merge different models with different inputs Keras

问题

I would like to train two different Conv models in Keras with different input dimensions.

I have:

 input_size=4
 input_sizeB=6

 model=Sequential()
 model.add(Conv2D(filters=10,input_shape= 
 (1,time_steps,input_size),kernel_size(24,3),activation='relu',data_format='channels_first',kernel_regularizer=regularizers.l2(0.001)))
 model.add(Flatten())
A= model.add(Dense(25, 
activation='tanh',kernel_regularizer=regularizers.l2(0.003)))

 model2=Sequential()
 model2.add(Conv2D(filters=10,input_shape= 
 (1,time_steps,input_sizeB),kernel_size(24,3),activation='relu',data_format='channels_first',kernel_regularizer=regularizers.l2(0.001)))
  model2.add(Flatten())
B= model2.add(Dense(25, 
activation='tanh',kernel_regularizer=regularizers.l2(0.003)))

Now I would merge the two dense layers at the end of both Conv net.

How I should do?

回答1:

Using the Sequential API, you can use the Merge layer (doc) as follows:

merged_layer = Merge([model, model2], mode='concat') # mode='sum', 'ave', etc.
merged_model = Sequential()
merged_model.add(merged_layer)

Note that this will throw a warning (depending on your version, the code should still work), as sequential Merge is getting deprecated. You could otherwise consider the Functional API, which offers some more flexibility in that regards c.f. the several pre-defined merge layers Keras provides depending on the operation you want to use (doc). Find an example below:

merged_layer = Concatenate()([model.output, model2.output])
merged_model = Model([model.input, model2.input], merged_layer)

来源：https://stackoverflow.com/questions/50214679/merge-different-models-with-different-inputs-keras