Method get_variable

tensorflow/python/ops/variable_scope.py:496–805

Gets an existing variable with these parameters or create a new one. If a variable with the given name is already stored, we return the stored variable. Otherwise, we create a new one. Set `reuse` to `True` when you only want to reuse existing Variables. Set `reuse` to `False` when

(self,
                   name,
                   shape=None,
                   embedding_block_num=None,
                   dtype=dtypes.float32,
                   initializer=None,
                   regularizer=None,
                   reuse=None,
                   trainable=None,
                   collections=None,
                   caching_device=None,
                   partitioner=None,
                   validate_shape=True,
                   use_resource=None,
                   custom_getter=None,
                   constraint=None,
                   synchronization=VariableSynchronization.AUTO,
                   aggregation=VariableAggregation.NONE,
                   invalid_key=None,
                   evconfig=variables.EmbeddingVariableConfig(),
                   ht_partition_num=1000)

    return distribute_hashtable_var

  def get_variable(self,
                   name,
                   shape=None,
                   embedding_block_num=None,
                   dtype=dtypes.float32,
                   initializer=None,
                   regularizer=None,
                   reuse=None,
                   trainable=None,
                   collections=None,
                   caching_device=None,
                   partitioner=None,
                   validate_shape=True,
                   use_resource=None,
                   custom_getter=None,
                   constraint=None,
                   synchronization=VariableSynchronization.AUTO,
                   aggregation=VariableAggregation.NONE,
                   invalid_key=None,
                   evconfig=variables.EmbeddingVariableConfig(),
                   ht_partition_num=1000):
    """Gets an existing variable with these parameters or create a new one.

    If a variable with the given name is already stored, we return the stored
    variable. Otherwise, we create a new one.

    Set `reuse` to `True` when you only want to reuse existing Variables.
    Set `reuse` to `False` when you only want to create new Variables.
    Set `reuse` to None (the default) or tf.compat.v1.AUTO_REUSE when you want
    variables to be created if they don't exist or returned if they do.

    If initializer is `None` (the default), the default initializer passed in
    the constructor is used. If that one is `None` too, we use a new
    `glorot_uniform_initializer`. If initializer is a Tensor, we use
    it as a value and derive the shape from the initializer.

    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.
      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.
      reuse: a Boolean, None, or tf.AUTO_REUSE. Controls reuse or creation of
        variables. When eager execution is enabled  this argument is always
        forced to be False.
      trainable: If `True` also add the variable to the graph collection
        `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`). `trainable`
        defaults to `True`, unless `synchronization` is set to `ON_READ`, in
        which case it defaults to `False`.