model.py 文件源码

def Model1(dim, max_ques_len, max_ans_len, vocab_lim, embedding):
    inp_q = Input(shape=(max_ques_len,))
    embedding_q  = Embedding(vocab_lim, dim, input_length=max_ques_len, weights=[embedding], trainable=False)(inp_q)
    conv_q= Convolution1D(100, 5, border_mode='same', activation='relu')(embedding_q)
    conv_q = Dropout(0.25)(conv_q)
    pool_q = GlobalMaxPooling1D()(conv_q)

    inp_a = Input(shape=(max_ans_len,))
    embedding_a  = Embedding(vocab_lim, dim, input_length=max_ans_len, weights=[embedding], trainable=False)(inp_a)
    conv_a = Convolution1D(100, 5, border_mode='same', activation='relu')(embedding_a)
    conv_a = Dropout(0.25)(conv_a)
    pool_a = GlobalMaxPooling1D()(conv_a)

    #sim = SimLayer(1)([pool_q, pool_a])
    sim = merge([Dense(100, bias=False)(pool_q), pool_a], mode='dot')
    # print pool_a, pool_q

    # model1 = merge([pool_q, pool_a, sim], mode='concat')
    # model = Model(input=[inp_q, inp_a], output=[model1])
    # model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
    # print model.summary()
    # return model


    model_sim = merge([pool_q, pool_a, sim], mode='concat')
    print model_sim

    # #model_final = Flatten()(model_sim)
    model_final = Dropout(0.5)(model_sim)
    model_final = Dense(201)(model_final)
    model_final = Dropout(0.5)(model_final)
    model_final = Dense(1, activation='sigmoid')(model_final)

    model = Model(input=[inp_q, inp_a], output=[model_final])
    print(model.output_shape)
    model.compile(loss='binary_crossentropy', optimizer='nadam', metrics=['accuracy'])
    print model.summary()
    return model