net.py 文件源码

def __init__(self, n_layers, n_source_vocab, n_target_vocab, n_units,
                 max_length=50, dropout=0.2, width=3):
        init_emb = chainer.initializers.Normal(0.1)
        init_out = VarInNormal(1.)
        super(Seq2seq, self).__init__(
            embed_x=L.EmbedID(n_source_vocab, n_units, ignore_label=-1,
                              initialW=init_emb),
            embed_y=L.EmbedID(n_target_vocab, n_units, ignore_label=-1,
                              initialW=init_emb),
            embed_position_x=L.EmbedID(max_length, n_units,
                                       initialW=init_emb),
            embed_position_y=L.EmbedID(max_length, n_units,
                                       initialW=init_emb),
            encoder=ConvGLUEncoder(n_layers, n_units, width, dropout),
            decoder=ConvGLUDecoder(n_layers, n_units, width, dropout),
            W=L.Linear(n_units, n_target_vocab, initialW=init_out),
        )
        self.n_layers = n_layers
        self.n_units = n_units
        self.n_target_vocab = n_target_vocab
        self.max_length = max_length
        self.width = width
        self.dropout = dropout