COVID Neural Net.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "center-collective",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "established-temperature",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import keras\n",
    "import sklearn\n",
    "import matplotlib.pyplot as plt\n",
    "from PIL import Image\n",
    "import os, os.path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "entertaining-waste",
   "metadata": {},
   "outputs": [],
   "source": [
    "config = tf.compat.v1.ConfigProto()\n",
    "config.gpu_options.allow_growth = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "invalid-travel",
   "metadata": {},
   "outputs": [],
   "source": [
    "sess = tf.compat.v1.Session(config=config)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "revolutionary-strategy",
   "metadata": {},
   "source": [
    "# Loading in Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "manufactured-soundtrack",
   "metadata": {},
   "outputs": [],
   "source": [
    "base_dir = r'C:\\Users\\jaych\\Resilio Sync\\SDP\\ct images'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "fluid-accreditation",
   "metadata": {},
   "outputs": [],
   "source": [
    "covid_dir = os.path.join(base_dir, 'CT_COVID')\n",
    "non_covid_dir = os.path.join(base_dir, 'CT_NonCOVID')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "applicable-basics",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Covid Positive Images: 349\n",
      "Total Non-Covid Images: 397\n"
     ]
    }
   ],
   "source": [
    "print(\"Total Covid Positive Images:\" ,len(os.listdir(covid_dir)))\n",
    "print(\"Total Non-Covid Images:\" ,len(os.listdir(non_covid_dir)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "brazilian-skill",
   "metadata": {},
   "outputs": [],
   "source": [
    "covidImgs = []\n",
    "nonCovidImgs = []\n",
    "def load_images(path, imgs):\n",
    "    for f in os.listdir(path):\n",
    "        imgs.append(Image.open(os.path.join(path,f)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "located-religion",
   "metadata": {},
   "outputs": [],
   "source": [
    "load_images(covid_dir, covidImgs)\n",
    "load_images(non_covid_dir, nonCovidImgs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "alive-windsor",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x2831a5f12b0>"
      ]
     },
     "execution_count": 11,
     "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.imshow(covidImgs[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "mysterious-flood",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x2831c1e45e0>"
      ]
     },
     "execution_count": 12,
     "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.imshow(nonCovidImgs[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "continued-nicaragua",
   "metadata": {},
   "outputs": [],
   "source": [
    "allImgs = covidImgs + nonCovidImgs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "female-cement",
   "metadata": {},
   "outputs": [],
   "source": [
    "labels=[]\n",
    "def label (imgs, string):\n",
    "    for i,image in enumerate(imgs):\n",
    "        labels.append(string)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "sealed-oasis",
   "metadata": {},
   "outputs": [],
   "source": [
    "label(covidImgs, 'Covid')\n",
    "label(nonCovidImgs, 'Non-Covid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "listed-arkansas",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid',\n",
       " 'Non-Covid']"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "interpreted-compact",
   "metadata": {},
   "source": [
    "# Loading in Data 2.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "express-lesson",
   "metadata": {},
   "outputs": [],
   "source": [
    "base_dir = r'C:\\Users\\jaych\\Documents\\Senior Design\\output'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "alien-might",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Directories for our training,\n",
    "# validation and test splits\n",
    "train_dir = os.path.join(base_dir, 'train')\n",
    "validation_dir = os.path.join(base_dir, 'val')\n",
    "test_dir = os.path.join(base_dir, 'test')\n",
    "\n",
    "# Directory with our training male pictures\n",
    "train_covid_dir = os.path.join(train_dir, 'CT_COVID')\n",
    "\n",
    "# Directory with our training female pictures\n",
    "train_non_dir = os.path.join(train_dir, 'CT_NonCOVID')\n",
    "\n",
    "# Directory with our validation male pictures\n",
    "validation_covid_dir = os.path.join(validation_dir, 'CT_COVID')\n",
    "\n",
    "# Directory with our validation female pictures\n",
    "validation_non_dir = os.path.join(validation_dir, 'CT_NonCOVID')\n",
    "\n",
    "# Directory with our validation male pictures\n",
    "test_covid_dir = os.path.join(test_dir, 'CT_COVID')\n",
    "\n",
    "# Directory with our validation female pictures\n",
    "test_non_dir = os.path.join(test_dir, 'CT_NonCOVID')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "annual-boundary",
   "metadata": {},
   "source": [
    "Fairly Balanced in the Training Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "lasting-persian",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total training covid: 279\n"
     ]
    }
   ],
   "source": [
    "print('total training covid:', len(os.listdir(train_covid_dir)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "assisted-lincoln",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total training non-covid: 317\n"
     ]
    }
   ],
   "source": [
    "print('total training non-covid:', len(os.listdir(train_non_dir)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "animated-litigation",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total validation covid: 34\n"
     ]
    }
   ],
   "source": [
    "print('total validation covid:', len(os.listdir(validation_covid_dir)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "listed-crystal",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total validation non-covid: 39\n"
     ]
    }
   ],
   "source": [
    "print('total validation non-covid:', len(os.listdir(validation_non_dir)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "stupid-cemetery",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "competitive-criticism",
   "metadata": {},
   "source": [
    "# Data Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "understanding-english",
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "\n",
    "# # All images will be rescaled by 1./255\n",
    "# train_datagen = ImageDataGenerator(rescale=1./255)\n",
    "# test_datagen = ImageDataGenerator(rescale=1./255)\n",
    "\n",
    "# train_generator = train_datagen.flow_from_directory(\n",
    "#         # This is the target directory\n",
    "#         train_dir,\n",
    "#         # All images will be resized to 150x150\n",
    "#         target_size=(218, 178),\n",
    "#         batch_size=20,\n",
    "#         # Since we use binary_crossentropy loss, we need binary labels\n",
    "#         class_mode='binary')\n",
    "\n",
    "# validation_generator = test_datagen.flow_from_directory(\n",
    "#         validation_dir,\n",
    "#         target_size=(218, 178),\n",
    "#         batch_size=20,\n",
    "#         class_mode='binary')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "preliminary-serbia",
   "metadata": {},
   "outputs": [],
   "source": [
    "# for data_batch, labels_batch in train_generator:\n",
    "#     print('data batch shape:', data_batch.shape)\n",
    "#     print('labels batch shape:', labels_batch.shape)\n",
    "#     break"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "recognized-thickness",
   "metadata": {},
   "source": [
    "# Building the Model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "respiratory-source",
   "metadata": {},
   "source": [
    "We will be using a VGG-16 pre-trained model as the base for our image classification. I am keeping the base of the model, but am setting include_top to be False so that we can use our own densely connected classifier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "regular-rescue",
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.applications import VGG16\n",
    "\n",
    "conv_base = VGG16(weights='imagenet',\n",
    "                  include_top=False,\n",
    "                  input_shape=(218, 178, 3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "aging-advocacy",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"vgg16\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         [(None, 218, 178, 3)]     0         \n",
      "_________________________________________________________________\n",
      "block1_conv1 (Conv2D)        (None, 218, 178, 64)      1792      \n",
      "_________________________________________________________________\n",
      "block1_conv2 (Conv2D)        (None, 218, 178, 64)      36928     \n",
      "_________________________________________________________________\n",
      "block1_pool (MaxPooling2D)   (None, 109, 89, 64)       0         \n",
      "_________________________________________________________________\n",
      "block2_conv1 (Conv2D)        (None, 109, 89, 128)      73856     \n",
      "_________________________________________________________________\n",
      "block2_conv2 (Conv2D)        (None, 109, 89, 128)      147584    \n",
      "_________________________________________________________________\n",
      "block2_pool (MaxPooling2D)   (None, 54, 44, 128)       0         \n",
      "_________________________________________________________________\n",
      "block3_conv1 (Conv2D)        (None, 54, 44, 256)       295168    \n",
      "_________________________________________________________________\n",
      "block3_conv2 (Conv2D)        (None, 54, 44, 256)       590080    \n",
      "_________________________________________________________________\n",
      "block3_conv3 (Conv2D)        (None, 54, 44, 256)       590080    \n",
      "_________________________________________________________________\n",
      "block3_pool (MaxPooling2D)   (None, 27, 22, 256)       0         \n",
      "_________________________________________________________________\n",
      "block4_conv1 (Conv2D)        (None, 27, 22, 512)       1180160   \n",
      "_________________________________________________________________\n",
      "block4_conv2 (Conv2D)        (None, 27, 22, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block4_conv3 (Conv2D)        (None, 27, 22, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block4_pool (MaxPooling2D)   (None, 13, 11, 512)       0         \n",
      "_________________________________________________________________\n",
      "block5_conv1 (Conv2D)        (None, 13, 11, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block5_conv2 (Conv2D)        (None, 13, 11, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block5_conv3 (Conv2D)        (None, 13, 11, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block5_pool (MaxPooling2D)   (None, 6, 5, 512)         0         \n",
      "=================================================================\n",
      "Total params: 14,714,688\n",
      "Trainable params: 14,714,688\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "conv_base.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "small-gazette",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 596 images belonging to 2 classes.\n",
      "Found 73 images belonging to 2 classes.\n",
      "Found 77 images belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "datagen = ImageDataGenerator(rescale=1./255)\n",
    "batch_size = 20\n",
    "\n",
    "def extract_features(directory, sample_count):\n",
    "    features = np.zeros(shape=(sample_count, 6, 5, 512))\n",
    "    labels = np.zeros(shape=(sample_count))\n",
    "    generator = datagen.flow_from_directory(\n",
    "        directory,\n",
    "        target_size=(218, 178),\n",
    "        batch_size=batch_size,\n",
    "        class_mode='binary')\n",
    "    i = 0\n",
    "    for inputs_batch, labels_batch in generator:\n",
    "        features_batch = conv_base.predict(inputs_batch)\n",
    "        features[i * batch_size : (i + 1) * batch_size] = features_batch\n",
    "        labels[i * batch_size : (i + 1) * batch_size] = labels_batch\n",
    "        i += 1\n",
    "        if i * batch_size >= sample_count:\n",
    "            # Note that since generators yield data indefinitely in a loop,\n",
    "            # we must `break` after every image has been seen once.\n",
    "            break\n",
    "    return features, labels\n",
    "\n",
    "train_features, train_labels = extract_features(train_dir, 596)\n",
    "validation_features, validation_labels = extract_features(validation_dir, 73)\n",
    "test_features, test_labels = extract_features(test_dir, 77)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "mechanical-driving",
   "metadata": {},
   "source": [
    "flatten the features so they can be processed in a densely connected classifier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "organic-seating",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_features = np.reshape(train_features, (596, 6 * 5 * 512))\n",
    "validation_features = np.reshape(validation_features, (73, 6 * 5 * 512))\n",
    "test_features = np.reshape(test_features, (77, 6 * 5 * 512))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "frozen-dynamics",
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras import models\n",
    "from keras import layers\n",
    "\n",
    "model = models.Sequential()\n",
    "model.add(conv_base)\n",
    "model.add(layers.Flatten())\n",
    "model.add(layers.Dense(256, activation='relu'))\n",
    "model.add(layers.Dense(1, activation='sigmoid'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "improved-snake",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "vgg16 (Functional)           (None, 6, 5, 512)         14714688  \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (None, 15360)             0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 256)               3932416   \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 1)                 257       \n",
      "=================================================================\n",
      "Total params: 18,647,361\n",
      "Trainable params: 18,647,361\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "limited-refund",
   "metadata": {},
   "source": [
    "Before we compile and train our model, a very important thing to do is to freeze the convolutional base. \"Freezing\" a layer or set of \n",
    "layers means preventing their weights from getting updated during training. If we don't do this, then the representations that were \n",
    "previously learned by the convolutional base would get modified during training. Since the `Dense` layers on top are randomly initialized, \n",
    "very large weight updates would be propagated through the network, effectively destroying the representations previously learned."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "elect-thanksgiving",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This is the number of trainable weights before freezing the conv base: 30\n"
     ]
    }
   ],
   "source": [
    "print('This is the number of trainable weights '\n",
    "      'before freezing the conv base:', len(model.trainable_weights))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "chronic-species",
   "metadata": {},
   "outputs": [],
   "source": [
    "conv_base.trainable = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "enormous-marking",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This is the number of trainable weights before freezing the conv base: 4\n"
     ]
    }
   ],
   "source": [
    "print('This is the number of trainable weights '\n",
    "      'before freezing the conv base:', len(model.trainable_weights))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "surgical-aquarium",
   "metadata": {},
   "source": [
    "Two weights in each dense layer, the bias and the weight vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "legendary-analysis",
   "metadata": {},
   "outputs": [],
   "source": [
    " from keras import optimizers\n",
    "\n",
    "# train_generator = train_datagen.flow_from_directory(\n",
    "#         # This is the target directory\n",
    "#         train_dir,\n",
    "#         # All images will be resized to 150x150\n",
    "#         target_size=(218, 178),\n",
    "#         batch_size=20,\n",
    "#         # Since we use binary_crossentropy loss, we need binary labels\n",
    "#         class_mode='binary')\n",
    "\n",
    "# validation_generator = test_datagen.flow_from_directory(\n",
    "#         validation_dir,\n",
    "#         target_size=(218, 178),\n",
    "#         batch_size=20,\n",
    "#         class_mode='binary')\n",
    "\n",
    "# model.compile(loss='binary_crossentropy',\n",
    "#               optimizer=optimizers.RMSprop(lr=2e-5),\n",
    "#               metrics=['acc'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "prescription-sunglasses",
   "metadata": {},
   "outputs": [],
   "source": [
    "# print(596//batch_size, 73//batch_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "smaller-opportunity",
   "metadata": {},
   "outputs": [],
   "source": [
    "# history = model.fit(\n",
    "#       train_generator,\n",
    "#       steps_per_epoch=29,\n",
    "#       epochs=35,\n",
    "#       validation_data=validation_generator,\n",
    "#       validation_steps=3,\n",
    "#       verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "rocky-compensation",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[name: \"/device:CPU:0\"\n",
      "device_type: \"CPU\"\n",
      "memory_limit: 268435456\n",
      "locality {\n",
      "}\n",
      "incarnation: 10009789183137472670\n",
      ", name: \"/device:GPU:0\"\n",
      "device_type: \"GPU\"\n",
      "memory_limit: 6910041152\n",
      "locality {\n",
      "  bus_id: 1\n",
      "  links {\n",
      "  }\n",
      "}\n",
      "incarnation: 4216352920101208748\n",
      "physical_device_desc: \"device: 0, name: GeForce RTX 3070, pci bus id: 0000:09:00.0, compute capability: 8.6\"\n",
      "]\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.python.client import device_lib\n",
    "print(device_lib.list_local_devices())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "another-framework",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Device mapping:\n",
      "/job:localhost/replica:0/task:0/device:GPU:0 -> device: 0, name: GeForce RTX 3070, pci bus id: 0000:09:00.0, compute capability: 8.6\n",
      "\n"
     ]
    }
   ],
   "source": [
    "sess = tf.compat.v1.Session(config=tf.compat.v1.ConfigProto(log_device_placement=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "reserved-arthritis",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import matplotlib.pyplot as plt\n",
    "\n",
    "# acc = history.history['acc']\n",
    "# val_acc = history.history['val_acc']\n",
    "# loss = history.history['loss']\n",
    "# val_loss = history.history['val_loss']\n",
    "\n",
    "# epochs = range(len(acc))\n",
    "\n",
    "# plt.plot(epochs, acc, 'bo', label='Training acc')\n",
    "# plt.plot(epochs, val_acc, 'b', label='Validation acc')\n",
    "# plt.title('Training and validation accuracy')\n",
    "# plt.legend()\n",
    "\n",
    "# plt.figure()\n",
    "\n",
    "# plt.plot(epochs, loss, 'bo', label='Training loss')\n",
    "# plt.plot(epochs, val_loss, 'b', label='Validation loss')\n",
    "# plt.title('Training and validation loss')\n",
    "# plt.legend()\n",
    "\n",
    "# plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "emotional-front",
   "metadata": {},
   "source": [
    "Lots of overfitting even from the start, data augmentation can help fix this. But it requires GPU usage in order to be feasible."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "psychological-nepal",
   "metadata": {},
   "source": [
    "# Data Preprocessing with Augmentation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "engaging-grocery",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 596 images belonging to 2 classes.\n",
      "Found 73 images belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "\n",
    "train_datagen = ImageDataGenerator(\n",
    "      rescale=1./255,\n",
    "      rotation_range=40,\n",
    "      width_shift_range=0.2,# data augmentation transformations\n",
    "      height_shift_range=0.2,\n",
    "      shear_range=0.2,\n",
    "      zoom_range=0.2,\n",
    "      horizontal_flip=True,\n",
    "      fill_mode='nearest')\n",
    "\n",
    "# Note that the validation data should not be augmented!\n",
    "test_datagen = ImageDataGenerator(rescale=1./255)\n",
    "\n",
    "train_generator = train_datagen.flow_from_directory(\n",
    "        # This is the target directory\n",
    "        train_dir,\n",
    "        # All images will be resized to 218x178\n",
    "        target_size=(218, 178),\n",
    "        batch_size=20,\n",
    "        # Since we use binary_crossentropy loss, we need binary labels\n",
    "        class_mode='binary')\n",
    "\n",
    "validation_generator = test_datagen.flow_from_directory(\n",
    "        validation_dir,\n",
    "        target_size=(218, 178),\n",
    "        batch_size=20,\n",
    "        class_mode='binary')\n",
    "\n",
    "model.compile(loss='binary_crossentropy',\n",
    "              optimizer=optimizers.RMSprop(lr=2e-5),\n",
    "              metrics=['acc'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "exceptional-parade",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data batch shape: (20, 218, 178, 3)\n",
      "labels batch shape: (20,)\n"
     ]
    }
   ],
   "source": [
    "for data_batch, labels_batch in train_generator:\n",
    "    print('data batch shape:', data_batch.shape)\n",
    "    print('labels batch shape:', labels_batch.shape)\n",
    "    break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "western-stranger",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.preprocessing.image.DirectoryIterator at 0x28359d638b0>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_generator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "baking-armor",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "29/29 [==============================] - 6s 182ms/step - loss: 0.7200 - acc: 0.5112 - val_loss: 0.6353 - val_acc: 0.6667\n",
      "Epoch 2/30\n",
      "29/29 [==============================] - 5s 172ms/step - loss: 0.6241 - acc: 0.6435 - val_loss: 0.5594 - val_acc: 0.6667\n",
      "Epoch 3/30\n",
      "29/29 [==============================] - 5s 171ms/step - loss: 0.6239 - acc: 0.6879 - val_loss: 0.5561 - val_acc: 0.6833\n",
      "Epoch 4/30\n",
      "29/29 [==============================] - 5s 172ms/step - loss: 0.5846 - acc: 0.6973 - val_loss: 0.6462 - val_acc: 0.6500\n",
      "Epoch 5/30\n",
      "29/29 [==============================] - 5s 170ms/step - loss: 0.5706 - acc: 0.6893 - val_loss: 0.5446 - val_acc: 0.7000\n",
      "Epoch 6/30\n",
      "29/29 [==============================] - 5s 172ms/step - loss: 0.5354 - acc: 0.7523 - val_loss: 0.5141 - val_acc: 0.7500\n",
      "Epoch 7/30\n",
      "29/29 [==============================] - 5s 172ms/step - loss: 0.5456 - acc: 0.7270 - val_loss: 0.5143 - val_acc: 0.7833\n",
      "Epoch 8/30\n",
      "29/29 [==============================] - 5s 170ms/step - loss: 0.5685 - acc: 0.7323 - val_loss: 0.4981 - val_acc: 0.7500\n",
      "Epoch 9/30\n",
      "29/29 [==============================] - 5s 173ms/step - loss: 0.5514 - acc: 0.7144 - val_loss: 0.5160 - val_acc: 0.7167\n",
      "Epoch 10/30\n",
      "29/29 [==============================] - 5s 177ms/step - loss: 0.5189 - acc: 0.7434 - val_loss: 0.4775 - val_acc: 0.7500\n",
      "Epoch 11/30\n",
      "29/29 [==============================] - 5s 172ms/step - loss: 0.5023 - acc: 0.7701 - val_loss: 0.4596 - val_acc: 0.7667\n",
      "Epoch 12/30\n",
      "29/29 [==============================] - 5s 173ms/step - loss: 0.4970 - acc: 0.7871 - val_loss: 0.4886 - val_acc: 0.7500\n",
      "Epoch 13/30\n",
      "29/29 [==============================] - 5s 174ms/step - loss: 0.4825 - acc: 0.7920 - val_loss: 0.4593 - val_acc: 0.8000\n",
      "Epoch 14/30\n",
      "29/29 [==============================] - 5s 171ms/step - loss: 0.4801 - acc: 0.7709 - val_loss: 0.4556 - val_acc: 0.7833\n",
      "Epoch 15/30\n",
      "29/29 [==============================] - 5s 172ms/step - loss: 0.5159 - acc: 0.7390 - val_loss: 0.4586 - val_acc: 0.8000\n",
      "Epoch 16/30\n",
      "29/29 [==============================] - 5s 173ms/step - loss: 0.5095 - acc: 0.7580 - val_loss: 0.4778 - val_acc: 0.7500\n",
      "Epoch 17/30\n",
      "29/29 [==============================] - 5s 172ms/step - loss: 0.5000 - acc: 0.7435 - val_loss: 0.4974 - val_acc: 0.7333\n",
      "Epoch 18/30\n",
      "29/29 [==============================] - 5s 172ms/step - loss: 0.4824 - acc: 0.7718 - val_loss: 0.4858 - val_acc: 0.7833\n",
      "Epoch 19/30\n",
      "29/29 [==============================] - 5s 173ms/step - loss: 0.4989 - acc: 0.7631 - val_loss: 0.4600 - val_acc: 0.7833\n",
      "Epoch 20/30\n",
      "29/29 [==============================] - 5s 172ms/step - loss: 0.4900 - acc: 0.7419 - val_loss: 0.4774 - val_acc: 0.7333\n",
      "Epoch 21/30\n",
      "29/29 [==============================] - 5s 172ms/step - loss: 0.4416 - acc: 0.7792 - val_loss: 0.4740 - val_acc: 0.8000\n",
      "Epoch 22/30\n",
      "29/29 [==============================] - 5s 172ms/step - loss: 0.4249 - acc: 0.8063 - val_loss: 0.4523 - val_acc: 0.8000\n",
      "Epoch 23/30\n",
      "29/29 [==============================] - 5s 173ms/step - loss: 0.4380 - acc: 0.7973 - val_loss: 0.4799 - val_acc: 0.7500\n",
      "Epoch 24/30\n",
      "29/29 [==============================] - 5s 172ms/step - loss: 0.4183 - acc: 0.8223 - val_loss: 0.4384 - val_acc: 0.8167\n",
      "Epoch 25/30\n",
      "29/29 [==============================] - 5s 173ms/step - loss: 0.4468 - acc: 0.7928 - val_loss: 0.5173 - val_acc: 0.7500\n",
      "Epoch 26/30\n",
      "29/29 [==============================] - 5s 171ms/step - loss: 0.4629 - acc: 0.7985 - val_loss: 0.4874 - val_acc: 0.7333\n",
      "Epoch 27/30\n",
      "29/29 [==============================] - 5s 172ms/step - loss: 0.4193 - acc: 0.8165 - val_loss: 0.4212 - val_acc: 0.8500\n",
      "Epoch 28/30\n",
      "29/29 [==============================] - 5s 171ms/step - loss: 0.4375 - acc: 0.7688 - val_loss: 0.4807 - val_acc: 0.7000\n",
      "Epoch 29/30\n",
      "29/29 [==============================] - 5s 172ms/step - loss: 0.4335 - acc: 0.7780 - val_loss: 0.4413 - val_acc: 0.8000\n",
      "Epoch 30/30\n",
      "29/29 [==============================] - 5s 172ms/step - loss: 0.4693 - acc: 0.7795 - val_loss: 0.4325 - val_acc: 0.8167\n"
     ]
    }
   ],
   "source": [
    "import random\n",
    "\n",
    "random.seed(123)\n",
    "\n",
    "history = model.fit(\n",
    "      train_generator,\n",
    "      steps_per_epoch=29,\n",
    "      epochs=30,\n",
    "      validation_data=validation_generator,\n",
    "      validation_steps=3,\n",
    "      verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "shared-jacob",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save('covid_model1.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "nutritional-baghdad",
   "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"
    },
    {
     "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": [
    "acc = history.history['acc']\n",
    "val_acc = history.history['val_acc']\n",
    "loss = history.history['loss']\n",
    "val_loss = history.history['val_loss']\n",
    "\n",
    "epochs = range(len(acc))\n",
    "\n",
    "plt.plot(epochs, acc, 'bo', label='Training acc')\n",
    "plt.plot(epochs, val_acc, 'b', label='Validation acc')\n",
    "plt.title('Training and validation accuracy')\n",
    "plt.legend()\n",
    "\n",
    "plt.figure()\n",
    "\n",
    "plt.plot(epochs, loss, 'bo', label='Training loss')\n",
    "plt.plot(epochs, val_loss, 'b', label='Validation loss')\n",
    "plt.title('Training and validation loss')\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "distinguished-nature",
   "metadata": {},
   "source": [
    "## Fine-tuning\n",
    "\n",
    "Another widely used technique for model reuse, complementary to feature extraction, is _fine-tuning_. \n",
    "Fine-tuning consists in unfreezing a few of the top layers \n",
    "of a frozen model base used for feature extraction, and jointly training both the newly added part of the model (in our case, the \n",
    "fully-connected classifier) and these top layers. This is called \"fine-tuning\" because it slightly adjusts the more abstract \n",
    "representations of the model being reused, in order to make them more relevant for the problem at hand.\n",
    "\n",
    "![fine-tuning VGG16](https://s3.amazonaws.com/book.keras.io/img/ch5/vgg16_fine_tuning.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "skilled-pickup",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"vgg16\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         [(None, 218, 178, 3)]     0         \n",
      "_________________________________________________________________\n",
      "block1_conv1 (Conv2D)        (None, 218, 178, 64)      1792      \n",
      "_________________________________________________________________\n",
      "block1_conv2 (Conv2D)        (None, 218, 178, 64)      36928     \n",
      "_________________________________________________________________\n",
      "block1_pool (MaxPooling2D)   (None, 109, 89, 64)       0         \n",
      "_________________________________________________________________\n",
      "block2_conv1 (Conv2D)        (None, 109, 89, 128)      73856     \n",
      "_________________________________________________________________\n",
      "block2_conv2 (Conv2D)        (None, 109, 89, 128)      147584    \n",
      "_________________________________________________________________\n",
      "block2_pool (MaxPooling2D)   (None, 54, 44, 128)       0         \n",
      "_________________________________________________________________\n",
      "block3_conv1 (Conv2D)        (None, 54, 44, 256)       295168    \n",
      "_________________________________________________________________\n",
      "block3_conv2 (Conv2D)        (None, 54, 44, 256)       590080    \n",
      "_________________________________________________________________\n",
      "block3_conv3 (Conv2D)        (None, 54, 44, 256)       590080    \n",
      "_________________________________________________________________\n",
      "block3_pool (MaxPooling2D)   (None, 27, 22, 256)       0         \n",
      "_________________________________________________________________\n",
      "block4_conv1 (Conv2D)        (None, 27, 22, 512)       1180160   \n",
      "_________________________________________________________________\n",
      "block4_conv2 (Conv2D)        (None, 27, 22, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block4_conv3 (Conv2D)        (None, 27, 22, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block4_pool (MaxPooling2D)   (None, 13, 11, 512)       0         \n",
      "_________________________________________________________________\n",
      "block5_conv1 (Conv2D)        (None, 13, 11, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block5_conv2 (Conv2D)        (None, 13, 11, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block5_conv3 (Conv2D)        (None, 13, 11, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block5_pool (MaxPooling2D)   (None, 6, 5, 512)         0         \n",
      "=================================================================\n",
      "Total params: 14,714,688\n",
      "Trainable params: 0\n",
      "Non-trainable params: 14,714,688\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "conv_base.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "excellent-windsor",
   "metadata": {},
   "source": [
    "\n",
    "We will fine-tune the last 3 convolutional layers, which means that all layers up until `block4_pool` should be frozen, and the layers \n",
    "`block5_conv1`, `block5_conv2` and `block5_conv3` should be trainable.\n",
    "\n",
    "Why not fine-tune more layers? Why not fine-tune the entire convolutional base? We could. However, we need to consider that:\n",
    "\n",
    "* Earlier layers in the convolutional base encode more generic, reusable features, while layers higher up encode more specialized features. It is \n",
    "more useful to fine-tune the more specialized features, as these are the ones that need to be repurposed on our new problem. There would \n",
    "be fast-decreasing returns in fine-tuning lower layers.\n",
    "* The more parameters we are training, the more we are at risk of overfitting. The convolutional base has 15M parameters, so it would be \n",
    "risky to attempt to train it on our small dataset.\n",
    "\n",
    "Thus, in our situation, it is a good strategy to only fine-tune the top 2 to 3 layers in the convolutional base.\n",
    "\n",
    "Let's set this up, starting from where we left off in the previous example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "romantic-blackberry",
   "metadata": {},
   "outputs": [],
   "source": [
    "conv_base.trainable = True\n",
    "\n",
    "set_trainable = False\n",
    "for layer in conv_base.layers:\n",
    "    if layer.name == 'block5_conv1':\n",
    "        set_trainable = True\n",
    "    if set_trainable:\n",
    "        layer.trainable = True\n",
    "    else:\n",
    "        layer.trainable = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "thermal-porcelain",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\jaych\\anaconda3\\envs\\SDP-GPU\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\training.py:1844: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.\n",
      "  warnings.warn('`Model.fit_generator` is deprecated and '\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "29/29 [==============================] - 11s 218ms/step - loss: 0.4142 - acc: 0.7980 - val_loss: 0.4281 - val_acc: 0.8000\n",
      "Epoch 2/50\n",
      "29/29 [==============================] - 5s 174ms/step - loss: 0.4081 - acc: 0.8046 - val_loss: 0.4504 - val_acc: 0.8000\n",
      "Epoch 3/50\n",
      "29/29 [==============================] - 5s 174ms/step - loss: 0.3876 - acc: 0.8379 - val_loss: 0.5194 - val_acc: 0.7167\n",
      "Epoch 4/50\n",
      "29/29 [==============================] - 5s 178ms/step - loss: 0.4223 - acc: 0.8063 - val_loss: 0.4536 - val_acc: 0.7833\n",
      "Epoch 5/50\n",
      "29/29 [==============================] - 5s 176ms/step - loss: 0.3860 - acc: 0.8193 - val_loss: 0.4335 - val_acc: 0.7500\n",
      "Epoch 6/50\n",
      "29/29 [==============================] - 5s 174ms/step - loss: 0.3687 - acc: 0.8472 - val_loss: 0.4138 - val_acc: 0.8000\n",
      "Epoch 7/50\n",
      "29/29 [==============================] - 5s 172ms/step - loss: 0.3575 - acc: 0.8418 - val_loss: 0.3892 - val_acc: 0.8500\n",
      "Epoch 8/50\n",
      "29/29 [==============================] - 5s 177ms/step - loss: 0.3418 - acc: 0.8633 - val_loss: 0.5656 - val_acc: 0.7667\n",
      "Epoch 9/50\n",
      "29/29 [==============================] - 5s 174ms/step - loss: 0.3603 - acc: 0.8489 - val_loss: 0.4100 - val_acc: 0.8333\n",
      "Epoch 10/50\n",
      "29/29 [==============================] - 5s 173ms/step - loss: 0.3357 - acc: 0.8319 - val_loss: 0.4074 - val_acc: 0.8000\n",
      "Epoch 11/50\n",
      "29/29 [==============================] - 5s 179ms/step - loss: 0.2893 - acc: 0.8637 - val_loss: 0.4802 - val_acc: 0.7500\n",
      "Epoch 12/50\n",
      "29/29 [==============================] - 5s 176ms/step - loss: 0.3006 - acc: 0.8605 - val_loss: 0.4265 - val_acc: 0.8000\n",
      "Epoch 13/50\n",
      "29/29 [==============================] - 5s 175ms/step - loss: 0.3555 - acc: 0.8261 - val_loss: 0.3927 - val_acc: 0.8167\n",
      "Epoch 14/50\n",
      "29/29 [==============================] - 5s 174ms/step - loss: 0.3609 - acc: 0.8393 - val_loss: 0.4127 - val_acc: 0.8000\n",
      "Epoch 15/50\n",
      "29/29 [==============================] - 5s 175ms/step - loss: 0.3061 - acc: 0.8687 - val_loss: 0.3916 - val_acc: 0.8667\n",
      "Epoch 16/50\n",
      "29/29 [==============================] - 5s 176ms/step - loss: 0.2545 - acc: 0.8935 - val_loss: 0.4475 - val_acc: 0.7667\n",
      "Epoch 17/50\n",
      "29/29 [==============================] - 5s 173ms/step - loss: 0.2599 - acc: 0.8939 - val_loss: 0.4097 - val_acc: 0.8000\n",
      "Epoch 18/50\n",
      "29/29 [==============================] - 5s 176ms/step - loss: 0.3367 - acc: 0.8597 - val_loss: 0.4205 - val_acc: 0.8167\n",
      "Epoch 19/50\n",
      "29/29 [==============================] - 5s 174ms/step - loss: 0.2781 - acc: 0.8748 - val_loss: 0.4452 - val_acc: 0.8500\n",
      "Epoch 20/50\n",
      "29/29 [==============================] - 5s 171ms/step - loss: 0.2929 - acc: 0.8455 - val_loss: 0.4453 - val_acc: 0.8000\n",
      "Epoch 21/50\n",
      "29/29 [==============================] - 5s 172ms/step - loss: 0.2763 - acc: 0.8794 - val_loss: 0.4106 - val_acc: 0.8333\n",
      "Epoch 22/50\n",
      "29/29 [==============================] - 5s 171ms/step - loss: 0.2996 - acc: 0.8699 - val_loss: 0.3621 - val_acc: 0.8167\n",
      "Epoch 23/50\n",
      "29/29 [==============================] - 5s 171ms/step - loss: 0.2572 - acc: 0.8813 - val_loss: 0.6007 - val_acc: 0.7333\n",
      "Epoch 24/50\n",
      "29/29 [==============================] - 5s 171ms/step - loss: 0.2595 - acc: 0.8779 - val_loss: 0.4118 - val_acc: 0.7833\n",
      "Epoch 25/50\n",
      "29/29 [==============================] - 5s 172ms/step - loss: 0.2492 - acc: 0.8883 - val_loss: 0.3396 - val_acc: 0.8167\n",
      "Epoch 26/50\n",
      "29/29 [==============================] - 5s 171ms/step - loss: 0.2444 - acc: 0.8887 - val_loss: 0.4979 - val_acc: 0.8000\n",
      "Epoch 27/50\n",
      "29/29 [==============================] - 5s 171ms/step - loss: 0.2619 - acc: 0.8787 - val_loss: 0.3875 - val_acc: 0.8333\n",
      "Epoch 28/50\n",
      "29/29 [==============================] - 5s 171ms/step - loss: 0.2518 - acc: 0.8770 - val_loss: 0.4676 - val_acc: 0.8000\n",
      "Epoch 29/50\n",
      "29/29 [==============================] - 5s 175ms/step - loss: 0.2266 - acc: 0.8929 - val_loss: 0.3966 - val_acc: 0.8167\n",
      "Epoch 30/50\n",
      "29/29 [==============================] - 5s 175ms/step - loss: 0.2043 - acc: 0.9102 - val_loss: 0.5099 - val_acc: 0.8167\n",
      "Epoch 31/50\n",
      "29/29 [==============================] - 5s 173ms/step - loss: 0.2443 - acc: 0.8717 - val_loss: 0.4444 - val_acc: 0.8333\n",
      "Epoch 32/50\n",
      "29/29 [==============================] - 5s 173ms/step - loss: 0.2290 - acc: 0.9006 - val_loss: 0.3245 - val_acc: 0.8333\n",
      "Epoch 33/50\n",
      "29/29 [==============================] - 5s 172ms/step - loss: 0.1786 - acc: 0.9424 - val_loss: 0.3532 - val_acc: 0.8500\n",
      "Epoch 34/50\n",
      "29/29 [==============================] - 5s 177ms/step - loss: 0.2153 - acc: 0.9151 - val_loss: 0.4316 - val_acc: 0.8167\n",
      "Epoch 35/50\n",
      "29/29 [==============================] - 5s 171ms/step - loss: 0.1965 - acc: 0.9361 - val_loss: 0.4256 - val_acc: 0.8500\n",
      "Epoch 36/50\n",
      "29/29 [==============================] - 5s 171ms/step - loss: 0.2662 - acc: 0.8737 - val_loss: 0.3961 - val_acc: 0.8167\n",
      "Epoch 37/50\n",
      "29/29 [==============================] - 5s 174ms/step - loss: 0.1835 - acc: 0.9193 - val_loss: 0.4914 - val_acc: 0.8333\n",
      "Epoch 38/50\n",
      "29/29 [==============================] - 5s 171ms/step - loss: 0.1771 - acc: 0.9316 - val_loss: 0.6900 - val_acc: 0.8000\n",
      "Epoch 39/50\n",
      "29/29 [==============================] - 5s 170ms/step - loss: 0.1932 - acc: 0.9201 - val_loss: 0.4495 - val_acc: 0.7833\n",
      "Epoch 40/50\n",
      "29/29 [==============================] - 5s 170ms/step - loss: 0.2163 - acc: 0.9058 - val_loss: 0.5080 - val_acc: 0.8667\n",
      "Epoch 41/50\n",
      "29/29 [==============================] - 5s 171ms/step - loss: 0.2098 - acc: 0.9262 - val_loss: 0.3225 - val_acc: 0.8500\n",
      "Epoch 42/50\n",
      "29/29 [==============================] - 5s 173ms/step - loss: 0.1621 - acc: 0.9353 - val_loss: 0.4470 - val_acc: 0.8833\n",
      "Epoch 43/50\n",
      "29/29 [==============================] - 5s 170ms/step - loss: 0.1876 - acc: 0.9185 - val_loss: 0.2944 - val_acc: 0.8333\n",
      "Epoch 44/50\n",
      "29/29 [==============================] - 5s 171ms/step - loss: 0.1272 - acc: 0.9601 - val_loss: 0.3339 - val_acc: 0.8333\n",
      "Epoch 45/50\n",
      "29/29 [==============================] - 5s 173ms/step - loss: 0.1609 - acc: 0.9323 - val_loss: 0.3098 - val_acc: 0.8500\n",
      "Epoch 46/50\n",
      "29/29 [==============================] - 5s 170ms/step - loss: 0.1500 - acc: 0.9472 - val_loss: 0.4263 - val_acc: 0.8333\n",
      "Epoch 47/50\n",
      "29/29 [==============================] - 5s 172ms/step - loss: 0.1740 - acc: 0.9311 - val_loss: 0.3486 - val_acc: 0.8333\n",
      "Epoch 48/50\n",
      "29/29 [==============================] - 5s 172ms/step - loss: 0.1576 - acc: 0.9395 - val_loss: 0.5968 - val_acc: 0.8333\n",
      "Epoch 49/50\n",
      "29/29 [==============================] - 5s 170ms/step - loss: 0.2000 - acc: 0.9099 - val_loss: 0.4477 - val_acc: 0.8167\n",
      "Epoch 50/50\n",
      "29/29 [==============================] - 5s 172ms/step - loss: 0.1385 - acc: 0.9443 - val_loss: 0.3172 - val_acc: 0.8667\n"
     ]
    }
   ],
   "source": [
    "model.compile(loss='binary_crossentropy',\n",
    "              optimizer=optimizers.RMSprop(lr=1e-5),\n",
    "              metrics=['acc'])\n",
    "\n",
    "history = model.fit_generator(\n",
    "      train_generator,\n",
    "      steps_per_epoch=29,\n",
    "      epochs=50,\n",
    "      validation_data=validation_generator,\n",
    "      validation_steps=3,\n",
    "    verbose = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "liquid-muslim",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save('covid_fine_tuned.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "julian-impossible",
   "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"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAEICAYAAABPgw/pAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAABHBklEQVR4nO2deZgU5bX/v4dhGYadAUEZdkEEQZYBRAKKaMKioGhU5EaJC+5rEsWYCDGX5PoLN1e5URPEqBEUl0REwYtRICKKYRFQFBAQdHDYhn1n4Pz+OP0yRU9Vd1V3VVcv5/M88/R0dXXVW93V3zr1fc97XmJmKIqiKJlPlbAboCiKoviDCrqiKEqWoIKuKIqSJaigK4qiZAkq6IqiKFmCCrqiKEqWoIKu2EJE7xLRDX6vGyZEtJGILg5gu0xEZ0b+/zMR/drNugnsZxQRvZdoO2Ns90IiKvF7u0rqqRp2AxT/IKL9lqcFAI4AOB55fiszT3O7LWYeHMS62Q4z3+bHdoioFYBvAFRj5vLItqcBcP0dKrmHCnoWwcy1zf9EtBHAzcz8fvR6RFTViISiKNmDWi45gLmlJqKHiGgLgOeJqAERvUNE24loV+T/Ist75hPRzZH/RxPRR0Q0MbLuN0Q0OMF1WxPRh0S0j4jeJ6KniGiqQ7vdtPG3RLQwsr33iKiR5fWfENEmIiojokdifD69iWgLEeVZll1BRCsj//ciok+IaDcRlRLRn4iousO2XiCi/7Q8/0XkPd8T0Y1R6w4los+IaC8RfUdE4y0vfxh53E1E+4moj/lsLe8/n4gWE9GeyOP5bj+bWBDR2ZH37yaiVUQ0zPLaECL6MrLNzUT088jyRpHvZzcR7SSiBUSk+pJi9APPHZoCaAigJYAxkO/++cjzFgAOAfhTjPf3BrAGQCMA/w/Ac0RECaz7MoB/AygEMB7AT2Ls000brwPwUwCnAagOwAhMRwDPRLZ/RmR/RbCBmT8FcADARVHbfTny/3EA90eOpw+AgQDuiNFuRNowKNKeSwC0AxDt3x8AcD2A+gCGAridiC6PvNY/8lifmWsz8ydR224IYBaASZFj+yOAWURUGHUMlT6bOG2uBuBtAO9F3nc3gGlEdFZklecg9l0dAOcAmBtZ/jMAJQAaA2gC4JcAtK5IilFBzx1OABjHzEeY+RAzlzHz35n5IDPvAzABwAUx3r+JmZ9l5uMAXgRwOuSH63pdImoBoCeAR5n5KDN/BGCm0w5dtvF5Zl7LzIcAvAaga2T5VQDeYeYPmfkIgF9HPgMnXgEwEgCIqA6AIZFlYOalzLyImcuZeSOAv9i0w46rI+37gpkPQC5g1uObz8yfM/MJZl4Z2Z+b7QJyAfiamV+KtOsVAKsBXGZZx+mzicV5AGoD+K/IdzQXwDuIfDYAjgHoSER1mXkXMy+zLD8dQEtmPsbMC1gLRaUcFfTcYTszHzZPiKiAiP4SsST2Qm7x61tthyi2mH+Y+WDk39oe1z0DwE7LMgD4zqnBLtu4xfL/QUubzrBuOyKoZU77gkTjI4ioBoARAJYx86ZIO9pH7IQtkXb8DhKtx+OUNgDYFHV8vYloXsRS2gPgNpfbNdveFLVsE4BmludOn03cNjOz9eJn3e6VkIvdJiL6FxH1iSz/A4B1AN4jog1ENNbdYSh+ooKeO0RHSz8DcBaA3sxcFxW3+E42ih+UAmhIRAWWZc1jrJ9MG0ut247ss9BpZWb+EiJcg3Gq3QKIdbMaQLtIO36ZSBsgtpGVlyF3KM2ZuR6AP1u2Gy+6/R5iRVlpAWCzi3bF227zKP/75HaZeTEzD4fYMTMgkT+YeR8z/4yZ2wAYBuABIhqYZFsUj6ig5y51IJ707ogfOy7oHUYi3iUAxhNR9Uh0d1mMtyTTxjcAXEpEP4h0YD6G+Of7ywDuhVw4Xo9qx14A+4moA4DbXbbhNQCjiahj5IIS3f46kDuWw0TUC3IhMWyHWERtHLY9G0B7IrqOiKoS0TUAOkLskWT4FBLNP0hE1YjoQsh3ND3ynY0ionrMfAzymZwAACK6lIjOjPSV7IH0O8SyuJQAUEHPXZ4AUBPADgCLAPxfivY7CtKxWAbgPwG8CsmXt+MJJNhGZl4F4E6ISJcC2AXptIuF8bDnMvMOy/KfQ8R2H4BnI21204Z3I8cwF2JHzI1a5Q4AjxHRPgCPIhLtRt57ENJnsDCSOXJe1LbLAFwKuYspA/AggEuj2u0ZZj4KEfDBkM/9aQDXM/PqyCo/AbAxYj3dBvk+Aen0fR/AfgCfAHiamecl0xbFO6T9FkqYENGrAFYzc+B3CIqS7WiErqQUIupJRG2JqEokrW84xItVFCVJdKSokmqaAvgHpIOyBMDtzPxZuE1SlOxALRdFUZQsQS0XRVGULCE0y6VRo0bcqlWrsHavKIqSkSxdunQHMze2ey00QW/VqhWWLFkS1u4VRVEyEiKKHiF8ErVcFEVRsgRXgk5Eg4hoDRGts6vRQET/Q0TLI39riWi37y1VFEVRYhLXcokUQnoKUgK0BMBiIpoZqX0BAGDm+y3r3w2gWwBtVRRFUWLgxkPvBWAdM28AACKaDhkM8qXD+iORYF2QY8eOoaSkBIcPH46/shIq+fn5KCoqQrVq1cJuiqIoEdwIejOcWgK0BDKBQSWIqCWA1qhcs8IVJSUlqFOnDlq1agXnuROUsGFmlJWVoaSkBK1btw67OYqiRPC7U/RaAG9EJjaoBBGNIaIlRLRk+/btlV4/fPgwCgsLVczTHCJCYWGh3kkpSprhRtA349SazkVwrrl8LSKzvNjBzJOZuZiZixs3tk2jVDHPEPR7UpT0w42gLwbQjmRy3+oQ0a40bVikTnQDSOlMRVFyiNWrgbkJGa2Kn8QVdGYuB3AXgDkAvgLwGjOvIqLHrLOBQ4R+eibPI1hWVoauXbuia9euaNq0KZo1a3by+dGjR2O+d8mSJbjnnnvi7uP888+Pu44b5s+fj0svvdSXbSlKsvzmN8BPfxp2KxRXI0WZeTZkhhTrskejno/3r1numDYNeOQR4NtvgRYtgAkTgFGj4r/PicLCQixfvhwAMH78eNSuXRs//3nFROnl5eWoWtX+IysuLkZxcXHcfXz88ceJN1BR0pQtWwCbbjElxWTsSNFp04AxY4BNmwBmeRwzRpb7yejRo3Hbbbehd+/eePDBB/Hvf/8bffr0Qbdu3XD++edjzZo1AE6NmMePH48bb7wRF154Idq0aYNJkyad3F7t2rVPrn/hhRfiqquuQocOHTBq1CiYm5vZs2ejQ4cO6NGjB+655564kfjOnTtx+eWXo0uXLjjvvPOwcuVKAMC//vWvk3cY3bp1w759+1BaWor+/fuja9euOOecc7BgwQJ/PzAlJ9m2DTh0SP6U8MjYeuiPPAIcPHjqsoMHZXkyUbodJSUl+Pjjj5GXl4e9e/diwYIFqFq1Kt5//3388pe/xN///vdK71m9ejXmzZuHffv24ayzzsLtt99eKWf7s88+w6pVq3DGGWegb9++WLhwIYqLi3Hrrbfiww8/ROvWrTFy5Mi47Rs3bhy6deuGGTNmYO7cubj++uuxfPlyTJw4EU899RT69u2L/fv3Iz8/H5MnT8aPfvQjPPLIIzh+/DgORn+IipIA27bJ465dQM2a4bYll8lYQf/2W2/Lk+HHP/4x8vLyAAB79uzBDTfcgK+//hpEhGPHjtm+Z+jQoahRowZq1KiB0047DVu3bkVRUdEp6/Tq1evksq5du2Ljxo2oXbs22rRpczK/e+TIkZg8eXLM9n300UcnLyoXXXQRysrKsHfvXvTt2xcPPPAARo0ahREjRqCoqAg9e/bEjTfeiGPHjuHyyy9H165dk/loFAXl5UBZmfxfVgaccUa47cllMtZyadHC2/JkqFWr1sn/f/3rX2PAgAH44osv8PbbbzvmYteoUePk/3l5eSgvL09onWQYO3YspkyZgkOHDqFv375YvXo1+vfvjw8//BDNmjXD6NGj8be//c3XfSq5x44dYnsCwM6d4bYl18lYQZ8wASgoOHVZQYEsD5I9e/agWbNmAIAXXnjB9+2fddZZ2LBhAzZu3AgAePXV+BPM9+vXD9MinQfz589Ho0aNULduXaxfvx6dO3fGQw89hJ49e2L16tXYtGkTmjRpgltuuQU333wzli1b5vsxKLmFsVsAFfSwyVhBHzUKmDwZaNkSIJLHyZP998+jefDBB/Hwww+jW7duvkfUAFCzZk08/fTTGDRoEHr06IE6deqgXr16Md8zfvx4LF26FF26dMHYsWPx4osvAgCeeOIJnHPOOejSpQuqVauGwYMHY/78+Tj33HPRrVs3vPrqq7j33nt9PwYlt7AKurFelHAIbU7R4uJijp7g4quvvsLZZ58dSnvSif3796N27dpgZtx5551o164d7r///vhvTDH6fSkA8PLLFYHU448DDz4YbnuyHSJaysy2OdIZG6FnM88++yy6du2KTp06Yc+ePbj11lvDbpKiOKKWS/qQsVku2cz999+flhG5otixdStQtSpQWKiCHjYq6IqiJMW2bcBppwH166uHHjYq6IqiJIUR9Dp1NEIPG/XQFUVJim3bgCZNgIYNVdDDRgVdUZSkMBF6w4ZquYSNCrqFAQMGYM6cOacse+KJJ3D77bc7vufCCy+ESb8cMmQIdu/eXWmd8ePHY+LEiTH3PWPGDHz5ZcU0rY8++ijef/99D623R8vsKkFjBF07RcNHBd3CyJEjMX369FOWTZ8+3VWBLECqJNavXz+hfUcL+mOPPYaLL744oW0pSqrYv1+K4pkIXSsuhosKuoWrrroKs2bNOjmZxcaNG/H999+jX79+uP3221FcXIxOnTph3Lhxtu9v1aoVduzYAQCYMGEC2rdvjx/84AcnS+wCkmPes2dPnHvuubjyyitx8OBBfPzxx5g5cyZ+8YtfoGvXrli/fj1Gjx6NN954AwDwwQcfoFu3bujcuTNuvPFGHDly5OT+xo0bh+7du6Nz585YvXp1zOPTMruK35gcdCPogEbpYZK2WS733QdE5prwja5dgSeecH69YcOG6NWrF959910MHz4c06dPx9VXXw0iwoQJE9CwYUMcP34cAwcOxMqVK9GlSxfb7SxduhTTp0/H8uXLUV5eju7du6NHjx4AgBEjRuCWW24BAPzqV7/Cc889h7vvvhvDhg3DpZdeiquuuuqUbR0+fBijR4/GBx98gPbt2+P666/HM888g/vuuw8A0KhRIyxbtgxPP/00Jk6ciClTpjgen5bZVfzGKujmFNm5E4iUO1JSjEboUVhtF6vd8tprr6F79+7o1q0bVq1adYo9Es2CBQtwxRVXoKCgAHXr1sWwYRUz9X3xxRfo168fOnfujGnTpmHVqlUx27NmzRq0bt0a7du3BwDccMMN+PDDD0++PmLECABAjx49Thb0cuKjjz7CT37yEwD2ZXYnTZqE3bt3o2rVqujZsyeef/55jB8/Hp9//jnq1KkTc9tKbmIE3WS5ABqhh0naRuixIukgGT58OO6//34sW7YMBw8eRI8ePfDNN99g4sSJWLx4MRo0aIDRo0c7ls2Nx+jRozFjxgyce+65eOGFFzB//vyk2mtK8CZTfnfs2LEYOnQoZs+ejb59+2LOnDkny+zOmjULo0ePxgMPPIDrr78+qbYq2Yc1Qo9MGaCZLiGiEXoUtWvXxoABA3DjjTeejM737t2LWrVqoV69eti6dSvefffdmNvo378/ZsyYgUOHDmHfvn14++23T762b98+nH766Th27NjJkrcAUKdOHezbt6/Sts466yxs3LgR69atAwC89NJLuOCCCxI6Ni2zq/jN1q3y2LixZLkAGqGHSdpG6GEycuRIXHHFFSetF1NutkOHDmjevDn69u0b8/3du3fHNddcg3PPPRennXYaevbsefK13/72t+jduzcaN26M3r17nxTxa6+9FrfccgsmTZp0sjMUAPLz8/H888/jxz/+McrLy9GzZ0/cdtttCR2Xmeu0S5cuKCgoOKXM7rx581ClShV06tQJgwcPxvTp0/GHP/wB1apVQ+3atXUiDMWWbduAunWB/Hy1XNIBLZ+rJIx+X8rIkcCSJcDXX8usRfn5ktDw+ONhtyx70fK5iqIEghlUBMhEMzq4KFxU0BVFSRhTx8Wg9VzCJe0EPSwLSPGGfk8KIJ2iJkIHtJ5L2KSVoOfn56OsrEzFIs1hZpSVlSE/Pz/spighcvw4sGNHZUHXCD080irLpaioCCUlJdi+fXvYTVHikJ+fj6KiorCboYRIWZl0hFoFvbBQOkmVcEgrQa9WrRpat24ddjMURXGBdVCRQS2XcHFluRDRICJaQ0TriGiswzpXE9GXRLSKiF72t5mKoqQb1mH/hoYNgcOHteJiWMSN0IkoD8BTAC4BUAJgMRHNZOYvLeu0A/AwgL7MvIuITrPfmqIEz7ZtQPXqMselEhx2Ebp1tKgW6Eo9biL0XgDWMfMGZj4KYDqA4VHr3ALgKWbeBQDMvM3fZiqKe666CrjjjrBbkf2YYf/RlgugtktYuBH0ZgC+szwviSyz0h5AeyJaSESLiGiQ3YaIaAwRLSGiJdrxqQTFxo1ApPSNEiDbtklBrgYNKpbl2vD/2bOBa66RzuF0wK+0xaoA2gG4EMBIAM8SUf3olZh5MjMXM3Nx48aNfdq1opzKzp0V0aMSHNu2SVGuKhYVybUCXXPmAK+9BuzZE3ZLBDeCvhlAc8vzosgyKyUAZjLzMWb+BsBaiMArSko5cgQ4cADYsiV9oqZsxTrs35BrEfquXfL43Xex10sVbgR9MYB2RNSaiKoDuBbAzKh1ZkCicxBRI4gFs8G/ZiqKO4yQHD2aPlFTthI97B/IPQ/dnG/ffhtuOwxxBZ2ZywHcBWAOgK8AvMbMq4joMSIyU/HMAVBGRF8CmAfgF8ycI1+pkk5YI0O1XYIletg/ABQUADVq5E6Ebo4zXSJ0VwOLmHk2gNlRyx61/M8AHoj8KUpoWCPDLVuAs84Kry3Zjp3lQpRbw/8zLkJXlExCI/TUcOCA/EULOpBbo0WNh66CrigBYBUSFfTgMFnHdoKeKzXRmdPPclFBV7IKq5Bs2RJeO7Idu2H/hlyxXPbvB8y87BqhK0oAlJXJsP+mTTVCDxK7Yf+GXLFczEWrcWNg82YpJxw2KuhKVrFzpwiKCnqw2A37N+SK5WL8865dgWPH0uN8U0EPmClTgNWrw25F7lBWJoLSpIlaLkFiInS7Ad+5UnHRXLTOPVce08FHV0EPkKNHgVtuAZ55JuyW5A4aoaeGbduA2rUl7zyaXBlcFC3o6eCjq6AHiIkQS0rCbUcuYY3Qt27V4f9BYZeDbsiVei4aoecYpaXymA5fdK5gIvQmTeQOaffusFuUndgN+zfkSj0X46G3aSN3KxqhZzlG0DVCTx0mQm/aVJ6r7RIMdsP+DblkuVSvLrZT8+Yq6FmPEfQtW6QXXAmWQ4ekM85E6IAKelCo5VJxN0gEtGiRHnfiKugBYgSdGfj++3DbkgsYATEeOqCZLkFw4oSMFI0XoeeKoAMi6BqhZzlG0AG1XVKBucU3WS6ARuhBsHOniLqToNesKRUXs91y2bWrQtCbN5e7lsOHw22TCnqAlJbKyQ2ooKcCa4TesKFMj6aC7j+xhv0DYkHkwuCinTsrpt9r0UIew/6dq6AHSGkp0K2b/J8O/lq2Y43Qq1TRwUVBEWuUqCEX6rlYLZfmkTndwv6dq6AHSGkp0KGDpDSFfeXOBawROlCRi674S6w6LoZcqOcS7aED4fvoKugBcfy4iMnpp8vVWwU9eKwROqCCHhRuBD3bLZejR6UevDnXiorkUSP0LGX7duk4Ov10+bLD/qJzgZ07gfz8in6Lpk3VcgmCbdvE0jJiZke2Wy5mUJH5DPLz5QKnEXqWYjJcjKBrhB48ZlCRoUkTER8d/u8v27YBjRpJp7MT2S7o5thMpyiQHoOLVNADIlrQS0t1cFHQWD1NQIf/B0WsYf8GU3Hx4MHUtCnVGEG3nm/pMLhIBT0grILevLlEida8dMV/oiN0k4uutou/xBr2b8j20aLRlgtQEaGHeUeogh4QRrybNq3oMFHbJVjsInRAO0b9Jtawf0O2jxZ1itD37wf27AmnTUAOCPqRI0BxMTBjRmr3W1oq/lp+vgp6qrDz0AEVdL/xIujZmrro5KED4froWS/oixcDS5cCL72U2v2WlordAqTPoINsxszAbo2Y1HLxn0OHgH371HLZuVNGxNarV7HM5KKH+TvPekGfP18e581L7SSuVkGvVw+oVUsj9CA5cEA6QK0ReoMGQNWqGqH7yfbt8uimUxTIXkHftQuoX//UTJ90GFyUE4JOJF/A8uWp269V0Ik0dTFo7DzNKlUkkkxnQT94EHj2WRmzkAm4GfYP5IblEp2H36SJBBAq6AFx5Ajw8cfA1VfL8w8+SM1+TUaLEXRAbBe1XIIjeti/Id0HF73+OjBmDPD++2G3xB1uRokCMulDfr5zhL5tGzBoUOb+JqyFuQx5eeEPInQl6EQ0iIjWENE6Ihpr8/poItpORMsjfzf731TvLF4snt+11wKdOqVO0Hftktt/q6BrhB4s0cP+Dek+/H/1anlM1bmZLG4FHYg9uOj114E5c4CPPvKvbanELkIHwh9cFFfQiSgPwFMABgPoCGAkEXW0WfVVZu4a+ZviczsTwtgt/fsDF10ELFggUXvQWHPQDWZwUXl58PvPRewsFyD9BX3NGnnMVkF3slxmzZLHdL57ioWToIc9uMhNhN4LwDpm3sDMRwFMBzA82Gb5w/z5QJcu8sEPHCjR+qJFwe/XnKTRlsuJEzq4KCiMcNhZLlu3Jj7YwwwgCQoj6MuWZUYH4rZtYqfUqhV/XacCXQcOAHPnyv+ZKujWyS2smEJ8qUzAsOJG0JsBsF5zSiLLormSiFYS0RtE1NxuQ0Q0hoiWENGS7aa7PCCOHhX//IIL5PkFF0gnWSoiIacIHVDbJShiRejHjiUmzJs2SSQalL99/Diwbh3Qt69ccExGVjrjZti/wclymTu34k45ne+enDhxQs6naA8dkAi9vDy84/KrU/RtAK2YuQuAfwJ40W4lZp7MzMXMXNy4cWOfdm2P8c8vvFCe168vA4xU0LOTsjKJGmvUOHV5MoOLVq+WH+ennybfPjs2bpTA4z/+Q2rmZ0LHqJth/wYny2XWLDneLl0yM0Lfu1dE3SlCB8Lz0d0I+mYA1oi7KLLsJMxcxszGnZ4CoIc/zUscE+3071+xbOBA4N//loERQVJaKuJSp07FMh1cFCxOnmYyg4s2R85y03HpN8ZuOeccOU8zwUffuhVwG4sZy8VqdzGLoF9yCdCyZWYKutPdIBD+4CI3gr4YQDsiak1E1QFcC2CmdQUissSiGAbgK/+amBjGP7d6qgMHSsT14YfB7js6ZRGQO4SCAo3QgyJ62L8hmQjdfFdBC/pZZwEXXwysXZve54exiNq2dbd+w4ZirRw6VLFs5Uo5xqFD0z+l1Am7wlyGtI/QmbkcwF0A5kCE+jVmXkVEjxHRsMhq9xDRKiJaAeAeAKODarAbjh4FFi6ssFsM558vt+RBR0KlpRWRoUEHFwWLU4RuBD0R4bAKehAV9NauFR+2USMJNoD0jtI3bJCBUOee6259u8FFJrtlyBD5brZvD68DMVFiRej164udlLaCDgDMPJuZ2zNzW2aeEFn2KDPPjPz/MDN3YuZzmXkAMwcU07gj2j831KwpHVCpEPToCB3QwUVB4hShN2gAVKuWXIS+fz/w/ffJtc+ONWuA9u3lYn/OOWJlpLOPvnKlPHbp4m59u3ous2YBPXrI76NpU/Gid+zwt51BY1eYy0AUbupiVo4U/de/5NHqnxsGDpQT0+TTBoGToCcToa9dCwwbFm5pznTGKUJPZvh/SUlF8aUgbJc1a8RuAaSdF10kwUa6zrC0cqW0s6PdKBQbouu57NghacNDh8rzTC2eFitCB8IdXJSVgm7nnxvMre28ecHs+8AB6XR1EvREBxe98grw9tupLwOcCZhKi3bfNyC39olaLhddJP/7Lej79knUbwQdEB+9tDQ4zz5ZVqyQ9po5W+MRbbn83/9JRJ7pgm48dLsIHdAI3Vec/HNDjx5A3brB2S52KYuGoiLxCxM5gRcskMd//CPxtmUr+/bJRdIpYjKDi7xw8KD8cIuL5XzxW2TXrpVHq6Cnu4++cqV7uwWobLnMmiV3S8XF8jxT69Xv3FlRq8aO5s3FATh8OLXtArJQ0JcskR+jk6BXrSqvhSHopgfcq+1y7JjcqublSf2L/fuTa2O24TRK1JDI8H+Tsti8uYiu34JuzXAxtG4tf+noo+/dC3zzjTdBt1ou5eUSoQ8ZIrYNkLkRul1hLismdTGMBIisE3STf96vn/M6AwdKj/3Gjf7vP16EDnj/opcvFyvnttskDWz27KSamHXE8zSNoHspUWu+o6IioEMH94K+fXtFzfBYrFkjHWhnnnnq8oED5RxOt5o/X3whj14EvWbNioqLn3wik3UbuwWQbJBatTJT0J3ONSDc1MWsFPTOnSUVzIkgb22DEHRTkW7sWMmEUNvlVJwqLRqaNhWB9DL8P1rQS0rc3Rldcw1w5ZXx11uzBmjVqvJt+8UXS8f3smXu25oKVqyQR7cpi4bCQvl+3nlH7o4vueTU1zMxF92pjoshzMFFWSXox47F9s8NHTvKiRSUoFerZn/737ChRC1ev+gFC4A2bURcLr9cvMgw/Ll0xakWuiERr9YIerNmIuhAhe/thKkf9Mkn8cV/7dpT7RaD6YRNNx995UrJsTZBiVtMPZdZsyTrzDplG5BY/0bYxIvQzWekEXqSxPPPDUTyw5k71/8UMTOoiMh+v15TF5klQjcW0ogRIhbp6LOGhRvLBfAWCZaUiE9aUFAh6PFsl5UrxRIrL49d1ZPZWdAbNxZbI92+X9Mhandex6JhQ+Czz4BVq061WwyJZiCFSTwPPT9fOn9V0OMwbZrcplapIo/Tpp36ul39FicGDpTIYNUqf9volINu8Croa9eKJ/uDH8jziy6SrAu1XSpwY7kA3iN0E2m1bSsd0vEE3VrEK1Z5ic2bpU+kfXv71wcOlDtN65D5MDlxAvj8c2/+uaGwUKpWAvaCnomWS7wIHQhvEGHGCPq0aTJV16ZNEuFs2iTPraLuxj83BOWjxxN0r1+08c9NhF69OnDZZcBbb6Vfx1lY7NwphdCqVbN/PRHLZfPmCkGvUUMsLzeC3qQJ0L17bEG3y3CxcvHFFdMnpgMbN0pqaCKCboSvbVv7C1jTpvL9HT2aVBNTxqFDYnfGE/QWLTRCj8kjj4idYuXgQVkOiH/+0Ufx7RZDy5Zygv35z/6OvnQToX//vfv6FQsWyG249cdw5ZXyIwi6yFim4DTs32CG/3u1XKx+sZtMl08/BXr3ltr7ixY5z44VT9D795cOxHTx0c2Qf68dokCF8F16qb1dY+6eghy57SexCnNZMYFbqkf9ZoygO13tzPIZM9z551aeeUaqx/34x3JBSJajR0Vc4gn68ePuo8UFC8Rusf4YfvQj6VxV20WIdwtM5C0X/ehRWTda0Neudb4Q79olr/fuLYJ85IjUFLJjzRpJ12tmN00MJJ2vd+/08dFXrpTPsFMn7+81F1o7uwVIrnhaGMTrrzG0aCF9Xbt3B96kU8gYQTepQHbL168HbrlFRoE6nTh2XHSRROj//Cdw993JX03tpp6Lxktd9O+/l3x5458bCgqAwYOBN9/0lludKJ99Bpxxhj/9DeXl4seuWCH59cuWAUuXivh9801i24wXoQPeBN0U4ooW9CNHKvzgaIx49+5d8X2Z0b3RWItyOTFwoHwuqRYEO1aulHx5N9PORTNoEPCTn1TMHBaNl8FFv/898NRT3tvgJ7EKc1kxepVq2yVjBH3CBBEyKwUFwLhxwFVXSUfpG29UnrEmHjfdBDz0EPCXvwD/8z/JtTFWDrrBSy56tH9uZcQIEZ5//9tbGxPhpZfk2B5/PLntHD8O/PCH4sV27Qp06yYX4eJioFcvEbmvv/a+XTedVF6yKawpi4Z4mS6ffioC3bOn9OF06uRsiVmLcjlx8cVysTaF5sLE65B/K126AH/7m/T92OFF0P/yF9lWmLi1XFq1ksdEg5REyRhBHzUKmDxZvG8ieZw8WbIBli8X0TEfold+9zvxpX/+8+SKXwUh6AUFIn7RDB0qvnDQtgszMHOmfOavvJJcz/3EiVIUbdw4afebb0rn7syZwN//Lr7xhAnet+smQveS72wdVGQwAhxL0M8+WzKQALFdFi6s3HF9+LBE+fEEvUdkzq/PP3fX5qA4cEBsyUT8cze47bA+ckTOvbDLT7u1XNq0kccNG4JtTzQZI+iAiPrGjRK5mPkYn3tOOka9WC3RVKkiV/6ePWUfS5cmth03gl5YKHmqbk7MBQuAPn3sszfq15fb8r//PdiOly+/FEvroYdkP5MmJbad5cuBX/9aLpzjxgFXXCGDpIYNk6ydESOktMHUqbI/t5gJe91E6Nu2ubOoTB0Xq6AXFkrntJ2gM1d0iBr695fMEDPC0rBunawfT9ALCuQOYd26+O0Nki++kPYmGqHHo0YNOZfjRejmd79lS7gZMW4FvWFDubhrhO6S5cuBO+4QUfvNb5LfXkGBRIuNGonAJBIJlJZKJBtrEl23g4v27JFb3Wj/3MqIERIBmCyEIHjrLXm8+27pPP7LX7xnBR0+LBMhN2ok73fyjh98UC5eXqL0PXvkh+7GQy8vt5+FPpqSEumYNNG2walI1zffSK1vq6AbmyzadomX4WKlXbvELCg/8TqpRSK4yUU3F3nmYCYbccvOnTImwTpfsB1EEqVrhO6C3bsl0issFBsgL8+f7TZtKkOU9++XNKu9e729v7RUxLxq1djruRH0Tz4RoYpVZGz4cDlxgrRd3npL7lzOOEMsqX37xOrywsMPS4fq88/HFt7TT5exBX/7m/sfgtuIycvgIpOyGH3hcUpdNAOKrILerJnkXjsJutOgIit+CfqRI3Knlcid3MqVIl4tWybfDifcCLr1TiVM22XXLukQdTNitnVrFfS4MAOjR0vv8euvu5+B3C3nnCPbXbXKezpjvBx0g5vBRR99JBcqq0hEc9ppIvhBCbrpdB0+XJ736AEMGAA8+aT729733weeeAK4805Jt4zHQw/JBfF3v3O3/XijRA1eBhdF56AbOnSQUbvWOTIBEfSCAjl3rPTvL7aZ1eZZs0YujrVrx29Hu3ayv2THSfzxj9JJ27q1FHhbvty9uK9YIdF5lQCVwk3/htWGC1PQ3XTAG9q0kbu3VGSiGTJO0P/4R4kaJ04UfzkIfvQjsQbee098Xbcnv1tBdzO4aMECGXEY74d/5ZXic8YrHJUIb78tj0bQAeAXvxCPefr0+O/fuVMuvh06AP/v/7nb5xlnSArqiy+6K28crzCXwUu+cyxBByqibMOnn8rFLvrOrH9/Ef+vvqpY5ibDxWBK6yYbpf/rX3I8HTvK76ZbN/n/N7+Jfd4wJ5fh4ha3EbrpaAyyzvjnn8e25bwK+pEjqc2xzzhBv+wyuYW/555g93PTTdKJ99e/Av/5n+7e40XQy8udR8cdOSKRcSz/3DBihETyf/6zuzZ6YeZMOSmtA0oGDZJIdOLE+Be6O++UyGvq1Mopp7F46CGJCH//+/jruo3Q3Vou5eXyPdoN+rFLXTx6VPL0e/WqvL6pKWRsF2Zvgt6unTwm0zF64oRccIYMkTr6W7bIudK0qQj62Wc7D2D67ju5Owha0Js0ESvvwAHnddavl2yvevWCi9CNxWlGn9sRrzCXlTAyXTJO0Nu3l9txr1XfEuE3v5FBEY8+KhFjLMzoT7eWC+B8Yi5dKh2JsfxzQ1GRtPGZZ/ztLNq/X4aeG5/eQAT87GcSybz3nvP7p02TKH78+IoUPLcUFQE33yyee7yBGW4j9Pr1JRc6nqBv3SrfpV2E3qqVbMMq6CtWyAXYzhpr3VouDEbQt2+X/h+3gt62rTwmE6GvWSP7PO88ed6oEXDrrZI++t130sZ77rG3FlPRIQrEv9gePy7WxZlnBlv0avNmuYCZ8R92uMmoMrRuLY8q6AkQrxJjIusTAVOmyIjSm2+OXVtj+3a5wruN0AHnW0dzQvXtG39bgNxJlJe7i2jdMmeOCNWwYZVfu+46sUb+8IfKrx04IJ2nN9wAnH++RNuJYN4X75iMoNevH3s9k30U7/bXLmXRkJcnAYVV0O06RK377N9fBN2UzAXcC3rNmiJgyQi6KeNrBN1Ks2ZiYX71lf0ITJNy2blz4vt3QzxBLymRO6G2bYMVdPM5r1rl3G/hxXIxY2ZU0D3iphJjoutXry6djh06iL3hNNDDTQ66IZ6gL1ggP/pY6Y9W2rQBfvpTyT7x62R/6y05ce1sn+rVgXvvlQvcZ59VLJ89W+yZ//5vaY+ZpSYRWrQAbrxRxhnEOqayMhFzN/tx0/lmN6jISnSmy6efynbNXVc0/ftXlHDwkrJoOPPM5AW9fn3nfV52mfQZjR9feeq8lSvl3IqXopcs8UaLmg7RoCN0c8Fltq/Dc/y43O24FfT8fLlopjIXPSsEPV4lxmTXr1dPxKp2bfEi7eaM9CLojRrJgAq7E/PECRlh6MY/t/LII3IiusnhXr5c0iKdKC+X9M2hQ52FcswY+TwmTpQf4rXXyvo1a0pE+uyz7r1GJx5+WB7/67+c1/ESMbmp5+JG0DdsqKikaAYUOVmAVh99zRr53r2kACaburhokbTPKUuFSLKQDhyofP6nokMUiN9hbfoQTIS+fXswM3atXSvfD5H978PU1fFyXqc6Fz0rBD1eJcZklwNyIr39tnRk3npr5Q5BL4JuBhctWiQ13EtLK7b35Zfi07nxz620bCnZIc89Fzs7ZMUK2faAAadOyGBl4UIRSmt2SzT164uov/qqdKy9+ab0OSxf7r3tTrRsKVkyU6ZUWCHRuBn2bzj9dBHsWJ25JSXyo3baZocOEqmtXy+f0ddfx04tPftsuYAbQT/zTG/jJtq1k2P0Mh+qYd8+yYCys1usdOggA8emTKmYy/TQIRG4VAh648bym4gVoVerJr+ZROfldcPatfJZnH22vaC7reNiRQU9AWJVYvRjuaF7d8l4efNN4IUXTn3NCLq5fYxHr17ilQ8YIH503brSeXjTTfK61wgdAH75SxELp6yczZsliq5XT8TtiivsO1LfektslR/+MPb+7rtPIvKuXSWae/RR78XR4vHQQ+KfOhVl8hKhd+8u0V2sW2CnQUUGa+qiKYwWS9CJ5AJnBN2L3QIkl+myeLHc8cUTdEC+u0aNpIOUWQKLEydSI+hVq4qoxxL0Nm3k3PZSrdQra9dKH0mfPhJsReePux3EZqV1a/mNpWr2KVeCTkSDiGgNEa0jorEx1ruSiJiIiv1rYnycKjE62Q9e17fywANSCvSee0698paWyq1Y9CzuTkydKpH0e+8Bf/qTeM6NG4vg9O1bkfLkhWbNJG/+hRcqC4AZ/bpnj9gpM2fKSNgRI069fWUWQR84ML532ry5WBhz53oXKre0bSudq9Om2UfWXiJ0c+fgVNYWcM5BN1iLdJkKi8Vxzvb+/eVcMYLhhWRy0U2HaKwLjqF+fckeW7hQRl+bDtGginJFE6t/Y926ioyfoAT92DH5joyg79pV+TNPRNDN79ip7LLvMHPMPwB5ANYDaAOgOoAVADrarFcHwIcAFgEojrfdHj16sJ9MncrcsiUzkTxOnZrYcjds2sRcrx5z377M5eWy7IormDt29OlgkqC0lLlmTebrr69YVl7OPHQoc5UqzLNnVyx/4w1mgHn0aOYTJ2TZ55/Lsj//ObXtjsVTT0mbVqyo/FqDBsx33eVuO8ePy/o33eS8Tps2zNddF3s7zZrJ5zt4MHOnTvH3u2yZtB9gfv55d201HDok5+j48d7ex8x82WXMHTq4X7+8nLl7dzm+m29mLiiQzywVXHIJc+/elZefOMFcuzbzPffI8wMH5HOcMMHf/a9dK9t98UXmVavsv6tp02T56tXut7twobxn1iz/2gpgCTvoqpsIvReAdcy8gZmPApgOwM5d/S2AxwEE0F0Rn+hKjKNGxc5msVsfcJfO2KKFpHktXFhRI9ztoKKgadpUipZNnVqRjXH//RKV/+lPMjGG4cor5Vb7hReA//1fWWaKcV12WUqbHZOrr5bb8ujvwmQduI3Qq1QRK8upTjlz/AgdENvlq6/EcnET/XbpUlHoy+udTH5+YqmLzBKhu7FbDHl5Uk1z82bpi+ncOdgh/1acRotu2yZ3lyZCLyiQ79vvCN1kuLRvL99vvXqVfXTjoXvtFAVS56O7+bqaAbB+fCWRZSchou4AmjPzrFgbIqIxRLSEiJZst0sV8Rmv2Sxe0hmvuw645hopBbt0afoIOiC+c82awGOPSd2V//1fsYpuv73yuuPGSefnAw9IGqK1GFe60KiRpNa98sqpvubu3fI9ebkF7tdPxNFOPHbsEL/ejaAvXSp2jxtBz8ur6BNJxJpq1867h75hg9h3XgQdELtv1KhgS+baYQQ92lYzKYtG0IFgUheNoLdrJxex3r0rLCuD29mKrDRpIr/FdBL0mBBRFQB/BPCzeOsy82RmLmbm4sZ+V9WywWs2i5cLAJGMzmzSRErDppOgN24sWQvTp0t0fsUV9oOAADl5X3pJROqqq6QjLVZ2S1hcd538iK3+t9th/1ZMGqHdaMB4KYuGDh0qLixuBB2Qzu6rr/bWVkMiueixBhTF4/HH5bweOND7exOlaVNJBY2ucGouZKYvAQhO0Bs2rLjb69NHMoT27atYZ+dO6Veym5/ACSLpGE1VLrobQd8MwDpsoiiyzFAHwDkA5hPRRgDnAZiZ6o5RO7xms3i9ADRoICUBVq+WyC5dBB2Q0Zp160qH3dSpsW+d69SRyNxkdqSjoA8fLnNaWu+W3A77t9K9u9y223WMehF0QLbjduLkESMkxTMR2rWTY3VTy92waJF8XtEVIN3QrJkEKNdc4/29ieI0uGj9ejkvrbORFRUFI+jWDus+feSibZ3i0UtGlZVUpi66EfTFANoRUWsiqg7gWgAzzYvMvIeZGzFzK2ZuBekUHcbMSwJpsQe8ZrMkks44cKCk7wHpZVMUFkrq2YcfuiuM1batDJ763e8Sm909aGrVkhmOXn+9YlBPIhF6tWoStfoh6MXFiY+E9YJJXfQSpS9aJKmxic4VkIpaSVacBhetXy+/P2s6bPPm4mfHKubllWhBN8XWrD66qYXuFSPoQc4sZogr6MxcDuAuAHMAfAXgNWZeRUSPEZFNpY/0wWkeUtMBGk2i6Yy//7141UOG+NNuvzjjDPdplIAI3cMPp/7H7JZRo8Q3f/ddeZ5IhA6Ij75iReXb+5ISEeh4JReaNRPRj5en7xdeBf3QIRnglYjdEhZOEbo1ZdHgd+riwYPy3VsFvUEDGWBk9dGTidD375c+mqBx5aEz82xmbs/MbZl5QmTZo8w802bdC9MhOjc4ZbM4revlAmDIz5e89KBrXuQ6l1wi/QPGdkkkQgdE0E+cAD7++NTlmzeLbRYvqiWSQUJjHUdk+EubNrJPtx2jS5dK+YZMFPToXPT160/1zwH/Bd18rtFjBMwAIxNZJyropupiKnz0rBgp6ideLgAGp1RHrxUgldhUrSq+7ttvS3S9c6cIXbxKi9Gcd55sK9p2cZOyaCgo8G/qw3jUqCG2g9sIPZkO0bBo0EDsMGuEvmePRLVOEbpfw/+tGS5W+vSRoMF87slE6EBqfHQV9CRxSnW84w5vFSAVd4waJR76P/4hP7YGDbznSteqJZ2jyQh6qvFSpGvRIhERt9U604EqVSqXN7ZLWQQqJh/xK0I3gh59J2AuiJ98Ir/hRD30VNZFV0FPEqdUx8mTveXAK+7o3VvEato0iZi8+ueGfv0kg8F0sLodVBQWRtDddKx5HVCULkQPLrKWzbVSo4Z0ovop6M2aVZ7usWNHyRRbtEh+u0ePJhah16ol7VVBTyOc7BOnlEan+ULjzcCjxIZIctLnzpWJCBL5gQEi6EeOVNS93rNHsibSWdB3746fuvjdd9IXkKmCbvXQjbdtV9fIz1x0pxo7ZoDRJ58kVsfFSqpy0VXQXRBrBKlTSqOTvxqvoqMSn1GjpI/j888Tj9DNyE1ju7hNWQwLt0W6jH8e1ATqQWIXoTdpYp9s4GcueqyiaX36yHlmArFEBT1Vuegq6C6INYLUKdVxzBjnFEjtLE2ODh3EAwcS/4EVFsottanrEmvquXTAberiokWSdZXKYft+YSYgMaNw7VIWDX5F6Dt3Sl+Mk6Cfd560x8yfm4ygf/ut/dytfqKC7oJYI0idUh2fftp+OaCdpX5gso8SjdABsV0+/ljsMROhmw63dKNNGwkA3Ah6jx5Szz7TaNpUvguTjrp+fWxB37fPee5Pt5jPMzrDxWCsq1mRKlWJzsLVpo1cGIK2XFXQXRBvBKlTqqPdcq8FwxR7rr1WUg+TEeB+/ST9ceVKEXSi9CrfYKV6dQkKYgn60aOSg56J/jlwai76oUPynUR3iBr8ykW3Vlm0o0GDimJsQHIeOhC8j66C7oJkJsSIJpHp75TKnHGGjIa8447Et2Gd8KKkRG750zmyPfPM2IOLli+Xjt5M9M+BU0eLGuGLFaED/gh6Xl6F4NphvUAmY7kAwfvoKuguSHQEqR2JTn+nVKZTJ0kJS5QWLeTPCHq6+ueGeKmLmTigyIpV0J1SFg1+DS5au1bEPNaF3Fwgq1d3VxfJjmbNZOCUCnqakMgIUjv8jPaV5OnXTwT9u+8yQ9DN6Ek7/vlPOYZ07QeIh7VAl7kTcYrQTz9d+hT8iNDjTQtoBL1Bg8TrHOXlSQKECnqW4We0ryRPv37i2X71VWYIOmDvo8+dC7zzDnDLLaltk5/UqSOTQWzdKhF63brOnd7VqklEn4ygM8tnGU/QO3aUtiVqtxhSkbqYguKfSjSjRqmApwvGRz9xIv0F3ZqLfv75FcuPHgXuuksE48EHw2mbHxBV5KJv3y7HGysiTjZ1sbRUBpPFE/S8PODii6XgWTK0bl0xkC0oVNCVnObssyUKLCtLf0Fv3VpshuiO0UmT5A7jnXe8lUtOR4ygb9wIdOsWe93mzSVDKVGcinLZ4UdacZs2kve+e7f3gnJuUcslx9BBTadCVBGlp7v3XL26fGdWy6WkBBg/Xib1Hjo0rJb5R5MmckwbNzp3iBpMhJ7oxBHxUhat1Kwpf8lgMl2CTF1UQc8hvEyCnUtccIE8tmwZbjvcEF118ec/l8E4Tz4ZXpv8pGlTqTVfXu7cIWpo3lzy1b1MzWdl7Vq5o0nVnVkqUhdV0HMIHdRkz623il0RKxc5XTATRjNLR+irr8osU5nQdjc0bVoRcbuJ0IHYPnqs6H3tWrlAei2/nCipGFykgp5D6KAme2rWzBy7ol07GfK+eXN2dIRGY3LRAXcROuCci/7ee3JR+OIL+9fdpCz6Sf36kvqoEbriGTuvXAc1ZT6mA++uu6QjdNKkzO8ItWJy0WvUiD/perwI/bHHRDxHjgQOHz71tfJySY1MpaADwacuqqCnEX51WDp55UOG6KCmTMcI+ltvZU9HqBUTobdtG98KadJE6vnYCfqSJcDChcDll0uE/tBDp76+aZOIupsMFz9RQc8RYnVYxhJ6u9ecvPLZs3VQU6bTqpXkRefnZ09HqBWroMcjL0+ieDtBf/JJGQz04osygfukSXL+G7xkuPhJ69by23aaACdZNA89TXAS4XvvlZ5885oResOYMZVfi96OwZT7VQHPXKpVA264QUrkZktHqBVjubgRdMB+cFFpqXQW3367jDZ9/HFg3jzgpz+VvPUmTcIT9DZtZCDY999XWEZ+ohF6muDUMVlW5pyZ4nQRCHu2pFTkuudyPv1zzyVXZTKdqVkTmDIFuPNOd+vbCfozz4idcvfd8jw/H3jlFSmVPHq0jApeu1Y6KRs18rP18Qk8dZGZQ/nr0aMHKxW0bMksZou7PyL5c3q9oKDy86lTgz+OqVOD33cq9qFkBr/4BXP16szHj8vzQ4eYGzViHjas8rp/+pOcK088wXzxxcy9eqW2rczM69ZJG/7618S3AWAJO+iqRuhpglMVRqfiRKb0qx3GGw/DK09Frrvm0yuG5s3Fwti+XZ6//LJUo7z33srr3nEHcOmlkua5eHHq7RZAfrMPPCAFv4JAPfQ0wYjtI4+I/dKiRUX2SbQvbs1McXotLK88Fbnumk+vGKypi6edJp2hnTsDAwZUXpcI+OtfZb7VLVtSn+ECSB/If/93cNtXQU8jYolwtNBb14v1Wqpp0UI6Z+2WZ9I+lMzADNsvKZEBVytXSh+DU5XGxo0l82Xw4IqJxrMKJy/G+gdgEIA1ANYBGGvz+m0APgewHMBHADrG26Z66OnF1Kni4xPJY6J+tHroSirZskW+/0mTxDdv1Eh89Hjs2MF84kTw7QsCxPDQ3Yh5HoD1ANoAqA5gRbRgA6hr+X8YgP+Lt10V9PTBb4H06+IQ9j6U9Of4cekUHTFCzoVf/SrsFgVPLEEned0ZIuoDYDwz/yjy/OFIZP97h/VHAriemQfH2m5xcTEvWbIk5r6V1NCqlb2F0bKllDH1CzPoKV3sISU7aNtW0gCrVZPzNV7JgEyHiJYyc7Hda2489GYArJmeJQB62+zkTgAPQKL4ixwaMgbAGABooYZn2pCKTkYzEtZugJSKupIMzZuLoF9zTfaLeTx8S1tk5qeYuS2AhwD8ymGdycxczMzFjRs39mvXSpKkomiXphoqQWEyXe67L9RmpAVuBH0zAOsg1aLIMiemA7g8iTYpKcYpB37CBP9GZGqqoRIUo0cDv/61lEPIddwI+mIA7YioNRFVB3AtgJnWFYjImtE5FIDNvORKujJqlP1AJMC/GY60dK8SFAMHSqlcBfE7RQGAiIYAeAKS8fJXZp5ARI9BeltnEtGTAC4GcAzALgB3MfOqWNvUTtH0x8/O0mgPHZC7AK32qCjeiNUp6krQg0AFPf2pUsV+Ci8iKXDkFc1yUZTkiSXoWstFccRvm2TUKInsT5yQRyPmuVw5UVH8RAVdcSRWZ6lfxJrYQ1EUb6igK444dZb6aZNoOqOi+IcKuhITJ5vEL1KVzpiIraNWkJJpqKAroeK3T28nwonYOmoFKZmICrqSMH5EsH769E4ifO+93m0dtYKUTEQFXUkIvyJYP316JxEuK7NfP5atoyNblUxE89CVhEhVhUYvOOXNOxGrrel4fIoCaB66EgDpGME6+e6Fhd5tnVSkbCqK36igKwmRjrVZnET4ySe92zqpSNlUFL9RQVcSIh0j2FginEj6pY5sVTINnSRaSQgjbulWmyXWRNt+oBN1KOmMRuhKwgQ96CgVeI22NZ1RSWdU0JW0Jkh7I5HUy3Qe2RrkdpQMwWn26KD/evTo4eM82Eo2MnUqc0EBs8it/BUUyHI/aNny1G2bv5Yt/X2PV/w67qA/PyUcIPNQ2Oqq5qEraUvQueCJ1HtPxUQdfh235tJnJ5qHrmQkQdsbiaRexsqkScX8q172kY5jBZSAcQrdg/5Ty0WJR9D2hp+WhJ/bcjruwkJv+0iFPaSkHsSwXDRCV9KWoHPdU1FHJpHsF6fjNtt0u490HCugBIyT0gf9pxG64oapUyWiJJLHdO3QI7KPhokSOwa798Tah5ftKJkNtFNUUYLFqQOysBA4dMifTlTt5FQA7RRVlMDxyyZJZB9qoSgGFXRF8QEnP37nTvv1E8k00YJhSjxU0BXFJ+xKIcRKjUwkzTEbyi0owaGCrigB4mSTDBmic5Yq/qOCrigB4mSTzJ7tb5EvrdmiACroihI4djaJn6M4EykyFusCoBeHzMVV2iIRDQLwJIA8AFOY+b+iXn8AwM0AygFsB3AjM9skWFWgaYtKLuNnCqLXbcWqRwMEX6tGSY5YaYtxBZ2I8gCsBXAJgBIAiwGMZOYvLesMAPApMx8kotsBXMjM18Targq6ksv4WeTLa5GxWBcAQHPd051k89B7AVjHzBuY+SiA6QCGW1dg5nnMbE7NRQCKkmmwomQ7fqYgei0yFsvuybR678qpuBH0ZgC+szwviSxz4iYA79q9QERjiGgJES3Zvn27+1YqShbiVwqi1wFHsS4AqZj8OxHPP9a29MJgwakmgPkDcBXENzfPfwLgTw7r/gckQq8Rb7tay0VR/MNLzZZYlSH9rkBp1ya/qkDm6gQeiFHLxY2g9wEwx/L8YQAP26x3MYCvAJwWb5usgq4ooRLrAuBHQa9YYptIkTE7crU8cCxBd9MpWhXSKToQwGZIp+h1zLzKsk43AG8AGMTMX7u5M9BOUUXJLKZNkzz5b78VC2bCBGebKBUdr4nMOJUNJNUpyszlAO4CMAcSgb/GzKuI6DEiGhZZ7Q8AagN4nYiWE9FMn9quKEoa4NX3jtW56leRsVT4/ZmGls9VFCUuXnPd463vJdp3IhXzu6YjWj5XUZSk8DrPabwo3CnDx0vWStjVJ9Myw8bJXA/6TztFFSVzSGSeU6+dq5mUtRJmW6EzFimKkgxO9kbNmkBZWeX1U1HCIEzCbKtaLoqiJEUqJvDwauuESbq2VSN0RVESJhVFxvycl9UvwmyrRuiKogSCn/OcpmJe1lh4iazDbqsTKuiKoiSMn5kmqbB1AHvh9ppnn6q2ekUtF0VR0ho/bZ2gO3dT0VmqlouiKBmLn7bOI4/YWyJ2Yg54j6z9bGsiqKAripLW+GnreBXoFi0ya7CTWi6KouQMXrNTbrgBePHF9MqwUctFURQFzpbIk0/aR9azZ/ubtRJ0jroKuqIoWYeTcMayROzqy/g5JZ+fMzU5oZaLoihZhZ9VGFMxcMrrttRyURQlZ3DKZEnEJvEzayUVE3CroCuKklX4KZx+Zq2kYkIOFXRFUbIKv4XTqXa7V1KRo66CrihKVhH24B4nUpGjXtW/TSmKooSPEchkp7gLApNNExQq6IqiZB1BC2e6opaLoihKlqCCriiKkiWooCuKomQJKuiKoihZggq6oihKlhBaLRci2g7AprKBKxoB2OFjczKFXD1uIHePXY87t3Bz3C2ZubHdC6EJejIQ0RKn4jTZTK4eN5C7x67HnVske9xquSiKomQJKuiKoihZQqYK+uSwGxASuXrcQO4eux53bpHUcWekh64oiqJUJlMjdEVRFCUKFXRFUZQsIeMEnYgGEdEaIlpHRGPDbk9QENFfiWgbEX1hWdaQiP5JRF9HHhuE2cYgIKLmRDSPiL4kolVEdG9keVYfOxHlE9G/iWhF5Lh/E1nemog+jZzvrxJR9bDbGgRElEdEnxHRO5HnWX/cRLSRiD4nouVEtCSyLKnzPKMEnYjyADwFYDCAjgBGElHHcFsVGC8AGBS1bCyAD5i5HYAPIs+zjXIAP2PmjgDOA3Bn5DvO9mM/AuAiZj4XQFcAg4joPACPA/gfZj4TwC4AN4XXxEC5F8BXlue5ctwDmLmrJfc8qfM8owQdQC8A65h5AzMfBTAdwPCQ2xQIzPwhgJ1Ri4cDeDHy/4sALk9lm1IBM5cy87LI//sgP/JmyPJjZ2F/5Gm1yB8DuAjAG5HlWXfcAEBERQCGApgSeU7IgeN2IKnzPNMEvRmA7yzPSyLLcoUmzFwa+X8LgCZhNiZoiKgVgG4APkUOHHvEdlgOYBuAfwJYD2A3M5dHVsnW8/0JAA8COBF5XojcOG4G8B4RLSWiMZFlSZ3nOmNRhsLMTERZm3NKRLUB/B3Afcy8V4I2IVuPnZmPA+hKRPUBvAmgQ7gtCh4iuhTANmZeSkQXhtycVPMDZt5MRKcB+CcRrba+mMh5nmkR+mYAzS3PiyLLcoWtRHQ6AEQet4XcnkAgomoQMZ/GzP+ILM6JYwcAZt4NYB6APgDqE5EJvLLxfO8LYBgRbYRYqBcBeBLZf9xg5s2Rx22QC3gvJHmeZ5qgLwbQLtIDXh3AtQBmhtymVDITwA2R/28A8FaIbQmEiH/6HICvmPmPlpey+tiJqHEkMgcR1QRwCaT/YB6AqyKrZd1xM/PDzFzEzK0gv+e5zDwKWX7cRFSLiOqY/wH8EMAXSPI8z7iRokQ0BOK55QH4KzNPCLdFwUBErwC4EFJOcyuAcQBmAHgNQAtI6eGrmTm64zSjIaIfAFgA4HNUeKq/hPjoWXvsRNQF0gmWBwm0XmPmx4ioDSRybQjgMwD/wcxHwmtpcEQsl58z86XZftyR43sz8rQqgJeZeQIRFSKJ8zzjBF1RFEWxJ9MsF0VRFMUBFXRFUZQsQQVdURQlS1BBVxRFyRJU0BVFUbIEFXRFUZQsQQVdURQlS/j/ESRkB9Y3PWYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "acc = history.history['acc']\n",
    "val_acc = history.history['val_acc']\n",
    "loss = history.history['loss']\n",
    "val_loss = history.history['val_loss']\n",
    "\n",
    "epochs = range(len(acc))\n",
    "\n",
    "plt.plot(epochs, acc, 'bo', label='Training acc')\n",
    "plt.plot(epochs, val_acc, 'b', label='Validation acc')\n",
    "plt.title('Training and validation accuracy')\n",
    "plt.legend()\n",
    "\n",
    "plt.figure()\n",
    "\n",
    "plt.plot(epochs, loss, 'bo', label='Training loss')\n",
    "plt.plot(epochs, val_loss, 'b', label='Validation loss')\n",
    "plt.title('Training and validation loss')\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "still-plain",
   "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"
    },
    {
     "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": [
    "def smooth_curve(points, factor=0.8):\n",
    "  smoothed_points = []\n",
    "  for point in points:\n",
    "    if smoothed_points:\n",
    "      previous = smoothed_points[-1]\n",
    "      smoothed_points.append(previous * factor + point * (1 - factor))\n",
    "    else:\n",
    "      smoothed_points.append(point)\n",
    "  return smoothed_points\n",
    "\n",
    "plt.plot(epochs,\n",
    "         smooth_curve(acc), 'bo', label='Smoothed training acc')\n",
    "plt.plot(epochs,\n",
    "         smooth_curve(val_acc), 'b', label='Smoothed validation acc')\n",
    "plt.title('Training and validation accuracy')\n",
    "plt.legend()\n",
    "\n",
    "plt.figure()\n",
    "\n",
    "plt.plot(epochs,\n",
    "         smooth_curve(loss), 'bo', label='Smoothed training loss')\n",
    "plt.plot(epochs,\n",
    "         smooth_curve(val_loss), 'b', label='Smoothed validation loss')\n",
    "plt.title('Training and validation loss')\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "central-american",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 77 images belonging to 2 classes.\n",
      "test acc: 0.7922077775001526\n"
     ]
    }
   ],
   "source": [
    "test_generator = test_datagen.flow_from_directory(\n",
    "        test_dir,\n",
    "        target_size=(218, 178),\n",
    "        batch_size=20,\n",
    "        class_mode='binary')\n",
    "\n",
    "test_loss, test_acc = model.evaluate_generator(test_generator, steps=4)\n",
    "print('test acc:', test_acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "greenhouse-louisville",
   "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}