解决方法:

如果安装的是GPU版本

如果你有一个GPU,你不应该关心AVX的支持,因为大多数昂贵的操作将被分派到一个GPU设备上(除非明确地设置)。在这种情况下,您可以简单地忽略此警告:

import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = ''

如果安装的是CPU版本(pip install tensorflow)

1.在代码中加入如下代码,忽略警告:

import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = ''

2.编译TensorFlow源码
如果您没有GPU并且希望尽可能多地利用CPU,那么如果您的CPU支持AVX,AVX2和FMA,则应该从针对CPU优化的源构建tensorflow。在这个问题中已经讨论过这个问题,也是这个GitHub问题。 Tensorflow使用称为bazel的ad-hoc构建系统,构建它并不是那么简单,但肯定是可行的。在此之后,不仅警告消失,tensorflow性能也应该改善。

参考:

https://blog.csdn.net/hq86937375/article/details/79696023

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