notebooks/05_Neural_Network_Uncertainty_Quantification_with_Dropout.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "parallel-donna",
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'google.colab'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-14-ca870b942523>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m### for colab\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mgoogle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolab\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mdrive\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mdrive\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmount\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'/content/gdrive'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'gdrive/MyDrive/ML_data/UQ_methods'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'google.colab'"
     ]
    }
   ],
   "source": [
    "### for colab\n",
    "from google.colab import drive\n",
    "drive.mount('/content/gdrive')\n",
    "import sys\n",
    "sys.path.append('gdrive/MyDrive/ML_data/UQ_methods')\n",
    "!pip install extra_keras_datasets\n",
    "!pip install mediapy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "fitted-preservation",
   "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_ext autoreload\n",
    "%autoreload 2\n",
    "import tensorflow as tf\n",
    "import tensorflow_probability as tfp\n",
    "import sys\n",
    "sys.path.append('../')\n",
    "\n",
    "from utility.mnist_data import adversarial_data\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense,Dropout,BatchNormalization\n",
    "from keras.datasets import mnist\n",
    "import numpy as np\n",
    "from keras.utils import to_categorical\n",
    "from sklearn.model_selection import KFold\n",
    "import tensorflow.keras.backend as K\n",
    "from tensorflow.python.keras import backend\n",
    "from tensorflow.keras.models import Model\n",
    "import seaborn as sns\n",
    "try:\n",
    "    plt.style.use('rossidata')\n",
    "except:\n",
    "    sns.set_style(\"white\")\n",
    "    \n",
    "import mediapy as media\n",
    "from extra_keras_datasets import emnist\n",
    "import pandas as pd\n",
    "from scipy.stats import ttest_ind,kstest\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "import pickle\n",
    "import matplotlib.image as mpimg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "functional-bottom",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Building out MINST dataset\n",
    "from keras.layers import Conv2D\n",
    "from keras.layers import MaxPooling2D\n",
    "from keras.layers import Dense\n",
    "from keras.layers import Flatten"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "first-excitement",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:Loading dataset = emnist\n",
      "WARNING:root:Please cite the following paper when using or referencing this Extra Keras Dataset:\n",
      "WARNING:root:Cohen, G., Afshar, S., Tapson, J., & van Schaik, A. (2017). EMNIST: an extension of MNIST to handwritten letters. Retrieved from http://arxiv.org/abs/1702.05373\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "sys.path.append('../')\n",
    "\n",
    "from utility.mnist_data.adversarial_data import MNIST_EMNIST,compute_dropout_uncertainty\n",
    "\n",
    "data=MNIST_EMNIST()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "english-diagnosis",
   "metadata": {},
   "source": [
    "# Setting up the data: training with numbers, attacking with letters\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "optional-choir",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Data\n"
     ]
    },
    {
     "data": {
      "text/html": [
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      "text/plain": [
       "<IPython.core.display.HTML object>"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Adversarial Data\n"
     ]
    },
    {
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image-rendering:pixelated; object-fit:cover;\" src=\"data:image/png;base64,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\"/></td><td><img width=\"100\" height=\"100\" style=\"border:1px solid black; image-rendering:pixelated; object-fit:cover;\" src=\"data:image/png;base64,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\"/></td><td><img width=\"100\" height=\"100\" style=\"border:1px solid black; image-rendering:pixelated; object-fit:cover;\" src=\"data:image/png;base64,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\"/></td><td><img width=\"100\" height=\"100\" style=\"border:1px solid black; image-rendering:pixelated; object-fit:cover;\" src=\"data:image/png;base64,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\"/></td></tr></table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# vanilla data\n",
    "trainers,adversarials=data.get_taste_test()\n",
    "print('Training Data')\n",
    "\n",
    "media.show_images(trainers,columns=10,  border=True, height=100,cmap='flare')\n",
    "print('Adversarial Data')\n",
    "media.show_images(adversarials,columns=10,  border=True, height=100,cmap='flare')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "cleared-southeast",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(28, 28, 1)"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainers[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 330,
   "id": "informative-american",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/nicholas.rossi/miniconda3/envs/py36/lib/python3.6/site-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n",
      "/Users/nicholas.rossi/miniconda3/envs/py36/lib/python3.6/site-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n"
     ]
    },
    {
     "ename": "AttributeError",
     "evalue": "'AxesSubplot' object has no attribute 'set_titile'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-330-19809e9b6866>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_xticklabels\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m6\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m7\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m8\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m9\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'A'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'C'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'E'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'F'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'H'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'K'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'M'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'N'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'R'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'X'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m \u001b[0max\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_titile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Training MNIST data'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     11\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_titile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Adversarial Data'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'AxesSubplot' object has no attribute 'set_titile'"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# data counts\n",
    "fig,ax=plt.subplots(1,2,figsize=(12,6),sharey=True)\n",
    "\n",
    "sns.countplot(data.train_keys,palette='flare',ax=ax[0])\n",
    "# ax[1].set_xticklabels([0,1,2,3,4,5,6,7,8,9])\n",
    "\n",
    "sns.countplot(data.holdout_training_keys,palette='flare',ax=ax[1])\n",
    "ax[1].set_xticklabels([0,1,2,3,4,5,6,7,8,9,'A','C','E','F','H','K','M','N','R','X'])\n",
    "\n",
    "ax[0].set_title('Training MNIST data')\n",
    "ax[1].set_title('Adversarial Data')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "id": "stable-diving",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/nicholas.rossi/miniconda3/envs/py36/lib/python3.6/site-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax=plt.subplots()\n",
    "sns.countplot(data.adversarial_training_labels,palette='flare',ax=ax)\n",
    "ax.set_title('Adversarial Counter')\n",
    "fig.savefig('figures/adv_count.png',bbox_inches='tight',dpi=300)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "inside-hardware",
   "metadata": {},
   "source": [
    "# Training Model and Testing Performance"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "announced-discount",
   "metadata": {},
   "source": [
    "# Why you need dropout\n",
    "\n",
    "Lets compare our shuffling and resampling of dropout for two charecters : the number 3 and the letter N. The model will be trained on digits (0-9), and expecting to see them. The letter N will be a suprise that it is not prepared for."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "unknown-antibody",
   "metadata": {},
   "source": [
    "# Grabbing our \"3\" and \"N\" data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "lonely-status",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3s\n"
     ]
    },
    {
     "data": {
      "text/html": [
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      "Ns\n"
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image-rendering:pixelated; object-fit:cover;\" src=\"data:image/png;base64,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\"/></td><td><img width=\"100\" height=\"100\" style=\"border:1px solid black; image-rendering:pixelated; object-fit:cover;\" src=\"data:image/png;base64,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\"/></td><td><img width=\"100\" height=\"100\" style=\"border:1px solid black; image-rendering:pixelated; object-fit:cover;\" src=\"data:image/png;base64,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\"/></td></tr></table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "N_samples=data.X_adv_train[data.holdout_training_keys==23]\n",
    "threes_samples=data.X_adv_train[data.holdout_training_keys==3]\n",
    "eights_samples=data.X_adv_train[data.holdout_training_keys==8]\n",
    "sevens_samples=data.X_adv_train[data.holdout_training_keys==7]\n",
    "\n",
    "print('3s')\n",
    "media.show_images(threes_samples[:10],columns=10,  border=True, height=100,cmap='flare')\n",
    "\n",
    "print('Ns')\n",
    "media.show_images(N_samples[:10],columns=10,  border=True, height=100,cmap='flare')\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "chicken-disposal",
   "metadata": {},
   "source": [
    "# Comparing Models with and without Dropout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "significant-printing",
   "metadata": {},
   "outputs": [],
   "source": [
    "from utility.mnist_data.model_warehouse import *\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "enclosed-mathematics",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      " 257/1875 [===>..........................] - ETA: 4:02 - loss: 0.7772 - accuracy: 0.7457"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-79-47cd09b4b9d8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     11\u001b[0m     \u001b[0mmetrics\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m     \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 13\u001b[0;31m     \u001b[0mhistory\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m20\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m32\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalidation_data\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     14\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m     \u001b[0mletter_dropout_means\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mletter_dropout_uncertainy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcompute_dropout_uncertainty\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mN_samples\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/py36/lib/python3.6/site-packages/tensorflow/python/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[1;32m   1098\u001b[0m                 _r=1):\n\u001b[1;32m   1099\u001b[0m               \u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_train_batch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1100\u001b[0;31m               \u001b[0mtmp_logs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1101\u001b[0m               \u001b[0;32mif\u001b[0m \u001b[0mdata_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshould_sync\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1102\u001b[0m                 \u001b[0mcontext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masync_wait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/py36/lib/python3.6/site-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    826\u001b[0m     \u001b[0mtracing_count\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexperimental_get_tracing_count\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    827\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0mtrace\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTrace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_name\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mtm\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 828\u001b[0;31m       \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    829\u001b[0m       \u001b[0mcompiler\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"xla\"\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_experimental_compile\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34m\"nonXla\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    830\u001b[0m       \u001b[0mnew_tracing_count\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexperimental_get_tracing_count\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/py36/lib/python3.6/site-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m_call\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    853\u001b[0m       \u001b[0;31m# In this case we have created variables on the first call, so we run the\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    854\u001b[0m       \u001b[0;31m# defunned version which is guaranteed to never create variables.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 855\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stateless_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# pylint: disable=not-callable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    856\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stateful_fn\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    857\u001b[0m       \u001b[0;31m# Release the lock early so that multiple threads can perform the call\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/py36/lib/python3.6/site-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   2941\u001b[0m        filtered_flat_args) = self._maybe_define_function(args, kwargs)\n\u001b[1;32m   2942\u001b[0m     return graph_function._call_flat(\n\u001b[0;32m-> 2943\u001b[0;31m         filtered_flat_args, captured_inputs=graph_function.captured_inputs)  # pylint: disable=protected-access\n\u001b[0m\u001b[1;32m   2944\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2945\u001b[0m   \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/py36/lib/python3.6/site-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_call_flat\u001b[0;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[1;32m   1917\u001b[0m       \u001b[0;31m# No tape is watching; skip to running the function.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1918\u001b[0m       return self._build_call_outputs(self._inference_function.call(\n\u001b[0;32m-> 1919\u001b[0;31m           ctx, args, cancellation_manager=cancellation_manager))\n\u001b[0m\u001b[1;32m   1920\u001b[0m     forward_backward = self._select_forward_and_backward_functions(\n\u001b[1;32m   1921\u001b[0m         \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/py36/lib/python3.6/site-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36mcall\u001b[0;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[1;32m    558\u001b[0m               \u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    559\u001b[0m               \u001b[0mattrs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mattrs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 560\u001b[0;31m               ctx=ctx)\n\u001b[0m\u001b[1;32m    561\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    562\u001b[0m           outputs = execute.execute_with_cancellation(\n",
      "\u001b[0;32m~/miniconda3/envs/py36/lib/python3.6/site-packages/tensorflow/python/eager/execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m     58\u001b[0m     \u001b[0mctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mensure_initialized\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     59\u001b[0m     tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\n\u001b[0;32m---> 60\u001b[0;31m                                         inputs, attrs, num_outputs)\n\u001b[0m\u001b[1;32m     61\u001b[0m   \u001b[0;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     62\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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8wKmkVSh60lGq1Squ63J6+uDl4jMzM0xPTwc4lbQSRU86RqVSwXVdCoUHr36cnZ1lamoqwKmk1Sh60hEqlQrpdJpisejvzc/PMzExEeBU0ooUPWl75XIZ13UvBW9hYYHx8fEAp5JWpehJWyuXy6TTaUqlkr+3uLjI2NhYcENJS1P0pG2VSiXS6TTlctnfSyQSjI6OPuajpNspetKWrgYvFAqxuLio4MmNFD1pO8VikXQ6TaVy9s5loVCIRCLByMhIwJNJO1D0pK0UCgVc170UvKWlJYaHhwOeTNqFoidto1AokE6nqVarwFnwkskkQ0NDN3ykyAOKnrSF09NTXNf1g9fT00MymWRwcDDgyaTdKHrS8k5OTnBdl1qtBpwFz3EcYrFYwJNJO9JJAaSlKXjSaHqmJy3r+PiYTCbjB6+3txfHcRgYuHoaZpH6KXrSko6OjshkMlycuKq3t5dUKkV/f3/Ak0m7U/Sk5eTzebLZrB+8vr4+HMdR8KQhFD1pKYeHh+RyuUvBS6VSRKPRgCeTTqHoScs4ODggl8v563A4TCqVIhKJBDiVdBpFT1rC/v4+y8vL/joSieA4joInDafoSeD29vZYWVnx15FIhFQqRTgcDnAq6VSKngRqd3eX1dVVfx2NRnEcR8GTO6PoSWB2dnZYW1vz1/39/TiOQ1+fvi3l7ui7SwKxvb3N+vq6v1bwpFn0HSZNt7W1xcbGhr8eGBjAcRx6e3sDnEq6haInTbW5ucnm5qa/jsViJJNJBU+aRtGTpvA8j83NTba2tvy9wcFBlpaWFDxpKkVP7pzneWxsbLC9ve3vDQ4Okkwm6enRG/1Icyl6cqc8z2N9fZ2dnR1/b2hoiKWlJQVPAqHoyZ3xPI+1tTV2d3f9veHhYRKJhIIngVH05E54nsfq6ip7e3v+3sjICIlEglAoFOBk0u0UPWk4z/NYWVlhf3/f3xsdHWVxcVHBk8ApetJQnuexvLzMwcGBvzc2NsbCwoKCJy1B0ZOGqdVqLC8vc3h46O+Nj48zPz+v4EnLUPSkIWq1Grlcjnw+7+9NTEwwNzen4ElLqSt6xph3AV8EZoFXgeettWvXXHYY+BbwKWvtlxo0p7SwWq1GNpvl6OjI35ucnGR2dlbBk5Zz43EDxpgQ8CLwCWvtDPB14POP+ZBfB77TmPGk1dVqNTKZzKXgTU1NKXjSsuo5WOpZoGCtfel8/QLwHmPM6NULGmN+BIgC/9C4EaVVVatVXNfl+PjY34vH48zMzCh40rLqid4zwOsXC2ttGcgCb334QsaYMeAzwC81cD5pURfBOzk58ffi8TjxeFzBk5ZWT/RiwOmVvTwwdGXvN4HPWmv3kI52EbzT0wffFrOzs8Tj8QCnEqlPPX/IOAaunlJ++HwfAGPMTwC91to/beBs0oIqlQqu61IoFPy9ubk5JicnA5xKpH71RO814C0XC2NMGHCANx66zPPAjxhj9s/X/UDNGPNfrbW/0JhRJWiVSoV0Ok2xWPT35ufnmZiYCHAqkdu5MXrW2m8bYzxjzPvO/5jxEeAVIGKMeRn4MWvt+x/+GGPMpwBXh6x0jnK5jOu6l4K3sLDA+Ph4gFOJ3F69Byc/B3zZGPMFzg5H+QAwArzt/DrKdzOetIJyuUw6naZUKvl7i4uLjI2NBTeUyBMKeZ4XyA3fv3/fv+F79+4FMoPcrFQqkU6nKZcf/HctkUgwOvqmI5ZEWsm1hxDoZWhyrWKxiOu6fvBCoRCJRIKRkZGAJxN5coqePFKxWCSdTlOpVICz4C0tLTE8PBzwZCJPR9GTNykUCriuq+BJR1L05JJCoUA6naZarQJnwUsmkwwNXT0WXaQ9KXriOz09xXVdP3g9PT0kk0kGBwcDnkykcRQ9AeDk5ATXdanVasBZ8BzHIRaLBTyZSGMpesLx8TGZTMYPXm9vL47jMDBw9dWHIu1P0etyR0dHZLNZBU+6hqLXxY6OjshkMlwcoN7X14fjOPT39wc8mcjdUfS6VD6fJ5vNXgpeKpUiGo0GPJnI3VL0utDh4SG5XM4PXjgcxnEcBU+6gqLXZQ4ODsjlcv46HA6TSqWIRCIBTiXSPIpeF9nf32d5edlfRyIRHMdR8KSrKHpdYm9vj5WVFX8diURIpVKEw+EApxJpPkWvC+zu7rK6uuqvo9EoqVSKvj59+aX76Lu+w+3s7LC29uC87P39/TiOo+BJ19J3fgfb3t5mfX3dXyt4Iopex9ra2mJjY8NfDwwM4DgOvb29AU4lEjxFr8N4nsfW1habm5v+XiwWI5lMKngiKHodxfM8Njc32dra8vcGBwdJJpP09NRzXneRzqfodQjP89jY2GB7e9vfGxoaYmlpScETeYii1wE8z2N9fZ2dnR1/b3h4mEQioeCJXKHotTnP81hdXWVvb8/fU/BErqfotTHP81hZWWF/f9/fGxkZIZFIEApde9pPka6m6LUpz/NYXl7m4ODA3xsdHWVxcVHBE3kMRa8NeZ5HLpfj8PDQ3xsbG2NhYUHBE7mBotdmarUay8vLl4I3Pj7O/Py8gidSB0WvjdRqNXK5HPl83t+bmJhgbm5OwROpk6LXJmq1GtlslqOjI39vamqKmZkZBU/kFhS9NlCr1chkMhwfH/t709PTxONxBU/klhS9FletVslkMpycnPh78XiceDwe4FQi7UvRa2HVahXXdTk9PfX3ZmZmmJ6eDnAqkfam6LWoSqWC67oUCgV/b3Z2lqmpqQCnEml/il4LelTw5ubmmJycDHAqkc6g6LWYSqVCOp2mWCz6e/Pz80xMTAQ4lUjnUPRaSLlcxnXdS8FbWFhgfHw8wKlEOoui1yJKpRKu61Iqlfy9xcVFxsbGghtKpAMpei2gVCqRTqcpl8v+XiKRYHR0NMCpRDqTohewYrGI67p+8EKhEIlEgpGRkYAnE+lMdUXPGPMu4IvALPAq8Ly1du2hf+8DPg58EOgFvgN82FqbafjEHaRYLJJOp6lUKsBZ8JaWlhgeHg54MpHOdeNb6xpjQsCLwCestTPA14HPX7lY7Px/32mtTQJ/BfxGIwftNIVC4U3BSyaTCp7IHavnmd6zQMFa+9L5+gXgfxpjRq21BwDW2kPgsw99zEucPeuTRzg9PcV1XarVKgA9PT0sLS0xNDQU8GQina+ekyg8A7x+sbDWloEs8NbHfMz3A998utE606OCl0wmFTyRJqnnmV4MOL2ylwce+Sg1xkxx9vu9n3yqyTrQyckJrutSq9WAs+A5jkMsFrvhI0WkUeqJ3jEwcGVv+Hz/EmPMIPBnwKesta8+/Xid4/j4mEwm4wevt7cXx3EYGLj6qRWRu1RP9F4D3nKxMMaEAQd44+ELGWOiwJ8AX7PWfrVxI7a/o6MjMpkMnucBCp5IkG78nZ619tuAZ4x53/nWR4BXgIgx5mVjTPj8kJU/BF621v7W3Y3bfvL5/KXg9fX1kUqlFDyRgIQuHoyPY4z5L8CXgQRnx+B9AAgDfwG8Hfgp4PeB5Ssf+rPW2n981HXev3/fv+F79+49yewtL5/Pk81m3xS8aDQa8GQiHe/atxSvK3p3odOjd3h4SC6X84MXDodxHEfBE2mOa6Onl6HdgYODA3K5nL8Oh8OkUikikUiAU4kIKHoNt7+/z/Lyg5/yI5EIjuMoeCItQtFroL29PVZWVvx1NBrFcRzC4XCAU4nIwxS9Btnd3WV1ddVfR6NRUqkUfX36FIu0Ej0iG2BnZ4e1Nf9NZ+jv78dxHAVPpAXpUfmUtre3WV9f99cDAwMkk0kFT6RF6ZH5FDY3N9nc3PTXAwMDOI5Db29vgFOJyOMoek/A8zw2NzfZ2try92KxGMlkUsETaXGK3i15nsfGxgbb29v+3uDgIMlkkp6eet6pS0SCpOjdgud5rK+vs7Oz4+8NDQ2xtLSk4Im0CUWvTp7nsba2xu7urr83PDxMIpFQ8ETaiKJXB8/zWF1dZW9vz98bGRlhcXFRwRNpM4reDTzPY2Vlhf39fX9vdHSUxcVFQqFrX9MsIi1K0XsMz/NYXl7m4ODA3xsbG2NhYUHBE2lTit41PM8jl8txeHjo7yl4Iu1P0XuEWq1GLpcjn8/7exMTE8zNzSl4Im1O0buiVquRzWY5Ojry9yYnJ5mdnVXwRDqAoveQWq1GJpPh+PjBid6mpqaYmZlR8EQ6hKJ3rlqtks1mLwVvenqaeDyu4Il0EEWPs+BlMhlOTk78vXg8TjweD3AqEbkLXR+9arWK67qcnp76ezMzM0xPTwc4lYjcla6OXqVSwXVdCoWCvzc7O8vU1FSAU4nIXera6D0qeHNzc0xOTgY4lYjcta6MXrlcxnVdisWivzc/P8/ExESAU4lIM3Rd9MrlMul0mlKp5O8tLCwwPj4e4FQi0ixdFb1SqYTrupeCt7i4yNjYWHBDiUhTdU30SqUS6XSacrns7yUSCUZHRwOcSkSarSuiVywWSafTVCoVAEKhEIlEgpGRkYAnE5Fm6/joFQoFXNe9FLylpSWGh4cDnkxEgtDR0SsUCqTTaarVKnAWvGQyydDQUMCTiUhQOjZ6p6enuK7rB6+np4dkMsng4GDAk4lIkDoyeo8KnuM4xGKxgCcTkaB1XPROTk5wXZdarQYoeCJyWUdF7/j4mEwm4wevt7cXx3EYGBgIeDIRaRUdE72joyMymQye5wFnwUulUvT39wc8mYi0ko6IXj6fJ5vN+sHr6+vDcRwFT0TepO2jd3h4SC6XuxS8VCpFNBoNeDIRaUVtHb2rwQuHwziOo+CJyLXaNnoHBwfkcjl/HQ6HSaVSRCKRAKcSkVbXltHb399neXnZX0ciEVKpFOFwOMCpRKQd1BU9Y8y7gC8Cs8CrwPPW2rXbXqYR9vb2WFlZ8dfRaBTHcRQ8EalLz00XMMaEgBeBT1hrZ4CvA5+/7WUaYXd3903B0zM8EbmNG6MHPAsUrLUvna9fAN5jjBm95WWeyuHhIaurq/66v7+fVCpFX19b/oQuIgGpJ3rPAK9fLKy1ZSALvPWWl3kqBwcH/v8fGBhQ8ETkidRTjRhwemUvDwzd8jJPZXJykmKxSH9/P3Nzc/T29jbqqkWki9QTvWPg6otXh8/3b3OZpxKLxXjmmWcadXUi0qXq+fH2NeAtFwtjTBhwgDdueRkRkcDdGD1r7bcBzxjzvvOtjwCvABFjzMvGmPB1l7HW7t7J1CIiT6jevwQ8B3zZGPMF4DvAB4AR4G3n11G+5jIiIi0ldPG61Wa7f/++f8P37t0LZAYR6Vih6/6hnt/piYh0DEVPRLqKoiciXUXRE5GuouiJSFdpiRev3r9/P+gRRKSzeAD37t17019x9UxPRLqKoiciXSWwg5NFRIKgZ3oi0lUUPRHpKoqeiHQVRU9EuoqiJyJdpSUOTr6qlc6z+7RumtMY0wd8HPgg0MvZexF+2FqbCWDca93m822MGQa+BXzKWvulpg1Zpzq/v0LArwAfBvqB37PWfrLZs96kzvvy48DnODtnzQ7wUWvtPzR71psYY34S+ArwDmut+4h//2Hgtzl7L8+/A37eWnt029tpuWd6rXSe3adV55yx8/99p7U2CfwV8BvNm/JmT/D5/nXO4t1ybnFfPg28G/g+a+1iiwavnsdKFPgD4DlrrcPZf2C/3ORRb2SM+Tjwi4C95t+Hgd/n7M2KZwGXs5DfWstFjxY5z26D3DintfbQWvtZa23+fOsl4O1NnvMmdX++jTE/AkSBlnsmce7G+2KMiQEfAj7U4qc8qOfrMgDUODuPDcA3gVZ7nAD8E/CjwHXP3H6Qs1NQvGKt9YBf4yyAt9aK0WuJ8+w2yJPM+f2cfWO2krruhzFmDPgM8EvNHO6W6rkv7z7f+6QxxjXG/LMx5geaO2Zdbrwv1tp94HeBF40x7+Xsx8dfbuaQ9bDWfsNaW3rMRa7e1zXOztMzfdvbasXotcR5dhvkVnMaY6Y4+/HjM3c8123Vez9+E/istXavKVM9mXruywLw3cDfA98F/CrwR8aY/qZMWL96vy4vAPeA3wEiwF/e/WgN17DHfCtGryXOs9sgdc9pjBkE/oyzX/6/2oTZbuPG+2GM+Qmg11r7p80c7AnU8zWpAH9jrf26tbZqrf0LYIPW+7VDPV+XKeDPgZ+21r4d+BLwty0Y8Js07DHfitHrpPPs1jXn+S+b/wT4mrX2q02cr1713I/ngR8zxuwbY/Y5e8b6f40xrfYHpnruS5qzZ3sP6wMKdz3cLdVzX94L/Ju19lsA1tqvACfA9zRryAa5el/ngJq1dvO2V9Ry0euk8+zWc1/OD1n5Q+Bla+1vBTXr49T5NXm/tXbEWjtmrR0D/jfw36y1vxDQ2I9Uz30B/gUIG2PeD2CM+SHODlt5/VHXGZQ678sbwPcaYxYAjDHvAJLAfwQx820YY541xnztfPkN4FljzLvP/2r9MeCPn+R6W/I4PTrrPLs33Zef4uyvVu80xnz0oY/7WWvtPzZ72Meo52vSLh57X6y1ZWPMzwFfMMb8H2ANeL+1thLYxNe76b78qzHmc8BfG2MinP0e7IPW2q3AJq7fJGf3A2vtqTHmA8BXgQng//GEj3m9tZSIdJWW+/FWROQuKXoi0lUUPRHpKoqeiHQVRU9EuoqiJyJdRdETka6i6IlIV1H0RKSr/H+C6U9Yfwy2qQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax=plt.subplots(1,2,figsize=(12,4),sharex=True)\n",
    "labels=['thicc','thicc_norm']\n",
    "meta_metrics={}\n",
    "palette='flare'\n",
    "colors=sns.color_palette(palette, 2)\n",
    "\n",
    "roc_fig,roc_ax=plt.subplots(figsize=(5,5))\n",
    "roc_ax.plot([0,1],[0,1],color='#d3d3d3',linewidth=3)\n",
    "\n",
    "for i,model_type in enumerate([thicc_CNN_dropout,thicc_CNN_dropout_with_norm]):\n",
    "    metrics={}\n",
    "    model = model_type()\n",
    "    history = model.fit(data.X_train, data.y_train, epochs=20, batch_size=32, validation_data=(data.X_test, data.y_test), verbose=1)\n",
    "    \n",
    "    letter_dropout_means,letter_dropout_uncertainy=compute_dropout_uncertainty(model,N_samples)\n",
    "    number_dropout_means,number_dropout_uncertainy=compute_dropout_uncertainty(model,sevens_samples)\n",
    "    subset=pd.DataFrame({0:np.mean(number_dropout_uncertainy,axis=1),1:np.mean(letter_dropout_uncertainy,axis=1)}).melt()\n",
    "\n",
    "    letter_values=np.mean(letter_dropout_uncertainy,axis=1)\n",
    "    number_values=np.mean(number_dropout_uncertainy,axis=1)\n",
    "    letter_mean=np.round(np.mean(letter_dropout_uncertainy),2)\n",
    "    number_mean=np.round(np.mean(number_dropout_uncertainy),2)\n",
    "    \n",
    "    metrics['k_metric']=kstest(letter_values,number_values)[0]\n",
    "    \n",
    "    metrics['diff_means']=letter_mean-number_mean\n",
    "\n",
    "    print(f'{labels[i]}, letter uq : {letter_mean}, number uq :{number_mean}')\n",
    "    \n",
    "    sns.regplot(x=\"value\", y=\"variable\", data=subset,\n",
    "                     logistic=True,\n",
    "                n_boot=500, y_jitter=.1,line_kws={\"color\": colors[-1]},scatter_kws={'alpha':0.05,\"color\": colors[0]},ax=ax[i])\n",
    "    \n",
    "    ax[i].set_title(labels[i])\n",
    "    ax[i].set_xlabel('Uncertainty')\n",
    "    ax[i].set_yticks([0,1])\n",
    "    ax[i].set_yticklabels(['8','N'])\n",
    "    \n",
    "    \n",
    "    ### computing logistic regression\n",
    "    ground=subset['variable'].values\n",
    "    clf = LogisticRegression(random_state=0).fit(subset['value'].values.reshape(-1,1), ground)\n",
    "    predictions=clf.predict_proba(subset['value'].values.reshape(-1,1))[:,1]\n",
    "    fpr, tpr, _ = roc_curve(ground, predictions)\n",
    "    roc_auc = auc(fpr, tpr)\n",
    "    metrics['AUC']=roc_auc\n",
    "    meta_metrics[labels[i]]=metrics\n",
    "\n",
    "    roc_ax.plot(fpr, tpr, \n",
    "         lw=3, label=f'{labels[i]} {np.round(roc_auc,2)}',color=colors[i])\n",
    "\n",
    "roc_ax.set_xlabel('False Positive Rate')\n",
    "roc_ax.set_ylabel('True Positive Rate')\n",
    "roc_ax.set_title(f'ROC curve \\n Distinguishing between 3 and N \\n based on uncertainty' )\n",
    "roc_ax.legend()\n",
    "roc_fig.savefig('figures/thicc_gauntlet_ROC_8.png',dpi=300,bbox_inches='tight')\n",
    "\n",
    "    \n",
    "fig.savefig('figures/thicc_gauntlet_8.png',dpi=300,bbox_inches='tight')\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "british-special",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Computing classifier for adversarial /non-adversarial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "id": "editorial-inspector",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "id": "valued-elevation",
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = LogisticRegression(random_state=0).fit(subset['value'].values.reshape(-1,1), subset['variable'].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "id": "intense-sperm",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "predictions=clf.predict_proba(subset['value'].values.reshape(-1,1))[:,1]\n",
    "ground=subset['variable'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "id": "skilled-bundle",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'ROC curve (AUC = 0.84)')"
      ]
     },
     "execution_count": 267,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "fpr, tpr, _ = roc_curve(ground, predictions)\n",
    "roc_auc = auc(fpr, tpr)\n",
    "\n",
    "lw = 2\n",
    "# plt.figure()\n",
    "\n",
    "fig,ax=plt.subplots(figsize=(5,5))\n",
    "\n",
    "ax.plot(fpr, tpr, color='darkorange',\n",
    "         lw=lw, label='ROC curve (AUC = %0.2f)' % roc_auc)\n",
    "\n",
    "ax.plot([0,1],[0,1],color='#d3d3d3',linewidth=3)\n",
    "ax.set_xlabel('False Positive Rate')\n",
    "ax.set_ylabel('True Positive Rate')\n",
    "ax.set_title(f'ROC curve (AUC = {np.round(roc_auc,2)})' )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "exotic-binding",
   "metadata": {},
   "source": [
    "# Running Complete Ecosystem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "crucial-armenia",
   "metadata": {},
   "outputs": [],
   "source": [
    "from utility.mnist_data.dropout_framework import DropoutMinstModel,KfoldMinstModel\n",
    "from utility.mnist_data.model_warehouse import basic_CNN,deep_CNN\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "adaptive-muscle",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:Loading dataset = emnist\n",
      "WARNING:root:Please cite the following paper when using or referencing this Extra Keras Dataset:\n",
      "WARNING:root:Cohen, G., Afshar, S., Tapson, J., & van Schaik, A. (2017). EMNIST: an extension of MNIST to handwritten letters. Retrieved from http://arxiv.org/abs/1702.05373\n",
      "INFO:root:Starting mnist model training\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "1875/1875 [==============================] - 142s 75ms/step - loss: 0.3929 - accuracy: 0.8807 - val_loss: 0.0416 - val_accuracy: 0.9855\n",
      "Epoch 2/30\n",
      "1875/1875 [==============================] - 134s 71ms/step - loss: 0.0735 - accuracy: 0.9784 - val_loss: 0.0303 - val_accuracy: 0.9903\n",
      "Epoch 3/30\n",
      "1875/1875 [==============================] - 134s 71ms/step - loss: 0.0575 - accuracy: 0.9826 - val_loss: 0.0332 - val_accuracy: 0.9892\n",
      "Epoch 4/30\n",
      "1875/1875 [==============================] - 141s 75ms/step - loss: 0.0490 - accuracy: 0.9853 - val_loss: 0.0226 - val_accuracy: 0.9925\n",
      "Epoch 5/30\n",
      "1875/1875 [==============================] - 147s 78ms/step - loss: 0.0438 - accuracy: 0.9865 - val_loss: 0.0215 - val_accuracy: 0.9931\n",
      "Epoch 6/30\n",
      "1875/1875 [==============================] - 150s 80ms/step - loss: 0.0356 - accuracy: 0.9894 - val_loss: 0.0241 - val_accuracy: 0.9925\n",
      "Epoch 7/30\n",
      "1875/1875 [==============================] - 154s 82ms/step - loss: 0.0320 - accuracy: 0.9903 - val_loss: 0.0224 - val_accuracy: 0.9928\n",
      "Epoch 8/30\n",
      "1875/1875 [==============================] - 156s 83ms/step - loss: 0.0279 - accuracy: 0.9910 - val_loss: 0.0172 - val_accuracy: 0.9947\n",
      "Epoch 9/30\n",
      "1875/1875 [==============================] - 156s 83ms/step - loss: 0.0253 - accuracy: 0.9923 - val_loss: 0.0176 - val_accuracy: 0.9946\n",
      "Epoch 10/30\n",
      "1875/1875 [==============================] - 149s 79ms/step - loss: 0.0233 - accuracy: 0.9929 - val_loss: 0.0183 - val_accuracy: 0.9947\n",
      "Epoch 11/30\n",
      "1875/1875 [==============================] - 148s 79ms/step - loss: 0.0226 - accuracy: 0.9932 - val_loss: 0.0192 - val_accuracy: 0.9940\n",
      "Epoch 12/30\n",
      "1875/1875 [==============================] - 147s 78ms/step - loss: 0.0196 - accuracy: 0.9941 - val_loss: 0.0160 - val_accuracy: 0.9949\n",
      "Epoch 13/30\n",
      "1875/1875 [==============================] - 145s 78ms/step - loss: 0.0165 - accuracy: 0.9951 - val_loss: 0.0210 - val_accuracy: 0.9935\n",
      "Epoch 14/30\n",
      "1875/1875 [==============================] - 146s 78ms/step - loss: 0.0181 - accuracy: 0.9945 - val_loss: 0.0184 - val_accuracy: 0.9952\n",
      "Epoch 15/30\n",
      "1875/1875 [==============================] - 145s 77ms/step - loss: 0.0153 - accuracy: 0.9950 - val_loss: 0.0158 - val_accuracy: 0.9944\n",
      "Epoch 16/30\n",
      "1875/1875 [==============================] - 143s 76ms/step - loss: 0.0154 - accuracy: 0.9950 - val_loss: 0.0183 - val_accuracy: 0.9947\n",
      "Epoch 17/30\n",
      "1875/1875 [==============================] - 142s 76ms/step - loss: 0.0134 - accuracy: 0.9958 - val_loss: 0.0181 - val_accuracy: 0.9947\n",
      "Epoch 18/30\n",
      "1875/1875 [==============================] - 143s 76ms/step - loss: 0.0136 - accuracy: 0.9956 - val_loss: 0.0196 - val_accuracy: 0.9942\n",
      "Epoch 19/30\n",
      "1875/1875 [==============================] - 142s 76ms/step - loss: 0.0130 - accuracy: 0.9959 - val_loss: 0.0174 - val_accuracy: 0.9950\n",
      "Epoch 20/30\n",
      "1875/1875 [==============================] - 152s 81ms/step - loss: 0.0120 - accuracy: 0.9963 - val_loss: 0.0187 - val_accuracy: 0.9948\n",
      "Epoch 21/30\n",
      "1875/1875 [==============================] - 156s 83ms/step - loss: 0.0105 - accuracy: 0.9969 - val_loss: 0.0184 - val_accuracy: 0.9954\n",
      "Epoch 22/30\n",
      "1875/1875 [==============================] - 157s 84ms/step - loss: 0.0104 - accuracy: 0.9965 - val_loss: 0.0182 - val_accuracy: 0.9960\n",
      "Epoch 23/30\n",
      "1875/1875 [==============================] - 157s 84ms/step - loss: 0.0109 - accuracy: 0.9965 - val_loss: 0.0173 - val_accuracy: 0.9955\n",
      "Epoch 24/30\n",
      "1875/1875 [==============================] - 147s 79ms/step - loss: 0.0095 - accuracy: 0.9969 - val_loss: 0.0171 - val_accuracy: 0.9960\n",
      "Epoch 25/30\n",
      "1875/1875 [==============================] - 148s 79ms/step - loss: 0.0106 - accuracy: 0.9971 - val_loss: 0.0638 - val_accuracy: 0.9848\n",
      "Epoch 26/30\n",
      "1875/1875 [==============================] - 141s 75ms/step - loss: 0.0089 - accuracy: 0.9975 - val_loss: 0.0195 - val_accuracy: 0.9951\n",
      "Epoch 27/30\n",
      "1875/1875 [==============================] - 141s 75ms/step - loss: 0.0101 - accuracy: 0.9967 - val_loss: 0.0192 - val_accuracy: 0.9947\n",
      "Epoch 28/30\n",
      "1875/1875 [==============================] - 141s 75ms/step - loss: 0.0088 - accuracy: 0.9973 - val_loss: 0.0192 - val_accuracy: 0.9950\n",
      "Epoch 29/30\n",
      "1875/1875 [==============================] - 140s 75ms/step - loss: 0.0082 - accuracy: 0.9974 - val_loss: 0.0215 - val_accuracy: 0.9948\n",
      "Epoch 30/30\n",
      "1875/1875 [==============================] - 140s 75ms/step - loss: 0.0075 - accuracy: 0.9980 - val_loss: 0.0208 - val_accuracy: 0.9952\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:mnist model trained\n",
      "INFO:root:Collecting UQ from dropout\n",
      "100%|██████████| 100/100 [35:43<00:00, 21.44s/it]\n",
      "INFO:root:Finished collecting UQ from dropout\n",
      "100%|██████████| 100/100 [04:40<00:00,  2.81s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: serialized_data/kaggle_example/mnist.mdl/assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: serialized_data/kaggle_example/mnist.mdl/assets\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "titles=['basic_CNN','deep_CNN']\n",
    "for i, model in enumerate([kaggle_example]):\n",
    "\n",
    "    complete_model=DropoutMinstModel(base_model=model,n_epochs=30,n_dropout_shuffles=100,title=titles[i])\n",
    "    complete_model.train()\n",
    "    complete_model.make_plots(f'figures/{titles[i]}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "capital-belarus",
   "metadata": {},
   "source": [
    "# Visualizing Data / Comparing two solutions\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "worldwide-benefit",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loading Data\n",
    "basic_uq=pd.read_csv('serialized_data/basic_cnn/uq_dataframe.csv')\n",
    "deep_uq=pd.read_csv('serialized_data/deep_cnn/uq_dataframe.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "unexpected-italian",
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_uncerainty_distributions(ax,uq_dataframe,title='Distributions of Uncertainty by class'):\n",
    "    sns.boxplot(x='Labels', y='UQ', data=uq_dataframe, palette='flare', showfliers=False,ax=ax)\n",
    "\n",
    "    ax.set_xticklabels([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 'A', 'C', 'E', 'F', 'K', 'M', 'N', 'R','V', 'X'])\n",
    "    ax.set_title(title)\n",
    "    return ax\n",
    "\n",
    "def generate_roc(ax,fpr,tpr,auc,color,label='basic'):\n",
    "    ax.plot(fpr,tpr,lw=3,color=color,label=f'{label} : {np.round(auc,2)}')\n",
    "\n",
    "    ax.plot([0, 1], [0, 1], color='#d3d3d3', linewidth=3)\n",
    "    ax.set_xlabel('False Positive Rate')\n",
    "    ax.set_ylabel('True Positive Rate')\n",
    "    ax.set_title(f'ROC for Numbers/Letters')\n",
    "    #fig.savefig(f'{output_folder}/final_roc_curve.png',dpi=300,bbox_inches='tight')\n",
    "    return ax\n",
    "\n",
    "def plot_cms(ax,cm,ticks,accuracy,label='basic'):\n",
    "    \n",
    "    sns.heatmap(cm, annot=True,ax=ax,yticklabels=ticks,xticklabels=ticks)\n",
    "    ax.set_title(f'{label} \\n accuracy:{accuracy}')\n",
    "    #fig.savefig(f'{output_folder}/mnist_cm.png',dpi=300,bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "lasting-consideration",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,2,figsize=(12,4),sharey=True)\n",
    "\n",
    "ax[0]=generate_uncerainty_distributions(ax[0],basic_uq,title='Distributions of Uncertainty by class \\n Basic CNN')\n",
    "ax[1]=generate_uncerainty_distributions(ax[1],deep_uq,title='Distributions of Uncertainty by class \\n Deep CNN')\n",
    "fig.savefig('figures/UQ_comp_basic_deep.png',dpi=300,bbox_inches='tight')\n",
    "# fig=generate_uncerainty_distributions(deep_uq,title='Distributions of Uncertainty by class \\n Deep CNN')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "proved-theater",
   "metadata": {},
   "outputs": [],
   "source": [
    "basic_metrics=pickle.load( open( 'serialized_data/basic_cnn/metrics.pkl', \"rb\" ) )\n",
    "deep_metrics=pickle.load( open( 'serialized_data/deep_cnn/metrics.pkl', \"rb\" ) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "original-cameroon",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(5,5))\n",
    "colors=sns.color_palette('flare', 3)\n",
    "\n",
    "ax=generate_roc(ax,basic_metrics['UQ_fpr'],basic_metrics['UQ_tpr'],basic_metrics['UQ_auc'],color=colors[0],label='basic_CNN')\n",
    "ax=generate_roc(ax,deep_metrics['UQ_fpr'],deep_metrics['UQ_tpr'],deep_metrics['UQ_auc'],color=colors[2],label='deep_CNN')\n",
    "\n",
    "ax.legend()\n",
    "fig.savefig('figures/UQ_comp_basic_deep_ROC.png',dpi=300,bbox_inches='tight')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "special-seating",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8472916666666667"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# computing accuracy\n",
    "loaded_model = pickle.load(open('serialized_data/deep_cnn/uq.mdl', 'rb'))\n",
    "predictions=loaded_model.predict(deep_uq.UQ.values.reshape(-1,1))\n",
    "accuracy_score(predictions,deep_uq.is_adversarial.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "choice-beast",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,2,figsize=(10,4))\n",
    "plot_cms(ax[0],basic_metrics['mnist_confusion_matrix'],np.arange(10),basic_metrics['mnist_accuracy'],label='basic_CNN')\n",
    "plot_cms(ax[1],deep_metrics['mnist_confusion_matrix'],np.arange(10),deep_metrics['mnist_accuracy'],label='deep_CNN')\n",
    "fig.savefig('figures/confusion_mats.png',dpi=300,bbox_inches='tight')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "quick-deviation",
   "metadata": {},
   "source": [
    "# Testing a trained model on external challenges"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "balanced-murder",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:Loading dataset = emnist\n",
      "WARNING:root:Please cite the following paper when using or referencing this Extra Keras Dataset:\n",
      "WARNING:root:Cohen, G., Afshar, S., Tapson, J., & van Schaik, A. (2017). EMNIST: an extension of MNIST to handwritten letters. Retrieved from http://arxiv.org/abs/1702.05373\n"
     ]
    }
   ],
   "source": [
    "resurection=DropoutMinstModel(base_model=deep_CNN,\n",
    "                              n_epochs=30,\n",
    "                              n_dropout_shuffles=100,\n",
    "                              title=titles[i],\n",
    "                             previous_trained_models='serialized_data/deep_cnn')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "id": "liberal-compiler",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "def rgb2gray(rgb):\n",
    "    resultant=np.dot(rgb[...,:3], [0.2989, 0.5870, 0.1140])\n",
    "    resultant = resultant.reshape((1, 28, 28, 1))\n",
    "    resultant=1-resultant\n",
    "\n",
    "    return resultant\n",
    "\n",
    "img_loc='figures/yoda.png'\n",
    "img = mpimg.imread(img_loc)     \n",
    "yoda = rgb2gray(img)\n",
    "\n",
    "img_loc='figures/zhong.png'\n",
    "img = mpimg.imread(img_loc)     \n",
    "zhong = rgb2gray(img)\n",
    "\n",
    "img_loc='figures/1.png'\n",
    "img = mpimg.imread(img_loc)     \n",
    "eight = rgb2gray(img)\n",
    "\n",
    "media.show_images(np.vstack([zhong,yoda,eight]),height=100,cmap='flare')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "id": "rough-humanitarian",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [00:00<00:00, 161.18it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.58240023]\n",
      "is Zhong adversarial : [ True] what number is it closest to : [9]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "is_adversarial,predicted_classes=resurection.predict(zhong)\n",
    "print(f'is Zhong adversarial : {is_adversarial} what number is it closest to : {predicted_classes}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "id": "rental-population",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [00:00<00:00, 159.82it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.5850559]\n",
      "is Yoda adversarial : [ True] what number is it closest to : [5]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "is_adversarial,predicted_classes=resurection.predict(yoda)\n",
    "print(f'is Yoda adversarial : {is_adversarial} what number is it closest to : {predicted_classes}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "id": "ready-harbor",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [00:00<00:00, 159.61it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.5652839]\n",
      "is eight adversarial : [ True] what number is it closest to : [1]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "is_adversarial,predicted_classes=resurection.predict(eight)\n",
    "print(f'is eight adversarial : {is_adversarial} what number is it closest to : {predicted_classes}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "cooperative-salem",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "partial_model = Model(model.inputs, model.layers[-1].output)\n",
    "result = []\n",
    "n_iter=10\n",
    "for i in range(n_iter):\n",
    "    result.append(partial_model(testX))\n",
    "\n",
    "result = np.array(result)\n",
    "\n",
    "prediction_mean = result.mean(axis=0)\n",
    "prediction_std=result.std(axis=0)\n",
    "        \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "sufficient-radius",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "prediction_mean = result.mean(axis=0)\n",
    "prediction_std=result.std(axis=0)\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "respected-wisconsin",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([6.7762636e-21, 5.5511151e-17, 0.0000000e+00, 0.0000000e+00,\n",
       "       0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
       "       0.0000000e+00, 6.9388939e-18], dtype=float32)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction_std[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "upper-authorization",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 28, 28, 1)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testX.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "ceramic-place",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(28, 28, 1)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testX[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "complicated-advertiser",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:Loading dataset = emnist\n",
      "WARNING:root:Please cite the following paper when using or referencing this Extra Keras Dataset:\n",
      "WARNING:root:Cohen, G., Afshar, S., Tapson, J., & van Schaik, A. (2017). EMNIST: an extension of MNIST to handwritten letters. Retrieved from http://arxiv.org/abs/1702.05373\n"
     ]
    }
   ],
   "source": [
    "X, y= emnist.load_data()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "promising-speaking",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[[0, 0, 0, ..., 0, 0, 0],\n",
       "         [0, 0, 0, ..., 0, 0, 0],\n",
       "         [0, 0, 0, ..., 0, 0, 0],\n",
       "         ...,\n",
       "         [0, 0, 0, ..., 0, 0, 0],\n",
       "         [0, 0, 0, ..., 0, 0, 0],\n",
       "         [0, 0, 0, ..., 0, 0, 0]],\n",
       " \n",
       "        [[0, 0, 0, ..., 0, 0, 0],\n",
       "         [0, 0, 0, ..., 0, 0, 0],\n",
       "         [0, 0, 0, ..., 0, 0, 0],\n",
       "         ...,\n",
       "         [0, 0, 0, ..., 0, 0, 0],\n",
       "         [0, 0, 0, ..., 0, 0, 0],\n",
       "         [0, 0, 0, ..., 0, 0, 0]],\n",
       " \n",
       "        [[0, 0, 0, ..., 0, 0, 0],\n",
       "         [0, 0, 0, ..., 0, 0, 0],\n",
       "         [0, 0, 0, ..., 0, 0, 0],\n",
       "         ...,\n",
       "         [0, 0, 0, ..., 0, 0, 0],\n",
       "         [0, 0, 0, ..., 0, 0, 0],\n",
       "         [0, 0, 0, ..., 0, 0, 0]],\n",
       " \n",
       "        ...,\n",
       " \n",
       "        [[0, 0, 0, ..., 0, 0, 0],\n",
       "         [0, 0, 0, ..., 0, 0, 0],\n",
       "         [0, 0, 0, ..., 0, 0, 0],\n",
       "         ...,\n",
       "         [0, 0, 0, ..., 0, 0, 0],\n",
       "         [0, 0, 0, ..., 0, 0, 0],\n",
       "         [0, 0, 0, ..., 0, 0, 0]],\n",
       " \n",
       "        [[0, 0, 0, ..., 0, 0, 0],\n",
       "         [0, 0, 0, ..., 0, 0, 0],\n",
       "         [0, 0, 0, ..., 0, 0, 0],\n",
       "         ...,\n",
       "         [0, 0, 0, ..., 0, 0, 0],\n",
       "         [0, 0, 0, ..., 0, 0, 0],\n",
       "         [0, 0, 0, ..., 0, 0, 0]],\n",
       " \n",
       "        [[0, 0, 0, ..., 0, 0, 0],\n",
       "         [0, 0, 0, ..., 0, 0, 0],\n",
       "         [0, 0, 0, ..., 0, 0, 0],\n",
       "         ...,\n",
       "         [0, 0, 0, ..., 0, 0, 0],\n",
       "         [0, 0, 0, ..., 0, 0, 0],\n",
       "         [0, 0, 0, ..., 0, 0, 0]]], dtype=uint8),\n",
       " array([41, 39,  9, ...,  1, 26, 33], dtype=uint8))"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ruled-approval",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "language_info": {
   "codemirror_mode": {
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   "file_extension": ".py",
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