PlotNeuralNet

Latex code for drawing neural networks for reports and presentation. Have a look into examples to see how they are made. Additionally, lets consolidate any improvements that you make and fix any bugs to help more people with this code.

Getting Started

1. Install the following packages on Ubuntu.

   • Ubuntu 16.04

     sudo apt-get install texlive-latex-extra
     

   • Ubuntu 18.04.2 Base on this website, please install the following packages.

     sudo apt-get install texlive-latex-base
     sudo apt-get install texlive-fonts-recommended
     sudo apt-get install texlive-fonts-extra
     sudo apt-get install texlive-latex-extra
     

2. Execute the example as followed.

   cd pyexamples/
   bash ../tikzmake.sh test_simple
   

TODO

• Python interface
• Add easy legend functionality
• Add more layer shapes like TruncatedPyramid, 2DSheet etc
• Add examples for RNN and likes.

Latex usage

See examples directory for usage.

Python usage

First, create a new directory and a new Python file:

$ mkdir my_project
$ cd my_project
vim my_arch.py

Add the following code to your new file:

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()

Now, run the program as follows:

bash ../tikzmake.sh my_arch

Examples

Following are some network representations:

FCN-8

FCN-32

Holistically-Nested Edge Detection