Windows10 Faster R-CNN（GPU版） 配置训练自己的模型

参考链接

1. 找到合适自己的版本，下载安装Anaconda

点击跳转下载安装 Anaconda，双击下载好的 .exe 文件安装，只勾选第一个把 conda 添加到 PATH 路径。
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安装完成之后，Windows + R 输入 cmd 打开 terminal，输入 conda 后按回车键，如下图所示及 conda 环境安装成功。

2. 安装虚拟环境

继续在 terminal 中输入以下代码块

1 conda create -n tensorflow python=3.7.4

完成之后就建立了一个叫做 tensorflow 的虚拟环境，python 版本为 3.7.4，之后所有的训练都是在这个环境下进行。

1. Windows 下用 activate tensorflow 激活环境，deactivate tensorflow 关闭环境
   macOS Linux 下用 source activate tensorflow 激活环境，source deactivate 关闭环境
2. conda env list 安装了哪些环境
3. conda remove -name tensorflow -all 删除该环境
4. conda clean -p 删除一些没用的包
5. conda clean -t 删除 conda 保存下来的 tar 包

3. 安装版本依赖

cmd 输入以下代码块查看显卡型号和显存大小

1 cd C:\Program Files\NVIDIA Corporation\NCSMI
2
3 nvidia-smi

安装 CUDA 和 cuDNN，版本适配问题得注意，本文选用的是 CUDA9.0 和 cuDNN7.3.1，具体可参考下面图片。
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安装 CUDA

CUDA9.0 下载地址：点击跳转下载，Windows10 x86_64 local 下载。
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双击点击下载好的 .exe 文件进行安装，cmd 输入以下代码块，成功的话会出现 Result = PASS 表示安装成功。

1 cd C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v9.0\extras\demo_suite
2
3 bandwidthTest.exe

安装cuDNN

cuDNN 下载地址：点击跳转下载，本文安装的是与 CUDA9.0 适配的 cuDNN7.3.1 Windows10 版本。
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解压下载好的文件包，将对应的三个文件夹里面的内容分别复制到 C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v9.0 下面对应的文件夹内

4. 安装 tensorflow

1. 激活 tensorflow activate tensorflow
2. pip install -i http://pypi.douban.com/simple tensorflow-gpu==1.8.0 --trusted-host pypi.douban.com，如果下载安装速度缓慢参看下载加速文章
3. conda search tensorflow-gpu 可以查看提供了哪些版本。
4. 运行以下代码块查看 tensorflow-gpu 安装情况，图片是安装成功的情况。

1 python
2 import tensorflow as tf
3 tf.__version__
4 tf.__path__