models.py 文件源码

def create_word_rnn_model(self, emb_dim, emb_path, vocab_word,
                              vocab_word_size, word_maxlen):
        from keras.layers import SimpleRNN
        logger.info('Building word SimpleRNN model')
        input_word = Input(shape=(word_maxlen, ), name='input_word')
        word_emb = Embedding(
            vocab_word_size, emb_dim, mask_zero=True,
            name='word_emb')(input_word)
        rnn = SimpleRNN(
            300,
            return_sequences=True,
            dropout=self.dropout,
            recurrent_dropout=self.recurrent_dropout)(word_emb)
        dropped = Dropout(0.5)(rnn)
        mot = MeanOverTime(mask_zero=True)(dropped)
        densed = Dense(self.num_outputs, name='dense')(mot)
        output = Activation('sigmoid')(densed)
        model = Model(inputs=input_word, outputs=output)
        model.get_layer('dense').bias.set_value(self.bias)
        if emb_path:
            from emb_reader import EmbReader as EmbReader
            logger.info('Initializing lookup table')
            emb_reader = EmbReader(emb_path, emb_dim=emb_dim)
            model.get_layer('word_emb').embeddings.set_value(
                emb_reader.get_emb_matrix_given_vocab(
                    vocab_word,
                    model.get_layer('word_emb').embeddings.get_value()))
        logger.info('  Done')
        return model