Debugging

      if len(self._layer_list) == 0:

        raise ValueError("No layers have been added to model.")

      for ind, layer in enumerate(self._layer_list):

        if not len(layer.in_layers) == 0:

          raise ValueError("Cannot specify in_layers for Sequential.")

        #if not len(layer.in_layers) == 0:

        #  raise ValueError("Cannot specify in_layers for Sequential.")

        layer.in_layers += [prev_layer]

        self._add_layer(layer)

        #self._add_layer(layer)

        prev_layer = layer

      # The last layer is the output of the model

      self.outputs.append(prev_layer)

      if loss == "binary_crossentropy":

        smce = SoftMaxCrossEntropy(in_layers=[labels, prev_layer])

        self._add_layer(smce)

        #self._add_layer(smce)

        self.set_loss(ReduceMean(in_layers=[smce]))

      elif loss == "mse":

        mse = ReduceSquareDifference(in_layers=[prev_layer, labels])

      else:

        # TODO(rbharath): Add in support for additional losses.

        raise ValueError("Unsupported loss.")

    self._built = True

    #self._built = True

    super(Sequential, self).fit(dataset, **kwargs)

  def restore(self, checkpoint=None):

    """Not currently supported.

    """

    raise ValueError("Restore is not yet supported "

                     "for sequential models.")