好吧，我能想到的是真正地手动遍历模型的每一层，并再次将它们重新连接，就像这样：

  l = model.layers [1]（已解码）＃层0是输入层，我们将用range（2， len（model.layers））：
l = model.layers [i]（l）
 stackedmodel = Model（ae_input，l）
stacked_model.compile（...）
 

这可以正常工作并产生正确的绘图且没有错误，但这似乎不是最优雅的解决方案...

（顺便说一句，实际上复制模型似乎是不必要的，因为我没有再训练任何东西。）

This questions is similar to Keras replacing input layer.

I have a classifier network and an autoencoder network and I want to use the output of the autoencoder (i.e. encoding + decoding, as a preprocessing step) as the input to the classifier - but after the classifier was already trained on the regular data.

The classification network was built with the functional API like this (based on this example):

clf_input = Input(shape=(28,28,1))
clf_layer = Conv2D(...)(clf_input)
clf_layer = MaxPooling2D(...)(clf_layer)
...
clf_output = Dense(num_classes, activation='softmax')(clf_layer)
model = Model(clf_input, clf_output)
model.compile(...)
model.fit(...)

And the autoencoder like this (based on this example):

ae_input = Input(shape=(28,28,1))
x = Conv2D(...)(ae_input)
x = MaxPooling2D(...)(x)
...
encoded = MaxPooling2D(...)(x)
x = Conv2d(...)(encoded)
x = UpSampling2D(...)(x)
...
decoded = Conv2D(...)(x)
autoencoder = Model(ae_input, decoded)
autoencoder.compile(...)
autoencoder.fit(...)

I can concatenate the two models like this (I still need the original models, hence the copying):

model_copy = keras.models.clone_model(model)
model_copy.set_weights(model.get_weights())
# remove original input layer
model_copy.layers.pop(0)
# set the new input
new_clf_output = model_copy(decoded)
# get the stacked model
stacked_model = Model(ae_input, new_clf_output)
stacked_model.compile(...)

And this works great when all I want to do is apply the model to new test data, but it gives an error on something like this:

for layer in stacked_model.layers:
    print layer.get_config()

where it gets to the end of the autoencoder but then fails with a KeyError at the point where the classifier model gets its input. Also when plotting the model with keras.utils.plot_model I get this:

where you can see the autoencoder layers but then at the end, instead of the individual layers from the classifier model, there is only the complete model in one block.

Is there a way to connect two models such the new stacked model is actually made up of all the individual layers?

Ok, what I could come up with is to really manually go through each layer of the model and reconnect them one by one again like this:

l = model.layers[1](decoded)  # layer 0 is the input layer, which we're replacing
for i in range(2, len(model.layers)):
    l = model.layers[i](l)
stacked_model = Model(ae_input, l)
stacked_model.compile(...)

while this works and produces the correct plot and no errors, this does not seem like the most elegant solution...

(btw, the copying of the model actually seems to be unnecessary as I'm not retraining anything.)