Keras CNN with skip connections and gates

cnn.py

def get_cnn_architecture(weights_path=None):
    
    input_img = Input(shape=(64,64,3))  # adapt this if using `channels_first` image data format
    x1 = Conv2D(64, (3, 3), activation='relu', padding='same')(input_img)
    
    gateFactor = Input(tensor = K.variable([0.3]))
    fractionG = Multiply()([x1,gateFactor])
    complement = Lambda(lambda x: x[0] - x[1])([x1,fractionG])
    
    x = MaxPooling2D((2, 2), padding='same')(fractionG)
    x2 = Conv2D(64, (3, 3), activation='relu', padding='same')(x) 
    x = MaxPooling2D((2, 2), padding='same')(x2)
    x3 = Conv2D(128, (3, 3), activation='relu', padding='same')(x) 
    x = MaxPooling2D((2, 2), padding='same')(x3)
    x4 = Conv2D(256, (3, 3), activation='relu', padding='same')(x) 
    x = MaxPooling2D((2, 2), padding='same')(x4)
    x5 = Conv2D(512, (3, 3), activation='relu', padding='same')(x) 
    
    x = UpSampling2D((2, 2))(x5)
    y1 = Conv2DTranspose(256, (3, 3), activation='relu', padding='same')(x) 
    x = UpSampling2D((2, 2))(y1)
    y2 = Conv2DTranspose(128, (3, 3), activation='relu', padding='same')(x) 
    x = UpSampling2D((2, 2))(y2)
    y3 = Conv2DTranspose(64, (3, 3), activation='relu', padding='same')(x) 
    x = UpSampling2D((2, 2))(y3)
    
    joinedTensor = Add()([x,complement])
    
    y4 = Conv2DTranspose(64, (3, 3), activation='relu', padding='same')(joinedTensor)
    y5 = Conv2DTranspose(3, (3, 3), activation='relu', padding='same')(y4) 
    
    layers = y5
    
    model = Model(input_img,layers)
    print model.summary()
    return model