Function model_variable

tensorflow/contrib/framework/python/ops/variables.py:285–352

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 va

(name,
                   shape=None,
                   dtype=dtypes.float32,
                   initializer=None,
                   regularizer=None,
                   trainable=True,
                   collections=None,
                   caching_device=None,
                   device=None,
                   partitioner=None,
                   custom_getter=None,
                   use_resource=None,
                   synchronization=variables.VariableSynchronization.AUTO,
                   aggregation=variables.VariableAggregation.NONE)

@contrib_add_arg_scope
def model_variable(name,
                   shape=None,
                   dtype=dtypes.float32,
                   initializer=None,
                   regularizer=None,
                   trainable=True,
                   collections=None,
                   caching_device=None,
                   device=None,
                   partitioner=None,
                   custom_getter=None,
                   use_resource=None,
                   synchronization=variables.VariableSynchronization.AUTO,
                   aggregation=variables.VariableAggregation.NONE):
  """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.
    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.
  """
  collections = list(collections or [])
  collections += [ops.GraphKeys.GLOBAL_VARIABLES, ops.GraphKeys.MODEL_VARIABLES]
  var = variable(
      name,
      shape=shape,
      dtype=dtype,
      initializer=initializer,
      regularizer=regularizer,