models.py 文件源码

def create_char_rnn_model(self, emb_dim, word_maxlen, vocab_char_size,
                              char_maxlen):
        from keras.layers import SimpleRNN
        logger.info('Building character RNN model')
        input_char = Input(shape=(char_maxlen, ), name='input_char')
        char_emb = Embedding(
            vocab_char_size, emb_dim, mask_zero=True)(input_char)
        rnn = SimpleRNN(
            300,
            return_sequences=True,
            dropout=self.dropout,
            recurrent_dropout=self.recurrent_dropout)(char_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_char, outputs=output)
        model.get_layer('dense').bias.set_value(self.bias)
        logger.info('  Done')
        return model