Sequential models are not suited for creating models with branches.

You can have the two independent models as Sequential models, as you did, but from the Concatenate on, you should start using the functional Model API.

The idea is to get the output tensors of the two models and feed them in other layers to get new output tensors.

So, considering you have model and extra:

mergedOutput = Concatenate()([model.output, extra.output]) 

This mergetOutput is a tensor. You can either create the last part of the model using this tensor, or create the last part independently, and call it on this tensor. The second approach may be good if you want to train each model separately (doesn't seem to be your case).

Now, creating the new model as a functional API model:

out = Dense(128, activation='relu')(mergetOutput) out = Dropout(0.8)(out) out = Dense(32, activation='sigmoid')(out) out = Dense(num_classes, activation='softmax')(out) new_model = Model( [model.input, extra.input], #model with two input tensors out #and one output tensor ) 

An easier approach is to take all three models you have already created and use them to create a combined model:

model = Sequential() #your first model extra = Sequential() #your second model new_model = Sequential() #all these three exactly as you did 

Join them like this:

mergedOutput = Concatenate()([model.output, extra.output]) finalOutput = new_model(mergedOutput) fullModel = Model([model.input,extra.input],finalOutput) 

Sequential models are not suited for creating models with branches.

You can have the two independent models as Sequential models, as you did, but from the Concatenate on, you should start using the functional Model API.

The idea is to get the output tensors of the two models and feed them in other layers to get new output tensors.

So, considering you have model and extra:

mergedOutput = Concatenate()([model.output, extra.output]) 

This mergetOutput is a tensor. You can either create the last part of the model using this tensor, or create the last part independently, and call it on this tensor. The second approach may be good if you want to train each model separately (doesn't seem to be your case).

Now, creating the new model as a functional API model:

out = Dense(128, activation='relu')(mergetOutput) out = Dropout(0.8)(out) out = Dense(32, activation='sigmoid')(out) out = Dense(num_classes, activation='softmax')(out) new_model = Model( [model.input, extra.input], #model with two input tensors out #and one output tensor ) 

An easier approach is to take all three models you have already created and use them to create a combined model:

model = Sequential() #your first model extra = Sequential() #your second model new_model = Sequential() #all these three exactly as you did    #in this case, you just need to add an input shape to new_model, compatible with the concatenated output of the previous models. new_model.add(FirstNewModelLayer(...,input_shape=(someValue,))) 

Join them like this:

mergedOutput = Concatenate()([model.output, extra.output]) finalOutput = new_model(mergedOutput) fullModel = Model([model.input,extra.input],finalOutput)