enc_dec_model.py 文件源码

def __init__(self, N_SOURCE_VOCAB, N_TARGET_VOCAB, N_EMBED, N_HIDDEN, train=True):
        super(EncDecModel, self).__init__(
                # Encoder
                enc_embed=L.EmbedID(N_SOURCE_VOCAB, N_EMBED),
                enc_lstm_1=L.LSTM(N_EMBED, N_HIDDEN),
                enc_lstm_2=L.LSTM(N_HIDDEN, N_HIDDEN),
                # Decoder initializer
                enc_dec_1_c=L.Linear(N_HIDDEN, N_HIDDEN),
                enc_dec_1_h=L.Linear(N_HIDDEN, N_HIDDEN),
                enc_dec_2_c=L.Linear(N_HIDDEN, N_HIDDEN),
                enc_dec_2_h=L.Linear(N_HIDDEN, N_HIDDEN),
                # Decoder
                dec_embed=L.EmbedID(N_TARGET_VOCAB, N_EMBED),
                dec_lstm_1=L.LSTM(N_EMBED, N_HIDDEN),
                dec_lstm_2=L.LSTM(N_HIDDEN, N_HIDDEN),
                dec_output=L.Linear(N_HIDDEN, N_TARGET_VOCAB),
        )
        for param in self.params():
            param.data[...] = self.xp.random.uniform(-0.08, 0.08, param.data.shape)
        self.train = train
        self.src_vocab_size = N_SOURCE_VOCAB
        self.trg_vocab_size = N_TARGET_VOCAB
        self.embed_size = N_EMBED
        self.hidden_size = N_HIDDEN