如何在keras中递归地扩展/解析/展平嵌套模型？

Question

Let's say I built a nested model like this:

from keras.models import Sequential, Model
from keras.layers.core import Input, Dense

model_1 = Sequential()
model_1.add(Dense(...))
model_1.add(Dense(...))

input_2 = Input(...)
output_2 = Dense(...)(input_2)
model_2 = Model(inputs=input_2, outputs=output_2) 

model = Sequential()
model.add(model_1)
model.add(model_2)

How can I transform this recursively into a "flat" model, that does not contain any Model or Sequential layers.

Since model_1 and model_2 might have been trained in advance the parameters should be conserved during the transformation.

Answer 1

I had a similar problem, and I got a working solution, but this doesn't seem very elegant.

The basic idea is to iterate through the layers of the sub-models and add them to the overall model one by one rather than adding the entire sub-models.

model = Sequential()

for layer1 in model1.layers:
    model.add(layer1)

for layer2 in model2.layers:
    model.add(layer2)

If the model already includes nested models, it is possible to iterate over them via:

model_flat = Sequential()

for layer_nested in model.get_layer('nested_model').layers:
    model_flat.add(layer_nested)