使用具有可调参数的多个输入实现网络干净

Question

我目前处于需要训练此类网络的情况。

def model3():

    #stride = 1
    #dim = 40
    #window_height = 5
    #splits = ((40-5)+1)/1 = 18
    next(test_generator())
    next(train_generator(batch_size))

    kernel_number = 200#int(math.ceil(splits))
    list_of_input = [Input(shape = (window_height,total_frames_with_deltas,3)) for i in range(splits)]
    list_of_conv_output = []
    list_of_max_out = []
    for i in range(splits):
        list_of_conv_output.append(Conv2D(filters = kernel_number , kernel_size = (window_height,3), activation = 'relu')(list_of_input[i]))
        list_of_max_out.append((MaxPooling2D(pool_size=((1,11)))(list_of_conv_output[i])))

    merge = keras.layers.concatenate(list_of_max_out)
    print merge.shape
    reshape = Reshape((total_frames/total_frames,-1))(merge)

    dense1 = Dense(units = 1000, activation = 'relu',    name = "dense_1")(reshape)
    dense2 = Dense(units = 1000, activation = 'relu',    name = "dense_2")(dense1)
    dense3 = Dense(units = 145 , activation = 'softmax', name = "dense_3")(dense2)
    #dense4 = Dense(units = 1, activation = 'linear', name = "dense_4")(dense3)

我希望可以看到我有33个输入，每个输入都有自己的卷积和池化层。问题是每个卷积和池化层具有相同的参数，并且我可以看到可以改变每个卷积和池对的唯一方法是，如果我当时定义了每个单独的层 - 而不是定义它在for循环中。

有没有一种干净的方法来实现这一点，而不必编写许多代码行，或者我定义每个代码的长列表 - 但更容易访问每个元素并更改它们。