将模型创建和训练部分封装在一个函数中，然后使用子流程进行主要工作。训练完成后，子进程将终止，GPU内存将释放。
类似于：

import multiprocessing

def create_model_and_train( ):
      .....
      .....

p = multiprocessing.Process(target=create_model_and_train) 
p.start() 
p.join()

或者你可以创建下面的函数，并在每次运行之前调用它：

from keras.backend.tensorflow_backend import set_session
from keras.backend.tensorflow_backend import clear_session
from keras.backend.tensorflow_backend import get_session
import tensorflow
import gc

# Reset Keras Session
def reset_keras():
    sess = get_session()
    clear_session()
    sess.close()
    sess = get_session()

    try:
        del classifier # this is from global space - change this as you need
    except:
        pass

    print(gc.collect()) # if it does something you should see a number as output

    # use the same config as you used to create the session
    config = tensorflow.ConfigProto()
    config.gpu_options.per_process_gpu_memory_fraction = 1
    config.gpu_options.visible_device_list = "0"
    set_session(tensorflow.Session(config=config))