noContextLSTM.py 文件源码

def __init__(self, word_index, embedding_matrix):
        embedding_layer_q = Embedding(len(word_index) + 1,
                                      EMBEDDING_DIM,
                                      weights=[embedding_matrix],
                                      input_length=MAX_SEQUENCE_LENGTH_Q,
                                      trainable=False)
        embedding_layer_a = Embedding(len(word_index) + 1,
                                      EMBEDDING_DIM,
                                      weights=[embedding_matrix],
                                      input_length=MAX_SEQUENCE_LENGTH_A,
                                      trainable=False)
        question = Input(shape=(MAX_SEQUENCE_LENGTH_Q,), dtype='int32', name='question')
        answer = Input(shape=(MAX_SEQUENCE_LENGTH_A,), dtype='int32', name='answer')
        embedded_question = embedding_layer_q(question)
        embedded_answer = embedding_layer_a(answer)

        l_lstm_q = Bidirectional(LSTM(60))(embedded_question)
        l_lstm_a = Bidirectional(LSTM(60))(embedded_answer)

        concat_c_q_a = concatenate([l_lstm_a, l_lstm_q], axis = 1)

        softmax_c_q_a = Dense(2, activation='softmax')(concat_c_q_a)
        self.model = Model([question, answer], softmax_c_q_a)
        opt = Nadam()
        self.model.compile(loss='categorical_crossentropy',
                      optimizer=opt,
                      metrics=['acc'])