How to implement Merge from Keras.layers

Question

I have been trying to merge the following sequential models but haven\'t been able to. Could somebody please point out my mistake, thank you.

The code compiles while

Answer 1

To be honest, I was struggling on this issue for a long time...

Luckily I found the panacea expected finally. For anyone who would like to make the minimal changes on their original codes with Sequential, here comes the solution:

def linear_model_combined(optimizer='Adadelta'): 
    from keras.models import Model, Sequential
    from keras.layers.core import Dense, Flatten, Activation, Dropout
    from keras.layers import add

    modela = Sequential()
    modela.add(Flatten(input_shape=(100, 34)))
    modela.add(Dense(1024))
    modela.add(Activation('relu'))
    modela.add(Dense(512))

    modelb = Sequential()
    modelb.add(Flatten(input_shape=(100, 34)))
    modelb.add(Dense(1024))
    modelb.add(Activation('relu'))
    modelb.add(Dense(512))

    merged_output = add([modela.output, modelb.output])   

    model_combined = Sequential()
    model_combined.add(Activation('relu'))
    model_combined.add(Dense(256))
    model_combined.add(Activation('relu'))
    model_combined.add(Dense(4))
    model_combined.add(Activation('softmax'))

    final_model = Model([modela.input, modelb.input], model_combined(merged_output))

    final_model.compile(loss='categorical_crossentropy', optimizer=optimizer, metrics=['accuracy'])

    return final_model

For more information, refer to https://github.com/keras-team/keras/issues/3921#issuecomment-335457553 for farizrahman4u's comment. ;)