Method add_weight

tensorflow/python/keras/engine/base_layer.py:402–561

Adds a new variable to the layer. Arguments: name: Variable name. shape: Variable shape. Defaults to scalar if unspecified. dtype: The type of the variable. Defaults to `self.dtype` or `float32`. initializer: Initializer instance (callable). regularizer: Regularize

(self,
                 name=None,
                 shape=None,
                 dtype=None,
                 initializer=None,
                 regularizer=None,
                 trainable=None,
                 constraint=None,
                 partitioner=None,
                 use_resource=None,
                 synchronization=tf_variables.VariableSynchronization.AUTO,
                 aggregation=tf_variables.VariableAggregation.NONE,
                 **kwargs)

  @doc_controls.for_subclass_implementers
  def add_weight(self,
                 name=None,
                 shape=None,
                 dtype=None,
                 initializer=None,
                 regularizer=None,
                 trainable=None,
                 constraint=None,
                 partitioner=None,
                 use_resource=None,
                 synchronization=tf_variables.VariableSynchronization.AUTO,
                 aggregation=tf_variables.VariableAggregation.NONE,
                 **kwargs):
    """Adds a new variable to the layer.

    Arguments:
      name: Variable name.
      shape: Variable shape. Defaults to scalar if unspecified.
      dtype: The type of the variable. Defaults to `self.dtype` or `float32`.
      initializer: Initializer instance (callable).
      regularizer: Regularizer instance (callable).
      trainable: Boolean, whether the variable should be part of the layer's
        "trainable_variables" (e.g. variables, biases)
        or "non_trainable_variables" (e.g. BatchNorm mean and variance).
        Note that `trainable` cannot be `True` if `synchronization`
        is set to `ON_READ`.
      constraint: Constraint instance (callable).
      partitioner: Partitioner to be passed to the `Trackable` API.
      use_resource: Whether to use `ResourceVariable`.
      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`.
      **kwargs: Additional keyword arguments. Accepted values are `getter` and
        `collections`.

    Returns:
      The created variable. Usually either a `Variable` or `ResourceVariable`
      instance. If `partitioner` is not `None`, a `PartitionedVariable`
      instance is returned.

    Raises:
      RuntimeError: If called with partitioned variable regularization and
        eager execution is enabled.
      ValueError: When giving unsupported dtype and no initializer or when
        trainable has been set to True with synchronization set as `ON_READ`.
    """
    if shape is None:
      shape = ()
    # Validate optional keyword arguments.
    for kwarg in kwargs:
      if kwarg not in ['getter', 'collections', 'experimental_autocast', 'ev_option']:
        raise TypeError('Unknown keyword argument:', kwarg)