layer_normalization_RNN.py 文件源码

def reset_states(self):
        assert self.stateful, 'Layer must be stateful.'
        input_shape = self.input_spec[0].shape
        if not input_shape[0]:
            raise Exception('If a RNN is stateful, it needs to know '
                            'its batch size. Specify the batch size '
                            'of your input tensors: \n'
                            '- If using a Sequential model, '
                            'specify the batch size by passing '
                            'a `batch_input_shape` '
                            'argument to your first layer.\n'
                            '- If using the functional API, specify '
                            'the time dimension by passing a '
                            '`batch_shape` argument to your Input layer.')
        if hasattr(self, 'states'):
            K.set_value(self.states[0],
                        np.zeros((input_shape[0], self.output_dim)))
        else:
            self.states = [K.zeros((input_shape[0], self.output_dim))]