1.安裝Python3.5

在cmd中輸入conda create --name python35 python=3.5 ，如果一次不成功可以多試幾次

如圖所示：

輸入activate python35進入Python3.5環境中

輸入deactivate退出

查詢conda環境

conda info --envs

2.安裝TensorFlow

如上圖所示，安裝過程出現錯誤

使用指令pip install --upgrade tensorflow

還會有錯誤

仔細看報錯資訊：

socket.timeout:the read operation timed out

出現超時錯誤，則需要設定超時時間

輸入指令：

pip --default-timeout=100 install -U tensorflow

或pip --default-timeout=100 install --upgrade tensorflow

最後則可以安裝成功

測試一下Tensorflow能不能使用：

可以使用但是報了一行提醒：

Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2

這個是什麼意思呢？

Modern CPUs provide a lot of low-level instructions, besides the usual arithmetic and logic, known as extensions, e.g. SSE2, SSE4, AVX, etc. From the 

Advanced Vector Extensions (AVX) are extensions to the x86 instruction set architecture for microprocessors from Intel and AMD proposed by Intel in March 2008 and first supported by Intel with the Sandy Bridge processor shipping in Q1 2011 and later on by AMD with the Bulldozer processor shipping in Q3 2011. AVX provides new features, new instructions and a new coding scheme.

In particular, AVX introduces fused multiply-accumulate (FMA) operations, which speed up linear algebra computation, namely dot-product, matrix multiply, convolution, etc. Almost every machine-learning training involves a great deal of these operations, hence will be faster on a CPU that supports AVX and FMA (up to 300%). The warning states that your CPU does support AVX (hooray!).

I'd like to stress here: it's all about CPU only.

Why isn't it used then?

Because tensorflow default distribution is built without CPU extensions, such as SSE4.1, SSE4.2, AVX, AVX2, FMA, etc. The default builds (ones from pip install tensorflow) are intended to be compatible with as many CPUs as possible. Another argument is that even with these extensions CPU is a lot slower than a GPU, and it's expected for medium- and large-scale machine-learning training to be performed on a GPU.

What should you do?

If you have a GPU, you shouldn't care about AVX support, because most expensive ops will be dispatched on a GPU device (unless explicitly set not to). In this case, you can simply ignore this warning by

# Just disables the warning, doesn't enable AVX/FMA
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'

... or by setting export TF_CPP_MIN_LOG_LEVEL=2 if you're on Unix. Tensorflow is working fine anyway, but you won't see these annoying warnings.

If you don't have a GPU and want to utilize CPU as much as possible, you should build tensorflow from the source optimized for your CPU with AVX, AVX2, and FMA enabled if your CPU supports them. It's been discussed in this question and also this GitHub issue. Tensorflow uses an ad-hoc build system called bazel and building it is not that trivial, but is certainly doable. After this, not only will the warning disappear, tensorflow performance should also improve.

或者還有一種解決方法，還沒有嘗試：