rnn_encoder.py 文件源码

def call(self, inputs, mask=None, initial_state=None, training=None):
        inputs_shape = K.shape(inputs)
        zeros = tf.zeros(
            shape=[
                inputs_shape[0],
                inputs_shape[1] - 1,
                self.layer.units
            ]
        )
        outputs = self.layer.call(
            inputs=inputs,
            mask=mask,
            initial_state=initial_state,
            training=training
        )
        outputs = K.reshape(
            tf.slice(outputs, [0, inputs_shape[1] - 1, 0], [-1, 1, -1]),
            shape=(inputs_shape[0], 1, self.layer.units)
        )
        outputs = K.concatenate([outputs, zeros], axis=1)

        if 0 < self.layer.dropout + self.layer.recurrent_dropout:
            outputs._uses_learning_phase = True
        return outputs