# 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.

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

"""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.

name: optional name to use for the variable op.

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).

name: Optional name for the op.

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.

name: A name for the operation (optional).

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)