____tz_zs學習筆記

tf.Variable

def __init__(self,
               initial_value=None, 
               trainable=True,
               collections=None,
               validate_shape=True,
               caching_device=None,
               name=None,
               variable_def=None,
               dtype=None,
               expected_shape=None,
               import_scope=None):
 

initial_value：初始化的值，可以是隨機數、常數或者是通過其他變數的初始值得到的。

trainable：標記是否加入GraphKeys.TRAINABLE_VARIABLES集合

validate_shape：如果為false則可以更改shape

dtype：變數的型別，不可改變

變數被使用前，需要通過會話（session）執行其初始化方法完成初始化賦值

sess.run(tf.global_variables_initializer)

#!/usr/bin/python2.7
# -*- coding:utf-8 -*-

"""
@author:    tz_zs
"""

import tensorflow as tf

variable_b = tf.Variable(initial_value=2.2,
                         name="b")  # initial_value 必須指定 ， <tf.Variable 'b:0' shape=() dtype=int32_ref>
variable_d = tf.Variable(initial_value=tf.constant(4.4, shape=[3, 4]),
                         name="d")  # <tf.Variable 'd:0' shape=(3, 4) dtype=float32_ref>

get_variable_a = tf.get_variable("a", (2, 5))  # <tf.Variable 'a:0' shape=(2, 5) dtype=float32_ref>

with tf.Session() as sess:
    tf.global_variables_initializer().run()

    sess_run_a = sess.run(get_variable_a)
    print sess_run_a
    print type(sess_run_a)
    '''
    [[-0.82719386 -0.73760307  0.73487639 -0.56606793  0.80296755]
    [ 0.19305325 -0.55414021  0.03531528 -0.75267315  0.73095655]]
    <type 'numpy.ndarray'>
    '''

    sess_run_b = sess.run(variable_b)
    print sess_run_b
    print type(sess_run_b)  # <type 'numpy.float32'>
    '''
    2.2
    <type 'numpy.float32'>
    '''

    sess_run_d = sess.run(variable_d)
    print sess_run_d
    print type(sess_run_d)  # <type 'numpy.ndarray'>
    '''
    [[ 4.4000001  4.4000001  4.4000001  4.4000001]
     [ 4.4000001  4.4000001  4.4000001  4.4000001]
     [ 4.4000001  4.4000001  4.4000001  4.4000001]]
    <type 'numpy.ndarray'
    '''
 

.

# -*- coding: utf-8 -*-
"""
@author: tz_zs

變數 初始值為常數的情況
"""
import tensorflow as tf

v1 = tf.Variable(tf.constant(1.0, shape=[1]), name="v1")
v2 = tf.Variable(tf.constant(2.0), name="v2")
v3 = tf.Variable(2.0, name="v3")
result1 = v1 + v2
result2 = v1 + v3
print(v1)  # <tf.Variable 'v1:0' shape=(1,) dtype=float32_ref>
print(v2)  # <tf.Variable 'v2:0' shape=() dtype=float32_ref>
print(v3)  # <tf.Variable 'v3:0' shape=() dtype=float32_ref>
print(result1)  # Tensor("add:0", shape=(1,), dtype=float32)
print(result2)  # Tensor("add_1:0", shape=(1,), dtype=float32)

with tf.Session() as sess:
    tf.global_variables_initializer().run()# sess.run(tf.global_variables_initializer())
    print(sess.run(v1))  # [ 1.]
    print(sess.run(v2))  # 2.0
    print(sess.run(v3))  # 2.0
    print(sess.run(result1))  # [ 3.]
    print(sess.run(result2))  # [ 3.]
 

.

tf.get_variable & tf.variable_scope

tf.get_variable函式可以用來建立或者獲取變數，當建立變數時，與tf.Variable是一樣的。

tf.variable_scope函式生成一個上下文管理器，用於控制tf.get_variable

code1

# -*- coding: utf-8 -*-
"""
@author: tz_zs

變數生成之tf.get_variable與tf.variable_scope  reuse引數
"""

import tensorflow as tf

with tf.variable_scope("a"):
    v1 = tf.get_variable("v", [1], initializer=tf.constant_initializer(1.0))

# with tf.variable_scope("a"):
#     v2 = tf.get_variable("v", [1])   # 報錯 ValueError: Variable a/v already exists,

with tf.variable_scope("a", reuse=True):
    v3 = tf.get_variable("v", [1])
    print(v3 == v1)  # True

with tf.variable_scope("b", reuse=True):
    v4 = tf.get_variable("v",
                         [1])  # 報錯 ValueError: Variable b/v does not exist, or was not created with tf.get_variable().

code2

# -*- coding: utf-8 -*-
"""
@author: tz_zs

變數生成之tf.get_variable與tf.variable_scope名稱空間
"""

import tensorflow as tf

v1 = tf.get_variable("a", [1])
print(v1.name)

with tf.variable_scope("foo"):
    v2 = tf.get_variable("a", [1])
    print(v2.name)

with tf.variable_scope("foo"):
    with tf.variable_scope("bar"):
        v3 = tf.get_variable("a", [1])
        print(v3.name)

    v4 = tf.get_variable("b", [1])
    print(v4.name)

# with tf.variable_scope(""):
#     v8 = tf.get_variable("a",[1])  # 會報錯，因為名稱為空的名稱空間，等價於v1的情況，a已經存在了。而這裡reuse為None，不能複用，於是報錯
#     print(v8.name)


with tf.variable_scope("", reuse=True):
    v5 = tf.get_variable("foo/bar/a", [1])
    print(v5.name)
    print(v3.name)
    print(v5 == v3)

    v6 = tf.get_variable("a", [1])
    print(v6.name)
    print(v1.name)
    print(v6 == v1)

a:0
foo/a:0
foo/bar/a:0
foo/b:0
foo/bar/a:0
foo/bar/a:0
True
a:0
a:0
True

附錄1 variables.py：

# 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.
# ==============================================================================
"""Variable class."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

from tensorflow.core.framework import attr_value_pb2
from tensorflow.core.framework import variable_pb2
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.framework import tensor_shape
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import control_flow_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import state_ops
from tensorflow.python.util import compat
from tensorflow.python.util.deprecation import deprecated


class Variable(object):
  """See the @{$variables$Variables How To} for a high
  level overview.

  A variable maintains state in the graph across calls to `run()`. You add a
  variable to the graph by constructing an instance of the class `Variable`.

  The `Variable()` constructor requires an initial value for the variable,
  which can be a `Tensor` of any type and shape. The initial value defines the
  type and shape of the variable. After construction, the type and shape of
  the variable are fixed. The value can be changed using one of the assign
  methods.

  If you want to change the shape of a variable later you have to use an
  `assign` Op with `validate_shape=False`.

  Just like any `Tensor`, variables created with `Variable()` can be used as
  inputs for other Ops in the graph. Additionally, all the operators
  overloaded for the `Tensor` class are carried over to variables, so you can
  also add nodes to the graph by just doing arithmetic on variables.

  ```python
  import tensorflow as tf

  # Create a variable.
  w = tf.Variable(<initial-value>, name=<optional-name>)

  # Use the variable in the graph like any Tensor.
  y = tf.matmul(w, ...another variable or tensor...)

  # The overloaded operators are available too.
  z = tf.sigmoid(w + y)

  # Assign a new value to the variable with `assign()` or a related method.
  w.assign(w + 1.0)
  w.assign_add(1.0)
  ```

  When you launch the graph, variables have to be explicitly initialized before
  you can run Ops that use their value. You can initialize a variable by
  running its *initializer op*, restoring the variable from a save file, or
  simply running an `assign` Op that assigns a value to the variable. In fact,
  the variable *initializer op* is just an `assign` Op that assigns the
  variable's initial value to the variable itself.

  ```python
  # Launch the graph in a session.
  with tf.Session() as sess:
      # Run the variable initializer.
      sess.run(w.initializer)
      # ...you now can run ops that use the value of 'w'...
  ```

  The most common initialization pattern is to use the convenience function
  `global_variables_initializer()` to add an Op to the graph that initializes
  all the variables. You then run that Op after launching the graph.

  ```python
  # Add an Op to initialize global variables.
  init_op = tf.global_variables_initializer()

  # Launch the graph in a session.
  with tf.Session() as sess:
      # Run the Op that initializes global variables.
      sess.run(init_op)
      # ...you can now run any Op that uses variable values...
  ```

  If you need to create a variable with an initial value dependent on another
  variable, use the other variable's `initialized_value()`. This ensures that
  variables are initialized in the right order.

  All variables are automatically collected in the graph where they are
  created. By default, the constructor adds the new variable to the graph
  collection `GraphKeys.GLOBAL_VARIABLES`. The convenience function
  `global_variables()` returns the contents of that collection.

  When building a machine learning model it is often convenient to distinguish
  between variables holding the trainable model parameters and other variables
  such as a `global step` variable used to count training steps. To make this
  easier, the variable constructor supports a `trainable=<bool>` parameter. If
  `True`, the new variable is also added to the graph collection
  `GraphKeys.TRAINABLE_VARIABLES`. The convenience function
  `trainable_variables()` returns the contents of this collection. The
  various `Optimizer` classes use this collection as the default list of
  variables to optimize.
  """

  def __init__(self,
               initial_value=None,
               trainable=True,
               collections=None,
               validate_shape=True,
               caching_device=None,
               name=None,
               variable_def=None,
               dtype=None,
               expected_shape=None,
               import_scope=None):
    """Creates a new variable with value `initial_value`.

    The new variable is added to the graph collections listed in `collections`,
    which defaults to `[GraphKeys.GLOBAL_VARIABLES]`.

    If `trainable` is `True` the variable is also added to the graph collection
    `GraphKeys.TRAINABLE_VARIABLES`.

    This constructor creates both a `variable` Op and an `assign` Op to set the
    variable to its initial value.

    Args:
      initial_value: A `Tensor`, or Python object convertible to a `Tensor`,
        which is the initial value for the Variable. The initial value must have
        a shape specified unless `validate_shape` is set to False. Can also be a
        callable with no argument that returns the initial value when called. In
        that case, `dtype` must be specified. (Note that initializer functions
        from init_ops.py must first be bound to a shape before being used here.)
      trainable: If `True`, the default, also adds the variable to the graph
        collection `GraphKeys.TRAINABLE_VARIABLES`. This collection is used as
        the default list of variables to use by the `Optimizer` classes.
      collections: List of graph collections keys. The new variable is added to
        these collections. Defaults to `[GraphKeys.GLOBAL_VARIABLES]`.
      validate_shape: If `False`, allows the variable to be initialized with a
        value of unknown shape. If `True`, the default, the shape of
        `initial_value` must be known.
      caching_device: Optional device string describing where the Variable
        should be cached for reading.  Defaults to the Variable's device.
        If not `None`, caches on another device.  Typical use is to cache
        on the device where the Ops using the Variable reside, to deduplicate
        copying through `Switch` and other conditional statements.
      name: Optional name for the variable. Defaults to `'Variable'` and gets
        uniquified automatically.
      variable_def: `VariableDef` protocol buffer. If not `None`, recreates
        the Variable object with its contents. `variable_def` and the other
        arguments are mutually exclusive.
      dtype: If set, initial_value will be converted to the given type.
        If `None`, either the datatype will be kept (if `initial_value` is
        a Tensor), or `convert_to_tensor` will decide.
      expected_shape: A TensorShape. If set, initial_value is expected
        to have this shape.
      import_scope: Optional `string`. Name scope to add to the
        `Variable.` Only used when initializing from protocol buffer.

    Raises:
      ValueError: If both `variable_def` and initial_value are specified.
      ValueError: If the initial value is not specified, or does not have a
        shape and `validate_shape` is `True`.
    """
    if variable_def:
      # If variable_def is provided, recreates the variable from its fields.
      if initial_value:
        raise ValueError("variable_def and initial_value are mutually "
                         "exclusive.")
      self._init_from_proto(variable_def, import_scope=import_scope)
    else:
      # Create from initial_value.
      self._init_from_args(
          initial_value=initial_value,
          trainable=trainable,
          collections=collections,
          validate_shape=validate_shape,
          caching_device=caching_device,
          name=name,
          dtype=dtype,
          expected_shape=expected_shape)

  def __repr__(self):
    return "<tf.Variable '%s' shape=%s dtype=%s>" % (
            self.name, self.get_shape(), self.dtype.name)

  def _init_from_args(self,
                      initial_value=None,
                      trainable=True,
                      collections=None,
                      validate_shape=True,
                      caching_device=None,
                      name=None,
                      dtype=None,
                      expected_shape=None):
    """Creates a new variable from arguments.

    Args:
      initial_value: A `Tensor`, or Python object convertible to a `Tensor`,
        which is the initial value for the Variable. The initial value must have
        a shape specified unless `validate_shape` is set to False. Can also be a
        callable with no argument that returns the initial value when called.
        (Note that initializer functions  from init_ops.py must first be bound
         to a shape before being used here.)
      trainable: If `True`, the default, also adds the variable to the graph
        collection `GraphKeys.TRAINABLE_VARIABLES`. This collection is used as
        the default list of variables to use by the `Optimizer` classes.
      collections: List of graph collections keys. The new variable is added to
        these collections. Defaults to `[GraphKeys.GLOBAL_VARIABLES]`.
      validate_shape: If `False`, allows the variable to be initialized with a
        value of unknown shape. If `True`, the default, the shape of
        `initial_value` must be known.
      caching_device: Optional device string or function describing where the
        Variable should be cached for reading.  Defaults to the Variable's
        device.  If not `None`, caches on another device.  Typical use is to
        cache on the device where the Ops using the Variable reside, to
        deduplicate copying through `Switch` and other conditional statements.
      name: Optional name for the variable. Defaults to `'Variable'` and gets
        uniquified automatically.
      dtype: If set, initial_value will be converted to the given type.
        If None, either the datatype will be kept (if initial_value is
       a Tensor) or float32 will be used (if it is a Python object convertible
       to a Tensor).
      expected_shape: Deprecated. Ignored.

    Raises:
      ValueError: If the initial value is not specified, or does not have a
        shape and `validate_shape` is `True`.
    """
    _ = expected_shape
    if initial_value is None:
      raise ValueError("initial_value must be specified.")
    init_from_fn = callable(initial_value)

    if collections is None:
      collections = [ops.GraphKeys.GLOBAL_VARIABLES]
    if not isinstance(collections, (list, tuple, set)):
      raise ValueError(
          "collections argument to Variable constructor must be a list, tuple, "
          "or set. Got %s of type %s" % (collections, type(collections)))
    if trainable and ops.GraphKeys.TRAINABLE_VARIABLES not in collections:
      collections = list(collections) + [ops.GraphKeys.TRAINABLE_VARIABLES]
    with ops.control_dependencies(None):
      with ops.name_scope(name, "Variable", [] if init_from_fn else
                          [initial_value]) as name:

        if init_from_fn:
          # Use attr_scope and device(None) to simulate the behavior of
          # colocate_with when the variable we want to colocate with doesn't
          # yet exist.
          true_name = ops._name_from_scope_name(name)
          attr = attr_value_pb2.AttrValue(
              list=attr_value_pb2.AttrValue.ListValue(
                  s=[compat.as_bytes("loc:@%s" % true_name)]))
          # pylint: disable=protected-access
          with ops.get_default_graph()._attr_scope({"_class": attr}):
            with ops.name_scope("Initializer"),  ops.device(None):
              self._initial_value = ops.convert_to_tensor(
                  initial_value(), name="initial_value", dtype=dtype)
              shape = (self._initial_value.get_shape()
                       if validate_shape else tensor_shape.unknown_shape())
            self._variable = state_ops.variable_op_v2(
                shape,
                self._initial_value.dtype.base_dtype,
                name=name)

        # Or get the initial value from a Tensor or Python object.
        else:
          self._initial_value = ops.convert_to_tensor(
              initial_value, name="initial_value", dtype=dtype)
          shape = (self._initial_value.get_shape()
                   if validate_shape else tensor_shape.unknown_shape())
          # In this case, the variable op can't be created until after the
          # initial_value has been converted to a Tensor with a known type.
          self._variable = state_ops.variable_op_v2(
              shape,
              self._initial_value.dtype.base_dtype,
              name=name)

        # Manually overrides the variable's shape with the initial value's.
        if validate_shape:
          initial_value_shape = self._initial_value.get_shape()
          if not initial_value_shape.is_fully_defined():
            raise ValueError("initial_value must have a shape specified: %s" %
                             self._initial_value)

        # Assigns initial value.
        self._initializer_op = state_ops.assign(
            self._variable, self._initial_value,
            validate_shape=validate_shape).op

        # TODO(vrv): Change this class to not take caching_device, but
        # to take the op to colocate the snapshot with, so we can use
        # colocation rather than devices.
        if caching_device is not None:
          with ops.device(caching_device):
            self._snapshot = array_ops.identity(self._variable, name="read")
        else:
          with ops.colocate_with(self._variable.op):
            self._snapshot = array_ops.identity(self._variable, name="read")

    ops.add_to_collections(collections, self)
    self._caching_device = caching_device
    self._save_slice_info = None

  def _init_from_proto(self, variable_def, import_scope=None):
    """Creates a new variable from `VariableDef` protocol buffer.

    Args:
      variable_def: `VariableDef` protocol buffer.
      import_scope: Optional `string`. Name scope to add.
    """
    assert isinstance(variable_def, variable_pb2.VariableDef)
    # Create from variable_def.
    g = ops.get_default_graph()
    self._variable = g.as_graph_element(
        ops.prepend_name_scope(variable_def.variable_name,
                               import_scope=import_scope))
    self._initializer_op = g.as_graph_element(
        ops.prepend_name_scope(variable_def.initializer_name,
                               import_scope=import_scope))
    self._snapshot = g.as_graph_element(
        ops.prepend_name_scope(variable_def.snapshot_name,
                               import_scope=import_scope))
    if variable_def.HasField("save_slice_info_def"):
      self._save_slice_info = Variable.SaveSliceInfo(
          save_slice_info_def=variable_def.save_slice_info_def)
    else:
      self._save_slice_info = None
    self._caching_device = None

  def _as_graph_element(self):
    """Conversion function for Graph.as_graph_element()."""
    return self._variable

  def _AsTensor(self):  # pylint: disable=invalid-name
    """Converts this variable to a Tensor.

    See @{tf.Variable.value}.

    Returns:
      A `Tensor` containing the value of the variable.
    """
    return self._snapshot

  def __iter__(self):
    """Dummy method to prevent iteration. Do not call.

    NOTE(mrry): If we register __getitem__ as an overloaded operator,
    Python will valiantly attempt to iterate over the variable's Tensor from 0
    to infinity.  Declaring this method prevents this unintended behavior.

    Raises:
      TypeError: when invoked.
    """
    raise TypeError("'Variable' object is not iterable.")

  def value(self):
    """Returns the last snapshot of this variable.

    You usually do not need to call this method as all ops that need the value
    of the variable call it automatically through a `convert_to_tensor()` call.

    Returns a `Tensor` which holds the value of the variable.  You can not
    assign a new value to this tensor as it is not a reference to the variable.

    To avoid copies, if the consumer of the returned value is on the same device
    as the variable, this actually returns the live value of the variable, not
    a copy.  Updates to the variable are seen by the consumer.  If the consumer
    is on a different device it will get a copy of the variable.

    Returns:
      A `Tensor` containing the value of the variable.
    """
    return self._snapshot

  def read_value(self):
    """Returns the value of this variable, read in the current context.

    Can be different from value() if it's on another device, with control
    dependencies, etc.

    Returns:
      A `Tensor` containing the value of the variable.
    """
    return array_ops.identity(self._variable, name="read")

  def _ref(self):
    """Returns a reference to this variable.

    You usually do not need to call this method as all ops that need a reference
    to the variable call it automatically.

    Returns is a `Tensor` which holds a reference to the variable.  You can
    assign a new value to the variable by passing the tensor to an assign op.
    See @{tf.Variable.value} if you want to get the value of the
    variable.

    Returns:
      A `Tensor` that is a reference to the variable.
    """
    return self._variable

  def set_shape(self, shape):
    """Overrides the shape for this variable.

    Args:
      shape: the `TensorShape` representing the overridden shape.
    """
    self._ref().set_shape(shape)
    self.value().set_shape(shape)

  def eval(self, session=None):
    """In a session, computes and returns the value of this variable.

    This is not a graph construction method, it does not add ops to the graph.

    This convenience method requires a session where the graph
    containing this variable has been launched. If no session is
    passed, the default session is used.  See @{tf.Session} for more
    information on launching a graph and on sessions.

    ```python
    v = tf.Variable([1, 2])
    init = tf.global_variables_initializer()

    with tf.Session() as sess:
        sess.run(init)
        # Usage passing the session explicitly.
        print(v.eval(sess))
        # Usage with the default session.  The 'with' block
        # above makes 'sess' the default session.
        print(v.eval())
    ```

    Args:
      session: The session to use to evaluate this variable. If
        none, the default session is used.

    Returns:
      A numpy `ndarray` with a copy of the value of this variable.
    """
    return self._variable.eval(session=session)

  def initialized_value(self):
    """Returns the value of the initialized variable.

    You should use this instead of the variable itself to initialize another
    variable with a value that depends on the value of this variable.

    ```python
    # Initialize 'v' with a random tensor.
    v = tf.Variable(tf.truncated_normal([10, 40]))
    # Use `initialized_value` to guarantee that `v` has been
    # initialized before its value is used to initialize `w`.
    # The random values are picked only once.
    w = tf.Variable(v.initialized_value() * 2.0)
    ```

    Returns:
      A `Tensor` holding the value of this variable after its initializer
      has run.
    """
    with ops.control_dependencies(None):
      return control_flow_ops.cond(is_variable_initialized(self),
                                   self.read_value,
                                   lambda: self.initial_value)

  @property
  def initial_value(self):
    """Returns the Tensor used as the initial value for the variable.

    Note that this is different from `initialized_value()` which runs
    the op that initializes the variable before returning its value.
    This method returns the tensor that is used by the op that initializes
    the variable.

    Returns:
      A `Tensor`.
    """
    return self._initial_value

  def assign(self, value, use_locking=False):
    """Assigns a new value to the variable.

    This is essentially a shortcut for `assign(self, value)`.

    Args:
      value: A `Tensor`. The new value for this variable.
      use_locking: If `True`, use locking during the assignment.

    Returns:
      A `Tensor` that will hold the new value of this variable after
      the assignment has completed.
    """
    return state_ops.assign(self._variable, value, use_locking=use_locking)

  def assign_add(self, delta, use_locking=False):
    """Adds a value to this variable.

     This is essentially a shortcut for `assign_add(self, delta)`.

    Args:
      delta: A `Tensor`. The value to add to this variable.
      use_locking: If `True`, use locking during the operation.

    Returns:
      A `Tensor` that will hold the new value of this variable after
      the addition has completed.
    """
    return state_ops.assign_add(self._variable, delta, use_locking=use_locking)

  def assign_sub(self, delta, use_locking=False):
    """Subtracts a value from this variable.

    This is essentially a shortcut for `assign_sub(self, delta)`.

    Args:
      delta: A `Tensor`. The value to subtract from this variable.
      use_locking: If `True`, use locking during the operation.

    Returns:
      A `Tensor` that will hold the new value of this variable after
      the subtraction has completed.
    """
    return state_ops.assign_sub(self._variable, delta, use_locking=use_locking)

  def scatter_sub(self, sparse_delta, use_locking=False):
    """Subtracts `IndexedSlices` from this variable.

    This is essentially a shortcut for `scatter_sub(self, sparse_delta.indices,
    sparse_delta.values)`.

    Args:
      sparse_delta: `IndexedSlices` to be subtracted from this variable.
      use_locking: If `True`, use locking during the operation.

    Returns:
      A `Tensor` that will hold the new value of this variable after
      the scattered subtraction has completed.

    Raises:
      ValueError: if `sparse_delta` is not an `IndexedSlices`.
    """
    if not isinstance(sparse_delta, ops.IndexedSlices):
      raise ValueError("sparse_delta is not IndexedSlices: %s" % sparse_delta)
    return state_ops.scatter_sub(
        self._variable,
        sparse_delta.indices,
        sparse_delta.values,
        use_locking=use_locking)

  def count_up_to(self, limit):
    """Increments this variable until it reaches `limit`.

    When that Op is run it tries to increment the variable by `1`. If
    incrementing the variable would bring it above `limit` then the Op raises
    the exception `OutOfRangeError`.

    If no error is raised, the Op outputs the value of the variable before
    the increment.

    This is essentially a shortcut for `count_up_to(self, limit)`.

    Args:
      limit: value at which incrementing the variable raises an error.

    Returns:
      A `Tensor` that will hold the variable value before the increment. If no
      other Op modifies this variable, the values produced will all be
      distinct.
    """
    return state_ops.count_up_to(self._variable, limit=limit)

  def load(self, value, session=None):
    """Load new value into this variable

    Writes new value to variable's memory. Doesn't add ops to the graph.

    This convenience method requires a session where the graph
    containing this variable has been launched. If no session is
    passed, the default session is used.  See @{tf.Session} for more
    information on launching a graph and on sessions.

    ```python
    v = tf.Variable([1, 2])
    init = tf.global_variables_initializer()

    with tf.Session() as sess:
        sess.run(init)
        # Usage passing the session explicitly.
        v.load([2, 3], sess)
        print(v.eval(sess)) # prints [2 3]
        # Usage with the default session.  The 'with' block
        # above makes 'sess' the default session.
        v.load([3, 4], sess)
        print(v.eval()) # prints [3 4]
    ```

    Args:
        value: New variable value
        session: The session to use to evaluate this variable. If
          none, the default session is used.

    Raises:
        ValueError: Session is not passed and no default session
    """
    session = session or ops.get_default_session()
    if session is None:
      raise ValueError(
          "Either session argument should be provided or default session "
          "should be established")
    session.run(self._initializer_op, {self._initializer_op.inputs[1]: value})

  # Conversion to tensor.
  @staticmethod
  def _TensorConversionFunction(v, dtype=None, name=None, as_ref=False):  # pylint: disable=invalid-name
    """Utility function for converting a Variable to a Tensor."""
    _ = name
    if dtype and not dtype.is_compatible_with(v.dtype):
      raise ValueError(
          "Incompatible type conversion requested to type '%s' for variable "
          "of type '%s'" % (dtype.name, v.dtype.name))
    if as_ref:
      return v._ref()  # pylint: disable=protected-access
    else:
      return v.value()

  @staticmethod
  def _OverloadAllOperators():  # pylint: disable=invalid-name
    """Register overloads for all operators."""
    for operator in ops.Tensor.OVERLOADABLE_OPERATORS:
      Variable._OverloadOperator(operator)
    # For slicing, bind getitem differently than a tensor (use SliceHelperVar
    # instead)
    # pylint: disable=protected-access
    setattr(Variable, "__getitem__", array_ops._SliceHelperVar)

  @staticmethod
  def _OverloadOperator(operator):  # pylint: disable=invalid-name
    """Defer an operator overload to `ops.Tensor`.

    We pull the operator out of ops.Tensor dynamically to avoid ordering issues.

    Args:
      operator: string. The operator name.
    """

    def _run_op(a, *args):
      # pylint: disable=protected-access
      return getattr(ops.Tensor, operator)(a._AsTensor(), *args)
    # Propagate __doc__ to wrapper
    try:
      _run_op.__doc__ = getattr(ops.Tensor, operator).__doc__
    except AttributeError:
      pass

    setattr(Variable, operator, _run_op)

  # NOTE(mrry): This enables the Variable's overloaded "right" binary
  # operators to run when the left operand is an ndarray, because it
  # accords the Variable class higher priority than an ndarray, or a
  # numpy matrix.
  # TODO(mrry): Convert this to using numpy's __numpy_ufunc__
  # mechanism, which allows more control over how Variables interact
  # with ndarrays.
  __array_priority__ = 100

  @property
  def name(self):
    """The name of this variable."""
    return self._variable.name

  @property
  def initializer(self):
    """The initializer operation for this variable."""
    return self._initializer_op

  @property
  def device(self):
    """The device of this variable."""
    return self._variable.device

  @property
  def dtype(self):
    """The `DType` of this variable."""
    return self._variable.dtype

  @property
  def op(self):
    """The `Operation` of this variable."""
    return self._variable.op

  @property
  def graph(self):
    """The `Graph` of this variable."""
    return self._variable.graph

  @property
  def shape(self):
    """The `TensorShape` of this variable.

    Returns:
      A `TensorShape`.
    """
    return self._variable.get_shape()

  def get_shape(self):
    """Alias of Variable.shape."""
    return self.shape

  def to_proto(self, export_scope=None):
    """Converts a `Variable` to a `VariableDef` protocol buffer.

    Args:
      export_scope: Optional `string`. Name scope to remove.

    Returns:
      A `VariableDef` protocol buffer, or `None` if the `Variable` is not
      in the specified name scope.
    """
    if (export_scope is None or
        self._variable.name.startswith(export_scope)):
      var_def = variable_pb2.VariableDef()
      var_def.variable_name = ops.strip_name_scope(
          self._variable.name, export_scope)
      var_def.initializer_name = ops.strip_name_scope(
          self.initializer.name, export_scope)
      var_def.snapshot_name = ops.strip_name_scope(
          self._snapshot.name, export_scope)
      if self._save_slice_info:
        var_def.save_slice_info_def.MergeFrom(self._save_slice_info.to_proto(
            export_scope=export_scope))
      return var_def
    else:
      return None

  @staticmethod
  def from_proto(variable_def, import_scope=None):
    """Returns a `Variable` object created from `variable_def`."""
    return Variable(variable_def=variable_def,
                    import_scope=import_scope)

  class SaveSliceInfo(object):
    """Information on how to save this Variable as a slice.

    Provides internal support for saving variables as slices of a larger
    variable.  This API is not public and is subject to change.

    Available properties:

    * full_name
    * full_shape
    * var_offset
    * var_shape
    """

    def __init__(self,
                 full_name=None,
                 full_shape=None,
                 var_offset=None,
                 var_shape=None,
                 save_slice_info_def=None,
                 import_scope=None):
      """Create a `SaveSliceInfo`.

      Args:
        full_name: Name of the full variable of which this `Variable` is a
            slice.
        full_shape: Shape of the full variable, as a list of int.
        var_offset: Offset of this `Variable` into the full variable, as a
            list of int.
        var_shape: Shape of this `Variable`, as a list of int.
        save_slice_info_def: `SaveSliceInfoDef` protocol buffer. If not `None`,
          recreates the SaveSliceInfo object its contents.
          `save_slice_info_def` and other arguments are mutually
          exclusive.
        import_scope: Optional `string`. Name scope to add. Only used
          when initializing from protocol buffer.
      """
      if save_slice_info_def:
        assert isinstance(save_slice_info_def, variable_pb2.SaveSliceInfoDef)
        self.full_name = ops.prepend_name_scope(
            save_slice_info_def.full_name, import_scope=import_scope)
        self.full_shape = [i for i in save_slice_info_def.full_shape]
        self.var_offset = [i for i in save_slice_info_def.var_offset]
        self.var_shape = [i for i in save_slice_info_def.var_shape]
      else:
        self.full_name = full_name
        self.full_shape = full_shape
        self.var_offset = var_offset
        self.var_shape = var_shape

    @property
    def spec(self):
      """Computes the spec string used for saving."""
      full_shape_str = " ".join(["%d" % d for d in self.full_shape]) + " "
      sl_spec = ":".join([
          "%d,%d" % (o, s) for o, s in zip(self.var_offset, self.var_shape)
      ])
      return full_shape_str + sl_spec

    def to_proto(self, export_scope=None):
      """Returns a SaveSliceInfoDef() proto.

      Args:
        export_scope: Optional `string`. Name scope to remove.

      Returns:
        A `SaveSliceInfoDef` protocol buffer, or None if the `Variable` is not
        in the specified name scope.
      """
      if (export_scope is None or
          self.full_name.startswith(export_scope)):
        save_slice_info_def = variable_pb2.SaveSliceInfoDef()
        save_slice_info_def.full_name = ops.strip_name_scope(
            self.full_name, export_scope)
        for i in self.full_shape:
          save_slice_info_def.full_shape.append(i)
        for i in self.var_offset:
          save_slice_info_def.var_offset.append(i)
        for i in self.var_shape:
          save_slice_info_def.var_shape.append(i)
        return save_slice_info_def
      else:
        return None

  def _set_save_slice_info(self, save_slice_info):
    """Sets the slice info for this `Variable`.

    Args:
      save_slice_info: A `Variable.SaveSliceInfo` object.
    """
    self._save_slice_info = save_slice_info

  def _get_save_slice_info(self):
    return self._save_slice_info


class PartitionedVariable(object):
  """A container for partitioned `Variable` objects."""

  class PartitionedVariableIterator(object):
    """An iterator that allows accessing the underlying `Variable` objects.

    This iterator is necessary to control order of access when Variables
    are not partitioned in a standard way along a single axis.

    Allows e.g. `list(partitioned_variable)` to return a proper list.
    """

    def __init__(self, partitioned_variable):
      self._ix = 0
      self._partitioned_variable = partitioned_variable

    def __iter__(self):
      return self

    def __next__(self):  # For python3 compatibility.
      return self.next()

    def next(self):
      # pylint: disable=protected-access
      if self._ix >= len(self._partitioned_variable._variable_list):
        raise StopIteration()
      variable = self._partitioned_variable._variable_list[self._ix]
      # pylint: enable=protected-access
      self._ix += 1
      return variable

  def __init__(self, name, shape, dtype, variable_list, partitions):
    """Creates a new partitioned variable wrapper.

    Variables passed via the variable_list must contain a save_slice_info
    field.  Concatenation and iteration is in lexicographic order according
    to the var_offset property of the save_slice_info.

    Args:
      name: String. Overall name of the variables.
      shape: List of integers.  Overall shape of the variables.
      dtype: Type of the variables.
      variable_list: List of `Variable` that comprise this partitioned variable.
      partitions: List of integers.  Number of partitions for each dimension.

    Raises:
      TypeError: If `variable_list` is not a list of `Variable` objects, or
        `partitions` is not a list.
      ValueError: If `variable_list` is empty, or the `Variable` shape
        information does not match `shape`, or `partitions` has invalid values.
    """
    if not isinstance(variable_list, (list, tuple)):
      raise TypeError(
          "variable_list is not a list or tuple: %s" % variable_list)
    if not isinstance(partitions, (list, tuple)):
      raise TypeError("partitions is not a list or tuple: %s" % partitions)
    if not all([p >= 1 for p in partitions]):
      raise ValueError("partition values must be positive: %s" % partitions)
    if not variable_list:
      raise ValueError("variable_list may not be empty")
    # pylint: disable=protected-access
    for v in variable_list:
      # Sort the variable_list lexicographically according to var offset value.
      if not all([v._get_save_slice_info() is not None for v in variable_list]):
        raise ValueError(
            "All variables must have a save_slice_info available: %s"
            % [v.name for v in variable_list])
      if len(shape) != len(partitions):
        raise ValueError("len(shape) != len(partitions): %s vs. %s"
                         % (shape, partitions))
      if not all([v._get_save_slice_info().full_shape == shape]):
        raise ValueError(
            "All variables' full shapes must match shape: %s; "
            "but full shapes were: %s"
            % (shape, str([v._get_save_slice_info().full_shape])))
    self._variable_list = sorted(
        variable_list, key=lambda v: v._get_save_slice_info().var_offset)
    # pylint: enable=protected-access

    self._name = name
    self._shape = shape
    self._dtype = dtype
    self._partitions = partitions
    self._as_tensor = None

  def __iter__(self):
    """Return an iterable for accessing the underlying partition Variables."""
    return self.PartitionedVariableIterator(self)

  def __len__(self):
    num_partition_axes = len(self._partition_axes())
    if num_partition_axes > 1:
      raise ValueError("Cannot get a length for %d > 1 partition axes"
                       % num_partition_axes)
    return len(self._variable_list)

  def _partition_axes(self):
    if all([p == 1 for p in self._partitions]):
      return [0]
    else:
      return [i for i, p in enumerate(self._partitions) if p > 1]

  def _concat(self):
    """Returns the overall concatenated value as a `Tensor`.

    This is different from using the partitioned variable directly as a tensor
    (through tensor conversion and `as_tensor`) in that it creates a new set of
    operations that keeps the control dependencies from its scope.

    Returns:
      `Tensor` containing the concatenated value.
    """
    if len(self._variable_list) == 1:
      with ops.name_scope(None):
        return array_ops.identity(self._variable_list[0], name=self._name)

    partition_axes = self._partition_axes()

    if len(partition_axes) > 1:
      raise NotImplementedError(
          "Cannot concatenate along more than one dimension: %s.  "
          "Multi-axis partition concat is not supported" % str(partition_axes))
    partition_ix = partition_axes[0]

    with ops.name_scope(self._name + "/ConcatPartitions/"):
      concatenated = array_ops.concat(self._variable_list, partition_ix)

    with ops.name_scope(None):
      return array_ops.identity(concatenated, name=self._name)

  def as_tensor(self):
    """Returns the overall concatenated value as a `Tensor`.

    The returned tensor will not inherit the control dependencies from the scope
    where the value is used, which is similar to getting the value of
    `Variable`.

    Returns:
      `Tensor` containing the concatenated value.
    """
    with ops.control_dependencies(None):
      return self._concat()

  @staticmethod
  def _TensorConversionFunction(v, dtype=None, name=None, as_ref=False):
    # pylint: disable=invalid-name
    _ = name
    if dtype is not None and not dtype.is_compatible_with(v.dtype):
      raise ValueError(
          "Incompatible type conversion requested to type '%s' for variable "
          "of type '%s'" % (dtype.name, v.dtype.name))
    if as_ref:
      raise NotImplementedError(
          "PartitionedVariable doesn't support being used as a reference.")
    else:
      return v.as_tensor()

  @property
  def name(self):
    return self._name

  @property
  def dtype(self):
    return self._dtype

  def get_shape(self):
    return self._shape

  def _get_variable_list(self):
    return self._variable_list

  def _get_partitions(self):
    return self._partitions

  def assign(self, value, use_locking=False):
    _ = value, use_locking
    raise NotImplementedError(
        "assign() has not been implemented for PartitionedVariable.")


def global_variables():
  """Returns global variables.

  Global variables are variables that are shared across machines in a
  distributed environment. The `Variable()` constructor or `get_variable()`
  automatically adds new variables to the graph collection
  `GraphKeys.GLOBAL_VARIABLES`.
  This convenience function returns the contents of that collection.

  An alternative to global variables are local variables. See
  @{tf.local_variables}

  Returns:
    A list of `Variable` objects.
  """
  return ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES)


@deprecated("2017-03-02", "Please use tf.global_variables instead.")
def all_variables():
  """See `tf.global_variables`."""
  return global_variables()


def _all_saveable_objects():
  """Returns all variables and `SaveableObject`s that must be checkpointed.

  Returns:
    A list of `Variable` and `SaveableObject` to be checkpointed
  """
  # TODO(andreasst): make this function public once things are settled.
  return (ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES) +
          ops.get_collection(ops.GraphKeys.SAVEABLE_OBJECTS))


def local_variables():
  """Returns local variables.

  Local variables - per process variables, usually not saved/restored to
  checkpoint and used for temporary or intermediate values.
  For example, they can be used as counters for metrics computation or
  number of epochs this machine has read data.
  The `tf.contrib.framework.local_variable()` function automatically adds the
  new variable to `GraphKeys.LOCAL_VARIABLES`.
  This convenience function returns the contents of that collection.

  An alternative to local variables are global variables. See
  @{tf.global_variables}

  Returns:
    A list of local `Variable` objects.
  """
  return ops.get_collection(ops.GraphKeys.LOCAL_VARIABLES)


def model_variables():
  """Returns all variables in the MODEL_VARIABLES collection.

  Returns:
    A list of local Variable objects.
  """
  return ops.get_collection(ops.GraphKeys.MODEL_VARIABLES)


def trainable_variables():
  """Returns all variables created with `trainable=True`.

  When passed `trainable=True`, the `Variable()` constructor automatically
  adds new variables to the graph collection
  `GraphKeys.TRAINABLE_VARIABLES`. This convenience function returns the
  contents of that collection.

  Returns:
    A list of Variable objects.
  """
  return ops.get_collection(ops.GraphKeys.TRAINABLE_VARIABLES)


def moving_average_variables():
  """Returns all variables that maintain their moving averages.

  If an `ExponentialMovingAverage` object is created and the `apply()`
  method is called on a list of variables, these variables will
  be added to the `GraphKeys.MOVING_AVERAGE_VARIABLES` collection.
  This convenience function returns the contents of that collection.

  Returns:
    A list of Variable objects.
  """
  return ops.get_collection(ops.GraphKeys.MOVING_AVERAGE_VARIABLES)


def variables_initializer(var_list, name="init"):
  """Returns an Op that initializes a list of variables.

  After you launch the graph in a session, you can run the returned Op to
  initialize all the variables in `var_list`. This Op runs all the
  initializers of the variables in `var_list` in parallel.

  Calling `initialize_variables()` is equivalent to passing the list of
  initializers to `Group()`.

  If `var_list` is empty, however, the function still returns an Op that can
  be run. That Op just has no effect.

  Args:
    var_list: List of `Variable` objects to initialize.
    name: Optional name for the returned operation.

  Returns:
    An Op that run the initializers of all the specified variables.
  """
  if var_list:
    return control_flow_ops.group(*[v.initializer for v in var_list], name=name)
  return control_flow_ops.no_op(name=name)


@deprecated("2017-03-02", "Use `tf.variables_initializer` instead.")
def initialize_variables(var_list, name="init"):
  """See `tf.variables_initializer`."""
  return variables_initializer(var_list, name=name)


def global_variables_initializer():
  """Returns an Op that initializes global variables.

  This is just a shortcut for `variable_initializers(global_variables())`

  Returns:
    An Op that initializes global variables in the graph.
  """
  return variables_initializer(global_variables())


@deprecated("2017-03-02", "Use `tf.global_variables_initializer` instead.")
def initialize_all_variables():
  """See `tf.global_variables_initializer`."""
  return global_variables_initializer()


def local_variables_initializer():
  """Returns an Op that initializes all local variables.

  This is just a shortcut for `variable_initializers(local_variables())`

  Returns:
    An Op that initializes all local variables in the graph.
  """
  return variables_initializer(local_variables())


@deprecated("2017-03-02", "Use `tf.local_variables_initializer` instead.")
def initialize_local_variables():
  """See `tf.local_variables_initializer`."""
  return local_variables_initializer()


def is_variable_initialized(variable):
  """Tests if a variable has been initialized.

  Args:
    variable: A `Variable`.

  Returns:
    Returns a scalar boolean Tensor, `True` if the variable has been
    initialized, `False` otherwise.
  """
  return state_ops.is_variable_initialized(variable)


def assert_variables_initialized(var_list=None):
  """Returns an Op to check if variables are initialized.

  NOTE: This function is obsolete and will be removed in 6 months.  Please
  change your implementation to use `report_uninitialized_variables()`.

  When run, the returned Op will raise the exception `FailedPreconditionError`
  if any of the variables has not yet been initialized.

  Note: This function is implemented by trying to fetch the values of the
  variables. If one of the variables is not initialized a message may be
  logged by the C++ runtime. This is expected.

  Args:
    var_list: List of `Variable` objects to check. Defaults to the
      value of `global_variables().`

  Returns:
    An Op, or None if there are no variables.
  """
  if var_list is None:
    var_list = global_variables() + local_variables()
  # Backwards compatibility for old-style variables. TODO(touts): remove.
  if not var_list:
    var_list = []
    for op in ops.get_default_graph().get_operations():
      if op.type in ["Variable", "VariableV2", "AutoReloadVariable"]:
        var_list.append(op.outputs[0])
  if not var_list:
    return None
  else:
    ranks = []
    for var in var_list:
      with ops.colocate_with(var.op):
        ranks.append(array_ops.rank_internal(var, optimize=False))
    if len(ranks) == 1:
      return ranks[0]
    else:
      return array_ops.stack(ranks)


def report_uninitialized_variables(var_list=None,
                                   name="report_uninitialized_variables"):
  """Adds ops to list the names of uninitialized variables.

  When run, it returns a 1-D tensor containing the names of uninitialized
  variables if there are any, or an empty array if there are none.

  Args:
    var_list: List of `Variable` objects to check. Defaults to the
      value of `global_variables() + local_variables()`
    name: Optional name of the `Operation`.

  Returns:
    A 1-D tensor containing names of the uninitialized variables, or an empty
    1-D tensor if there are no variables or no uninitialized variables.
  """
  if var_list is None:
    var_list = global_variables() + local_variables()
    # Backwards compatibility for old-style variables. TODO(touts): remove.
    if not var_list:
      var_list = []
      for op in ops.get_default_graph().get_operations():
        if op.type in ["Variable", "VariableV2", "AutoReloadVariable"]:
          var_list.append(op.outputs[0])
  with ops.name_scope(name):
    if not var_list:
      # Return an empty tensor so we only need to check for returned tensor
      # size being 0 as an indication of model ready.
      return array_ops.constant([], dtype=dtypes.string)
    else:
      # Get a 1-D boolean tensor listing whether each variable is initialized.
      variables_mask = math_ops.logical_not(
          array_ops.stack(
              [state_ops.is_variable_initialized(v) for v in var_list]))
      # Get a 1-D string tensor containing all the variable names.
      variable_names_tensor = array_ops.constant([s.op.name for s in var_list])
      # Return a 1-D tensor containing all the names of uninitialized variables.
      return array_ops.boolean_mask(variable_names_tensor, variables_mask)

# pylint: disable=protected-access
ops.register_tensor_conversion_function(Variable,
                                        Variable._TensorConversionFunction)
Variable._OverloadAllOperators()

ops.register_tensor_conversion_function(
    PartitionedVariable, PartitionedVariable._TensorConversionFunction)
# pylint: enable=protected-access


ops.register_dense_tensor_like_type(Variable)

附錄2 variable_scope.py

# 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.
# ==============================================================================

"""A class to store named variables and a scope operator to manage sharing."""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import collections as collections_lib
import copy
import functools
import traceback

import six
from six.moves import xrange  # pylint: disable=redefined-builtin

from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.framework import tensor_shape
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import init_ops
from tensorflow.python.ops import resource_variable_ops
from tensorflow.python.ops import variables
from tensorflow.python.platform import tf_logging as logging
from tensorflow.python.util import tf_contextlib

__all__ = ["VariableScope", "get_variable_scope",
           "get_variable", "get_local_variable", "variable_scope",
           "variable_op_scope", "no_regularizer"]


class _PartitionInfo(object):
  """Holds partition info used by initializer functions.
  """

  def __init__(self, full_shape, var_offset):
    """Constructor.

    Args:
      full_shape: Tuple or list of `int` indicating the full combined shape
        of the partitioned variables.
      var_offset: Tuple or list of `int` specifying offset of this partition
        with respect to the full variable for each dimension.

    Raises:
      TypeError: If `full_shape` or `var_offset` is not a sequence.
      ValueError: If `full_shape` or `var_offset` differ in length. If
        `var_offset` exceeds `full_shape` in any dimension.
    """
    if not isinstance(full_shape, collections_lib.Sequence) or isinstance(
        full_shape, six.string_types):
      raise TypeError(
          "`full_shape` must be a sequence (like tuple or list) instead of " +
          type(full_shape).__name__)

    if not isinstance(var_offset, collections_lib.Sequence) or isinstance(
        var_offset, six.string_types):
      raise TypeError(
          "`var_offset` must be a sequence (like tuple or list) instead of " +
          type(var_offset).__name__)

    if len(var_offset) != len(full_shape):
      raise ValueError(
          "Expected equal length, but `var_offset` is of length {} while "
          "full_shape is of length {}.".format(
              len(var_offset), len(full_shape)))

    for i in xrange(len(full_shape)):
      offset = var_offset[i]
      shape = full_shape[i]
      if offset < 0 or offset >= shape:
        raise ValueError(
            "Expected 0 <= offset < shape but found offset={}, shape={} for "
            "var_offset={}, full_shape={}".format(offset, shape, var_offset,
                                                  full_shape))

    self._full_shape = full_shape
    self._var_offset = var_offset

  @property
  def full_shape(self):
    return self._full_shape

  @property
  def var_offset(self):
    return self._var_offset

  def single_offset(self, shape):
    """Returns the offset when the variable is partitioned in at most one dim.

    Args:
      shape: Tuple or list of `int` indicating the shape of one specific
        variable partition.

    Returns:
      `int` representing the offset in the dimension along which the variable is
       partitioned. Returns 0 if the variable is not being partitioned.

    Raises:
      ValueError: Depending on self.single_slice_dim().
    """

    single_slice_dim = self.single_slice_dim(shape)
    # If this variable is not being partitioned at all, single_slice_dim() could
    # return None.
    if single_slice_dim is None:
      return 0
    return self.var_offset[single_slice_dim]

  def single_slice_dim(self, shape):
    """Returns the slice dim when the variable is partitioned only in one dim.

    Args:
      shape: Tuple or list of `int` indicating the shape of one specific
        variable partition.

    Returns:
      `int` representing the dimension that the variable is partitioned in, or
      `None` if the variable doesn't seem to be partitioned at all.

    Raises:
      TypeError: If `shape` is not a sequence.
      ValueError: If `shape` is not the same length as `self.full_shape`. If
        the variable is partitioned in more than one dimension.
    """
    if not isinstance(shape, collections_lib.Sequence) or isinstance(
        shape, six.string_types):
      raise TypeError(
          "`shape` must be a sequence (like tuple or list) instead of " +
          type(shape).__name__)

    if len(shape) != len(self.full_shape):
      raise ValueError(
          "Expected equal length, but received shape={} of length {} while "
          "self.full_shape={} is of length {}.".format(shape, len(
              shape), self.full_shape, len(self.full_shape)))

    for i in xrange(len(shape)):
      if self.var_offset[i] + shape[i] > self.full_shape[i]:
        raise ValueError(
            "With self.var_offset={}, a partition of shape={} would exceed "
            "self.full_shape={} in dimension {}.".format(
                self.var_offset, shape, self.full_shape, i))

    slice_dim = None
    for i in xrange(len(shape)):
      if shape[i] == self.full_shape[i]:
        continue
      if slice_dim is not None:
        raise ValueError(
            "Cannot use single_slice_dim() with shape={} and "
            "self.full_shape={} since slice dim could be either dimension {} "
            "or {}.".format(shape, self.full_shape, i, slice_dim))
      slice_dim = i

    return slice_dim


class _VariableStore(object):
  """Variable store that carries a number of named Variables.

  New variable names and new variables can be created; all stored
  variables are initialized with the initializer passed to __init__.

  Attributes:
    vars: a dictionary with string names (same as passed in GetVar) as keys
          and the corresponding TensorFlow Variables as values.
  """

  def __init__(self):
    """Create a variable store."""
    self._vars = {}  # A dictionary of the stored TensorFlow variables.
    self._partitioned_vars = {}  # A dict of the stored PartitionedVariables.
    self.variable_scopes_count = {}  # Count re-used variable scopes.

  def open_variable_scope(self, scope_name):
    if scope_name in self.variable_scopes_count:
      self.variable_scopes_count[scope_name] += 1
    else:
      self.variable_scopes_count[scope_name] = 1

  def close_variable_subscopes(self, scope_name):
    for k in self.variable_scopes_count:
      if not scope_name or k.startswith(scope_name + "/"):
        self.variable_scopes_count[k] = 0

  def variable_scope_count(self, scope_name):
    return self.variable_scopes_count.get(scope_name, 0)

  def get_variable(self, name, shape=None, dtype=dtypes.float32,
                   initializer=None, regularizer=None, reuse=None,
                   trainable=True, collections=None, caching_device=None,
                   partitioner=None, validate_shape=True, use_resource=None,
                   custom_getter=None):
    """Gets an existing variable with these parameters or create a new one.

    If a variable with the given name is already stored, we return the stored
    variable. Otherwise, we create a new one.

    Set `reuse` to `True` when you only want to reuse existing Variables.
    Set `reuse` to `False` when you only want to create new Variables.
    If `reuse` is `None` (the default), both new and existing variables are
    returned.

    If initializer is `None` (the default), the default initializer passed in
    the constructor is used. If that one is `None` too, we use a new
    `glorot_uniform_initializer`. If initializer is a Tensor, we use
    it as a value and derive the shape from the initializer.

    If a partitioner is provided, a `PartitionedVariable` is returned.
    Accessing this object as a `Tensor` returns the shards concatenated along
    the partition axis.

    Some useful partitioners are available.  See, e.g.,
    `variable_axis_size_partitioner` and `min_max_variable_partitioner`.

    Args:
      name: The name of the new or existing variable.
      shape: Shape of the new or existing variable.
      dtype: Type of the new or existing variable (defaults to `DT_FLOAT`).
      initializer: Initializer for the variable.
      regularizer: A (Tensor -> Tensor or None) function; the result of
        applying it on a newly created variable will be added to the collection
        GraphKeys.REGULARIZATION_LOSSES and can be used for regularization.
      reuse: a Boolean or `None`. Controls reuse or creation of variables.
      trainable: If `True` also add the variable to the graph collection
        `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).
      collections: List of graph collections keys to add the `Variable` to.
        Defaults to `[GraphKeys.GLOBAL_VARIABLES]` (see `tf.Variable`).
      caching_device: Optional device string or function describing where the
        Variable should be cached for reading.  Defaults to the Variable's
        device.  If not `None`, caches on another device.  Typical use is to
        cache on the device where the Ops using the `Variable` reside, to
        deduplicate copying through `Switch` and other conditional statements.
      partitioner: Optional callable that accepts a fully defined `TensorShape`
        and dtype of the `Variable` to be created, and returns a list of
        partitions for each axis (currently only one axis can be partitioned).
      validate_shape: If False, allows the variable to be initialized with a
        value of unknown shape. If True, the default, the shape of initial_value
        must be known.
      use_resource: If False, creates a regular Variable. If True, creates
        instead an experimental ResourceVariable which has well-defined
        semantics. Defaults to False (will later change to True).
      custom_getter: Callable that takes as a first argument the true getter,
        and allows overwriting the internal get_variable method.
        The signature of `custom_getter` should match that of this method,
        but the most future-proof version will allow for changes:
        `def custom_getter(getter, *args, **kwargs)`.  Direct access to
        all `get_variable` parameters is also allowed:
        `def custom_getter(getter, name, *args, **kwargs)`.  A simple identity
        custom getter that simply creates variables with modified names is:
        ```python
        def custom_getter(getter, name, *args, **kwargs):
          return getter(name + '_suffix', *args, **kwargs)
        ```

    Returns:
      The created or existing `Variable` (or `PartitionedVariable`, if a
      partitioner was used).

    Raises:
      ValueError: when creating a new variable and shape is not declared,
        when reusing a variable