keras combine pretrained model

I would do this in following steps:

1. Define function for building a clean model with the same architecture:

   def build_base():
       input = Input(shape=(200,))
       dnn = Dense(400, activation="relu")(input)
       dnn = Dense(400, activation="relu")(dnn)
       output = Dense(5, activation="softmax")(dnn)
       model = keras.models.Model(inputs=input, outputs=output)
       return input, output, model
   

2. Build two copies of the same model:

   input_1, output_1, model_1 = build_base()
   input_2, output_2, model_2 = build_base()
   

3. Set weights in both models:

   model_1.set_weights(old_model.get_weights())
   model_2.set_weights(old_model.get_weights())
   

4. Now do the rest:

   merge_layer = concatenate([input_1, output_2])
   dnn_layer = Dense(200, activation="relu")(merge_layer)
   dnn_layer = Dense(200, activation="relu")(dnn_layer)
   output = Dense(10, activation="sigmoid")(dnn_layer)
   new_model = keras.models.Model(inputs=[input_1, input_2], outputs=output)