noContextCNN.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)

        conv_blocksA = []
        conv_blocksQ = []
        for sz in [3,5]:
            conv = Convolution1D(filters=20,
                                 kernel_size=sz,
                                 padding="valid",
                                 activation="relu",
                                 strides=1)(embedded_answer)
            conv = MaxPooling1D(pool_size=2)(conv)
            conv = Flatten()(conv)
            conv_blocksA.append(conv)
        for sz in [5,7, 9]:
            conv = Convolution1D(filters=20,
                                 kernel_size=sz,
                                 padding="valid",
                                 activation="relu",
                                 strides=1)(embedded_question)
            conv = MaxPooling1D(pool_size=3)(conv)
            conv = Flatten()(conv)
            conv_blocksQ.append(conv)

        z = Concatenate()(conv_blocksA + conv_blocksQ)
        z = Dropout(0.5)(z)
        z = Dense(100, activation="relu")(z)
        softmax_c_q = Dense(2, activation='softmax')(z)
        self.model = Model([question, answer], softmax_c_q)
        opt = Nadam()
        self.model.compile(loss='categorical_crossentropy',
                      optimizer=opt,
                      metrics=['acc'])