nb_ex2_2_ann_rg.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "# %load /home/sjkim/.jupyter/head.py\n",
    "%matplotlib inline\n",
    "%load_ext autoreload \n",
    "%autoreload 2\n",
    "from importlib import reload\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "import pandas as pd\n",
    "import os\n",
    "#os.environ[\"CUDA_VISIBLE_DEVICES\"]=\"0\"\n",
    "\n",
    "# seaborn\n",
    "#import seaborn as sns\n",
    "#sns.set( style = 'white', font_scale = 1.7)\n",
    "#sns.set_style('ticks')\n",
    "#plt.rcParams['savefig.dpi'] = 200\n",
    "\n",
    "# font for matplotlib\n",
    "#import matplotlib\n",
    "#import matplotlib.font_manager as fm\n",
    "#fm.get_fontconfig_fonts()\n",
    "#font_location = '/usr/share/fonts/truetype/nanum/NanumGothicBold.ttf'\n",
    "#font_name = fm.FontProperties(fname=font_location).get_name()\n",
    "#matplotlib.rc('font', family=font_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<module 'ex2_2_ann_rg' from '/home/sjkim/Dropbox/Aspuru-Guzik/python_lab/py3/keraspp/ex2_2_ann_rg.py'>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import ex2_2_ann_rg\n",
    "reload(ex2_2_ann_rg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 323 samples, validate on 81 samples\n",
      "Epoch 1/100\n",
      "0s - loss: 549.2300 - val_loss: 544.1247\n",
      "Epoch 2/100\n",
      "0s - loss: 390.5700 - val_loss: 103.3929\n",
      "Epoch 3/100\n",
      "0s - loss: 98.1901 - val_loss: 85.2140\n",
      "Epoch 4/100\n",
      "0s - loss: 77.4438 - val_loss: 81.0683\n",
      "Epoch 5/100\n",
      "0s - loss: 66.6654 - val_loss: 61.9228\n",
      "Epoch 6/100\n",
      "0s - loss: 61.4270 - val_loss: 65.4058\n",
      "Epoch 7/100\n",
      "0s - loss: 71.9419 - val_loss: 58.9549\n",
      "Epoch 8/100\n",
      "0s - loss: 51.9858 - val_loss: 93.1046\n",
      "Epoch 9/100\n",
      "0s - loss: 62.5920 - val_loss: 62.6678\n",
      "Epoch 10/100\n",
      "0s - loss: 47.1879 - val_loss: 53.1253\n",
      "Epoch 11/100\n",
      "0s - loss: 42.8130 - val_loss: 49.0748\n",
      "Epoch 12/100\n",
      "0s - loss: 40.8272 - val_loss: 41.4224\n",
      "Epoch 13/100\n",
      "0s - loss: 38.3668 - val_loss: 54.4867\n",
      "Epoch 14/100\n",
      "0s - loss: 42.8160 - val_loss: 41.1169\n",
      "Epoch 15/100\n",
      "0s - loss: 40.6437 - val_loss: 44.5767\n",
      "Epoch 16/100\n",
      "0s - loss: 34.1632 - val_loss: 44.1284\n",
      "Epoch 17/100\n",
      "0s - loss: 32.6142 - val_loss: 35.6701\n",
      "Epoch 18/100\n",
      "0s - loss: 31.8371 - val_loss: 41.4578\n",
      "Epoch 19/100\n",
      "0s - loss: 31.4799 - val_loss: 33.7412\n",
      "Epoch 20/100\n",
      "0s - loss: 31.6695 - val_loss: 31.0509\n",
      "Epoch 21/100\n",
      "0s - loss: 30.1381 - val_loss: 29.6005\n",
      "Epoch 22/100\n",
      "0s - loss: 29.6422 - val_loss: 29.8011\n",
      "Epoch 23/100\n",
      "0s - loss: 27.3097 - val_loss: 32.7661\n",
      "Epoch 24/100\n",
      "0s - loss: 28.1749 - val_loss: 31.3858\n",
      "Epoch 25/100\n",
      "0s - loss: 29.1247 - val_loss: 37.3145\n",
      "Epoch 26/100\n",
      "0s - loss: 47.5037 - val_loss: 61.4627\n",
      "Epoch 27/100\n",
      "0s - loss: 43.6486 - val_loss: 35.6210\n",
      "Epoch 28/100\n",
      "0s - loss: 33.7183 - val_loss: 31.1215\n",
      "Epoch 29/100\n",
      "0s - loss: 32.7949 - val_loss: 91.2134\n",
      "Epoch 30/100\n",
      "0s - loss: 84.2713 - val_loss: 25.8957\n",
      "Epoch 31/100\n",
      "0s - loss: 25.5216 - val_loss: 36.2650\n",
      "Epoch 32/100\n",
      "0s - loss: 28.5257 - val_loss: 40.8993\n",
      "Epoch 33/100\n",
      "0s - loss: 72.1279 - val_loss: 43.6365\n",
      "Epoch 34/100\n",
      "0s - loss: 41.9866 - val_loss: 27.7356\n",
      "Epoch 35/100\n",
      "0s - loss: 26.8115 - val_loss: 33.6829\n",
      "Epoch 36/100\n",
      "0s - loss: 27.2886 - val_loss: 49.3238\n",
      "Epoch 37/100\n",
      "0s - loss: 63.5212 - val_loss: 25.4363\n",
      "Epoch 38/100\n",
      "0s - loss: 24.6916 - val_loss: 25.2577\n",
      "Epoch 39/100\n",
      "0s - loss: 25.0479 - val_loss: 24.3853\n",
      "Epoch 40/100\n",
      "0s - loss: 26.0277 - val_loss: 24.0724\n",
      "Epoch 41/100\n",
      "0s - loss: 24.5822 - val_loss: 26.5253\n",
      "Epoch 42/100\n",
      "0s - loss: 27.0171 - val_loss: 25.3956\n",
      "Epoch 43/100\n",
      "0s - loss: 24.5532 - val_loss: 26.1544\n",
      "Epoch 44/100\n",
      "0s - loss: 24.5788 - val_loss: 24.1822\n",
      "Epoch 45/100\n",
      "0s - loss: 27.3841 - val_loss: 36.1446\n",
      "Epoch 46/100\n",
      "0s - loss: 32.5138 - val_loss: 25.3374\n",
      "Epoch 47/100\n",
      "0s - loss: 25.1175 - val_loss: 49.5074\n",
      "Epoch 48/100\n",
      "0s - loss: 39.5790 - val_loss: 40.8372\n",
      "Epoch 49/100\n",
      "0s - loss: 27.3132 - val_loss: 25.7616\n",
      "Epoch 50/100\n",
      "0s - loss: 24.0742 - val_loss: 33.7171\n",
      "Epoch 51/100\n",
      "0s - loss: 39.2199 - val_loss: 23.2041\n",
      "Epoch 52/100\n",
      "0s - loss: 23.2444 - val_loss: 24.4851\n",
      "Epoch 53/100\n",
      "0s - loss: 27.8802 - val_loss: 46.7543\n",
      "Epoch 54/100\n",
      "0s - loss: 29.9209 - val_loss: 46.1203\n",
      "Epoch 55/100\n",
      "0s - loss: 37.3527 - val_loss: 77.2600\n",
      "Epoch 56/100\n",
      "0s - loss: 43.2760 - val_loss: 47.8863\n",
      "Epoch 57/100\n",
      "0s - loss: 33.8143 - val_loss: 24.2472\n",
      "Epoch 58/100\n",
      "0s - loss: 23.8081 - val_loss: 22.6879\n",
      "Epoch 59/100\n",
      "0s - loss: 23.1451 - val_loss: 27.6580\n",
      "Epoch 60/100\n",
      "0s - loss: 24.2453 - val_loss: 40.6752\n",
      "Epoch 61/100\n",
      "0s - loss: 28.9283 - val_loss: 25.5648\n",
      "Epoch 62/100\n",
      "0s - loss: 23.6439 - val_loss: 23.7677\n",
      "Epoch 63/100\n",
      "0s - loss: 30.2104 - val_loss: 22.3435\n",
      "Epoch 64/100\n",
      "0s - loss: 23.2911 - val_loss: 27.7983\n",
      "Epoch 65/100\n",
      "0s - loss: 34.9106 - val_loss: 34.5690\n",
      "Epoch 66/100\n",
      "0s - loss: 36.8702 - val_loss: 23.0353\n",
      "Epoch 67/100\n",
      "0s - loss: 27.9201 - val_loss: 24.5019\n",
      "Epoch 68/100\n",
      "0s - loss: 25.6354 - val_loss: 22.2898\n",
      "Epoch 69/100\n",
      "0s - loss: 24.0518 - val_loss: 22.5758\n",
      "Epoch 70/100\n",
      "0s - loss: 22.6869 - val_loss: 22.1801\n",
      "Epoch 71/100\n",
      "0s - loss: 24.7605 - val_loss: 21.6672\n",
      "Epoch 72/100\n",
      "0s - loss: 25.1309 - val_loss: 23.6435\n",
      "Epoch 73/100\n",
      "0s - loss: 22.6495 - val_loss: 24.3107\n",
      "Epoch 74/100\n",
      "0s - loss: 25.7740 - val_loss: 38.4270\n",
      "Epoch 75/100\n",
      "0s - loss: 31.0539 - val_loss: 25.8740\n",
      "Epoch 76/100\n",
      "0s - loss: 23.7850 - val_loss: 24.1559\n",
      "Epoch 77/100\n",
      "0s - loss: 29.6644 - val_loss: 24.6882\n",
      "Epoch 78/100\n",
      "0s - loss: 24.3368 - val_loss: 21.3049\n",
      "Epoch 79/100\n",
      "0s - loss: 22.8251 - val_loss: 21.4472\n",
      "Epoch 80/100\n",
      "0s - loss: 23.4458 - val_loss: 24.6260\n",
      "Epoch 81/100\n",
      "0s - loss: 29.0920 - val_loss: 34.8721\n",
      "Epoch 82/100\n",
      "0s - loss: 37.4682 - val_loss: 22.1323\n",
      "Epoch 83/100\n",
      "0s - loss: 22.7357 - val_loss: 21.7718\n",
      "Epoch 84/100\n",
      "0s - loss: 22.8009 - val_loss: 24.7953\n",
      "Epoch 85/100\n",
      "0s - loss: 26.4292 - val_loss: 21.6159\n",
      "Epoch 86/100\n",
      "0s - loss: 26.7697 - val_loss: 29.5336\n",
      "Epoch 87/100\n",
      "0s - loss: 34.8803 - val_loss: 38.1017\n",
      "Epoch 88/100\n",
      "0s - loss: 31.0725 - val_loss: 21.2199\n",
      "Epoch 89/100\n",
      "0s - loss: 22.3300 - val_loss: 22.7985\n",
      "Epoch 90/100\n",
      "0s - loss: 22.4448 - val_loss: 21.4731\n",
      "Epoch 91/100\n",
      "0s - loss: 24.7583 - val_loss: 25.1043\n",
      "Epoch 92/100\n",
      "0s - loss: 23.1121 - val_loss: 21.2430\n",
      "Epoch 93/100\n",
      "0s - loss: 22.2409 - val_loss: 24.0846\n",
      "Epoch 94/100\n",
      "0s - loss: 22.1329 - val_loss: 26.7777\n",
      "Epoch 95/100\n",
      "0s - loss: 21.9800 - val_loss: 20.7085\n",
      "Epoch 96/100\n",
      "0s - loss: 22.2884 - val_loss: 20.5597\n",
      "Epoch 97/100\n",
      "0s - loss: 23.3004 - val_loss: 22.8056\n",
      "Epoch 98/100\n",
      "0s - loss: 22.8027 - val_loss: 23.2885\n",
      "Epoch 99/100\n",
      "0s - loss: 22.1984 - val_loss: 26.6096\n",
      "Epoch 100/100\n",
      "0s - loss: 26.8275 - val_loss: 31.8822\n",
      "100/102 [============================>.] - ETA: 0s\n",
      "Test Loss -> 30.14\n"
     ]
    },
    {
     "data": {
      "image/png": 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8wTkVRETk7lpNqCgoKMDKlSvxyiuvQBAEp+eSkpIQFhaGqKgop8djYmJgMBiQ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\nBQU88sgjjB49miFDhnDzzTezYsUKLBZLi7rr1q3j1ltvJTY2lkmTJrF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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4680676b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ex2_2_ann_rg.main()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [default]",
   "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": "3.6.3"
  },
  "toc": {
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": true,
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": "block",
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}