【神经网络结构可视化】PlotNeuralNet的安装、测试及创建自己的神经网络结构可视化图形

文章目录

• • 前提准备
  • • 1、下载MikTeX
    • [2、下载Git bash](#2、下载Git bash)
    • 3、下载PlotNeuralNet
  • 进行测试
  • • 1、解压PlotNeuralNet-master.zip
    • [2、打开Git bash](#2、打开Git bash)
    • [3、 在my_project中查看生成的pdf文件](#3、 在my_project中查看生成的pdf文件)
  • 创建自己的神经网络结构可视化图形

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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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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

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(),
    
    #input
    to_input( '../examples/fcn8s/cats.jpg' ),

    #block-001
    to_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-005
    to_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() 
    ]