor`. Has the same type as `params`. + + +- - - + +### tf.dynamic_partition(data, partitions, num_partitions, name=None) <div class="md-anchor" id="dynamic_partition">{#dynamic_partition}</div> + +Partitions `data` into `num_partitions` tensors using indices from `partitions`. + +For each index tuple `js` of size `partitions.ndim`, the slice `data[js, ...]` +becomes part of `outputs[partitions[js]]`. The slices with `partitions[js] = i` +are placed in `outputs[i]` in lexicographic order of `js`, and the first +dimension of `outputs[i]` is the number of entries in `partitions` equal to `i`. +In detail, + + outputs[i].shape = [sum(partitions == i)] + data.shape[partitions.ndim:] + + outputs[i] = pack([data[js, ...] for js if partitions[js] == i]) + +`data.shape` must start with `partitions.shape`. + +For example: + + # Scalar partitions + partitions = 1 + num_partitions = 2 + data = [10, 20] + outputs[0] = [] # Empty with shape [0, 2] + outputs[1] = [[10, 20]] + + # Vector partitions + partitions = [0, 0, 1, 1, 0] + num_partitions = 2 + data = [10, 20, 30, 40, 50] + outputs[0] = [10, 20, 50] + outputs[1] = [30, 40] + +<div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;"> +<img style="width:100%" src="../images/DynamicPartition.png" alt> +</div> + +##### Args: + + +* <b>data</b>: A `Tensor`. +* <b>partitions</b>: A `Tensor` of type `int32`. + Any shape. Indices in the range `[0, num_partitions)`. +* <b>num_partitions</b>: An `int` that is `>= 1`. + The number of partitions to output. +* <b>name</b>: A name for the operation (optional). + +##### Returns: + + A list of `num_partitions` `Tensor` objects of the same type as data. + + +- - - + +### tf.dynamic_stitch(indices, data, name=None) <div class="md-anchor" id="dynamic_stitch">{#dynamic_stitch}</div> + +Interleave the values from the `data` tensors into a single tensor. + +Builds a merged tensor such that + + merged[indices[m][i, ..., j], ...] = data[m][i, ..., j, ...] + +For example, if each `indices[m]` is scalar or vector, we have + + # Scalar indices + merged[indices[m], ...] = data[m][...] + + # Vector indices + merged[indices[m][i], ...] = data[m][i, ...] + +Each `data[i].shape` must start with the corresponding `indices[i].shape`, +and the rest of `data[i].shape` must be constant w.r.t. `i`. That is, we +must have `data[i].shape = indices[i].shape + constant`. In terms of this +`constant`, the output shape is + + merged.shape = [max(indices)] + constant + +Values are merged in order, so if an index appears in both `indices[m][i]` and +`indices[n][j]` for `(m,i) < (n,j)` the slice `data[n][j]` will appear in the +merged result. + +For example: + + indices[0] = 6 + indices[1] = [4, 1] + indices[2] = [[5, 2], [0, 3]] + data[0] = [61, 62] + data[1] = [[41, 42], [11, 12]] + data[2] = [[[51, 52], [21, 22]], [[1, 2], [31, 32]]] + merged = [[1, 2], [11, 12], [21, 22], [31, 32], [41, 42], + [51, 52], [61, 62]] + +<div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;"> +<img style="width:100%" src="../images/DynamicStitch.png" alt> +</div> + +##### Args: + + +* <b>indices</b>: A list of at least 2 `Tensor` objects of type `int32`. +* <b>data</b>: A list with the same number of `Tensor` objects as `indices` of `Tensor` objects of the same type. +* <b>name</b>: A name for the operation (optional). + +##### Returns: + + A `Tensor`. Has the same type as `data`. + + |