tensorflow/tensorflow/python/ops/state_ops.py at master · GraphicsCoder/tensorflow

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# 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.

# ==============================================================================

"""Variables. See the @{python/state_ops} guide.

@@Variable

@@global_variables

@@local_variables

@@model_variables

@@trainable_variables

@@moving_average_variables

@@global_variables_initializer

@@local_variables_initializer

@@variables_initializer

@@is_variable_initialized

@@report_uninitialized_variables

@@assert_variables_initialized

@@assign

@@assign_add

@@assign_sub

@@Saver

@@latest_checkpoint

@@get_checkpoint_state

@@update_checkpoint_state

@@get_variable

@@get_local_variable

@@VariableScope

@@variable_scope

@@variable_op_scope

@@get_variable_scope

@@make_template

@@no_regularizer

@@constant_initializer

@@random_normal_initializer

@@truncated_normal_initializer

@@random_uniform_initializer

@@uniform_unit_scaling_initializer

@@zeros_initializer

@@ones_initializer

@@orthogonal_initializer

@@fixed_size_partitioner

@@variable_axis_size_partitioner

@@min_max_variable_partitioner

@@scatter_update

@@scatter_add

@@scatter_sub

@@scatter_mul

@@scatter_div

@@scatter_nd_update

@@scatter_nd_add

@@scatter_nd_sub

@@sparse_mask

@@IndexedSlices

@@initialize_all_tables

@@tables_initializer

@@export_meta_graph

@@import_meta_graph

@@all_variables

@@initialize_all_variables

@@initialize_local_variables

@@initialize_variables

"""

from __future__ import absolute_import

from __future__ import division

from __future__ import print_function

from tensorflow.python.framework import ops

from tensorflow.python.framework import tensor_shape

from tensorflow.python.ops import gen_resource_variable_ops

from tensorflow.python.ops import gen_state_ops

# go/tf-wildcard-import

# pylint: disable=wildcard-import

from tensorflow.python.ops.gen_state_ops import *

# pylint: enable=wildcard-import

# pylint: disable=protected-access,g-doc-return-or-yield,g-doc-args

def variable_op(shape, dtype, name="Variable", set_shape=True, container="",

shared_name=""):

"""Deprecated. Used variable_op_v2 instead."""

if not set_shape:

shape = tensor_shape.unknown_shape()

ret = gen_state_ops._variable(shape=shape, dtype=dtype, name=name,

container=container, shared_name=shared_name)

# TODO(mrry): Move this to where it is used, so we can get rid of this op

# wrapper?

if set_shape:

ret.set_shape(shape)

return ret

def variable_op_v2(shape, dtype, name="Variable", container="", shared_name=""):

"""Create a variable Operation.

See also variables.Variable.

Args:

shape: The shape of the tensor managed by this variable

dtype: The underlying type of the tensor values.

container: An optional string. Defaults to "".

If non-empty, this variable is placed in the given container.

Otherwise, a default container is used.

shared_name: An optional string. Defaults to "".

If non-empty, this variable is named in the given bucket

with this shared_name. Otherwise, the node name is used instead.

Returns:

A variable tensor.1;5A

"""

return gen_state_ops._variable_v2(shape=shape,

dtype=dtype,

name=name,

container=container,

shared_name=shared_name)

def init_variable(v, init, name="init"):

"""Initializes variable with "init".

This op does the following:

if init is a Tensor, v = init

if callable(init): v = init(VariableShape(v), v.dtype)

Args:

v: Variable to initialize

init: Tensor to assign to v,

Or an object convertible to Tensor e.g. nparray,

Or an Initializer that generates a tensor given the shape and type of v.

An "Initializer" is a callable that returns a tensor that "v" should be

set to. It will be called as init(shape, dtype).

Returns:

The operation that initializes v.

"""

with ops.name_scope(None, v.op.name + "/", [v, init]):

with ops.name_scope(name) as scope:

with ops.colocate_with(v):

if callable(init):

assert v.get_shape().is_fully_defined(), "Variable shape unknown."

# TODO(mrry): Convert to v.shape when the property and

# accessor are reconciled (and all initializers support

# tf.TensorShape objects).

value = init(v.get_shape().as_list(), v.dtype.base_dtype)

value = ops.convert_to_tensor(value, name="value")

return gen_state_ops.assign(v, value, name=scope)

else:

init = ops.convert_to_tensor(init, name="init")

return gen_state_ops.assign(v, init, name=scope)

def is_variable_initialized(ref, name=None):

"""Checks whether a tensor has been initialized.

Outputs boolean scalar indicating whether the tensor has been initialized.

Args:

ref: A mutable `Tensor`.

Should be from a `Variable` node. May be uninitialized.

Returns:

A `Tensor` of type `bool`.

"""

if ref.dtype._is_ref_dtype:

return gen_state_ops.is_variable_initialized(ref=ref, name=name)

# Handle resource variables.

if ref.op.type == "VarHandleOp":

return gen_resource_variable_ops.var_is_initialized_op(ref.handle,

name=name)

def assign_sub(ref, value, use_locking=None, name=None):

"""Update 'ref' by subtracting 'value' from it.

This operation outputs "ref" after the update is done.

This makes it easier to chain operations that need to use the reset value.

Args:

ref: A mutable `Tensor`. Must be one of the following types:

`float32`, `float64`, `int64`, `int32`, `uint8`, `uint16`, `int16`,

`int8`, `complex64`, `complex128`, `qint8`, `quint8`, `qint32`, `half`.

Should be from a `Variable` node.

value: A `Tensor`. Must have the same type as `ref`.

The value to be subtracted to the variable.

use_locking: An optional `bool`. Defaults to `False`.

If True, the subtraction will be protected by a lock;

otherwise the behavior is undefined, but may exhibit less contention.

Returns:

Same as "ref". Returned as a convenience for operations that want

to use the new value after the variable has been updated.

"""

if ref.dtype._is_ref_dtype:

return gen_state_ops.assign_sub(

ref, value, use_locking=use_locking, name=name)

return ref.assign_sub(value, name=name)

def assign_add(ref, value, use_locking=None, name=None):

"""Update 'ref' by adding 'value' to it.

This operation outputs "ref" after the update is done.

This makes it easier to chain operations that need to use the reset value.

Args:

ref: A mutable `Tensor`. Must be one of the following types:

`float32`, `float64`, `int64`, `int32`, `uint8`, `uint16`, `int16`,

`int8`, `complex64`, `complex128`, `qint8`, `quint8`, `qint32`, `half`.

Should be from a `Variable` node.

value: A `Tensor`. Must have the same type as `ref`.

The value to be added to the variable.

use_locking: An optional `bool`. Defaults to `False`.

If True, the addition will be protected by a lock;

otherwise the behavior is undefined, but may exhibit less contention.

Returns:

Same as "ref". Returned as a convenience for operations that want

to use the new value after the variable has been updated.

"""

if ref.dtype._is_ref_dtype:

return gen_state_ops.assign_add(

ref, value, use_locking=use_locking, name=name)

return ref.assign_add(value, name=name)

def assign(ref, value, validate_shape=None, use_locking=None, name=None):

"""Update 'ref' by assigning 'value' to it.

This operation outputs "ref" after the assignment is done.

This makes it easier to chain operations that need to use the reset value.

Args:

ref: A mutable `Tensor`.

Should be from a `Variable` node. May be uninitialized.

value: A `Tensor`. Must have the same type as `ref`.

The value to be assigned to the variable.

validate_shape: An optional `bool`. Defaults to `True`.

If true, the operation will validate that the shape

of 'value' matches the shape of the Tensor being assigned to. If false,

'ref' will take on the shape of 'value'.

use_locking: An optional `bool`. Defaults to `True`.

If True, the assignment will be protected by a lock;

otherwise the behavior is undefined, but may exhibit less contention.

Returns:

Same as "ref". Returned as a convenience for operations that want

to use the new value after the variable has been reset.

"""

if ref.dtype._is_ref_dtype:

return gen_state_ops.assign(

ref, value, use_locking=use_locking, name=name,

validate_shape=validate_shape)

return ref.assign(value, name=name)

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