Function _get_partitioned_variable

tensorflow/python/ops/variable_scope.py:2415–2529

Gets or creates a sharded variable list with these parameters. The `partitioner` must be a callable that accepts a fully defined `TensorShape` and returns a sequence of integers (the `partitions`). These integers describe how to partition the given sharded `Variable` along the given dimensi

(name,
                              shape=None,
                              dtype=None,
                              initializer=None,
                              regularizer=None,
                              trainable=True,
                              collections=None,
                              caching_device=None,
                              partitioner=None,
                              validate_shape=True,
                              use_resource=None,
                              constraint=None,
                              synchronization=VariableSynchronization.AUTO,
                              aggregation=VariableAggregation.NONE)

      ht_partition_num=ht_partition_num)

def _get_partitioned_variable(name,
                              shape=None,
                              dtype=None,
                              initializer=None,
                              regularizer=None,
                              trainable=True,
                              collections=None,
                              caching_device=None,
                              partitioner=None,
                              validate_shape=True,
                              use_resource=None,
                              constraint=None,
                              synchronization=VariableSynchronization.AUTO,
                              aggregation=VariableAggregation.NONE):
  """Gets or creates a sharded variable list with these parameters.

  The `partitioner` must be a callable that accepts a fully defined
  `TensorShape` and returns a sequence of integers (the `partitions`).
  These integers describe how to partition the given sharded `Variable`
  along the given dimension.  That is, `partitions[1] = 3` means split
  the `Variable` into 3 shards along dimension 1.  Currently, sharding along
  only one axis is supported.

  If the list of variables with the given name (prefix) is already stored,
  we return the stored variables. Otherwise, we create a new one.

  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 the initializer is a callable, then it will be called for each
  shard.  Otherwise the initializer should match the shape of the entire
  sharded Variable, and it will be sliced accordingly for each shard.

  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 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: List of graph collections keys to add the Variable to. Defaults
      to `[GraphKeys.GLOBAL_VARIABLES]` (see `tf.Variable`).
    caching_device: Optional device string or function describing where the
      Variable should be cached for reading.  Defaults to the Variable's device.
      If not `None`, caches on another device.  Typical use is to cache on the
      device where the Ops using the Variable reside, to deduplicate copying
      through `Switch` and other conditional statements.
    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).