我安装了Tensorflow，到目前为止效果很好。但是，当我执行代码时，它将显示以下内容
2017-05-07 12:35:10.449884: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.1 instructions, but these are available on your machine and could speed up CPU computations.
2017-05-07 12:35:10.449909: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations.
2017-05-07 12:35:10.449919: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations.
2017-05-07 12:35:10.449927: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations.
2017-05-07 12:35:10.449934: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use FMA instructions, but these are available on your machine and could speed up CPU computations.
我尝试了许多不同的解决方案，但是没有用。有bazel解决方案，但我认为我不使用此应用程序。我该如何解决这个问题？
┌─╼ [~]
└────╼ python3 test.py
2017-05-07 13:23:04.148596: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.1 instructions, but these are available on your machine and could speed up CPU computations.
2017-05-07 13:23:04.148624: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations.
2017-05-07 13:23:04.148629: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations.
2017-05-07 13:23:04.148633: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations.
2017-05-07 13:23:04.148637: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use FMA instructions, but these are available on your machine and could speed up CPU computations.
Tensor("add:0", shape=(), dtype=int32)
5
┌─╼ [~]
└────╼ export TF_CPP_MIN_LOG_LEVEL=3
┌─╼ [~]
└────╼ python3 test.py
Tensor("add:0", shape=(), dtype=int32)
5
但是，如果我关闭终端然后再次打开它，我会得到同样的错误。怎样才能使伟大的解决方案export TF_CPP_MIN_LOG_LEVEL=3永久化？
提前致谢！
一个类似的问题已经在堆栈溢出中发布。您可以忽略这些警告。如果它们使您烦恼，则可以使用来掩盖它们os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'，例如：
import tensorflow as tf
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
print('TensorFlow version: {0}'.format(tf.__version__))
hello = tf.constant('Hello, TensorFlow!')
sess = tf.Session()
print(sess.run(hello))
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我来说两句