我可以手动将输入神经元插入 Keras 中的隐藏层吗？

Question

I have a CNN and I want to sneak in some extra information into one of the final layers.

Here's a simplified version of the code. Watch for the comment

def define_model():
    model = Sequential()
    model.add(Conv2D(32, (3,3))
    model.add(Conv2D(32, (3,3))
    model.add(MaxPooling2D((2,2))
    model.add(Conv2D(64, (3,3))
    model.add(Conv2D(64, (3,3))
    model.add(MaxPooling2D((2,2)))
    model.add(Flatten())
    # this next layer is where I want to sneak the neuron(s) in
    model.add(Dense(1024))
    model.add(Dropout(rate=0.4))
    model.add(Dense(168))
    model.compile()
    return model

So I have some additional information about the input image which might be able to help the network. Think of it as a clue which may or may not deserve a reasonable amount of weighting.

The clue is in the form of an integer which technically is in [0, inf) but practically is probably in [0, 20].

So my questions are

1. What's the appropriate way to represent that hint speaking in terms of NN architecture in general.

2. How do I tweak the Keras model to make that happen in practice?

3. Bonus: If I wanted to, could I prevent the subsequent dropout from ever dropping out this added feature?

Answer 1

This could work by using Keras' functional API:

def define_model():
    inputs = Input(input_shape=(...))
    hints = Input(input_shape=(...))

    x = Conv2D(32, (3,3))(inputs)
    x = Conv2D(32, (3,3))(x)
    x = MaxPooling2D((2,2))(x)
    x = Conv2D(64, (3,3))(x)
    x = Conv2D(64, (3,3))(x)
    x = MaxPooling2D((2,2))(x)
    x = Flatten()(x)

    x = Add()([x, hints])

    x = Dense(1024)(x)
    x = Dropout(rate=0.4)(x)
    outputs = Dense(168)(x)

    model = Model([inputs, hints], outputs)

    model.compile()
    return model

I don't know about protecting it from the dropout using Keras though.