train_nets.py 文件源码

def model_cnn(net_layers, input_shape):

    inp = Input(shape=input_shape)
    model = inp

    for cl in net_layers['conv_layers']:
        model = Conv2D(filters=cl[0], kernel_size=cl[1], activation='relu')(model)
        if cl[4]:
            model = MaxPooling2D()(model)
        if cl[2]:
            model = BatchNormalization()(model)
        if cl[3]:
            model = Dropout(0.2)(model)

    model = Flatten()(model)

    for dl in net_layers['dense_layers']:
        model = Dense(dl[0])(model)
        model = Activation('relu')(model)
        if dl[1]:
            model = BatchNormalization()(model)
        if dl[2]:
            model = Dropout(0.2)(model)

    model = Dense(1)(model)
    model = Activation('sigmoid')(model)

    model = Model(inp, model)
    return model



# %%

# LSTM architecture
# conv_layers -> [(filters, kernel_size, BatchNormaliztion, Dropout, MaxPooling)]
# dense_layers -> [(num_neurons, BatchNormaliztion, Dropout)]