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,
    synchronization=tf.VariableSynchronization.AUTO,
    aggregation=tf.VariableAggregation.NONE
)

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

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.
  • 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.

Returns:

The created or existing variable.