tf.Variable

class tf.Variable

See the guide: Variables > Variables

See the Variables How To for a high level overview.

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

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

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

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

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)


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.

# 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.

# 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.

Child Classes

class SaveSliceInfo

Properties

device

The device of this variable.

dtype

The DType of this variable.

graph

The Graph of this variable.

initial_value

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.

initializer

The initializer operation for this variable.

name

The name of this variable.

op

The Operation of this variable.

Methods

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

Creates a new variable with value initial_value.

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

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

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

Args:

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

Raises:

• ValueError: If both variable_def and initial_value are specified.
• ValueError: If the initial value is not specified, or does not have a shape and validate_shape is True.

assign(value, use_locking=False)

Assigns a new value to the variable.

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

Args:

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

Returns:

A Tensor that will hold the new value of this variable after the assignment has completed.

assign_add(delta, use_locking=False)

Adds a value to this variable.

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

Args:

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

Returns:

A Tensor that will hold the new value of this variable after the addition has completed.

assign_sub(delta, use_locking=False)

Subtracts a value from this variable.

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

Args:

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

Returns:

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

count_up_to(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.

eval(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.

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.

from_proto(variable_def, import_scope=None)

Returns a Variable object created from variable_def.

get_shape()

The TensorShape of this variable.

Returns:

A TensorShape.

initialized_value()

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.

Beware of using initialized_value except during initialization: initialized_value causes the Variable's initializer op to be run, so running this op resets the variable to the initial value.

# 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.

load(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.

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)) # prints [2 3]
# Usage with the default session.  The 'with' block
# above makes 'sess' the default session.
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


read_value()

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.

scatter_sub(sparse_delta, use_locking=False)

Subtracts IndexedSlices from this variable.

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

Args:

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

Returns:

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

Raises:

• ValueError: if sparse_delta is not an IndexedSlices.

set_shape(shape)

Overrides the shape for this variable.

Args:

• shape: the TensorShape representing the overridden shape.

to_proto(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.

value()

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.

Defined in tensorflow/python/ops/variables.py.