train_word2vec_subword_chainer_input.py 文件源码

def __init__(self, n_vocab_char, n_units, n_units_char):
        super(RNN, self).__init__()
        with self.init_scope():
            self.embed = L.EmbedID(
                n_vocab_char, n_units_char, initialW=I.Uniform(1. / n_units_char))  # word embedding
            self.mid = L.LSTM(n_units_char, n_units_char)  # the first LSTM layer
            self.out = L.Linear(n_units_char, n_units)  # the feed-forward output layer