tf.get_variable(name, shape=None, dtype=None, initializer=None, regularizer=None, trainable=True, collections=None, caching_device=None, partitioner=None, validate_shape=True, custom_getter=None)

tf.get_variable(name, shape=None, dtype=None, initializer=None, regularizer=None, trainable=True, collections=None, caching_device=None, partitioner=None, validate_shape=True, custom_getter=None)

See the guide: Variables > Sharing Variables

Gets an existing variable with these parameters or create a new one.

This function prefixes the name with the current variable scope and performs reuse checks. See the Variable Scope How To for an extensive description of how reusing works. Here is a basic example:

with tf.variable_scope("foo"):
    v = tf.get_variable("v", [1])  # v.name == "foo/v:0"
    w = tf.get_variable("w", [1])  # w.name == "foo/w:0"
with tf.variable_scope("foo", reuse=True):
    v1 = tf.get_variable("v")  # The same as v above.

If initializer is None (the default), the default initializer passed in the variable scope will be used. If that one is None too, a glorot_uniform_initializer will be used. The initializer can also be a Tensor, in which case the variable is initialized to this value and shape.

Similarly, if the regularizer is None (the default), the default regularizer passed in the variable scope will be used (if that is None too, then by default no regularization is performed).

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

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

Args:

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

Returns:

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

Raises:

• ValueError: when creating a new variable and shape is not declared, when violating reuse during variable creation, or when initializer dtype and dtype don't match. Reuse is set inside variable_scope.

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