main_inputs=[]
outputs=[]

def convnet(channels,rows,columns):


        input=Input(shape=(channels,rows,columns))
        main_inputs.append(input)

        conv1=Convolution2D(kernel_size=(3,3) ,filters=64, padding="same")(input)
        activation1= Activation('relu')(conv1)
        conv2=Convolution2D(kernel_size=(3,3), filters=64, padding="same")(activation1)
        activation2 = Activation('relu')(conv2)
        conv3=Convolution2D(kernel_size=(3,3), filters=64, padding="same")(activation2)
        activation3 = Activation('relu')(conv3)
        conv4=Convolution2D(kernel_size=(3,3), filters=channels, padding="same")(activation3)

        activation4 = Activation('linear')(conv4)
        outputs.append(activation4)
        print(np.shape(outputs))


        main_output =  keras.layers.average(outputs)




        model = Model(inputs=main_inputs, outputs=main_output)

        return model

ValueError: A merge layer should be called on a list of inputs

out=K.mean(activation4,axis=1)

'Tensor' object has no attribute '_keras_history'