Changes and additions based on PlotNeuralNet

Box.sty

Path: layers/Box.sty

diff --git a/B.sty b/Box.sty
index 38818a8..854edbb 100644
--- a/B.sty
+++ b/Box.sty
@@ -42,13 +42,13 @@

         \coordinate (a1) at (0 , \y/2 , \z/2);
         \coordinate (b1) at (0 ,-\y/2 , \z/2);
-        \tikzstyle{depthlabel}=[pos=0,text width=14*\z,text centered,sloped]
+        \tikzstyle{depthlabel}=[pos=0.5,text width=14*\z,text centered,sloped]

         \path (c) edge ["\small\zlabel"',depthlabel](f); %depth label
         \path (b1) edge ["\ylabel",midway] (a1);  %height label


-        \tikzstyle{captionlabel}=[text width=15*\LastEastx/\scale,text centered]
+        \tikzstyle{captionlabel}=[text width=2cm,text centered]
         \path (\LastEastx/2,-\y/2,+\z/2) + (0,-25pt) coordinate (cap)
         edge ["\textcolor{black}{ \bf \caption}"',captionlabel](cap) ; %Block caption/pic object label

Box.sty is the revised version.

RightBandedBox.sty

Path: layers/RightBandedBox.sty

diff --git a/R.sty b/RightBandedBox.sty
index 7d74a51..adad188 100644
--- a/R.sty
+++ b/RightBandedBox.sty
@@ -60,12 +60,12 @@

         \coordinate (a1) at (0 , \y/2 , \z/2);
         \coordinate (b1) at (0 ,-\y/2 , \z/2);
-        \tikzstyle{depthlabel}=[pos=0,text width=14*\z,text centered,sloped]
+        \tikzstyle{depthlabel}=[pos=0.5,text width=14*\z,text centered,sloped]

         \path (c) edge ["\small\zlabels"',depthlabel](f); %depth label
         \path (b1) edge ["\ylabel",midway] (a1);  %height label

-        \tikzstyle{captionlabel}=[text width=15*\LastEastx/\scale,text centered]
+        \tikzstyle{captionlabel}=[text width=3cm,text centered]
         \path (\LastEastx/2,-\y/2,+\z/2) + (0,-25pt) coordinate (cap)
         edge ["\textcolor{black}{ \bf \caption}"',captionlabel] (cap); %Block caption/pic object label

RightBandedBox.sty is the revised version.

tikzeng.py

Path: pycore/tikzeng.py

Modify SoftmaxColor
Add PoolReluColor
Add to_Linear function
Add to_LinearRelu function
Modify to_Conv function
- Input parameters changed from "s_fiter, n_filter" to "x, y, n_filter"
  - x: depth (the length of feature map)
  - y: height (the width of feature map)
  - n_filter: width (number of the convolution kernel)
Modify to_Pool function
Add to_PoolRelu function
Add to_ConvRelu function
Add to_dashed_connection function
Add to_dashed_connection_half function
Add to_Flatten function

More details at tikzeng.py

My example

my example

Code of the example in my_example.py

EEGNet

Code of the example in EEGNet.py

The following is the original description

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.

Examples

Following are some network representations:

FCN-8

FCN-32

Holistically-Nested Edge Detection

Getting Started

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
```
- Windows
1. Download and install MikTeX.
2. Download and install bash runner on Windows, recommends Git bash or Cygwin(https://www.cygwin.com/)

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

quoccuonglqd / PlotNeuralNet