variational_autoencoder.py 文件源码

def _vae_loss(self, x, x_decoded_mean):
        n_inputs = self._model.get_input_shape_at(0)[1]
        z_mean = self._model.get_layer('z_mean').inbound_nodes[0].output_tensors[0]
        z_log_var = self._model.get_layer('z_log_var').inbound_nodes[0].output_tensors[0]

        xent_loss = n_inputs * objectives.binary_crossentropy(x, x_decoded_mean)
        kl_loss = - 0.5 * K.sum(1 + z_log_var
                                - K.square(z_mean)
                                - K.exp(z_log_var), axis=-1)
        return xent_loss + kl_loss