從谷歌下載TensorFlow使用，在測試使用過程中，每次運行代碼，都會輸出一大堆的warning信息（見本文的最後部分），其大致意思是：本機CPU架構支持SSE、AVX等加速指令，而當前使用的TensorFlow庫並沒有充分利用這些指令集，如果能夠使用開啓了這些指令集的TensorFlow版本，則可以提高TensorFlow的執行效率。
就目前而言，查閱了谷歌的相關文檔，提供的TensorFlow庫還相對單一，沒喲開啓這麼多指令集支持，其主要考慮的是讓這些release版本的TensorFlow庫可以安裝在更多的計算機中使用，因爲有很多老架構的cpu並不支持高級的指令集，比如AVX。
因此，如果想充分利用SSE和AVX來加速TensorFlow的CPU版本的運算，需要自己編譯TensorFlow的源碼。
另外，在StackOverflow中也找到了網友提供的用於暫時屏蔽這些warning輸出的辦法，如下：
import os
os.environ['TF_CPP_MIN_LOG_LEVEL']='2'
import tensorflow as tf——————————-TensorFlow輸出的關於CPU指令集SSE、AVX的warning信息————————————-
2017-08-01 17:14:18.620000: 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.
2017-08-01 17:14:18.620138: 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.
2017-08-01 17:14:18.620223: 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.
2017-08-01 17:14:18.620243: 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.
2017-08-01 17:14:18.620259: 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.
[array([ 14.], dtype=float32)]