models.py 文件源码

def bag_of_convs_model(optimizer="adam",compile=True):
    main_input = Input(shape=(100,), dtype='int32', name='main_input')
    embedding = Embedding(output_dim=32, input_dim=100, input_length=100,
        dropout=0)(main_input)

    conv1 = getconvmodel(2,256)(embedding)
    conv2 = getconvmodel(3,256)(embedding)
    conv3 = getconvmodel(4,256)(embedding)
    conv4 = getconvmodel(5,256)(embedding)

    merged = merge([conv1,conv2,conv3,conv4],mode="concat")

    middle = Dense(1024,activation='relu')(merged)
    middle = Dropout(0.5)(middle)

    middle = Dense(1024,activation='relu')(middle)
    middle = Dropout(0.5)(middle)

    output = Dense(1,activation='sigmoid')(middle)

    model = Model(input=main_input,output=output)
    if compile:
        model.compile(loss='binary_crossentropy', optimizer=optimizer)
    return model