如何使用来自一个预先训练的MLP的最后一个隐藏层权重作为Keras的新MLP（转移学习）的输入？

Question

I want to do transfer learning with simple MLP models. First I train a 1 hidden layer feed forward network on large data:

net = Sequential()
net.add(Dense(500, input_dim=2048, kernel_initializer='normal', activation='relu'))
net.add(Dense(1, kernel_initializer='normal'))
net.compile(loss='mean_absolute_error', optimizer='adam')
net.fit(x_transf, 
        y_transf,
        epochs=1000, 
        batch_size=8, 
        verbose=0)

Then I want to pass the unique hidden layer as input to a new network, in which I want to add a second layer. The re-used layer should not be trainable.

idx = 1  # index of desired layer
input_shape = net.layers[idx].get_input_shape_at(0) # get the input shape of desired layer
input_layer = net.layers[idx]
input_layer.trainable = False

transf_model = Sequential()
transf_model.add(input_layer)
transf_model.add(Dense(input_shape[1], activation='relu'))
transf_model.compile(loss='mean_absolute_error', optimizer='adam')
transf_model.fit(x, 
                 y,
                 epochs=10, 
                 batch_size=8, 
                 verbose=0)

EDIT: The above code returns:

ValueError: Error when checking target: expected dense_9 to have shape (None, 500) but got array with shape (436, 1)

What's the trick to make this work?

Answer 1

I would simply use Functional API to build such a model:

shared_layer = net.layers[0] # you want the first layer, so index = 0
shared_layer.trainable = False

inp = Input(the_shape_of_one_input_sample) # e.g. (2048,)
x = shared_layer(inp)
x = Dense(800, ...)(x)
out = Dense(1, ...)(x)

model = Model(inp, out)

# the rest is the same...