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array_ops.py
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2554 lines (2048 loc) · 83.3 KB
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# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""## Casting
TensorFlow provides several operations that you can use to cast tensor data
types in your graph.
@@string_to_number
@@to_double
@@to_float
@@to_bfloat16
@@to_int32
@@to_int64
@@cast
@@bitcast
@@saturate_cast
## Shapes and Shaping
TensorFlow provides several operations that you can use to determine the shape
of a tensor and change the shape of a tensor.
@@broadcast_dynamic_shape
@@broadcast_static_shape
@@shape
@@shape_n
@@size
@@rank
@@reshape
@@squeeze
@@expand_dims
@@meshgrid
## Slicing and Joining
TensorFlow provides several operations to slice or extract parts of a tensor,
or join multiple tensors together.
@@slice
@@strided_slice
@@split
@@tile
@@pad
@@concat
@@stack
@@parallel_stack
@@unstack
@@reverse_sequence
@@reverse
@@reverse_v2
@@transpose
@@extract_image_patches
@@space_to_batch_nd
@@space_to_batch
@@required_space_to_batch_paddings
@@batch_to_space_nd
@@batch_to_space
@@space_to_depth
@@depth_to_space
@@gather
@@gather_nd
@@unique_with_counts
@@scatter_nd
@@dynamic_partition
@@dynamic_stitch
@@boolean_mask
@@one_hot
@@sequence_mask
@@dequantize
@@quantize_v2
@@quantized_concat
@@setdiff1d
## Fake quantization
Operations used to help train for better quantization accuracy.
@@fake_quant_with_min_max_args
@@fake_quant_with_min_max_args_gradient
@@fake_quant_with_min_max_vars
@@fake_quant_with_min_max_vars_gradient
@@fake_quant_with_min_max_vars_per_channel
@@fake_quant_with_min_max_vars_per_channel_gradient
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import sys
import numpy as np
import six
from tensorflow.python.framework import common_shapes
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.framework import sparse_tensor
from tensorflow.python.framework import tensor_shape
from tensorflow.python.framework import tensor_util
# 'Constant' gets imported in the module 'array_ops'.
from tensorflow.python.framework.constant_op import constant
from tensorflow.python.ops import gen_array_ops
from tensorflow.python.ops import gen_math_ops
# go/tf-wildcard-import
# pylint: disable=wildcard-import
from tensorflow.python.ops.gen_array_ops import *
from tensorflow.python.util import deprecation
from tensorflow.python.util.deprecation import deprecated
# pylint: enable=wildcard-import
# Used for slicing to specify a new 1 size dimension
newaxis = None
# We override the 'slice' for the "slice" op, so we keep python's
# existing 'slice' for later use in this module.
_baseslice = slice
# pylint: disable=redefined-builtin,protected-access
def expand_dims(input, axis=None, name=None, dim=None):
"""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 `axis` of `input`'s shape. The dimension index `axis` starts
at zero; if you specify a negative number for `axis` 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:
```python
# '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:
input: A `Tensor`.
axis: 0-D (scalar). Specifies the dimension index at which to
expand the shape of `input`.
name: The name of the output `Tensor`.
dim: 0-D (scalar). Equivalent to `axis`, to be deprecated.
Returns:
A `Tensor` with the same data as `input`, but its shape has an additional
dimension of size 1 added.
Raises:
ValueError: if both `dim` and `axis` are specified.
"""
# TODO(aselle): Remove argument dim
if dim is not None:
if axis is not None:
raise ValueError("can't specify both 'dim' and 'axis'")
axis = dim
return gen_array_ops._expand_dims(input, axis, name)
# pylint: enable=redefined-builtin,protected-access
# Aliases for some automatically-generated names.
# pylint: disable=protected-access
@deprecated(
"2016-11-30",
"This op will be removed after the deprecation date. "
"Please switch to tf.setdiff1d().")
def listdiff(x, y, out_idx=None, name=None):
return gen_array_ops._list_diff(x, y, out_idx, name)
listdiff.__doc__ = gen_array_ops._list_diff.__doc__ + "\n" + listdiff.__doc__
# pylint: enable=protected-access
# pylint: disable=undefined-variable,protected-access
def setdiff1d(x, y, index_dtype=dtypes.int32, name=None):
return gen_array_ops._list_diff(x, y, index_dtype, name)
setdiff1d.__doc__ = gen_array_ops._list_diff.__doc__
# pylint: enable=protected-access
def broadcast_dynamic_shape(shape_x, shape_y):
# pylint: disable=protected-access
"""Returns the broadcasted dynamic shape between `shape_x` and `shape_y`.
Args:
shape_x: A rank 1 integer `Tensor`, representing the shape of x.
shape_y: A rank 1 integer `Tensor`, representing the shape of x.
Returns:
A rank 1 integer `Tensor` representing the broadcasted shape.
"""
return gen_array_ops._broadcast_args(shape_x, shape_y)
# pylint: enable=protected-access
def broadcast_static_shape(shape_x, shape_y):
"""Returns the broadcasted static shape between `shape_x` and `shape_y`.
Args:
shape_x: A `TensorShape`
shape_y: A `TensorShape`
Returns:
A `TensorShape` representing the broadcasted shape.
Raises:
ValueError: If the two shapes can not be broadcasted.
"""
return common_shapes.broadcast_shape(shape_x, shape_y)
def shape(input, name=None, out_type=dtypes.int32):
# pylint: disable=redefined-builtin
"""Returns the shape of a tensor.
This operation returns a 1-D integer tensor representing the shape of `input`.
For example:
```python
# 't' is [[[1, 1, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]]]
shape(t) ==> [2, 2, 3]
```
Args:
input: A `Tensor` or `SparseTensor`.
name: A name for the operation (optional).
out_type: (Optional) The specified output type of the operation
(`int32` or `int64`). Defaults to `tf.int32`.
Returns:
A `Tensor` of type `out_type`.
"""
return shape_internal(input, name, optimize=True, out_type=out_type)
def shape_internal(input, name=None, optimize=True, out_type=dtypes.int32):
# pylint: disable=redefined-builtin
"""Returns the shape of a tensor.
Args:
input: A `Tensor` or `SparseTensor`.
name: A name for the operation (optional).
optimize: if true, encode the shape as a constant when possible.
out_type: (Optional) The specified output type of the operation
(`int32` or `int64`). Defaults to tf.int32.
Returns:
A `Tensor` of type `out_type`.
"""
with ops.name_scope(name, "Shape", [input]) as name:
if isinstance(
input, (sparse_tensor.SparseTensor, sparse_tensor.SparseTensorValue)):
return gen_math_ops.cast(input.dense_shape, out_type)
else:
input_tensor = ops.convert_to_tensor(input)
input_shape = input_tensor.get_shape()
if optimize and input_shape.is_fully_defined():
return constant(input_shape.as_list(), out_type, name=name)
return gen_array_ops.shape(input, name=name, out_type=out_type)
def size(input, name=None, out_type=dtypes.int32):
# pylint: disable=redefined-builtin
"""Returns the size of a tensor.
This operation returns an integer representing the number of elements in
`input`.
For example:
```python
# 't' is [[[1, 1, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]]]]
size(t) ==> 12
```
Args:
input: A `Tensor` or `SparseTensor`.
name: A name for the operation (optional).
out_type: (Optional) The specified output type of the operation
(`int32` or `int64`). Defaults to tf.int32.
Returns:
A `Tensor` of type `out_type`. Defaults to tf.int32.
"""
return size_internal(input, name, optimize=True, out_type=out_type)
def size_internal(input, name=None, optimize=True, out_type=dtypes.int32):
# pylint: disable=redefined-builtin,protected-access
"""Returns the size of a tensor.
Args:
input: A `Tensor` or `SparseTensor`.
name: A name for the operation (optional).
optimize: if true, encode the size as a constant when possible.
out_type: (Optional) The specified output type of the operation
(`int32` or `int64`). Defaults to tf.int32.
Returns:
A `Tensor` of type `out_type`.
"""
with ops.name_scope(name, "Size", [input]) as name:
if isinstance(
input, (sparse_tensor.SparseTensor, sparse_tensor.SparseTensorValue)):
return gen_math_ops._prod(
gen_math_ops.cast(input.dense_shape, out_type), 0, name=name)
else:
input_tensor = ops.convert_to_tensor(input)
input_shape = input_tensor.get_shape()
if optimize and input_shape.is_fully_defined():
return constant(input_shape.num_elements(), out_type, name=name)
return gen_array_ops.size(input, name=name, out_type=out_type)
def rank(input, name=None):
# pylint: disable=redefined-builtin
"""Returns the rank of a tensor.
This operation returns an integer representing the rank of `input`.
For example:
```python
# '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:
input: A `Tensor` or `SparseTensor`.
name: A name for the operation (optional).
Returns:
A `Tensor` of type `int32`.
@compatibility(numpy)
Equivalent to np.ndim
@end_compatibility
"""
return rank_internal(input, name, optimize=True)
def rank_internal(input, name=None, optimize=True):
# pylint: disable=redefined-builtin
"""Returns the rank of a tensor.
Args:
input: A `Tensor` or `SparseTensor`.
name: A name for the operation (optional).
optimize: if true, encode the rank as a constant when possible.
Returns:
A `Tensor` of type `int32`.
"""
with ops.name_scope(name, "Rank", [input]) as name:
if isinstance(
input, (sparse_tensor.SparseTensor, sparse_tensor.SparseTensorValue)):
return gen_array_ops.size(input.dense_shape, name=name)
else:
input_tensor = ops.convert_to_tensor(input)
input_shape = input_tensor.get_shape()
if optimize and input_shape.ndims is not None:
return constant(input_shape.ndims, dtypes.int32, name=name)
return gen_array_ops.rank(input, name=name)
def _SliceHelper(tensor, slice_spec, var=None):
"""Overload for Tensor.__getitem__.
This operation extracts the specified region from the tensor.
The notation is similar to NumPy with the restriction that
currently only support basic indexing. That means that
using a tensor as input is not currently allowed
Some useful examples:
```python
# strip leading and trailing 2 elements
foo = tf.constant([1,2,3,4,5,6])
print(foo[2:-2].eval()) # => [3,4]
# skip every row and reverse every column
foo = tf.constant([[1,2,3], [4,5,6], [7,8,9]])
print(foo[::2,::-1].eval()) # => [[3,2,1], [9,8,7]]
# Insert another dimension
foo = tf.constant([[1,2,3], [4,5,6], [7,8,9]])
print(foo[tf.newaxis, :, :].eval()) # => [[[3,2,1], [9,8,7]]]
print(foo[:, tf.newaxis, :].eval()) # => [[[3,2,1]], [[9,8,7]]]
print(foo[:, :, tf.newaxis].eval()) # => [[[3],[2],[1]], [[9],[8],[7]]]
# Ellipses (3 equivalent operations)
print(foo[tf.newaxis, :, :].eval()) # => [[[3,2,1], [9,8,7]]]
print(foo[tf.newaxis, ...].eval()) # => [[[3,2,1], [9,8,7]]]
print(foo[tf.newaxis].eval()) # => [[[3,2,1], [9,8,7]]]
```
Notes:
- `tf.newaxis` is `None` as in NumPy.
- An implicit ellipsis is placed at the end of the `slice_spec`
- NumPy advanced indexing is currently not supported.
Args:
tensor: An ops.Tensor object.
slice_spec: The arguments to Tensor.__getitem__.
var: In the case of variable slice assignment, the Variable
object to slice (i.e. tensor is the read-only view of this
variable).
Returns:
The appropriate slice of "tensor", based on "slice_spec".
Raises:
ValueError: If a slice range is negative size.
TypeError: If the slice indices aren't int, slice, or Ellipsis.
"""
if not isinstance(slice_spec, (list, tuple)):
slice_spec = [slice_spec]
begin, end, strides = [], [], []
index = 0
new_axis_mask, shrink_axis_mask = 0, 0
begin_mask, end_mask = 0, 0
ellipsis_mask = 0
for s in slice_spec:
if isinstance(s, _baseslice):
strides.append(s.step if s.step is not None else 1)
# python doesn't always use None when constructing ranges
# for example a[:] gives slice(None,sys.maxsize,None)
# whereas a[::1] gives slice(None,None,None)
if s.start is not None and s.start is not sys.maxsize:
begin.append(s.start)
else:
begin.append(0)
begin_mask |= (1 << index)
if s.stop is not None and s.stop != sys.maxsize:
end.append(s.stop)
else:
end.append(0)
end_mask |= (1 << index)
elif s is Ellipsis:
begin.append(0)
end.append(0)
strides.append(1)
ellipsis_mask |= (1 << index)
elif s is newaxis:
begin.append(0)
end.append(0)
strides.append(1)
new_axis_mask |= (1 << index)
else:
begin.append(s)
end.append(s + 1)
strides.append(1)
shrink_axis_mask |= (1 << index)
index += 1
# stack possibly involves no tensors, so we must use op_scope correct graph.
with ops.name_scope(None, "strided_slice",
[tensor] + begin + end + strides) as name:
if begin:
packed_begin, packed_end, packed_strides = (
stack(begin), stack(end), stack(strides))
else:
var_empty = constant([], dtype=dtypes.int32)
packed_begin = packed_end = packed_strides = var_empty
return strided_slice(
tensor,
packed_begin,
packed_end,
packed_strides,
begin_mask=begin_mask,
end_mask=end_mask,
shrink_axis_mask=shrink_axis_mask,
new_axis_mask=new_axis_mask,
ellipsis_mask=ellipsis_mask,
var=var,
name=name)
# pylint: disable=undefined-variable,protected-access
def slice(input_, begin, size, name=None):
# pylint: disable=redefined-builtin
"""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:
```python
# '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:
input_: A `Tensor`.
begin: An `int32` or `int64` `Tensor`.
size: An `int32` or `int64` `Tensor`.
name: A name for the operation (optional).
Returns:
A `Tensor` the same type as `input`.
"""
return gen_array_ops._slice(input_, begin, size, name=name)
# pylint: disable=invalid-name
def strided_slice(input_,
begin,
end,
strides=None,
begin_mask=0,
end_mask=0,
ellipsis_mask=0,
new_axis_mask=0,
shrink_axis_mask=0,
var=None,
name=None):
"""Extracts a strided slice from a tensor.
To a first order, this operation extracts a slice of size `end - begin`
from a tensor `input`
starting at the location specified by `begin`. The slice continues by adding
`stride` to the `begin` index until all dimensions are not less than `end`.
Note that components of stride can be negative, which causes a reverse
slice.
This operation can be thought of an encoding of a numpy style sliced
range. Given a python slice input[<spec0>, <spec1>, ..., <specn>]
this function will be called as follows.
`begin`, `end`, and `strides` will be all length n. n is in general
not the same dimensionality as `input`.
For the ith spec,
`begin_mask`, `end_mask`, `ellipsis_mask`, `new_axis_mask`,
and `shrink_axis_mask` will have the ith bit corresponding to
the ith spec.
If the ith bit of `begin_mask` is non-zero, `begin[i]` is ignored and
the fullest possible range in that dimension is used instead.
`end_mask` works analogously, except with the end range.
`foo[5:,:,:3]` on a 7x8x9 tensor is equivalent to `foo[5:7,0:8,0:3]`.
`foo[::-1]` reverses a tensor with shape 8.
If the ith bit of `ellipsis_mask`, as many unspecified dimensions
as needed will be inserted between other dimensions. Only one
non-zero bit is allowed in `ellipsis_mask`.
For example `foo[3:5,...,4:5]` on a shape 10x3x3x10 tensor is
equivalent to `foo[3:5,:,:,4:5]` and
`foo[3:5,...]` is equivalent to `foo[3:5,:,:,:]`.
If the ith bit of `new_axis_mask` is one, then a `begin`,
`end`, and `stride` are ignored and a new length 1 dimension is
added at this point in the output tensor.
For example `foo[3:5,4]` on a 10x8 tensor produces a shape 2 tensor
whereas `foo[3:5,4:5]` produces a shape 2x1 tensor with shrink_mask
being 1<<1 == 2.
If the ith bit of `shrink_axis_mask` is one, then `begin`,
`end[i]`, and `stride[i]` are used to do a slice in the appropriate
dimension, but the output tensor will be reduced in dimensionality
by one. This is only valid if the ith entry of slice[i]==1.
NOTE: `begin` and `end` are zero-indexed`.
`strides` entries must be non-zero.
```python
# 'input' is [[[1, 1, 1], [2, 2, 2]],
# [[3, 3, 3], [4, 4, 4]],
# [[5, 5, 5], [6, 6, 6]]]
tf.strided_slice(input, [1, 0, 0], [2, 1, 3], [1, 1, 1]) ==> [[[3, 3, 3]]]
tf.strided_slice(input, [1, 0, 0], [2, 2, 3], [1, 1, 1]) ==> [[[3, 3, 3],
[4, 4, 4]]]
tf.strided_slice(input, [1, 1, 0], [2, -1, 3], [1, -1, 1]) ==>[[[4, 4, 4],
[3, 3, 3]]]
```
Args:
input_: A `Tensor`.
begin: An `int32` or `int64` `Tensor`.
end: An `int32` or `int64` `Tensor`.
strides: An `int32` or `int64` `Tensor`.
begin_mask: An `int32` mask.
end_mask: An `int32` mask.
ellipsis_mask: An `int32` mask.
new_axis_mask: An `int32` mask.
shrink_axis_mask: An `int32` mask.
var: The variable corresponding to `input_` or None
name: A name for the operation (optional).
Returns:
A `Tensor` the same type as `input`.
"""
if strides is None:
strides = ones_like(begin)
op = gen_array_ops.strided_slice(
input=input_,
begin=begin,
end=end,
strides=strides,
name=name,
begin_mask=begin_mask,
end_mask=end_mask,
ellipsis_mask=ellipsis_mask,
new_axis_mask=new_axis_mask,
shrink_axis_mask=shrink_axis_mask)
def assign(val):
"""Closure that holds all the arguments to create an assignment."""
if var is None:
raise ValueError("Sliced assignment is only supported for variables")
return gen_array_ops.strided_slice_assign(
ref=var,
begin=begin,
end=end,
strides=strides,
value=val,
name=name + "_assign",
begin_mask=begin_mask,
end_mask=end_mask,
ellipsis_mask=ellipsis_mask,
new_axis_mask=new_axis_mask,
shrink_axis_mask=shrink_axis_mask)
op.assign = assign
return op
def _SliceHelperVar(var, slice_spec):
"""Creates a slice helper object given a variable.
This allows creating a sub-tensor from part of the current contents
of a variable.
See
[`Tensor.__getitem__`](../../api_docs/python/framework.md#Tensor.__getitem__)
for detailed examples of slicing.
This function in addition also allows assignment to a sliced range.
This is similar to `__setitem__` functionality in Python. However,
the syntax is different so that the user can capture the assignment
operation for grouping or passing to `sess.run()`.
For example,
```prettyprint
import tensorflow as tf
A = tf.Variable([[1,2,3], [4,5,6], [7,8,9]], dtype=tf.float32)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
print sess.run(A[:2, :2]) # => [[1,2], [4,5]]
op = A[:2,:2].assign(22. * tf.ones((2, 2)))
print sess.run(op) # => [[22, 22, 3], [22, 22, 6], [7,8,9]]
```
Note that assignments currently do not support NumPy broadcasting
semantics.
Args:
var: An `ops.Variable` object.
slice_spec: The arguments to `Tensor.__getitem__`.
Returns:
The appropriate slice of "tensor", based on "slice_spec".
As an operator. The operator also has a `assign()` method
that can be used to generate an assignment operator.
Raises:
ValueError: If a slice range is negative size.
TypeError: If the slice indices aren't int, slice, or Ellipsis.
"""
return _SliceHelper(var._AsTensor(), slice_spec, var)
ops.Tensor._override_operator("__getitem__", _SliceHelper)
def _inplace_helper(value, loc, update, op):
"""Applies an inplace op on `value` at `loc` with `update`.
op is one of gen_array_ops._inplace_update, gen_array_ops._inplace_add, or
gen_array_ops._inplace_subtract.
If `loc` is None, `value` and `update` must be the same size.
```
value op update
```
If `loc` is a scalar, `value` has rank 1 higher than `update`
```
value[i, :] op update
```
If `loc` is a vector, `value` has the same rank as `update`
```
value[loc, :] op update
```
Args:
value: A `Tensor` object that will be updated in-place.
loc: None, scalar or 1-D `Tensor`.
update: A `Tensor` of rank one less than `value` if `loc` is a scalar,
otherwise of rank equal to `value` that contains the new values
for `value`.
op: One of gen_array_ops._inplace_update, ._inplace_add, ._inplace_subtract
Returns:
output: `value` that has been updated accordingly.
"""
value = ops.convert_to_tensor(value)
update = ops.convert_to_tensor(update, value.dtype)
if loc is None:
# Full tensor
return reshape(op(reshape(value, [1, -1]),
gen_math_ops.cast([0], dtypes.int64),
reshape(update, [1, -1])),
shape(value))
loc = gen_math_ops.cast(loc, dtypes.int64)
if loc.get_shape().ndims == 0:
# Single 0-dim update
return op(value, reshape(loc, [1]), expand_dims(update, 0))
return op(value, loc, update)
def _empty(output_shape, dtype, init=False):
"""Creates an empty Tensor with shape `output_shape` and type `dtype`.
The memory can optionally be initialized. This is usually useful in
conjunction with in-place operations.
Args:
output_shape: 1-D `Tensor` indicating the shape of the output.
dtype: The element type of the returned tensor.
init: `bool` indicating whether or not to zero the allocated memory.
Returns:
output: An empty Tensor of the specified type.
"""
return gen_array_ops._empty(output_shape, dtype, init=init)
def _empty_like(value, init=None):
"""Creates an empty Tensor with the same shape and type `dtype` as value.
The memory can optionally be initialized. This op is usually useful in
conjunction with in-place operations.
Args:
value: A `Tensor` whose shape will be used.
init: Initalize the returned tensor with the default value of
`value.dtype()` if True. Otherwise do not initialize.
Returns:
output: An empty Tensor of the specified shape and type.
"""
value = ops.convert_to_tensor(value)
return gen_array_ops._empty(shape(value), value.dtype, init=init)
def _alias_inplace_update(value, loc, update):
"""Updates input `value` at `loc` with `update`. Aliases value.
If `loc` is None, `value` and `update` must be the same size.
```
value = update
```
If `loc` is a scalar, `value` has rank 1 higher than `update`
```
value[i, :] = update
```
If `loc` is a vector, `value` has the same rank as `update`
```
value[loc, :] = update
```
Warning: If you use this function you will almost certainly want to add
a control dependency as done in the implementation of parallel_stack to
avoid race conditions.
Args:
value: A `Tensor` object that will be updated in-place.
loc: None, scalar or 1-D `Tensor`.
update: A `Tensor` of rank one less than `value` if `loc` is a scalar,
otherwise of rank equal to `value` that contains the new values
for `value`.
Returns:
output: `value` that has been updated accordingly.
"""
return _inplace_helper(value, loc, update, gen_array_ops._inplace_update)
def _alias_inplace_add(value, loc, update):
"""Updates input `value` at `loc` with `update`. Aliases value.
If `loc` is None, `value` and `update` must be the same size.
```
value += update
```
If `loc` is a scalar, `value` has rank 1 higher than `update`
```
value[i, :] += update
```
If `loc` is a vector, `value` has the same rank as `update`
```
value[loc, :] += update
```
Warning: If you use this function you will almost certainly want to add
a control dependency as done in the implementation of parallel_stack to
avoid race conditions.
Args:
value: A `Tensor` object that will be updated in-place.
loc: None, scalar or 1-D `Tensor`.
update: A `Tensor` of rank one less than `value` if `loc` is a scalar,
otherwise of rank equal to `value` that contains the new values
for `value`.
Returns:
output: `value` that has been updated accordingly.
"""
return _inplace_helper(value, loc, update, gen_array_ops._inplace_add)
def _alias_inplace_subtract(value, loc, update):
"""Updates input `value` at `loc` with `update`. Aliases value.
If `loc` is None, `value` and `update` must be the same size.
```
value -= update
```
If `loc` is a scalar, `value` has rank 1 higher than `update`
```
value[i, :] -= update
```
If `loc` is a vector, `value` has the same rank as `update`
```
value[loc, :] -= update
```
Warning: If you use this function you will almost certainly want to add
a control dependency as done in the implementation of parallel_stack to
avoid race conditions.
Args:
value: A `Tensor` object that will be updated in-place.
loc: None, Scalar or 1-D `Tensor`.
update: A `Tensor` of rank one less than `value` if `loc` is a scalar,
otherwise of rank equal to `value` that contains the new values
for `value`.
Returns:
output: `value` that has been updated accordingly.
"""
return _inplace_helper(value, loc, update, gen_array_ops._inplace_subtract)
def parallel_stack(values, name="parallel_stack"):
"""Stacks a list of rank-`R` tensors into one rank-`(R+1)` tensor in parallel.
Requires that the shape of inputs be known at graph construction time.
Packs the list of tensors in `values` into a tensor with rank one higher than
each tensor in `values`, by packing them along the first dimension.
Given a list of length `N` of tensors of shape `(A, B, C)`; the `output`
tensor will have the shape `(N, A, B, C)`.
For example:
```prettyprint
# 'x' is [1, 4]
# 'y' is [2, 5]
# 'z' is [3, 6]
parallel_stack([x, y, z]) => [[1, 4], [2, 5], [3, 6]]
```
The difference between stack and parallel_stack is that stack requires all
of the inputs be computed before the operation will begin but doesn't require
that the input shapes be known during graph construction. Parallel stack
will copy pieces of the input into the output as they become available, in
some situations this can provide a performance benefit.
This is the opposite of unstack. The numpy equivalent is
tf.parallel_stack([x, y, z]) = np.asarray([x, y, z])
Args:
values: A list of `Tensor` objects with the same shape and type.
name: A name for this operation (optional).
Returns:
output: A stacked `Tensor` with the same type as `values`.
"""
with ops.name_scope(name):
value_t = ops.convert_to_tensor(values[0])
value_shape = ops.convert_to_tensor(value_t).get_shape()
output_shape = tensor_shape.TensorShape([len(values)])
output_shape = output_shape.concatenate(value_shape)
outputs = _empty(output_shape, values[0].dtype)
output_ops = []
for i in range(len(values)):
with ops.colocate_with(outputs):
output_op = _alias_inplace_update(outputs, i, values[i])
output_ops.append(output_op)
with ops.control_dependencies(output_ops):
outputs = identity(outputs)
return outputs
def stack(values, axis=0, name="stack"):
"""Stacks a list of rank-`R` tensors into one rank-`(R+1)` tensor.
Packs the list of tensors in `values` into a tensor with rank one higher than
each tensor in `values`, by packing them along the `axis` dimension.
Given a list of length `N` of tensors of shape `(A, B, C)`;
if `axis == 0` then the `output` tensor will have the shape `(N, A, B, C)`.
if `axis == 1` then the `output` tensor will have the shape `(A, N, B, C)`.
Etc.
For example:
```prettyprint
# 'x' is [1, 4]