Method add

tensorflow/python/keras/engine/sequential.py:132–215

Adds a layer instance on top of the layer stack. Arguments: layer: layer instance. Raises: TypeError: If `layer` is not a layer instance. ValueError: In case the `layer` argument does not know its input shape. ValueError: In case the `layer` argu

(self, layer)

  @trackable.no_automatic_dependency_tracking
  def add(self, layer):
    """Adds a layer instance on top of the layer stack.

    Arguments:
        layer: layer instance.

    Raises:
        TypeError: If `layer` is not a layer instance.
        ValueError: In case the `layer` argument does not
            know its input shape.
        ValueError: In case the `layer` argument has
            multiple output tensors, or is already connected
            somewhere else (forbidden in `Sequential` models).
    """
    # If we are passed a Keras tensor created by keras.Input(), we can extract
    # the input layer from its keras history and use that without any loss of
    # generality.
    if hasattr(layer, '_keras_history'):
      origin_layer = layer._keras_history[0]
      if isinstance(origin_layer, input_layer.InputLayer):
        layer = origin_layer

    if not isinstance(layer, base_layer.Layer):
      raise TypeError('The added layer must be '
                      'an instance of class Layer. '
                      'Found: ' + str(layer))

    tf_utils.assert_no_legacy_layers([layer])

    self.built = False
    set_inputs = False
    if not self._layers:
      if isinstance(layer, input_layer.InputLayer):
        # Corner case where the user passes an InputLayer layer via `add`.
        assert len(nest.flatten(layer._inbound_nodes[-1].output_tensors)) == 1
        set_inputs = True
      else:
        batch_shape, dtype = training_utils.get_input_shape_and_dtype(layer)
        if batch_shape:
          # Instantiate an input layer.
          x = input_layer.Input(
              batch_shape=batch_shape, dtype=dtype, name=layer.name + '_input')
          # This will build the current layer
          # and create the node connecting the current layer
          # to the input layer we just created.
          layer(x)
          set_inputs = True

      if set_inputs:
        # If an input layer (placeholder) is available.
        if len(nest.flatten(layer._inbound_nodes[-1].output_tensors)) != 1:
          raise ValueError('All layers in a Sequential model '
                           'should have a single output tensor. '
                           'For multi-output layers, '
                           'use the functional API.')
        self.outputs = [
            nest.flatten(layer._inbound_nodes[-1].output_tensors)[0]
        ]