解决方案是上述解决方法，它可以将后端导入为"k":

Solution was the workaround as described, which was to import backend as 'k':

train.py:

from keras import backend as K

#Other stuff...

model = Sequential()
model.add(Lambda(lambda x: K.tf.image.resize_images(x, (80, 160)), \
                 input_shape=(160, 320, 3))) #Resize 80x160x3

#Rest of model and training...

model.save('model.h5')

eval.py:

from keras import backend as K

#Other stuff...

model = load_model('model.h5') #Error is here