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Gets an existing model variable with these parameters or creates a new one.
tf.contrib.framework.model_variable( name, shape=None, dtype=tf.dtypes.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 )
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
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.
Truealso add the variable to the graph collection
collections: A list of collection names to which the Variable will be added. Note that the variable is always also added to the
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
TensorShapeand dtype of the
Variableto 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.
Trueuse 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
AUTOand the current
DistributionStrategychooses when to synchronize.
aggregation: Indicates how a distributed variable will be aggregated. Accepted values are constants defined in the class
The created or existing variable.