def variable_op(shape, dtype, name="Variable", set_shape=True, 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.
set_shape: If True, set the shape property of the returned Tensor to
the shape argument.
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.
"""
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 testUnknown(self):
tf_val = gen_state_ops._variable(shape=[3, 4, 7],
dtype=tf.float32,
name="tf_val",
container="",
shared_name="")
self.assertIs(None, tf.contrib.util.constant_value(tf_val))
def variable_op(shape, dtype, name="Variable", set_shape=True, 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.
set_shape: If True, set the shape property of the returned Tensor to
the shape argument.
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.
"""
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 testUnknown(self):
tf_val = gen_state_ops._variable(
shape=[3, 4, 7],
dtype=dtypes.float32,
name="tf_val",
container="",
shared_name="")
self.assertIs(None, tensor_util.constant_value(tf_val))
def testTwoDeviceFunctions(self):
with ops.Graph().as_default() as g:
var_0 = gen_state_ops._variable(shape=[1], dtype=dtypes.float32,
name="var_0", container="", shared_name="")
with g.device(test_device_func_pin_variable_to_cpu):
var_1 = gen_state_ops._variable(shape=[1], dtype=dtypes.float32,
name="var_1", container="", shared_name="")
var_2 = gen_state_ops._variable(shape=[1], dtype=dtypes.float32,
name="var_2", container="", shared_name="")
var_3 = gen_state_ops._variable(shape=[1], dtype=dtypes.float32,
name="var_3", container="", shared_name="")
with g.device(test_device_func_pin_variable_to_cpu):
var_4 = gen_state_ops._variable(shape=[1], dtype=dtypes.float32,
name="var_4", container="", shared_name="")
with g.device("/device:GPU:0"):
var_5 = gen_state_ops._variable(shape=[1], dtype=dtypes.float32,
name="var_5", container="", shared_name="")
var_6 = gen_state_ops._variable(shape=[1], dtype=dtypes.float32,
name="var_6", container="", shared_name="")
self.assertDeviceEqual(var_0.device, None)
self.assertDeviceEqual(var_1.device, "/device:CPU:0")
self.assertDeviceEqual(var_2.device, None)
self.assertDeviceEqual(var_3.device, None)
self.assertDeviceEqual(var_4.device, "/device:CPU:0")
self.assertDeviceEqual(var_5.device, "/device:GPU:0")
self.assertDeviceEqual(var_6.device, "/device:CPU:0")
def testTwoDeviceFunctions(self):
with ops.Graph().as_default() as g:
var_0 = gen_state_ops._variable(shape=[1], dtype=dtypes.float32,
name="var_0", container="", shared_name="")
with g.device(test_device_func_pin_variable_to_cpu):
var_1 = gen_state_ops._variable(shape=[1], dtype=dtypes.float32,
name="var_1", container="", shared_name="")
var_2 = gen_state_ops._variable(shape=[1], dtype=dtypes.float32,
name="var_2", container="", shared_name="")
var_3 = gen_state_ops._variable(shape=[1], dtype=dtypes.float32,
name="var_3", container="", shared_name="")
with g.device(test_device_func_pin_variable_to_cpu):
var_4 = gen_state_ops._variable(shape=[1], dtype=dtypes.float32,
name="var_4", container="", shared_name="")
with g.device("/device:GPU:0"):
var_5 = gen_state_ops._variable(shape=[1], dtype=dtypes.float32,
name="var_5", container="", shared_name="")
var_6 = gen_state_ops._variable(shape=[1], dtype=dtypes.float32,
name="var_6", container="", shared_name="")
self.assertDeviceEqual(var_0.device, None)
self.assertDeviceEqual(var_1.device, "/device:CPU:0")
self.assertDeviceEqual(var_2.device, None)
self.assertDeviceEqual(var_3.device, None)
self.assertDeviceEqual(var_4.device, "/device:CPU:0")
self.assertDeviceEqual(var_5.device, "/device:GPU:0")
self.assertDeviceEqual(var_6.device, "/device:CPU:0")
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(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 testDecay(self):
initial_lr = 0.1
k = 10
decay_rate = 0.96
step = gen_state_ops._variable(shape=[], dtype=dtypes.int32,
name="step", container="", shared_name="")
assign_step = state_ops.assign(step, 0)
increment_step = state_ops.assign_add(step, 1)
decayed_lr = learning_rate_decay.natural_exp_decay(initial_lr, step,
k, decay_rate)
with self.test_session():
assign_step.op.run()
for i in range(k+1):
expected = initial_lr * math.exp(-i / k * decay_rate)
self.assertAllClose(decayed_lr.eval(), expected, 1e-6)
increment_step.op.run()
def testDecay(self):
initial_lr = 0.1
k = 10
decay_rate = 0.96
step = gen_state_ops._variable(shape=[], dtype=dtypes.int32,
name="step", container="", shared_name="")
assign_step = state_ops.assign(step, 0)
increment_step = state_ops.assign_add(step, 1)
decayed_lr = learning_rate_decay.natural_exp_decay(initial_lr, step,
k, decay_rate)
with self.test_session():
assign_step.op.run()
for i in range(k+1):
expected = initial_lr * math.exp(-i / k * decay_rate)
self.assertAllClose(decayed_lr.eval(), expected, 1e-6)
increment_step.op.run()
def testAverageVariablesDeviceAssignment(self):
with tf.device("/job:dev_v0"):
v0 = tf.Variable(10.0, name="v0")
with tf.device("/job:dev_v1"):
v1 = gen_state_ops._variable(shape=[1], dtype=tf.float32,
name="v1", container="", shared_name="")
v1.set_shape([1])
tensor2 = v0 + v1
ema = tf.train.ExponentialMovingAverage(0.25, name="foo_avg")
with tf.device("/job:default"):
ema.apply([v0, v1, tensor2])
self.assertDeviceEqual("/job:dev_v0", ema.average(v0).device)
self.assertDeviceEqual("/job:dev_v1", ema.average(v1).device)
# However, the colocation property is maintained.
self.assertEqual([b"loc:@v1"],
ema.average(v1).op.colocation_groups())
self.assertDeviceEqual("/job:default", ema.average(tensor2).device)
def testAverageVariablesDeviceAssignment(self):
with tf.device("/job:dev_v0"):
v0 = tf.Variable(10.0, name="v0")
with tf.device("/job:dev_v1"):
v1 = gen_state_ops._variable(shape=[1], dtype=tf.float32,
name="v1", container="", shared_name="")
v1.set_shape([1])
tensor2 = v0 + v1
ema = tf.train.ExponentialMovingAverage(0.25, name="foo_avg")
with tf.device("/job:default"):
ema.apply([v0, v1, tensor2])
self.assertDeviceEqual("/job:dev_v0", ema.average(v0).device)
self.assertDeviceEqual("/job:dev_v1", ema.average(v1).device)
# However, the colocation property is maintained.
self.assertEqual([b"loc:@v1"],
ema.average(v1).op.colocation_groups())
self.assertDeviceEqual("/job:default", ema.average(tensor2).device)
def testContainer(self):
with tf.Graph().as_default():
v0 = tf.Variable([0])
with tf.container("l1"):
v1 = tf.Variable([1])
with tf.container("l2"):
v2 = tf.Variable([2])
special_v = gen_state_ops._variable(shape=[1], dtype=tf.float32,
name="VariableInL3", container="l3", shared_name="")
v3 = tf.Variable([3])
v4 = tf.Variable([4])
self.assertEqual(tf.compat.as_bytes(""), v0.op.get_attr("container"))
self.assertEqual(tf.compat.as_bytes("l1"), v1.op.get_attr("container"))
self.assertEqual(tf.compat.as_bytes("l2"), v2.op.get_attr("container"))
self.assertEqual(tf.compat.as_bytes("l3"),
special_v.op.get_attr("container"))
self.assertEqual(tf.compat.as_bytes("l1"), v3.op.get_attr("container"))
self.assertEqual(tf.compat.as_bytes(""), v4.op.get_attr("container"))
def testStaircase(self):
with self.test_session():
step = gen_state_ops._variable(shape=[], dtype=dtypes.int32,
name="step", container="", shared_name="")
assign_100 = state_ops.assign(step, 100)
assign_1 = state_ops.assign(step, 1)
assign_2 = state_ops.assign(step, 2)
decayed_lr = learning_rate_decay.exponential_decay(.1, step, 3, 0.96,
staircase=True)
# No change to learning rate
assign_1.op.run()
self.assertAllClose(decayed_lr.eval(), .1, 1e-6)
assign_2.op.run()
self.assertAllClose(decayed_lr.eval(), .1, 1e-6)
# Decayed learning rate
assign_100.op.run()
expected = .1 * 0.96 ** (100 // 3)
self.assertAllClose(decayed_lr.eval(), expected, 1e-6)
def testContainer(self):
with tf.Graph().as_default():
v0 = tf.Variable([0])
with tf.container("l1"):
v1 = tf.Variable([1])
with tf.container("l2"):
v2 = tf.Variable([2])
special_v = gen_state_ops._variable(shape=[1], dtype=tf.float32,
name="VariableInL3", container="l3", shared_name="")
v3 = tf.Variable([3])
v4 = tf.Variable([4])
self.assertEqual(tf.compat.as_bytes(""), v0.op.get_attr("container"))
self.assertEqual(tf.compat.as_bytes("l1"), v1.op.get_attr("container"))
self.assertEqual(tf.compat.as_bytes("l2"), v2.op.get_attr("container"))
self.assertEqual(tf.compat.as_bytes("l3"),
special_v.op.get_attr("container"))
self.assertEqual(tf.compat.as_bytes("l1"), v3.op.get_attr("container"))
self.assertEqual(tf.compat.as_bytes(""), v4.op.get_attr("container"))
def testStaircase(self):
with self.test_session():
step = gen_state_ops._variable(shape=[], dtype=dtypes.int32,
name="step", container="", shared_name="")
assign_100 = state_ops.assign(step, 100)
assign_1 = state_ops.assign(step, 1)
assign_2 = state_ops.assign(step, 2)
decayed_lr = learning_rate_decay.exponential_decay(.1, step, 3, 0.96,
staircase=True)
# No change to learning rate
assign_1.op.run()
self.assertAllClose(decayed_lr.eval(), .1, 1e-6)
assign_2.op.run()
self.assertAllClose(decayed_lr.eval(), .1, 1e-6)
# Decayed learning rate
assign_100.op.run()
expected = .1 * 0.96 ** (100 // 3)
self.assertAllClose(decayed_lr.eval(), expected, 1e-6)
def testStaircase(self):
initial_lr = 0.1
k = 10
decay_rate = 0.96
step = gen_state_ops._variable(shape=[], dtype=dtypes.int32,
name="step", container="", shared_name="")
assign_step = state_ops.assign(step, 0)
increment_step = state_ops.assign_add(step, 1)
decayed_lr = learning_rate_decay.inverse_time_decay(initial_lr,
step,
k,
decay_rate,
staircase=True)
with self.test_session():
assign_step.op.run()
for i in range(k+1):
expected = initial_lr / (1 + decay_rate * (i // k))
self.assertAllClose(decayed_lr.eval(), expected, 1e-6)
increment_step.op.run()
def testStaircase(self):
initial_lr = 0.1
k = 10
decay_rate = 0.96
step = gen_state_ops._variable(shape=[], dtype=dtypes.int32,
name="step", container="", shared_name="")
assign_step = state_ops.assign(step, 0)
increment_step = state_ops.assign_add(step, 1)
decayed_lr = learning_rate_decay.inverse_time_decay(initial_lr,
step,
k,
decay_rate,
staircase=True)
with self.test_session():
assign_step.op.run()
for i in range(k+1):
expected = initial_lr / (1 + decay_rate * (i // k))
self.assertAllClose(decayed_lr.eval(), expected, 1e-6)
increment_step.op.run()
def testSGDR(self):
k = 100
initial_lr = 0.5
t_0 = 2
mult_factor = 12
step = gen_state_ops._variable(shape=[],
dtype=dtypes.int32,
name="step",
container="",
shared_name="")
assign_step = state_ops.assign(step, 0)
increment_step = state_ops.assign_add(step, 1)
sgdr_lr = sgdr_decay(initial_lr, step, t_0, mult_factor)
with self.test_session():
assign_step.op.run()
for i in range(k + 1):
lr = sgdr_lr.eval()
print(lr)
increment_step.op.run()
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. 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 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: A TensorShape. If set, initial_value is expected
to have this shape.
Raises:
ValueError: If the initial value is not specified, or does not have a
shape and `validate_shape` is `True`.
"""
if initial_value is None:
raise ValueError("initial_value must be specified.")
init_from_fn = callable(initial_value)
if init_from_fn and dtype is None:
raise ValueError(
"dtype must also be specified when initial_value is callable.")
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]
expected_shape = tensor_shape.as_shape(expected_shape)
with ops.control_dependencies(None):
with ops.name_scope(name, "Variable", [] if init_from_fn else
[initial_value]) as name:
# Get the initial value from a callable function. The real shape of the
# variable will be set later, since under the init_from_fn case, the
# shape won't be known until after the function is invoked.
#
# NOTE: The current Variable OpKernel does not support
# partially defined shapes, so we only set the shape if it is
# fully defined. For historical reasons, we use the scalar
# shape (`[]`) to represent an unknown or partially known
# shape. A future version of the Variable ops will remove this
# limitation.
def full_shape_to_list(shape):
"""Returns shape as a list if shape is fully defined."""
if shape and shape.is_fully_defined():
return shape.as_list()
else:
return []
def assert_expected_shape():
"""Asserts that the initial value has the expected shape."""
if expected_shape:
expected_shape.assert_is_compatible_with(
self._initial_value.get_shape())
if init_from_fn:
expected_shape_list = full_shape_to_list(expected_shape)
set_shape = validate_shape and expected_shape.is_fully_defined()
self._variable = gen_state_ops._variable(
shape=expected_shape_list,
dtype=dtype.base_dtype,
name=name,
container="",
shared_name="")
if set_shape:
self._variable.set_shape(expected_shape_list)
with ops.colocate_with(self._variable.op):
with ops.name_scope("Initializer"):
# Colocate the tensors created by the initial_value() function
# with the variable itself.
self._initial_value = ops.convert_to_tensor(
initial_value(), name="initial_value", dtype=dtype)
assert_expected_shape()
# 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)
assert_expected_shape()
set_shape = (validate_shape
and self._initial_value.get_shape().is_fully_defined())
# 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 = gen_state_ops._variable(
shape=full_shape_to_list(self._initial_value.get_shape()),
dtype=self._initial_value.dtype.base_dtype,
name=name,
container="",
shared_name="")
if set_shape:
self._variable.set_shape(
full_shape_to_list(self._initial_value.get_shape()))
# 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)
self._variable.set_shape(initial_value_shape)
# TODO(b/28152992): Remove the below hack modifying the node_def shape
# directly once set_shape() handles it.
self._variable.op.node_def.attr["shape"].shape.CopyFrom(
initial_value_shape.as_proto())
# 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_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. 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.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: A TensorShape. If set, initial_value is expected
to have this shape.
Raises:
ValueError: If the initial value is not specified, or does not have a
shape and `validate_shape` is `True`.
"""
if initial_value is None:
raise ValueError("initial_value must be specified.")
init_from_fn = callable(initial_value)
if init_from_fn and dtype is None:
raise ValueError(
"dtype must also be specified when initial_value is callable.")
if collections is None:
collections = [ops.GraphKeys.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]
expected_shape = tensor_shape.as_shape(expected_shape)
with ops.control_dependencies(None):
with ops.name_scope(name, "Variable", [] if init_from_fn else
[initial_value]) as name:
# Get the initial value from a callable function. The real shape of the
# variable will be set later, since under the init_from_fn case, the
# shape won't be known until after the function is invoked.
#
# NOTE: The current Variable OpKernel does not support
# partially defined shapes, so we only set the shape if it is
# fully defined. For historical reasons, we use the scalar
# shape (`[]`) to represent an unknown or partially known
# shape. A future version of the Variable ops will remove this
# limitation.
def full_shape_to_list(shape):
"""Returns shape as a list if shape is fully defined."""
if shape and shape.is_fully_defined():
return shape.as_list()
else:
return []
def assert_expected_shape():
"""Asserts that the initial value has the expected shape."""
if expected_shape:
expected_shape.assert_is_compatible_with(
self._initial_value.get_shape())
if init_from_fn:
expected_shape_list = full_shape_to_list(expected_shape)
set_shape = validate_shape and expected_shape.is_fully_defined()
self._variable = gen_state_ops._variable(
shape=expected_shape_list,
dtype=dtype.base_dtype,
name=name,
container="",
shared_name="")
if set_shape:
self._variable.set_shape(expected_shape_list)
with ops.colocate_with(self._variable.op):
with ops.name_scope("Initializer"):
# Colocate the tensors created by the initial_value() function
# with the variable itself.
self._initial_value = ops.convert_to_tensor(
initial_value(), name="initial_value", dtype=dtype)
assert_expected_shape()
# 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)
assert_expected_shape()
set_shape = (validate_shape
and self._initial_value.get_shape().is_fully_defined())
# 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 = gen_state_ops._variable(
shape=full_shape_to_list(self._initial_value.get_shape()),
dtype=self._initial_value.dtype.base_dtype,
name=name,
container="",
shared_name="")
if set_shape:
self._variable.set_shape(
full_shape_to_list(self._initial_value.get_shape()))
# 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)
self._variable.set_shape(initial_value_shape)
# TODO(b/28152992): Remove the below hack modifying the node_def shape
# directly once set_shape() handles it.
self._variable.op.node_def.attr["shape"].shape.CopyFrom(
initial_value_shape.as_proto())
# 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
