tf.Variable(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)
Docstring:
See the @{$variables$Variables How To} for a high level overview.
A variable maintains state in the graph across calls to run(). You add a variable to the graph by constructing an instance of the class Variable.
The Variable() constructor requires an initial value for the variable, which can be a Tensor of any type and shape. The initial value defines the type and shape of the variable. After construction, the type and shape of the variable are fixed. The value can be changed using one of the assign methods.
If you want to change the shape of a variable later you have to use an assign Op with validate_shape=False.
Just like any Tensor, variables created with Variable() can be used as inputs for other Ops in the graph. Additionally, all the operators overloaded for the Tensor class are carried over to variables, so you can also add nodes to the graph by just doing arithmetic on variables.
import tensorflow as tf
# Create a variable.
w = tf.Variable(, 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.
# 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= 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.
Init docstring:
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.
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.
Type: type