Present_ALEX_BEAT_v2_1mod4project_notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "JILuvObhGJu-"
   },
   "source": [
    "# Mod 4 Project - Image Classification\n",
    "\n",
    "* Student name: Alex Beat\n",
    "* Student pace: part time\n",
    "* Scheduled project review date/time: 06/24/20 @12pm pacific\n",
    "* Instructor name: James Irving\n",
    "* Blog post URL: NA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "5yNKgJn4X97-"
   },
   "source": [
    "# Intro"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "L82wyLOxY_2H"
   },
   "source": [
    "* Problem: Build a model that can classify whether a given patient has pneumonia, given a chest x-ray image.\n",
    "* Audience: Medical business, imaging labs. \n",
    "* Business Questions: How can a successful model help save medical professionals time, money and promote better accuracy in patient diagnosis. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "j8el6CF1aHir"
   },
   "source": [
    "## Content\n",
    "The dataset is organized into 2 folders (train, test) and contains subfolders for each image category (Pneumonia/Normal). There are 5,385 X-Ray images (JPEG) and 2 categories (Pneumonia/Normal).\n",
    "\n",
    "Chest X-ray images (anterior-posterior) were selected from retrospective cohorts of pediatric patients of one to five years old from Guangzhou Women and Children’s Medical Center, Guangzhou. All chest X-ray imaging was performed as part of patients’ routine clinical care.\n",
    "\n",
    "For the analysis of chest x-ray images, all chest radiographs were initially screened for quality control by removing all low quality or unreadable scans. The diagnoses for the images were then graded by two expert physicians before being cleared for training the AI system. In order to account for any grading errors, the evaluation set was also checked by a third expert.\n",
    "\n",
    "(Content info and dataset provided by Paul Mooney on Kaggle: https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia?)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "InclPflMXvZ1"
   },
   "source": [
    "# Select Colab directory and unzip virtually in colab."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "cMMdd93Df0_n"
   },
   "source": [
    "This first section will help those who want to or need to run Keras and Tensorflow in Colab. I was forced to run my notebooks in the cloud using Colab because my OS was too old and wouldn't run the necesary versions of Keras. Working with Colab is very fast with image loading, as long as you set it up correctly. If not, it will be slower than a notebook on your own machine. This blog post was used to help load in your entire zip file and virtually unzip your image files in one move in colab as opposed to how Colab normally would load in each image individually: https://medium.com/datadriveninvestor/speed-up-your-image-training-on-google-colab-dc95ea1491cf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "GJF7ocAf-5Oc"
   },
   "source": [
    "### Mount google drive connection to colab. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 156
    },
    "colab_type": "code",
    "id": "qVAJAUjGHpOn",
    "outputId": "03f30b56-921f-4099-e030-3852fafd8573"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
      "\n",
      "Enter your authorization code:\n",
      "··········\n",
      "Mounted at /content/drive\n",
      "/root\n",
      "/\n"
     ]
    }
   ],
   "source": [
    "from google.colab import drive\n",
    "\n",
    "# choose your drive file path\n",
    "drive.mount('/content/drive', force_remount=True)\n",
    "\n",
    "# cd to the uppermost folder in gdrive to save your zip file\n",
    "%cd ~\n",
    "%cd .."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "w-FeLKZ6_4hF"
   },
   "source": [
    "### Create file paths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 52
    },
    "colab_type": "code",
    "id": "P7tGSw_oVzP3",
    "outputId": "b0695f9b-c5e0-4bd6-b1a9-ecb9506b53f7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/\n"
     ]
    },
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic": {
       "type": "string"
      },
      "text/plain": [
       "'content/drive/My Drive/Datasets/xray.zip'"
      ]
     },
     "execution_count": 2,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os,glob\n",
    "\n",
    "# check current directory to see if you're at the top\n",
    "print(os.path.abspath(os.curdir))\n",
    "\n",
    "# file path to save your zip file dataset\n",
    "source_folder = r'content/drive/My Drive/Datasets/'\n",
    "\n",
    "# variable for your filename\n",
    "file = glob.glob(source_folder+'xray.zip',recursive=True)[0]\n",
    "file"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "tWFS5rvF__Bm"
   },
   "source": [
    "### Zip virtual copy/unzip/removal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "IjfEc304WB0u"
   },
   "outputs": [],
   "source": [
    "# variable for the path\n",
    "zip_path = file\n",
    "\n",
    "# copies the zip file\n",
    "!cp \"{zip_path}\" .\n",
    "\n",
    "# unzip file virtually in colab\n",
    "!unzip -q xray.zip\n",
    "\n",
    "# removes copied zip\n",
    "!rm xray.zip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 527
    },
    "colab_type": "code",
    "id": "bkZicl01WEoO",
    "outputId": "3f9e9df3-2a5f-4e3a-c166-a37b1f8633c2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['lib',\n",
       " 'dev',\n",
       " 'etc',\n",
       " 'sbin',\n",
       " 'root',\n",
       " 'sys',\n",
       " 'proc',\n",
       " 'home',\n",
       " 'usr',\n",
       " 'var',\n",
       " 'tmp',\n",
       " 'run',\n",
       " 'srv',\n",
       " 'lib64',\n",
       " 'boot',\n",
       " 'bin',\n",
       " 'opt',\n",
       " 'mnt',\n",
       " 'media',\n",
       " 'xray',\n",
       " 'content',\n",
       " '.dockerenv',\n",
       " 'datalab',\n",
       " 'tools',\n",
       " 'swift',\n",
       " 'dlib-19.18.0-cp36-cp36m-linux_x86_64.whl',\n",
       " 'tensorflow-1.15.2',\n",
       " 'dlib-19.18.0-cp27-cp27mu-linux_x86_64.whl',\n",
       " 'lib32']"
      ]
     },
     "execution_count": 4,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check directory to see virtual files are in your main folder\n",
    "import os,glob\n",
    "print(os.path.abspath(os.curdir))\n",
    "os.listdir()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "9z81B-FVX7L3"
   },
   "source": [
    "# Step 1: Load the Data / Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 238
    },
    "colab_type": "code",
    "id": "H6e24655bfhf",
    "outputId": "9f68a1ec-1fa0-4f6d-b337-f93ac11bfd63"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fsds_1007219  v0.7.22 loaded.  Read the docs: https://fsds.readthedocs.io/en/latest/ \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_11440b02_bbbe_11ea_a283_0242ac1c0002\" ><caption>Loaded Packages and Handles</caption><thead>    <tr>        <th class=\"col_heading level0 col0\" >Handle</th>        <th class=\"col_heading level0 col1\" >Package</th>        <th class=\"col_heading level0 col2\" >Description</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                                <td id=\"T_11440b02_bbbe_11ea_a283_0242ac1c0002row0_col0\" class=\"data row0 col0\" >dp</td>\n",
       "                        <td id=\"T_11440b02_bbbe_11ea_a283_0242ac1c0002row0_col1\" class=\"data row0 col1\" >IPython.display</td>\n",
       "                        <td id=\"T_11440b02_bbbe_11ea_a283_0242ac1c0002row0_col2\" class=\"data row0 col2\" >Display modules with helpful display and clearing commands.</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_11440b02_bbbe_11ea_a283_0242ac1c0002row1_col0\" class=\"data row1 col0\" >fs</td>\n",
       "                        <td id=\"T_11440b02_bbbe_11ea_a283_0242ac1c0002row1_col1\" class=\"data row1 col1\" >fsds_100719</td>\n",
       "                        <td id=\"T_11440b02_bbbe_11ea_a283_0242ac1c0002row1_col2\" class=\"data row1 col2\" >Custom data science bootcamp student package</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_11440b02_bbbe_11ea_a283_0242ac1c0002row2_col0\" class=\"data row2 col0\" >mpl</td>\n",
       "                        <td id=\"T_11440b02_bbbe_11ea_a283_0242ac1c0002row2_col1\" class=\"data row2 col1\" >matplotlib</td>\n",
       "                        <td id=\"T_11440b02_bbbe_11ea_a283_0242ac1c0002row2_col2\" class=\"data row2 col2\" >Matplotlib's base OOP module with formatting artists</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_11440b02_bbbe_11ea_a283_0242ac1c0002row3_col0\" class=\"data row3 col0\" >plt</td>\n",
       "                        <td id=\"T_11440b02_bbbe_11ea_a283_0242ac1c0002row3_col1\" class=\"data row3 col1\" >matplotlib.pyplot</td>\n",
       "                        <td id=\"T_11440b02_bbbe_11ea_a283_0242ac1c0002row3_col2\" class=\"data row3 col2\" >Matplotlib's matlab-like plotting module</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_11440b02_bbbe_11ea_a283_0242ac1c0002row4_col0\" class=\"data row4 col0\" >np</td>\n",
       "                        <td id=\"T_11440b02_bbbe_11ea_a283_0242ac1c0002row4_col1\" class=\"data row4 col1\" >numpy</td>\n",
       "                        <td id=\"T_11440b02_bbbe_11ea_a283_0242ac1c0002row4_col2\" class=\"data row4 col2\" >scientific computing with Python</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_11440b02_bbbe_11ea_a283_0242ac1c0002row5_col0\" class=\"data row5 col0\" >pd</td>\n",
       "                        <td id=\"T_11440b02_bbbe_11ea_a283_0242ac1c0002row5_col1\" class=\"data row5 col1\" >pandas</td>\n",
       "                        <td id=\"T_11440b02_bbbe_11ea_a283_0242ac1c0002row5_col2\" class=\"data row5 col2\" >High performance data structures and tools</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_11440b02_bbbe_11ea_a283_0242ac1c0002row6_col0\" class=\"data row6 col0\" >sns</td>\n",
       "                        <td id=\"T_11440b02_bbbe_11ea_a283_0242ac1c0002row6_col1\" class=\"data row6 col1\" >seaborn</td>\n",
       "                        <td id=\"T_11440b02_bbbe_11ea_a283_0242ac1c0002row6_col2\" class=\"data row6 col2\" >High-level data visualization library based on matplotlib</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7fafe8632080>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[i] Pandas .iplot() method activated.\n"
     ]
    }
   ],
   "source": [
    "!pip install -U fsds_100719\n",
    "from fsds_100719.imports import *\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "np.random.seed(111)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "tZ-7YxuBjTbS",
    "outputId": "200a2a8f-8f45-4817-8aa7-abd999a60a86"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic": {
       "type": "string"
      },
      "text/plain": [
       "'2.2.0'"
      ]
     },
     "execution_count": 16,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import tensorflow\n",
    "tensorflow.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "OYWYYW5w3vFa"
   },
   "source": [
    "## Define folder structure for use with .flow."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "GUPy3XbnquCO"
   },
   "outputs": [],
   "source": [
    "#  Set up directory paths from gdrive\n",
    "train_dir_normal = 'xray/train/NORMAL'\n",
    "train_dir_pneum = 'xray/train/PNEUMONIA'\n",
    "test_dir_normal = 'xray/test/NORMAL'\n",
    "test_dir_pneum = 'xray/test/PNEUMONIA'\n",
    "all_dir_paths = [train_dir_normal, train_dir_pneum, test_dir_normal,\n",
    "                 test_dir_pneum]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 85
    },
    "colab_type": "code",
    "id": "z6pg5a6M0AIF",
    "outputId": "d606303d-fd23-4444-a901-023895e5b00e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 1342 images in xray/train/NORMAL folder.\n",
      "There are 3419 images in xray/train/PNEUMONIA folder.\n",
      "There are 234 images in xray/test/NORMAL folder.\n",
      "There are 390 images in xray/test/PNEUMONIA folder.\n"
     ]
    }
   ],
   "source": [
    "# use loop to get number of samples from all folders\n",
    "for dataset in all_dir_paths: \n",
    "  print(f'There are {len(os.listdir(dataset))} images in {str(dataset)} folder.') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Q2HXcdWk4xwV"
   },
   "outputs": [],
   "source": [
    "import glob,os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "myumPl0mDfLk"
   },
   "outputs": [],
   "source": [
    "# use glob to get filenames of all images in each folder\n",
    "train_files_normal = glob.glob(train_dir_normal+'/*.jpeg')\n",
    "train_files_pneum = glob.glob(train_dir_pneum+'/*.jpeg')\n",
    "all_train_files = [*train_files_normal,*train_files_pneum]\n",
    "\n",
    "test_files_normal = glob.glob(test_dir_normal+'/*.jpeg')\n",
    "test_files_pneum = glob.glob(test_dir_pneum+'/*.jpeg')\n",
    "all_test_files = [*test_files_normal,*test_files_pneum]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "aeBCGJ6Ua32c"
   },
   "source": [
    "## Functions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "gvgKkfPnLxcb"
   },
   "source": [
    "These functions were adapted from this Colab notebook about CNNs. https://colab.research.google.com/drive/1fwXPY3IDHxNiv7YgOpt3p5BvUaO4VruB?usp=sharing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "SSZUgInsBG_N"
   },
   "outputs": [],
   "source": [
    "from PIL import Image\n",
    "from keras.preprocessing import image\n",
    "from imageio import imread\n",
    "from skimage.transform import resize\n",
    "from tqdm import tqdm\n",
    "from keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img\n",
    "from PIL import Image\n",
    "import sklearn.metrics as metrics\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline \n",
    "import itertools\n",
    "\n",
    "def read_img(img_path,target_size=(64, 64)):\n",
    "  \"\"\"This function reads in the image using the image path filenames created by glob. \n",
    "  Then it turns the image into an arrary for use with modeling. Specify target pixel \n",
    "  size using target_size=(n,n). Default is 64,64 pixels.\"\"\"\n",
    "  \n",
    "  img = image.load_img(img_path, target_size=target_size)\n",
    "  img = image.img_to_array(img)\n",
    "  \n",
    "  return img\n",
    "\n",
    "def load_train_test_images(training_filenames_normal, training_filenames_pneum,\n",
    "                        test_filenames_normal, test_filenames_pneum,\n",
    "                        img_size=(64,64)):\n",
    "    \"\"\"Reads in training, test filenames, uses read_img() to change to \n",
    "    change img to numpy array, then produces X and y data splits, in addition to\n",
    "    creating proper binary labels for modeling.\n",
    "\n",
    "    ylabels are encoded as 0=normal, 1=pneumonia\n",
    "    Returns:  X_train, X_test, y_train, y_test\"\"\"\n",
    "    \n",
    "    display('[i] LOADING IMAGES')\n",
    "\n",
    "\n",
    " # create empty lists to contain the image filenames and another to contain\n",
    " # the classification ylabel for each image.\n",
    "    train_img = []\n",
    "    train_label = []\n",
    "\n",
    "# reads in and classifies training normal label\n",
    "    for img_path in tqdm(training_filenames_normal):\n",
    "        train_img.append(read_img(img_path,target_size=img_size))\n",
    "        train_label.append(0)\n",
    "\n",
    "# reads in and classifies training penumonia label\n",
    "    for img_path in tqdm(training_filenames_pneum):\n",
    "        train_img.append(read_img(img_path,target_size=img_size))\n",
    "        train_label.append(1)\n",
    "\n",
    "\n",
    " # create empty lists to contain the image filenames and another to contain\n",
    " # the classification ylabel for each image.\n",
    "    test_img = []\n",
    "    test_label = []\n",
    "\n",
    "# reads in and classifies test normal label\n",
    "    for img_path in tqdm(test_filenames_normal):\n",
    "        test_img.append(read_img(img_path,target_size=img_size))\n",
    "        test_label.append(0)\n",
    "\n",
    "# reads in and classifies test penumonia label\n",
    "    for img_path in tqdm(test_filenames_pneum):\n",
    "        test_img.append(read_img(img_path,target_size=img_size))\n",
    "        test_label.append(1)\n",
    "\n",
    "\n",
    "# create your X_train and y_train variables for use in modeling\n",
    "    X_train = np.array(train_img, np.float32)\n",
    "    y_train = np.array(train_label)\n",
    "\n",
    "    X_test = np.array(test_img, np.float32)\n",
    "    y_test = np.array(test_label)\n",
    "\n",
    "\n",
    "# Prints the length of each split for use in batching and knowledge of data. \n",
    "    print('\\n[i] Length of Splits:')\n",
    "    print(f\"X_train={len(X_train)}, X_test={len(X_test)}\")\n",
    "\n",
    "    # return X_train, X_test, y_train, y_test\n",
    "    return X_train, X_test, y_train, y_test\n",
    "\n",
    "\n",
    "\n",
    "# final eval of model showing report, confusion matrix and acc/loss graphs\n",
    "def evaluate_model(y_test, y_pred, model_history):\n",
    "    \"\"\"Takes in your target, target predictions, model history.\n",
    "        Returns a metric report, confusion matrix and plots for \n",
    "        accuracy and loss.\"\"\"\n",
    "    \n",
    "    ## Classification Report / Scores \n",
    "    print(metrics.classification_report(y_test,y_pred))\n",
    "\n",
    "    # confusion matrix\n",
    "    fig, ax = plt.subplots(figsize=(12,6))\n",
    "    cm = metrics.confusion_matrix(y_test, y_pred, normalize='true')\n",
    "\n",
    "    # Add title and axis labels\n",
    "    plt.imshow(cm, interpolation='nearest', cmap='Blues' )\n",
    "    plt.title('Confusion Matrix. 0=Norm, 1=Pneum')\n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel('Predicted label')\n",
    "\n",
    "    # Add appropriate axis scales\n",
    "    class_names = set(y_test) # Get class labels to add to matrix\n",
    "    tick_marks = np.arange(len(class_names))\n",
    "    plt.xticks(tick_marks, class_names)\n",
    "    plt.yticks(tick_marks, class_names)\n",
    "\n",
    "    # Add labels to each cell\n",
    "    thresh = cm.max() / 2. # Used for text coloring below\n",
    "    # Here we iterate through the confusion matrix and append labels to our visualization \n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "            plt.text(j, i, round(cm[i, j], 2),\n",
    "                    horizontalalignment='center',\n",
    "                    color='white' if cm[i, j] > thresh else 'black')\n",
    "\n",
    "    # classes = list(range(len(cm)))  \n",
    "    # plt.xticks(classes, classes)\n",
    "    # plt.yticks(classes, classes)\n",
    "\n",
    "    plt.colorbar()\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "    # loss and acc plots\n",
    "    fig, ax = plt.subplots(figsize=(12,6),ncols=2)\n",
    "\n",
    "    train_acc = model_history.history['acc']\n",
    "    test_acc = model_history.history['val_acc']\n",
    "    train_loss = model_history.history['loss']\n",
    "    test_loss = model_history.history['val_loss']\n",
    "\n",
    "    epochs = range(len(train_acc))\n",
    "    ax[0].plot(epochs, train_acc, 'g', label='Training acc')\n",
    "    ax[0].plot(epochs, test_acc, 'b', label='Test acc')\n",
    "    ax[0].legend()\n",
    "\n",
    "    ax[1].plot(epochs, train_loss, 'g', label='Training loss')\n",
    "    ax[1].plot(epochs, test_loss, 'b', label='Test loss')\n",
    "    ax[1].legend()\n",
    "\n",
    "    ax[0].set(title='Training and Testing accuracy')\n",
    "    ax[1].set(title='Training and Testing loss')\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "NKEXojGJxPbt"
   },
   "source": [
    "## Load images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 171
    },
    "colab_type": "code",
    "id": "5vZ5RhJ-Pjat",
    "outputId": "c0b1ec3b-abba-49ca-ac2b-a42570544497"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic": {
       "type": "string"
      },
      "text/plain": [
       "'[i] LOADING IMAGES'"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1341/1341 [00:29<00:00, 45.57it/s]\n",
      "100%|██████████| 3418/3418 [00:22<00:00, 151.60it/s]\n",
      "100%|██████████| 234/234 [00:03<00:00, 64.61it/s]\n",
      "100%|██████████| 390/390 [00:02<00:00, 179.74it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[i] Length of Splits:\n",
      "X_train=4759, X_test=624\n",
      "Training set shape  (100, 64, 64, 3)\n"
     ]
    }
   ],
   "source": [
    "## USING FUNCTION TO LOAD IN IMAGES \n",
    "X_train,X_test,y_train,y_test = load_train_test_images(train_files_normal, \n",
    "                                                           train_files_pneum, \n",
    "                                                           test_files_normal, \n",
    "                                                           test_files_pneum,\n",
    "                                                          img_size=(64,64))\n",
    "\n",
    "print('Training set shape ', training_set[0][0].shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 68
    },
    "colab_type": "code",
    "id": "njvK3BDKmTgY",
    "outputId": "a2810b06-9a77-4255-b1d6-2c7d9d808c93"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 3808 images belonging to 2 classes.\n",
      "Found 951 images belonging to 2 classes.\n",
      "Found 624 images belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "# create train set generator\n",
    "train_datagen = ImageDataGenerator(rescale = 1./255,\n",
    "                                       shear_range = 0.2, \n",
    "                                        zoom_range = 0.2,\n",
    "                                   validation_split=0.2)\n",
    "\n",
    "# create test set generator\n",
    "test_datagen = ImageDataGenerator(rescale = 1./255)\n",
    "\n",
    "# pulls training images from file path, creates a trainings subset and a val subset \n",
    "# in the following flow. Class mode is set to binary for labeling. \n",
    "training_set = train_datagen.flow_from_directory('xray/train/',batch_size=32,\n",
    "                                                 seed=1,subset='training', \n",
    "                                                 target_size=(64,64),\n",
    "                                                 class_mode='binary')\n",
    "val_set = train_datagen.flow_from_directory('xray/train/',batch_size=32,\n",
    "                                            seed=1,subset='validation',\n",
    "                                            target_size=(64,64),\n",
    "                                            class_mode='binary')\n",
    "\n",
    "# same thing but for a separate test set of images. \n",
    "test_set = test_datagen.flow_from_directory('xray/test/',batch_size=32,\n",
    "                                            target_size=(64,64),\n",
    "                                            class_mode='binary')\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "QYkX1aeQGJy0"
   },
   "source": [
    "# Step 2: EDA and Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ajnHbbfqx3vR"
   },
   "source": [
    "## View an image"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ciCcKGlHDuKq"
   },
   "source": [
    "Test to view an image using filename to make sure they loaded into notebook correctly. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 292
    },
    "colab_type": "code",
    "id": "5zbo5SDW3P8m",
    "outputId": "91f13b69-6b16-492a-e1fa-879835875156"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# view a regular image file\n",
    "from keras.preprocessing import image\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "file_normal = train_files_normal[0] # uses first normal filename from glob list\n",
    "file_pneum = train_files_pneum[0] # uses first pneumonia filename from glob list\n",
    "\n",
    "img_n = image.load_img(file_normal)\n",
    "img_p = image.load_img(file_pneum)\n",
    "\n",
    "# need subplot titles and each plot still\n",
    "fig, ax = plt.subplots(figsize=(12,6),ncols=2)\n",
    "\n",
    "\n",
    "ax[0].imshow(img_n)\n",
    "ax[0].set(title='Normal')\n",
    "ax[0].axis('off')\n",
    "\n",
    "ax[1].imshow(img_p)\n",
    "ax[1].set(title='Pneumonia')\n",
    "ax[1].axis('off')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "33h1C88DENSS"
   },
   "source": [
    "Test to view image as a tensor array. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 265
    },
    "colab_type": "code",
    "id": "kisL4iPM3c85",
    "outputId": "eeaf411a-5a85-47d8-afd0-e14b061b348e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 64, 64, 3)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature_img = train_files_normal[1] # uses first normal filename from glob list\n",
    "\n",
    "f_img = image.load_img(feature_img, target_size=(64,64))\n",
    "\n",
    "# view as a tensor image\n",
    "img_tensor = image.img_to_array(f_img)\n",
    "img_tensor = np.expand_dims(img_tensor, axis=0)\n",
    "\n",
    "# Follow the Original Model Preprocessing\n",
    "img_tensor /= 255.\n",
    "\n",
    "# Check tensor shape\n",
    "print(img_tensor.shape)\n",
    "\n",
    "# Preview an image\n",
    "plt.imshow(img_tensor[0])\n",
    "plt.axis('off')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "G_SqdBCJGJ3R"
   },
   "source": [
    "# Step 4: Modeling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "JQYokTp7gfu7"
   },
   "source": [
    "## BUILD BASELINE CNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 768
    },
    "colab_type": "code",
    "id": "ygeEcJrmixhh",
    "outputId": "75564f0e-d25a-4382-aff7-3ed138ace3d6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Model: \"sequential_20\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_58 (Conv2D)           (None, 62, 62, 32)        896       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_58 (MaxPooling (None, 31, 31, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_59 (Conv2D)           (None, 29, 29, 64)        18496     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_59 (MaxPooling (None, 14, 14, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_60 (Conv2D)           (None, 12, 12, 128)       73856     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_60 (MaxPooling (None, 6, 6, 128)         0         \n",
      "_________________________________________________________________\n",
      "flatten_20 (Flatten)         (None, 4608)              0         \n",
      "_________________________________________________________________\n",
      "dense_39 (Dense)             (None, 512)               2359808   \n",
      "_________________________________________________________________\n",
      "dense_40 (Dense)             (None, 1)                 513       \n",
      "=================================================================\n",
      "Total params: 2,453,569\n",
      "Trainable params: 2,453,569\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "None"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "500/500 [==============================] - 256s 512ms/step - loss: 0.5463 - acc: 0.7364 - val_loss: 0.3210 - val_acc: 0.8427\n",
      "Epoch 2/10\n",
      "500/500 [==============================] - 256s 513ms/step - loss: 0.3320 - acc: 0.8583 - val_loss: 0.3742 - val_acc: 0.8651\n",
      "Epoch 3/10\n",
      "500/500 [==============================] - 253s 507ms/step - loss: 0.2458 - acc: 0.8999 - val_loss: 0.2392 - val_acc: 0.8957\n",
      "Epoch 4/10\n",
      "500/500 [==============================] - 255s 510ms/step - loss: 0.2126 - acc: 0.9128 - val_loss: 0.1965 - val_acc: 0.9059\n",
      "Epoch 5/10\n",
      "500/500 [==============================] - 255s 510ms/step - loss: 0.1896 - acc: 0.9255 - val_loss: 0.2837 - val_acc: 0.8717\n",
      "Epoch 6/10\n",
      "500/500 [==============================] - 254s 508ms/step - loss: 0.1700 - acc: 0.9331 - val_loss: 0.2116 - val_acc: 0.9282\n",
      "Epoch 7/10\n",
      "500/500 [==============================] - 255s 509ms/step - loss: 0.1593 - acc: 0.9397 - val_loss: 0.2783 - val_acc: 0.9314\n",
      "Training took a total of 0:29:44.957211\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(111)\n",
    "import keras\n",
    "from keras import layers\n",
    "from keras import models\n",
    "from keras import optimizers\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "import datetime\n",
    "from keras.callbacks import EarlyStopping\n",
    "\n",
    "\n",
    "\n",
    "# timer for model\n",
    "original_start = datetime.datetime.now()\n",
    "start = datetime.datetime.now()\n",
    "\n",
    "# Initialising the CNN\n",
    "model = Sequential()\n",
    "\n",
    "# Step 1 - Convolution\n",
    "model.add(layers.Conv2D(32, (3, 3), activation='relu',\n",
    "                        input_shape=(64 ,64,  3)))\n",
    "# Step 2 - Pooling\n",
    "model.add(layers.MaxPooling2D((2, 2)))\n",
    "\n",
    "# Adding a second convolutional layer\n",
    "model.add(layers.Conv2D(64, (3, 3), activation='relu',\n",
    "                        input_shape=(64 ,64,  3)))\n",
    "# Pooling\n",
    "model.add(layers.MaxPooling2D((2, 2)))\n",
    "\n",
    "# Adding a third convolutional layer\n",
    "model.add(layers.Conv2D(128, (3, 3), activation='relu',\n",
    "                        input_shape=(64 ,64,  3)))\n",
    "# Pooling\n",
    "model.add(layers.MaxPooling2D((2, 2)))\n",
    "\n",
    "# Step 3 - Flattening\n",
    "model.add(layers.Flatten())\n",
    "\n",
    "# Step 4 - Full connection\n",
    "model.add(layers.Dense(units = 512, activation = 'relu'))\n",
    "\n",
    "model.add(layers.Dense(units = 1, activation = 'sigmoid'))\n",
    "\n",
    "# Compiling the CNN\n",
    "model.compile(optimizer = 'sgd', \n",
    "                   loss = 'binary_crossentropy',\n",
    "                   metrics = ['acc'])\n",
    "print()\n",
    "display(model.summary())\n",
    "\n",
    "# Define the callbacks for early stopping of model based on val loss change.\n",
    "early_stopping = [EarlyStopping(monitor='val_loss', min_delta= 0.01, patience=3)]\n",
    "                \n",
    "\n",
    "# Fitting the CNN to the images using fit_generator\n",
    "history = model.fit(training_set,\n",
    "                             steps_per_epoch = 500,\n",
    "                             epochs = 10,\n",
    "                            callbacks=early_stopping,\n",
    "                             validation_data = val_set,\n",
    "                             validation_steps =200, verbose=1, workers=-1)\n",
    "# end timer\n",
    "end = datetime.datetime.now()\n",
    "elapsed = end - start\n",
    "print('Training took a total of {}'.format(elapsed))                              "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "id": "Dv6xQX8xgvjx",
    "outputId": "86a3c3e1-afda-4f49-a229-cc44061a16dd"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4759/4759 [==============================] - 1s 218us/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[13.539220279744773, 0.9554527997970581]"
      ]
     },
     "execution_count": 68,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# training loss and accuracy\n",
    "results_train = model.evaluate(X_train, y_train)\n",
    "results_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "id": "uG2fWjQOgxcP",
    "outputId": "3aaa3f30-a376-49e4-bb87-5476cdde6ad2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "624/624 [==============================] - 0s 212us/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[57.79526121799763, 0.879807710647583]"
      ]
     },
     "execution_count": 69,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# test loss and accuracy\n",
    "results_test = model.evaluate(X_test, y_test)\n",
    "results_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "OOE0LwYTo2BI"
   },
   "outputs": [],
   "source": [
    "# # Your code here; save the model for future reference \n",
    "# model.save('baseline_model.h5')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "2h9EqXmfGJ46"
   },
   "source": [
    "### Interpreting Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 981
    },
    "colab_type": "code",
    "id": "qNB3F6HNe_Oa",
    "outputId": "b1657a19-14bc-4e36-b14a-1624bc83554a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.93      0.74      0.82       234\n",
      "           1       0.86      0.96      0.91       390\n",
      "\n",
      "    accuracy                           0.88       624\n",
      "   macro avg       0.89      0.85      0.87       624\n",
      "weighted avg       0.88      0.88      0.88       624\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_preds = model.predict_classes(X_test).flatten()\n",
    "\n",
    "evaluate_model(y_test,y_preds,history)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "rQbv3oOwuGpy"
   },
   "source": [
    "Training accuracy 95%. Test accuracy at 88% and loss at 58%. Pneumonia recall - 97%, F1 score 91."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "xz8ntdxLyW2c"
   },
   "source": [
    "# Visualize Feature Map Layers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "urHAMZdvpQEu"
   },
   "source": [
    "This will help show you what's happening with the images after each layer of the model network and how the patterns are developed. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "1mHxTKWEGJ0Y"
   },
   "outputs": [],
   "source": [
    "from keras import models\n",
    "import math \n",
    "\n",
    "# Extract model layer outputs\n",
    "layer_outputs = [layer.output for layer in model.layers[:6]]\n",
    "\n",
    "# Rather then a model with a single output, we are going to make a model to display the feature maps\n",
    "activation_model = models.Model(inputs=model.input, outputs=layer_outputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 459
    },
    "colab_type": "code",
    "id": "xDrvXQ-sGJ1f",
    "outputId": "4c11ae18-1c95-4564-83b4-717faf7fa329"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_20\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_58 (Conv2D)           (None, 62, 62, 32)        896       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_58 (MaxPooling (None, 31, 31, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_59 (Conv2D)           (None, 29, 29, 64)        18496     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_59 (MaxPooling (None, 14, 14, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_60 (Conv2D)           (None, 12, 12, 128)       73856     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_60 (MaxPooling (None, 6, 6, 128)         0         \n",
      "_________________________________________________________________\n",
      "flatten_20 (Flatten)         (None, 4608)              0         \n",
      "_________________________________________________________________\n",
      "dense_39 (Dense)             (None, 512)               2359808   \n",
      "_________________________________________________________________\n",
      "dense_40 (Dense)             (None, 1)                 513       \n",
      "=================================================================\n",
      "Total params: 2,453,569\n",
      "Trainable params: 2,453,569\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 271
    },
    "colab_type": "code",
    "id": "524AqvXmGJ2U",
    "outputId": "b82b3bb0-590b-4112-bd02-9a6cf5b5b702"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 62, 62, 32)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Returns an array for each activation layer\n",
    "activations = activation_model.predict(img_tensor)\n",
    "\n",
    "first_layer_activation = activations[0]\n",
    "print(first_layer_activation.shape)\n",
    "\n",
    "# We slice the third channel and preview the results\n",
    "plt.matshow(first_layer_activation[0, :, :, 3], cmap='viridis')\n",
    "plt.axis('off')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "wPqLIQ5pzk2I"
   },
   "source": [
    "REPEAT FOR ALL LAYERS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 695
    },
    "colab_type": "code",
    "id": "5Kq7-n-fzpMC",
    "outputId": "47e86e11-a906-4bdc-c0cf-351415b159ab"
   },
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "ignored",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-75-959193e8806f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      8\u001b[0m     \u001b[0mcolumn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m%\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m     \u001b[0max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maxes\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumn\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m     \u001b[0mcur_layer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mactivations\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     11\u001b[0m     \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmatshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcur_layer\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m29\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcmap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'viridis'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m     \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxaxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_ticks_position\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'bottom'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mIndexError\u001b[0m: list index out of range"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(2,4, figsize=(12,8))\n",
    "layer_names = []\n",
    "for layer in model.layers[:8]:\n",
    "    layer_names.append(layer.name)\n",
    "\n",
    "for i in range(8):\n",
    "    row = i//4\n",
    "    column = i%4\n",
    "    ax = axes[row, column]\n",
    "    cur_layer = activations[i]\n",
    "    ax.matshow(cur_layer[0, :, :, 29], cmap='viridis')\n",
    "    ax.xaxis.set_ticks_position('bottom')\n",
    "    ax.axis('off')\n",
    "    ax.set_title(layer_names[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "3B29kUQqpFXq"
   },
   "source": [
    "# Step 5: Model Hyperparams Updates"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "HG6m85_A7HJS"
   },
   "source": [
    "## Second model with RMSprop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 700
    },
    "colab_type": "code",
    "id": "SF1vCaMp695Y",
    "outputId": "640e72a7-ff8b-4c98-f086-6e684841464f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Model: \"sequential_21\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_61 (Conv2D)           (None, 62, 62, 32)        896       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_61 (MaxPooling (None, 31, 31, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_62 (Conv2D)           (None, 29, 29, 64)        18496     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_62 (MaxPooling (None, 14, 14, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_63 (Conv2D)           (None, 12, 12, 128)       73856     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_63 (MaxPooling (None, 6, 6, 128)         0         \n",
      "_________________________________________________________________\n",
      "flatten_21 (Flatten)         (None, 4608)              0         \n",
      "_________________________________________________________________\n",
      "dense_41 (Dense)             (None, 512)               2359808   \n",
      "_________________________________________________________________\n",
      "dense_42 (Dense)             (None, 1)                 513       \n",
      "=================================================================\n",
      "Total params: 2,453,569\n",
      "Trainable params: 2,453,569\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "None"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "500/500 [==============================] - 256s 512ms/step - loss: 0.3023 - acc: 0.8749 - val_loss: 0.1754 - val_acc: 0.9174\n",
      "Epoch 2/10\n",
      "500/500 [==============================] - 255s 509ms/step - loss: 0.1569 - acc: 0.9394 - val_loss: 0.0748 - val_acc: 0.9399\n",
      "Epoch 3/10\n",
      "500/500 [==============================] - 255s 510ms/step - loss: 0.1267 - acc: 0.9538 - val_loss: 0.4958 - val_acc: 0.9446\n",
      "Epoch 4/10\n",
      "500/500 [==============================] - 256s 511ms/step - loss: 0.1080 - acc: 0.9610 - val_loss: 0.2003 - val_acc: 0.9419\n",
      "Epoch 5/10\n",
      "500/500 [==============================] - 256s 512ms/step - loss: 0.0954 - acc: 0.9662 - val_loss: 0.0786 - val_acc: 0.9462\n",
      "Training took a total of 0:21:18.111254\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(111)\n",
    "\n",
    "from keras import layers\n",
    "from keras import models\n",
    "from keras import optimizers\n",
    "import datetime\n",
    "from keras.callbacks import EarlyStopping\n",
    "\n",
    "\n",
    "\n",
    "# timer for model\n",
    "start = datetime.datetime.now()\n",
    "\n",
    "# Initialising the CNN\n",
    "rms_model = Sequential()\n",
    "\n",
    "# Step 1 - Convolution\n",
    "rms_model.add(layers.Conv2D(32, (3, 3), activation='relu',\n",
    "                        input_shape=(64 ,64,  3)))\n",
    "# Step 2 - Pooling\n",
    "rms_model.add(layers.MaxPooling2D((2, 2)))\n",
    "\n",
    "# Adding a second convolutional layer\n",
    "rms_model.add(layers.Conv2D(64, (3, 3), activation='relu',\n",
    "                        input_shape=(64 ,64,  3)))\n",
    "# Pooling\n",
    "rms_model.add(layers.MaxPooling2D((2, 2)))\n",
    "\n",
    "# Adding a third convolutional layer\n",
    "rms_model.add(layers.Conv2D(128, (3, 3), activation='relu',\n",
    "                        input_shape=(64 ,64,  3)))\n",
    "# Pooling\n",
    "rms_model.add(layers.MaxPooling2D((2, 2)))\n",
    "\n",
    "# Step 3 - Flattening\n",
    "rms_model.add(layers.Flatten())\n",
    "\n",
    "# Step 4 - Full connection\n",
    "rms_model.add(layers.Dense(units = 512, activation = 'relu'))\n",
    "\n",
    "rms_model.add(layers.Dense(units = 1, activation = 'sigmoid'))\n",
    "\n",
    "# Compiling the CNN\n",
    "rms_model.compile(optimizer = 'rmsprop', \n",
    "                   loss = 'binary_crossentropy',\n",
    "                   metrics = ['acc'])\n",
    "print()\n",
    "display(rms_model.summary())\n",
    "\n",
    "# Define the callbacks for early stopping of model based on val loss change.\n",
    "early_stopping = [EarlyStopping(monitor='val_loss', min_delta= 0.01, patience=3)]\n",
    "                \n",
    "\n",
    "# Fitting the CNN to the images using fit_generator\n",
    "rms_history = rms_model.fit(training_set,\n",
    "                             steps_per_epoch = 500,\n",
    "                             epochs = 10,\n",
    "                            callbacks=early_stopping,\n",
    "                             validation_data = val_set,\n",
    "                             validation_steps =200, verbose=1, workers=-1)\n",
    "\n",
    "# end timer\n",
    "end = datetime.datetime.now()\n",
    "elapsed = end - start\n",
    "print('Training took a total of {}'.format(elapsed))                              "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "id": "yD0RkN0i695s",
    "outputId": "81ae5dfa-ec2c-44bc-a971-ff7097b07e48"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4759/4759 [==============================] - 1s 214us/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[38.46056850480846, 0.9384324550628662]"
      ]
     },
     "execution_count": 77,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# training loss and accuracy\n",
    "\n",
    "results_train = rms_model.evaluate(X_train, y_train)\n",
    "results_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "id": "5lqiW5B6695x",
    "outputId": "9751eae1-aa09-4102-80cd-2c14320aaf5b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "624/624 [==============================] - 0s 227us/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[72.4228279101543, 0.9150640964508057]"
      ]
     },
     "execution_count": 78,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# test loss and accuracy\n",
    "\n",
    "results_test = rms_model.evaluate(X_test, y_test)\n",
    "results_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "WREX8RF3JUHe"
   },
   "source": [
    "HOW TO SAVE h5 model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "XV-gJJhD696C"
   },
   "outputs": [],
   "source": [
    "# # Your code here; save the model for future reference \n",
    "# model.save('chest_xray_downsampled_data.h5')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "qUjf0B_wxPOB"
   },
   "source": [
    "HOW TO LOAD A PREVIOUS SAVED h5 model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ZepOVgVUxMm3"
   },
   "outputs": [],
   "source": [
    "# from keras.models import load_model\n",
    "\n",
    "# model = load_model('chest_xray_all_data.h5')\n",
    "# # As a reminder \n",
    "# model.summary()  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Ev8jtG_4BHL-"
   },
   "source": [
    "### Interpreting Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 981
    },
    "colab_type": "code",
    "id": "32Tf0bs1696N",
    "outputId": "c85dd5e1-e4c9-477b-ed19-795f28f37431"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.91      0.85      0.88       234\n",
      "           1       0.92      0.95      0.93       390\n",
      "\n",
      "    accuracy                           0.92       624\n",
      "   macro avg       0.91      0.90      0.91       624\n",
      "weighted avg       0.91      0.92      0.91       624\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_preds = rms_model.predict_classes(X_test).flatten()\n",
    "\n",
    "evaluate_model(y_test,y_preds,rms_history)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "_JioQq5J-1tN"
   },
   "source": [
    "Training accuracy 94%. Test accuracy at 92% and loss at 72%. Pneumonia recall - 95%, F1 score 93."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "u6rDPtpGAiKF"
   },
   "source": [
    "## Third model with ADAM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 666
    },
    "colab_type": "code",
    "id": "fYxOnvoxAPxF",
    "outputId": "aadab488-2aac-4733-aa5c-8b11aa01da16"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Model: \"sequential_22\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_64 (Conv2D)           (None, 62, 62, 32)        896       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_64 (MaxPooling (None, 31, 31, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_65 (Conv2D)           (None, 29, 29, 64)        18496     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_65 (MaxPooling (None, 14, 14, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_66 (Conv2D)           (None, 12, 12, 128)       73856     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_66 (MaxPooling (None, 6, 6, 128)         0         \n",
      "_________________________________________________________________\n",
      "flatten_22 (Flatten)         (None, 4608)              0         \n",
      "_________________________________________________________________\n",
      "dense_43 (Dense)             (None, 512)               2359808   \n",
      "_________________________________________________________________\n",
      "dense_44 (Dense)             (None, 1)                 513       \n",
      "=================================================================\n",
      "Total params: 2,453,569\n",
      "Trainable params: 2,453,569\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "None"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "500/500 [==============================] - 259s 517ms/step - loss: 0.2420 - acc: 0.9005 - val_loss: 0.1398 - val_acc: 0.9179\n",
      "Epoch 2/10\n",
      "500/500 [==============================] - 258s 516ms/step - loss: 0.1456 - acc: 0.9427 - val_loss: 0.2664 - val_acc: 0.9368\n",
      "Epoch 3/10\n",
      "500/500 [==============================] - 257s 514ms/step - loss: 0.1181 - acc: 0.9554 - val_loss: 0.2711 - val_acc: 0.9209\n",
      "Epoch 4/10\n",
      "500/500 [==============================] - 256s 513ms/step - loss: 0.1128 - acc: 0.9587 - val_loss: 0.2993 - val_acc: 0.9493\n",
      "Training took a total of 0:17:10.934737\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(111)\n",
    "\n",
    "from keras import layers\n",
    "from keras import models\n",
    "from keras import optimizers\n",
    "import datetime\n",
    "from keras.callbacks import EarlyStopping\n",
    "\n",
    "\n",
    "\n",
    "# timer for model\n",
    "start = datetime.datetime.now()\n",
    "\n",
    "# Initialising the CNN\n",
    "adam_model = Sequential()\n",
    "\n",
    "# Step 1 - Convolution\n",
    "adam_model.add(layers.Conv2D(32, (3, 3), activation='relu',\n",
    "                        input_shape=(64 ,64,  3)))\n",
    "# Step 2 - Pooling\n",
    "adam_model.add(layers.MaxPooling2D((2, 2)))\n",
    "\n",
    "# Adding a second convolutional layer\n",
    "adam_model.add(layers.Conv2D(64, (3, 3), activation='relu',\n",
    "                        input_shape=(64 ,64,  3)))\n",
    "# Pooling\n",
    "adam_model.add(layers.MaxPooling2D((2, 2)))\n",
    "\n",
    "# Adding a third convolutional layer\n",
    "adam_model.add(layers.Conv2D(128, (3, 3), activation='relu',\n",
    "                        input_shape=(64 ,64,  3)))\n",
    "# Pooling\n",
    "adam_model.add(layers.MaxPooling2D((2, 2)))\n",
    "\n",
    "# Step 3 - Flattening\n",
    "adam_model.add(layers.Flatten())\n",
    "\n",
    "# Step 4 - Full connection\n",
    "adam_model.add(layers.Dense(units = 512, activation = 'relu'))\n",
    "\n",
    "adam_model.add(layers.Dense(units = 1, activation = 'sigmoid'))\n",
    "\n",
    "# Compiling the CNN\n",
    "adam_model.compile(optimizer = 'adam', \n",
    "                   loss = 'binary_crossentropy',\n",
    "                   metrics = ['acc'])\n",
    "print()\n",
    "display(adam_model.summary())\n",
    "\n",
    "# Define the callbacks for early stopping of model based on val loss change.\n",
    "early_stopping = [EarlyStopping(monitor='val_loss', min_delta= 0.01, patience=3)]\n",
    "                \n",
    "\n",
    "# Fitting the CNN to the images using fit_generator\n",
    "adam_history = adam_model.fit(training_set,\n",
    "                             steps_per_epoch = 500,\n",
    "                             epochs = 10,\n",
    "                            callbacks=early_stopping,\n",
    "                             validation_data = val_set,\n",
    "                             validation_steps =200, verbose=1, workers=-1)\n",
    "\n",
    "# end timer\n",
    "end = datetime.datetime.now()\n",
    "elapsed = end - start\n",
    "print('Training took a total of {}'.format(elapsed))                              "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "id": "KUiBeY5dAPxY",
    "outputId": "6c1dcd6c-e740-4c18-e538-f08aca0ff06b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4759/4759 [==============================] - 1s 212us/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[13.576785112738067, 0.9546123147010803]"
      ]
     },
     "execution_count": 81,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# training loss and accuracy\n",
    "results_train = adam_model.evaluate(X_train, y_train)\n",
    "results_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "id": "aJzxwzxsAPxc",
    "outputId": "b692d3e5-073a-4755-ff81-dd3104e282a5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "624/624 [==============================] - 0s 216us/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[49.79576932734404, 0.9006410241127014]"
      ]
     },
     "execution_count": 82,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# test loss and accuracy\n",
    "results_test = adam_model.evaluate(X_test, y_test)\n",
    "results_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "FNho08weBLG2"
   },
   "source": [
    "### Interpreting Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 981
    },
    "colab_type": "code",
    "id": "vJaQ_32WAPxv",
    "outputId": "ae0404ad-3e10-4330-ec82-4d6a9dfa5b05"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.94      0.79      0.86       234\n",
      "           1       0.88      0.97      0.92       390\n",
      "\n",
      "    accuracy                           0.90       624\n",
      "   macro avg       0.91      0.88      0.89       624\n",
      "weighted avg       0.90      0.90      0.90       624\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_preds = adam_model.predict_classes(X_test).flatten()\n",
    "\n",
    "evaluate_model(y_test,y_preds,adam_history)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "T-3VfY8eALAe"
   },
   "source": [
    "Training accuracy 95%. Test accuracy at 90% and loss at 50%. Pneumonia recall - 97%, F1 score 92."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "RLfGFSOZB4JE"
   },
   "source": [
    "Best model so far for recall was adam with 90% accuracy and a recall of 97%. I'm going to move forward with the adam optimizer. \n",
    "\n",
    "- baseline sgd\n",
    "Training accuracy 95%. Test accuracy at 88% and loss at 58%. Pneumonia recall - 97%, F1 score 91.  \n",
    "- rmsprop\n",
    "Training accuracy 94%. Test accuracy at 92% and loss at 72%. Pneumonia recall - 95%, F1 score 93.  \n",
    "- adam\n",
    "Training accuracy 95%. Test accuracy at 90% and loss at 50%. Pneumonia recall - 97%, F1 score 92."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "_weKy5mQIRC5"
   },
   "source": [
    "## Fourth model with ADAM. Add Dropout. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 870
    },
    "colab_type": "code",
    "id": "xJEw5IKEIRC9",
    "outputId": "2c749fd9-c460-4031-e12f-3e535be3b558"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Model: \"sequential_23\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_67 (Conv2D)           (None, 62, 62, 32)        896       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_67 (MaxPooling (None, 31, 31, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_68 (Conv2D)           (None, 29, 29, 64)        18496     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_68 (MaxPooling (None, 14, 14, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_69 (Conv2D)           (None, 12, 12, 128)       73856     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_69 (MaxPooling (None, 6, 6, 128)         0         \n",
      "_________________________________________________________________\n",
      "flatten_23 (Flatten)         (None, 4608)              0         \n",
      "_________________________________________________________________\n",
      "dense_45 (Dense)             (None, 128)               589952    \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "dense_46 (Dense)             (None, 512)               66048     \n",
      "_________________________________________________________________\n",
      "dense_47 (Dense)             (None, 1)                 513       \n",
      "=================================================================\n",
      "Total params: 749,761\n",
      "Trainable params: 749,761\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "None"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "500/500 [==============================] - 256s 512ms/step - loss: 0.2793 - acc: 0.8810 - val_loss: 0.1483 - val_acc: 0.9357\n",
      "Epoch 2/10\n",
      "500/500 [==============================] - 255s 510ms/step - loss: 0.1571 - acc: 0.9392 - val_loss: 0.0613 - val_acc: 0.9427\n",
      "Epoch 3/10\n",
      "500/500 [==============================] - 256s 512ms/step - loss: 0.1268 - acc: 0.9546 - val_loss: 0.1829 - val_acc: 0.9475\n",
      "Epoch 4/10\n",
      "500/500 [==============================] - 255s 511ms/step - loss: 0.1090 - acc: 0.9587 - val_loss: 0.1235 - val_acc: 0.9523\n",
      "Epoch 5/10\n",
      "500/500 [==============================] - 255s 511ms/step - loss: 0.0950 - acc: 0.9646 - val_loss: 0.0348 - val_acc: 0.9465\n",
      "Epoch 6/10\n",
      "500/500 [==============================] - 257s 513ms/step - loss: 0.0870 - acc: 0.9674 - val_loss: 0.0935 - val_acc: 0.9463\n",
      "Epoch 7/10\n",
      "500/500 [==============================] - 255s 510ms/step - loss: 0.0841 - acc: 0.9693 - val_loss: 0.0807 - val_acc: 0.9518\n",
      "Epoch 8/10\n",
      "500/500 [==============================] - 256s 512ms/step - loss: 0.0735 - acc: 0.9724 - val_loss: 0.0761 - val_acc: 0.9628\n",
      "Training took a total of 0:34:06.772247\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(111)\n",
    "\n",
    "from keras import layers\n",
    "from keras import models\n",
    "from keras import optimizers\n",
    "import datetime\n",
    "from keras.callbacks import EarlyStopping\n",
    "\n",
    "\n",
    "\n",
    "# timer for model\n",
    "start = datetime.datetime.now()\n",
    "\n",
    "# Initialising the CNN\n",
    "adam_drop_model = Sequential()\n",
    "\n",
    "# Step 1 - Convolution\n",
    "adam_drop_model.add(layers.Conv2D(32, (3, 3), activation='relu',\n",
    "                        input_shape=(64 ,64,  3)))\n",
    "# Step 2 - Pooling\n",
    "adam_drop_model.add(layers.MaxPooling2D((2, 2)))\n",
    "\n",
    "# Adding a second convolutional layer\n",
    "adam_drop_model.add(layers.Conv2D(64, (3, 3), activation='relu',\n",
    "                        input_shape=(64 ,64,  3)))\n",
    "# Pooling\n",
    "adam_drop_model.add(layers.MaxPooling2D((2, 2)))\n",
    "\n",
    "# Adding a third convolutional layer\n",
    "adam_drop_model.add(layers.Conv2D(128, (3, 3), activation='relu',\n",
    "                        input_shape=(64 ,64,  3)))\n",
    "# Pooling\n",
    "adam_drop_model.add(layers.MaxPooling2D((2, 2)))\n",
    "\n",
    "# Step 3 - Flattening\n",
    "adam_drop_model.add(layers.Flatten())\n",
    "\n",
    "# Step 4 - Full connection\n",
    "adam_drop_model.add(layers.Dense(units = 128, activation = 'relu'))\n",
    "\n",
    "# Dropout applied to the full connection layer\n",
    "adam_drop_model.add(layers.Dropout(0.3))\n",
    "\n",
    "# Step 5 - Full connection\n",
    "adam_drop_model.add(layers.Dense(units = 512, activation = 'relu'))\n",
    "\n",
    "# output\n",
    "adam_drop_model.add(layers.Dense(units = 1, activation = 'sigmoid'))\n",
    "\n",
    "# Compiling the CNN\n",
    "adam_drop_model.compile(optimizer = 'adam', \n",
    "                   loss = 'binary_crossentropy',\n",
    "                   metrics = ['acc'])\n",
    "print()\n",
    "display(adam_drop_model.summary())\n",
    "\n",
    "# Define the callbacks for early stopping of model based on val loss change.\n",
    "early_stopping = [EarlyStopping(monitor='val_loss', min_delta= 0.01, patience=3)]\n",
    "                \n",
    "\n",
    "# Fitting the CNN to the images using fit_generator\n",
    "adam_drop_history = adam_drop_model.fit(training_set,\n",
    "                             steps_per_epoch = 500,\n",
    "                             epochs = 10,\n",
    "                            callbacks=early_stopping,\n",
    "                             validation_data = val_set,\n",
    "                             validation_steps =200, verbose=1, workers=-1)\n",
    "\n",
    "# end timer\n",
    "end = datetime.datetime.now()\n",
    "elapsed = end - start\n",
    "print('Training took a total of {}'.format(elapsed))                              "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "id": "8ogkhxzsIRDV",
    "outputId": "b118ca32-a73b-461a-9e51-ed6cc88cd33e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4759/4759 [==============================] - 1s 213us/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[15.08093405620532, 0.9617566466331482]"
      ]
     },
     "execution_count": 85,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# training loss and accuracy\n",
    "results_train = adam_drop_model.evaluate(X_train, y_train)\n",
    "results_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "id": "5A4G7ynLIRDa",
    "outputId": "07e566ee-433b-40de-f046-59d2c74fb803"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "624/624 [==============================] - 0s 219us/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[78.91844392434145, 0.8926281929016113]"
      ]
     },
     "execution_count": 86,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# test loss and accuracy\n",
    "results_test = adam_drop_model.evaluate(X_test, y_test)\n",
    "results_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "pXGIHYgUIRDz"
   },
   "source": [
    "\n",
    "### Interpreting Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 981
    },
    "colab_type": "code",
    "id": "CPvEVIcJIRDu",
    "outputId": "dafa7afa-8acc-49cb-9eb2-3b3ee75c7ab3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.93      0.77      0.84       234\n",
      "           1       0.87      0.97      0.92       390\n",
      "\n",
      "    accuracy                           0.89       624\n",
      "   macro avg       0.90      0.87      0.88       624\n",
      "weighted avg       0.90      0.89      0.89       624\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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Yaa+qv64muIg4JCLuioiZEXFyN/elvzhm4jiuuGUOi/o9CnCT9dZgxzHrc830h3oTmFY5Uy65kBl/+D3vOvHDi9dttsVYLrzyei675vf87Cc/Zv4jDpfXgQmuRUQMB84ADgUmAG+LiAnd2l/TPfj4c2zRUrFtvsGazGup6FoVVdqsl6w/cuIYfn7rXBb0z3zScthksy14aN6cxcsPPTiHjTd7aRV247W/4gdnfJPTzpy0eFiy1cabbs7LXj6B30+9vqvxanCY4Ja0DzAzM+/NzBeAScDRXdxfo936wONss8k6jNtoLVYbHhy991iuvv2lw0LbbboO66+9GtPufewl246ZOG6piU9aHhN23ZNZ9/8vc2bdz4svvMDVl/+U/Q88bIk2f5x+G1/+zEc47cxJbDh648XrH5o3h+eeK/4w+78nH+fWqTew1bbjV2r8WkExwFcPdHMW5Rig9dt0NrBv/0YRcSJwIsDwdUZ3MZx6W7go+cyFt/LjD7+K4cOCSdc/wN3z/swnj9yR2x54YnGyO3rvcVy2lHNsYzdaiy02XJMb7nl0ZYeuhhkxYgT/8MVvctK73sjCRQs5+i3v4GXb78i//8uXmbDLHux/0GGc/pXP8ezTT/OpD74bKIYlTztrEvfNvIvTvvxZIoLM5J1/8yHG77BTj49ITRWZ3Rmuiog3A4dk5l+Xy+8E9s3Mk5b1npEbb5ej3/T1rsQjLY+fnXxgr0OQAHjHUfsz4/bf93SO/uqbjs8xx58+oD7uO+3wmzNz4iCF1JFuVnBzgHEty2PLdZKkOqnpzZa7eQ5uKjA+IraJiJHAccDkLu5PktQFAUQM7NULXavgMnNBRJwEXAUMB87OzOnd2p8kSa26equuzJwCTOnmPiRJ3eYTvSVJDVXD/GaCkyRVs4KTJDVPDyeKDIQ3W5YkNZIVnCSprQCGDatfCWeCkyRVquMQpQlOklTJSSaSpOZxkokkSUOHFZwkqa3iXpT1K+FMcJKkCt6qS5LUUDXMb56DkyQ1kxWcJKmSQ5SSpOap6WUCJjhJUlvOopQkNVYN85uTTCRJzWQFJ0mq5BClJKmRapjfTHCSpAphBSdJaqBiFmWvo1h+TjKRJDWSFZwkqYI3W5YkNVQN85sJTpJUrY4VnOfgJEmNZAUnSWrPmy1LkprImy1LkhrLBCdJaqQa5jcnmUiSmskKTpJUySFKSVLzOItSktRE4a26JElNVcP85iQTSVIzmeAkSZWGRQzoVSUiDomIuyJiZkScvIw2b42IGRExPSJ+XNWnQ5SSpErdHKKMiOHAGcBBwGxgakRMzswZLW3GA58GXpWZj0fEJlX9muAkSW1FdP0ygX2AmZl5b7G/mAQcDcxoafM3wBmZ+ThAZj5c1alDlJKkXhsDzGpZnl2ua7U9sH1EXBcRv4uIQ6o6tYKTJFUaNvACbnRETGtZ/n5mfn853j8CGA8cAIwFfhMRu2TmE+3eIElSW4MwRPloZk5cxrY5wLiW5bHlulazgRsz80Xgvoi4myLhTV3WDh2ilCRVihjYq8JUYHxEbBMRI4HjgMn92lxKUb0REaMphizvbdepFZwkqa2guJtJt2Tmgog4CbgKGA6cnZnTI+JUYFpmTi63vT4iZgALgU9m5vx2/ZrgJEk9l5lTgCn91p3S8nMCHytfHTHBSZIqDcIkk5XOBCdJai+82bIkqaFqmN9McJKk9gI6up/kUONlApKkRrKCkyRVqmEBZ4KTJFVzkokkqXE6vBvJkLPMBBcR/wbksrZn5oe7EpEkSYOgXQU3rc02SdIqpI6zKJeZ4DLzh63LEbFWZj7T/ZAkSUNN/dJbB5cJRMQry5tb/rFc3i0ivtv1yCRJQ0aUdzNZ0VcvdHId3LeBg4H5AJl5G/CabgYlSRo6igu9B/bqhY4u9M7MWf1WLexCLJIkDZpOLhOYFRH7ARkRqwF/D9zZ3bAkSUNGg2+2/H7gdGAMMJfioXMf7GZQkqShpYb5rTrBZeajwPErIRZJ0hBVxwquk1mU20bE5RHxSEQ8HBGXRcS2KyM4SVLvNXmSyY+Bi4DNgS2Ai4ELuhmUJEkD1UmCWyszz83MBeXrPGCNbgcmSRo66ngdXLt7UW5Y/vjziDgZmERxb8pjgSkrITZJ0hBRvzNw7SeZ3EyR0PqO629btiXw6W4FJUkaOiKady/KbVZmIJIkDaaOngcXETsDE2g595aZP+pWUJKkoaWGBVx1gouIzwMHUCS4KcChwLWACU6SVhGNvA4OeDPwV8CDmfleYDdg/a5GJUkaUvqe6r2ir17oZIjy2cxcFBELImI94GFgXJfjkiQNEUE0a5JJi2kRsQFwJsXMyqeAG7oalSRJA9TJvSj/rvzxPyLiSmC9zLy9u2FJkoaMHg4zDkS7C733bLctM28Z7GB22XIDrvvOGwe7W2m5jdr7pF6HIAHw/Mw5vQ4BqOckk3YV3LfabEvgdYMciyRpiOro6dhDTLsLvV+7MgORJA1NQT0ruDomZUmSKnV0JxNJ0qqtV890GwgTnCSpUh0TXCdP9I6IeEdEnFIubxkR+3Q/NEnSUFDcjaR+z4Pr5Bzcd4FXAm8rl/8MnNG1iCRJGgSdDFHum5l7RsTvATLz8YgY2eW4JElDSB2HKDtJcC9GxHCKa9+IiI2BRV2NSpI0pNTwKoGOEty/ApcAm0TElymeLvDZrkYlSRoygoY90btPZp4fETdTPDIngGMy886uRyZJGjLqeNF0Jw883RJ4Bri8dV1m/qmbgUmSNBCdDFFeQXH+LYA1gG2Au4CduhiXJGkIqeEIZUdDlLu0LpdPGfi7ZTSXJDVMRHMfeLqEzLwlIvbtRjCSpKGphvmto3NwH2tZHAbsCcztWkSSJA2CTiq4dVt+XkBxTu4n3QlHkjQUNe5C7/IC73Uz8xMrKR5J0hDTuOvgImJEZi6IiFetzIAkSUNPDfNb2wruJorzbbdGxGTgYuDpvo2Z+dMuxyZJGgqigUOUpTWA+cDr+Mv1cAmY4CRJQ1a7BLdJOYPyDv6S2PpkV6OSJA0pQf1KuHYJbjiwDiz1qExwkrSKKCaZ9DqK5dcuwc3LzFNXWiSSpCGraQmuhocjSeqGqOE0ynZPQPirlRaFJEmDbJkVXGY+tjIDkSQNTU08BydJEkTzLvSWJAmo56266vgUckmSKlnBSZLaqus5OCs4SVKliIG9qvuPQyLiroiYGREnt2n3pojIiJhY1acVnCSpQjCsi5dGl49mOwM4CJgNTI2IyZk5o1+7dYG/B27spF8rOElSW0HXK7h9gJmZeW9mvgBMAo5eSrsvAV8DnuskbhOcJKnXxgCzWpZnl+sWi4g9gXGZeUWnnTpEKUlqb3CeBzc6Iqa1LH8/M7/f0e4jhgH/ArxneXZogpMkVRqE6+AezcxlTQyZA4xrWR5bruuzLrAzcE15T8zNgMkRcVRmtibNJZjgJElt9Z2D66KpwPiI2IYisR0HvL1vY2Y+CYxeHE/ENcAn2iU3MMFJkjrQzTuZZOaCiDgJuIriWaRnZ+b0iDgVmJaZk1ekXxOcJKnnMnMKMKXfulOW0faATvo0wUmSKtXwVpQmOElSe0E9rykzwUmS2ovmPdFbkqTasoKTJFWqX/1mgpMkVSgel1O/FGeCkyRVql96M8FJkjpQwwLOSSaSpGaygpMkVYhaXiZggpMkteWF3pKkxrKCkyQ1Uv3SWz2rTkmSKlnBSZLaq+m9KE1wkqS2nGQiSWqsOlZwdUzKkiRVsoKTJFWqX/1mgpMkdaCGI5QmOElSe8Ukk/plOBOcJKlSHSs4J5lIkhrJCk6SVCEIhyglSU1UxyFKE5wkqS0nmUiSminqWcE5yUSS1EhWcJKkSnWs4ExwkqRKzqKUJDVOAMPql988BydJaiYrOElSJYcoJUmNVMdJJg5R1sjVV13Jrju9nJ122I5vfP2rL9n+/PPP8463H8tOO2zHq/fblwfuvx+AqTfdxL577c6+e+3OPnvuxmWXXrKSI1fTHLTfjtx2yee447LP84n3HvSS7VtuPoop//Ehbrrw01x15t8zZpMNAHjNxPH8btLJi1+P/+40jjxg15UdvlZADPC/XrCCq4mFCxfykQ9/kCt+/t+MGTuW//eKvTniiKPYccKExW3OOfsHjNpgFNP/OJOLLpzEZ/7xU5z34wvZaeedue7GaYwYMYJ58+ax7167cfgRRzJihP/8Wn7DhgXfPvmtHP6B7zDnoSe49vxP8rNf/4E/3vvg4jZf+egbOP+Kmzj/8hvZf+/tOfVDR3HC537Eb6bdwyuOK/44G7XeWtwx+fP84nd39upQ1CEnmairpt50Ey972XZss+22jBw5krccexw/u/yyJdr87PLLOP6d7wbgjW96M9f8zy/JTNZaa63Fyez5554j6jjWoCFj75235n9nPcr9c+bz4oKFXHzVLRzRrwrbYdvN+fVNdwHw66l3c8QBu7yknzccuAdXXzeDZ597caXErVWPCa4m5s6dw9ix4xYvjxkzljlz5ry0zbiizYgRI1hv/fWZP38+ADfdeCN77rYTE/fYhX894z+s3rTCtthkfWY/9Pji5TkPPc6Yjddfos0f7p7D0a/bHYCjX7cb662zJhuuv/YSbd5y8J5cdOXN3Q9Yg2CgA5S9+aO6awkuIs6OiIcj4o5u7UOd22fffbnltulce8NUvvG1r/Dcc8/1OiQ12KdPu4RX77UdN1zwKV6913bMeehxFi5ctHj7ZqPXY6fxW/DfN8zoYZTqWHkvyoG8eqGbf8afA3wH+FEX97HK2GKLMcyePWvx8pw5sxkzZsxL28yaxdixY1mwYAH/9+STbLTRRku02WHHHVlnnXWYfscd7DVx4kqJXc0y9+EnGbvpqMXLYzYdxZxHnlyizbxHnuS4T5wFwNprjuSYv9qdJ596dvH2Nx20J5P/53YWLFiE6qGOJza6VsFl5m+Ax7rV/6pm4t57M3PmPdx/33288MILXHzhJA4/4qgl2hx+xFGcf+4PAfjpT/6L/V/7OiKC+++7jwULFgDwwAMPcNddf2Srrbde2Yeghpg2/QG223JjttpiI1YbMZy3HLwnV1xz+xJtNtpg7cXnej/5voP54WW/W2L7Ww/Zi4uunLbSYtbAFJNMYkCvXuj5iZiIOBE4EWDcllv2OJqha8SIEZx2+nc48vCDWbhwIe9+z/uYsNNOnPqFU9hzr4kcceRRvOd9J/C+97yTnXbYjlGjNuTc8ycBcP111/LNb3yV1UasxrBhwzj9377L6NGje3xEqquFCxfx0a9dxOXf/SDDhwU/vOx33Hnvg3zuA4dzy4w/ccWv/8BrJo7n1A8dRSZce8tMPvKVixa/f8vNN2TsZqP47c0ze3gUWhVEZnav84itgZ9l5s6dtN9rr4l53Y3+VafeG7X3Sb0OQQLg+bsuYtEzD/d0hHDHXfbI/7zkVwPq45XjR92cmSv1vEjPKzhJUg3U8CScCU6SVKmO96Ls5mUCFwA3AC+PiNkRcUK39iVJUn9dq+Ay823d6luStHLV8QZIDlFKkirVML+Z4CRJHahhhjPBSZLaCpxkIknSkGEFJ0lqr4c3TB4IE5wkqVIN85sJTpLUgRpmOBOcJKlC7x5aOhBOMpEkNZIVnCSpkpNMJEmNE9TyFJwJTpLUgRpmOM/BSZIayQpOklTJWZSSpEaKGNiruv84JCLuioiZEXHyUrZ/LCJmRMTtEfHLiNiqqk8TnCSpUgzw1bbviOHAGcChwATgbRExoV+z3wMTM3NX4L+Ar1fFbIKTJLU30OxWXcHtA8zMzHsz8wVgEnB0a4PM/FVmPlMu/g4YW9WpCU6S1GtjgFkty7PLdctyAvDzqk6dZCJJqjQIk0xGR8S0luXvZ+b3lzuOiHcAE4H9q9qa4CRJbQWDcieTRzNz4jK2zQHGtSyPLdctGUfEgcBngP0z8/mqHTpEKUmq1N1TcEwFxkfENhExEjgOmLzE/iP2AL4HHJWZD3cSswlOktRTmbkAOAm4CrgTuCgzp0fEqRFxVNnsG8A6wMURcWtETF5Gd4s5RClJqtbl67wzcwowpd+6U1p+PnB5+zTBSZIq1fFOJiY4SVIlH5cjSWqkGuY3J5lIkprJCk6SVDaxICUAAAXFSURBVK2GJZwJTpLUVnEtW/0ynAlOktReh4+8GWpMcJKkSjXMb04ykSQ1kxWcJKlaDUs4E5wkqUI4yUSS1Ex1nGTiOThJUiNZwUmS2urwmW5DjglOklSthhnOBCdJquQkE0lSIznJRJKkIcIKTpJUqYYFnAlOklTBmy1LkpqrfhnOBCdJaiuoZwXnJBNJUiNZwUmSKtWwgDPBSZKq1XGI0gQnSapUxzuZeA5OktRIVnCSpGr1K+BMcJKkajXMbyY4SVJ74Z1MJElN5SQTSZKGCCs4SVK1+hVwJjhJUrUa5jcTnCSpmpNMJEkNFE4ykSRpqLCCkyS15fPgJEkaQqzgJEmVrOAkSRoirOAkSZXqOIvSBCdJas+bLUuSmijwTiaSpKaqYYZzkokkqZGs4CRJlZxkIklqJCeZSJIaqYb5zQQnSepADTOck0wkSY1kBSdJquQkE0lS49T1cTmRmb2OYbGIeAR4oNdx1Nxo4NFeByHhZ3GwbJWZG/cygIi4kuLfcyAezcxDBiOeTg2pBKeBi4hpmTmx13FIfhbVa04ykSQ1kglOktRIJrjm+X6vA5BKfhbVU56DkyQ1khWcJKmRTHANEhGHRMRdETEzIk7udTxaNUXE2RHxcETc0etYtGozwTVERAwHzgAOBSYAb4uICb2NSquoc4CVer2TtDQmuObYB5iZmfdm5gvAJODoHsekVVBm/gZ4rNdxSCa45hgDzGpZnl2uk6RVkglOktRIJrjmmAOMa1keW66TpFWSCa45pgLjI2KbiBgJHAdM7nFMktQzJriGyMwFwEnAVcCdwEWZOb23UWlVFBEXADcAL4+I2RFxQq9j0qrJO5lIkhrJCk6S1EgmOElSI5ngJEmNZIKTJDWSCU6S1EgmOPVcRCyMiFsj4o6IuDgi1hpAX+dExJvLn89qd8PpiDggIvZbgX3cHxGjO13fr81Ty7mvL0TEJ5Y3RkkmOA0Nz2bm7pm5M/AC8P7WjRExYkU6zcy/zswZbZocACx3gpNUDyY4DTW/BbYrq6vfRsRkYEZEDI+Ib0TE1Ii4PSL+FiAK3ymfg/cLYJO+jiLimoiYWP58SETcEhG3RcQvI2JrikT60bJ6fHVEbBwRPyn3MTUiXlW+d6OIuDoipkfEWUBUHUREXBoRN5fvObHfttPK9b+MiI3LdS+LiCvL9/w2InYYjF+mtCpbob+MpW4oK7VDgSvLVXsCO2fmfWWSeDIz946I1YHrIuJqYA/g5RTPwNsUmAGc3a/fjYEzgdeUfW2YmY9FxH8AT2XmN8t2PwZOy8xrI2JLirvC7Ah8Hrg2M0+NiMOBTu7M8b5yH2sCUyPiJ5k5H1gbmJaZH42IU8q+TwK+D7w/M++JiH2B7wKvW4Ffo6SSCU5DwZoRcWv582+BH1AMHd6UmfeV618P7Np3fg1YHxgPvAa4IDMXAnMj4n+W0v8rgN/09ZWZy3pW2YHAhIjFBdp6EbFOuY83lu+9IiIe7+CYPhwRbyh/HlfGOh9YBFxYrj8P+Gm5j/2Ai1v2vXoH+5DUhglOQ8Gzmbl764ryi/7p1lXAhzLzqn7tDhvEOIYBr8jM55YSS8ci4gCKZPnKzHwmIq4B1lhG8yz3+0T/34GkgfEcnOriKuADEbEaQERsHxFrA78Bji3P0W0OvHYp7/0d8JqI2KZ874bl+j8D67a0uxr4UN9CRPQlnN8Aby/XHQqMqoh1feDxMrntQFFB9hkG9FWhb6cY+vw/4L6IeEu5j4iI3Sr2IamCCU51cRbF+bVbIuIO4HsUIxCXAPeU235EcRf7JWTmI8CJFMOBt/GXIcLLgTf0TTIBPgxMLCexzOAvszm/SJEgp1MMVf6pItYrgRERcSfwVYoE2+dpYJ/yGF4HnFquPx44oYxvOnB0B78TSW34NAFJUiNZwUmSGskEJ0lqJBOcJKmRTHCSpEYywUmSGskEJ0lqJBOcJKmRTHCSpEb6/8lnmWOWS7IfAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_preds = adam_drop_model.predict_classes(X_test).flatten()\n",
    "\n",
    "evaluate_model(y_test,y_preds,adam_drop_history)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "iV1VruSQIRD0"
   },
   "source": [
    "Training accuracy 96%. Test accuracy at 89% and loss at 79%. Pneumonia recall - 97%, F1 score 89.  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 357
    },
    "colab_type": "code",
    "id": "F-FYnjLkDInJ",
    "outputId": "4ef445f8-028e-4831-f4a7-a3dcb29297aa"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Adam model\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.94      0.79      0.86       234\n",
      "           1       0.88      0.97      0.92       390\n",
      "\n",
      "    accuracy                           0.90       624\n",
      "   macro avg       0.91      0.88      0.89       624\n",
      "weighted avg       0.90      0.90      0.90       624\n",
      "\n",
      "Adam model with dropout\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.93      0.77      0.84       234\n",
      "           1       0.87      0.97      0.92       390\n",
      "\n",
      "    accuracy                           0.89       624\n",
      "   macro avg       0.90      0.87      0.88       624\n",
      "weighted avg       0.90      0.89      0.89       624\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print('Adam model')\n",
    "adam_y_preds = adam_model.predict_classes(X_test).flatten()\n",
    "print(metrics.classification_report(y_test, adam_y_preds))\n",
    "'\\n'\n",
    "print('Adam model with dropout')\n",
    "dropout_y_preds = adam_drop_model.predict_classes(X_test).flatten()\n",
    "print(metrics.classification_report(y_test, dropout_y_preds))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "IsBntvhVD6jq"
   },
   "source": [
    "Results: The Adam model with no dropout was better by only a slight amount with an accuracy of 90% compared to the dropout model's 89%. Both have a recall of pneumonia patients at 97%. Adam model with no dropout was better at not overfitting, with training acc of 95% and test acc of 90%. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "VbTxXAMaqFmd"
   },
   "source": [
    "## Conclusion Summary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "sWKnH7rpzc6u"
   },
   "source": [
    "Based on this final Adam model without dropout, it seems that the process was able to classify 97% of all pneumonia patients in the test set. More tuning for the future to try and get more of the patients properly classified. \n",
    "\n",
    "With a high precision of 88% as well, the model is not only capturing a majority of the pneumonia case, but also being precise in diagnosis. Same goes for normal patients with a precision of 94%. \n",
    "\n",
    "CNN models are best for this type of work over dense layered networks. It uses such small images while still being able to break down the images using feature maps to search for patterns and accurately obtain results. Adjusting the model to work with higher res images could possibly increase accuracy performance with only a slight time increase.\n",
    "\n",
    "Using this model adds technical advantages to aid in doctor review. It functions faster and better with image processing than a normal dense neural network. This will greatly increase processing time and cutting costs in the medical field and will allow labor hours to be redirected towards other pressing matters and care for more patients. It's recommended to use the model to scan lab images before doctor’s manual review. This will save on labor hours. Expenses can be allocated, diagnosis accuracy will go up, catching pneumonia earlier. Business reputation will go up. Patients will recognize the better care and work from the company. Using this model will also help to prevent human error. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "aP8oA0Drzsgh"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "collapsed_sections": [],
   "name": "Present ALEX_BEAT_v2.1mod4project_notebook.ipynb",
   "provenance": [],
   "toc_visible": true
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