Working_ALEX_BEAT_v2_1mod4project_notebook.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/alexanderjohnny/dsc-mod-4-project-v2-1-online-ds-pt-100719/blob/master/Working_ALEX_BEAT_v2_1mod4project_notebook.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JILuvObhGJu-",
        "colab_type": "text"
      },
      "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": {
        "id": "5yNKgJn4X97-",
        "colab_type": "text"
      },
      "source": [
        "# Intro"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L82wyLOxY_2H",
        "colab_type": "text"
      },
      "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": {
        "id": "j8el6CF1aHir",
        "colab_type": "text"
      },
      "source": [
        "## Content\n",
        "The dataset is organized into 3 folders (train, test, val) and contains subfolders for each image category (Pneumonia/Normal). There are 5,863 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": {
        "id": "InclPflMXvZ1",
        "colab_type": "text"
      },
      "source": [
        "# Select Colab directory and unzip virtually in colab."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cMMdd93Df0_n",
        "colab_type": "text"
      },
      "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": {
        "id": "GJF7ocAf-5Oc",
        "colab_type": "text"
      },
      "source": [
        "### Mount google drive connection to colab. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qVAJAUjGHpOn",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 156
        },
        "outputId": "d43b2e6f-9e8f-4745-ed2f-208b56776a19"
      },
      "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 .."
      ],
      "execution_count": null,
      "outputs": [
        {
          "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"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w-FeLKZ6_4hF",
        "colab_type": "text"
      },
      "source": [
        "### Create file paths"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P7tGSw_oVzP3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "e2c619bb-51c1-4801-fb27-5a298f33a36e"
      },
      "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"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic": {
              "type": "string"
            },
            "text/plain": [
              "'content/drive/My Drive/Datasets/xray.zip'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tWFS5rvF__Bm",
        "colab_type": "text"
      },
      "source": [
        "### Zip virtual copy/unzip/removal."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IjfEc304WB0u",
        "colab_type": "code",
        "colab": {}
      },
      "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"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bkZicl01WEoO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 527
        },
        "outputId": "7939c61c-c26a-4494-dd2c-2539f10d5f6d"
      },
      "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"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['srv',\n",
              " 'home',\n",
              " 'etc',\n",
              " 'tmp',\n",
              " 'opt',\n",
              " 'run',\n",
              " 'lib',\n",
              " 'root',\n",
              " 'sys',\n",
              " 'usr',\n",
              " 'lib64',\n",
              " 'var',\n",
              " 'media',\n",
              " 'proc',\n",
              " 'boot',\n",
              " 'sbin',\n",
              " 'mnt',\n",
              " 'bin',\n",
              " 'dev',\n",
              " 'xray',\n",
              " 'content',\n",
              " '.dockerenv',\n",
              " 'tools',\n",
              " 'datalab',\n",
              " 'swift',\n",
              " 'tensorflow-1.15.2',\n",
              " 'dlib-19.18.0-cp27-cp27mu-linux_x86_64.whl',\n",
              " 'dlib-19.18.0-cp36-cp36m-linux_x86_64.whl',\n",
              " 'lib32']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9z81B-FVX7L3",
        "colab_type": "text"
      },
      "source": [
        "# Step 1: Load the Data / Preprocessing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H6e24655bfhf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "outputId": "fd092aee-045a-40f6-d081-41a9536567a8"
      },
      "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"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "fsds_1007219  v0.7.22 loaded.  Read the docs: https://fsds.readthedocs.io/en/latest/ \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<style  type=\"text/css\" >\n",
              "</style><table id=\"T_60023544_b721_11ea_9455_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_60023544_b721_11ea_9455_0242ac1c0002row0_col0\" class=\"data row0 col0\" >dp</td>\n",
              "                        <td id=\"T_60023544_b721_11ea_9455_0242ac1c0002row0_col1\" class=\"data row0 col1\" >IPython.display</td>\n",
              "                        <td id=\"T_60023544_b721_11ea_9455_0242ac1c0002row0_col2\" class=\"data row0 col2\" >Display modules with helpful display and clearing commands.</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                                <td id=\"T_60023544_b721_11ea_9455_0242ac1c0002row1_col0\" class=\"data row1 col0\" >fs</td>\n",
              "                        <td id=\"T_60023544_b721_11ea_9455_0242ac1c0002row1_col1\" class=\"data row1 col1\" >fsds_100719</td>\n",
              "                        <td id=\"T_60023544_b721_11ea_9455_0242ac1c0002row1_col2\" class=\"data row1 col2\" >Custom data science bootcamp student package</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                                <td id=\"T_60023544_b721_11ea_9455_0242ac1c0002row2_col0\" class=\"data row2 col0\" >mpl</td>\n",
              "                        <td id=\"T_60023544_b721_11ea_9455_0242ac1c0002row2_col1\" class=\"data row2 col1\" >matplotlib</td>\n",
              "                        <td id=\"T_60023544_b721_11ea_9455_0242ac1c0002row2_col2\" class=\"data row2 col2\" >Matplotlib's base OOP module with formatting artists</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                                <td id=\"T_60023544_b721_11ea_9455_0242ac1c0002row3_col0\" class=\"data row3 col0\" >plt</td>\n",
              "                        <td id=\"T_60023544_b721_11ea_9455_0242ac1c0002row3_col1\" class=\"data row3 col1\" >matplotlib.pyplot</td>\n",
              "                        <td id=\"T_60023544_b721_11ea_9455_0242ac1c0002row3_col2\" class=\"data row3 col2\" >Matplotlib's matlab-like plotting module</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                                <td id=\"T_60023544_b721_11ea_9455_0242ac1c0002row4_col0\" class=\"data row4 col0\" >np</td>\n",
              "                        <td id=\"T_60023544_b721_11ea_9455_0242ac1c0002row4_col1\" class=\"data row4 col1\" >numpy</td>\n",
              "                        <td id=\"T_60023544_b721_11ea_9455_0242ac1c0002row4_col2\" class=\"data row4 col2\" >scientific computing with Python</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                                <td id=\"T_60023544_b721_11ea_9455_0242ac1c0002row5_col0\" class=\"data row5 col0\" >pd</td>\n",
              "                        <td id=\"T_60023544_b721_11ea_9455_0242ac1c0002row5_col1\" class=\"data row5 col1\" >pandas</td>\n",
              "                        <td id=\"T_60023544_b721_11ea_9455_0242ac1c0002row5_col2\" class=\"data row5 col2\" >High performance data structures and tools</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                                <td id=\"T_60023544_b721_11ea_9455_0242ac1c0002row6_col0\" class=\"data row6 col0\" >sns</td>\n",
              "                        <td id=\"T_60023544_b721_11ea_9455_0242ac1c0002row6_col1\" class=\"data row6 col1\" >seaborn</td>\n",
              "                        <td id=\"T_60023544_b721_11ea_9455_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 0x7f5ff4cdfa58>"
            ]
          },
          "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": "stream",
          "text": [
            "[i] Pandas .iplot() method activated.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OYWYYW5w3vFa",
        "colab_type": "text"
      },
      "source": [
        "## Define folder structure for use with .flow."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GUPy3XbnquCO",
        "colab_type": "code",
        "colab": {}
      },
      "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",
        "val_dir_normal = 'xray/val/NORMAL'\n",
        "val_dir_pneum = 'xray/val/PNEUMONIA'\n",
        "all_dir_paths = [train_dir_normal, train_dir_pneum, test_dir_normal,\n",
        "                 test_dir_pneum, val_dir_normal, val_dir_pneum]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z6pg5a6M0AIF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        },
        "outputId": "632a1407-7152-4533-f717-8c47dd277a3b"
      },
      "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.') "
      ],
      "execution_count": null,
      "outputs": [
        {
          "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",
            "There are 0 images in xray/val/NORMAL folder.\n",
            "There are 0 images in xray/val/PNEUMONIA folder.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q2HXcdWk4xwV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import glob,os"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "myumPl0mDfLk",
        "colab_type": "code",
        "colab": {}
      },
      "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]\n",
        "\n",
        "# hashed out bc I don't have val files currently\n",
        "# val_files_normal = glob.glob(val_dir_normal+'/*.jpeg'\n",
        "# val_files_pneum = glob.glob(train_dir_pneum+'/*.jpeg'\n",
        "# all_val_files = [*val_files_normal,*val_files_pneum]\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aeBCGJ6Ua32c",
        "colab_type": "text"
      },
      "source": [
        "## Functions"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gvgKkfPnLxcb",
        "colab_type": "text"
      },
      "source": [
        "These functions were adapted from this Colab notebook about CNNs. https://colab.research.google.com/drive/1fwXPY3IDHxNiv7YgOpt3p5BvUaO4VruB?usp=sharing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SSZUgInsBG_N",
        "colab_type": "code",
        "colab": {}
      },
      "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",
        "\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_val_images(training_filenames_normal, training_filenames_pneum,\n",
        "                        test_filenames_normal, test_filenames_pneum,\n",
        "                        # val_filenames_normal, val_filenames_pneum,\n",
        "                        img_size=(64,64)):\n",
        "    \"\"\"Reads in training, test and val 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. The validation set is intended \n",
        "    to be used after training and testing model.\n",
        "\n",
        "    ylabels are encoded as 0=normal, 1=pneumonia\n",
        "    Returns:  X_train, X_test, X_val, y_train, y_test, y_val\"\"\"\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",
        "    # val_img = []\n",
        "    # val_label = []\n",
        "\n",
        "    # for img_path in tqdm(val_filenames_normal):\n",
        "    #     val_img.append(read_img(img_path,target_size=img_size))\n",
        "    #     val_label.append(0)\n",
        "\n",
        "    # for img_path in tqdm(val_filenames_pneum):\n",
        "    #     train_img.append(read_img(img_path,target_size=img_size))\n",
        "    #     train_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",
        "    # X_val = np.array(val_img, np.float32)\n",
        "    # y_val = np.array(val_label)\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)}, X_val={len(X_val)}\")\n",
        "    print(f\"X_train={len(X_train)}, X_test={len(X_test)}\")\n",
        "\n",
        "    # return X_train, X_test, X_val, y_train, y_test,y_val \n",
        "    return X_train, X_test, y_train, y_test\n",
        "\n",
        "\n",
        "# def train_test_val_datagens(X_train,X_test,X_val,y_train,y_test,y_val,\n",
        "def train_test_val_datagens(X_train, X_test, y_train, y_test,\n",
        "                            BATCH_SIZE = 32):\n",
        "                              \n",
        "    \"\"\"Takes in your training and test data and creates ImageDataGenerators \n",
        "    for train,test,val data. This will normalize your image array data.\n",
        "    Returns: training_set,test_set,val_set\"\"\"\n",
        "\n",
        "    ## Create training and test data image generators. \n",
        "    # This will normalize the image pixel data.\n",
        "    from keras.preprocessing.image import ImageDataGenerator\n",
        "\n",
        "    train_datagen = ImageDataGenerator(rescale = 1./255,\n",
        "                                       shear_range = 0.2, \n",
        "                                        zoom_range = 0.2)\n",
        "\n",
        "    test_datagen = ImageDataGenerator(rescale = 1./255)\n",
        "\n",
        "    # val_datagen = ImageDataGenerator(rescale = 1./255)\n",
        "\n",
        "    training_set = train_datagen.flow(X_train,y=y_train,batch_size=BATCH_SIZE)\n",
        "    test_set = test_datagen.flow(X_test,y=y_test,batch_size=BATCH_SIZE)\n",
        "    # val_set = val_datagen.flow(X_val,y=y_val,batch_size=BATCH_SIZE)\n",
        "\n",
        "    return training_set, test_set, # val_set\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",
        "    # subplot for confusion matrix\n",
        "    fig, ax = plt.subplots(figsize=(12,6))\n",
        "    cm = metrics.confusion_matrix(y_test, y_pred, normalize='true')\n",
        "\n",
        "    plt.imshow(cm, interpolation='nearest', cmap='Blues')\n",
        "    plt.title('Confusion Matrix. 0=Norm, 1=Pneum')\n",
        "    plt.colorbar()\n",
        "    plt.ylabel('True label')\n",
        "    plt.xlabel('Predicted label')\n",
        "\n",
        "    classes = list(range(len(cm)))  \n",
        "    plt.xticks(classes, classes)\n",
        "    plt.yticks(classes, classes)\n",
        "\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"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5vZ5RhJ-Pjat",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 171
        },
        "outputId": "ee91007b-8183-4643-f25d-10fbe6b21e4f"
      },
      "source": [
        "## USING FUNCTIONS TO LOAD IN IMAGES \n",
        "# X_train,X_test,X_val,y_train,y_test,y_val = load_train_test_val(*all_filename_vars,\n",
        "#                                                 img_size=(64,64)\n",
        "X_train,X_test,y_train,y_test = load_train_test_val_images(train_files_normal, \n",
        "                                                           train_files_pneum, \n",
        "                                                           test_files_normal, \n",
        "                                                           test_files_pneum,\n",
        "                                                          img_size=(64,64))\n",
        "\n",
        "# training_set,test_set,val_set = train_test_val_datagens(*train_test_val_vars,\n",
        "#                                                         BATCH_SIZE=100)\n",
        "training_set, test_set = train_test_val_datagens(X_train, X_test, y_train, y_test,\n",
        "                                                        BATCH_SIZE=100)\n",
        "\n",
        "print('Training set shape ', training_set[0][0].shape)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.google.colaboratory.intrinsic": {
              "type": "string"
            },
            "text/plain": [
              "'[i] LOADING IMAGES'"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 1341/1341 [00:30<00:00, 44.52it/s]\n",
            "100%|██████████| 3418/3418 [00:22<00:00, 149.47it/s]\n",
            "100%|██████████| 234/234 [00:03<00:00, 63.14it/s]\n",
            "100%|██████████| 390/390 [00:02<00:00, 180.56it/s]\n"
          ],
          "name": "stderr"
        },
        {
          "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"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QYkX1aeQGJy0",
        "colab_type": "text"
      },
      "source": [
        "# Step 2: EDA and Visualization"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ajnHbbfqx3vR",
        "colab_type": "text"
      },
      "source": [
        "## View an image"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ciCcKGlHDuKq",
        "colab_type": "text"
      },
      "source": [
        "Test to view an image using filename to make sure they loaded into notebook correctly. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5zbo5SDW3P8m",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "outputId": "97c66291-8794-4a30-b8f0-ca947f3473dc"
      },
      "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",
        "\n",
        "ax[1].imshow(img_p)\n",
        "ax[1].set(title='Pneumonia')\n",
        "plt.show()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "33h1C88DENSS",
        "colab_type": "text"
      },
      "source": [
        "Test to view image as a tensor array. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kisL4iPM3c85",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        },
        "outputId": "a6c5c106-9944-4140-b1e2-5e20772e4db8"
      },
      "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.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(1, 64, 64, 3)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G_SqdBCJGJ3R",
        "colab_type": "text"
      },
      "source": [
        "# Step 4: Modeling"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JQYokTp7gfu7",
        "colab_type": "text"
      },
      "source": [
        "## BUILD BASELINE CNN"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ygeEcJrmixhh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 666
        },
        "outputId": "bb5008c2-15d9-46e3-8f86-2687c08c732f"
      },
      "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",
        "\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",
        "# Fitting the CNN to the images using fit_generator\n",
        "history = model.fit_generator(training_set,\n",
        "                             steps_per_epoch = 500,\n",
        "                             epochs = 4,\n",
        "                             validation_data = test_set,\n",
        "                             validation_steps =100, 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))                              "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_3 (Conv2D)            (None, 62, 62, 32)        896       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_3 (MaxPooling2 (None, 31, 31, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_4 (Conv2D)            (None, 29, 29, 64)        18496     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_4 (MaxPooling2 (None, 14, 14, 64)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_5 (Conv2D)            (None, 12, 12, 128)       73856     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_5 (MaxPooling2 (None, 6, 6, 128)         0         \n",
            "_________________________________________________________________\n",
            "flatten_2 (Flatten)          (None, 4608)              0         \n",
            "_________________________________________________________________\n",
            "dense_3 (Dense)              (None, 512)               2359808   \n",
            "_________________________________________________________________\n",
            "dense_4 (Dense)              (None, 1)                 513       \n",
            "=================================================================\n",
            "Total params: 2,453,569\n",
            "Trainable params: 2,453,569\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "None"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/4\n",
            "500/500 [==============================] - 79s 158ms/step - loss: 0.5210 - acc: 0.7520 - val_loss: 0.7345 - val_acc: 0.6419\n",
            "Epoch 2/4\n",
            "500/500 [==============================] - 79s 158ms/step - loss: 0.3095 - acc: 0.8680 - val_loss: 0.4309 - val_acc: 0.8496\n",
            "Epoch 3/4\n",
            "500/500 [==============================] - 79s 158ms/step - loss: 0.2433 - acc: 0.8983 - val_loss: 0.4843 - val_acc: 0.8451\n",
            "Epoch 4/4\n",
            "500/500 [==============================] - 79s 158ms/step - loss: 0.2087 - acc: 0.9155 - val_loss: 0.3483 - val_acc: 0.8666\n",
            "Training took a total of 0:05:16.514135\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Dv6xQX8xgvjx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "6968ea30-d9e0-4d6d-afe2-9bca8c08547e"
      },
      "source": [
        "# training loss and accuracy\n",
        "results_train = model.evaluate(X_train, y_train)\n",
        "results_train"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "4759/4759 [==============================] - 1s 239us/step\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[14.55169740060336, 0.9462071657180786]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uG2fWjQOgxcP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "1a96f66f-d6c0-4e6e-9a98-dfd8bfddce78"
      },
      "source": [
        "# test loss and accuracy\n",
        "results_test = model.evaluate(X_test, y_test)\n",
        "results_test"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "624/624 [==============================] - 0s 301us/step\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[64.36060394996252, 0.8525640964508057]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OOE0LwYTo2BI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# # Your code here; save the model for future reference \n",
        "# model.save('baseline_model.h5')\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2h9EqXmfGJ46",
        "colab_type": "text"
      },
      "source": [
        "### Interpreting Results"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qNB3F6HNe_Oa",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 981
        },
        "outputId": "d430536a-e5b0-4a4a-9919-3a2d9f68e6ef"
      },
      "source": [
        "y_preds = model.predict_classes(X_test).flatten()\n",
        "\n",
        "evaluate_model(y_test,y_preds,history)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.94      0.65      0.77       234\n",
            "           1       0.82      0.98      0.89       390\n",
            "\n",
            "    accuracy                           0.85       624\n",
            "   macro avg       0.88      0.81      0.83       624\n",
            "weighted avg       0.87      0.85      0.85       624\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rQbv3oOwuGpy",
        "colab_type": "text"
      },
      "source": [
        "Model.evaluate shows my test accuracy at 84% and loss at 73%. My metrics classification report from sklearn also shows my accuracy at 84% with an excellent recall of 97% of pneumonia diagnosis. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xz8ntdxLyW2c",
        "colab_type": "text"
      },
      "source": [
        "# Visualize Feature Map Layers"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "urHAMZdvpQEu",
        "colab_type": "text"
      },
      "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",
      "metadata": {
        "id": "1mHxTKWEGJ0Y",
        "colab_type": "code",
        "colab": {}
      },
      "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)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xDrvXQ-sGJ1f",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 459
        },
        "outputId": "9995c232-ee74-44ba-859f-0b6905a77e02"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_3 (Conv2D)            (None, 62, 62, 32)        896       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_3 (MaxPooling2 (None, 31, 31, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_4 (Conv2D)            (None, 29, 29, 64)        18496     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_4 (MaxPooling2 (None, 14, 14, 64)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_5 (Conv2D)            (None, 12, 12, 128)       73856     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_5 (MaxPooling2 (None, 6, 6, 128)         0         \n",
            "_________________________________________________________________\n",
            "flatten_2 (Flatten)          (None, 4608)              0         \n",
            "_________________________________________________________________\n",
            "dense_3 (Dense)              (None, 512)               2359808   \n",
            "_________________________________________________________________\n",
            "dense_4 (Dense)              (None, 1)                 513       \n",
            "=================================================================\n",
            "Total params: 2,453,569\n",
            "Trainable params: 2,453,569\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "524AqvXmGJ2U",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 292
        },
        "outputId": "bd02ea50-254d-4319-eb0c-1ae66322a03b"
      },
      "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.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(1, 62, 62, 32)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 288x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wPqLIQ5pzk2I",
        "colab_type": "text"
      },
      "source": [
        "REPEAT FOR ALL LAYERS"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5Kq7-n-fzpMC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 695
        },
        "outputId": "3906606d-3acf-458b-cce1-812e08b41661"
      },
      "source": [
        "fig, axes = plt.subplots(2,4, figsize=(12,8))\n",
        "\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.set_title(layer_names[i])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "IndexError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-74-c4b608dc2808>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      9\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     10\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---> 11\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     12\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     13\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"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x576 with 8 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3B29kUQqpFXq",
        "colab_type": "text"
      },
      "source": [
        "# Step 5: Model Hyperparams Updates"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HG6m85_A7HJS",
        "colab_type": "text"
      },
      "source": [
        "## Second model with RMSprop"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "SF1vCaMp695Y",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 734
        },
        "outputId": "c33977c0-5016-45b1-abf6-36334574bcf1"
      },
      "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",
        "\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",
        "# Fitting the CNN to the images using fit_generator\n",
        "rms_history = rms_model.fit_generator(training_set,\n",
        "                             steps_per_epoch = 500,\n",
        "                             epochs = 6,\n",
        "                             validation_data = test_set,\n",
        "                             validation_steps =100, 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))                              "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Model: \"sequential_3\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_6 (Conv2D)            (None, 62, 62, 32)        896       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_6 (MaxPooling2 (None, 31, 31, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_7 (Conv2D)            (None, 29, 29, 64)        18496     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_7 (MaxPooling2 (None, 14, 14, 64)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_8 (Conv2D)            (None, 12, 12, 128)       73856     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_8 (MaxPooling2 (None, 6, 6, 128)         0         \n",
            "_________________________________________________________________\n",
            "flatten_3 (Flatten)          (None, 4608)              0         \n",
            "_________________________________________________________________\n",
            "dense_5 (Dense)              (None, 512)               2359808   \n",
            "_________________________________________________________________\n",
            "dense_6 (Dense)              (None, 1)                 513       \n",
            "=================================================================\n",
            "Total params: 2,453,569\n",
            "Trainable params: 2,453,569\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "None"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/6\n",
            "500/500 [==============================] - 80s 161ms/step - loss: 0.2759 - acc: 0.8904 - val_loss: 0.2993 - val_acc: 0.9241\n",
            "Epoch 2/6\n",
            "500/500 [==============================] - 80s 161ms/step - loss: 0.1235 - acc: 0.9533 - val_loss: 0.1814 - val_acc: 0.9142\n",
            "Epoch 3/6\n",
            "500/500 [==============================] - 80s 160ms/step - loss: 0.0874 - acc: 0.9677 - val_loss: 0.6231 - val_acc: 0.8892\n",
            "Epoch 4/6\n",
            "500/500 [==============================] - 80s 160ms/step - loss: 0.0676 - acc: 0.9754 - val_loss: 0.1884 - val_acc: 0.9295\n",
            "Epoch 5/6\n",
            "500/500 [==============================] - 80s 161ms/step - loss: 0.0532 - acc: 0.9805 - val_loss: 0.2703 - val_acc: 0.9310\n",
            "Epoch 6/6\n",
            "500/500 [==============================] - 81s 161ms/step - loss: 0.0434 - acc: 0.9837 - val_loss: 0.0874 - val_acc: 0.9360\n",
            "Training took a total of 0:08:02.329140\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "yD0RkN0i695s",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "34a9d40d-2867-494a-bc2f-f9f95be23529"
      },
      "source": [
        "# training loss and accuracy\n",
        "\n",
        "results_train = rms_model.evaluate(X_train, y_train)\n",
        "results_train"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "4759/4759 [==============================] - 1s 224us/step\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[42.102379328715095, 0.9417945146560669]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "5lqiW5B6695x",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "9fa6bdbe-5c48-4d2e-ea65-49f88061834d"
      },
      "source": [
        "# test loss and accuracy\n",
        "\n",
        "results_test = rms_model.evaluate(X_test, y_test)\n",
        "results_test"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "624/624 [==============================] - 0s 233us/step\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[108.10659354772324, 0.9102563858032227]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WREX8RF3JUHe",
        "colab_type": "text"
      },
      "source": [
        "HOW TO SAVE h5 model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "XV-gJJhD696C",
        "colab": {}
      },
      "source": [
        "# # Your code here; save the model for future reference \n",
        "# model.save('chest_xray_downsampled_data.h5')\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qUjf0B_wxPOB",
        "colab_type": "text"
      },
      "source": [
        "HOW TO LOAD A PREVIOUS SAVED h5 model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZepOVgVUxMm3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# from keras.models import load_model\n",
        "\n",
        "# model = load_model('chest_xray_all_data.h5')\n",
        "# # As a reminder \n",
        "# model.summary()  "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ev8jtG_4BHL-",
        "colab_type": "text"
      },
      "source": [
        "### Interpreting Results"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "32Tf0bs1696N",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 981
        },
        "outputId": "0eee7f4d-98de-43a7-dc44-27c527b78534"
      },
      "source": [
        "y_preds = rms_model.predict_classes(X_test).flatten()\n",
        "\n",
        "evaluate_model(y_test,y_preds,rms_history)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.92      0.83      0.87       234\n",
            "           1       0.90      0.96      0.93       390\n",
            "\n",
            "    accuracy                           0.91       624\n",
            "   macro avg       0.91      0.89      0.90       624\n",
            "weighted avg       0.91      0.91      0.91       624\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_JioQq5J-1tN",
        "colab_type": "text"
      },
      "source": [
        "Model.evaluate shows my test accuracy at 77% and loss at 607%. Is that possible? Seems like epoch 4 was the best when looking over the graphs. My metrics classification report from sklearn also shows my accuracy at 77% with an excellent recall of 99% of pneumonia diagnosis. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u6rDPtpGAiKF",
        "colab_type": "text"
      },
      "source": [
        "## Third model with ADAM"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "fYxOnvoxAPxF",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 734
        },
        "outputId": "7c98c9ad-3180-42f9-bf12-3f20d0ba55b6"
      },
      "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",
        "\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",
        "# Fitting the CNN to the images using fit_generator\n",
        "adam_history = adam_model.fit_generator(training_set,\n",
        "                             steps_per_epoch = 500,\n",
        "                             epochs = 6,\n",
        "                             validation_data = test_set,\n",
        "                             validation_steps =100, 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))                              "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Model: \"sequential_4\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_9 (Conv2D)            (None, 62, 62, 32)        896       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_9 (MaxPooling2 (None, 31, 31, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_10 (Conv2D)           (None, 29, 29, 64)        18496     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_10 (MaxPooling (None, 14, 14, 64)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_11 (Conv2D)           (None, 12, 12, 128)       73856     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_11 (MaxPooling (None, 6, 6, 128)         0         \n",
            "_________________________________________________________________\n",
            "flatten_4 (Flatten)          (None, 4608)              0         \n",
            "_________________________________________________________________\n",
            "dense_7 (Dense)              (None, 512)               2359808   \n",
            "_________________________________________________________________\n",
            "dense_8 (Dense)              (None, 1)                 513       \n",
            "=================================================================\n",
            "Total params: 2,453,569\n",
            "Trainable params: 2,453,569\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "None"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/6\n",
            "500/500 [==============================] - 82s 164ms/step - loss: 0.2046 - acc: 0.9146 - val_loss: 0.2017 - val_acc: 0.9263\n",
            "Epoch 2/6\n",
            "500/500 [==============================] - 82s 163ms/step - loss: 0.1100 - acc: 0.9585 - val_loss: 0.2518 - val_acc: 0.9227\n",
            "Epoch 3/6\n",
            "500/500 [==============================] - 82s 163ms/step - loss: 0.0865 - acc: 0.9680 - val_loss: 0.4835 - val_acc: 0.8800\n",
            "Epoch 4/6\n",
            "500/500 [==============================] - 82s 163ms/step - loss: 0.0708 - acc: 0.9741 - val_loss: 0.3154 - val_acc: 0.9168\n",
            "Epoch 5/6\n",
            "500/500 [==============================] - 82s 163ms/step - loss: 0.0600 - acc: 0.9783 - val_loss: 0.3124 - val_acc: 0.9277\n",
            "Epoch 6/6\n",
            "500/500 [==============================] - 82s 164ms/step - loss: 0.0477 - acc: 0.9833 - val_loss: 0.1642 - val_acc: 0.9346\n",
            "Training took a total of 0:08:11.213310\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "KUiBeY5dAPxY",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "24528f22-2f67-483f-ee53-8436243cd367"
      },
      "source": [
        "# training loss and accuracy\n",
        "results_train = adam_model.evaluate(X_train, y_train)\n",
        "results_train"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "4759/4759 [==============================] - 1s 208us/step\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[38.10190445747063, 0.9182601571083069]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "aJzxwzxsAPxc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "211be1a3-6849-4350-cb16-95f82c6e9198"
      },
      "source": [
        "# test loss and accuracy\n",
        "results_test = adam_model.evaluate(X_test, y_test)\n",
        "results_test"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "624/624 [==============================] - 0s 213us/step\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[60.72488844394684, 0.9150640964508057]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FNho08weBLG2",
        "colab_type": "text"
      },
      "source": [
        "### Interpreting Results"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "vJaQ_32WAPxv",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 981
        },
        "outputId": "927a16b3-699a-4e63-8bc0-8f2dfeefd63c"
      },
      "source": [
        "y_preds = adam_model.predict_classes(X_test).flatten()\n",
        "\n",
        "evaluate_model(y_test,y_preds,adam_history)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.88      0.90      0.89       234\n",
            "           1       0.94      0.92      0.93       390\n",
            "\n",
            "    accuracy                           0.92       624\n",
            "   macro avg       0.91      0.91      0.91       624\n",
            "weighted avg       0.92      0.92      0.92       624\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T-3VfY8eALAe",
        "colab_type": "text"
      },
      "source": [
        "Model.evaluate shows my test accuracy at 86% and loss at 210%. Seems like epoch 4 was the best when looking over the graphs. My metrics classification report from sklearn also shows my accuracy at 86% with a good recall of 99% of pneumonia diagnosis. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RLfGFSOZB4JE",
        "colab_type": "text"
      },
      "source": [
        "Best model so far for recall was adam and rms with 99% Why is that loss so high though? I'm going to move forward with the adam optimizer for it's generally high 99% recall and see what I can do about that loss. Better f1 score on adam. \n",
        "\n",
        "- baseline sgd\n",
        "test accuracy at 84% and loss at 73%. sklearn also shows my accuracy at 84% with an excellent recall of 97% of pneumonia diagnosis.  \n",
        "- rmsprop\n",
        "test accuracy at 77% and loss at 607%. sklearn also shows my accuracy at 77% with an excellent recall of 99% of pneumonia diagnosis.  \n",
        "- adam\n",
        "test accuracy at 86% and loss at 210%. sklearn also shows my accuracy at 86% with a good recall of 99% of pneumonia diagnosis. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "_weKy5mQIRC5"
      },
      "source": [
        "## Fourth model with ADAM. Add Dropout. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "xJEw5IKEIRC9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 768
        },
        "outputId": "17451df2-df7b-4bef-edf7-39df5e5cd385"
      },
      "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",
        "\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 with dropout\n",
        "adam_drop_model.add(layers.Dense(units = 512, activation = 'relu'))\n",
        "\n",
        "# Dropout applied to the full connection layer\n",
        "adam_drop_model.add(layers.Dropout(0.3))\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",
        "# Fitting the CNN to the images using fit_generator\n",
        "adam_drop_history = adam_drop_model.fit_generator(training_set,\n",
        "                             steps_per_epoch = 500,\n",
        "                             epochs = 6,\n",
        "                             validation_data = test_set,\n",
        "                             validation_steps =100, 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))                              "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Model: \"sequential_4\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_7 (Conv2D)            (None, 62, 62, 100)       2800      \n",
            "_________________________________________________________________\n",
            "max_pooling2d_7 (MaxPooling2 (None, 31, 31, 100)       0         \n",
            "_________________________________________________________________\n",
            "conv2d_8 (Conv2D)            (None, 29, 29, 100)       90100     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_8 (MaxPooling2 (None, 14, 14, 100)       0         \n",
            "_________________________________________________________________\n",
            "flatten_4 (Flatten)          (None, 19600)             0         \n",
            "_________________________________________________________________\n",
            "dense_7 (Dense)              (None, 100)               1960100   \n",
            "_________________________________________________________________\n",
            "dropout_1 (Dropout)          (None, 100)               0         \n",
            "_________________________________________________________________\n",
            "dense_8 (Dense)              (None, 100)               10100     \n",
            "_________________________________________________________________\n",
            "dropout_2 (Dropout)          (None, 100)               0         \n",
            "_________________________________________________________________\n",
            "dense_9 (Dense)              (None, 1)                 101       \n",
            "=================================================================\n",
            "Total params: 2,063,201\n",
            "Trainable params: 2,063,201\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "None"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/6\n",
            "500/500 [==============================] - 55s 110ms/step - loss: 0.2249 - acc: 0.9093 - val_loss: 0.2140 - val_acc: 0.8906\n",
            "Epoch 2/6\n",
            "500/500 [==============================] - 55s 110ms/step - loss: 0.1261 - acc: 0.9537 - val_loss: 0.1027 - val_acc: 0.9060\n",
            "Epoch 3/6\n",
            "500/500 [==============================] - 55s 110ms/step - loss: 0.1036 - acc: 0.9620 - val_loss: 0.6806 - val_acc: 0.8591\n",
            "Epoch 4/6\n",
            "500/500 [==============================] - 55s 110ms/step - loss: 0.0875 - acc: 0.9676 - val_loss: 0.5446 - val_acc: 0.9161\n",
            "Epoch 5/6\n",
            "500/500 [==============================] - 55s 111ms/step - loss: 0.0740 - acc: 0.9729 - val_loss: 0.2783 - val_acc: 0.9249\n",
            "Epoch 6/6\n",
            "500/500 [==============================] - 55s 109ms/step - loss: 0.0659 - acc: 0.9770 - val_loss: 0.5139 - val_acc: 0.9006\n",
            "Training took a total of 0:05:30.617854\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "NIFQbkuAIRDG",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "4441069b-d137-4e19-d8fc-810cbd3d21ad"
      },
      "source": [
        "results_train = adam_drop_model.evaluate(X_train, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "4759/4759 [==============================] - 1s 123us/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "1K5H6YdWIRDL",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "ff5395ce-5b6a-43bb-ca37-d58e236f204f"
      },
      "source": [
        "results_test = adam_drop_model.evaluate(X_test, y_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "624/624 [==============================] - 0s 126us/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "8ogkhxzsIRDV",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "28925c61-20c1-4377-b363-143c96fd3d9e"
      },
      "source": [
        "# training loss and accuracy\n",
        "results_train"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[10.746086222173949, 0.9691111445426941]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "5A4G7ynLIRDa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "3f346e6f-e4b9-4898-f401-c178c41187ca"
      },
      "source": [
        "# test loss and accuracy\n",
        "results_test"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[133.61955882341434, 0.8605769276618958]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "t6Guia9bIRDf",
        "colab": {}
      },
      "source": [
        "# # Your code here; save the model for future reference \n",
        "# model.save('chest_xray_downsampled_data.h5')\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "K5Rd5s5EIRDn"
      },
      "source": [
        "### Visualize accuracy and loss"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "_LMLaxH1IRDo",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 545
        },
        "outputId": "136494bb-5aba-4673-ff2e-70df87da019a"
      },
      "source": [
        "# view baseline cnn results evaluate\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline \n",
        "\n",
        "train_acc = adam_drop_history.history['acc']\n",
        "test_acc = adam_drop_history.history['val_acc']\n",
        "train_loss = adam_drop_history.history['loss']\n",
        "test_loss = adam_drop_history.history['val_loss']\n",
        "epochs = range(len(train_acc))\n",
        "plt.plot(epochs, train_acc, 'bo', label='Training acc')\n",
        "plt.plot(epochs, test_acc, 'b', label='Test acc')\n",
        "plt.title('Training and Testing accuracy')\n",
        "plt.legend()\n",
        "plt.figure()\n",
        "plt.plot(epochs, train_loss, 'bo', label='Training loss')\n",
        "plt.plot(epochs, test_loss, 'b', label='Test loss')\n",
        "plt.title('Training and Testing loss')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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dd93Fiy++yMMPP8wPP/xAuXLl2Lp1K1WrVmXQoEFUrlyZW2+99YjXvf7667n88su5/PLLefHFFxk6dCjTp09n5MiRzJkzh1q1arF161YAnnvuOW644QbS0tLYs2cP+yNwcX5roRvPbNoEH33kWufh3o+4Zk2YM8ctNzBzpivJfPRReGMwgfvrr79Yvnw5nTp1IiUlhQceeIDs7GwAkpOTSUtL49VXX6V06aK1UT/77DP69u0LQP/+/VmwYAEA7du3Z8CAAUyYMOFg4m7bti0PPvggo0ePZv369VSoUCGIP2FwWAvdeOatt9wOROEot+SnVCm49VY4+2zo2xc6doThw2HECMiz/0FcK0pLOlRUlVNPPZXPPvvssOdmzZrF/PnzmTlzJqNGjeKbIMwme+6551i0aBGzZs2iRYsWLFmyhL59+9K6dWtmzZrF+eefz7hx4+jYsWOJ7xVM1kI3nsnIgPr13cdvL7Vo4ZYNuOIKePBBOP10+P57b2MyhypXrhybNm06mND37t1LVlYWBw4cYMOGDZx99tmMHj2abdu2sXPnTqpUqcKOHTsKvW67du2YPHkyAOnp6ZxxxhkAfP/997Ru3ZqRI0eSmJjIhg0bWLt2LfXr12fo0KH06NHj4PZykcQSuvHEli1ubLgX5Zb8VK4Mzz8Pb7wB337r/si8/LJ1mEaKUqVK4fP5uOOOO2jWrBkpKSksXLiQ/fv3069fP5o2bUrz5s0ZOnQoVatW5cILL2TatGmFdor+97//ZeLEiSQnJ/PKK6/w9NNPA3DbbbfRtGlTmjRpQrt27WjWrBlTp06lSZMmpKSksHz5ci677LJw/fgBC2j5XBHpAjwNJADPq+rD+ZzzT2AEoMBXqtr3SNe05XPj26RJMGAALFoErVp5Hc2hNmyAfv3ccMo+feDZZ6GQZbBjji2fGxmCvnyuiCQAY4GuQGOgj4g0znNOA2A40F5VTwVuLF74Jl74fG5XopYtvY7kcLVrw4cfwgMPwNSprrUeh0OaTRQKpOTSClijqmtVdQ8wGeiR55yrgbGqugVAVX8Nbpgmlmzb5qbgR0q5JT8JCW7X+gULXIxnnAEjR0IINsYxJmgCSei1gA25Hmf7j+V2MnCyiHwqIp/7SzSHEZGBIpIpIpmbbKWkuPX227Bnj3ejW4qiTRs3BrtvX7j3XjciZv16r6MKD692MzNOcd7/YHWKlgYaAGcBfYAJIlI170mqOl5VU1U1NTExMUi3NtEmI8ONA2/TxutIAnPUUfDKK/Dqq/DVV26Z3wD2C45q5cuXZ/PmzZbUPaKqbN68mfLlyxfpdYGMQ98I5N6DPcl/LLdsYJGq7gV+EJFvcQl+cZGiMTFv50545x23D2ipKBtjlZYGbdu6r717uxmmY8ZAlSpeRxZ8SUlJZGdnY5+kvVO+fHmSkpKK9JpAEvpioIGI1MMl8t5A3hEs03Et84kiUh1XgllbpEhMXJg9G3bvjo5yS37q13ejX+6/H0aNcjX2116LzM7dkihTpgz16tXzOgxTRIW2kVR1HzAEmAOsBKaqapaIjBSR7v7T5gCbRWQFMA+4TVU3hypoE718PrdOefv2XkdSfGXKuA7SefPcMrzt2sHo0XDggNeRmXgX0Dj0ULBx6PFn1y5ITITLLnNju2PBli1ugS+fzy0d8PLLUCvvkAFjgqhE49CNCZZ333VJPVrLLfk55hg3Vv2FF9wG1cnJMH2611GZeGUJ3YSNzwfVqsGZZ3odSXCJuHVgli6FevXgootg0CD3x8uYcLKEbsJi9243/vyii6CIK5xGjZNPdjNKb78dxo2D1NTgryNuzJFYQjdhMXcu7NgRW+WW/JQt6zpI586FrVuhdWu3/Kx1mJpwsIRuwsLng6pV3UzLeHDuuW5XpC5d4KaboFs3t3+qMaFkCd2E3J49bjOLHj1cCzZeVK/uOkifecbthpSc7MbhGxMqltBNyH3wgVuQK9bLLfkRgcGDITMTjj/etdRvvNH1KRgTbJbQTcj5fG56fKdOXkfinVNPhS++gKFD4emnXW09K8vrqEyssYRuQmrvXld26N4dypXzOhpvlS/vkvmsWfDTT24UzLPP2q5IJngsoZuQ+vhj+P33+Cy3FOT8812H6ZlnwrXXQs+e8NtvXkdlYoEldBNSPh9UqgSdO3sdSWQ54QTXQfrkk24GbXKy62swpiQsoZuQ2b8f3nzTdQRWqOB1NJGnVCnXQbpokduztFMneOIJr6My0cwSugmZTz6BTZus3FKYlBRYssSVXm67zfYvNcVnCd2EjM/nWuZdu3odSeSrWBFeegnq1HEbaGzf7nVEJhpZQjchceCAK7d07QqVK3sdTXQ46ii3zd2PP8KQIV5HY6KRJXQTEp995obmWbmlaNq1g3vucXuYvv6619GYaGMJ3YSEz+fGnXfr5nUk0efuu90G2oMHw/r1XkdjookldBN0Bw64hN65sysjmKIpXRrS090oof793VdjAmEJ3QTd4sWQnQ29enkdSfSqXx/GjnUjhR55xOtoTLSwhG6CzudzGylfeKHXkUS3/v3hX/+C//zH/ZE0pjCW0E1QqbqEfu65br9NU3wibq2XGjXcUMadO72OyATDH3+E7tqW0E1QffklrFtno1uC5Zhj3IiXNWvcRhkmum3Y4LYqfOWV0FzfEroJqowMSEhwm1mY4DjzTBg2DJ5/3o3tN9Hpzz/dnro7dkDLlqG5R0AJXUS6iMhqEVkjIsPyeX6AiGwSkWX+f1cFP1QT6VThjTegY0eoVs3raGLLiBFuud2rr4aNG72OxhSVKlxzjVviIT0dGjUKzX0KTegikgCMBboCjYE+ItI4n1OnqGqK/9/zQY7TRIFvvnGlASu3BF/Zsi4R7N4Nl19um05HmzFjXJnlvvtCO1ggkBZ6K2CNqq5V1T3AZMA+UJvD+HxuBcGePb2OJDadfLLbIOODD9yyuyY6fPgh3HKL+724++7Q3iuQhF4L2JDrcbb/WF69RORrEfGJSO38LiQiA0UkU0QyN23aVIxwTSTz+aBDBzjuOK8jiV1XXunqsMOHw7JlXkdjCrNuHfzzn9CwIbz8smvwhFKwLj8TqKuqycBcYFJ+J6nqeFVNVdXUxMTEIN3aRIIVK2DlSiu3hJoITJgAiYnQty/s2uV1RKYgu3a5P7779rltGKtUCf09A0noG4HcLe4k/7GDVHWzqv7lf/g80CI44Zlo4fO5ZHPRRV5HEvuqVYNJk9wf0Ntu8zoakx9VuOoq+Oort8hagwbhuW8gCX0x0EBE6olIWaA3MCP3CSJSI9fD7sDK4IVookFGBrRvDzVreh1JfDj3XFeXfeYZePttr6MxeT3+uEvko0aFdz+AQhO6qu4DhgBzcIl6qqpmichIEenuP22oiGSJyFfAUGBAqAI2kefbb92mx1ZuCa9Ro6BZM7jiCvj5Z6+jMTnmzoU77nC/D8MOG+QdWqKq4b2jX2pqqmZmZnpybxNcDz0Ed97pNmaonW93uAmVFSugRQs46yy36bSI1xHFt7Vr3XyBpCS3lWAoNncRkSWqmprfczZT1JSYzwetW1sy90Ljxu7j/bvvwv/+53U08W3nzr+H7E6b5s1OXZbQTYmsXevWb7Fyi3cGD3Ybidx2Gyxf7nU08UkV/v1vyMqCyZPhpJO8icMSuimRjAz31dY+944IvPgiHH20G8q4e7fXEcWf0aPdJ9XRo+G887yLwxK6KZGMDFfDrVfP60ji23HHwcSJbvmF4cO9jia+vPOO60Pq08eNPPKSJXRTbD/+CIsWWbklUpx/PgwZAk89BXPmeB1NfPjuO5fImzVzq2F63SltCd0UW85SrlZuiRyPPOI6SgcMAFtdI7R27HCdoKVLu07QihW9jsgSuikBnw+Sk8M3C84UrkIFeO01+P13N1PRo1HJMe/AAbjsMli9GqZOhbp1vY7IsYRuimXjRvj0Uyu3RKJmzeDhh2HGDLfuiwm+UaPc+iyPPebW/48UltBNsUyb5r5aQo9MN9wAnTrBjTfCqlVeRxNbZs50G3f37+/e50hiCd0Ui8/narWnnOJ1JCY/pUrBSy+5um5aGuzZ43VEsWHVKujXz43sGjfO+07QvCyhmyL75Rf45BNrnUe6mjXdyIsvv3QtSlMy27a5TtBy5dwn1AoVvI7ocJbQTZFNn+46hSyhR76ePWHgQDf6Zd48r6OJXgcOuJb599+7T6eRusyFJXRTZD6f2w6tSROvIzGBeOIJNxKpf383+sUU3X33uWWKn3rK7coVqSyhmyL57TfX0uvVK/LqhyZ/lSq5oYy//OJ2nrehjEUzbRqMHOnWarn2Wq+jOTJL6KZI3noL9u+3cku0adECHnjAfbqalO8GkSY/WVluvHmrVm4zkUhvxFhCN0Xi87l1W5o39zoSU1S33urWTb/+elizxutoIt+WLa4PolIlNyu6fHmvIyqcJXQTsC1b4P33Xes80lsq5nAJCW7n+dKlXQff3r1eRxS59u93wz3Xr3cL0NWq5XVEgbGEbgI2c6bbwdzKLdGrdm0YP94tqnb//V5HE7nuucetovjf/7q9cqOFJXQTsJzhWi1beh2JKYlLL3WLd40aBQsWeB1N5HnjDbet4sCBrhM5mlhCNwHZvt0tyWqjW2LDmDFuQal+/dyEGeN8/bX7Y9eunXuPoo0ldBOQt99208et3BIbqlSB9HTIzobrrvM6msjw+++uE7RqVfdptFw5ryMqOkvoJiA+H9SoAW3beh2JCZY2beDee11iT0/3Ohpv7dsHvXu7VUQzMtz/69HIErop1M6droOoVy+36JOJHcOHu06/a6+Fdeu8jsY7d94Jc+fCs8+6P3TRKqBfTxHpIiKrRWSNiAw7wnm9RERFJDV4IRqvvfOO23jYyi2xp3RpePVV932/fq6lGm9efx0efdSVnq64wutoSqbQhC4iCcBYoCvQGOgjIo3zOa8KcAOwKNhBGm/5fG4T4tNP9zoSEwp167pZkJ9+6jbGiCdLl8KVV8IZZ8CTT3odTckF0kJvBaxR1bWqugeYDPTI57z7gdHA7iDGZzy2axfMmgUXX+wmppjYlJbmNjseMcKNUY8Hv/0GF10E1aq5oYplyngdUckFktBrARtyPc72HztIRE4DaqvqrCNdSEQGikimiGRush1so8KcOfDHH7YRdDx45hk3IzItzW2AHMv27YN//hN+/tktvnX88V5HFBwl7uISkVLAE8AthZ2rquNVNVVVUxMTE0t6axMGPp9rwZx5pteRmFCrWtXV03/4wW1dF8tuu82tGjp+PKTGUI9fIAl9I5B7Ofck/7EcVYAmwEcisg5oA8ywjtHot3u3m+7fs2dsfBw1hTvjDDfy5cUX3R/zWPTKK25d8xtucCspxpJAEvpioIGI1BORskBvYEbOk6q6TVWrq2pdVa0LfA50V9XMkERswmbuXPfR20a3xJd773XLOwwc6CYexZLMTLj6ajj7bDeyJdYUmtBVdR8wBJgDrASmqmqWiIwUke6hDtB4JyPDfQzv2NHrSEw4lSnjJhrt2eNasAcOeB1RcPzyi+sEPeEEmDIlNj91lg7kJFWdDczOcyzfbWdV9aySh2W8tmeP28yiRw8oW9braEy4NWjg1jK58kp4/HFXc45me/e6TtDNm93wzFjtwrN5fyZfH34IW7dauSWe/fvfbnTTXXfBl196HU3J3HwzzJ8Pzz8f25uzWEI3+fL53AJOnTp5HYnxiogbBXLccdC3r5uTEI1efBH+9z+3Y1Pfvl5HE1qW0M1h9u51Y3MvvDA6V5wzwXPssW6Xo2+/hVsKHZgceRYtgsGD4dxz3Rrnsc4SujnMxx+7pUSt3GLAdYrfeis89xzMmFH4+ZHi55/dDOdatWDyZLduTayzhG4O4/O5jXG7dPE6EhMp7r/f1Z6vvBJ++snraAq3Z4+r/2/dCtOnu8lx8cASujnE/v2u3NKtG1So4HU0JlKUK+eGMv7xh9vRJ9KHMg4dCgsXwksvQXKy19GEjyV0c4gFC+DXX63cYg53yinwxBPw3ntu8+RINX48jBsHw4a5/VPjiSV0cwifz7XMu3b1OhITia65xnWW3367238z0ixcCEOGuHLhAw94HU34WUI3B98IlWoAABHXSURBVB044GaHdukClSt7HY2JRCLwwgtwzDFuVcY///Q6or9t3Ojq5nXqwGuvxedyz5bQzUGffeY6vKzcYo4kMdHVppcvd2WNSLB7txvRsnOn6wQ95hivI/KGJXRzkM/npvlfcIHXkZhI16WLW61wzBi3RaGXVN32cV98AZMmwamnehuPlyyhG8D9UmRkQOfOcNRRXkdjosHDD0OTJm6JgF9/9S6OZ591s0Hvuce10uOZJXQDwOLFsGGDlVtM4MqXd7XqrVvd+HTV8Mcwf777pHDBBW77vHhnCd0ArtxSpowbwWBMoJo2hUcegbffdjNJwymnAXLSSW6npVKWzSyhG9ey8vngnHPitzPJFN/117ua+s03w8qV4bnnn3+6tc1373adoEcfHZ77RjpL6IalS90+klZuMcUhAhMnuqGuffvCX3+F9n6qMGgQLFniZq82ahTa+0UTS+gGn8+N2e3Rw+tITLQ64QQ3Pn3ZMtc5GUpjxrgVIO+7z0qEeVlCj3M55Zazz4bq1b2OxkSz7t1dy/nRR+GDD0Jzj3nz3DK+PXvC3XeH5h7RzBJ6nFu+HL77zsotJjgefxwaNoTLL3fbvQXTunVubZaGDV0L3TpBD2dvSZzz+dwvRs+eXkdiYkHFim4o46+/unVfgjWUcdcu1wm6b5/rBK1SJTjXjTWW0OOczwcdOsDxx3sdiYkVp50Go0a5iWoTJ5b8eqpw1VXw1Vfuj0WDBiW/ZqyyhB7HVqxw/3r18joSE2tuucXtdDR0qCvplcQTT8Drr7s/EuefH5z4YpUl9DiWkeG+xvt0aRN8pUq5dVXKlnWrMu7dW7zrzJ3rluq95JLIWQgskgWU0EWki4isFpE1InLY2yoig0TkGxFZJiILRKRx8EM1webzQfv2ULOm15GYWJSUBBMmuGUl7ruv6K9fuxb+9S+32NbEiW68uzmyQhO6iCQAY4GuQGOgTz4J+zVVbaqqKcAjwBNBj9QE1XffuQ0KbHSLCaVeveCKK+DBB926K4H644+/O+qnTbP1+QMVSAu9FbBGVdeq6h5gMnDIFBRV3Z7rYSXAg2V6TFFYucWEy9NPu/VW+vd3C3kVRtWt4JiVBZMnu9eawASS0GsBG3I9zvYfO4SIXCci3+Na6EODE54JFZ8PWreGE0/0OhIT6ypXdlP0N26EwYMLH8o4ejS88YZbnve888ITY6wIWqeoqo5V1ZOAO4B853CJyEARyRSRzE2bNgXr1qaIfvjBrYNh5RYTLq1auTr65MkuuRfk3Xfhzjuhd2+49dbwxRcrAknoG4HauR4n+Y8VZDKQ7zQVVR2vqqmqmpqYmBh4lCaocsotNlzRhNOwYXD66XDtta5Rkdd330GfPtCsmVsXxjpBiy6QhL4YaCAi9USkLNAbmJH7BBHJPdS/G1DCkacmlHw+N/mjXj2vIzHxJCHBrVsuAv36uVmfOXbscJ2gCQmuE7RiRe/ijGaFJnRV3QcMAeYAK4GpqpolIiNFpLv/tCEikiUiy4CbgctDFrEpkQ0bYNEiK7cYb9Sp4zbCWLjQjXwBOHDArf2yejVMnQp163oaYlQrHchJqjobmJ3n2H9yfX9DkOMyIfLmm+6rlVuMV/r0gdmzYeRI6NTJrcw4bRo8+aSbXWqKT9SLjQCB1NRUzczM9OTe8eyMM2D7drcuhjFe2bYNUlLcolubNrkSzKRJVjcPhIgsUdXU/J6zqf9x5P/+Dz791MotxntHH+3q6b/95vpzxo2zZB4MAZVcTGyYNs2NAbZyi4kE7du7ZQHq14cKFbyOJjZYQo8jPh+ccgo0tpV2TIQ47TSvI4gtVnKJE7/84tbSsHKLMbHLEnqcmD7dDQ+zhG5M7LKEHicyMtxOL02beh2JMSZULKHHgc2b4cMPXevcRhIYE7ssoceBt96C/fut3GJMrLOEHgd8PjedunlzryMxxoSSJfQYt2ULvP++lVuMiQeW0GPczJlug14rtxgT+yyhxzifD2rXdhsMGGNimyX0GLZ9O7z3npvqb+UWY2KfJfQYNmsW/PWXlVuMiReW0GOYzwc1akDbtl5HYowJh6hK6OnpbvhdqVLu65E2m413O3e6TQQuvti9X8aY2Bc1qy2mp8PAgW5BfID1691jgLQ07+KKVO+8A7t3W7nFmHgSNW23u+76O5nn2LXLHTeH8/kgMdHtUGSMiQ9Rk9B//LFox+PZn3+6DtGLL3a7qBtj4kPUJPQTTyza8Xg2Zw788YeVW4yJN1GT0EeNgooVDz1WsaI7bg7l80G1anDmmV5HYowJp6hJ6GlpMH48VK3qHpcuDYMHW4doXn/9BTNmQM+eUKaM19EYY8IpahI6uOS9ZYsrKfzjH/D449CtG3z7rdeRRY65c2HHDtsI2ph4FFBCF5EuIrJaRNaIyLB8nr9ZRFaIyNci8oGI1Al+qH877zz46iuX0D/5BJo0gWHDXCKLdz4fHH00nHOO15EYY8Kt0IQuIgnAWKAr0BjoIyJ5941fCqSqajLgAx4JdqB5lS0LN9/sWudpaTB6NDRq5Marq4b67pFpzx63mUWPHu79McbEl0Ba6K2ANaq6VlX3AJOBHrlPUNV5qpozSvxzICm4YRbshBNg4kT47DOoWRP69XNjr5cuDVcEkWPePNi61Ua3GBOvAknotYANuR5n+48V5ErgnfyeEJGBIpIpIpmbNm0KPMoAtGkDixbB88+7Vntqqus03bw5qLeJaD4fVKkCnTp5HYkxxgtB7RQVkX5AKvBofs+r6nhVTVXV1MTExGDeGnBrllx5pUvoQ4bAhAlup/tnnnF7asayfftg2jS48EIoX97raIwxXggkoW8Eaud6nOQ/dggRORe4C+iuqn8FJ7ziqVoVnn4ali2DlBS47jpo0QLmz/cyqtD6+GP3acRGtxgTvwJJ6IuBBiJST0TKAr2BGblPEJHmwDhcMv81+GEWT5Mm8MEHMHUq/P67m2jTty9kZ3sdWfD5fG6iVZcuXkdijPFKoQldVfcBQ4A5wEpgqqpmichIEenuP+1RoDLwhogsE5EZBVwu7ETg0kth1Sq45x548003Guahh9wknFiwf7/7ubp1O3w2rTEmfoh6NMYvNTVVMzMzw37ftWvhlltg+nQ46SR46im44IKwhxFUH38MZ50FU6bAP//pdTTGmFASkSWqmprfc1E1UzQY6td3nYdz5rjlAy680LVsv/vO68iKLyPDdYSef77XkRhjvBR3CT3HeefB11/DY4+52aannupmm+7c6XVkRXPggEvoXbtC5cpeR2OM8VLcJnRwsylvucUNc+zb1802bdgQXnstemabfv45/N//2WQiY0ycJ/QcJ5wAL70ECxe6TZXT0qBDBzfsMdL5fO4PU7T3AxhjSs4Sei5t28IXX7gJSatWubHr114bubNNVV1CP+88OOoor6MxxnjNEnoepUrBVVf9Pdt0/Hg4+WR49tnIm226eDFs2GDlFmOMYwm9AMcc42abLl0Kycmupd6ihetAjRQZGW6kTvfuhZ9rjIl9ltAL0bQpfPjh37NNO3RwHagbD1v8ILxyyi3nnuv++BhjjCX0AOTMNl258u/Zpg0bwsMPezfbdNkyN0nKyi3GmByW0IugUiUYORJWrHBL1A4f7taLmTUr/LH4fJCQ4DazMMYYsIReLLlnmyYkuCGDF1wQvtmmqvDGG266f/Xq4bmnMSbyWUIvgdyzTefPd6314cNDP9t0+XL3x8PKLcaY3Cyhl1DObNPVq6FPH1dXD/VsU5/P1fUvuig01zfGRCdL6EFSo8bhs03PPBO++ir498rIcKNtjj8++Nc2xkQvS+hB1rat29t0wgQ3Kua004I723TlSsjKsnKLMeZwltBDICHh79mm111Xstmm6elQt66bwVq3LvznP+74xRcHO2pjTLSzhB5CxxwDY8YUf7ZpejoMHAjr17t6/Pr1rtxy8slQs2ZoYzfGRB9L6GGQM9t0yhRXeunQwdXYC5ttetddsGvXocdUYdOm0MVqjIleltDDRMRtD7dqFdx9t2tpFzbb9Mcf8z++ZUvo4jTGRC9L6GFWqRLcf7+bbXruuW7cetOmMHv24eeeeGL+16hTJ7QxGmOikyV0j9Sv7zaqfvdd1+HZrZubbbpmzd/njBoFFSse+royZdxxY4zJyxK6xzp3drNNH30UPv7Y7W16551utmlamhshk7tFPnq0O26MMXlZQo8AZcvCrbe6YY69e8NDD0GjRvD6626p3nXroE0bN6b9ppu8jjb08g7VTE/3OiJjokNACV1EuojIahFZIyLD8nm+g4h8KSL7RMSmvBRTjRowaRJ8+qmbBdq3r5ttOmuW2wy6Vy+vIwy9/IZqDhxoSd2YQBSa0EUkARgLdAUaA31EpHGe034EBgCvBTvAeNSundvbdPx4NzM0ZwPoeJgdmt9QzV273HFjzJGVDuCcVsAaVV0LICKTgR7AipwTVHWd/7kDIYgxLiUkwNVXuyR+330uqZ18stdRhV5BQzULOm6M+VsgCb0WsCHX42ygdXFuJiIDgYEAJxY0Js8c4phj4KmnvI4ifE480ZVZ8jtujDmysHaKqup4VU1V1dTExMRw3tpEifyGalasGPtDNa0j2ARDIAl9I1A71+Mk/zFjgi73UE0R93X8+NgeqmkdwSZYRAvZhUFESgPfAufgEvlioK+qZuVz7kvA26rqK+zGqampmpmZWZyYjYkpdevmX2aqU8cNWTUmNxFZoqqp+T1XaAtdVfcBQ4A5wEpgqqpmichIEenuv0FLEckGLgXGichhyd4Ykz/rCDbBElANXVVnq+rJqnqSqo7yH/uPqs7wf79YVZNUtZKqVlPVU0MZtDGxpKAO31jvCLZ+g+CzmaLGeCweO4Kt3yA0LKEb47F47Ai2CWShYQndmAiQluY6QA8ccF9jOZlD/PYbhLrMZAndGBN28dhvEI4ykyV0Y0zYxWO/QTjKTJbQjTFhF4/9BuEoMwWylosxxgRdWlpsJ/C8wrFOkbXQjTEmDMJRZrKEbowxYRCOMpOVXIwxJkxCXWayFroxxsQIS+jGGBMjLKEbY0yMsIRujDExwhK6McbEiEJ3LArZjUU2AfkMsw9IdeC3IIYTDexnjg/2M8eHkvzMdVQ1302ZPUvoJSEimQVtwRSr7GeOD/Yzx4dQ/cxWcjHGmBhhCd0YY2JEtCb08V4H4AH7meOD/czxISQ/c1TW0I0xxhwuWlvoxhhj8rCEbowxMSLqErqIdBGR1SKyRkSGeR1PqInIiyLyq4gs9zqWcBGR2iIyT0RWiEiWiNzgdUyhJiLlReQLEfnK/zPf53VM4SAiCSKyVETe9jqWcBCRdSLyjYgsE5HMoF8/mmroIpIAfAt0ArKBxUAfVV3haWAhJCIdgJ3Ay6raxOt4wkFEagA1VPVLEakCLAF6xvh/ZwEqqepOESkDLABuUNXPPQ4tpETkZiAVOEpVL/A6nlATkXVAqqqGZCJVtLXQWwFrVHWtqu4BJgM9PI4ppFR1PvC713GEk6r+pKpf+r/fAawEankbVWips9P/sIz/X/S0topBRJKAbsDzXscSK6ItodcCNuR6nE2M/6LHOxGpCzQHFnkbSej5yw/LgF+Buaoa6z/zU8DtwAGvAwkjBd4TkSUiMjDYF4+2hG7iiIhUBjKAG1V1u9fxhJqq7lfVFCAJaCUiMVtiE5ELgF9VdYnXsYTZ6ap6GtAVuM5fUg2aaEvoG4HauR4n+Y+ZGOOvI2cA6ar6ptfxhJOqbgXmAV28jiWE2gPd/TXlyUBHEXnV25BCT1U3+r/+CkzDlZGDJtoS+mKggYjUE5GyQG9ghscxmSDzdxC+AKxU1Se8jiccRCRRRKr6v6+A6/hf5W1UoaOqw1U1SVXr4n6PP1TVfh6HFVIiUsnfyY+IVALOA4I6ei2qErqq7gOGAHNwHWVTVTXL26hCS0ReBz4DGopItohc6XVMYdAe6I9rtS3z/zvf66BCrAYwT0S+xjVc5qpqXAzliyPHAwtE5CvgC2CWqr4bzBtE1bBFY4wxBYuqFroxxpiCWUI3xpgYYQndGGNihCV0Y4yJEZbQjTEmRlhCN8aYGGEJ3RhjYsT/A/uzey17w6ZXAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "uwOuMkIjIRDt"
      },
      "source": [
        "### Visualize confusion matrix"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "CPvEVIcJIRDu",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 557
        },
        "outputId": "87581a8c-97f0-4928-a630-9d4f1448c4a9"
      },
      "source": [
        "import sklearn.metrics as metrics\n",
        "# from mod3 project\n",
        "def evaluate_model(y_test, y_pred,model_history=None):\n",
        "    \"\"\"Takes in your target, target predictions, model history.\n",
        "        Returns a metric report and confusion matrix.\"\"\"\n",
        "    \n",
        "    ## Classification Report / Scores \n",
        "    print(metrics.classification_report(y_test,y_pred))\n",
        "\n",
        "    # subplot for confusion matrix\n",
        "    fig, ax = plt.subplots(figsize=(12,6))\n",
        "    cm = metrics.confusion_matrix(y_test,y_pred)\n",
        "\n",
        "    plt.imshow(cm, interpolation='nearest', cmap='Blues')\n",
        "    plt.title('Confusion Matrix. 0=Norm, 1=Pneum')\n",
        "    plt.colorbar()\n",
        "    plt.ylabel('True label')\n",
        "    plt.xlabel('Predicted label')\n",
        "\n",
        "    classes = list(range(len(cm)))  \n",
        "    plt.xticks(classes, classes)\n",
        "    plt.yticks(classes, classes)\n",
        "\n",
        "\n",
        "    plt.show()\n",
        "\n",
        "y_preds = adam_drop_model.predict_classes(X_test).flatten()\n",
        "\n",
        "evaluate_model(y_test,y_preds,adam_drop_history)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.96      0.66      0.78       234\n",
            "           1       0.83      0.98      0.90       390\n",
            "\n",
            "    accuracy                           0.86       624\n",
            "   macro avg       0.89      0.82      0.84       624\n",
            "weighted avg       0.88      0.86      0.85       624\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "pXGIHYgUIRDz"
      },
      "source": [
        "\n",
        "### Interpreting Results"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "iV1VruSQIRD0"
      },
      "source": [
        "My test accuracy is 88%. Loss is 62. Recall is 94%. Better than the previous version of adam. There seems to be overfitting when comparing training acc of 95% to test at 88%. Epoch 3 had best results. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "jA3-6AkDIRD1"
      },
      "source": [
        "previous models\n",
        "- baseline sgd\n",
        "test accuracy at 84% and loss at 73%. sklearn also shows my accuracy at 84% with an excellent recall of 97% of pneumonia diagnosis.  \n",
        "- rmsprop\n",
        "test accuracy at 77% and loss at 607%. sklearn also shows my accuracy at 77% with an excellent recall of 99% of pneumonia diagnosis.  \n",
        "- adam\n",
        "test accuracy at 86% and loss at 210%. sklearn also shows my accuracy at 86% with a good recall of 99% of pneumonia diagnosis. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VbTxXAMaqFmd",
        "colab_type": "text"
      },
      "source": [
        "## Conclusion Summary"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sWKnH7rpzc6u",
        "colab_type": "text"
      },
      "source": [
        "Based on this final model, it seems that the process was able to classify 94% of all pneumonia patients in the test set. More tuning for the future to try and get more of the patients properly classified. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aP8oA0Drzsgh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}