seq2seq的实现方式（2）

书接上文：

本篇实现的是（3）的方式，Encoder的隐层和Embeding的集合方式是sum，当然也可以改成其他的方式。下面就直接上代码了：

    def build_model(self):
 
        encoder_input = layers.Input(shape=(self.input_seq_len,))
        encoder_embeding = layers.Embedding(input_dim=len(self.en_word_id_dict),
                                            output_dim=self.encode_embeding_len,
                                            mask_zero=True
                                            )(encoder_input)
        encoder_lstm, state_h, state_c = layers.LSTM(units=self.encode_embeding_len,
                                                     return_state=True)(encoder_embeding)

        encoder_state = [state_h, state_c]

        decoder_input = layers.Input(shape=(self.output_seq_len,))
        decoder_embeding = layers.Embedding(input_dim=len(self.ch_word_id_dict),
                                            output_dim=self.decode_embeding_len,
                                            mask_zero=True
                                            )(decoder_input)
        #embeding 和 中间状态平拼接
        encoder_lstm = layers.core.RepeatVector(self.output_seq_len)(encoder_lstm)
        # decoder_embeding = layers.merge([encoder_lstm, decoder_embeding], mode="concat", concat_axis=-1)
        decoder_embeding = layers.merge([encoder_lstm, decoder_embeding], mode="sum")

        decoder_lstm, _, _ = layers.LSTM(units=self.encode_embeding_len,
                                         return_state=True,
                                         return_sequences=True)(decoder_embeding, initial_state=encoder_state)
        decoder_out = layers.Dense(len(self.ch_word_id_dict), activation="softmax")(decoder_lstm)

        model = Model([encoder_input, decoder_input], decoder_out)
        model.compile(optimizer='rmsprop', loss='categorical_crossentropy')

        model.summary()
        return model