nb_ex3_2_dnn_cifar10_cl.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 예제 3-2"
   ]
  },
  {
   "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": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import ex3_2_dnn_cifar10_cl as example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/100\n",
      "40000/40000 [==============================] - 3s - loss: 1.9462 - acc: 0.3022 - val_loss: 1.8319 - val_acc: 0.3406\n",
      "Epoch 2/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.7791 - acc: 0.3667 - val_loss: 1.8153 - val_acc: 0.3597\n",
      "Epoch 3/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.7062 - acc: 0.3874 - val_loss: 1.6988 - val_acc: 0.3953\n",
      "Epoch 4/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.6547 - acc: 0.4099 - val_loss: 1.6765 - val_acc: 0.4016\n",
      "Epoch 5/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.6207 - acc: 0.4214 - val_loss: 1.6590 - val_acc: 0.4086\n",
      "Epoch 6/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.5863 - acc: 0.4350 - val_loss: 1.6295 - val_acc: 0.4232\n",
      "Epoch 7/100\n",
      "40000/40000 [==============================] - 1s - loss: 1.5693 - acc: 0.4400 - val_loss: 1.5910 - val_acc: 0.4356\n",
      "Epoch 8/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.5481 - acc: 0.4463 - val_loss: 1.6082 - val_acc: 0.4242\n",
      "Epoch 9/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.5263 - acc: 0.4572 - val_loss: 1.6203 - val_acc: 0.4268\n",
      "Epoch 10/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.5088 - acc: 0.4621 - val_loss: 1.5703 - val_acc: 0.4397\n",
      "Epoch 11/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.5026 - acc: 0.4613 - val_loss: 1.5655 - val_acc: 0.4409\n",
      "Epoch 12/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.4868 - acc: 0.4673 - val_loss: 1.5576 - val_acc: 0.4486\n",
      "Epoch 13/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.4794 - acc: 0.4723 - val_loss: 1.5565 - val_acc: 0.4484\n",
      "Epoch 14/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.4681 - acc: 0.4750 - val_loss: 1.5607 - val_acc: 0.4426\n",
      "Epoch 15/100\n",
      "40000/40000 [==============================] - 1s - loss: 1.4576 - acc: 0.4762 - val_loss: 1.5356 - val_acc: 0.4583\n",
      "Epoch 16/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.4501 - acc: 0.4808 - val_loss: 1.5671 - val_acc: 0.4470\n",
      "Epoch 17/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.4456 - acc: 0.4823 - val_loss: 1.5328 - val_acc: 0.4596\n",
      "Epoch 18/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.4338 - acc: 0.4861 - val_loss: 1.5621 - val_acc: 0.4520\n",
      "Epoch 19/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.4320 - acc: 0.4855 - val_loss: 1.5352 - val_acc: 0.4555\n",
      "Epoch 20/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.4222 - acc: 0.4908 - val_loss: 1.5133 - val_acc: 0.4633\n",
      "Epoch 21/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.4144 - acc: 0.4940 - val_loss: 1.5432 - val_acc: 0.4523\n",
      "Epoch 22/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.4144 - acc: 0.4925 - val_loss: 1.5543 - val_acc: 0.4608\n",
      "Epoch 23/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.4019 - acc: 0.4968 - val_loss: 1.5372 - val_acc: 0.4600\n",
      "Epoch 24/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.3950 - acc: 0.5012 - val_loss: 1.5409 - val_acc: 0.4556\n",
      "Epoch 25/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.3894 - acc: 0.5033 - val_loss: 1.5160 - val_acc: 0.4653\n",
      "Epoch 26/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.3888 - acc: 0.5027 - val_loss: 1.5336 - val_acc: 0.4621\n",
      "Epoch 27/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.3844 - acc: 0.5033 - val_loss: 1.4883 - val_acc: 0.4759\n",
      "Epoch 28/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.3732 - acc: 0.5067 - val_loss: 1.4881 - val_acc: 0.4751\n",
      "Epoch 29/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.3704 - acc: 0.5118 - val_loss: 1.5350 - val_acc: 0.4598\n",
      "Epoch 30/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.3629 - acc: 0.5135 - val_loss: 1.5024 - val_acc: 0.4670\n",
      "Epoch 31/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.3664 - acc: 0.5078 - val_loss: 1.4977 - val_acc: 0.4708\n",
      "Epoch 32/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.3632 - acc: 0.5115 - val_loss: 1.5067 - val_acc: 0.4732\n",
      "Epoch 33/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.3544 - acc: 0.5167 - val_loss: 1.5062 - val_acc: 0.4708\n",
      "Epoch 34/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.3481 - acc: 0.5186 - val_loss: 1.5056 - val_acc: 0.4765\n",
      "Epoch 35/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.3516 - acc: 0.5171 - val_loss: 1.5054 - val_acc: 0.4705\n",
      "Epoch 36/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.3406 - acc: 0.5199 - val_loss: 1.4959 - val_acc: 0.4762\n",
      "Epoch 37/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.3361 - acc: 0.5178 - val_loss: 1.4983 - val_acc: 0.4751\n",
      "Epoch 38/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.3338 - acc: 0.5238 - val_loss: 1.4828 - val_acc: 0.4810\n",
      "Epoch 39/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.3268 - acc: 0.5218 - val_loss: 1.4712 - val_acc: 0.4808\n",
      "Epoch 40/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.3265 - acc: 0.5226 - val_loss: 1.5044 - val_acc: 0.4700\n",
      "Epoch 41/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.3326 - acc: 0.5223 - val_loss: 1.5152 - val_acc: 0.4725\n",
      "Epoch 42/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.3246 - acc: 0.5243 - val_loss: 1.5290 - val_acc: 0.4669\n",
      "Epoch 43/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.3195 - acc: 0.5253 - val_loss: 1.5135 - val_acc: 0.4738\n",
      "Epoch 44/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.3142 - acc: 0.5307 - val_loss: 1.4776 - val_acc: 0.4826\n",
      "Epoch 45/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.3112 - acc: 0.5305 - val_loss: 1.4763 - val_acc: 0.4813\n",
      "Epoch 46/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.3079 - acc: 0.5313 - val_loss: 1.4959 - val_acc: 0.4737\n",
      "Epoch 47/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.3035 - acc: 0.5308 - val_loss: 1.5088 - val_acc: 0.4703\n",
      "Epoch 48/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.3063 - acc: 0.5318 - val_loss: 1.4994 - val_acc: 0.4794\n",
      "Epoch 49/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.2991 - acc: 0.5340 - val_loss: 1.5216 - val_acc: 0.4718\n",
      "Epoch 50/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.3036 - acc: 0.5335 - val_loss: 1.4801 - val_acc: 0.4837\n",
      "Epoch 51/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.2956 - acc: 0.5372 - val_loss: 1.4979 - val_acc: 0.4819\n",
      "Epoch 52/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.2938 - acc: 0.5364 - val_loss: 1.4784 - val_acc: 0.4846\n",
      "Epoch 53/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.2975 - acc: 0.5306 - val_loss: 1.5025 - val_acc: 0.4791\n",
      "Epoch 54/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.2943 - acc: 0.5361 - val_loss: 1.4855 - val_acc: 0.4835\n",
      "Epoch 55/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.2868 - acc: 0.5392 - val_loss: 1.4935 - val_acc: 0.4821\n",
      "Epoch 56/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.2801 - acc: 0.5416 - val_loss: 1.4938 - val_acc: 0.4804\n",
      "Epoch 57/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.2766 - acc: 0.5429 - val_loss: 1.4977 - val_acc: 0.4745\n",
      "Epoch 58/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.2769 - acc: 0.5407 - val_loss: 1.4844 - val_acc: 0.4853\n",
      "Epoch 59/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.2796 - acc: 0.5414 - val_loss: 1.4976 - val_acc: 0.4842\n",
      "Epoch 60/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.2742 - acc: 0.5413 - val_loss: 1.5275 - val_acc: 0.4689\n",
      "Epoch 61/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.2721 - acc: 0.5447 - val_loss: 1.5142 - val_acc: 0.4769\n",
      "Epoch 62/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.2695 - acc: 0.5432 - val_loss: 1.4987 - val_acc: 0.4784\n",
      "Epoch 63/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.2725 - acc: 0.5417 - val_loss: 1.5184 - val_acc: 0.4786\n",
      "Epoch 64/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.2609 - acc: 0.5487 - val_loss: 1.5041 - val_acc: 0.4793\n",
      "Epoch 65/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.2592 - acc: 0.5486 - val_loss: 1.4937 - val_acc: 0.4832\n",
      "Epoch 66/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.2605 - acc: 0.5482 - val_loss: 1.5339 - val_acc: 0.4682\n",
      "Epoch 67/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.2623 - acc: 0.5464 - val_loss: 1.5075 - val_acc: 0.4796\n",
      "Epoch 68/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.2584 - acc: 0.5496 - val_loss: 1.5146 - val_acc: 0.4755\n",
      "Epoch 69/100\n",
      "40000/40000 [==============================] - 1s - loss: 1.2523 - acc: 0.5520 - val_loss: 1.5416 - val_acc: 0.4707\n",
      "Epoch 70/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.2513 - acc: 0.5519 - val_loss: 1.5103 - val_acc: 0.4797\n",
      "Epoch 71/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.2508 - acc: 0.5526 - val_loss: 1.5127 - val_acc: 0.4805\n",
      "Epoch 72/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.2436 - acc: 0.5562 - val_loss: 1.4995 - val_acc: 0.4802\n",
      "Epoch 73/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.2544 - acc: 0.5522 - val_loss: 1.4992 - val_acc: 0.4833\n",
      "Epoch 74/100\n",
      "40000/40000 [==============================] - 1s - loss: 1.2408 - acc: 0.5561 - val_loss: 1.4907 - val_acc: 0.4919\n",
      "Epoch 75/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.2436 - acc: 0.5555 - val_loss: 1.5758 - val_acc: 0.4664\n",
      "Epoch 76/100\n",
      "40000/40000 [==============================] - 1s - loss: 1.2384 - acc: 0.5549 - val_loss: 1.5133 - val_acc: 0.4816\n",
      "Epoch 77/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.2470 - acc: 0.5513 - val_loss: 1.5228 - val_acc: 0.4748\n",
      "Epoch 78/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.2370 - acc: 0.5549 - val_loss: 1.5036 - val_acc: 0.4841\n",
      "Epoch 79/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.2412 - acc: 0.5542 - val_loss: 1.5075 - val_acc: 0.4839\n",
      "Epoch 80/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.2366 - acc: 0.5576 - val_loss: 1.5264 - val_acc: 0.4786\n",
      "Epoch 81/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.2300 - acc: 0.5602 - val_loss: 1.5467 - val_acc: 0.4696\n",
      "Epoch 82/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.2294 - acc: 0.5601 - val_loss: 1.5071 - val_acc: 0.4806\n",
      "Epoch 83/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.2272 - acc: 0.5609 - val_loss: 1.5303 - val_acc: 0.4730\n",
      "Epoch 84/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.2361 - acc: 0.5561 - val_loss: 1.5211 - val_acc: 0.4784\n",
      "Epoch 85/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.2296 - acc: 0.5600 - val_loss: 1.5002 - val_acc: 0.4854\n",
      "Epoch 86/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.2230 - acc: 0.5630 - val_loss: 1.5153 - val_acc: 0.4863\n",
      "Epoch 87/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.2180 - acc: 0.5616 - val_loss: 1.5339 - val_acc: 0.4754\n",
      "Epoch 88/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.2306 - acc: 0.5601 - val_loss: 1.5255 - val_acc: 0.4799\n",
      "Epoch 89/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.2167 - acc: 0.5619 - val_loss: 1.5098 - val_acc: 0.4845\n",
      "Epoch 90/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.2239 - acc: 0.5604 - val_loss: 1.4977 - val_acc: 0.4878\n",
      "Epoch 91/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.2171 - acc: 0.5637 - val_loss: 1.5386 - val_acc: 0.4726\n",
      "Epoch 92/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.2134 - acc: 0.5676 - val_loss: 1.5220 - val_acc: 0.4768\n",
      "Epoch 93/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.2143 - acc: 0.5666 - val_loss: 1.5718 - val_acc: 0.4642\n",
      "Epoch 94/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.2159 - acc: 0.5650 - val_loss: 1.5227 - val_acc: 0.4818\n",
      "Epoch 95/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.2147 - acc: 0.5641 - val_loss: 1.4982 - val_acc: 0.4901\n",
      "Epoch 96/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.2073 - acc: 0.5645 - val_loss: 1.5341 - val_acc: 0.4776\n",
      "Epoch 97/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.2104 - acc: 0.5654 - val_loss: 1.5304 - val_acc: 0.4826\n",
      "Epoch 98/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.2041 - acc: 0.5669 - val_loss: 1.5196 - val_acc: 0.4867\n",
      "Epoch 99/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.2091 - acc: 0.5666 - val_loss: 1.5065 - val_acc: 0.4867\n",
      "Epoch 100/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.2018 - acc: 0.5705 - val_loss: 1.5234 - val_acc: 0.4823\n",
      " 8300/10000 [=======================>......] - ETA: 0sTest Loss and Accuracy -> [1.5101678037643433, 0.47739998131990435]\n"
     ]
    },
    {
     "data": {
      "image/png": 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StfLOXyunY9P5YO0pSst0jH+4NeMHtK6xF+N2UqmElyd04PXswxVzKCYMaM2E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z0rKwsRERHPdKVh165d8PLygr//09k1JRIJYmNjkZmZqdevoaEBBw4cQFxc3LN/A0RERNQkuxYa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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f222e194c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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LC6SkpGDPnj0oKirC9OnTVa47ceIEVaJ8DVECQQySiYkJysrKqj1eU8Ela2trlJeXq7RX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\nMZvCeXBfAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f222aebda20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "example.main(Pd_l=[0.0, 0.0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/100\n",
      "40000/40000 [==============================] - 2s - loss: 2.1348 - acc: 0.1996 - val_loss: 1.9382 - val_acc: 0.3008\n",
      "Epoch 2/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.9595 - acc: 0.2768 - val_loss: 1.8489 - val_acc: 0.3310\n",
      "Epoch 3/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.8947 - acc: 0.3087 - val_loss: 1.8124 - val_acc: 0.3417\n",
      "Epoch 4/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.8616 - acc: 0.3242 - val_loss: 1.7711 - val_acc: 0.3592\n",
      "Epoch 5/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.8353 - acc: 0.3347 - val_loss: 1.7689 - val_acc: 0.3600\n",
      "Epoch 6/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.8211 - acc: 0.3441 - val_loss: 1.7424 - val_acc: 0.3736\n",
      "Epoch 7/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.8053 - acc: 0.3473 - val_loss: 1.8260 - val_acc: 0.3457\n",
      "Epoch 8/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.7871 - acc: 0.3539 - val_loss: 1.7284 - val_acc: 0.3783\n",
      "Epoch 9/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.7738 - acc: 0.3600 - val_loss: 1.7216 - val_acc: 0.3794\n",
      "Epoch 10/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.7645 - acc: 0.3622 - val_loss: 1.6943 - val_acc: 0.3909\n",
      "Epoch 11/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.7673 - acc: 0.3624 - val_loss: 1.6992 - val_acc: 0.3887\n",
      "Epoch 12/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.7561 - acc: 0.3650 - val_loss: 1.6846 - val_acc: 0.3963\n",
      "Epoch 13/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.7431 - acc: 0.3715 - val_loss: 1.6980 - val_acc: 0.3905\n",
      "Epoch 14/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.7431 - acc: 0.3716 - val_loss: 1.6855 - val_acc: 0.3966\n",
      "Epoch 15/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.7369 - acc: 0.3738 - val_loss: 1.6877 - val_acc: 0.3913\n",
      "Epoch 16/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.7229 - acc: 0.3829 - val_loss: 1.6756 - val_acc: 0.3906\n",
      "Epoch 17/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.7192 - acc: 0.3827 - val_loss: 1.6578 - val_acc: 0.4006\n",
      "Epoch 18/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.7212 - acc: 0.3802 - val_loss: 1.6627 - val_acc: 0.3991\n",
      "Epoch 19/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.7112 - acc: 0.3829 - val_loss: 1.6629 - val_acc: 0.3997\n",
      "Epoch 20/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.7119 - acc: 0.3841 - val_loss: 1.6605 - val_acc: 0.3963\n",
      "Epoch 21/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.7075 - acc: 0.3870 - val_loss: 1.6512 - val_acc: 0.4045\n",
      "Epoch 22/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.7065 - acc: 0.3862 - val_loss: 1.6482 - val_acc: 0.4034\n",
      "Epoch 23/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.7014 - acc: 0.3902 - val_loss: 1.6479 - val_acc: 0.4098\n",
      "Epoch 24/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.6919 - acc: 0.3934 - val_loss: 1.6356 - val_acc: 0.4125\n",
      "Epoch 25/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.6957 - acc: 0.3932 - val_loss: 1.6469 - val_acc: 0.4126\n",
      "Epoch 26/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.6915 - acc: 0.3920 - val_loss: 1.6426 - val_acc: 0.4075\n",
      "Epoch 27/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.6927 - acc: 0.3933 - val_loss: 1.6438 - val_acc: 0.4122\n",
      "Epoch 28/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.6877 - acc: 0.3959 - val_loss: 1.6328 - val_acc: 0.4157\n",
      "Epoch 29/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.6845 - acc: 0.3979 - val_loss: 1.6271 - val_acc: 0.4148\n",
      "Epoch 30/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.6759 - acc: 0.4013 - val_loss: 1.6276 - val_acc: 0.4145\n",
      "Epoch 31/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.6817 - acc: 0.3969 - val_loss: 1.6456 - val_acc: 0.4088\n",
      "Epoch 32/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.6776 - acc: 0.3962 - val_loss: 1.6697 - val_acc: 0.3916\n",
      "Epoch 33/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.6725 - acc: 0.4007 - val_loss: 1.6422 - val_acc: 0.4080\n",
      "Epoch 34/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.6733 - acc: 0.4000 - val_loss: 1.6566 - val_acc: 0.4045\n",
      "Epoch 35/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.6713 - acc: 0.4014 - val_loss: 1.6341 - val_acc: 0.4135\n",
      "Epoch 36/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.6680 - acc: 0.4016 - val_loss: 1.6296 - val_acc: 0.4159\n",
      "Epoch 37/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.6701 - acc: 0.4013 - val_loss: 1.6440 - val_acc: 0.4108\n",
      "Epoch 38/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.6567 - acc: 0.4053 - val_loss: 1.6422 - val_acc: 0.4107\n",
      "Epoch 39/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.6573 - acc: 0.4043 - val_loss: 1.6147 - val_acc: 0.4188\n",
      "Epoch 40/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.6550 - acc: 0.4075 - val_loss: 1.6290 - val_acc: 0.4098\n",
      "Epoch 41/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.6557 - acc: 0.4054 - val_loss: 1.6257 - val_acc: 0.4173\n",
      "Epoch 42/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.6555 - acc: 0.4054 - val_loss: 1.6420 - val_acc: 0.4073\n",
      "Epoch 43/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.6624 - acc: 0.4044 - val_loss: 1.6327 - val_acc: 0.4147\n",
      "Epoch 44/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.6505 - acc: 0.4095 - val_loss: 1.6112 - val_acc: 0.4220\n",
      "Epoch 45/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.6410 - acc: 0.4128 - val_loss: 1.6191 - val_acc: 0.4231\n",
      "Epoch 46/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.6457 - acc: 0.4063 - val_loss: 1.6244 - val_acc: 0.4149\n",
      "Epoch 47/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.6495 - acc: 0.4083 - val_loss: 1.6419 - val_acc: 0.4033\n",
      "Epoch 48/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.6425 - acc: 0.4119 - val_loss: 1.6036 - val_acc: 0.4225\n",
      "Epoch 49/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.6398 - acc: 0.4113 - val_loss: 1.6258 - val_acc: 0.4142\n",
      "Epoch 50/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.6446 - acc: 0.4097 - val_loss: 1.5947 - val_acc: 0.4272\n",
      "Epoch 51/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.6313 - acc: 0.4139 - val_loss: 1.6295 - val_acc: 0.4178\n",
      "Epoch 52/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.6404 - acc: 0.4106 - val_loss: 1.6379 - val_acc: 0.4143\n",
      "Epoch 53/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.6379 - acc: 0.4132 - val_loss: 1.6143 - val_acc: 0.4193\n",
      "Epoch 54/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.6361 - acc: 0.4118 - val_loss: 1.6147 - val_acc: 0.4173\n",
      "Epoch 55/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.6279 - acc: 0.4140 - val_loss: 1.6026 - val_acc: 0.4255\n",
      "Epoch 56/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.6326 - acc: 0.4129 - val_loss: 1.6253 - val_acc: 0.4158\n",
      "Epoch 57/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.6285 - acc: 0.4142 - val_loss: 1.6213 - val_acc: 0.4195\n",
      "Epoch 58/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.6281 - acc: 0.4202 - val_loss: 1.6331 - val_acc: 0.4120\n",
      "Epoch 59/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.6294 - acc: 0.4183 - val_loss: 1.6096 - val_acc: 0.4253\n",
      "Epoch 60/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.6265 - acc: 0.4175 - val_loss: 1.6133 - val_acc: 0.4210\n",
      "Epoch 61/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.6301 - acc: 0.4174 - val_loss: 1.6288 - val_acc: 0.4141\n",
      "Epoch 62/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.6205 - acc: 0.4167 - val_loss: 1.6408 - val_acc: 0.4126\n",
      "Epoch 63/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.6247 - acc: 0.4164 - val_loss: 1.6227 - val_acc: 0.4196\n",
      "Epoch 64/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.6178 - acc: 0.4193 - val_loss: 1.6042 - val_acc: 0.4234\n",
      "Epoch 65/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.6219 - acc: 0.4213 - val_loss: 1.5909 - val_acc: 0.4309\n",
      "Epoch 66/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.6164 - acc: 0.4220 - val_loss: 1.5968 - val_acc: 0.4312\n",
      "Epoch 67/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.6211 - acc: 0.4181 - val_loss: 1.6156 - val_acc: 0.4222\n",
      "Epoch 68/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.6204 - acc: 0.4202 - val_loss: 1.6153 - val_acc: 0.4218\n",
      "Epoch 69/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.6205 - acc: 0.4166 - val_loss: 1.6110 - val_acc: 0.4231\n",
      "Epoch 70/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.6159 - acc: 0.4199 - val_loss: 1.6046 - val_acc: 0.4295\n",
      "Epoch 71/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.6181 - acc: 0.4188 - val_loss: 1.6127 - val_acc: 0.4211\n",
      "Epoch 72/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.6139 - acc: 0.4220 - val_loss: 1.6155 - val_acc: 0.4199\n",
      "Epoch 73/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.6100 - acc: 0.4203 - val_loss: 1.6196 - val_acc: 0.4189\n",
      "Epoch 74/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.6123 - acc: 0.4202 - val_loss: 1.6043 - val_acc: 0.4226\n",
      "Epoch 75/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.6112 - acc: 0.4244 - val_loss: 1.6075 - val_acc: 0.4208\n",
      "Epoch 76/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.6147 - acc: 0.4230 - val_loss: 1.6079 - val_acc: 0.4184\n",
      "Epoch 77/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.6090 - acc: 0.4215 - val_loss: 1.5963 - val_acc: 0.4276\n",
      "Epoch 78/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.6112 - acc: 0.4236 - val_loss: 1.6166 - val_acc: 0.4203\n",
      "Epoch 79/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.6095 - acc: 0.4200 - val_loss: 1.6102 - val_acc: 0.4196\n",
      "Epoch 80/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.6049 - acc: 0.4254 - val_loss: 1.6043 - val_acc: 0.4285\n",
      "Epoch 81/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.6095 - acc: 0.4207 - val_loss: 1.6022 - val_acc: 0.4275\n",
      "Epoch 82/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.6071 - acc: 0.4242 - val_loss: 1.6255 - val_acc: 0.4195\n",
      "Epoch 83/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.6055 - acc: 0.4224 - val_loss: 1.5970 - val_acc: 0.4280\n",
      "Epoch 84/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.6109 - acc: 0.4240 - val_loss: 1.6514 - val_acc: 0.4124\n",
      "Epoch 85/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.6044 - acc: 0.4242 - val_loss: 1.6254 - val_acc: 0.4174\n",
      "Epoch 86/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.6067 - acc: 0.4251 - val_loss: 1.6039 - val_acc: 0.4254\n",
      "Epoch 87/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.5996 - acc: 0.4246 - val_loss: 1.6005 - val_acc: 0.4275\n",
      "Epoch 88/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.6005 - acc: 0.4265 - val_loss: 1.6085 - val_acc: 0.4204\n",
      "Epoch 89/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.6058 - acc: 0.4283 - val_loss: 1.6076 - val_acc: 0.4236\n",
      "Epoch 90/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.6036 - acc: 0.4246 - val_loss: 1.6183 - val_acc: 0.4152\n",
      "Epoch 91/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.5997 - acc: 0.4237 - val_loss: 1.6059 - val_acc: 0.4254\n",
      "Epoch 92/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.5980 - acc: 0.4249 - val_loss: 1.6035 - val_acc: 0.4237\n",
      "Epoch 93/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.5995 - acc: 0.4257 - val_loss: 1.6470 - val_acc: 0.4136\n",
      "Epoch 94/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.6007 - acc: 0.4231 - val_loss: 1.6039 - val_acc: 0.4255\n",
      "Epoch 95/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.6007 - acc: 0.4245 - val_loss: 1.6042 - val_acc: 0.4274\n",
      "Epoch 96/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.5973 - acc: 0.4276 - val_loss: 1.6201 - val_acc: 0.4168\n",
      "Epoch 97/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.5937 - acc: 0.4291 - val_loss: 1.5964 - val_acc: 0.4278\n",
      "Epoch 98/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.6014 - acc: 0.4261 - val_loss: 1.6205 - val_acc: 0.4164\n",
      "Epoch 99/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.5868 - acc: 0.4309 - val_loss: 1.6123 - val_acc: 0.4228\n",
      "Epoch 100/100\n",
      "40000/40000 [==============================] - 2s - loss: 1.5938 - acc: 0.4281 - val_loss: 1.6105 - val_acc: 0.4182\n",
      " 9500/10000 [===========================>..] - ETA: 0sTest Loss and Accuracy -> [1.592626223564148, 0.42449998617172241]\n"
     ]
    },
    {
     "data": {
      "image/png": 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r3nt8Eq+sDsHdwZR549xYtcCL6vo2XtkQSV3TX+9dVUsN38VtJ73qr8+jtrGd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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f222aaadd68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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pcHBw0JkAdO3aFRKJBImJiejdu7eyfc+ePejUqVONn/YVz1es7yCRSCCVSlFW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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f222af316a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "example.main(Pd_l=[0.05, 0.5])"
   ]
  }
 ],
 "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
}