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# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Variable class."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import enum # pylint: disable=g-bad-import-order
import six
from tensorflow.core.framework import attr_value_pb2
from tensorflow.core.framework import variable_pb2
from tensorflow.python.eager import context
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 gen_array_ops
from tensorflow.python.ops import gen_state_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import state_ops
from tensorflow.python.platform import tf_logging as logging
from tensorflow.python.training.checkpointable import base as checkpointable
from tensorflow.python.util import compat
from tensorflow.python.util import tf_should_use
from tensorflow.python.util.deprecation import deprecated
from tensorflow.python.util.tf_export import tf_export
def default_variable_creator(_, **kwds):
del kwds
raise NotImplementedError("variable_scope needs to be imported")
def _make_getter(captured_getter, captured_previous):
"""To avoid capturing loop variables."""
def getter(**kwargs):
return captured_getter(captured_previous, **kwargs)
return getter
@tf_export("VariableSynchronization")
class VariableSynchronization(enum.Enum):
"""Indicates when a distributed variable will be synced.
* `AUTO`: Indicates that the synchronization will be determined by the current
`DistributionStrategy` (eg. With `MirroredStrategy` this would be
`ON_WRITE`).
* `NONE`: Indicates that there will only be one copy of the variable, so
there is no need to sync.
* `ON_WRITE`: Indicates that the variable will be updated across devices
every time it is written.
* `ON_READ`: Indicates that the variable will be aggregated across devices
when it is read (eg. when checkpointing or when evaluating an op that uses
the variable).
"""
AUTO = 0
NONE = 1
ON_WRITE = 2
ON_READ = 3
@tf_export("VariableAggregation")
class VariableAggregation(enum.Enum):
"""Indicates how a distributed variable will be aggregated.
`tf.contrib.distribute.DistributionStrategy` distributes a model by making
multiple copies (called "towers") acting data-parallel on different elements
of the input batch. When performing some variable-update operation, say
`var.assign_add(x)`, in a model, we need to resolve how to combine the
different values for `x` computed in the different towers.
* `NONE`: This is the default, giving an error if you use a
variable-update operation with multiple towers.
* `SUM`: Add the updates across towers.
* `MEAN`: Take the arithmetic mean ("average") of the updates across towers.
* `ONLY_FIRST_TOWER`: This is for when every tower is performing the same
update, but we only want to perform the update once. Used, e.g., for the
global step counter.
"""
NONE = 0
SUM = 1
MEAN = 2
ONLY_FIRST_TOWER = 3
class VariableMetaclass(type):
"""Metaclass to allow construction of tf.Variable to be overridden."""
def _variable_call(cls,
initial_value=None,
trainable=None,
collections=None,
validate_shape=True,
caching_device=None,
name=None,
variable_def=None,
dtype=None,
expected_shape=None,
import_scope=None,
constraint=None,
use_resource=None,
synchronization=VariableSynchronization.AUTO,
aggregation=VariableAggregation.NONE):
"""Call on Variable class. Useful to force the signature."""
previous_getter = lambda **kwargs: default_variable_creator(None, **kwargs)
for getter in ops.get_default_graph()._variable_creator_stack: # pylint: disable=protected-access
previous_getter = _make_getter(getter, previous_getter)
# Reset `aggregation` that is explicitly set as `None` to the enum NONE.
if aggregation is None:
aggregation = VariableAggregation.NONE
return previous_getter(
initial_value=initial_value,
trainable=trainable,
collections=collections,
validate_shape=validate_shape,
caching_device=caching_device,
name=name,
variable_def=variable_def,
dtype=dtype,
expected_shape=expected_shape,
import_scope=import_scope,
constraint=constraint,
use_resource=use_resource,
synchronization=synchronization,
aggregation=aggregation)
def __call__(cls, *args, **kwargs):
if cls is Variable:
return cls._variable_call(*args, **kwargs)
else:
return super(VariableMetaclass, cls).__call__(*args, **kwargs)
@tf_export("Variable")
class Variable(six.with_metaclass(VariableMetaclass,
checkpointable.CheckpointableBase)):
"""See the [Variables Guide](https://tensorflow.org/guide/variables).
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.
WARNING: tf.Variable objects by default have a non-intuitive memory model. A
Variable is represented internally as a mutable Tensor which can
non-deterministically alias other Tensors in a graph. The set of operations
which consume a Variable and can lead to aliasing is undetermined and can
change across TensorFlow versions. Avoid writing code which relies on the
value of a Variable either changing or not changing as other operations
happen. For example, using Variable objects or simple functions thereof as
predicates in a `tf.cond` is dangerous and error-prone:
```
v = tf.Variable(True)
tf.cond(v, lambda: v.assign(False), my_false_fn) # Note: this is broken.
```
Here replacing adding `use_resource=True` when constructing the variable will
fix any nondeterminism issues:
```
v = tf.Variable(True, use_resource=True)
tf.cond(v, lambda: v.assign(False), my_false_fn)
```
To use the replacement for variables which does
not have these issues:
* Add `use_resource=True` when constructing `tf.Variable`;
* Call `tf.get_variable_scope().set_use_resource(True)` inside a
`tf.variable_scope` before the `tf.get_variable()` call.
"""
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,
constraint=None,
use_resource=None,
synchronization=VariableSynchronization.AUTO,
aggregation=VariableAggregation.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, referencing the variable's nodes
in the graph, which must already exist. The graph is not changed.
`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.
constraint: An optional projection function to be applied to the variable
after being updated by an `Optimizer` (e.g. used to implement norm
constraints or value constraints for layer weights). The function must
take as input the unprojected Tensor representing the value of the
variable and return the Tensor for the projected value
(which must have the same shape). Constraints are not safe to
use when doing asynchronous distributed training.
use_resource: if True, a ResourceVariable is created; otherwise an
old-style ref-based variable is created. When eager execution is enabled
a resource variable is always created.
synchronization: Indicates when a distributed a variable will be
aggregated. Accepted values are constants defined in the class
`tf.VariableSynchronization`. By default the synchronization is set to
`AUTO` and the current `DistributionStrategy` chooses
when to synchronize. If `synchronization` is set to `ON_READ`,
`trainable` must not be set to `True`.
aggregation: Indicates how a distributed variable will be aggregated.
Accepted values are constants defined in the class
`tf.VariableAggregation`.
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`.
RuntimeError: If eager execution is enabled.
"""
raise NotImplementedError
def __repr__(self):
raise NotImplementedError
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.
"""
raise NotImplementedError
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.
"""
raise NotImplementedError
def set_shape(self, shape):
"""Overrides the shape for this variable.
Args:
shape: the `TensorShape` representing the overridden shape.
"""
raise NotImplementedError
@property
def trainable(self):
raise NotImplementedError
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.
"""
raise NotImplementedError
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.
"""
raise NotImplementedError
@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`.
"""
raise NotImplementedError
@property
def constraint(self):
"""Returns the constraint function associated with this variable.
Returns:
The constraint function that was passed to the variable constructor.
Can be `None` if no constraint was passed.
"""
raise NotImplementedError
def assign(self, value, use_locking=False, name=None, read_value=True):
"""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.
name: The name of the operation to be created
read_value: if True, will return something which evaluates to the
new value of the variable; if False will return the assign op.
Returns:
A `Tensor` that will hold the new value of this variable after
the assignment has completed.
"""
raise NotImplementedError
def assign_add(self, delta, use_locking=False, name=None, read_value=True):
"""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.
name: The name of the operation to be created
read_value: if True, will return something which evaluates to the
new value of the variable; if False will return the assign op.
Returns:
A `Tensor` that will hold the new value of this variable after
the addition has completed.
"""
raise NotImplementedError
def assign_sub(self, delta, use_locking=False, name=None, read_value=True):
"""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.
name: The name of the operation to be created
read_value: if True, will return something which evaluates to the
new value of the variable; if False will return the assign op.
Returns:
A `Tensor` that will hold the new value of this variable after
the subtraction has completed.
"""
raise NotImplementedError
def scatter_sub(self, sparse_delta, use_locking=False, name=None):
"""Subtracts `IndexedSlices` from this variable.
Args:
sparse_delta: `IndexedSlices` to be subtracted from this variable.
use_locking: If `True`, use locking during the operation.
name: the name of 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`.
"""
raise NotImplementedError
def scatter_add(self, sparse_delta, use_locking=False, name=None):
"""Adds `IndexedSlices` to this variable.
Args:
sparse_delta: `IndexedSlices` to be assigned to this variable.
use_locking: If `True`, use locking during the operation.
name: the name of 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`.
"""
raise NotImplementedError
def scatter_update(self, sparse_delta, use_locking=False, name=None):
"""Assigns `IndexedSlices` to this variable.
Args:
sparse_delta: `IndexedSlices` to be assigned to this variable.
use_locking: If `True`, use locking during the operation.
name: the name of 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`.
"""
raise NotImplementedError
def scatter_nd_sub(self, indices, updates, name=None):
"""Applies sparse subtraction to individual values or slices in a Variable.
`ref` is a `Tensor` with rank `P` and `indices` is a `Tensor` of rank `Q`.
`indices` must be integer tensor, containing indices into `ref`.
It must be shape `[d_0, ..., d_{Q-2}, K]` where `0 < K <= P`.
The innermost dimension of `indices` (with length `K`) corresponds to
indices into elements (if `K = P`) or slices (if `K < P`) along the `K`th
dimension of `ref`.
`updates` is `Tensor` of rank `Q-1+P-K` with shape:
```
[d_0, ..., d_{Q-2}, ref.shape[K], ..., ref.shape[P-1]].
```
For example, say we want to add 4 scattered elements to a rank-1 tensor to
8 elements. In Python, that update would look like this:
```python
ref = tf.Variable([1, 2, 3, 4, 5, 6, 7, 8])
indices = tf.constant([[4], [3], [1] ,[7]])
updates = tf.constant([9, 10, 11, 12])
op = ref.scatter_nd_sub(indices, updates)
with tf.Session() as sess:
print sess.run(op)
```
The resulting update to ref would look like this:
[1, -9, 3, -6, -6, 6, 7, -4]
See `tf.scatter_nd` for more details about how to make updates to
slices.
Args:
indices: The indices to be used in the operation.
updates: The values to be used in the operation.
name: the name of 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`.
"""
raise NotImplementedError
def scatter_nd_add(self, indices, updates, name=None):
"""Applies sparse addition to individual values or slices in a Variable.
`ref` is a `Tensor` with rank `P` and `indices` is a `Tensor` of rank `Q`.
`indices` must be integer tensor, containing indices into `ref`.
It must be shape `[d_0, ..., d_{Q-2}, K]` where `0 < K <= P`.
The innermost dimension of `indices` (with length `K`) corresponds to
indices into elements (if `K = P`) or slices (if `K < P`) along the `K`th
dimension of `ref`.
`updates` is `Tensor` of rank `Q-1+P-K` with shape:
```
[d_0, ..., d_{Q-2}, ref.shape[K], ..., ref.shape[P-1]].
```
For example, say we want to add 4 scattered elements to a rank-1 tensor to
8 elements. In Python, that update would look like this:
```python
ref = tf.Variable([1, 2, 3, 4, 5, 6, 7, 8])
indices = tf.constant([[4], [3], [1] ,[7]])
updates = tf.constant([9, 10, 11, 12])
add = ref.scatter_nd_add(indices, updates)
with tf.Session() as sess:
print sess.run(add)
```
The resulting update to ref would look like this:
[1, 13, 3, 14, 14, 6, 7, 20]
See `tf.scatter_nd` for more details about how to make updates to
slices.
Args:
indices: The indices to be used in the operation.
updates: The values to be used in the operation.
name: the name of 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`.
"""
raise NotImplementedError
def scatter_nd_update(self, indices, updates, name=None):
"""Applies sparse assignment to individual values or slices in a Variable.
`ref` is a `Tensor` with rank `P` and `indices` is a `Tensor` of rank `Q`.
`indices` must be integer tensor, containing indices into `ref`.
It must be shape `[d_0, ..., d_{Q-2}, K]` where `0 < K <= P`.
The innermost dimension of `indices` (with length `K`) corresponds to
indices into elements (if `K = P`) or slices (if `K < P`) along the `K`th
dimension of `ref`.
`updates` is `Tensor` of rank `Q-1+P-K` with shape:
```
[d_0, ..., d_{Q-2}, ref.shape[K], ..., ref.shape[P-1]].
```
For example, say we want to add 4 scattered elements to a rank-1 tensor to
8 elements. In Python, that update would look like this:
```python
ref = tf.Variable([1, 2, 3, 4, 5, 6, 7, 8])
indices = tf.constant([[4], [3], [1] ,[7]])
updates = tf.constant([9, 10, 11, 12])
op = ref.scatter_nd_assign(indices, updates)
with tf.Session() as sess:
print sess.run(op)
```
The resulting update to ref would look like this:
[1, 11, 3, 10, 9, 6, 7, 12]
See `tf.scatter_nd` for more details about how to make updates to
slices.
Args:
indices: The indices to be used in the operation.
updates: The values to be used in the operation.
name: the name of 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`.
"""
raise NotImplementedError
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.
"""
raise NotImplementedError
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
"""
raise NotImplementedError
# 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."""
raise NotImplementedError
@property
def initializer(self):
"""The initializer operation for this variable."""
raise NotImplementedError
@property
def device(self):
"""The device of this variable."""
raise NotImplementedError
@property
def dtype(self):
"""The `DType` of this variable."""
raise NotImplementedError
@property
def op(self):
"""The `Operation` of this variable."""
raise NotImplementedError
@property
def graph(self):
"""The `Graph` of this variable."""
raise NotImplementedError
@property
def shape(self):
"""The `TensorShape` of this variable.
Returns:
A `TensorShape`.
"""
raise NotImplementedError
def get_shape(self):
"""Alias of Variable.shape."""
raise NotImplementedError
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.
"""
raise NotImplementedError
@staticmethod
def from_proto(variable_def, import_scope=None):
"""Returns a `Variable` object created from `variable_def`."""
return RefVariable(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 __iadd__(self, other):
raise NotImplementedError
def __isub__(self, other):
raise NotImplementedError
def __imul__(self, other):
raise NotImplementedError
def __idiv__(self, other):
raise NotImplementedError
def __itruediv__(self, other):
raise NotImplementedError
def __irealdiv__(self, other):
raise NotImplementedError
def __ipow__(self, other):
raise NotImplementedError
# TODO(apassos): do not repeat all comments here
class RefVariable(Variable):
"""Ref-based implementation of variables."""
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,
constraint=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, referencing the variable's nodes
in the graph, which must already exist. The graph is not changed.
`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.
constraint: An optional projection function to be applied to the variable
after being updated by an `Optimizer` (e.g. used to implement norm
constraints or value constraints for layer weights). The function must
take as input the unprojected Tensor representing the value of the
variable and return the Tensor for the projected value
(which must have the same shape). Constraints are not safe to
use when doing asynchronous distributed training.
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`.
RuntimeError: If eager execution is enabled.
"""
self._in_graph_mode = 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,
constraint=constraint)
def __repr__(self):
if context.executing_eagerly() and not self._in_graph_mode:
return "<tf.Variable '%s' shape=%s dtype=%s, numpy=%s>" % (
self.name, self.get_shape(), self.dtype.name,
ops.numpy_text(self.read_value(), is_repr=True))
else:
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,
constraint=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.
constraint: An optional projection function to be applied to the variable
after being updated by an `Optimizer` (e.g. used to implement norm
constraints or value constraints for layer weights). The function must
take as input the unprojected Tensor representing the value of the
variable and return the Tensor for the projected value
(which must have the same shape). Constraints are not safe to
use when doing asynchronous distributed training.
Raises:
ValueError: If the initial value is not specified, or does not have a
shape and `validate_shape` is `True`.
RuntimeError: If lifted into the eager context.
"""
_ = 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 constraint is not None and not callable(constraint):
raise ValueError("The `constraint` argument must be a callable.")
# Store the graph key so optimizers know how to only retrieve variables from
# this graph.
self._graph_key = ops.get_default_graph()._graph_key # pylint: disable=protected-access
if isinstance(initial_value, checkpointable.CheckpointInitialValue):
self._maybe_initialize_checkpointable()
self._update_uid = initial_value.checkpoint_position.restore_uid
initial_value = initial_value.wrapped_value
self._trainable = trainable
if trainable and ops.GraphKeys.TRAINABLE_VARIABLES not in collections:
collections = list(collections) + [ops.GraphKeys.TRAINABLE_VARIABLES]
with ops.init_scope():
# Ensure that we weren't lifted into the eager context.
if context.executing_eagerly():
raise RuntimeError(
"RefVariable not supported when eager execution is enabled. ")
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) # pylint: disable=protected-access
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)
# pylint: enable=protected-access
# 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)
# pylint: disable=protected-access
if self._initial_value.op._get_control_flow_context() is not None:
raise ValueError(
"Initializer for variable %s is from inside a control-flow "
"construct, such as a loop or conditional. When creating a "
"variable inside a loop or conditional, use a lambda as the "
"initializer." % name)
# pylint: enable=protected-access
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)
# If 'initial_value' makes use of other variables, make sure we don't
# have an issue if these other variables aren't initialized first by
# using their initialized_value() method.
self._initializer_op = state_ops.assign(
self._variable,
self._try_guard_against_uninitialized_dependencies(
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
self._constraint = constraint
def _init_from_proto(self, variable_def, import_scope=None):
"""Recreates the Variable object from a `VariableDef` protocol buffer.
Args:
variable_def: `VariableDef` protocol buffer, describing a variable
whose nodes already exists in the graph.
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))
# Tests whether initial_value_name exists first for backwards compatibility.
if (hasattr(variable_def, "initial_value_name") and
variable_def.initial_value_name):
self._initial_value = g.as_graph_element(
ops.prepend_name_scope(variable_def.initial_value_name,
import_scope=import_scope))
else:
self._initial_value = None
self._trainable = getattr(variable_def, "trainable", True)
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,
import_scope=import_scope)
else:
self._save_slice_info = None
self._caching_device = None
self._constraint = 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)
@property
def trainable(self):
return self._trainable
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.init_scope():
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
@property
def constraint(self):
"""Returns the constraint function associated with this variable.
Returns:
The constraint function that was passed to the variable constructor.
Can be `None` if no constraint was passed.
"""
return self._constraint
def assign(self, value, use_locking=False, name=None, read_value=True):
"""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.
name: The name of the operation to be created
read_value: if True, will return something which evaluates to the
new value of the variable; if False will return the assign op.
Returns:
A `Tensor` that will hold the new value of this variable after
the assignment has completed.
"""
assign = state_ops.assign(self._variable, value, use_locking=use_locking,
name=name)
if read_value:
return assign
return assign.op
def assign_add(self, delta, use_locking=False, name=None, read_value=True):
"""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.
name: The name of the operation to be created
read_value: if True, will return something which evaluates to the
new value of the variable; if False will return the assign op.
Returns:
A `Tensor` that will hold the new value of this variable after
the addition has completed.
"""
assign = state_ops.assign_add(
self._variable, delta, use_locking=use_locking, name=name)
if read_value:
return assign
return assign.op
def assign_sub(self, delta, use_locking=False, name=None, read_value=True):
"""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.
name: The name of the operation to be created
read_value: if True, will return something which evaluates to the
new value of the variable; if False will return the assign op.
Returns:
A `Tensor` that will hold the new value of this variable after
the subtraction has completed.
"""
assign = state_ops.assign_sub(
self._variable, delta, use_locking=use_locking, name=name)
if read_value:
return assign
return assign.op
def scatter_sub(self, sparse_delta, use_locking=False, name=None):
"""Subtracts `IndexedSlices` from this variable.
Args:
sparse_delta: `IndexedSlices` to be subtracted from this variable.
use_locking: If `True`, use locking during the operation.
name: the name of 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 gen_state_ops.scatter_sub(
self._variable,
sparse_delta.indices,
sparse_delta.values,
use_locking=use_locking,
name=name)
def scatter_add(self, sparse_delta, use_locking=False, name=None):
"""Adds `IndexedSlices` from this variable.
Args:
sparse_delta: `IndexedSlices` to be added to this variable.
use_locking: If `True`, use locking during the operation.
name: the name of 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 gen_state_ops.scatter_add(
self._variable,
sparse_delta.indices,
sparse_delta.values,
use_locking=use_locking,
name=name)
def scatter_update(self, sparse_delta, use_locking=False, name=None):
"""Assigns `IndexedSlices` to this variable.
Args:
sparse_delta: `IndexedSlices` to be assigned to this variable.
use_locking: If `True`, use locking during the operation.
name: the name of 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 gen_state_ops.scatter_update(
self._variable,
sparse_delta.indices,
sparse_delta.values,
use_locking=use_locking,
name=name)
def scatter_nd_sub(self, indices, updates, name=None):
"""Applies sparse subtraction to individual values or slices in a Variable.
`ref` is a `Tensor` with rank `P` and `indices` is a `Tensor` of rank `Q`.
`indices` must be integer tensor, containing indices into `ref`.
It must be shape `[d_0, ..., d_{Q-2}, K]` where `0 < K <= P`.
The innermost dimension of `indices` (with length `K`) corresponds to
indices into elements (if `K = P`) or slices (if `K < P`) along the `K`th
dimension of `ref`.
`updates` is `Tensor` of rank `Q-1+P-K` with shape:
```
[d_0, ..., d_{Q-2}, ref.shape[K], ..., ref.shape[P-1]].
```
For example, say we want to add 4 scattered elements to a rank-1 tensor to
8 elements. In Python, that update would look like this:
```python
ref = tf.Variable([1, 2, 3, 4, 5, 6, 7, 8])
indices = tf.constant([[4], [3], [1] ,[7]])
updates = tf.constant([9, 10, 11, 12])
op = ref.scatter_nd_sub(indices, updates)
with tf.Session() as sess:
print sess.run(op)
```
The resulting update to ref would look like this:
[1, -9, 3, -6, -6, 6, 7, -4]
See `tf.scatter_nd` for more details about how to make updates to
slices.
Args:
indices: The indices to be used in the operation.
updates: The values to be used in the operation.
name: the name of 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`.
"""
return gen_state_ops.scatter_nd_sub(
self._variable, indices, updates, use_locking=True, name=name)
def scatter_nd_add(self, indices, updates, name=None):
"""Applies sparse addition to individual values or slices in a Variable.
`ref` is a `Tensor` with rank `P` and `indices` is a `Tensor` of rank `Q`.
`indices` must be integer tensor, containing indices into `ref`.
It must be shape `[d_0, ..., d_{Q-2}, K]` where `0 < K <= P`.
The innermost dimension of `indices` (with length `K`) corresponds to
indices into elements (if `K = P`) or slices (if `K < P`) along the `K`th
dimension of `ref`.
`updates` is `Tensor` of rank `Q-1+P-K` with shape:
```
[d_0, ..., d_{Q-2}, ref.shape[K], ..., ref.shape[P-1]].
```
For example, say we want to add 4 scattered elements to a rank-1 tensor to
8 elements. In Python, that update would look like this:
```python
ref = tf.Variable([1, 2, 3, 4, 5, 6, 7, 8])
indices = tf.constant([[4], [3], [1] ,[7]])
updates = tf.constant([9, 10, 11, 12])
add = ref.scatter_nd_add(indices, updates)
with tf.Session() as sess:
print sess.run(add)
```
The resulting update to ref would look like this:
[1, 13, 3, 14, 14, 6, 7, 20]
See `tf.scatter_nd` for more details about how to make updates to
slices.
Args:
indices: The indices to be used in the operation.
updates: The values to be used in the operation.
name: the name of 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`.
"""
return gen_state_ops.scatter_nd_add(
self._variable, indices, updates, use_locking=True, name=name)
def scatter_nd_update(self, indices, updates, name=None):
"""Applies sparse assignment to individual values or slices in a Variable.
`ref` is a `Tensor` with rank `P` and `indices` is a `Tensor` of rank `Q`.
`indices` must be integer tensor, containing indices into `ref`.
It must be shape `[d_0, ..., d_{Q-2}, K]` where `0 < K <= P`.
The innermost dimension of `indices` (with length `K`) corresponds to
indices into elements (if `K = P`) or slices (if `K < P`) along the `K`th
dimension of `ref`.
`updates` is `Tensor` of rank `Q-1+P-K` with shape:
```
[d_0, ..., d_{Q-2}, ref.shape[K], ..., ref.shape[P-1]].
```
For example, say we want to add 4 scattered elements to a rank-1 tensor to
8 elements. In Python, that update would look like this:
```python
ref = tf.Variable([1, 2, 3, 4, 5, 6, 7, 8])
indices = tf.constant([[4], [3], [1] ,[7]])
updates = tf.constant([9, 10, 11, 12])
op = ref.scatter_nd_update(indices, updates)
with tf.Session() as sess:
print sess.run(op)
```
The resulting update to ref would look like this:
[1, 11, 3, 10, 9, 6, 7, 12]
See `tf.scatter_nd` for more details about how to make updates to
slices.
Args:
indices: The indices to be used in the operation.
updates: The values to be used in the operation.
name: the name of 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`.
"""
return gen_state_ops.scatter_nd_update(
self._variable, indices, updates, use_locking=True, name=name)
def _strided_slice_assign(self,
begin,
end,
strides,
value,
name,
begin_mask,
end_mask,
ellipsis_mask,
new_axis_mask,
shrink_axis_mask):
return gen_array_ops.strided_slice_assign(ref=self._ref(),
begin=begin,
end=end,
strides=strides,
value=value,
name=name,
begin_mask=begin_mask,
end_mask=end_mask,
ellipsis_mask=ellipsis_mask,
new_axis_mask=new_axis_mask,
shrink_axis_mask=shrink_axis_mask)
def 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
"""
if context.executing_eagerly():
self.assign(value)
else:
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)
def _gather_saveables_for_checkpoint(self):
"""For implementing `Checkpointable`. This object is saveable on its own."""
return {checkpointable.VARIABLE_VALUE_KEY: self}
def _try_guard_against_uninitialized_dependencies(self, initial_value):
"""Attempt to guard against dependencies on uninitialized variables.
Replace references to variables in `initial_value` with references to the
variable's initialized values. The initialized values are essentially
conditional TensorFlow graphs that return a variable's value if it is
initialized or its `initial_value` if it hasn't been initialized. This
replacement is done on a best effort basis:
- If the `initial_value` graph contains cycles, we don't do any
replacements for that graph.
- If the variables that `initial_value` depends on are not present in the
`GLOBAL_VARIABLES` or `LOCAL_VARIABLES` we don't replace them.
In these cases, it is up to the caller to ensure that the `initial_value`
graph uses initialized variables or that they guard access to variables
using their `initialized_value` method.
Args:
initial_value: `Tensor`. The initial value.
Returns:
A `Tensor` suitable to initialize a variable.
Raises:
TypeError: If `initial_value` is not a `Tensor`.
"""
if not isinstance(initial_value, ops.Tensor):
raise TypeError("initial_value needs to be a Tensor: %s" % initial_value)
# Don't modify initial_value if it contains any cyclic dependencies.
def has_cycle(op, path):
"""Detect cycles in the dependencies of `initial_value`."""
if op.name in path:
return True
path.add(op.name)
for op_input in op.inputs:
if has_cycle(op_input.op, path):
return True
for op_control_input in op.control_inputs:
if has_cycle(op_control_input, path):
return True
path.remove(op.name)
return False
if has_cycle(initial_value.op, path=set()):
return initial_value
return self._safe_initial_value_from_tensor(initial_value, op_cache={})
def _safe_initial_value_from_tensor(self, tensor, op_cache):
"""Replace dependencies on variables with their initialized values.
Args:
tensor: A `Tensor`. The tensor to replace.
op_cache: A dict mapping operation names to `Operation`s. Used to memoize
the results so as to avoid creating redundant operations.
Returns:
A `Tensor` compatible with `tensor`. Any inputs that lead to variable
values will be replaced with a corresponding graph that uses the
variable's initialized values. This is done on a best-effort basis. If no
modifications need to be made then `tensor` will be returned unchanged.
"""
op = tensor.op
new_op = op_cache.get(op.name)
if new_op is None:
new_op = self._safe_initial_value_from_op(op, op_cache)
op_cache[op.name] = new_op
return new_op.outputs[tensor.value_index]
def _safe_initial_value_from_op(self, op, op_cache):
"""Replace dependencies on variables with their initialized values.
Args:
op: An `Operation`. The operation to replace.
op_cache: A dict mapping operation names to `Operation`s. Used to memoize
the results so as to avoid creating redundant operations.
Returns:
An `Operation` compatible with `op`. Any inputs that lead to variable
values will be replaced with a corresponding graph that uses the
variable's initialized values. This is done on a best-effort basis. If no
modifications need to be made then `op` will be returned unchanged.
"""
op_type = op.node_def.op
if op_type in ("IsVariableInitialized", "VarIsInitializedOp",
"ReadVariableOp"):
return op
# Attempt to find the initialized_value of any variable reference / handles.
# TODO(b/70206927): Fix handling of ResourceVariables.
if op_type in ("Variable", "VariableV2", "VarHandleOp"):
initialized_value = self._find_initialized_value_for_variable(op)
return op if initialized_value is None else initialized_value.op
# Recursively build initializer expressions for inputs.
modified = False
new_op_inputs = []
for op_input in op.inputs:
new_op_input = self._safe_initial_value_from_tensor(op_input, op_cache)
new_op_inputs.append(new_op_input)
modified = modified or (new_op_input != op_input)
# If at least one input was modified, replace the op.
if modified:
new_op_type = op_type
if new_op_type == "RefSwitch":
new_op_type = "Switch"
new_op_name = op.node_def.name + "_" + self.name
new_op_name = new_op_name.replace(":", "_")
return self.graph.create_op(
new_op_type, new_op_inputs,
op._output_types, # pylint: disable=protected-access
name=new_op_name, attrs=op.node_def.attr)
return op
def _find_initialized_value_for_variable(self, variable_op):
"""Find the initialized value for a variable op.
To do so, lookup the variable op in the variables collection.
Args:
variable_op: A variable `Operation`.
Returns:
A `Tensor` representing the initialized value for the variable or `None`
if the initialized value could not be found.
"""
try:
var_names = [variable_op.node_def.name, variable_op.node_def.name + ":0"]
for collection_name in (ops.GraphKeys.GLOBAL_VARIABLES,
ops.GraphKeys.LOCAL_VARIABLES):
for var in self.graph.get_collection(collection_name):
if var.name in var_names:
return var.initialized_value()
except AttributeError:
# Return None when an incomplete user-defined variable type was put in
# the collection.
return None
return None
# 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 _shared_name(self):
"""The shared name of the variable.
Unlike name(), shared_name doesn't have ":0" suffix. It is user-specified
name with name scope prefix.
Returns:
variable name.
"""
return self.name[:-2]
@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)
if self._initial_value is not None:
# For backwards compatibility.
var_def.initial_value_name = ops.strip_name_scope(
self._initial_value.name, export_scope)
var_def.trainable = self.trainable
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
def __iadd__(self, other):
logging.log_first_n(
logging.WARN,
"Variable += will be deprecated. Use variable.assign_add"
" if you want assignment to the variable value or 'x = x + y'"
" if you want a new python Tensor object.", 1)
return self + other
def __isub__(self, other):
logging.log_first_n(
logging.WARN,
"Variable -= will be deprecated. Use variable.assign_sub"
" if you want assignment to the variable value or 'x = x - y'"
" if you want a new python Tensor object.", 1)
return self - other
def __imul__(self, other):
logging.log_first_n(
logging.WARN,
"Variable *= will be deprecated. Use `var.assign(var * other)`"
" if you want assignment to the variable value or `x = x * y`"
" if you want a new python Tensor object.", 1)
return self * other
def __idiv__(self, other):
logging.log_first_n(
logging.WARN,
"Variable /= will be deprecated. Use `var.assign(var / other)`"
" if you want assignment to the variable value or `x = x / y`"
" if you want a new python Tensor object.", 1)
return self / other
def __itruediv__(self, other):
logging.log_first_n(
logging.WARN,
"Variable /= will be deprecated. Use `var.assign(var / other)`"
" if you want assignment to the variable value or `x = x / y`"
" if you want a new python Tensor object.", 1)
return self / other
def __irealdiv__(self, other):
logging.log_first_n(
logging.WARN,
"Variable /= will be deprecated. Use `var.assign(var / other)`"
" if you want assignment to the variable value or `x = x / y`"
" if you want a new python Tensor object.", 1)
return self / other
def __ipow__(self, other):
logging.log_first_n(
logging.WARN,
"Variable **= will be deprecated. Use `var.assign(var ** other)`"
" if you want assignment to the variable value or `x = x ** y`"
" if you want a new python Tensor object.", 1)
return self ** other
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.
@compatibility(eager) `tf.PartitionedVariable` is not compatible with
eager execution. Use `tf.Variable` instead which is compatible
with both eager execution and graph construction. See [the
TensorFlow Eager Execution
guide](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/eager/python/g3doc/guide.md#variables-and-optimizers)
for details on how variables work in eager execution.
@end_compatibility
"""
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.
RuntimeError: If eager execution is enabled
"""
if context.executing_eagerly():
raise RuntimeError(
"tf.PartitionedVariable not supported with eager execution enabled.")
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
@property
def shape(self):
return self.get_shape()
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.")
@tf_export("global_variables")
def global_variables(scope=None):
"""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`
Args:
scope: (Optional.) A string. If supplied, the resulting list is filtered
to include only items whose `name` attribute matches `scope` using
`re.match`. Items without a `name` attribute are never returned if a
scope is supplied. The choice of `re.match` means that a `scope` without
special tokens filters by prefix.
Returns:
A list of `Variable` objects.
"""
return ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES, scope)
@tf_export("all_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(scope=None):
"""Returns all variables and `SaveableObject`s that must be checkpointed.
Args:
scope: (Optional.) A string. If supplied, the resulting list is filtered
to include only items whose `name` attribute matches `scope` using
`re.match`. Items without a `name` attribute are never returned if a
scope is supplied. The choice of `re.match` means that a `scope` without
special tokens filters by prefix.
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, scope) +
ops.get_collection(ops.GraphKeys.SAVEABLE_OBJECTS, scope))
@tf_export("local_variables")
def local_variables(scope=None):
"""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`
Args:
scope: (Optional.) A string. If supplied, the resulting list is filtered
to include only items whose `name` attribute matches `scope` using
`re.match`. Items without a `name` attribute are never returned if a
scope is supplied. The choice of `re.match` means that a `scope` without
special tokens filters by prefix.
Returns:
A list of local `Variable` objects.
"""
return ops.get_collection(ops.GraphKeys.LOCAL_VARIABLES, scope)
@tf_export("model_variables")
def model_variables(scope=None):
"""Returns all variables in the MODEL_VARIABLES collection.
Args:
scope: (Optional.) A string. If supplied, the resulting list is filtered
to include only items whose `name` attribute matches `scope` using
`re.match`. Items without a `name` attribute are never returned if a
scope is supplied. The choice of `re.match` means that a `scope` without
special tokens filters by prefix.
Returns:
A list of local Variable objects.
"""
return ops.get_collection(ops.GraphKeys.MODEL_VARIABLES, scope)
@tf_export("trainable_variables")
def trainable_variables(scope=None):
"""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.
Args:
scope: (Optional.) A string. If supplied, the resulting list is filtered
to include only items whose `name` attribute matches `scope` using
`re.match`. Items without a `name` attribute are never returned if a
scope is supplied. The choice of `re.match` means that a `scope` without
special tokens filters by prefix.
Returns:
A list of Variable objects.
"""
return ops.get_collection(ops.GraphKeys.TRAINABLE_VARIABLES, scope)
@tf_export("moving_average_variables")
def moving_average_variables(scope=None):
"""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.
Args:
scope: (Optional.) A string. If supplied, the resulting list is filtered
to include only items whose `name` attribute matches `scope` using
`re.match`. Items without a `name` attribute are never returned if a
scope is supplied. The choice of `re.match` means that a `scope` without
special tokens filters by prefix.
Returns:
A list of Variable objects.
"""
return ops.get_collection(ops.GraphKeys.MOVING_AVERAGE_VARIABLES, scope)
@tf_export("initializers.variables", "variables_initializer")
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 and not context.executing_eagerly():
return control_flow_ops.group(*[v.initializer for v in var_list], name=name)
return control_flow_ops.no_op(name=name)
@tf_export("initialize_variables")
@tf_should_use.should_use_result
@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)
@tf_export("initializers.global_variables", "global_variables_initializer")
def global_variables_initializer():
"""Returns an Op that initializes global variables.
This is just a shortcut for `variables_initializer(global_variables())`
Returns:
An Op that initializes global variables in the graph.
"""
if context.executing_eagerly():
return control_flow_ops.no_op(name="global_variables_initializer")
return variables_initializer(global_variables())
@tf_export("initialize_all_variables")
@tf_should_use.should_use_result
@deprecated("2017-03-02", "Use `tf.global_variables_initializer` instead.")
def initialize_all_variables():
"""See `tf.global_variables_initializer`."""
return global_variables_initializer()
@tf_export("initializers.local_variables", "local_variables_initializer")
def local_variables_initializer():
"""Returns an Op that initializes all local variables.
This is just a shortcut for `variables_initializer(local_variables())`
Returns:
An Op that initializes all local variables in the graph.
"""
if context.executing_eagerly():
return control_flow_ops.no_op(name="local_variables_initializer")
return variables_initializer(local_variables())
@tf_export("initialize_local_variables")
@tf_should_use.should_use_result
@deprecated("2017-03-02", "Use `tf.local_variables_initializer` instead.")
def initialize_local_variables():
"""See `tf.local_variables_initializer`."""
return local_variables_initializer()
@tf_export("is_variable_initialized")
@tf_should_use.should_use_result
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)
@tf_export("assert_variables_initialized")
@tf_should_use.should_use_result
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)
@tf_export("report_uninitialized_variables")
@tf_should_use.should_use_result
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):
# Run all operations on CPU
if var_list:
init_vars = [state_ops.is_variable_initialized(v) for v in var_list]
with ops.device("/cpu:0"):
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(init_vars))
# 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
Variable._OverloadAllOperators()
ops.register_tensor_conversion_function(
PartitionedVariable, PartitionedVariable._TensorConversionFunction)
# pylint: enable=protected-access
ops.register_dense_tensor_like_type(Variable)