Function _clone_sequential_model

tensorflow/python/keras/models.py:261–363

Clone a `Sequential` model instance. Model cloning is similar to calling a model on new inputs, except that it creates new layers (and thus new weights) instead of sharing the weights of the existing layers. Arguments: model: Instance of `Sequential`. input_tensors: optional li

(model, input_tensors=None, layer_fn=_clone_layer)

def _clone_sequential_model(model, input_tensors=None, layer_fn=_clone_layer):
  """Clone a `Sequential` model instance.

  Model cloning is similar to calling a model on new inputs,
  except that it creates new layers (and thus new weights) instead
  of sharing the weights of the existing layers.

  Arguments:
      model: Instance of `Sequential`.
      input_tensors: optional list of input tensors
          to build the model upon. If not provided,
          placeholders will be created.
      layer_fn: callable to be applied on non-input layers in the model. By
          default it clones the layer. Another example is to preserve the layer
          to share the weights. This is required when we create a per-replica
          copy of the model with distribution strategy; we want the weights to
          be shared but still feed inputs separately so we create new input
          layers.

  Returns:
      An instance of `Sequential` reproducing the behavior
      of the original model, on top of new inputs tensors,
      using newly instantiated weights.

  Raises:
      ValueError: in case of invalid `model` argument value or `layer_fn`
      argument value.
  """
  if not isinstance(model, Sequential):
    raise ValueError('Expected `model` argument '
                     'to be a `Sequential` model instance, '
                     'but got:', model)

  if not callable(layer_fn):
    raise ValueError('Expected `layer_fn` argument to be a callable.')

  layers = []  # Layers needed to compute the model's outputs.
  layer_map = {}
  # Use model._layers to ensure that all layers are cloned. The model's layers
  # property will exclude the initial InputLayer (if it exists) in the model,
  # resulting in a different Sequential model structure.
  for layer in model._layers:
    if isinstance(layer, InputLayer) and input_tensors is not None:
      # If input tensors are provided, the original model's InputLayer is
      # overwritten with a different InputLayer.
      continue
    cloned_layer = (
        _clone_layer(layer)
        if isinstance(layer, InputLayer) else layer_fn(layer))
    layers.append(cloned_layer)
    layer_map[layer] = cloned_layer
  layers, ancillary_layers = _remove_ancillary_layers(model, layer_map, layers)

  if input_tensors is None:
    cloned_model = Sequential(layers=layers, name=model.name)
  elif len(generic_utils.to_list(input_tensors)) != 1:
    raise ValueError('To clone a `Sequential` model, we expect '
                     ' at most one tensor '