环境：Windows10，Anaconda，CUDA10.0，PaddlePaddle1.8.5
1、PaddlePaddle-GPU的依赖项：CUDA安装
CUDA：CUDA英文全称是Compute Unified Device Architecture，是显卡厂商NVIDIA推出的运算平台。 CUDA™是一种由NVIDIA推出的通用并行计算架构，该架构使GPU能够解决复杂的计算问题。按照官方的说法是，CUDA是一个并行计算平台和编程模型，能够使得使用GPU进行通用计算变得简单和优雅（该解释来源知乎）。
在Windows10上安装深度学习框架PaddlePaddle（GPU版，百度出品）前，需要安装与框架版本相匹配的CUDA驱动，使用CMD查看当前CUDA版本：
C:\Users\WangYue>nvcc --version
# 报错如下：
'nvcc' 不是内部或外部命令，也不是可运行的程序
或批处理文件。
表明本机尚未安装CUDA驱动。使用NVIDIA控制面板查看本机当前使用的GPU支持的CUDA最高版本。顺序为系统信息——组件——3D设置，本机为11.0.228。
图1，使用NVIDIA控制面板查看本机使用GPU支持的CUDA最高上限，本机为11.0.228
然后在NVIDIA官网下载所需版本的CUDA安装包。
图2，选择对应版本的CUDA驱动，Installer Type选择exe(local)
双击选择安装位置，开始安装，以及签署许可协议。
图3，安装中
关键的地方在于自定义安装设置：
图4，务必选择自定义
选择驱动程序组件的勾选方法如下：
（1）取消Visual Studio Integration；
（2）若当前版本（452.11）比待安装版本（411.31）高，则取消Display Driver。
图5，选择驱动程序组件
选择安装位置，按照默认路径即可。
图6，选择安装位置，按照默认路径
图7，安装完毕
安装完成后，使用CMD命令查看是否安装成功。可以看到，已经安装成功了。
C:\Users\WangYue>nvcc --version
#
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2018 NVIDIA Corporation
Built on Sat_Aug_25_21:08:04_Central_Daylight_Time_2018
Cuda compilation tools, release 10.0, V10.0.130
2、PaddlePaddle-GPU的依赖项：cudnn7.6.5安装
cuDNN：是一个专门为深度学习计算设计的软件库，里面提供了很多专门的计算函数，如卷积等。从上图也可以看到，还有很多其他的软件库和中间件，包括实现c++ STL的thrust、实现gpu版本blas的cublas、实现快速傅里叶变换的cuFFT、实现稀疏矩阵运算操作的cuSparse以及实现深度学习网络加速的cuDNN等等，具体细节可参阅GPU-Accelerated Libraries。
根据PaddlePaddle安装指南的提示，cuDNN>7.3都可以，在这里选择了V7.6.5。（需要注册NVIDIA账号才可以继续下载）
图8，选择cuDNN Library for Windows10
随后解压压缩文件，修改解压出的文件夹的名称，由cuda改为cudnn，并将该文件夹移动到如图所示路径。
图9，将改名后的cudnn文件夹移动到如图所示路径。
3、PaddlePaddle-GPU的依赖项：环境变量设置
首先，依次选择：我的电脑——属性——高级系统设置——系统变量——Path——编辑
图10，修改环境变量
其次，新建环境变量，新建——浏览——CUPTI\libx64路径导入，同理将cudnn\bin路径导入，然后将新添加的路径上移至顶端，和已经存在的2个CUDA相关环境变量并列。完成后关闭。
图11，创建环境变量并上移
4、利用Anaconda安装PaddlePaddle和图神经学习库PGL
首先，利用conda创建3.6版本的python环境。
(base) C:\Users\WangYue>conda create -n PGL python=3.6
激活环境，
(base) C:\Users\WangYue>conda activate PGL
检查python版本，确认为3.6版本，并退出python。
(PGL) C:\Users\WangYue>python
Python 3.6.12 |Anaconda, Inc.| (default, Sep 9 2020, 00:29:25) [MSC v.1916 64 bit (AMD64)] on win32
Type "help", "copyright", "credits" or "license" for more information.
>>> quit()
(PGL) C:\Users\WangYue>
根据PaddlePaddle官网的提示使用conda安装GPU版本的框架：
图12，选择合适的版本
安装之前首先添加国内镜像（安装时报错HTTP错误通常都是因为没有添加镜像的原因）：
conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/
conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main/
conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/Paddle/
conda config --set show_channel_urls yes
然后安装：
(PGL) C:\Users\WangYue>conda install paddlepaddle-gpu==1.8.5 cudatoolkit=10.0 -c paddle
done
检查是否安装成功，提示Your Paddle Fluid works well on MUTIPLE GPU or CPU.Your Paddle Fluid is installed successfully! Let's start deep Learning with Paddle Fluid now.表示已安装成功PaddlePaddle，并能够使用GPU进行计算。
(PGL) C:\Users\WangYue>python
Python 3.6.12 |Anaconda, Inc.| (default, Sep 9 2020, 00:29:25) [MSC v.1916 64 bit (AMD64)] on win32
Type "help", "copyright", "credits" or "license" for more information.
>>> import paddle
>>> paddle.fluid.install_check.run_check()
Running Verify Fluid Program ...
W1124 16:46:09.452337 15492 device_context.cc:252] Please NOTE: device: 0, CUDA Capability: 75, Driver API Version: 11.0, Runtime API Version: 10.0
W1124 16:46:12.831832 15492 device_context.cc:260] device: 0, cuDNN Version: 7.6.
Your Paddle Fluid works well on SINGLE GPU or CPU.
W1124 16:46:16.209867 15492 build_strategy.cc:170] fusion_group is not enabled for Windows/MacOS now, and only effective when running with CUDA GPU.
Your Paddle Fluid works well on MUTIPLE GPU or CPU.
Your Paddle Fluid is installed successfully! Let's start deep Learning with Paddle Fluid now
>>>
接下来，安装PGL，并下载PGL代码库。
# pip安装PGL
(PGL) C:\Users\WangYue>pip install PGL
# conda安装git
(PGL) C:\Users\WangYue>conda install git
# 切换到工作目录
(PGL) C:\Users\WangYue>e:
(PGL) E:>cd pythonproject_win
# git clone PGL代码库
(PGL) E:\pythonproject_win>git clone --depth=1 https://github.com/PaddlePaddle/PGL
Cloning into 'PGL'...
remote: Enumerating objects: 480, done.
remote: Counting objects: 100% (480/480), done.
remote: Compressing objects: 100% (416/416), done.
Receiving objects: 100% (480/480), 15.47 MiB | 18.00 KiB/s, done.
Resolving deltas: 100% (88/88), done.
测试PGL
(PGL) E:\pythonproject_win\PGL\examples\gcn>python train.py
[INFO] 2020-11-24 17:38:14,396 [ train.py: 152]: Namespace(dataset='cora', use_cuda=False)
D:\Anaconda3\envs\PGL\lib\site-packages\numpy\core\fromnumeric.py:3373: RuntimeWarning: Mean of empty slice.
out=out, **kwargs)
D:\Anaconda3\envs\PGL\lib\site-packages\numpy\core\_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars
ret = ret.dtype.type(ret / rcount)
[INFO] 2020-11-24 17:38:15,892 [ train.py: 135]: Epoch 0 (nan sec) Train Loss: 1.946376 Train Acc: 0.121429 Val Loss: 1.936659 Val Acc: 0.343333
[INFO] 2020-11-24 17:38:15,925 [ train.py: 135]: Epoch 1 (nan sec) Train Loss: 1.935814 Train Acc: 0.321429 Val Loss: 1.926904 Val Acc: 0.373333
[INFO] 2020-11-24 17:38:15,960 [ train.py: 135]: Epoch 2 (nan sec) Train Loss: 1.924818 Train Acc: 0.392857 Val Loss: 1.916334 Val Acc: 0.380000
[INFO] 2020-11-24 17:38:15,990 [ train.py: 135]: Epoch 3 (0.01900 sec) Train Loss: 1.909246 Train Acc: 0.392857 Val Loss: 1.905591 Val Acc: 0.450000
[INFO] 2020-11-24 17:38:16,021 [ train.py: 135]: Epoch 4 (0.01900 sec) Train Loss: 1.894153 Train Acc: 0.407143 Val Loss: 1.894562 Val Acc: 0.463333
[INFO] 2020-11-24 17:38:16,052 [ train.py: 135]: Epoch 5 (0.01900 sec) Train Loss: 1.883368 Train Acc: 0.414286 Val Loss: 1.883252 Val Acc: 0.463333
[INFO] 2020-11-24 17:38:16,082 [ train.py: 135]: Epoch 6 (0.01901 sec) Train Loss: 1.871770 Train Acc: 0.392857 Val Loss: 1.871455 Val Acc: 0.463333
[INFO] 2020-11-24 17:38:16,115 [ train.py: 135]: Epoch 7 (0.01920 sec) Train Loss: 1.853012 Train Acc: 0.435714 Val Loss: 1.859422 Val Acc: 0.466667
[INFO] 2020-11-24 17:38:16,146 [ train.py: 135]: Epoch 8 (0.01917 sec) Train Loss: 1.837925 Train Acc: 0.450000 Val Loss: 1.847324 Val Acc: 0.466667
[INFO] 2020-11-24 17:38:16,177 [ train.py: 135]: Epoch 9 (0.01900 sec) Train Loss: 1.824604 Train Acc: 0.435714 Val Loss: 1.835141 Val Acc: 0.473333
[INFO] 2020-11-24 17:38:16,208 [ train.py: 135]: Epoch 10 (0.01900 sec) Train Loss: 1.797683 Train Acc: 0.464286 Val Loss: 1.822889 Val Acc: 0.473333
[INFO] 2020-11-24 17:38:16,239 [ train.py: 135]: Epoch 11 (0.01900 sec) Train Loss: 1.790788 Train Acc: 0.428571 Val Loss: 1.810861 Val Acc: 0.473333
[INFO] 2020-11-24 17:38:16,269 [ train.py: 135]: Epoch 12 (0.01900 sec) Train Loss: 1.779723 Train Acc: 0.435714 Val Loss: 1.799263 Val Acc: 0.473333
[INFO] 2020-11-24 17:38:16,300 [ train.py: 135]: Epoch 13 (0.01909 sec) Train Loss: 1.765455 Train Acc: 0.414286 Val Loss: 1.788079 Val Acc: 0.473333
[INFO] 2020-11-24 17:38:16,330 [ train.py: 135]: Epoch 14 (0.01909 sec) Train Loss: 1.738075 Train Acc: 0.421429 Val Loss: 1.777288 Val Acc: 0.473333
[INFO] 2020-11-24 17:38:16,361 [ train.py: 135]: Epoch 15 (0.01908 sec) Train Loss: 1.722855 Train Acc: 0.407143 Val Loss: 1.766843 Val Acc: 0.473333
[INFO] 2020-11-24 17:38:16,393 [ train.py: 135]: Epoch 16 (0.01907 sec) Train Loss: 1.728457 Train Acc: 0.385714 Val Loss: 1.756886 Val Acc: 0.470000
[INFO] 2020-11-24 17:38:16,423 [ train.py: 135]: Epoch 17 (0.01907 sec) Train Loss: 1.701181 Train Acc: 0.428571 Val Loss: 1.747185 Val Acc: 0.473333
[INFO] 2020-11-24 17:38:16,453 [ train.py: 135]: Epoch 18 (0.01906 sec) Train Loss: 1.691437 Train Acc: 0.442857 Val Loss: 1.737742 Val Acc: 0.466667
[INFO] 2020-11-24 17:38:16,484 [ train.py: 135]: Epoch 19 (0.01906 sec) Train Loss: 1.678157 Train Acc: 0.478571 Val Loss: 1.728544 Val Acc: 0.456667
[INFO] 2020-11-24 17:38:16,515 [ train.py: 135]: Epoch 20 (0.01911 sec) Train Loss: 1.654645 Train Acc: 0.435714 Val Loss: 1.719246 Val Acc: 0.450000
[INFO] 2020-11-24 17:38:16,546 [ train.py: 135]: Epoch 21 (0.01916 sec) Train Loss: 1.649073 Train Acc: 0.428571 Val Loss: 1.709656 Val Acc: 0.430000
[INFO] 2020-11-24 17:38:16,578 [ train.py: 135]: Epoch 22 (0.01925 sec) Train Loss: 1.642157 Train Acc: 0.407143 Val Loss: 1.700160 Val Acc: 0.430000
[INFO] 2020-11-24 17:38:16,608 [ train.py: 135]: Epoch 23 (0.01919 sec) Train Loss: 1.624353 Train Acc: 0.450000 Val Loss: 1.690708 Val Acc: 0.430000
[INFO] 2020-11-24 17:38:16,638 [ train.py: 135]: Epoch 24 (0.01918 sec) Train Loss: 1.616144 Train Acc: 0.450000 Val Loss: 1.681005 Val Acc: 0.426667
[INFO] 2020-11-24 17:38:16,669 [ train.py: 135]: Epoch 25 (0.01918 sec) Train Loss: 1.610140 Train Acc: 0.421429 Val Loss: 1.671384 Val Acc: 0.430000
[INFO] 2020-11-24 17:38:16,699 [ train.py: 135]: Epoch 26 (0.01917 sec) Train Loss: 1.587404 Train Acc: 0.457143 Val Loss: 1.661528 Val Acc: 0.430000
[INFO] 2020-11-24 17:38:16,729 [ train.py: 135]: Epoch 27 (0.01916 sec) Train Loss: 1.567697 Train Acc: 0.457143 Val Loss: 1.651543 Val Acc: 0.433333
[INFO] 2020-11-24 17:38:16,760 [ train.py: 135]: Epoch 28 (0.01914 sec) Train Loss: 1.572376 Train Acc: 0.435714 Val Loss: 1.641517 Val Acc: 0.446667
[INFO] 2020-11-24 17:38:16,796 [ train.py: 135]: Epoch 29 (0.01932 sec) Train Loss: 1.539159 Train Acc: 0.464286 Val Loss: 1.631305 Val Acc: 0.460000
[INFO] 2020-11-24 17:38:16,827 [ train.py: 135]: Epoch 30 (0.01934 sec) Train Loss: 1.537863 Train Acc: 0.450000 Val Loss: 1.621206 Val Acc: 0.466667
[INFO] 2020-11-24 17:38:16,857 [ train.py: 135]: Epoch 31 (0.01933 sec) Train Loss: 1.513063 Train Acc: 0.471429 Val Loss: 1.611248 Val Acc: 0.470000
[INFO] 2020-11-24 17:38:16,890 [ train.py: 135]: Epoch 32 (0.01938 sec) Train Loss: 1.538276 Train Acc: 0.471429 Val Loss: 1.601295 Val Acc: 0.470000
[INFO] 2020-11-24 17:38:16,920 [ train.py: 135]: Epoch 33 (0.01937 sec) Train Loss: 1.483976 Train Acc: 0.507143 Val Loss: 1.591422 Val Acc: 0.470000
[INFO] 2020-11-24 17:38:16,952 [ train.py: 135]: Epoch 34 (0.01936 sec) Train Loss: 1.484524 Train Acc: 0.485714 Val Loss: 1.581631 Val Acc: 0.470000
[INFO] 2020-11-24 17:38:16,982 [ train.py: 135]: Epoch 35 (0.01935 sec) Train Loss: 1.412567 Train Acc: 0.514286 Val Loss: 1.571641 Val Acc: 0.480000
[INFO] 2020-11-24 17:38:17,013 [ train.py: 135]: Epoch 36 (0.01934 sec) Train Loss: 1.456412 Train Acc: 0.500000 Val Loss: 1.561643 Val Acc: 0.486667
[INFO] 2020-11-24 17:38:17,045 [ train.py: 135]: Epoch 37 (0.01933 sec) Train Loss: 1.422783 Train Acc: 0.578571 Val Loss: 1.551188 Val Acc: 0.503333
[INFO] 2020-11-24 17:38:17,077 [ train.py: 135]: Epoch 38 (0.01938 sec) Train Loss: 1.401480 Train Acc: 0.550000 Val Loss: 1.540481 Val Acc: 0.520000
[INFO] 2020-11-24 17:38:17,107 [ train.py: 135]: Epoch 39 (0.01937 sec) Train Loss: 1.381318 Train Acc: 0.571429 Val Loss: 1.529427 Val Acc: 0.533333
[INFO] 2020-11-24 17:38:17,137 [ train.py: 135]: Epoch 40 (0.01936 sec) Train Loss: 1.385848 Train Acc: 0.585714 Val Loss: 1.518172 Val Acc: 0.536667
[INFO] 2020-11-24 17:38:17,169 [ train.py: 135]: Epoch 41 (0.01935 sec) Train Loss: 1.373476 Train Acc: 0.614286 Val Loss: 1.506592 Val Acc: 0.540000
[INFO] 2020-11-24 17:38:17,199 [ train.py: 135]: Epoch 42 (0.01934 sec) Train Loss: 1.327903 Train Acc: 0.600000 Val Loss: 1.494827 Val Acc: 0.540000
[INFO] 2020-11-24 17:38:17,231 [ train.py: 135]: Epoch 43 (0.01936 sec) Train Loss: 1.330398 Train Acc: 0.578571 Val Loss: 1.482955 Val Acc: 0.540000
[INFO] 2020-11-24 17:38:17,262 [ train.py: 135]: Epoch 44 (0.01937 sec) Train Loss: 1.320188 Train Acc: 0.614286 Val Loss: 1.471008 Val Acc: 0.546667
[INFO] 2020-11-24 17:38:17,293 [ train.py: 135]: Epoch 45 (0.01936 sec) Train Loss: 1.292543 Train Acc: 0.664286 Val Loss: 1.458795 Val Acc: 0.553333
[INFO] 2020-11-24 17:38:17,324 [ train.py: 135]: Epoch 46 (0.01935 sec) Train Loss: 1.280111 Train Acc: 0.635714 Val Loss: 1.446541 Val Acc: 0.563333
[INFO] 2020-11-24 17:38:17,354 [ train.py: 135]: Epoch 47 (0.01935 sec) Train Loss: 1.268894 Train Acc: 0.664286 Val Loss: 1.433995 Val Acc: 0.570000
[INFO] 2020-11-24 17:38:17,386 [ train.py: 135]: Epoch 48 (0.01934 sec) Train Loss: 1.264615 Train Acc: 0.657143 Val Loss: 1.421343 Val Acc: 0.580000
[INFO] 2020-11-24 17:38:17,417 [ train.py: 135]: Epoch 49 (0.01935 sec) Train Loss: 1.235874 Train Acc: 0.664286 Val Loss: 1.408745 Val Acc: 0.600000
[INFO] 2020-11-24 17:38:17,448 [ train.py: 135]: Epoch 50 (0.01934 sec) Train Loss: 1.212078 Train Acc: 0.678571 Val Loss: 1.396486 Val Acc: 0.606667
[INFO] 2020-11-24 17:38:17,479 [ train.py: 135]: Epoch 51 (0.01936 sec) Train Loss: 1.184450 Train Acc: 0.728571 Val Loss: 1.384343 Val Acc: 0.616667
[INFO] 2020-11-24 17:38:17,511 [ train.py: 135]: Epoch 52 (0.01935 sec) Train Loss: 1.195419 Train Acc: 0.707143 Val Loss: 1.372334 Val Acc: 0.626667
[INFO] 2020-11-24 17:38:17,545 [ train.py: 135]: Epoch 53 (0.01940 sec) Train Loss: 1.192992 Train Acc: 0.685714 Val Loss: 1.360279 Val Acc: 0.646667
[INFO] 2020-11-24 17:38:17,576 [ train.py: 135]: Epoch 54 (0.01939 sec) Train Loss: 1.201842 Train Acc: 0.728571 Val Loss: 1.348460 Val Acc: 0.650000
[INFO] 2020-11-24 17:38:17,608 [ train.py: 135]: Epoch 55 (0.01941 sec) Train Loss: 1.139250 Train Acc: 0.750000 Val Loss: 1.336654 Val Acc: 0.653333
[INFO] 2020-11-24 17:38:17,640 [ train.py: 135]: Epoch 56 (0.01944 sec) Train Loss: 1.113972 Train Acc: 0.757143 Val Loss: 1.325034 Val Acc: 0.660000
[INFO] 2020-11-24 17:38:17,673 [ train.py: 135]: Epoch 57 (0.01946 sec) Train Loss: 1.094335 Train Acc: 0.800000 Val Loss: 1.313636 Val Acc: 0.666667
[INFO] 2020-11-24 17:38:17,703 [ train.py: 135]: Epoch 58 (0.01946 sec) Train Loss: 1.106194 Train Acc: 0.750000 Val Loss: 1.302360 Val Acc: 0.676667
[INFO] 2020-11-24 17:38:17,734 [ train.py: 135]: Epoch 59 (0.01945 sec) Train Loss: 1.081495 Train Acc: 0.785714 Val Loss: 1.291049 Val Acc: 0.680000
[INFO] 2020-11-24 17:38:17,764 [ train.py: 135]: Epoch 60 (0.01944 sec) Train Loss: 1.118300 Train Acc: 0.750000 Val Loss: 1.279665 Val Acc: 0.683333
[INFO] 2020-11-24 17:38:17,800 [ train.py: 135]: Epoch 61 (0.01952 sec) Train Loss: 1.057867 Train Acc: 0.792857 Val Loss: 1.268428 Val Acc: 0.693333
[INFO] 2020-11-24 17:38:17,831 [ train.py: 135]: Epoch 62 (0.01952 sec) Train Loss: 1.017038 Train Acc: 0.764286 Val Loss: 1.257187 Val Acc: 0.696667
[INFO] 2020-11-24 17:38:17,864 [ train.py: 135]: Epoch 63 (0.01955 sec) Train Loss: 1.005523 Train Acc: 0.800000 Val Loss: 1.246129 Val Acc: 0.710000
[INFO] 2020-11-24 17:38:17,895 [ train.py: 135]: Epoch 64 (0.01954 sec) Train Loss: 1.049122 Train Acc: 0.728571 Val Loss: 1.235504 Val Acc: 0.713333
[INFO] 2020-11-24 17:38:17,925 [ train.py: 135]: Epoch 65 (0.01953 sec) Train Loss: 1.030980 Train Acc: 0.750000 Val Loss: 1.224965 Val Acc: 0.720000
[INFO] 2020-11-24 17:38:17,956 [ train.py: 135]: Epoch 66 (0.01952 sec) Train Loss: 0.969386 Train Acc: 0.800000 Val Loss: 1.214571 Val Acc: 0.730000
[INFO] 2020-11-24 17:38:17,987 [ train.py: 135]: Epoch 67 (0.01952 sec) Train Loss: 0.939819 Train Acc: 0.778571 Val Loss: 1.204163 Val Acc: 0.733333
[INFO] 2020-11-24 17:38:18,020 [ train.py: 135]: Epoch 68 (0.01951 sec) Train Loss: 0.942907 Train Acc: 0.814286 Val Loss: 1.193557 Val Acc: 0.736667
[INFO] 2020-11-24 17:38:18,050 [ train.py: 135]: Epoch 69 (0.01949 sec) Train Loss: 0.951270 Train Acc: 0.842857 Val Loss: 1.182744 Val Acc: 0.740000
[INFO] 2020-11-24 17:38:18,081 [ train.py: 135]: Epoch 70 (0.01948 sec) Train Loss: 0.931611 Train Acc: 0.785714 Val Loss: 1.172386 Val Acc: 0.743333
[INFO] 2020-11-24 17:38:18,111 [ train.py: 135]: Epoch 71 (0.01947 sec) Train Loss: 0.917022 Train Acc: 0.814286 Val Loss: 1.162505 Val Acc: 0.746667
[INFO] 2020-11-24 17:38:18,142 [ train.py: 135]: Epoch 72 (0.01945 sec) Train Loss: 0.927033 Train Acc: 0.828571 Val Loss: 1.152792 Val Acc: 0.746667
[INFO] 2020-11-24 17:38:18,174 [ train.py: 135]: Epoch 73 (0.01944 sec) Train Loss: 0.892688 Train Acc: 0.835714 Val Loss: 1.143111 Val Acc: 0.750000
[INFO] 2020-11-24 17:38:18,204 [ train.py: 135]: Epoch 74 (0.01944 sec) Train Loss: 0.931898 Train Acc: 0.821429 Val Loss: 1.133670 Val Acc: 0.750000
[INFO] 2020-11-24 17:38:18,234 [ train.py: 135]: Epoch 75 (0.01943 sec) Train Loss: 0.914224 Train Acc: 0.828571 Val Loss: 1.124703 Val Acc: 0.753333
[INFO] 2020-11-24 17:38:18,265 [ train.py: 135]: Epoch 76 (0.01943 sec) Train Loss: 0.876583 Train Acc: 0.857143 Val Loss: 1.116272 Val Acc: 0.753333
[INFO] 2020-11-24 17:38:18,296 [ train.py: 135]: Epoch 77 (0.01942 sec) Train Loss: 0.835312 Train Acc: 0.828571 Val Loss: 1.107984 Val Acc: 0.753333
[INFO] 2020-11-24 17:38:18,326 [ train.py: 135]: Epoch 78 (0.01941 sec) Train Loss: 0.860001 Train Acc: 0.828571 Val Loss: 1.099642 Val Acc: 0.753333
[INFO] 2020-11-24 17:38:18,357 [ train.py: 135]: Epoch 79 (0.01941 sec) Train Loss: 0.811797 Train Acc: 0.857143 Val Loss: 1.091735 Val Acc: 0.746667
[INFO] 2020-11-24 17:38:18,388 [ train.py: 135]: Epoch 80 (0.01940 sec) Train Loss: 0.827785 Train Acc: 0.807143 Val Loss: 1.083777 Val Acc: 0.743333
[INFO] 2020-11-24 17:38:18,418 [ train.py: 135]: Epoch 81 (0.01939 sec) Train Loss: 0.823327 Train Acc: 0.842857 Val Loss: 1.075461 Val Acc: 0.746667
[INFO] 2020-11-24 17:38:18,448 [ train.py: 135]: Epoch 82 (0.01938 sec) Train Loss: 0.807287 Train Acc: 0.864286 Val Loss: 1.067636 Val Acc: 0.746667
[INFO] 2020-11-24 17:38:18,479 [ train.py: 135]: Epoch 83 (0.01938 sec) Train Loss: 0.749915 Train Acc: 0.871429 Val Loss: 1.059620 Val Acc: 0.753333
[INFO] 2020-11-24 17:38:18,512 [ train.py: 135]: Epoch 84 (0.01941 sec) Train Loss: 0.788638 Train Acc: 0.864286 Val Loss: 1.051789 Val Acc: 0.753333
[INFO] 2020-11-24 17:38:18,543 [ train.py: 135]: Epoch 85 (0.01940 sec) Train Loss: 0.777396 Train Acc: 0.842857 Val Loss: 1.044784 Val Acc: 0.756667
[INFO] 2020-11-24 17:38:18,574 [ train.py: 135]: Epoch 86 (0.01941 sec) Train Loss: 0.806583 Train Acc: 0.842857 Val Loss: 1.037960 Val Acc: 0.756667
[INFO] 2020-11-24 17:38:18,605 [ train.py: 135]: Epoch 87 (0.01941 sec) Train Loss: 0.792139 Train Acc: 0.857143 Val Loss: 1.031266 Val Acc: 0.760000
[INFO] 2020-11-24 17:38:18,636 [ train.py: 135]: Epoch 88 (0.01941 sec) Train Loss: 0.774478 Train Acc: 0.864286 Val Loss: 1.024997 Val Acc: 0.766667
[INFO] 2020-11-24 17:38:18,669 [ train.py: 135]: Epoch 89 (0.01943 sec) Train Loss: 0.758760 Train Acc: 0.864286 Val Loss: 1.018678 Val Acc: 0.766667
[INFO] 2020-11-24 17:38:18,700 [ train.py: 135]: Epoch 90 (0.01943 sec) Train Loss: 0.718107 Train Acc: 0.885714 Val Loss: 1.012541 Val Acc: 0.766667
[INFO] 2020-11-24 17:38:18,731 [ train.py: 135]: Epoch 91 (0.01942 sec) Train Loss: 0.755768 Train Acc: 0.850000 Val Loss: 1.006553 Val Acc: 0.763333
[INFO] 2020-11-24 17:38:18,761 [ train.py: 135]: Epoch 92 (0.01942 sec) Train Loss: 0.671023 Train Acc: 0.871429 Val Loss: 1.000243 Val Acc: 0.766667
[INFO] 2020-11-24 17:38:18,799 [ train.py: 135]: Epoch 93 (0.01949 sec) Train Loss: 0.736484 Train Acc: 0.871429 Val Loss: 0.993748 Val Acc: 0.770000
[INFO] 2020-11-24 17:38:18,829 [ train.py: 135]: Epoch 94 (0.01949 sec) Train Loss: 0.684531 Train Acc: 0.900000 Val Loss: 0.987403 Val Acc: 0.773333
[INFO] 2020-11-24 17:38:18,860 [ train.py: 135]: Epoch 95 (0.01948 sec) Train Loss: 0.704644 Train Acc: 0.878571 Val Loss: 0.980671 Val Acc: 0.776667
[INFO] 2020-11-24 17:38:18,890 [ train.py: 135]: Epoch 96 (0.01947 sec) Train Loss: 0.700402 Train Acc: 0.842857 Val Loss: 0.974380 Val Acc: 0.776667
[INFO] 2020-11-24 17:38:18,921 [ train.py: 135]: Epoch 97 (0.01948 sec) Train Loss: 0.701402 Train Acc: 0.892857 Val Loss: 0.967968 Val Acc: 0.776667
[INFO] 2020-11-24 17:38:18,952 [ train.py: 135]: Epoch 98 (0.01949 sec) Train Loss: 0.688109 Train Acc: 0.892857 Val Loss: 0.961627 Val Acc: 0.786667
[INFO] 2020-11-24 17:38:18,985 [ train.py: 135]: Epoch 99 (0.01948 sec) Train Loss: 0.698456 Train Acc: 0.878571 Val Loss: 0.955670 Val Acc: 0.783333
[INFO] 2020-11-24 17:38:19,015 [ train.py: 135]: Epoch 100 (0.01948 sec) Train Loss: 0.715043 Train Acc: 0.892857 Val Loss: 0.949948 Val Acc: 0.783333
[INFO] 2020-11-24 17:38:19,046 [ train.py: 135]: Epoch 101 (0.01947 sec) Train Loss: 0.680752 Train Acc: 0.900000 Val Loss: 0.945099 Val Acc: 0.783333
[INFO] 2020-11-24 17:38:19,076 [ train.py: 135]: Epoch 102 (0.01947 sec) Train Loss: 0.699681 Train Acc: 0.864286 Val Loss: 0.940839 Val Acc: 0.780000
[INFO] 2020-11-24 17:38:19,106 [ train.py: 135]: Epoch 103 (0.01945 sec) Train Loss: 0.713673 Train Acc: 0.871429 Val Loss: 0.937625 Val Acc: 0.783333
[INFO] 2020-11-24 17:38:19,136 [ train.py: 135]: Epoch 104 (0.01945 sec) Train Loss: 0.683508 Train Acc: 0.871429 Val Loss: 0.935034 Val Acc: 0.780000
[INFO] 2020-11-24 17:38:19,166 [ train.py: 135]: Epoch 105 (0.01943 sec) Train Loss: 0.617904 Train Acc: 0.900000 Val Loss: 0.932254 Val Acc: 0.783333
[INFO] 2020-11-24 17:38:19,196 [ train.py: 135]: Epoch 106 (0.01943 sec) Train Loss: 0.691874 Train Acc: 0.871429 Val Loss: 0.929585 Val Acc: 0.786667
[INFO] 2020-11-24 17:38:19,227 [ train.py: 135]: Epoch 107 (0.01943 sec) Train Loss: 0.600925 Train Acc: 0.935714 Val Loss: 0.926206 Val Acc: 0.786667
[INFO] 2020-11-24 17:38:19,257 [ train.py: 135]: Epoch 108 (0.01941 sec) Train Loss: 0.626542 Train Acc: 0.871429 Val Loss: 0.921885 Val Acc: 0.786667
[INFO] 2020-11-24 17:38:19,287 [ train.py: 135]: Epoch 109 (0.01941 sec) Train Loss: 0.623590 Train Acc: 0.900000 Val Loss: 0.916823 Val Acc: 0.786667
[INFO] 2020-11-24 17:38:19,318 [ train.py: 135]: Epoch 110 (0.01940 sec) Train Loss: 0.628510 Train Acc: 0.892857 Val Loss: 0.911530 Val Acc: 0.786667
[INFO] 2020-11-24 17:38:19,348 [ train.py: 135]: Epoch 111 (0.01940 sec) Train Loss: 0.623448 Train Acc: 0.885714 Val Loss: 0.906081 Val Acc: 0.786667
[INFO] 2020-11-24 17:38:19,379 [ train.py: 135]: Epoch 112 (0.01940 sec) Train Loss: 0.666084 Train Acc: 0.878571 Val Loss: 0.900852 Val Acc: 0.783333
[INFO] 2020-11-24 17:38:19,410 [ train.py: 135]: Epoch 113 (0.01939 sec) Train Loss: 0.583377 Train Acc: 0.928571 Val Loss: 0.896402 Val Acc: 0.786667
[INFO] 2020-11-24 17:38:19,443 [ train.py: 135]: Epoch 114 (0.01941 sec) Train Loss: 0.671777 Train Acc: 0.878571 Val Loss: 0.892135 Val Acc: 0.786667
[INFO] 2020-11-24 17:38:19,478 [ train.py: 135]: Epoch 115 (0.01944 sec) Train Loss: 0.617062 Train Acc: 0.892857 Val Loss: 0.888102 Val Acc: 0.786667
[INFO] 2020-11-24 17:38:19,508 [ train.py: 135]: Epoch 116 (0.01944 sec) Train Loss: 0.632892 Train Acc: 0.871429 Val Loss: 0.884869 Val Acc: 0.786667
[INFO] 2020-11-24 17:38:19,539 [ train.py: 135]: Epoch 117 (0.01943 sec) Train Loss: 0.621602 Train Acc: 0.885714 Val Loss: 0.881907 Val Acc: 0.786667
[INFO] 2020-11-24 17:38:19,574 [ train.py: 135]: Epoch 118 (0.01945 sec) Train Loss: 0.601480 Train Acc: 0.885714 Val Loss: 0.879309 Val Acc: 0.790000
[INFO] 2020-11-24 17:38:19,604 [ train.py: 135]: Epoch 119 (0.01945 sec) Train Loss: 0.608852 Train Acc: 0.928571 Val Loss: 0.876792 Val Acc: 0.793333
[INFO] 2020-11-24 17:38:19,636 [ train.py: 135]: Epoch 120 (0.01945 sec) Train Loss: 0.582225 Train Acc: 0.892857 Val Loss: 0.874423 Val Acc: 0.786667
[INFO] 2020-11-24 17:38:19,666 [ train.py: 135]: Epoch 121 (0.01944 sec) Train Loss: 0.576704 Train Acc: 0.907143 Val Loss: 0.871257 Val Acc: 0.786667
[INFO] 2020-11-24 17:38:19,698 [ train.py: 135]: Epoch 122 (0.01944 sec) Train Loss: 0.593480 Train Acc: 0.892857 Val Loss: 0.868069 Val Acc: 0.790000
[INFO] 2020-11-24 17:38:19,728 [ train.py: 135]: Epoch 123 (0.01944 sec) Train Loss: 0.572575 Train Acc: 0.907143 Val Loss: 0.864672 Val Acc: 0.793333
[INFO] 2020-11-24 17:38:19,759 [ train.py: 135]: Epoch 124 (0.01943 sec) Train Loss: 0.556454 Train Acc: 0.928571 Val Loss: 0.861215 Val Acc: 0.790000
[INFO] 2020-11-24 17:38:19,794 [ train.py: 135]: Epoch 125 (0.01947 sec) Train Loss: 0.583361 Train Acc: 0.892857 Val Loss: 0.856351 Val Acc: 0.793333
[INFO] 2020-11-24 17:38:19,825 [ train.py: 135]: Epoch 126 (0.01947 sec) Train Loss: 0.575858 Train Acc: 0.892857 Val Loss: 0.851848 Val Acc: 0.790000
[INFO] 2020-11-24 17:38:19,855 [ train.py: 135]: Epoch 127 (0.01946 sec) Train Loss: 0.586449 Train Acc: 0.907143 Val Loss: 0.848449 Val Acc: 0.786667
[INFO] 2020-11-24 17:38:19,886 [ train.py: 135]: Epoch 128 (0.01946 sec) Train Loss: 0.569864 Train Acc: 0.907143 Val Loss: 0.845945 Val Acc: 0.786667
[INFO] 2020-11-24 17:38:19,917 [ train.py: 135]: Epoch 129 (0.01945 sec) Train Loss: 0.549760 Train Acc: 0.914286 Val Loss: 0.844092 Val Acc: 0.793333
[INFO] 2020-11-24 17:38:19,949 [ train.py: 135]: Epoch 130 (0.01947 sec) Train Loss: 0.525366 Train Acc: 0.914286 Val Loss: 0.842285 Val Acc: 0.796667
[INFO] 2020-11-24 17:38:19,980 [ train.py: 135]: Epoch 131 (0.01946 sec) Train Loss: 0.570853 Train Acc: 0.907143 Val Loss: 0.841248 Val Acc: 0.800000
[INFO] 2020-11-24 17:38:20,010 [ train.py: 135]: Epoch 132 (0.01946 sec) Train Loss: 0.545706 Train Acc: 0.885714 Val Loss: 0.840566 Val Acc: 0.796667
[INFO] 2020-11-24 17:38:20,040 [ train.py: 135]: Epoch 133 (0.01945 sec) Train Loss: 0.522932 Train Acc: 0.942857 Val Loss: 0.839006 Val Acc: 0.796667
[INFO] 2020-11-24 17:38:20,081 [ train.py: 135]: Epoch 134 (0.01950 sec) Train Loss: 0.549455 Train Acc: 0.935714 Val Loss: 0.836572 Val Acc: 0.800000
[INFO] 2020-11-24 17:38:20,111 [ train.py: 135]: Epoch 135 (0.01949 sec) Train Loss: 0.540828 Train Acc: 0.914286 Val Loss: 0.833662 Val Acc: 0.800000
[INFO] 2020-11-24 17:38:20,142 [ train.py: 135]: Epoch 136 (0.01949 sec) Train Loss: 0.532241 Train Acc: 0.914286 Val Loss: 0.829949 Val Acc: 0.806667
[INFO] 2020-11-24 17:38:20,174 [ train.py: 135]: Epoch 137 (0.01949 sec) Train Loss: 0.513366 Train Acc: 0.935714 Val Loss: 0.825624 Val Acc: 0.806667
[INFO] 2020-11-24 17:38:20,205 [ train.py: 135]: Epoch 138 (0.01949 sec) Train Loss: 0.523155 Train Acc: 0.928571 Val Loss: 0.821903 Val Acc: 0.806667
[INFO] 2020-11-24 17:38:20,236 [ train.py: 135]: Epoch 139 (0.01949 sec) Train Loss: 0.507928 Train Acc: 0.921429 Val Loss: 0.818555 Val Acc: 0.800000
[INFO] 2020-11-24 17:38:20,267 [ train.py: 135]: Epoch 140 (0.01948 sec) Train Loss: 0.529753 Train Acc: 0.907143 Val Loss: 0.816106 Val Acc: 0.793333
[INFO] 2020-11-24 17:38:20,297 [ train.py: 135]: Epoch 141 (0.01948 sec) Train Loss: 0.581101 Train Acc: 0.864286 Val Loss: 0.814416 Val Acc: 0.793333
[INFO] 2020-11-24 17:38:20,329 [ train.py: 135]: Epoch 142 (0.01948 sec) Train Loss: 0.529025 Train Acc: 0.942857 Val Loss: 0.813017 Val Acc: 0.796667
[INFO] 2020-11-24 17:38:20,359 [ train.py: 135]: Epoch 143 (0.01947 sec) Train Loss: 0.547050 Train Acc: 0.914286 Val Loss: 0.811976 Val Acc: 0.800000
[INFO] 2020-11-24 17:38:20,390 [ train.py: 135]: Epoch 144 (0.01947 sec) Train Loss: 0.477046 Train Acc: 0.957143 Val Loss: 0.810994 Val Acc: 0.803333
[INFO] 2020-11-24 17:38:20,422 [ train.py: 135]: Epoch 145 (0.01947 sec) Train Loss: 0.523743 Train Acc: 0.900000 Val Loss: 0.809442 Val Acc: 0.800000
[INFO] 2020-11-24 17:38:20,453 [ train.py: 135]: Epoch 146 (0.01946 sec) Train Loss: 0.511287 Train Acc: 0.900000 Val Loss: 0.808221 Val Acc: 0.806667
[INFO] 2020-11-24 17:38:20,484 [ train.py: 135]: Epoch 147 (0.01946 sec) Train Loss: 0.492253 Train Acc: 0.942857 Val Loss: 0.806867 Val Acc: 0.806667
[INFO] 2020-11-24 17:38:20,514 [ train.py: 135]: Epoch 148 (0.01946 sec) Train Loss: 0.480283 Train Acc: 0.935714 Val Loss: 0.804443 Val Acc: 0.803333
[INFO] 2020-11-24 17:38:20,545 [ train.py: 135]: Epoch 149 (0.01945 sec) Train Loss: 0.453111 Train Acc: 0.971429 Val Loss: 0.802412 Val Acc: 0.800000
[INFO] 2020-11-24 17:38:20,576 [ train.py: 135]: Epoch 150 (0.01946 sec) Train Loss: 0.490314 Train Acc: 0.928571 Val Loss: 0.800740 Val Acc: 0.803333
[INFO] 2020-11-24 17:38:20,607 [ train.py: 135]: Epoch 151 (0.01946 sec) Train Loss: 0.475686 Train Acc: 0.928571 Val Loss: 0.798504 Val Acc: 0.803333
[INFO] 2020-11-24 17:38:20,637 [ train.py: 135]: Epoch 152 (0.01946 sec) Train Loss: 0.495609 Train Acc: 0.914286 Val Loss: 0.795318 Val Acc: 0.793333
[INFO] 2020-11-24 17:38:20,668 [ train.py: 135]: Epoch 153 (0.01945 sec) Train Loss: 0.516428 Train Acc: 0.921429 Val Loss: 0.792276 Val Acc: 0.800000
[INFO] 2020-11-24 17:38:20,698 [ train.py: 135]: Epoch 154 (0.01945 sec) Train Loss: 0.502076 Train Acc: 0.950000 Val Loss: 0.788550 Val Acc: 0.800000
[INFO] 2020-11-24 17:38:20,728 [ train.py: 135]: Epoch 155 (0.01944 sec) Train Loss: 0.462659 Train Acc: 0.942857 Val Loss: 0.785604 Val Acc: 0.800000
[INFO] 2020-11-24 17:38:20,759 [ train.py: 135]: Epoch 156 (0.01944 sec) Train Loss: 0.499720 Train Acc: 0.914286 Val Loss: 0.784087 Val Acc: 0.800000
[INFO] 2020-11-24 17:38:20,795 [ train.py: 135]: Epoch 157 (0.01947 sec) Train Loss: 0.501638 Train Acc: 0.950000 Val Loss: 0.783460 Val Acc: 0.806667
[INFO] 2020-11-24 17:38:20,827 [ train.py: 135]: Epoch 158 (0.01947 sec) Train Loss: 0.474494 Train Acc: 0.942857 Val Loss: 0.783603 Val Acc: 0.816667
[INFO] 2020-11-24 17:38:20,858 [ train.py: 135]: Epoch 159 (0.01946 sec) Train Loss: 0.463639 Train Acc: 0.921429 Val Loss: 0.784178 Val Acc: 0.816667
[INFO] 2020-11-24 17:38:20,889 [ train.py: 135]: Epoch 160 (0.01946 sec) Train Loss: 0.506787 Train Acc: 0.921429 Val Loss: 0.783240 Val Acc: 0.816667
[INFO] 2020-11-24 17:38:20,923 [ train.py: 135]: Epoch 161 (0.01948 sec) Train Loss: 0.470841 Train Acc: 0.907143 Val Loss: 0.781275 Val Acc: 0.813333
[INFO] 2020-11-24 17:38:20,954 [ train.py: 135]: Epoch 162 (0.01947 sec) Train Loss: 0.472303 Train Acc: 0.935714 Val Loss: 0.780728 Val Acc: 0.813333
[INFO] 2020-11-24 17:38:20,985 [ train.py: 135]: Epoch 163 (0.01947 sec) Train Loss: 0.470098 Train Acc: 0.928571 Val Loss: 0.780580 Val Acc: 0.816667
[INFO] 2020-11-24 17:38:21,015 [ train.py: 135]: Epoch 164 (0.01946 sec) Train Loss: 0.446667 Train Acc: 0.935714 Val Loss: 0.779541 Val Acc: 0.816667
[INFO] 2020-11-24 17:38:21,045 [ train.py: 135]: Epoch 165 (0.01946 sec) Train Loss: 0.452989 Train Acc: 0.935714 Val Loss: 0.777650 Val Acc: 0.813333
[INFO] 2020-11-24 17:38:21,076 [ train.py: 135]: Epoch 166 (0.01946 sec) Train Loss: 0.427969 Train Acc: 0.928571 Val Loss: 0.775191 Val Acc: 0.813333
[INFO] 2020-11-24 17:38:21,107 [ train.py: 135]: Epoch 167 (0.01945 sec) Train Loss: 0.475839 Train Acc: 0.928571 Val Loss: 0.772008 Val Acc: 0.813333
[INFO] 2020-11-24 17:38:21,139 [ train.py: 135]: Epoch 168 (0.01946 sec) Train Loss: 0.491018 Train Acc: 0.921429 Val Loss: 0.769636 Val Acc: 0.806667
[INFO] 2020-11-24 17:38:21,171 [ train.py: 135]: Epoch 169 (0.01946 sec) Train Loss: 0.490486 Train Acc: 0.928571 Val Loss: 0.768028 Val Acc: 0.813333
[INFO] 2020-11-24 17:38:21,201 [ train.py: 135]: Epoch 170 (0.01946 sec) Train Loss: 0.450003 Train Acc: 0.907143 Val Loss: 0.766701 Val Acc: 0.813333
[INFO] 2020-11-24 17:38:21,232 [ train.py: 135]: Epoch 171 (0.01945 sec) Train Loss: 0.465099 Train Acc: 0.957143 Val Loss: 0.765610 Val Acc: 0.813333
[INFO] 2020-11-24 17:38:21,265 [ train.py: 135]: Epoch 172 (0.01946 sec) Train Loss: 0.425242 Train Acc: 0.935714 Val Loss: 0.764214 Val Acc: 0.813333
[INFO] 2020-11-24 17:38:21,295 [ train.py: 135]: Epoch 173 (0.01946 sec) Train Loss: 0.425517 Train Acc: 0.942857 Val Loss: 0.762162 Val Acc: 0.813333
[INFO] 2020-11-24 17:38:21,327 [ train.py: 135]: Epoch 174 (0.01946 sec) Train Loss: 0.440138 Train Acc: 0.942857 Val Loss: 0.760464 Val Acc: 0.806667
[INFO] 2020-11-24 17:38:21,357 [ train.py: 135]: Epoch 175 (0.01946 sec) Train Loss: 0.451479 Train Acc: 0.942857 Val Loss: 0.760121 Val Acc: 0.803333
[INFO] 2020-11-24 17:38:21,391 [ train.py: 135]: Epoch 176 (0.01946 sec) Train Loss: 0.456870 Train Acc: 0.900000 Val Loss: 0.759870 Val Acc: 0.803333
[INFO] 2020-11-24 17:38:21,423 [ train.py: 135]: Epoch 177 (0.01946 sec) Train Loss: 0.466615 Train Acc: 0.928571 Val Loss: 0.760071 Val Acc: 0.803333
[INFO] 2020-11-24 17:38:21,455 [ train.py: 135]: Epoch 178 (0.01947 sec) Train Loss: 0.421555 Train Acc: 0.942857 Val Loss: 0.761534 Val Acc: 0.810000
[INFO] 2020-11-24 17:38:21,487 [ train.py: 135]: Epoch 179 (0.01947 sec) Train Loss: 0.435196 Train Acc: 0.928571 Val Loss: 0.762187 Val Acc: 0.810000
[INFO] 2020-11-24 17:38:21,518 [ train.py: 135]: Epoch 180 (0.01946 sec) Train Loss: 0.475000 Train Acc: 0.942857 Val Loss: 0.761095 Val Acc: 0.810000
[INFO] 2020-11-24 17:38:21,548 [ train.py: 135]: Epoch 181 (0.01946 sec) Train Loss: 0.442108 Train Acc: 0.914286 Val Loss: 0.759001 Val Acc: 0.813333
[INFO] 2020-11-24 17:38:21,578 [ train.py: 135]: Epoch 182 (0.01946 sec) Train Loss: 0.444315 Train Acc: 0.928571 Val Loss: 0.756658 Val Acc: 0.813333
[INFO] 2020-11-24 17:38:21,609 [ train.py: 135]: Epoch 183 (0.01946 sec) Train Loss: 0.456490 Train Acc: 0.921429 Val Loss: 0.753842 Val Acc: 0.816667
[INFO] 2020-11-24 17:38:21,639 [ train.py: 135]: Epoch 184 (0.01946 sec) Train Loss: 0.445454 Train Acc: 0.921429 Val Loss: 0.752500 Val Acc: 0.813333
[INFO] 2020-11-24 17:38:21,670 [ train.py: 135]: Epoch 185 (0.01946 sec) Train Loss: 0.474711 Train Acc: 0.907143 Val Loss: 0.751830 Val Acc: 0.816667
[INFO] 2020-11-24 17:38:21,700 [ train.py: 135]: Epoch 186 (0.01945 sec) Train Loss: 0.435747 Train Acc: 0.935714 Val Loss: 0.751131 Val Acc: 0.816667
[INFO] 2020-11-24 17:38:21,730 [ train.py: 135]: Epoch 187 (0.01945 sec) Train Loss: 0.454062 Train Acc: 0.914286 Val Loss: 0.750023 Val Acc: 0.816667
[INFO] 2020-11-24 17:38:21,762 [ train.py: 135]: Epoch 188 (0.01945 sec) Train Loss: 0.398557 Train Acc: 0.928571 Val Loss: 0.748987 Val Acc: 0.813333
[INFO] 2020-11-24 17:38:21,797 [ train.py: 135]: Epoch 189 (0.01947 sec) Train Loss: 0.435511 Train Acc: 0.935714 Val Loss: 0.746897 Val Acc: 0.813333
[INFO] 2020-11-24 17:38:21,829 [ train.py: 135]: Epoch 190 (0.01946 sec) Train Loss: 0.432686 Train Acc: 0.950000 Val Loss: 0.745300 Val Acc: 0.816667
[INFO] 2020-11-24 17:38:21,860 [ train.py: 135]: Epoch 191 (0.01946 sec) Train Loss: 0.404004 Train Acc: 0.971429 Val Loss: 0.743253 Val Acc: 0.810000
[INFO] 2020-11-24 17:38:21,892 [ train.py: 135]: Epoch 192 (0.01947 sec) Train Loss: 0.433725 Train Acc: 0.935714 Val Loss: 0.742340 Val Acc: 0.810000
[INFO] 2020-11-24 17:38:21,923 [ train.py: 135]: Epoch 193 (0.01946 sec) Train Loss: 0.429871 Train Acc: 0.935714 Val Loss: 0.741186 Val Acc: 0.813333
[INFO] 2020-11-24 17:38:21,954 [ train.py: 135]: Epoch 194 (0.01946 sec) Train Loss: 0.397349 Train Acc: 0.957143 Val Loss: 0.740313 Val Acc: 0.820000
[INFO] 2020-11-24 17:38:21,985 [ train.py: 135]: Epoch 195 (0.01946 sec) Train Loss: 0.423486 Train Acc: 0.921429 Val Loss: 0.739777 Val Acc: 0.820000
[INFO] 2020-11-24 17:38:22,016 [ train.py: 135]: Epoch 196 (0.01946 sec) Train Loss: 0.453004 Train Acc: 0.935714 Val Loss: 0.738899 Val Acc: 0.816667
[INFO] 2020-11-24 17:38:22,046 [ train.py: 135]: Epoch 197 (0.01945 sec) Train Loss: 0.427575 Train Acc: 0.950000 Val Loss: 0.738128 Val Acc: 0.820000
[INFO] 2020-11-24 17:38:22,076 [ train.py: 135]: Epoch 198 (0.01945 sec) Train Loss: 0.438130 Train Acc: 0.935714 Val Loss: 0.736901 Val Acc: 0.820000
[INFO] 2020-11-24 17:38:22,107 [ train.py: 135]: Epoch 199 (0.01945 sec) Train Loss: 0.390269 Train Acc: 0.935714 Val Loss: 0.736106 Val Acc: 0.816667
[INFO] 2020-11-24 17:38:22,119 [ train.py: 143]: Accuracy: 0.809000
至此，CUDA、cuDNN、PaddlePaddle-GPU、PGL全部安装完毕。
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