Method from_config

tensorflow/python/keras/engine/network.py:966–1120

Instantiates a Model from its config (output of `get_config()`). Arguments: config: Model config dictionary. custom_objects: Optional dictionary mapping names (strings) to custom classes or functions to be considered during deserialization. Returns:

(cls, config, custom_objects=None)

  @classmethod
  def from_config(cls, config, custom_objects=None):
    """Instantiates a Model from its config (output of `get_config()`).

    Arguments:
        config: Model config dictionary.
        custom_objects: Optional dictionary mapping names
            (strings) to custom classes or functions to be
            considered during deserialization.

    Returns:
        A model instance.

    Raises:
        ValueError: In case of improperly formatted config dict.
    """
    # Layer instances created during the graph reconstruction process.
    created_layers = collections.OrderedDict()

    # Dictionary mapping layer instances to
    # node data that specifies a layer call.
    # It acts as a queue that maintains any unprocessed
    # layer call until it becomes possible to process it
    # (i.e. until the input tensors to the call all exist).
    unprocessed_nodes = {}

    def add_unprocessed_node(layer, node_data):
      if layer not in unprocessed_nodes:
        unprocessed_nodes[layer] = [node_data]
      else:
        unprocessed_nodes[layer].append(node_data)

    def process_node(layer, node_data):
      """Deserialize a node.

      Arguments:
          layer: layer instance.
          node_data: Nested structure of `ListWrapper`.

      Raises:
          ValueError: In case of improperly formatted `node_data`.
      """
      input_tensors = []
      for input_data in nest.flatten(node_data):
        input_data = input_data.as_list()
        inbound_layer_name = input_data[0]
        inbound_node_index = input_data[1]
        inbound_tensor_index = input_data[2]
        if len(input_data) == 3:
          kwargs = {}
        elif len(input_data) == 4:
          kwargs = input_data[3]
          kwargs = _deserialize_keras_tensors(kwargs, created_layers)
        else:
          raise ValueError('Improperly formatted model config.')

        inbound_layer = created_layers[inbound_layer_name]
        if len(inbound_layer._inbound_nodes) <= inbound_node_index:
          add_unprocessed_node(layer, node_data)