model=Model(...) #定义模型结构
model_parallel=multi_gpu_model(model,gpu=n) #使用几张显卡n等于几
model_parallel.compile(...) #注意是model_parallel，不是model

class OwnCheckpoint(keras.callbacks.Callback):
 def __init__(self,model):
  self.model_to_save=model
 def on_epoch_end(self,epoch,logs=None): #这里logs必须写
  self.model_to_save.save('model_advanced/model_%d.h5' % epoch)

def img_image_generator(path_img, path_lab, batch_size, data_list):
 while True:
 # 'train_list.csv'
 file_list = pd.read_csv(data_list, sep=',',usecols=[1]).values.tolist()
 file_list = [i[0] for i in file_list]
 cnt = 0
 X = []
 Y1 = []
 for file_i in file_list:
 x = cv2.imread(path_img+'/'+file_i, cv2.IMREAD_GRAYSCALE)
 x = x.astype('float32')
 x /= 255.
 y = cv2.imread(path_lab+'/'+file_i, cv2.IMREAD_GRAYSCALE)
 y = y.astype('float32')
 y /= 255.
 X.append(x.reshape(256, 256, 1))
 Y1.append(y.reshape(256, 256, 1))
 cnt += 1
 if cnt == batch_size:
 cnt = 0
 yield (np.array(X), [np.array(Y1), np.array(Y1)])
 X = []
 Y1 = []

 generator_train = img_image_generator(path1, path2, 4, pathcsv_train)
 generator_test= img_image_generator(path1, path2, 4, pathcsv_test)
 model.fit_generator(generator_train, steps_per_epoch=237*2, epochs=50, callbacks=callbacks_list, validation_data=generator_test, validation_steps=60*2)

class ParallelModelCheckpoint(ModelCheckpoint):
  def __init__(self, model, filepath, monitor='val_out_final_score', verbose=0,   save_best_only=False, save_weights_only=False, mode='auto', period=1):
   self.single_model = model 
   super(ParallelModelCheckpoint, self).__init__(filepath, monitor, verbose, save_best_only, save_weights_only, mode, period)
  
  def set_model(self, model):
   super(ParallelModelCheckpoint, self).set_model(self.single_model)