Simpler Chainer RNN

sentence_model.py

class RNN_Sentence(Chain):
    def __init__(self, model_dim, word_embedding_dim, vocab_size, compose_network,
                 seq_length,
                 num_classes,
                 initial_embeddings,
                 ):
        super(RNN_Sentence, self).__init__(
            embed=L.EmbedID(vocab_size, word_embedding_dim, initialW=initial_embeddings),  # word embedding
            mid=L.LSTM(word_embedding_dim, model_dim),  # the first LSTM layer
            out=L.Linear(model_dim, num_classes),  # the feed-forward output layer
        )
        self.seq_length = seq_length

    def reset_state(self):
        self.mid.reset_state()

    def __call__(self, sent):
        x = self.embed(sent)
        for i in range(self.seq_length):
            self.mid(x[:, i:i+1])
        h = self.mid.h
        y = self.out(h)
        return y