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前提准备

1、下载MikTeX

下载链接： MikTeX ( https://miktex.org/download )

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2、下载Git bash

下载链接： Git bash ( https://git-scm.com/download/win )

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PlotNeuralNet_17">3、下载PlotNeuralNet

下载链接： PlotNeuralNet ( https://github.com/HarisIqbal88/PlotNeuralNet?tab=readme-ov-file )

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进行测试

PlotNeuralNetmasterzip_24">1、解压PlotNeuralNet-master.zip

将下载的PlotNeuralNet-master.zip解压到当前文件夹，并把解压的PlotNeuralNet-master文件夹更名为PlotNeuralNet。PlotNeuralNet文件夹里的文件如下：

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2、打开Git bash

• 在PlotNeuralNet文件夹里空白处右击鼠标，找到Git Bash Here，点击进入。如下图所示：
  
• 输入以下命令在PlotNeuralNet文件夹创建一个新的文件夹my_project：
  mkdir my_project
• 输入以下命令将Git bash 里的路径切换到文件夹my_project：
  cd my_project
• 输入以下命令使用vim编写my_arch.py：
  vim my_arch.py
  
• 将以下代码复制进去

import sys
sys.path.append('../')
from pycore.tikzeng import *# defined your arch
arch = [to_head( '..' ),to_cor(),to_begin(),to_Conv("conv1", 512, 64, offset="(0,0,0)", to="(0,0,0)", height=64, depth=64, width=2 ),to_Pool("pool1", offset="(0,0,0)", to="(conv1-east)"),to_Conv("conv2", 128, 64, offset="(1,0,0)", to="(pool1-east)", height=32, depth=32, width=2 ),to_connection( "pool1", "conv2"),to_Pool("pool2", offset="(0,0,0)", to="(conv2-east)", height=28, depth=28, width=1),to_SoftMax("soft1", 10 ,"(3,0,0)", "(pool1-east)", caption="SOFT"  ),to_connection("pool2", "soft1"),to_end()]def main():namefile = str(sys.argv[0]).split('.')[0]to_generate(arch, namefile + '.tex' )if __name__ == '__main__':main()

• 代码复制完后，按Esc键，然后再按:wq后回车保存退出。
  
• 执行以下命令
  bash ../tikzmake.sh my_arch
  
• 宏包安装
  在弹出的窗口界面安装宏包
  
  【可能的报错】
  如果在Git bash 中输出以下报错：
  ! LaTeX Error: File ’import.sty‘ not found. Type X to quit or to proceed, or enter new name. (Default extension: sty) Enter file name:
  【解决方式】
  1、直接关闭Git bash
  2、找到并打开MikTex Console
  
  3、按下图进行操作
  
  4、执行以下命令
bash ../tikzmake.sh my_arch
  

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3、 在my_project中查看生成的pdf文件

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创建自己的神经网络结构可视化图形

找到pyexamples文件夹下的test_simple.py或unet.py文件，更改defined your arch部分，运行代码后会生成一个.tex文件，运行.tex文件即可得到自己的可视化模型的pdf文件。
test_simple.py中的defined your arch部分：

# defined your arch
arch = [to_head( '..' ),to_cor(),to_begin(),to_Conv("conv1", 512, 64, offset="(0,0,0)", to="(0,0,0)", height=64, depth=64, width=2 ),to_Pool("pool1", offset="(0,0,0)", to="(conv1-east)"),to_Conv("conv2", 128, 64, offset="(1,0,0)", to="(pool1-east)", height=32, depth=32, width=2 ),to_connection( "pool1", "conv2"), to_Pool("pool2", offset="(0,0,0)", to="(conv2-east)", height=28, depth=28, width=1),to_SoftMax("soft1", 10 ,"(3,0,0)", "(pool1-east)", caption="SOFT"  ),to_connection("pool2", "soft1"),    to_Sum("sum1", offset="(1.5,0,0)", to="(soft1-east)", radius=2.5, opacity=0.6),to_connection("soft1", "sum1"),to_end()]

unet.py中的defined your arch部分：

arch = [ to_head('..'), to_cor(),to_begin(),#inputto_input( '../examples/fcn8s/cats.jpg' ),#block-001to_ConvConvRelu( name='ccr_b1', s_filer=500, n_filer=(64,64), offset="(0,0,0)", to="(0,0,0)", width=(2,2), height=40, depth=40  ),to_Pool(name="pool_b1", offset="(0,0,0)", to="(ccr_b1-east)", width=1, height=32, depth=32, opacity=0.5),*block_2ConvPool( name='b2', botton='pool_b1', top='pool_b2', s_filer=256, n_filer=128, offset="(1,0,0)", size=(32,32,3.5), opacity=0.5 ),*block_2ConvPool( name='b3', botton='pool_b2', top='pool_b3', s_filer=128, n_filer=256, offset="(1,0,0)", size=(25,25,4.5), opacity=0.5 ),*block_2ConvPool( name='b4', botton='pool_b3', top='pool_b4', s_filer=64,  n_filer=512, offset="(1,0,0)", size=(16,16,5.5), opacity=0.5 ),#Bottleneck#block-005to_ConvConvRelu( name='ccr_b5', s_filer=32, n_filer=(1024,1024), offset="(2,0,0)", to="(pool_b4-east)", width=(8,8), height=8, depth=8, caption="Bottleneck"  ),to_connection( "pool_b4", "ccr_b5"),#Decoder*block_Unconv( name="b6", botton="ccr_b5", top='end_b6', s_filer=64,  n_filer=512, offset="(2.1,0,0)", size=(16,16,5.0), opacity=0.5 ),to_skip( of='ccr_b4', to='ccr_res_b6', pos=1.25),*block_Unconv( name="b7", botton="end_b6", top='end_b7', s_filer=128, n_filer=256, offset="(2.1,0,0)", size=(25,25,4.5), opacity=0.5 ),to_skip( of='ccr_b3', to='ccr_res_b7', pos=1.25),    *block_Unconv( name="b8", botton="end_b7", top='end_b8', s_filer=256, n_filer=128, offset="(2.1,0,0)", size=(32,32,3.5), opacity=0.5 ),to_skip( of='ccr_b2', to='ccr_res_b8', pos=1.25),    *block_Unconv( name="b9", botton="end_b8", top='end_b9', s_filer=512, n_filer=64,  offset="(2.1,0,0)", size=(40,40,2.5), opacity=0.5 ),to_skip( of='ccr_b1', to='ccr_res_b9', pos=1.25),to_ConvSoftMax( name="soft1", s_filer=512, offset="(0.75,0,0)", to="(end_b9-east)", width=1, height=40, depth=40, caption="SOFT" ),to_connection( "end_b9", "soft1"),to_end() ]