深度学习网络画图卷积反卷积

# PlotNeuralNet [](https://doi.org/10.5281/zenodo.2526396) 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: <p align="center"><img src="https://user-images.githubusercontent.com/17570785/50308846-c2231880-049c-11e9-8763-3daa1024de78.png" width="85%" height="85%"></p> <h6 align="center">FCN-8 (<a href="https://www.overleaf.com/read/kkqntfxnvbsk">view on Overleaf</a>)</h6> <p align="center"><img src="https://user-images.githubusercontent.com/17570785/50308873-e2eb6e00-049c-11e9-9587-9da6bdec011b.png" width="85%" height="85%"></p> <h6 align="center">FCN-32 (<a href="https://www.overleaf.com/read/wsxpmkqvjnbs">view on Overleaf</a>)</h6> <p align="center"><img src="https://user-images.githubusercontent.com/17570785/50308911-03b3c380-049d-11e9-92d9-ce15669017ad.png" width="85%" height="85%"></p> <h6 align="center">Holistically-Nested Edge Detection (<a href="https://www.overleaf.com/read/jxhnkcnwhfxp">view on Overleaf</a>)</h6> ## 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](https://gist.github.com/rain1024/98dd5e2c6c8c28f9ea9d), 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](https://miktex.org/download). 2. Download and install bash runner on Windows, recommends [Git bash](https://git-scm.com/download/win) or Cygwin(https://www.cygwin.com/) 2. Execute the example as followed. ``` cd pyexamples/ bash ../tikzmake.sh test_simple ``` ## TODO - [X] Python interface - [ ] Add easy legend functionality - [ ] Add more layer shapes like TruncatedPyramid, 2DSheet etc - [ ] Add examples for RNN and likes. ## Latex usage See [`examples`](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: ```python 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