rnn_decoder.py 文件源码

def step_with_training(self, training=None):

        def step(inputs, states):
            input_shape = K.int_shape(inputs)
            y_tm1 = self.layer.preprocess_input(
                K.expand_dims(states[0], axis=1),
                training
            )
            y_tm1 = K.reshape(y_tm1, (-1, input_shape[-1]))

            inputs_sum = tf.reduce_sum(inputs)

            def inputs_f(): return inputs
            def output_f(): return y_tm1
            current_inputs = tf.case(
                [(tf.equal(inputs_sum, 0.0), output_f)],
                default=inputs_f
            )

            return self.layer.step(
                current_inputs,
                states
            )

        return step