tf.contrib.framework.model_variable

tf.contrib.framework.model_variable(
    name,
    shape=None,
    dtype=tf.float32,
    initializer=None,
    regularizer=None,
    trainable=True,
    collections=None,
    caching_device=None,
    device=None,
    partitioner=None,
    custom_getter=None,
    use_resource=None
)

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

See the guide: Framework (contrib) > Variables

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

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 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: A list of collection names to which the Variable will be added. Note that the variable is always also added to the GraphKeys.GLOBAL_VARIABLES and GraphKeys.MODEL_VARIABLES collections.
• caching_device: Optional device string or function describing where the Variable should be cached for reading. Defaults to the Variable's device.
• device: Optional device to place the variable. It can be an string or a function that is called to get the device for the variable.
• 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).
• custom_getter: Callable that allows overwriting the internal get_variable method and has to have the same signature.
• use_resource: If True use a ResourceVariable instead of a Variable.

Returns:

The created or existing variable.