Error: The model expects 3 input arrays, but only receives one array. Found: array with shape (10, 20, 50, 50, 1)

 main_model = Sequential()
 main_model.add(Conv3D(32, 3, 3,3, input_shape=(20,50,50,1)))' 
 main_model.add(Activation('relu'))
 main_model.add(MaxPooling3D(pool_size=(2, 2,2))
 main_model.add(Conv3D(64, 3, 3,3))
 main_model.add(Activation('relu'))
 main_model.add(MaxPooling3D(pool_size=(2, 2,2)))
 main_model.add(Dropout(0.8))
 main_model.add(Flatten())

 #lower features model - CNN2
 lower_model1 = Sequential()
 lower_model1.add(Conv3D(32, 3, 3,3, input_shape=(20,50,50,1))) 
 lower_model1.add(Activation('relu'))
 lower_model1.add(MaxPooling3D(pool_size=(2, 2,2))) 
 lower_model1.add(Dropout(0.8))
 lower_model1.add(Flatten())

 #lower features model - CNN3
 lower_model2 = Sequential()
 lower_model2.add(Conv3D(32, 3, 3,3, input_shape=(20,50,50,1))) 
 lower_model2.add(Activation('relu'))
 lower_model2.add(MaxPooling3D(pool_size=(2, 2,2))) 
 lower_model2.add(Dropout(0.8))
 lower_model2.add(Flatten()) 

 merged_model = Merge([main_model, lower_model1,lower_model2],mode='concat') 
 final_model = Sequential()
 final_model.add(merged_model)
 final_model.add(Dense(1024,init='normal'))
 final_model.add(Activation('relu'))
 final_model.add(Dropout(0.5))
 final_model.add(Dense(2,init='normal'))
 final_model.add(Activation('softmax')) 
 final_model.compile(loss='binary_crossentropy', optimizer='Adam', metrics=
 ['accuracy'])
 train=train_data[-10:]
 test=train_data[-2:]
 X = np.array([i[0] for i in train]).reshape(-1,20,50,50,1)
 Y = [i[1] for i in train]
 test_x = np.array([i[0] for i in test]).reshape(-1,20,50,50,1)
 test_y = [i[1] for i in test]
 final_model.fit(np.array(X),np.array(Y),validation_data=
 (np.array(test_x),np.array(test_y)),batch_size=batch_size,nb_epoch = 
 nb_epoch,validation_split=0.2,shuffle=True,verbose=1)

      

        
        
        
      

    

I am using 50x50 images contained in 20 chunks and that y my numpy array is 20x50x50 1st and 2nd models I am using sequential model for 3d cnn multiscale network ... i dont know i get this kind of result

see val_acc, val_loss remains the same in every epoch