Mod1_Capstone Project.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Capstone Project\n",
    "## Image classifier for the SVHN dataset\n",
    "### Instructions\n",
    "\n",
    "In this notebook, you will create a neural network that classifies real-world images digits. You will use concepts from throughout this course in building, training, testing, validating and saving your Tensorflow classifier model.\n",
    "\n",
    "This project is peer-assessed. Within this notebook you will find instructions in each section for how to complete the project. Pay close attention to the instructions as the peer review will be carried out according to a grading rubric that checks key parts of the project instructions. Feel free to add extra cells into the notebook as required.\n",
    "\n",
    "### How to submit\n",
    "\n",
    "When you have completed the Capstone project notebook, you will submit a pdf of the notebook for peer review. First ensure that the notebook has been fully executed from beginning to end, and all of the cell outputs are visible. This is important, as the grading rubric depends on the reviewer being able to view the outputs of your notebook. Save the notebook as a pdf (File -> Download as -> PDF via LaTeX). You should then submit this pdf for review.\n",
    "\n",
    "### Let's get started!\n",
    "\n",
    "We'll start by running some imports, and loading the dataset. For this project you are free to make further imports throughout the notebook as you wish. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from scipy.io import loadmat"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![SVHN overview image](data/svhn_examples.jpg)\n",
    "For the capstone project, you will use the [SVHN dataset](http://ufldl.stanford.edu/housenumbers/). This is an  image dataset of over 600,000 digit images in all, and is a harder dataset than MNIST as the numbers appear in the context of natural scene images. SVHN is obtained from house numbers in Google Street View images. \n",
    "\n",
    "* Y. Netzer, T. Wang, A. Coates, A. Bissacco, B. Wu and A. Y. Ng. \"Reading Digits in Natural Images with Unsupervised Feature Learning\". NIPS Workshop on Deep Learning and Unsupervised Feature Learning, 2011.\n",
    "\n",
    "Your goal is to develop an end-to-end workflow for building, training, validating, evaluating and saving a neural network that classifies a real-world image into one of ten classes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Run this cell to load the dataset\n",
    "\n",
    "train = loadmat('data/train_32x32.mat')\n",
    "test = loadmat('data/test_32x32.mat')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Both `train` and `test` are dictionaries with keys `X` and `y` for the input images and labels respectively."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Inspect and preprocess the dataset\n",
    "* Extract the training and testing images and labels separately from the train and test dictionaries loaded for you.\n",
    "* Select a random sample of images and corresponding labels from the dataset (at least 10), and display them in a figure.\n",
    "* Convert the training and test images to grayscale by taking the average across all colour channels for each pixel. _Hint: retain the channel dimension, which will now have size 1._\n",
    "* Select a random sample of the grayscale images and corresponding labels from the dataset (at least 10), and display them in a figure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Extract the training and testing images and labels separately from the train and test dictionaries loaded for you.\n",
    "\n",
    "X_train = train['X']\n",
    "X_test = test['X']\n",
    "y_train = train['y']\n",
    "y_test = test['y']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((32, 32, 3, 73257), (32, 32, 3, 26032))"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape,X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((73257, 32, 32, 3), (26032, 32, 32, 3))"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "X_train = np.moveaxis(X_train, -1, 0)\n",
    "X_test = np.moveaxis(X_test, -1 , 0)\n",
    "\n",
    "X_train.shape,X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[6]\n"
     ]
    },
    {
     "data": {
      "image/png": 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u/uCGoecB7C6/3g3guemHJ4SYFaNk9V0P4FsA3jSz18tj9wC4H8BTZnYHgHcB3DqbEEkWHqv7V6eI3JCLhh2SSD04NsYhP5c9zoxrBVlzzGI777zzw7E+s6hYrbvA4sxYVt9CXBNwoabVlwUttJhdytp/gdSGZO3XWMZfNEY7zoVW3+gW4FDxu/uPESvixpFnEkKcUegTfkIkisQvRKJI/EIkisQvRKJI/EIkyhwKeI7PNDKYNkJtQJZdGBVNpBlizBoiFmE4wjMPLbC2WGutxU3xXLxtWGyXgRSfDAliB7id1yLnRbB7wLLYjjSSaVfX1A2dbGpl153s/9A7vxCJIvELkSgSvxCJIvELkSgSvxCJIvELkSiNW32Ru8XcvMjWoEU/a7qDTjyU6JJ91rOu7hi1cliR0eqf58y+apHMPSNjtEdhUJQyig9ALZt1EAexFYP4mQVb10Vj60GvGVi3LfKatdvVY+Nks+qdX4hEkfiFSBSJX4hEkfiFSBSJX4hEaXS33zFOhbGN59WpV8ayIthc44+xXfuC7vbXi4NmdQQ7x1HCz7Axnl1CkpaCWnc8mammM0IWK3IJuFNRL1GLuQ5ZFq9xKxjLgh19NqbdfiHEUCR+IRJF4hciUSR+IRJF4hciUSR+IRJlqNVnZpcDeAzAFzHoVbTX3R8ys/sAfBvA++VD73H3F/jVvFY9vugUZqPRGm3TKIC2kdqeXQyvMUdsu3aQ2MOSd/J6a0XHAvuQ2oqkNiGzHFkcHkxXsDZkxLKj9xxzTMnzzoIEnix4LQHAssnv4VF8/hzA99z9NTPbAuBVM3uxHPu+u//ZxFEIIRpnlF59xwAcK7/+xMwOA7h01oEJIWbLWH/zm9l2ANcAeLk8dKeZHTSzfWZ2wZRjE0LMkJHFb2bnAngawF3u/jGAhwFcAWAHBr8ZPBCct8fMVsxsZXV1dQohCyGmwUjiN7MOBsJ/3N2fAQB3P+7ufXcvADwCYGfVue6+192X3X15aWlpWnELISZkqPhtsJX6KIDD7v7ghuPbNjzsGwAOTT88IcSsGGW3/3oA3wLwppm9Xh67B8DtZrYDAzPrCIDvjDJhZJU4sVeijC4n6Xk8C4zUfCN+TRgHbZ9FWj+RGm00o6sgY17dlqtPrC3vkbEW8SqJ3WRB9p6xtSJ3Y2TZAUOyKqOsvpr1EyMrFQBaHj+BjLUbW6gea3VI3cXo/hjDARxlt//HwSWHePpCiDMZfcJPiESR+IVIFIlfiESR+IVIFIlfiERpvIBnZL0wuyZK6yOGHU2ma9EMsfg8C3wUdr2oOCPALTvrVFt2AG9dFXs98Tl5v7q11nDINfPqV8eIldohz5mNMfrBc2OZgC2SgVcEbciAODsPiNtrAcDi4kLlcfacsyhrcgyvT+/8QiSKxC9Eokj8QiSKxC9Eokj8QiSKxC9EojRq9cFjm4pn4UXn0KlCWL81agPWGSHXY9aQkRS3jJicYeYhsQc3b45j7BO7qd2JY+z3q+czi59zp02sLWKVsWZ9kY2Z57Fll/fycKwg9mybxNEmGXoLi4uVxzOyHq0oS1C9+oQQw5D4hUgUiV+IRJH4hUgUiV+IRJH4hUiUZq0+i60vlqgWDdVsg8d7u5FAwoKhxF2Jsq+GYaRwJrOUosKZGSkkurhI+tYFlh0A5H1miVUfN1KJk60Vy46MMveAOLuw1+uF56xn6+EYh7xmLKtvU7XVt7DICoJWX2+cNpR65xciUSR+IRJF4hciUSR+IRJF4hciUYbu9pvZJgAvAVgsH/837n6vmV0I4EkA2zFo13Wbu39Ir4W4PhrbZbdgaIyNzVPPI3XkSDep+HossYck77CtWWPttdrxWFjbra41Qt4fWNuzMHeK9MJiLgwby/PYdYgSeLpkt5/VzmNxsKqSrC7g5k2bxo4jTAqbcmJPF8BvuPtXMGjHvcvMrgNwN4AD7n4lgAPl90KIs4Sh4vcBn5bfdsp/DuBmAPvL4/sB3DKTCIUQM2Gkv/nNLCs79J4A8KK7vwzgEnc/BgDl/xfPLkwhxLQZSfzu3nf3HQAuA7DTzK4edQIz22NmK2a2srq6WjdOIcSUGWu3393/G8A/AtgF4LiZbQOA8v8TwTl73X3Z3ZeXlpYmDFcIMS2Git/MvmBmW8uvNwP4TQA/AfA8gN3lw3YDeG5WQQohps8oiT3bAOy3QfG1FoCn3P1vzeyfADxlZncAeBfArcMvZbWsvjoZPE4sJXoemSyq78dmomPM2iLWUIbYPoySj5jVx5JmOKSFVvR6UquP2IokClYLsdUObMB2HEd7Ib5eRgIpiD3LXuvI0mtHdfrI9ca564eK390PArim4vh/AbhxjLmEEGcQ+oSfEIki8QuRKBK/EIki8QuRKBK/EIli1GKb9mRm7wP4j/LbiwD8orHJYxTHqSiOUznb4vhld//CKBdsVPynTGy24u7Lc5lccSgOxaFf+4VIFYlfiESZp/j3znHujSiOU1Ecp/L/No65/c0vhJgv+rVfiESZi/jNbJeZ/ZuZvW1mc6v9Z2ZHzOxNM3vdzFYanHefmZ0ws0Mbjl1oZi+a2U/L/y+YUxz3mdl/lmvyupnd1EAcl5vZP5jZYTN7y8x+vzze6JqQOBpdEzPbZGb/bGZvlHH8cXl8uuvh7o3+A5AB+BmALwNYAPAGgKuajqOM5QiAi+Yw71cBXAvg0IZjfwrg7vLruwH8yZziuA/AHzS8HtsAXFt+vQXAvwO4quk1IXE0uiYYZOaeW37dAfAygOumvR7zeOffCeBtd3/H3dcB/BCDYqDJ4O4vAfjgtMONF0QN4mgcdz/m7q+VX38C4DCAS9HwmpA4GsUHzLxo7jzEfymAn2/4/ijmsMAlDuBHZvaqme2ZUwyfcSYVRL3TzA6WfxbM/M+PjZjZdgzqR8y1SOxpcQANr0kTRXPnIf6qYiPzshyud/drAfw2gO+a2VfnFMeZxMMArsCgR8MxAA80NbGZnQvgaQB3ufvHTc07QhyNr4lPUDR3VOYh/qMALt/w/WUA3ptDHHD398r/TwB4FoM/SebFSAVRZ427Hy9vvALAI2hoTcysg4HgHnf3Z8rDja9JVRzzWpNy7rGL5o7KPMT/CoArzexLZrYA4JsYFANtFDM7x8y2fPY1gK8BOMTPmilnREHUz26ukm+ggTWxQUG6RwEcdvcHNww1uiZRHE2vSWNFc5vawTxtN/MmDHZSfwbgD+cUw5cxcBreAPBWk3EAeAKDXx97GPwmdAeAX8Kg7dlPy/8vnFMcfwXgTQAHy5ttWwNx/BoGf/odBPB6+e+mpteExNHomgD4VQD/Us53CMAflcenuh76hJ8QiaJP+AmRKBK/EIki8QuRKBK/EIki8QuRKBK/EIki8QuRKBK/EInyv/1HnwJSD+9JAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[6]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[4]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[5]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[8]\n"
     ]
    }
   ],
   "source": [
    "#Select a random sample of images and corresponding labels from the dataset (at least 10), and display them in a figure.\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "for i in range(20,30):\n",
    "    plt.imshow(X_train[i, :, :, :,])\n",
    "    plt.show()\n",
    "    print(y_train[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Convert the training and test images to grayscale by taking the average across all colour channels for each pixel. \n",
    "#Hint: retain the channel dimension, which will now have size 1.\n",
    "\n",
    "X_train_grayscale = np.mean(X_train, 3).reshape(73257, 32, 32, 1)/255\n",
    "X_test_grayscale = np.mean(X_test,3).reshape(26032, 32,32 ,1)/255"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_grayscale_plot = np.mean(X_train,3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[6]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2]\n"
     ]
    },
    {
     "data": {
      "image/png": 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SX6piKyPLiW1z9Bl4ndmK4CXvFE3smRS88xMSKQx+QiKFwU9IpDD4CYkUBj8hkcLgJyRSBmnXdRuArwK4CT0147iqflFElgB8E8BR9Fp2PaSq696+tJNg99fhtlFVR/aavhjWayobjq5l1NsDAGk5LbkcxEkIskhaTrurpi0O1bZsiS2zCrgBqO6E58pRyuB1hXJyZtzEHjXaryWOPjgrtmhXdWoJZk4bNas+ntf+qyhO+Uf3Lmupqd6YmjEf4szTMPu/SgfAp1X1twG8B8AnReQdAB4D8Lyq3gng+fxvQshbhL7Br6rnVPWH+eMtAKcA3ALgQQAn8qedAPDBcTlJCBk9Q32OFZGjAN4N4EUAN6rqOaB3gQBweNTOEULGx8DBLyJzAL4F4FOqujnEuGMisioiq9nWlSI+EkLGwEDBLyJV9AL/a6r67XzzeRE5ktuPALgQGquqx1V1RVVX0nm7YQMhpFz6Br/0lg+fBHBKVT+/x/QMgIfzxw8D+O7o3SOEjItBsvruBfAxAK+JyMv5tscBfBbA0yLyCIDXAXy4754yQWUjfMiKI21ZdfCkYUtDunTQtHnj4Egl3TtuDW5vL9TNMbvLdp2+1qwjyzgSm98CbLRpbJVd25HKrl2PL2uH7yvbmZ292XAKBu6o/Zp5p2xJevOJfV515z1QF6ftWcGvzVjnttF12rIZ8mbXkT2vpW/wq+r3AVMUfd/ARyKEXFfwG36ERAqDn5BIYfATEikMfkIihcFPSKSUWsATADQxpIjElld2li1J6QZzjDgpVqmTTSeevDIXnq7Goi15tQ7Y59UO1zLtS23TObdWeLt3XknHtmWpbfOyAdUo4Nnp2nO12bUl0/mucWJ9SIysvraxHQCc+qjInHEZ7DTHtjotxYaQ50YJ7/yERAqDn5BIYfATEikMfkIihcFPSKQw+AmJlHKlPgGsxK2sZssdzcWw9qIVWzaqGIUsAaC2bWs5ScuRcqbC4zJboXLlvG7B2c8cLcpS0rx+fONAHInQoqF2BuSvMnsil1O7SEzdqDJad6aj7DuiJRCO2w/e+QmJFAY/IZHC4CckUhj8hEQKg5+QSCk/scdYBVanBVUn3OELYiSPAIA47am8NlNJ207ASFvG8byF7YKtsBKnzKCHGqv9XqKTZ/NqGqpz60gq4ZM7ULFbrKXOhLSd+n5Vp+ChIdCMhWHq510P8M5PSKQw+AmJFAY/IZHC4CckUhj8hEQKg5+QSOkr9YnIbQC+CuAm9JpIHVfVL4rIEwA+DuBi/tTHVfVZf2cwLzeebGTle7hSkyP1uXKeY+tWwwf0JDtXcrRNffwf3ua18ZKOfQJenb7OjG2bn20Etx+u2Q2eZ8Wu0+cl/RTBUXthp4sVx3mLmHi5WNZsDHM3H0Tn7wD4tKr+UETmAfxARJ7LbV9Q1b8a4niEkOuEQXr1nQNwLn+8JSKnANwybscIIeNlqP/5ReQogHcDeDHf9KiIvCoiT4nI4oh9I4SMkYGDX0TmAHwLwKdUdRPAlwDcAeAu9D4ZfM4Yd0xEVkVkNdveHoHLhJBRMFDwi0gVvcD/mqp+GwBU9byqZqraBfBlAPeExqrqcVVdUdWVdK5glwpCyMjpG/wiIgCeBHBKVT+/Z/uRPU/7EICTo3ePEDIuBlntvxfAxwC8JiIv59seB/BREbkLvZy20wA+0XdPolAj28urMWfVpes6mYBZzXZDndZg3Yp9PewaKWKuTFlQN3K6OyF16gyaNi/hzJmPrO7Mh6O+VSthMW0+CUuAgF+L743OgmlrO/ewrQIa23xiD6o6E+m1AGs4GX+WCps5YrDVNmyY0x1ktf/7CEvSvqZPCLmu4Tf8CIkUBj8hkcLgJyRSGPyERAqDn5BIKbeAZ6JIZsLpapmTPpa0wnqZV4jTw5UVjcw9AOhWDKmvoJznFvB0svo82c6SHb1zzur2CXSmnXEztiMH62FJbz61C3h60lZd7FTG1NUxh2fHeS92C+XnAQ0vPdJg1Od1LbzzExIpDH5CIoXBT0ikMPgJiRQGPyGRwuAnJFJKlfrSVHHgQFjquVK1dbtOK9ysr7JjS1TtzJZWdjN7XNKx5ZWOkdXXmnekISe7sHCPP+fcrMt5t2Kfczpn3wMai7atvWDrkcv1cOGWhWTHHOP13PNoOFpr1dBTvWN5srOdk+jTdlI/E+ON4EmfFt0hxvDOT0ikMPgJiRQGPyGRwuAnJFIY/IRECoOfkEgpV+pLuliYsbO6LDYXw1JOI7F1tPasI1HNOBmEjoyWTYW3d2ZtzS6r25KSdOxjpQ3HD6dwadoM29KwWtrzo+tIW0v2uPSA3VvvcH0ruP2AU8Cz7qU5FqRryHZNpyNfIl7apNPL0ZHZLDmv37gi+xt8H4SQKGHwExIpDH5CIoXBT0ikMPgJiZS+q/0iUgfwAoCp/Pl/r6qfEZElAN8EcBS9dl0Pqeq6t69qkuHIzGbQNpXaSSL1Wrh+W/Og7f5u01YCGldsmzqr/clUOPmoPm2veqepvTrcaNj9rjqOLVuzbdZqv7uQ7iwctxfsgYcO2u21fmNqLbh9KbVX++edtmGJcwJFat25STMFE66KMoqV+2LH7U8TwO+r6rvQa8d9v4i8B8BjAJ5X1TsBPJ//TQh5i9A3+LXH1fzMav6jAB4EcCLffgLAB8fiISFkLAz0P7+IpHmH3gsAnlPVFwHcqKrnACD/fXh8bhJCRs1Awa+qmareBeBWAPeIyDsHPYCIHBORVRFZba4XLYVACBk1Q632q+plAP8C4H4A50XkCADkvy8YY46r6oqqrkwt1vfpLiFkVPQNfhFZFpGF/PE0gD8A8GMAzwB4OH/awwC+Oy4nCSGjZ5DEniMATohIit7F4mlV/QcR+VcAT4vIIwBeB/DhfjuqJRlumwmrgYemwjXfiuIlZ6y1Zkxbpzt8wsdspWmOaXXtKT6/O2/aNpv2p6QLU/a4TtM4XuIlq9im6Tn73H5z4ZJpu70Wti05t5u0oI7mvdZF6wJatJ37ZdH2Wqnhv7e/llMTcFD6Br+qvgrg3YHtvwbwvn17QAiZCPyGHyGRwuAnJFIY/IRECoOfkEhh8BMSKaJaXkaRiFwE8N/5n4cA2FpRedCPN0M/3sxbzY/bVXV5kB2WGvxvOrDIqqquTOTg9IN+0A9+7CckVhj8hETKJIP/+ASPvRf68Wbox5v5f+vHxP7nJ4RMFn7sJyRSJhL8InK/iPyniPxURCZW+09ETovIayLysoislnjcp0Tkgoic3LNtSUSeE5Gf5L8XJ+THEyLyy3xOXhaRD5Tgx20i8s8ickpEfiQif5xvL3VOHD9KnRMRqYvIv4nIK7kff55vH+18qGqpPwBSAD8D8HYANQCvAHhH2X7kvpwGcGgCx30vgLsBnNyz7S8BPJY/fgzAX0zIjycA/EnJ83EEwN3543kA/wXgHWXPieNHqXOCXo3gufxxFcCLAN4z6vmYxJ3/HgA/VdWfq2oLwDfQKwYaDar6AoBra1uXXhDV8KN0VPWcqv4wf7wF4BSAW1DynDh+lIr2GHvR3EkE/y0A3tjz9xlMYIJzFMD3ROQHInJsQj5c5XoqiPqoiLya/1sw9n8/9iIiR9GrHzHRIrHX+AGUPCdlFM2dRPCHyrVMSnK4V1XvBvBHAD4pIu+dkB/XE18CcAd6PRrOAfhcWQcWkTkA3wLwKVUNd3eZjB+lz4nuo2juoEwi+M8AuG3P37cCODsBP6CqZ/PfFwB8B71/SSbFQAVRx42qns/feF0AX0ZJcyIiVfQC7muq+u18c+lzEvJjUnOSH3voormDMongfwnAnSLyNhGpAfgIesVAS0VEZkVk/upjAO8HcNIfNVaui4KoV99cOR9CCXMiIgLgSQCnVPXze0ylzonlR9lzUlrR3LJWMK9ZzfwAeiupPwPwpxPy4e3oKQ2vAPhRmX4A+Dp6Hx/b6H0SegTADei1PftJ/ntpQn78LYDXALyav9mOlODH76L3r9+rAF7Ofz5Q9pw4fpQ6JwB+B8C/58c7CeDP8u0jnQ9+w4+QSOE3/AiJFAY/IZHC4CckUhj8hEQKg5+QSGHwExIpDH5CIoXBT0ik/A/A2N6F6qrkcQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[6]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[4]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[5]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[8]\n"
     ]
    }
   ],
   "source": [
    "#Select a random sample of the grayscale images and corresponding labels from the dataset (at least 10), \n",
    "#and display them in a figure\n",
    "\n",
    "for i in range(20,30):\n",
    "    plt.imshow(X_train_grayscale_plot[i, :, :,])\n",
    "    plt.show()\n",
    "    print(y_train[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. MLP neural network classifier\n",
    "* Build an MLP classifier model using the Sequential API. Your model should use only Flatten and Dense layers, with the final layer having a 10-way softmax output. \n",
    "* You should design and build the model yourself. Feel free to experiment with different MLP architectures. _Hint: to achieve a reasonable accuracy you won't need to use more than 4 or 5 layers._\n",
    "* Print out the model summary (using the summary() method)\n",
    "* Compile and train the model (we recommend a maximum of 30 epochs), making use of both training and validation sets during the training run. \n",
    "* Your model should track at least one appropriate metric, and use at least two callbacks during training, one of which should be a ModelCheckpoint callback.\n",
    "* As a guide, you should aim to achieve a final categorical cross entropy training loss of less than 1.0 (the validation loss might be higher).\n",
    "* Plot the learning curves for loss vs epoch and accuracy vs epoch for both training and validation sets.\n",
    "* Compute and display the loss and accuracy of the trained model on the test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, Flatten, Dropout,BatchNormalization, Conv2D, MaxPool2D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from tensorflow.keras.utils import to_categorical\n",
    "# y_train_binary = to_categorical(y_train)\n",
    "# y_test_binary = to_categorical(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import OneHotEncoder\n",
    "\n",
    "enc = OneHotEncoder().fit(y_train)\n",
    "y_train_encoded = enc.transform(y_train).toarray()\n",
    "y_test_encoded = enc.transform(y_test).toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(32, 32, 3)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "flatten (Flatten)            (None, 3072)              0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 256)               786688    \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 128)               32896     \n",
      "_________________________________________________________________\n",
      "batch_normalization (BatchNo (None, 128)               512       \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 128)               16512     \n",
      "_________________________________________________________________\n",
      "dropout (Dropout)            (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 64)                8256      \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 32)                2080      \n",
      "_________________________________________________________________\n",
      "dense_5 (Dense)              (None, 10)                330       \n",
      "=================================================================\n",
      "Total params: 847,274\n",
      "Trainable params: 847,018\n",
      "Non-trainable params: 256\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model_mlp = Sequential([\n",
    "    Flatten(input_shape=(32,32,3)),\n",
    "    Dense(256, activation='relu'),\n",
    "    Dense(128,activation='relu'),\n",
    "    BatchNormalization(momentum=0.98, epsilon=0.002),\n",
    "    Dense(128,activation='relu'),\n",
    "    Dropout(0.2),\n",
    "    Dense(64,activation='relu'),\n",
    "    Dense(32,activation='relu'),\n",
    "    Dense(10,activation='softmax')\n",
    "])\n",
    "model_mlp.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['acc'])\n",
    "model_mlp.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping\n",
    "\n",
    "checkpoint = ModelCheckpoint(filepath = \"mlp_weights\", \n",
    "                             save_best_only=True, save_weights_only=True,\n",
    "                             monitor='val_loss', verbose=1)\n",
    "early_stopping = EarlyStopping(patience=3, monitor='loss')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 73257 samples, validate on 26032 samples\n",
      "Epoch 1/25\n",
      "73216/73257 [============================>.] - ETA: 0s - loss: 1.8046 - acc: 0.3625\n",
      "Epoch 00001: val_loss improved from inf to 1.88761, saving model to mlp_weights\n",
      "73257/73257 [==============================] - 60s 817us/sample - loss: 1.8045 - acc: 0.3626 - val_loss: 1.8876 - val_acc: 0.3897\n",
      "Epoch 2/25\n",
      "73216/73257 [============================>.] - ETA: 0s - loss: 1.3235 - acc: 0.5642\n",
      "Epoch 00002: val_loss improved from 1.88761 to 1.70144, saving model to mlp_weights\n",
      "73257/73257 [==============================] - 52s 708us/sample - loss: 1.3235 - acc: 0.5642 - val_loss: 1.7014 - val_acc: 0.5164\n",
      "Epoch 3/25\n",
      "73216/73257 [============================>.] - ETA: 0s - loss: 1.1750 - acc: 0.6258\n",
      "Epoch 00003: val_loss improved from 1.70144 to 1.68834, saving model to mlp_weights\n",
      "73257/73257 [==============================] - 52s 707us/sample - loss: 1.1749 - acc: 0.6259 - val_loss: 1.6883 - val_acc: 0.5216\n",
      "Epoch 4/25\n",
      "73088/73257 [============================>.] - ETA: 0s - loss: 1.0862 - acc: 0.6575\n",
      "Epoch 00004: val_loss did not improve from 1.68834\n",
      "73257/73257 [==============================] - 52s 710us/sample - loss: 1.0859 - acc: 0.6576 - val_loss: 2.1784 - val_acc: 0.4385\n",
      "Epoch 5/25\n",
      "73216/73257 [============================>.] - ETA: 0s - loss: 1.0364 - acc: 0.6768\n",
      "Epoch 00005: val_loss improved from 1.68834 to 1.40558, saving model to mlp_weights\n",
      "73257/73257 [==============================] - 52s 716us/sample - loss: 1.0364 - acc: 0.6768 - val_loss: 1.4056 - val_acc: 0.5908\n",
      "Epoch 6/25\n",
      "73088/73257 [============================>.] - ETA: 0s - loss: 0.9791 - acc: 0.6965\n",
      "Epoch 00006: val_loss did not improve from 1.40558\n",
      "73257/73257 [==============================] - 52s 709us/sample - loss: 0.9788 - acc: 0.6966 - val_loss: 1.4457 - val_acc: 0.5622\n",
      "Epoch 7/25\n",
      "73216/73257 [============================>.] - ETA: 0s - loss: 0.9332 - acc: 0.7105\n",
      "Epoch 00007: val_loss improved from 1.40558 to 1.19084, saving model to mlp_weights\n",
      "73257/73257 [==============================] - 52s 715us/sample - loss: 0.9332 - acc: 0.7105 - val_loss: 1.1908 - val_acc: 0.6280\n",
      "Epoch 8/25\n",
      "73216/73257 [============================>.] - ETA: 0s - loss: 0.9144 - acc: 0.7170\n",
      "Epoch 00008: val_loss improved from 1.19084 to 1.12903, saving model to mlp_weights\n",
      "73257/73257 [==============================] - 52s 711us/sample - loss: 0.9144 - acc: 0.7170 - val_loss: 1.1290 - val_acc: 0.6672\n",
      "Epoch 9/25\n",
      "73216/73257 [============================>.] - ETA: 0s - loss: 0.9079 - acc: 0.7187\n",
      "Epoch 00009: val_loss did not improve from 1.12903\n",
      "73257/73257 [==============================] - 52s 715us/sample - loss: 0.9080 - acc: 0.7187 - val_loss: 1.3257 - val_acc: 0.6127\n",
      "Epoch 10/25\n",
      "73088/73257 [============================>.] - ETA: 0s - loss: 0.8791 - acc: 0.7288\n",
      "Epoch 00010: val_loss improved from 1.12903 to 1.09685, saving model to mlp_weights\n",
      "73257/73257 [==============================] - 51s 703us/sample - loss: 0.8789 - acc: 0.7287 - val_loss: 1.0969 - val_acc: 0.6688\n",
      "Epoch 11/25\n",
      "73088/73257 [============================>.] - ETA: 0s - loss: 0.8624 - acc: 0.7328\n",
      "Epoch 00011: val_loss did not improve from 1.09685\n",
      "73257/73257 [==============================] - 50s 680us/sample - loss: 0.8624 - acc: 0.7327 - val_loss: 1.2647 - val_acc: 0.6513\n",
      "Epoch 12/25\n",
      "73216/73257 [============================>.] - ETA: 0s - loss: 0.8388 - acc: 0.7404\n",
      "Epoch 00012: val_loss did not improve from 1.09685\n",
      "73257/73257 [==============================] - 50s 680us/sample - loss: 0.8389 - acc: 0.7404 - val_loss: 1.4452 - val_acc: 0.6329\n",
      "Epoch 13/25\n",
      "73216/73257 [============================>.] - ETA: 0s - loss: 0.8168 - acc: 0.7475\n",
      "Epoch 00013: val_loss did not improve from 1.09685\n",
      "73257/73257 [==============================] - 50s 683us/sample - loss: 0.8169 - acc: 0.7475 - val_loss: 1.7784 - val_acc: 0.6207\n",
      "Epoch 14/25\n",
      "73216/73257 [============================>.] - ETA: 0s - loss: 0.7977 - acc: 0.7527\n",
      "Epoch 00014: val_loss did not improve from 1.09685\n",
      "73257/73257 [==============================] - 50s 685us/sample - loss: 0.7977 - acc: 0.7527 - val_loss: 1.1444 - val_acc: 0.6648\n",
      "Epoch 15/25\n",
      "73088/73257 [============================>.] - ETA: 0s - loss: 0.7994 - acc: 0.7512\n",
      "Epoch 00015: val_loss did not improve from 1.09685\n",
      "73257/73257 [==============================] - 50s 683us/sample - loss: 0.7994 - acc: 0.7511 - val_loss: 1.1516 - val_acc: 0.6706\n",
      "Epoch 16/25\n",
      "73216/73257 [============================>.] - ETA: 0s - loss: 0.7656 - acc: 0.7623\n",
      "Epoch 00016: val_loss did not improve from 1.09685\n",
      "73257/73257 [==============================] - 49s 674us/sample - loss: 0.7655 - acc: 0.7623 - val_loss: 1.5406 - val_acc: 0.5922\n",
      "Epoch 17/25\n",
      "73216/73257 [============================>.] - ETA: 0s - loss: 0.7500 - acc: 0.7667\n",
      "Epoch 00017: val_loss did not improve from 1.09685\n",
      "73257/73257 [==============================] - 49s 666us/sample - loss: 0.7501 - acc: 0.7666 - val_loss: 1.1348 - val_acc: 0.6999\n",
      "Epoch 18/25\n",
      "73216/73257 [============================>.] - ETA: 0s - loss: 0.7374 - acc: 0.7702\n",
      "Epoch 00018: val_loss improved from 1.09685 to 1.08935, saving model to mlp_weights\n",
      "73257/73257 [==============================] - 49s 674us/sample - loss: 0.7373 - acc: 0.7702 - val_loss: 1.0893 - val_acc: 0.7226\n",
      "Epoch 19/25\n",
      "73216/73257 [============================>.] - ETA: 0s - loss: 0.7313 - acc: 0.7734\n",
      "Epoch 00019: val_loss did not improve from 1.08935\n",
      "73257/73257 [==============================] - 50s 676us/sample - loss: 0.7315 - acc: 0.7733 - val_loss: 1.1385 - val_acc: 0.6926\n",
      "Epoch 20/25\n",
      "73216/73257 [============================>.] - ETA: 0s - loss: 0.7220 - acc: 0.7755\n",
      "Epoch 00020: val_loss improved from 1.08935 to 1.01681, saving model to mlp_weights\n",
      "73257/73257 [==============================] - 50s 687us/sample - loss: 0.7219 - acc: 0.7755 - val_loss: 1.0168 - val_acc: 0.6840\n",
      "Epoch 21/25\n",
      "73216/73257 [============================>.] - ETA: 0s - loss: 0.7235 - acc: 0.7755\n",
      "Epoch 00021: val_loss did not improve from 1.01681\n",
      "73257/73257 [==============================] - 49s 673us/sample - loss: 0.7235 - acc: 0.7755 - val_loss: 1.0213 - val_acc: 0.6854\n",
      "Epoch 22/25\n",
      "73216/73257 [============================>.] - ETA: 0s - loss: 0.6982 - acc: 0.7821\n",
      "Epoch 00022: val_loss did not improve from 1.01681\n",
      "73257/73257 [==============================] - 51s 696us/sample - loss: 0.6982 - acc: 0.7821 - val_loss: 1.0483 - val_acc: 0.6834\n",
      "Epoch 23/25\n",
      "73216/73257 [============================>.] - ETA: 0s - loss: 0.6945 - acc: 0.7840\n",
      "Epoch 00023: val_loss improved from 1.01681 to 1.01678, saving model to mlp_weights\n",
      "73257/73257 [==============================] - 52s 714us/sample - loss: 0.6944 - acc: 0.7841 - val_loss: 1.0168 - val_acc: 0.6956\n",
      "Epoch 24/25\n",
      "73216/73257 [============================>.] - ETA: 0s - loss: 0.6849 - acc: 0.7852\n",
      "Epoch 00024: val_loss did not improve from 1.01678\n",
      "73257/73257 [==============================] - 52s 707us/sample - loss: 0.6849 - acc: 0.7852 - val_loss: 1.1012 - val_acc: 0.6638\n",
      "Epoch 25/25\n",
      "73216/73257 [============================>.] - ETA: 0s - loss: 0.6788 - acc: 0.7885\n",
      "Epoch 00025: val_loss did not improve from 1.01678\n",
      "73257/73257 [==============================] - 53s 719us/sample - loss: 0.6788 - acc: 0.7885 - val_loss: 1.0339 - val_acc: 0.7031\n"
     ]
    }
   ],
   "source": [
    "history = model_mlp.fit(X_train, y_train_encoded,\n",
    "                        batch_size=128,\n",
    "                        callbacks=[checkpoint, early_stopping],\n",
    "                        validation_data=(X_test, y_test_encoded), epochs=25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Loss vs Epoch')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend(['loss','val_loss'], loc='upper right')\n",
    "plt.title(\"Loss vs Epoch\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Accuracy vs Epoch')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['acc'])\n",
    "plt.plot(history.history['val_acc'])\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.legend(['loss','val_acc'], loc='lower right')\n",
    "plt.title(\"Accuracy vs Epoch\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. CNN neural network classifier\n",
    "* Build a CNN classifier model using the Sequential API. Your model should use the Conv2D, MaxPool2D, BatchNormalization, Flatten, Dense and Dropout layers. The final layer should again have a 10-way softmax output. \n",
    "* You should design and build the model yourself. Feel free to experiment with different CNN architectures. _Hint: to achieve a reasonable accuracy you won't need to use more than 2 or 3 convolutional layers and 2 fully connected layers.)_\n",
    "* The CNN model should use fewer trainable parameters than your MLP model.\n",
    "* Compile and train the model (we recommend a maximum of 30 epochs), making use of both training and validation sets during the training run.\n",
    "* Your model should track at least one appropriate metric, and use at least two callbacks during training, one of which should be a ModelCheckpoint callback.\n",
    "* You should aim to beat the MLP model performance with fewer parameters!\n",
    "* Plot the learning curves for loss vs epoch and accuracy vs epoch for both training and validation sets.\n",
    "* Compute and display the loss and accuracy of the trained model on the test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d (Conv2D)              (None, 30, 30, 32)        896       \n",
      "_________________________________________________________________\n",
      "max_pooling2d (MaxPooling2D) (None, 10, 10, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_1 (Conv2D)            (None, 10, 10, 64)        18496     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 3, 3, 64)          0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_1 (Batch (None, 3, 3, 64)          256       \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 3, 3, 64)          36928     \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 3, 3, 64)          0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 576)               0         \n",
      "_________________________________________________________________\n",
      "dense_6 (Dense)              (None, 32)                18464     \n",
      "_________________________________________________________________\n",
      "dense_7 (Dense)              (None, 10)                330       \n",
      "=================================================================\n",
      "Total params: 75,370\n",
      "Trainable params: 75,242\n",
      "Non-trainable params: 128\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model_cnn = Sequential([\n",
    "    Conv2D(filters=32, input_shape=(32,32,3),kernel_size=(3,3),activation='relu'),\n",
    "    MaxPool2D(pool_size=(3,3)),\n",
    "    Conv2D(filters=64,kernel_size=(3,3),padding='same',activation='relu'),\n",
    "    MaxPool2D(pool_size=(3,3)),\n",
    "    BatchNormalization(momentum=0.98),\n",
    "    Conv2D(filters=64,kernel_size=(3,3),padding='same',activation='relu'),\n",
    "    Dropout(0.02),\n",
    "    Flatten(),\n",
    "    Dense(32,activation='relu'),\n",
    "    Dense(10,activation='softmax')\n",
    "])\n",
    "model_cnn.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['acc'])\n",
    "model_cnn.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "checkpoint_cnn = ModelCheckpoint(filepath=\"cnn_weights\",save_best_only=True, save_weights_only=True, save_freq='epoch', \n",
    "                                    monitor='val_loss',mode='min',verbose=1)\n",
    "earlystopping_cnn = EarlyStopping(patience=3,monitor='val_loss')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 73257 samples, validate on 26032 samples\n",
      "Epoch 1/15\n",
      "73216/73257 [============================>.] - ETA: 0s - loss: 0.8068 - acc: 0.7407\n",
      "Epoch 00001: val_loss improved from inf to 0.55817, saving model to cnn_weights\n",
      "73257/73257 [==============================] - 378s 5ms/sample - loss: 0.8066 - acc: 0.7407 - val_loss: 0.5582 - val_acc: 0.8485\n",
      "Epoch 2/15\n",
      "73216/73257 [============================>.] - ETA: 0s - loss: 0.4293 - acc: 0.8706\n",
      "Epoch 00002: val_loss improved from 0.55817 to 0.49575, saving model to cnn_weights\n",
      "73257/73257 [==============================] - 360s 5ms/sample - loss: 0.4292 - acc: 0.8707 - val_loss: 0.4958 - val_acc: 0.8560\n",
      "Epoch 3/15\n",
      "73216/73257 [============================>.] - ETA: 0s - loss: 0.3586 - acc: 0.8923\n",
      "Epoch 00003: val_loss improved from 0.49575 to 0.41634, saving model to cnn_weights\n",
      "73257/73257 [==============================] - 372s 5ms/sample - loss: 0.3586 - acc: 0.8923 - val_loss: 0.4163 - val_acc: 0.8783\n",
      "Epoch 4/15\n",
      "73216/73257 [============================>.] - ETA: 0s - loss: 0.3188 - acc: 0.9041\n",
      "Epoch 00004: val_loss improved from 0.41634 to 0.41275, saving model to cnn_weights\n",
      "73257/73257 [==============================] - 371s 5ms/sample - loss: 0.3188 - acc: 0.9041 - val_loss: 0.4127 - val_acc: 0.8798\n",
      "Epoch 5/15\n",
      "73216/73257 [============================>.] - ETA: 0s - loss: 0.2890 - acc: 0.9137\n",
      "Epoch 00005: val_loss improved from 0.41275 to 0.38219, saving model to cnn_weights\n",
      "73257/73257 [==============================] - 382s 5ms/sample - loss: 0.2890 - acc: 0.9138 - val_loss: 0.3822 - val_acc: 0.8874\n",
      "Epoch 6/15\n",
      "73216/73257 [============================>.] - ETA: 0s - loss: 0.2652 - acc: 0.9204- ETA: 7s - lo\n",
      "Epoch 00006: val_loss improved from 0.38219 to 0.36283, saving model to cnn_weights\n",
      "73257/73257 [==============================] - 371s 5ms/sample - loss: 0.2652 - acc: 0.9204 - val_loss: 0.3628 - val_acc: 0.8979\n",
      "Epoch 7/15\n",
      "73216/73257 [============================>.] - ETA: 0s - loss: 0.2447 - acc: 0.9269\n",
      "Epoch 00007: val_loss did not improve from 0.36283\n",
      "73257/73257 [==============================] - 359s 5ms/sample - loss: 0.2448 - acc: 0.9268 - val_loss: 0.4356 - val_acc: 0.8799\n",
      "Epoch 8/15\n",
      "73216/73257 [============================>.] - ETA: 0s - loss: 0.2277 - acc: 0.9309\n",
      "Epoch 00008: val_loss did not improve from 0.36283\n",
      "73257/73257 [==============================] - 364s 5ms/sample - loss: 0.2276 - acc: 0.9310 - val_loss: 0.3674 - val_acc: 0.8917\n",
      "Epoch 9/15\n",
      "73216/73257 [============================>.] - ETA: 0s - loss: 0.2095 - acc: 0.9365\n",
      "Epoch 00009: val_loss did not improve from 0.36283\n",
      "73257/73257 [==============================] - 364s 5ms/sample - loss: 0.2095 - acc: 0.9365 - val_loss: 0.3951 - val_acc: 0.8925\n"
     ]
    }
   ],
   "source": [
    "history = model_cnn.fit(X_train, y_train_encoded, \n",
    "                     batch_size=64,\n",
    "                     callbacks=[checkpoint_cnn, earlystopping_cnn],\n",
    "                     validation_data=(X_test, y_test_encoded), epochs=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Loss vs Epoch')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend(['loss','val_loss'], loc='upper right')\n",
    "plt.title(\"Loss vs Epoch\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Accuracy vs Epoch')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['acc'])\n",
    "plt.plot(history.history['val_acc'])\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.legend(['loss','val_acc'], loc='lower right')\n",
    "plt.title(\"Accuracy vs Epoch\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Get model predictions\n",
    "* Load the best weights for the MLP and CNN models that you saved during the training run.\n",
    "* Randomly select 5 images and corresponding labels from the test set and display the images with their labels.\n",
    "* Alongside the image and label, show each model’s predictive distribution as a bar chart, and the final model prediction given by the label with maximum probability."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.training.tracking.util.CheckpointLoadStatus at 0x7f7d686603c8>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_cnn.load_weights(\"cnn_weights\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x864 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import random\n",
    "\n",
    "num_test_images = X_test.shape[0]\n",
    "\n",
    "random_inx = np.random.choice(num_test_images, 5)\n",
    "random_test_images = X_test[random_inx, ...]\n",
    "random_test_images = tf.cast(random_test_images, tf.float32)\n",
    "random_test_labels = y_test[random_inx, ...]\n",
    "\n",
    "predictions = model_cnn.predict(random_test_images)\n",
    "\n",
    "fig, axes = plt.subplots(5, 2, figsize=(16, 12))\n",
    "fig.subplots_adjust(hspace=0.4, wspace=-0.2)\n",
    "\n",
    "for i, (prediction, image, label) in enumerate(zip(predictions, random_test_images, random_test_labels)):\n",
    "    axes[i, 0].imshow(np.squeeze(image))\n",
    "    axes[i, 0].get_xaxis().set_visible(False)\n",
    "    axes[i, 0].get_yaxis().set_visible(False)\n",
    "    axes[i, 0].text(10., -1.5, f'Digit {label}')\n",
    "    axes[i, 1].bar(np.arange(1,11), prediction)\n",
    "    axes[i, 1].set_xticks(np.arange(1,11))\n",
    "    axes[i, 1].set_title(\"model_cnn prediction\")\n",
    "    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.training.tracking.util.CheckpointLoadStatus at 0x7f7d68540668>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_mlp.load_weights(\"mlp_weights\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x864 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_test_images = X_test.shape[0]\n",
    "\n",
    "random_inx = np.random.choice(num_test_images, 5)\n",
    "random_test_images = X_test[random_inx, ...]\n",
    "random_test_labels = y_test[random_inx, ...]\n",
    "\n",
    "predictions = model_mlp.predict(random_test_images)\n",
    "\n",
    "fig, axes = plt.subplots(5, 2, figsize=(16, 12))\n",
    "fig.subplots_adjust(hspace=0.4, wspace=-0.2)\n",
    "\n",
    "for i, (prediction, image, label) in enumerate(zip(predictions, random_test_images, random_test_labels)):\n",
    "    axes[i, 0].imshow(np.squeeze(image))\n",
    "    axes[i, 0].get_xaxis().set_visible(False)\n",
    "    axes[i, 0].get_yaxis().set_visible(False)\n",
    "    axes[i, 0].text(10., -1.5, f'Digit {label}')\n",
    "    axes[i, 1].bar(np.arange(1,11), prediction)\n",
    "    axes[i, 1].set_xticks(np.arange(1,11))\n",
    "    axes[i, 1].set_title(\"model_mlp prediction\")\n",
    "    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "! ls -lh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}