如果您正在使用TensorFlow后端，则将为for循环中的每个img建立一个模型。 TensorFlow只是将图形追加到图形等上，这意味着内存刚刚增加。这是一个众所周知的情况，当您要构建许多模型时，必须在超参数优化期间进行处理。

If you are using TensorFlow backend you will be building a model for each img in the for loop. TensorFlow just keeps appending graph onto graph etc. which means memory just rises. This is a well known occurrence and must be dealt with during hyperparameter optimization when you will be building many models, but also here.

from keras import backend as K

并将其放在预测（）的末尾：

and put this at the end of predict():

K.clear_session()

或者您可以只构建一个模型并将其作为预测函数的输入，因此不必每次都构建一个新模型。

Or you can just build one model and feed that as input to the predict function so you are not building a new one each time.