run.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Deep Reinforcement Learning using AlphaZero methodology\n",
    "\n",
    "Please see https://applied-data.science/blog/how-to-build-your-own-alphazero-ai-using-python-and-keras/ for further notes on the codebase"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. First load the core libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "# -*- coding: utf-8 -*-\n",
    "# %matplotlib inline\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "import numpy as np\n",
    "np.set_printoptions(suppress=True)\n",
    "\n",
    "from shutil import copyfile\n",
    "import random\n",
    "from importlib import reload\n",
    "\n",
    "\n",
    "from keras.utils import plot_model\n",
    "\n",
    "from game import Game, GameState\n",
    "from agent import Agent\n",
    "from memory import Memory\n",
    "from model import Residual_CNN\n",
    "from funcs import playMatches, playMatchesBetweenVersions\n",
    "\n",
    "import loggers as lg\n",
    "\n",
    "from settings import run_folder, run_archive_folder\n",
    "import initialise\n",
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "os.environ[\"PATH\"] += os.pathsep + 'C:/Program Files (x86)/Graphviz2.38/bin/'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Now run this block to start the learning process\n",
    "\n",
    "This block loops for ever, continually learning from new game data.\n",
    "\n",
    "The current best model and memories are saved in the run folder so you can kill the process and restart from the last checkpoint."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LOADING MODEL VERSION 98...\n",
      "\n",
      "\n",
      "ITERATION NUMBER 1\n",
      "BEST PLAYER VERSION 98\n",
      "SELF PLAYING 25 EPISODES...\n",
      "1 2 3 4 "
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-7-d4b5dcf02dd7>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     63\u001b[0m     \u001b[1;31m######## SELF PLAY ########\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     64\u001b[0m     \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'SELF PLAYING '\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mconfig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mEPISODES\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;34m' EPISODES...'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 65\u001b[1;33m     \u001b[0m_\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmemory\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mplayMatches\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbest_player\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbest_player\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mconfig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mEPISODES\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlogger_main\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mturns_until_tau0\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mconfig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTURNS_UNTIL_TAU0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmemory\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmemory\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     66\u001b[0m     \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'\\n'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     67\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Desktop\\DSMLL\\Reinforcement Learning\\DeepReinforcementLearning-master\\funcs.py\u001b[0m in \u001b[0;36mplayMatches\u001b[1;34m(player1, player2, EPISODES, logger, turns_until_tau0, memory, goes_first)\u001b[0m\n\u001b[0;32m     90\u001b[0m                 \u001b[0maction\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpi\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mMCTS_value\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mNN_value\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mplayers\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mstate\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplayerTurn\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'agent'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mact\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstate\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     91\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 92\u001b[1;33m                 \u001b[0maction\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpi\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mMCTS_value\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mNN_value\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mplayers\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mstate\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplayerTurn\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'agent'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mact\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstate\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     93\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     94\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mmemory\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Desktop\\DSMLL\\Reinforcement Learning\\DeepReinforcementLearning-master\\agent.py\u001b[0m in \u001b[0;36mact\u001b[1;34m(self, state, tau)\u001b[0m\n\u001b[0;32m     91\u001b[0m                         \u001b[0mlg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlogger_mcts\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minfo\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'****** SIMULATION %d ******'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msim\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     92\u001b[0m                         \u001b[0mlg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlogger_mcts\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minfo\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'***************************'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 93\u001b[1;33m                         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msimulate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     94\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     95\u001b[0m                 \u001b[1;31m#### get action values\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Desktop\\DSMLL\\Reinforcement Learning\\DeepReinforcementLearning-master\\agent.py\u001b[0m in \u001b[0;36msimulate\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m     76\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     77\u001b[0m                 \u001b[1;31m##### BACKFILL THE VALUE THROUGH THE TREE\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 78\u001b[1;33m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmcts\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbackFill\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mleaf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbreadcrumbs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     79\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     80\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Desktop\\DSMLL\\Reinforcement Learning\\DeepReinforcementLearning-master\\MCTS.py\u001b[0m in \u001b[0;36mbackFill\u001b[1;34m(self, leaf, value, breadcrumbs)\u001b[0m\n\u001b[0;32m    128\u001b[0m \t\t\t\t)\n\u001b[0;32m    129\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 130\u001b[1;33m                         \u001b[0medge\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moutNode\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstate\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrender\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlogger_mcts\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    131\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    132\u001b[0m         \u001b[1;32mdef\u001b[0m \u001b[0maddNode\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnode\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Desktop\\DSMLL\\Reinforcement Learning\\DeepReinforcementLearning-master\\game.py\u001b[0m in \u001b[0;36mrender\u001b[1;34m(self, logger)\u001b[0m\n\u001b[0;32m    223\u001b[0m         \u001b[1;32mdef\u001b[0m \u001b[0mrender\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlogger\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    224\u001b[0m                 \u001b[1;32mfor\u001b[0m \u001b[0mr\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m6\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 225\u001b[1;33m                         \u001b[0mlogger\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minfo\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpieces\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mboard\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m7\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mr\u001b[0m \u001b[1;33m:\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;36m7\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mr\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;36m7\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    226\u001b[0m                 \u001b[0mlogger\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minfo\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'--------------'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    227\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\logging\\__init__.py\u001b[0m in \u001b[0;36minfo\u001b[1;34m(self, msg, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1305\u001b[0m         \"\"\"\n\u001b[0;32m   1306\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0misEnabledFor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mINFO\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1307\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_log\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mINFO\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmsg\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1308\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1309\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mwarning\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmsg\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\logging\\__init__.py\u001b[0m in \u001b[0;36m_log\u001b[1;34m(self, level, msg, args, exc_info, extra, stack_info)\u001b[0m\n\u001b[0;32m   1441\u001b[0m         record = self.makeRecord(self.name, level, fn, lno, msg, args,\n\u001b[0;32m   1442\u001b[0m                                  exc_info, func, extra, sinfo)\n\u001b[1;32m-> 1443\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhandle\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrecord\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1444\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1445\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mhandle\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrecord\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\logging\\__init__.py\u001b[0m in \u001b[0;36mhandle\u001b[1;34m(self, record)\u001b[0m\n\u001b[0;32m   1451\u001b[0m         \"\"\"\n\u001b[0;32m   1452\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdisabled\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfilter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrecord\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1453\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcallHandlers\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrecord\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1454\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1455\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0maddHandler\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhdlr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\logging\\__init__.py\u001b[0m in \u001b[0;36mcallHandlers\u001b[1;34m(self, record)\u001b[0m\n\u001b[0;32m   1513\u001b[0m                 \u001b[0mfound\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfound\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1514\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[0mrecord\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlevelno\u001b[0m \u001b[1;33m>=\u001b[0m \u001b[0mhdlr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlevel\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1515\u001b[1;33m                     \u001b[0mhdlr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhandle\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrecord\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1516\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpropagate\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1517\u001b[0m                 \u001b[0mc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m    \u001b[1;31m#break out\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\logging\\__init__.py\u001b[0m in \u001b[0;36mhandle\u001b[1;34m(self, record)\u001b[0m\n\u001b[0;32m    862\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0macquire\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    863\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 864\u001b[1;33m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0memit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrecord\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    865\u001b[0m             \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    866\u001b[0m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrelease\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\logging\\__init__.py\u001b[0m in \u001b[0;36memit\u001b[1;34m(self, record)\u001b[0m\n\u001b[0;32m   1069\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstream\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1070\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstream\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_open\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1071\u001b[1;33m         \u001b[0mStreamHandler\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0memit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrecord\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1072\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1073\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__repr__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\logging\\__init__.py\u001b[0m in \u001b[0;36memit\u001b[1;34m(self, record)\u001b[0m\n\u001b[0;32m    995\u001b[0m             \u001b[0mstream\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    996\u001b[0m             \u001b[0mstream\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mterminator\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 997\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mflush\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    998\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    999\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhandleError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrecord\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\logging\\__init__.py\u001b[0m in \u001b[0;36mflush\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    975\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    976\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstream\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstream\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"flush\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 977\u001b[1;33m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstream\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mflush\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    978\u001b[0m         \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    979\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrelease\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "lg.logger_main.info('=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*')\n",
    "lg.logger_main.info('=*=*=*=*=*=.      NEW LOG      =*=*=*=*=*')\n",
    "lg.logger_main.info('=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*')\n",
    "\n",
    "env = Game()\n",
    "\n",
    "# If loading an existing neural network, copy the config file to root\n",
    "if initialise.INITIAL_RUN_NUMBER != None:\n",
    "    copyfile(run_archive_folder + env.name + '/run' + str(initialise.INITIAL_RUN_NUMBER).zfill(4) + '/config.py', './config.py')\n",
    "\n",
    "import config\n",
    "\n",
    "######## LOAD MEMORIES IF NECESSARY ########\n",
    "\n",
    "if initialise.INITIAL_MEMORY_VERSION == None:\n",
    "    memory = Memory(config.MEMORY_SIZE)\n",
    "else:\n",
    "    print('LOADING MEMORY VERSION ' + str(initialise.INITIAL_MEMORY_VERSION) + '...')\n",
    "    memory = pickle.load( open( run_archive_folder + env.name + '/run' + str(initialise.INITIAL_RUN_NUMBER).zfill(4) + \"/memory/memory\" + str(initialise.INITIAL_MEMORY_VERSION).zfill(4) + \".p\",   \"rb\" ) )\n",
    "\n",
    "######## LOAD MODEL IF NECESSARY ########\n",
    "\n",
    "# create an untrained neural network objects from the config file\n",
    "current_NN = Residual_CNN(config.REG_CONST, config.LEARNING_RATE, (2,) + env.grid_shape,   env.action_size, config.HIDDEN_CNN_LAYERS)\n",
    "best_NN = Residual_CNN(config.REG_CONST, config.LEARNING_RATE, (2,) +  env.grid_shape,   env.action_size, config.HIDDEN_CNN_LAYERS)\n",
    "\n",
    "#If loading an existing neural netwrok, set the weights from that model\n",
    "if initialise.INITIAL_MODEL_VERSION != None:\n",
    "    best_player_version  = initialise.INITIAL_MODEL_VERSION\n",
    "    print('LOADING MODEL VERSION ' + str(initialise.INITIAL_MODEL_VERSION) + '...')\n",
    "    m_tmp = best_NN.read(env.name, initialise.INITIAL_RUN_NUMBER, best_player_version)\n",
    "    current_NN.model.set_weights(m_tmp.get_weights())\n",
    "    best_NN.model.set_weights(m_tmp.get_weights())\n",
    "#otherwise just ensure the weights on the two players are the same\n",
    "else:\n",
    "    best_player_version = 0\n",
    "    best_NN.model.set_weights(current_NN.model.get_weights())\n",
    "\n",
    "#copy the config file to the run folder\n",
    "copyfile('./config.py', run_folder + 'config.py')\n",
    "plot_model(current_NN.model, to_file=run_folder + 'models/model.png', show_shapes = True)\n",
    "\n",
    "print('\\n')\n",
    "\n",
    "######## CREATE THE PLAYERS ########\n",
    "\n",
    "current_player = Agent('current_player', env.state_size, env.action_size, config.MCTS_SIMS, config.CPUCT, current_NN)\n",
    "best_player = Agent('best_player', env.state_size, env.action_size, config.MCTS_SIMS, config.CPUCT, best_NN)\n",
    "#user_player = User('player1', env.state_size, env.action_size)\n",
    "iteration = 0\n",
    "\n",
    "while 1:\n",
    "\n",
    "    iteration += 1\n",
    "    reload(lg)\n",
    "    reload(config)\n",
    "    \n",
    "    print('ITERATION NUMBER ' + str(iteration))\n",
    "    \n",
    "    lg.logger_main.info('BEST PLAYER VERSION: %d', best_player_version)\n",
    "    print('BEST PLAYER VERSION ' + str(best_player_version))\n",
    "\n",
    "    ######## SELF PLAY ########\n",
    "    print('SELF PLAYING ' + str(config.EPISODES) + ' EPISODES...')\n",
    "    _, memory, _, _ = playMatches(best_player, best_player, config.EPISODES, lg.logger_main, turns_until_tau0 = config.TURNS_UNTIL_TAU0, memory = memory)\n",
    "    print('\\n')\n",
    "    \n",
    "    memory.clear_stmemory()\n",
    "    \n",
    "    if len(memory.ltmemory) >= config.MEMORY_SIZE:\n",
    "\n",
    "        ######## RETRAINING ########\n",
    "        print('RETRAINING...')\n",
    "        current_player.replay(memory.ltmemory)\n",
    "        print('')\n",
    "\n",
    "        if iteration % 5 == 0:\n",
    "            pickle.dump( memory, open( run_folder + \"memory/memory\" + str(iteration).zfill(4) + \".p\", \"wb\" ) )\n",
    "\n",
    "        lg.logger_memory.info('====================')\n",
    "        lg.logger_memory.info('NEW MEMORIES')\n",
    "        lg.logger_memory.info('====================')\n",
    "        \n",
    "        memory_samp = random.sample(memory.ltmemory, min(1000, len(memory.ltmemory)))\n",
    "        \n",
    "        for s in memory_samp:\n",
    "            current_value, current_probs, _ = current_player.get_preds(s['state'])\n",
    "            best_value, best_probs, _ = best_player.get_preds(s['state'])\n",
    "\n",
    "            lg.logger_memory.info('MCTS VALUE FOR %s: %f', s['playerTurn'], s['value'])\n",
    "            lg.logger_memory.info('CUR PRED VALUE FOR %s: %f', s['playerTurn'], current_value)\n",
    "            lg.logger_memory.info('BES PRED VALUE FOR %s: %f', s['playerTurn'], best_value)\n",
    "            lg.logger_memory.info('THE MCTS ACTION VALUES: %s', ['%.2f' % elem for elem in s['AV']]  )\n",
    "            lg.logger_memory.info('CUR PRED ACTION VALUES: %s', ['%.2f' % elem for elem in  current_probs])\n",
    "            lg.logger_memory.info('BES PRED ACTION VALUES: %s', ['%.2f' % elem for elem in  best_probs])\n",
    "            lg.logger_memory.info('ID: %s', s['state'].id)\n",
    "            lg.logger_memory.info('INPUT TO MODEL: %s', current_player.model.convertToModelInput(s['state']))\n",
    "\n",
    "            s['state'].render(lg.logger_memory)\n",
    "            \n",
    "        ######## TOURNAMENT ########\n",
    "        print('TOURNAMENT...')\n",
    "        scores, _, points, sp_scores = playMatches(best_player, current_player, config.EVAL_EPISODES, lg.logger_tourney, turns_until_tau0 = 0, memory = None)\n",
    "        print('\\nSCORES')\n",
    "        print(scores)\n",
    "        print('\\nSTARTING PLAYER / NON-STARTING PLAYER SCORES')\n",
    "        print(sp_scores)\n",
    "        #print(points)\n",
    "\n",
    "        print('\\n\\n')\n",
    "\n",
    "        if scores['current_player'] > scores['best_player'] * config.SCORING_THRESHOLD:\n",
    "            best_player_version = best_player_version + 1\n",
    "            best_NN.model.set_weights(current_NN.model.get_weights())\n",
    "            best_NN.write(env.name, best_player_version)\n",
    "\n",
    "    else:\n",
    "        print('MEMORY SIZE: ' + str(len(memory.ltmemory)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The following panels are not involved in the learning process\n",
    "\n",
    "### Play matches between versions (use -1 for human player)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------\n",
      "[[1 2 3 4 5 6 7]]\n",
      "[[0 0 0 0 0 0 0]\n",
      " [0 0 0 2 0 0 0]\n",
      " [0 2 0 1 0 0 0]\n",
      " [0 2 1 2 0 0 0]\n",
      " [0 1 2 1 1 0 0]\n",
      " [1 2 1 1 2 0 0]]\n",
      "--------------\n",
      "column 3 was played\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "({'player1': 1, 'drawn': 0, 'player2(ver:85)': 0},\n",
       " None,\n",
       " {'player1': [1], 'player2(ver:85)': [-1]},\n",
       " {'sp': 1, 'drawn': 0, 'nsp': 0})"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from game import Game\n",
    "from funcs import playMatchesBetweenVersions\n",
    "import loggers as lg\n",
    "env = Game()\n",
    "playMatchesBetweenVersions(\n",
    "env\n",
    ", 1  # the run version number where the computer player is located\n",
    ", -1 # the version number of the first player (-1 for human)\n",
    ", 85 # the version number of the second player (-1 for human)\n",
    ", 1 # how many games to play\n",
    ", lg.logger_tourney # where to log the game to\n",
    ", 1  # which player to go first - 0 for random\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pass a particular game state through the neural network (setup below for Connect4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(array([0.06609119], dtype=float32), array([0.        , 0.        , 0.        , 0.        , 0.        ,\n",
      "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
      "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
      "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
      "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
      "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
      "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
      "       0.01976066, 0.0250197 , 0.02031646, 0.88036674, 0.01832377,\n",
      "       0.02242872, 0.01378396], dtype=float32), [35, 36, 37, 38, 39, 40, 41])\n"
     ]
    }
   ],
   "source": [
    "gs = GameState(np.array([\n",
    "    0,0,0,0,0,0,0,\n",
    "    0,0,0,0,0,0,0,\n",
    "    0,0,0,0,0,0,0,\n",
    "    0,0,0,0,0,0,0,\n",
    "    0,0,0,0,0,0,0,\n",
    "    0,0,0,0,0,0,0\n",
    "]), 1)\n",
    "\n",
    "preds = current_player.get_preds(gs)\n",
    "\n",
    "print(preds)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### See the layers of the current neural network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LAYER 0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 216x0 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LAYER 1\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 144x5400 with 150 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LAYER 2\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 216x288 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LAYER 3\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 216x0 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LAYER 4\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-9-36be611761b0>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mcurrent_player\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mviewLayers\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\Desktop\\DSMLL\\Reinforcement Learning\\DeepReinforcementLearning-master\\model.py\u001b[0m in \u001b[0;36mviewLayers\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    102\u001b[0m                                                 \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    103\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 104\u001b[1;33m                         \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    105\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    106\u001b[0m                 \u001b[0mlg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlogger_model\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minfo\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'------------------'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\matplotlib\\pyplot.py\u001b[0m in \u001b[0;36mshow\u001b[1;34m(*args, **kw)\u001b[0m\n\u001b[0;32m    251\u001b[0m     \"\"\"\n\u001b[0;32m    252\u001b[0m     \u001b[1;32mglobal\u001b[0m \u001b[0m_show\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 253\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0m_show\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    254\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    255\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\ipykernel\\pylab\\backend_inline.py\u001b[0m in \u001b[0;36mshow\u001b[1;34m(close, block)\u001b[0m\n\u001b[0;32m     34\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     35\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mfigure_manager\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mGcf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_all_fig_managers\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 36\u001b[1;33m             \u001b[0mdisplay\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfigure_manager\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     37\u001b[0m     \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     38\u001b[0m         \u001b[0mshow\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_to_draw\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\IPython\\core\\display.py\u001b[0m in \u001b[0;36mdisplay\u001b[1;34m(include, exclude, metadata, transient, display_id, *objs, **kwargs)\u001b[0m\n\u001b[0;32m    296\u001b[0m             \u001b[0mpublish_display_data\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmetadata\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmetadata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    297\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 298\u001b[1;33m             \u001b[0mformat_dict\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmd_dict\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minclude\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0minclude\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mexclude\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mexclude\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    299\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mformat_dict\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    300\u001b[0m                 \u001b[1;31m# nothing to display (e.g. _ipython_display_ took over)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36mformat\u001b[1;34m(self, obj, include, exclude)\u001b[0m\n\u001b[0;32m    178\u001b[0m             \u001b[0mmd\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    179\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 180\u001b[1;33m                 \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mformatter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    181\u001b[0m             \u001b[1;32mexcept\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    182\u001b[0m                 \u001b[1;31m# FIXME: log the exception\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<decorator-gen-9>\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36mcatch_format_error\u001b[1;34m(method, self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    222\u001b[0m     \u001b[1;34m\"\"\"show traceback on failed format call\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    223\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 224\u001b[1;33m         \u001b[0mr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    225\u001b[0m     \u001b[1;32mexcept\u001b[0m \u001b[0mNotImplementedError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    226\u001b[0m         \u001b[1;31m# don't warn on NotImplementedErrors\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m    339\u001b[0m                 \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    340\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 341\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    342\u001b[0m             \u001b[1;31m# Finally look for special method names\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    343\u001b[0m             \u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\IPython\\core\\pylabtools.py\u001b[0m in \u001b[0;36m<lambda>\u001b[1;34m(fig)\u001b[0m\n\u001b[0;32m    239\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    240\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[1;34m'png'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 241\u001b[1;33m         \u001b[0mpng_formatter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mprint_figure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'png'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    242\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[1;34m'retina'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;34m'png2x'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    243\u001b[0m         \u001b[0mpng_formatter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mretina_figure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\IPython\\core\\pylabtools.py\u001b[0m in \u001b[0;36mprint_figure\u001b[1;34m(fig, fmt, bbox_inches, **kwargs)\u001b[0m\n\u001b[0;32m    123\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    124\u001b[0m     \u001b[0mbytes_io\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mBytesIO\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 125\u001b[1;33m     \u001b[0mfig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_figure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbytes_io\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    126\u001b[0m     \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbytes_io\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetvalue\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    127\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mfmt\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'svg'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\matplotlib\\backend_bases.py\u001b[0m in \u001b[0;36mprint_figure\u001b[1;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, **kwargs)\u001b[0m\n\u001b[0;32m   2261\u001b[0m                 \u001b[0morientation\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morientation\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2262\u001b[0m                 \u001b[0mbbox_inches_restore\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0m_bbox_inches_restore\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2263\u001b[1;33m                 **kwargs)\n\u001b[0m\u001b[0;32m   2264\u001b[0m         \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2265\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mbbox_inches\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mrestore_bbox\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py\u001b[0m in \u001b[0;36mprint_png\u001b[1;34m(self, filename_or_obj, *args, **kwargs)\u001b[0m\n\u001b[0;32m    511\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    512\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mprint_png\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfilename_or_obj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 513\u001b[1;33m         \u001b[0mFigureCanvasAgg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    514\u001b[0m         \u001b[0mrenderer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_renderer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    515\u001b[0m         \u001b[0moriginal_dpi\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdpi\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py\u001b[0m in \u001b[0;36mdraw\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    431\u001b[0m             \u001b[1;31m# if toolbar:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    432\u001b[0m             \u001b[1;31m#     toolbar.set_cursor(cursors.WAIT)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 433\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    434\u001b[0m             \u001b[1;31m# A GUI class may be need to update a window using this draw, so\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    435\u001b[0m             \u001b[1;31m# don't forget to call the superclass.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\matplotlib\\artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[1;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[0;32m     53\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     54\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 55\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0martist\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     56\u001b[0m         \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     57\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\matplotlib\\figure.py\u001b[0m in \u001b[0;36mdraw\u001b[1;34m(self, renderer)\u001b[0m\n\u001b[0;32m   1473\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1474\u001b[0m             mimage._draw_list_compositing_images(\n\u001b[1;32m-> 1475\u001b[1;33m                 renderer, self, artists, self.suppressComposite)\n\u001b[0m\u001b[0;32m   1476\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1477\u001b[0m             \u001b[0mrenderer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclose_group\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'figure'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\matplotlib\\image.py\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[1;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[0;32m    139\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mnot_composite\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mhas_images\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    140\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[1;32min\u001b[0m \u001b[0martists\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 141\u001b[1;33m             \u001b[0ma\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    142\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    143\u001b[0m         \u001b[1;31m# Composite any adjacent images together\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\matplotlib\\artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[1;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[0;32m     53\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     54\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 55\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0martist\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     56\u001b[0m         \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     57\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_base.py\u001b[0m in \u001b[0;36mdraw\u001b[1;34m(self, renderer, inframe)\u001b[0m\n\u001b[0;32m   2598\u001b[0m         \u001b[1;31m# will draw the edges\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2599\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maxison\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_frameon\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2600\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2601\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2602\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0martists_rasterized\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\matplotlib\\artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[1;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[0;32m     53\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     54\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 55\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0martist\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     56\u001b[0m         \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     57\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\matplotlib\\patches.py\u001b[0m in \u001b[0;36mdraw\u001b[1;34m(self, renderer)\u001b[0m\n\u001b[0;32m    548\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    549\u001b[0m         \u001b[0mpath\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_path\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 550\u001b[1;33m         \u001b[0mtransform\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_transform\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    551\u001b[0m         \u001b[0mtpath\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtransform\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtransform_path_non_affine\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    552\u001b[0m         \u001b[0maffine\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtransform\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_affine\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\matplotlib\\patches.py\u001b[0m in \u001b[0;36mget_transform\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    197\u001b[0m         \u001b[0mto\u001b[0m \u001b[0mthe\u001b[0m \u001b[1;33m:\u001b[0m\u001b[1;32mclass\u001b[0m\u001b[1;33m:\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0mPatch\u001b[0m\u001b[0;31m`\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    198\u001b[0m         \"\"\"\n\u001b[1;32m--> 199\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_patch_transform\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0martist\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mArtist\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_transform\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    200\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    201\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mget_data_transform\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\matplotlib\\patches.py\u001b[0m in \u001b[0;36mget_patch_transform\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    735\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    736\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mget_patch_transform\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 737\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_update_patch_transform\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    738\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_rect_transform\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    739\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\matplotlib\\patches.py\u001b[0m in \u001b[0;36m_update_patch_transform\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    714\u001b[0m         \u001b[0mbbox\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtransforms\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mBbox\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfrom_extents\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    715\u001b[0m         \u001b[0mrot_trans\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtransforms\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mAffine2D\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 716\u001b[1;33m         \u001b[0mrot_trans\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrotate_deg_around\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mangle\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    717\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_rect_transform\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtransforms\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mBboxTransformTo\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbbox\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    718\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_rect_transform\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[0mrot_trans\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\matplotlib\\transforms.py\u001b[0m in \u001b[0;36mrotate_deg_around\u001b[1;34m(self, x, y, degrees)\u001b[0m\n\u001b[0;32m   2058\u001b[0m         \u001b[1;31m# Cast to float to avoid wraparound issues with uint8's\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2059\u001b[0m         \u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfloat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfloat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2060\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtranslate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m-\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m-\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrotate_deg\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdegrees\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtranslate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2061\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2062\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mtranslate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mty\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\matplotlib\\transforms.py\u001b[0m in \u001b[0;36mtranslate\u001b[1;34m(self, tx, ty)\u001b[0m\n\u001b[0;32m   2070\u001b[0m         translate_mtx = np.array(\n\u001b[0;32m   2071\u001b[0m             [[1.0, 0.0, tx], [0.0, 1.0, ty], [0.0, 0.0, 1.0]], float)\n\u001b[1;32m-> 2072\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_mtx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtranslate_mtx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_mtx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2073\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minvalidate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2074\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "current_player.model.viewLayers()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Output a diagram of the neural network architecture"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.utils import plot_model\n",
    "plot_model(current_NN.model, to_file=run_folder + 'models/model.png', show_shapes = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}