Adding Binder Support and Simple Notebook

README.md

@@ -1,11 +1,11 @@
 # PlotNeuralNet
-[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.2526396.svg)](https://doi.org/10.5281/zenodo.2526396)
+[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.2526396.svg)](https://doi.org/10.5281/zenodo.2526396) [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/HarisIqbal88/PlotNeuralNet/master)
 
 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.
 
 ## TODO
 
-- [X] Python interfaz
+- [X] Python interface
 - [ ] Add easy legend functionality
 - [ ] Add more layer shapes like TruncatedPyramid, 2DSheet etc
 - [ ] Add examples for RNN and likes.

binder/apt.txt

@@ -0,0 +1 @@
+texlive-full

pyexamples/UNetNotebook.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys, os\n",
+    "PLOTNN_DIR = os.path.join('..')\n",
+    "sys.path.append(PLOTNN_DIR)\n",
+    "from pycore.tikzeng import *\n",
+    "from pycore.blocks  import *\n",
+    "from IPython.display import display_pdf, FileLink"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "arch = [ \n",
+    "    to_head(PLOTNN_DIR), \n",
+    "    to_cor(),\n",
+    "    to_begin(),\n",
+    "    \n",
+    "    #input\n",
+    "    to_input( os.path.join(PLOTNN_DIR, 'examples/fcn8s/cats.jpg') ),\n",
+    "\n",
+    "    #block-001\n",
+    "    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  ),\n",
+    "    to_Pool(name=\"pool_b1\", offset=\"(0,0,0)\", to=\"(ccr_b1-east)\", width=1, height=32, depth=32, opacity=0.5),\n",
+    "    \n",
+    "    *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 ),\n",
+    "    *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 ),\n",
+    "    *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 ),\n",
+    "\n",
+    "    #Bottleneck\n",
+    "    #block-005\n",
+    "    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\"  ),\n",
+    "    to_connection( \"pool_b4\", \"ccr_b5\"),\n",
+    "\n",
+    "    #Decoder\n",
+    "    *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 ),\n",
+    "    to_skip( of='ccr_b4', to='ccr_res_b6', pos=1.25),\n",
+    "    *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 ),\n",
+    "    to_skip( of='ccr_b3', to='ccr_res_b7', pos=1.25),    \n",
+    "    *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 ),\n",
+    "    to_skip( of='ccr_b2', to='ccr_res_b8', pos=1.25),    \n",
+    "    \n",
+    "    *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 ),\n",
+    "    to_skip( of='ccr_b1', to='ccr_res_b9', pos=1.25),\n",
+    "    \n",
+    "    to_ConvSoftMax( name=\"soft1\", s_filer=512, offset=\"(0.75,0,0)\", to=\"(end_b9-east)\", width=1, height=40, depth=40, caption=\"SOFT\" ),\n",
+    "    to_connection( \"end_b9\", \"soft1\"),\n",
+    "     \n",
+    "    to_end() \n",
+    "    ]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "to_generate(arch, os.path.join(PLOTNN_DIR, 'u_out.tex'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pdflatex {os.path.join(PLOTNN_DIR, 'u_out.tex')}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "FileLink('u_out.pdf')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}