Multiclass Models.ipynb

{
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  {
   "cell_type": "markdown",
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   "source": [
    "# EE 460J: Final Project\n",
    "## Evan Bausbacher, Colby Janecka, Corey Karnei, Rijul Nanda, Youchan Lee\n",
    "\n",
    "\n",
    "##### In this project, we will analyze different images of the eye to detect eye diseases. The original dataset was taken from here: \n",
    "##### https://www.kaggle.com/andrewmvd/ocular-disease-recognition-odir5k\n",
    "##### Additional data for glaucoma: https://www.kaggle.com/sshikamaru/glaucoma-detection/version/2\n",
    "##### Additional data for diabetic retinopathy: https://www.kaggle.com/c/diabetic-retinopathy-detection/data\n",
    "##### Additional data for hypertension: https://retinagallery.com/thumbnails.php?album=731\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.image as mpimg\n",
    "import collections\n",
    "import pandas as pd\n",
    "import skimage.io\n",
    "import scipy.io\n",
    "import math\n",
    "import cv2\n",
    "from PIL import Image, ImageOps\n",
    "\n",
    "import os\n",
    "from os import path\n",
    "import shutil\n",
    "\n",
    "import keras\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Activation, Dropout, Flatten, Conv2D, MaxPooling2D\n",
    "from keras.layers.normalization import BatchNormalization\n",
    "\n",
    "from random import sample\n",
    "import seaborn as sns\n",
    "from tensorflow.keras.applications.vgg19 import VGG19\n",
    "from tensorflow.keras.models import Model, Sequential\n",
    "from tensorflow.keras.layers import Conv2D, Dense, Dropout, MaxPooling2D, Flatten\n",
    "from keras.utils import plot_model\n",
    "from scikitplot.metrics import plot_confusion_matrix as plt_con_mat\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from tensorflow.keras.applications.resnet50 import preprocess_input\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "\n",
    "import tensorflow as tf\n",
    "import tensorflow_addons as tfa\n",
    "import pathlib\n",
    "from tensorflow.keras.applications import ResNet50\n",
    "from tensorflow.keras.models import Model\n",
    "import tensorflow.keras as keras\n",
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Patient Age</th>\n",
       "      <th>Patient Sex</th>\n",
       "      <th>Left-Fundus</th>\n",
       "      <th>Right-Fundus</th>\n",
       "      <th>Left-Diagnostic Keywords</th>\n",
       "      <th>Right-Diagnostic Keywords</th>\n",
       "      <th>N</th>\n",
       "      <th>D</th>\n",
       "      <th>G</th>\n",
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       "      <th>A</th>\n",
       "      <th>H</th>\n",
       "      <th>M</th>\n",
       "      <th>O</th>\n",
       "      <th>filepath</th>\n",
       "      <th>labels</th>\n",
       "      <th>target</th>\n",
       "      <th>filename</th>\n",
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       "      <td>cataract</td>\n",
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       "      <td>2_left.jpg</td>\n",
       "      <td>2_right.jpg</td>\n",
       "      <td>laser spot，moderate non proliferative retinopathy</td>\n",
       "      <td>moderate non proliferative retinopathy</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>../input/ocular-disease-recognition-odir5k/ODI...</td>\n",
       "      <td>['D']</td>\n",
       "      <td>[0, 1, 0, 0, 0, 0, 0, 0]</td>\n",
       "      <td>2_right.jpg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>53</td>\n",
       "      <td>Male</td>\n",
       "      <td>4_left.jpg</td>\n",
       "      <td>4_right.jpg</td>\n",
       "      <td>macular epiretinal membrane</td>\n",
       "      <td>mild nonproliferative retinopathy</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>5_left.jpg</td>\n",
       "      <td>5_right.jpg</td>\n",
       "      <td>moderate non proliferative retinopathy</td>\n",
       "      <td>moderate non proliferative retinopathy</td>\n",
       "      <td>0</td>\n",
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       "      <td>4686_right.jpg</td>\n",
       "      <td>severe nonproliferative retinopathy</td>\n",
       "      <td>proliferative diabetic retinopathy</td>\n",
       "      <td>0</td>\n",
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       "      <td>4688_right.jpg</td>\n",
       "      <td>moderate non proliferative retinopathy</td>\n",
       "      <td>moderate non proliferative retinopathy</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>mild nonproliferative retinopathy</td>\n",
       "      <td>mild nonproliferative retinopathy</td>\n",
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       "      <td>4784_right.jpg</td>\n",
       "      <td>hypertensive retinopathy，age-related macular d...</td>\n",
       "      <td>hypertensive retinopathy，age-related macular d...</td>\n",
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      ],
      "text/plain": [
       "        ID  Patient Age Patient Sex    Left-Fundus    Right-Fundus  \\\n",
       "0        0           69      Female     0_left.jpg     0_right.jpg   \n",
       "1        1           57        Male     1_left.jpg     1_right.jpg   \n",
       "2        2           42        Male     2_left.jpg     2_right.jpg   \n",
       "3        4           53        Male     4_left.jpg     4_right.jpg   \n",
       "4        5           50      Female     5_left.jpg     5_right.jpg   \n",
       "...    ...          ...         ...            ...             ...   \n",
       "6387  4686           63        Male  4686_left.jpg  4686_right.jpg   \n",
       "6388  4688           42        Male  4688_left.jpg  4688_right.jpg   \n",
       "6389  4689           54        Male  4689_left.jpg  4689_right.jpg   \n",
       "6390  4690           57        Male  4690_left.jpg  4690_right.jpg   \n",
       "6391  4784           58        Male  4784_left.jpg  4784_right.jpg   \n",
       "\n",
       "                               Left-Diagnostic Keywords  \\\n",
       "0                                              cataract   \n",
       "1                                         normal fundus   \n",
       "2     laser spot，moderate non proliferative retinopathy   \n",
       "3                           macular epiretinal membrane   \n",
       "4                moderate non proliferative retinopathy   \n",
       "...                                                 ...   \n",
       "6387                severe nonproliferative retinopathy   \n",
       "6388             moderate non proliferative retinopathy   \n",
       "6389                  mild nonproliferative retinopathy   \n",
       "6390                  mild nonproliferative retinopathy   \n",
       "6391  hypertensive retinopathy，age-related macular d...   \n",
       "\n",
       "                              Right-Diagnostic Keywords  N  D  G  C  A  H  M  \\\n",
       "0                                         normal fundus  0  0  0  1  0  0  0   \n",
       "1                                         normal fundus  1  0  0  0  0  0  0   \n",
       "2                moderate non proliferative retinopathy  0  1  0  0  0  0  0   \n",
       "3                     mild nonproliferative retinopathy  0  1  0  0  0  0  0   \n",
       "4                moderate non proliferative retinopathy  0  1  0  0  0  0  0   \n",
       "...                                                 ... .. .. .. .. .. .. ..   \n",
       "6387                 proliferative diabetic retinopathy  0  1  0  0  0  0  0   \n",
       "6388             moderate non proliferative retinopathy  0  1  0  0  0  0  0   \n",
       "6389                                      normal fundus  0  1  0  0  0  0  0   \n",
       "6390                  mild nonproliferative retinopathy  0  1  0  0  0  0  0   \n",
       "6391  hypertensive retinopathy，age-related macular d...  0  0  0  0  1  1  0   \n",
       "\n",
       "      O                                           filepath labels  \\\n",
       "0     0  ../input/ocular-disease-recognition-odir5k/ODI...  ['N']   \n",
       "1     0  ../input/ocular-disease-recognition-odir5k/ODI...  ['N']   \n",
       "2     1  ../input/ocular-disease-recognition-odir5k/ODI...  ['D']   \n",
       "3     1  ../input/ocular-disease-recognition-odir5k/ODI...  ['D']   \n",
       "4     0  ../input/ocular-disease-recognition-odir5k/ODI...  ['D']   \n",
       "...  ..                                                ...    ...   \n",
       "6387  0  ../input/ocular-disease-recognition-odir5k/ODI...  ['D']   \n",
       "6388  0  ../input/ocular-disease-recognition-odir5k/ODI...  ['D']   \n",
       "6389  0  ../input/ocular-disease-recognition-odir5k/ODI...  ['D']   \n",
       "6390  0  ../input/ocular-disease-recognition-odir5k/ODI...  ['D']   \n",
       "6391  0  ../input/ocular-disease-recognition-odir5k/ODI...  ['H']   \n",
       "\n",
       "                        target       filename  \n",
       "0     [1, 0, 0, 0, 0, 0, 0, 0]    0_right.jpg  \n",
       "1     [1, 0, 0, 0, 0, 0, 0, 0]    1_right.jpg  \n",
       "2     [0, 1, 0, 0, 0, 0, 0, 0]    2_right.jpg  \n",
       "3     [0, 1, 0, 0, 0, 0, 0, 0]    4_right.jpg  \n",
       "4     [0, 1, 0, 0, 0, 0, 0, 0]    5_right.jpg  \n",
       "...                        ...            ...  \n",
       "6387  [0, 1, 0, 0, 0, 0, 0, 0]  4686_left.jpg  \n",
       "6388  [0, 1, 0, 0, 0, 0, 0, 0]  4688_left.jpg  \n",
       "6389  [0, 1, 0, 0, 0, 0, 0, 0]  4689_left.jpg  \n",
       "6390  [0, 1, 0, 0, 0, 0, 0, 0]  4690_left.jpg  \n",
       "6391  [0, 0, 0, 0, 0, 1, 0, 0]  4784_left.jpg  \n",
       "\n",
       "[6392 rows x 19 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read the csv describing the images\n",
    "df = pd.read_csv('ocular_disease_fundus/full_df.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
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       "      <td>1_right.jpg</td>\n",
       "      <td>normal fundus</td>\n",
       "      <td>normal fundus</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>42</td>\n",
       "      <td>Male</td>\n",
       "      <td>2_left.jpg</td>\n",
       "      <td>2_right.jpg</td>\n",
       "      <td>laser spot，moderate non proliferative retinopathy</td>\n",
       "      <td>moderate non proliferative retinopathy</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>66</td>\n",
       "      <td>Male</td>\n",
       "      <td>3_left.jpg</td>\n",
       "      <td>3_right.jpg</td>\n",
       "      <td>normal fundus</td>\n",
       "      <td>branch retinal artery occlusion</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>53</td>\n",
       "      <td>Male</td>\n",
       "      <td>4_left.jpg</td>\n",
       "      <td>4_right.jpg</td>\n",
       "      <td>macular epiretinal membrane</td>\n",
       "      <td>mild nonproliferative retinopathy</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3495</th>\n",
       "      <td>4686</td>\n",
       "      <td>63</td>\n",
       "      <td>Male</td>\n",
       "      <td>4686_left.jpg</td>\n",
       "      <td>4686_right.jpg</td>\n",
       "      <td>severe nonproliferative retinopathy</td>\n",
       "      <td>proliferative diabetic retinopathy</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3496</th>\n",
       "      <td>4688</td>\n",
       "      <td>42</td>\n",
       "      <td>Male</td>\n",
       "      <td>4688_left.jpg</td>\n",
       "      <td>4688_right.jpg</td>\n",
       "      <td>moderate non proliferative retinopathy</td>\n",
       "      <td>moderate non proliferative retinopathy</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3497</th>\n",
       "      <td>4689</td>\n",
       "      <td>54</td>\n",
       "      <td>Male</td>\n",
       "      <td>4689_left.jpg</td>\n",
       "      <td>4689_right.jpg</td>\n",
       "      <td>mild nonproliferative retinopathy</td>\n",
       "      <td>normal fundus</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3498</th>\n",
       "      <td>4690</td>\n",
       "      <td>57</td>\n",
       "      <td>Male</td>\n",
       "      <td>4690_left.jpg</td>\n",
       "      <td>4690_right.jpg</td>\n",
       "      <td>mild nonproliferative retinopathy</td>\n",
       "      <td>mild nonproliferative retinopathy</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3499</th>\n",
       "      <td>4784</td>\n",
       "      <td>58</td>\n",
       "      <td>Male</td>\n",
       "      <td>4784_left.jpg</td>\n",
       "      <td>4784_right.jpg</td>\n",
       "      <td>hypertensive retinopathy，age-related macular d...</td>\n",
       "      <td>hypertensive retinopathy，age-related macular d...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3500 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        ID  Patient Age Patient Sex    Left-Fundus    Right-Fundus  \\\n",
       "0        0           69      Female     0_left.jpg     0_right.jpg   \n",
       "1        1           57        Male     1_left.jpg     1_right.jpg   \n",
       "2        2           42        Male     2_left.jpg     2_right.jpg   \n",
       "3        3           66        Male     3_left.jpg     3_right.jpg   \n",
       "4        4           53        Male     4_left.jpg     4_right.jpg   \n",
       "...    ...          ...         ...            ...             ...   \n",
       "3495  4686           63        Male  4686_left.jpg  4686_right.jpg   \n",
       "3496  4688           42        Male  4688_left.jpg  4688_right.jpg   \n",
       "3497  4689           54        Male  4689_left.jpg  4689_right.jpg   \n",
       "3498  4690           57        Male  4690_left.jpg  4690_right.jpg   \n",
       "3499  4784           58        Male  4784_left.jpg  4784_right.jpg   \n",
       "\n",
       "                               Left-Diagnostic Keywords  \\\n",
       "0                                              cataract   \n",
       "1                                         normal fundus   \n",
       "2     laser spot，moderate non proliferative retinopathy   \n",
       "3                                         normal fundus   \n",
       "4                           macular epiretinal membrane   \n",
       "...                                                 ...   \n",
       "3495                severe nonproliferative retinopathy   \n",
       "3496             moderate non proliferative retinopathy   \n",
       "3497                  mild nonproliferative retinopathy   \n",
       "3498                  mild nonproliferative retinopathy   \n",
       "3499  hypertensive retinopathy，age-related macular d...   \n",
       "\n",
       "                              Right-Diagnostic Keywords  N  D  G  C  A  H  M  \\\n",
       "0                                         normal fundus  0  0  0  1  0  0  0   \n",
       "1                                         normal fundus  1  0  0  0  0  0  0   \n",
       "2                moderate non proliferative retinopathy  0  1  0  0  0  0  0   \n",
       "3                       branch retinal artery occlusion  0  0  0  0  0  0  0   \n",
       "4                     mild nonproliferative retinopathy  0  1  0  0  0  0  0   \n",
       "...                                                 ... .. .. .. .. .. .. ..   \n",
       "3495                 proliferative diabetic retinopathy  0  1  0  0  0  0  0   \n",
       "3496             moderate non proliferative retinopathy  0  1  0  0  0  0  0   \n",
       "3497                                      normal fundus  0  1  0  0  0  0  0   \n",
       "3498                  mild nonproliferative retinopathy  0  1  0  0  0  0  0   \n",
       "3499  hypertensive retinopathy，age-related macular d...  0  0  0  0  1  1  0   \n",
       "\n",
       "      O  \n",
       "0     0  \n",
       "1     0  \n",
       "2     1  \n",
       "3     1  \n",
       "4     1  \n",
       "...  ..  \n",
       "3495  0  \n",
       "3496  0  \n",
       "3497  0  \n",
       "3498  0  \n",
       "3499  0  \n",
       "\n",
       "[3500 rows x 15 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xl = pd.read_excel('ocular_disease_fundus/ODIR-5K/ODIR-5K/data.xlsx')\n",
    "xl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Patient Age</th>\n",
       "      <th>Patient Sex</th>\n",
       "      <th>Left-Fundus</th>\n",
       "      <th>Right-Fundus</th>\n",
       "      <th>Left-Diagnostic Keywords</th>\n",
       "      <th>Right-Diagnostic Keywords</th>\n",
       "      <th>N</th>\n",
       "      <th>D</th>\n",
       "      <th>G</th>\n",
       "      <th>C</th>\n",
       "      <th>A</th>\n",
       "      <th>H</th>\n",
       "      <th>M</th>\n",
       "      <th>O</th>\n",
       "      <th>Labels</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>69</td>\n",
       "      <td>Female</td>\n",
       "      <td>0_left.jpg</td>\n",
       "      <td>0_right.jpg</td>\n",
       "      <td>cataract</td>\n",
       "      <td>normal fundus</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[C]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>57</td>\n",
       "      <td>Male</td>\n",
       "      <td>1_left.jpg</td>\n",
       "      <td>1_right.jpg</td>\n",
       "      <td>normal fundus</td>\n",
       "      <td>normal fundus</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[N]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>42</td>\n",
       "      <td>Male</td>\n",
       "      <td>2_left.jpg</td>\n",
       "      <td>2_right.jpg</td>\n",
       "      <td>laser spot，moderate non proliferative retinopathy</td>\n",
       "      <td>moderate non proliferative retinopathy</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>[D, O]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>66</td>\n",
       "      <td>Male</td>\n",
       "      <td>3_left.jpg</td>\n",
       "      <td>3_right.jpg</td>\n",
       "      <td>normal fundus</td>\n",
       "      <td>branch retinal artery occlusion</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>[O]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>53</td>\n",
       "      <td>Male</td>\n",
       "      <td>4_left.jpg</td>\n",
       "      <td>4_right.jpg</td>\n",
       "      <td>macular epiretinal membrane</td>\n",
       "      <td>mild nonproliferative retinopathy</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>[D, O]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3495</th>\n",
       "      <td>4686</td>\n",
       "      <td>63</td>\n",
       "      <td>Male</td>\n",
       "      <td>4686_left.jpg</td>\n",
       "      <td>4686_right.jpg</td>\n",
       "      <td>severe nonproliferative retinopathy</td>\n",
       "      <td>proliferative diabetic retinopathy</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[D]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3496</th>\n",
       "      <td>4688</td>\n",
       "      <td>42</td>\n",
       "      <td>Male</td>\n",
       "      <td>4688_left.jpg</td>\n",
       "      <td>4688_right.jpg</td>\n",
       "      <td>moderate non proliferative retinopathy</td>\n",
       "      <td>moderate non proliferative retinopathy</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[D]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3497</th>\n",
       "      <td>4689</td>\n",
       "      <td>54</td>\n",
       "      <td>Male</td>\n",
       "      <td>4689_left.jpg</td>\n",
       "      <td>4689_right.jpg</td>\n",
       "      <td>mild nonproliferative retinopathy</td>\n",
       "      <td>normal fundus</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[D]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3498</th>\n",
       "      <td>4690</td>\n",
       "      <td>57</td>\n",
       "      <td>Male</td>\n",
       "      <td>4690_left.jpg</td>\n",
       "      <td>4690_right.jpg</td>\n",
       "      <td>mild nonproliferative retinopathy</td>\n",
       "      <td>mild nonproliferative retinopathy</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[D]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3499</th>\n",
       "      <td>4784</td>\n",
       "      <td>58</td>\n",
       "      <td>Male</td>\n",
       "      <td>4784_left.jpg</td>\n",
       "      <td>4784_right.jpg</td>\n",
       "      <td>hypertensive retinopathy，age-related macular d...</td>\n",
       "      <td>hypertensive retinopathy，age-related macular d...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[A, H]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3500 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        ID  Patient Age Patient Sex    Left-Fundus    Right-Fundus  \\\n",
       "0        0           69      Female     0_left.jpg     0_right.jpg   \n",
       "1        1           57        Male     1_left.jpg     1_right.jpg   \n",
       "2        2           42        Male     2_left.jpg     2_right.jpg   \n",
       "3        3           66        Male     3_left.jpg     3_right.jpg   \n",
       "4        4           53        Male     4_left.jpg     4_right.jpg   \n",
       "...    ...          ...         ...            ...             ...   \n",
       "3495  4686           63        Male  4686_left.jpg  4686_right.jpg   \n",
       "3496  4688           42        Male  4688_left.jpg  4688_right.jpg   \n",
       "3497  4689           54        Male  4689_left.jpg  4689_right.jpg   \n",
       "3498  4690           57        Male  4690_left.jpg  4690_right.jpg   \n",
       "3499  4784           58        Male  4784_left.jpg  4784_right.jpg   \n",
       "\n",
       "                               Left-Diagnostic Keywords  \\\n",
       "0                                              cataract   \n",
       "1                                         normal fundus   \n",
       "2     laser spot，moderate non proliferative retinopathy   \n",
       "3                                         normal fundus   \n",
       "4                           macular epiretinal membrane   \n",
       "...                                                 ...   \n",
       "3495                severe nonproliferative retinopathy   \n",
       "3496             moderate non proliferative retinopathy   \n",
       "3497                  mild nonproliferative retinopathy   \n",
       "3498                  mild nonproliferative retinopathy   \n",
       "3499  hypertensive retinopathy，age-related macular d...   \n",
       "\n",
       "                              Right-Diagnostic Keywords  N  D  G  C  A  H  M  \\\n",
       "0                                         normal fundus  0  0  0  1  0  0  0   \n",
       "1                                         normal fundus  1  0  0  0  0  0  0   \n",
       "2                moderate non proliferative retinopathy  0  1  0  0  0  0  0   \n",
       "3                       branch retinal artery occlusion  0  0  0  0  0  0  0   \n",
       "4                     mild nonproliferative retinopathy  0  1  0  0  0  0  0   \n",
       "...                                                 ... .. .. .. .. .. .. ..   \n",
       "3495                 proliferative diabetic retinopathy  0  1  0  0  0  0  0   \n",
       "3496             moderate non proliferative retinopathy  0  1  0  0  0  0  0   \n",
       "3497                                      normal fundus  0  1  0  0  0  0  0   \n",
       "3498                  mild nonproliferative retinopathy  0  1  0  0  0  0  0   \n",
       "3499  hypertensive retinopathy，age-related macular d...  0  0  0  0  1  1  0   \n",
       "\n",
       "      O  Labels  \n",
       "0     0     [C]  \n",
       "1     0     [N]  \n",
       "2     1  [D, O]  \n",
       "3     1     [O]  \n",
       "4     1  [D, O]  \n",
       "...  ..     ...  \n",
       "3495  0     [D]  \n",
       "3496  0     [D]  \n",
       "3497  0     [D]  \n",
       "3498  0     [D]  \n",
       "3499  0  [A, H]  \n",
       "\n",
       "[3500 rows x 16 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diseases = list(xl.columns[7:15])\n",
    "xl['Labels'] = xl.apply(lambda x:[disease for disease in diseases if x[disease]==1], axis=1)\n",
    "xl"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's look at an example image just to see what they all look like, and to ensure we can open images in python."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Image</th>\n",
       "      <th>Keywords</th>\n",
       "      <th>Age</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Labels</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0_left.jpg</td>\n",
       "      <td>cataract</td>\n",
       "      <td>69</td>\n",
       "      <td>Female</td>\n",
       "      <td>[C]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1_left.jpg</td>\n",
       "      <td>normal fundus</td>\n",
       "      <td>57</td>\n",
       "      <td>Male</td>\n",
       "      <td>[N]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2_left.jpg</td>\n",
       "      <td>laser spot，moderate non proliferative retinopathy</td>\n",
       "      <td>42</td>\n",
       "      <td>Male</td>\n",
       "      <td>[D, O]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3_left.jpg</td>\n",
       "      <td>normal fundus</td>\n",
       "      <td>66</td>\n",
       "      <td>Male</td>\n",
       "      <td>[O]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4_left.jpg</td>\n",
       "      <td>macular epiretinal membrane</td>\n",
       "      <td>53</td>\n",
       "      <td>Male</td>\n",
       "      <td>[D, O]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3495</th>\n",
       "      <td>4686_right.jpg</td>\n",
       "      <td>proliferative diabetic retinopathy</td>\n",
       "      <td>63</td>\n",
       "      <td>Male</td>\n",
       "      <td>[D]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3496</th>\n",
       "      <td>4688_right.jpg</td>\n",
       "      <td>moderate non proliferative retinopathy</td>\n",
       "      <td>42</td>\n",
       "      <td>Male</td>\n",
       "      <td>[D]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3497</th>\n",
       "      <td>4689_right.jpg</td>\n",
       "      <td>normal fundus</td>\n",
       "      <td>54</td>\n",
       "      <td>Male</td>\n",
       "      <td>[D]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3498</th>\n",
       "      <td>4690_right.jpg</td>\n",
       "      <td>mild nonproliferative retinopathy</td>\n",
       "      <td>57</td>\n",
       "      <td>Male</td>\n",
       "      <td>[D]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3499</th>\n",
       "      <td>4784_right.jpg</td>\n",
       "      <td>hypertensive retinopathy，age-related macular d...</td>\n",
       "      <td>58</td>\n",
       "      <td>Male</td>\n",
       "      <td>[A, H]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7000 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               Image                                           Keywords  Age  \\\n",
       "0         0_left.jpg                                           cataract   69   \n",
       "1         1_left.jpg                                      normal fundus   57   \n",
       "2         2_left.jpg  laser spot，moderate non proliferative retinopathy   42   \n",
       "3         3_left.jpg                                      normal fundus   66   \n",
       "4         4_left.jpg                        macular epiretinal membrane   53   \n",
       "...              ...                                                ...  ...   \n",
       "3495  4686_right.jpg                 proliferative diabetic retinopathy   63   \n",
       "3496  4688_right.jpg             moderate non proliferative retinopathy   42   \n",
       "3497  4689_right.jpg                                      normal fundus   54   \n",
       "3498  4690_right.jpg                  mild nonproliferative retinopathy   57   \n",
       "3499  4784_right.jpg  hypertensive retinopathy，age-related macular d...   58   \n",
       "\n",
       "         Sex  Labels  \n",
       "0     Female     [C]  \n",
       "1       Male     [N]  \n",
       "2       Male  [D, O]  \n",
       "3       Male     [O]  \n",
       "4       Male  [D, O]  \n",
       "...      ...     ...  \n",
       "3495    Male     [D]  \n",
       "3496    Male     [D]  \n",
       "3497    Male     [D]  \n",
       "3498    Male     [D]  \n",
       "3499    Male  [A, H]  \n",
       "\n",
       "[7000 rows x 5 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Split up the left and right eye information\n",
    "\n",
    "cols = ['Image', 'Keywords', 'Age', 'Sex', 'Labels']\n",
    "\n",
    "left = xl[['Left-Fundus', 'Left-Diagnostic Keywords', 'Patient Age', 'Patient Sex', 'Labels']]\n",
    "left.columns = cols\n",
    "\n",
    "right = xl[['Right-Fundus', 'Right-Diagnostic Keywords', 'Patient Age', 'Patient Sex', 'Labels']]\n",
    "right.columns = cols\n",
    "\n",
    "# combine them back\n",
    "both_eyes = pd.concat([left, right], axis=0)\n",
    "\n",
    "both_eyes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Start with the basic 7 diseases."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cataract(diagnostic_keyword):\n",
    "    if \"cataract\" in diagnostic_keyword:\n",
    "        return 1\n",
    "    else:\n",
    "        return 0\n",
    "    \n",
    "def normal(diagnostic_keyword):\n",
    "    if \"normal\" in diagnostic_keyword:\n",
    "        return 1\n",
    "    else:\n",
    "        return 0\n",
    "    \n",
    "def diabetes(diagnostic_keyword):\n",
    "    if \"proliferative retinopathy\" in diagnostic_keyword or 'diabetic' in diagnostic_keyword:\n",
    "        return 1\n",
    "    else:\n",
    "        return 0\n",
    "    \n",
    "def glaucoma(diagnostic_keyword):\n",
    "    if \"glaucoma\" in diagnostic_keyword:\n",
    "        return 1\n",
    "    else:\n",
    "        return 0\n",
    "    \n",
    "def amd(diagnostic_keyword):\n",
    "    if \"age-related\" in diagnostic_keyword:\n",
    "        return 1\n",
    "    else:\n",
    "        return 0\n",
    "\n",
    "def hypertension(diagnostic_keyword):\n",
    "    if \"hypertens\" in diagnostic_keyword:\n",
    "        return 1\n",
    "    else:\n",
    "        return 0\n",
    "\n",
    "def myopia(diagnostic_keyword):\n",
    "    if \"myopia\" in diagnostic_keyword:\n",
    "        return 1\n",
    "    else:\n",
    "        return 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Patient Age</th>\n",
       "      <th>Patient Sex</th>\n",
       "      <th>Left-Fundus</th>\n",
       "      <th>Right-Fundus</th>\n",
       "      <th>Left-Diagnostic Keywords</th>\n",
       "      <th>Right-Diagnostic Keywords</th>\n",
       "      <th>N</th>\n",
       "      <th>D</th>\n",
       "      <th>G</th>\n",
       "      <th>...</th>\n",
       "      <th>left_diabetes</th>\n",
       "      <th>right_diabetes</th>\n",
       "      <th>left_glaucoma</th>\n",
       "      <th>right_glaucoma</th>\n",
       "      <th>left_amd</th>\n",
       "      <th>right_amd</th>\n",
       "      <th>left_hypertension</th>\n",
       "      <th>right_hypertension</th>\n",
       "      <th>left_myopia</th>\n",
       "      <th>right_myopia</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>69</td>\n",
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       "      <td>0_left.jpg</td>\n",
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       "      <td>cataract</td>\n",
       "      <td>normal fundus</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>57</td>\n",
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       "      <td>1_left.jpg</td>\n",
       "      <td>1_right.jpg</td>\n",
       "      <td>normal fundus</td>\n",
       "      <td>normal fundus</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>42</td>\n",
       "      <td>Male</td>\n",
       "      <td>2_left.jpg</td>\n",
       "      <td>2_right.jpg</td>\n",
       "      <td>laser spot，moderate non proliferative retinopathy</td>\n",
       "      <td>moderate non proliferative retinopathy</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>66</td>\n",
       "      <td>Male</td>\n",
       "      <td>3_left.jpg</td>\n",
       "      <td>3_right.jpg</td>\n",
       "      <td>normal fundus</td>\n",
       "      <td>branch retinal artery occlusion</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>53</td>\n",
       "      <td>Male</td>\n",
       "      <td>4_left.jpg</td>\n",
       "      <td>4_right.jpg</td>\n",
       "      <td>macular epiretinal membrane</td>\n",
       "      <td>mild nonproliferative retinopathy</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3495</th>\n",
       "      <td>4686</td>\n",
       "      <td>63</td>\n",
       "      <td>Male</td>\n",
       "      <td>4686_left.jpg</td>\n",
       "      <td>4686_right.jpg</td>\n",
       "      <td>severe nonproliferative retinopathy</td>\n",
       "      <td>proliferative diabetic retinopathy</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3496</th>\n",
       "      <td>4688</td>\n",
       "      <td>42</td>\n",
       "      <td>Male</td>\n",
       "      <td>4688_left.jpg</td>\n",
       "      <td>4688_right.jpg</td>\n",
       "      <td>moderate non proliferative retinopathy</td>\n",
       "      <td>moderate non proliferative retinopathy</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3497</th>\n",
       "      <td>4689</td>\n",
       "      <td>54</td>\n",
       "      <td>Male</td>\n",
       "      <td>4689_left.jpg</td>\n",
       "      <td>4689_right.jpg</td>\n",
       "      <td>mild nonproliferative retinopathy</td>\n",
       "      <td>normal fundus</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3498</th>\n",
       "      <td>4690</td>\n",
       "      <td>57</td>\n",
       "      <td>Male</td>\n",
       "      <td>4690_left.jpg</td>\n",
       "      <td>4690_right.jpg</td>\n",
       "      <td>mild nonproliferative retinopathy</td>\n",
       "      <td>mild nonproliferative retinopathy</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3499</th>\n",
       "      <td>4784</td>\n",
       "      <td>58</td>\n",
       "      <td>Male</td>\n",
       "      <td>4784_left.jpg</td>\n",
       "      <td>4784_right.jpg</td>\n",
       "      <td>hypertensive retinopathy，age-related macular d...</td>\n",
       "      <td>hypertensive retinopathy，age-related macular d...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3500 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        ID  Patient Age Patient Sex    Left-Fundus    Right-Fundus  \\\n",
       "0        0           69      Female     0_left.jpg     0_right.jpg   \n",
       "1        1           57        Male     1_left.jpg     1_right.jpg   \n",
       "2        2           42        Male     2_left.jpg     2_right.jpg   \n",
       "3        3           66        Male     3_left.jpg     3_right.jpg   \n",
       "4        4           53        Male     4_left.jpg     4_right.jpg   \n",
       "...    ...          ...         ...            ...             ...   \n",
       "3495  4686           63        Male  4686_left.jpg  4686_right.jpg   \n",
       "3496  4688           42        Male  4688_left.jpg  4688_right.jpg   \n",
       "3497  4689           54        Male  4689_left.jpg  4689_right.jpg   \n",
       "3498  4690           57        Male  4690_left.jpg  4690_right.jpg   \n",
       "3499  4784           58        Male  4784_left.jpg  4784_right.jpg   \n",
       "\n",
       "                               Left-Diagnostic Keywords  \\\n",
       "0                                              cataract   \n",
       "1                                         normal fundus   \n",
       "2     laser spot，moderate non proliferative retinopathy   \n",
       "3                                         normal fundus   \n",
       "4                           macular epiretinal membrane   \n",
       "...                                                 ...   \n",
       "3495                severe nonproliferative retinopathy   \n",
       "3496             moderate non proliferative retinopathy   \n",
       "3497                  mild nonproliferative retinopathy   \n",
       "3498                  mild nonproliferative retinopathy   \n",
       "3499  hypertensive retinopathy，age-related macular d...   \n",
       "\n",
       "                              Right-Diagnostic Keywords  N  D  G  ...  \\\n",
       "0                                         normal fundus  0  0  0  ...   \n",
       "1                                         normal fundus  1  0  0  ...   \n",
       "2                moderate non proliferative retinopathy  0  1  0  ...   \n",
       "3                       branch retinal artery occlusion  0  0  0  ...   \n",
       "4                     mild nonproliferative retinopathy  0  1  0  ...   \n",
       "...                                                 ... .. .. ..  ...   \n",
       "3495                 proliferative diabetic retinopathy  0  1  0  ...   \n",
       "3496             moderate non proliferative retinopathy  0  1  0  ...   \n",
       "3497                                      normal fundus  0  1  0  ...   \n",
       "3498                  mild nonproliferative retinopathy  0  1  0  ...   \n",
       "3499  hypertensive retinopathy，age-related macular d...  0  0  0  ...   \n",
       "\n",
       "      left_diabetes  right_diabetes  left_glaucoma  right_glaucoma  left_amd  \\\n",
       "0                 0               0              0               0         0   \n",
       "1                 0               0              0               0         0   \n",
       "2                 1               1              0               0         0   \n",
       "3                 0               0              0               0         0   \n",
       "4                 0               1              0               0         0   \n",
       "...             ...             ...            ...             ...       ...   \n",
       "3495              1               1              0               0         0   \n",
       "3496              1               1              0               0         0   \n",
       "3497              1               0              0               0         0   \n",
       "3498              1               1              0               0         0   \n",
       "3499              0               0              0               0         1   \n",
       "\n",
       "     right_amd  left_hypertension  right_hypertension  left_myopia  \\\n",
       "0            0                  0                   0            0   \n",
       "1            0                  0                   0            0   \n",
       "2            0                  0                   0            0   \n",
       "3            0                  0                   0            0   \n",
       "4            0                  0                   0            0   \n",
       "...        ...                ...                 ...          ...   \n",
       "3495         0                  0                   0            0   \n",
       "3496         0                  0                   0            0   \n",
       "3497         0                  0                   0            0   \n",
       "3498         0                  0                   0            0   \n",
       "3499         1                  1                   1            0   \n",
       "\n",
       "      right_myopia  \n",
       "0                0  \n",
       "1                0  \n",
       "2                0  \n",
       "3                0  \n",
       "4                0  \n",
       "...            ...  \n",
       "3495             0  \n",
       "3496             0  \n",
       "3497             0  \n",
       "3498             0  \n",
       "3499             0  \n",
       "\n",
       "[3500 rows x 30 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xl[\"left_cataract\"] = xl[\"Left-Diagnostic Keywords\"].apply(lambda word: cataract(word))\n",
    "xl[\"right_cataract\"] = xl[\"Right-Diagnostic Keywords\"].apply(lambda word: cataract(word))\n",
    "\n",
    "xl[\"left_normal\"] = xl[\"Left-Diagnostic Keywords\"].apply(lambda word: normal(word))\n",
    "xl[\"right_normal\"] = xl[\"Right-Diagnostic Keywords\"].apply(lambda word: normal(word))\n",
    "\n",
    "xl[\"left_diabetes\"] = xl[\"Left-Diagnostic Keywords\"].apply(lambda word: diabetes(word))\n",
    "xl[\"right_diabetes\"] = xl[\"Right-Diagnostic Keywords\"].apply(lambda word: diabetes(word))\n",
    "\n",
    "xl[\"left_glaucoma\"] = xl[\"Left-Diagnostic Keywords\"].apply(lambda word: glaucoma(word))\n",
    "xl[\"right_glaucoma\"] = xl[\"Right-Diagnostic Keywords\"].apply(lambda word: glaucoma(word))\n",
    "\n",
    "xl[\"left_amd\"] = xl[\"Left-Diagnostic Keywords\"].apply(lambda word: amd(word))\n",
    "xl[\"right_amd\"] = xl[\"Right-Diagnostic Keywords\"].apply(lambda word: amd(word))\n",
    "\n",
    "xl[\"left_hypertension\"] = xl[\"Left-Diagnostic Keywords\"].apply(lambda word: hypertension(word))\n",
    "xl[\"right_hypertension\"] = xl[\"Right-Diagnostic Keywords\"].apply(lambda word: hypertension(word))\n",
    "\n",
    "xl[\"left_myopia\"] = xl[\"Left-Diagnostic Keywords\"].apply(lambda word: myopia(word))\n",
    "xl[\"right_myopia\"] = xl[\"Right-Diagnostic Keywords\"].apply(lambda word: myopia(word))\n",
    "\n",
    "xl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get all images that have cataracts\n",
    "left_cataract = xl.loc[(xl.left_cataract == 1) & (xl.C == 1)]['Left-Fundus'].values\n",
    "right_cataract = xl.loc[(xl.right_cataract == 1) & (xl.C == 1)]['Right-Fundus'].values\n",
    "\n",
    "# get all images that have normal\n",
    "left_normal = xl.loc[(xl.left_normal == 1) & (xl.N == 1)]['Left-Fundus'].values\n",
    "right_normal = xl.loc[(xl.right_normal == 1) & (xl.N == 1)]['Right-Fundus'].values\n",
    "\n",
    "# get all images that have diabetes\n",
    "left_diabetes = xl.loc[(xl.left_diabetes == 1) & (xl.D == 1)]['Left-Fundus'].values\n",
    "right_diabetes = xl.loc[(xl.right_diabetes == 1) & (xl.D == 1)]['Right-Fundus'].values\n",
    "\n",
    "# get all images that have glaucoma\n",
    "left_glaucoma = xl.loc[(xl.left_glaucoma == 1) & (xl.G == 1)]['Left-Fundus'].values\n",
    "right_glaucoma = xl.loc[(xl.right_glaucoma == 1) & (xl.G == 1)]['Right-Fundus'].values\n",
    "\n",
    "# get all images that have AMD\n",
    "left_amd = xl.loc[(xl.left_amd == 1) & (xl.A == 1)]['Left-Fundus'].values\n",
    "right_amd = xl.loc[(xl.right_amd == 1) & (xl.A == 1)]['Right-Fundus'].values\n",
    "\n",
    "# get all images that have hypertension\n",
    "left_hyper = xl.loc[(xl.left_hypertension == 1) & (xl.H == 1)]['Left-Fundus'].values\n",
    "right_hyper = xl.loc[(xl.right_hypertension == 1) & (xl.H == 1)]['Right-Fundus'].values\n",
    "\n",
    "# get all images that have myopia\n",
    "left_myopia = xl.loc[(xl.left_myopia == 1) & (xl.M == 1)]['Left-Fundus'].values\n",
    "right_myopia = xl.loc[(xl.right_myopia == 1) & (xl.M == 1)]['Right-Fundus'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "cataract = np.concatenate((left_cataract, right_cataract), axis=0)\n",
    "normal = np.concatenate((left_normal, right_normal), axis=0)\n",
    "diabetes = np.concatenate((left_diabetes, right_diabetes), axis=0)\n",
    "glaucoma = np.concatenate((left_glaucoma, right_glaucoma), axis=0)\n",
    "amd = np.concatenate((left_amd, right_amd), axis=0)\n",
    "hyper = np.concatenate((left_hyper, right_hyper), axis=0)\n",
    "myopia = np.concatenate((left_myopia, right_myopia), axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1551\n"
     ]
    }
   ],
   "source": [
    "# how many images total that are other?\n",
    "print(7000 - (len(diabetes) + len(cataract) + len(normal) + len(glaucoma) + len(amd) + len(hyper) + len(myopia)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create folders with all of the images classified into them - training images\n",
    "src = \"ocular_disease_fundus/ODIR-5K/ODIR-5K/ALL Training Images\"\n",
    "dst_D = \"ocular_disease_fundus/ODIR-5K/ODIR-5K/ALL Training Images/diabetes\"\n",
    "dst_N = \"ocular_disease_fundus/ODIR-5K/ODIR-5K/ALL Training Images/normal\"\n",
    "dst_G = \"ocular_disease_fundus/ODIR-5K/ODIR-5K/ALL Training Images/glaucoma\"\n",
    "dst_A = \"ocular_disease_fundus/ODIR-5K/ODIR-5K/ALL Training Images/amd\"\n",
    "dst_H = \"ocular_disease_fundus/ODIR-5K/ODIR-5K/ALL Training Images/hypertension\"\n",
    "dst_M = \"ocular_disease_fundus/ODIR-5K/ODIR-5K/ALL Training Images/myopia\"\n",
    "dst_C = \"ocular_disease_fundus/ODIR-5K/ODIR-5K/ALL Training Images/cataracts\"\n",
    "\n",
    "for f in cataract:\n",
    "    shutil.copy(path.join(src, f), dst_C)\n",
    "\n",
    "for f in diabetes:\n",
    "    shutil.copy(path.join(src, f), dst_D)\n",
    "\n",
    "for f in normal:\n",
    "    shutil.copy(path.join(src, f), dst_N)\n",
    "\n",
    "for f in glaucoma:\n",
    "    shutil.copy(path.join(src, f), dst_G)\n",
    "\n",
    "for f in amd:\n",
    "    shutil.copy(path.join(src, f), dst_A)\n",
    "    \n",
    "for f in hyper:\n",
    "    shutil.copy(path.join(src, f), dst_H)\n",
    "\n",
    "for f in myopia:\n",
    "    shutil.copy(path.join(src, f), dst_M)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Inspiration for the following multiclass models is taken from the following sites and papers: https://towardsdatascience.com/understanding-and-coding-a-resnet-in-keras-446d7ff84d33\n",
    "##### - https://arxiv.org/abs/1512.03385\n",
    "##### - https://www.kaggle.com/gpreda/cataract-prediction-using-transfer-learning\n",
    "##### - https://towardsdatascience.com/alexnet-the-architecture-that-challenged-cnns-e406d5297951\n",
    "##### - https://towardsdatascience.com/implementing-alexnet-cnn-architecture-using-tensorflow-2-0-and-keras-2113e090ad98\n",
    "\n",
    "\n",
    "\n",
    "#### Model 1: Multiclass ResNet50\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [],
   "source": [
    "IMG_WIDTH=224\n",
    "IMG_HEIGHT=224\n",
    "IMG_DIM = (IMG_WIDTH, IMG_HEIGHT)\n",
    "BATCH_SIZE = 25\n",
    "IMG_DIR = pathlib.Path('ocular_disease_fundus/preprocessed_images')\n",
    "TRAIN_DIR = 'ocular_disease_fundus/ODIR-5K/ODIR-5K/Actual Training Images'\n",
    "VAL_DIR = 'ocular_disease_fundus/ODIR-5K/ODIR-5K/Actual Testing Images'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Specify the values for all arguments to data_generator_with_aug.\n",
    "data_generator_with_aug = ImageDataGenerator(preprocessing_function=preprocess_input,\n",
    "                                              horizontal_flip = True,\n",
    "                                              width_shift_range = 0.2,\n",
    "                                              height_shift_range = 0.2\n",
    "                                                )\n",
    "            \n",
    "data_generator_no_aug = ImageDataGenerator(preprocessing_function=preprocess_input)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 3340 images belonging to 7 classes.\n",
      "Found 2291 images belonging to 7 classes.\n"
     ]
    }
   ],
   "source": [
    "train_generator = data_generator_with_aug.flow_from_directory(\n",
    "                                        directory=TRAIN_DIR,\n",
    "                                        target_size=IMG_DIM,\n",
    "                                        batch_size=BATCH_SIZE,\n",
    "                                        class_mode='categorical')\n",
    "\n",
    "validation_generator = data_generator_no_aug.flow_from_directory(\n",
    "                                        directory=VAL_DIR,\n",
    "                                        target_size=IMG_DIM,batch_size=BATCH_SIZE,\n",
    "                                        class_mode='categorical')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/resnet/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
      "94773248/94765736 [==============================] - 6s 0us/step\n"
     ]
    }
   ],
   "source": [
    "resnet = ResNet50(include_top=False, weights='imagenet', input_shape=(IMG_HEIGHT,IMG_WIDTH,3),pooling='max')\n",
    "\n",
    "output = resnet.layers[-1].output\n",
    "output = tf.keras.layers.Flatten()(output)\n",
    "resnet = Model(resnet.input, output)\n",
    "\n",
    "res_name = []\n",
    "for layer in resnet.layers:\n",
    "    res_name.append(layer.name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "set_trainable = False\n",
    "for layer in resnet.layers:\n",
    "    if layer.name in res_name[-22:]:\n",
    "        set_trainable = True\n",
    "    if set_trainable:\n",
    "        layer.trainable = True\n",
    "    else:\n",
    "        layer.trainable = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_2\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "functional_1 (Functional)    (None, 2048)              23587712  \n",
      "_________________________________________________________________\n",
      "dense_5 (Dense)              (None, 1024)              2098176   \n",
      "_________________________________________________________________\n",
      "dropout_3 (Dropout)          (None, 1024)              0         \n",
      "_________________________________________________________________\n",
      "dense_6 (Dense)              (None, 1024)              1049600   \n",
      "_________________________________________________________________\n",
      "dropout_4 (Dropout)          (None, 1024)              0         \n",
      "_________________________________________________________________\n",
      "dense_7 (Dense)              (None, 7)                 7175      \n",
      "=================================================================\n",
      "Total params: 26,742,663\n",
      "Trainable params: 26,689,543\n",
      "Non-trainable params: 53,120\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras.applications import ResNet50\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, Flatten, GlobalAveragePooling2D, Dropout\n",
    "\n",
    "num_classes = 7\n",
    "\n",
    "model = Sequential()\n",
    "model.add(resnet)\n",
    "model.add(Dense(1024, activation='relu'))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Dense(1024, activation='relu'))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Dense(num_classes, activation='softmax'))\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "adam = tf.keras.optimizers.Adam(learning_rate=0.0001)\n",
    "\n",
    "early_stop = tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=8,\n",
    "                                              restore_best_weights=False\n",
    "                                              )\n",
    "\n",
    "reduce_lr = tf.keras.callbacks.ReduceLROnPlateau(monitor='val_loss',\n",
    "                                   factor=0.2,\n",
    "                                   patience=4,\n",
    "                                   verbose=1,\n",
    "                                   min_delta=5*1e-3,min_lr = 5*1e-7,\n",
    "                                   )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer = adam, loss = 'categorical_crossentropy', metrics=['accuracy',tfa.metrics.F1Score(num_classes=num_classes)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.fit(train_generator,\n",
    "          steps_per_epoch=np.ceil(float(3340) / float(BATCH_SIZE)),\n",
    "          epochs = 35,callbacks=[early_stop,reduce_lr],\n",
    "          validation_steps=np.ceil(float(2291) / float(BATCH_SIZE)),\n",
    "          validation_data = validation_generator)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Model 2: AlexNet Multiclass Attempt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "IMG_WIDTH=224\n",
    "IMG_HEIGHT=224\n",
    "IMG_DIM = (IMG_WIDTH, IMG_HEIGHT)\n",
    "BATCH_SIZE = 25\n",
    "IMG_DIR = pathlib.Path('ocular_disease_fundus/preprocessed_images')\n",
    "TRAIN_DIR = 'ocular_disease_fundus/ODIR-5K/ODIR-5K/Actual Training Images'\n",
    "VAL_DIR = 'ocular_disease_fundus/ODIR-5K/ODIR-5K/Actual Testing Images'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_2\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_5 (Conv2D)            (None, 56, 56, 96)        34944     \n",
      "_________________________________________________________________\n",
      "batch_normalization_8 (Batch (None, 56, 56, 96)        384       \n",
      "_________________________________________________________________\n",
      "activation_8 (Activation)    (None, 56, 56, 96)        0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2 (None, 28, 28, 96)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_6 (Conv2D)            (None, 28, 28, 256)       614656    \n",
      "_________________________________________________________________\n",
      "batch_normalization_9 (Batch (None, 28, 28, 256)       1024      \n",
      "_________________________________________________________________\n",
      "activation_9 (Activation)    (None, 28, 28, 256)       0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_4 (MaxPooling2 (None, 14, 14, 256)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_7 (Conv2D)            (None, 14, 14, 384)       885120    \n",
      "_________________________________________________________________\n",
      "batch_normalization_10 (Batc (None, 14, 14, 384)       1536      \n",
      "_________________________________________________________________\n",
      "activation_10 (Activation)   (None, 14, 14, 384)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_8 (Conv2D)            (None, 14, 14, 384)       1327488   \n",
      "_________________________________________________________________\n",
      "batch_normalization_11 (Batc (None, 14, 14, 384)       1536      \n",
      "_________________________________________________________________\n",
      "activation_11 (Activation)   (None, 14, 14, 384)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_9 (Conv2D)            (None, 14, 14, 256)       884992    \n",
      "_________________________________________________________________\n",
      "batch_normalization_12 (Batc (None, 14, 14, 256)       1024      \n",
      "_________________________________________________________________\n",
      "activation_12 (Activation)   (None, 14, 14, 256)       0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_5 (MaxPooling2 (None, 7, 7, 256)         0         \n",
      "_________________________________________________________________\n",
      "flatten_2 (Flatten)          (None, 12544)             0         \n",
      "_________________________________________________________________\n",
      "dense_6 (Dense)              (None, 64)                802880    \n",
      "_________________________________________________________________\n",
      "batch_normalization_13 (Batc (None, 64)                256       \n",
      "_________________________________________________________________\n",
      "activation_13 (Activation)   (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dropout_4 (Dropout)          (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dense_7 (Dense)              (None, 64)                4160      \n",
      "_________________________________________________________________\n",
      "batch_normalization_14 (Batc (None, 64)                256       \n",
      "_________________________________________________________________\n",
      "activation_14 (Activation)   (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dropout_5 (Dropout)          (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dense_8 (Dense)              (None, 7)                 455       \n",
      "_________________________________________________________________\n",
      "batch_normalization_15 (Batc (None, 7)                 28        \n",
      "_________________________________________________________________\n",
      "activation_15 (Activation)   (None, 7)                 0         \n",
      "=================================================================\n",
      "Total params: 4,560,739\n",
      "Trainable params: 4,557,717\n",
      "Non-trainable params: 3,022\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(1000)\n",
    "AlexNet = Sequential()\n",
    "AlexNet.add(Conv2D(filters=96, input_shape=(224,224,3), kernel_size=(11,11), strides=(4,4), padding='same'))\n",
    "AlexNet.add(BatchNormalization())\n",
    "AlexNet.add(Activation('relu'))\n",
    "AlexNet.add(MaxPooling2D(pool_size=(2,2), strides=(2,2), padding='same'))\n",
    "\n",
    "AlexNet.add(Conv2D(filters=256, kernel_size=(5, 5), strides=(1,1), padding='same'))\n",
    "AlexNet.add(BatchNormalization())\n",
    "AlexNet.add(Activation('relu'))\n",
    "AlexNet.add(MaxPooling2D(pool_size=(2,2), strides=(2,2), padding='same'))\n",
    "\n",
    "AlexNet.add(Conv2D(filters=384, kernel_size=(3,3), strides=(1,1), padding='same'))\n",
    "AlexNet.add(BatchNormalization())\n",
    "AlexNet.add(Activation('relu'))\n",
    "\n",
    "AlexNet.add(Conv2D(filters=384, kernel_size=(3,3), strides=(1,1), padding='same'))\n",
    "AlexNet.add(BatchNormalization())\n",
    "AlexNet.add(Activation('relu'))\n",
    "\n",
    "AlexNet.add(Conv2D(filters=256, kernel_size=(3,3), strides=(1,1), padding='same'))\n",
    "AlexNet.add(BatchNormalization())\n",
    "AlexNet.add(Activation('relu'))\n",
    "AlexNet.add(MaxPooling2D(pool_size=(2,2), strides=(2,2), padding='same'))\n",
    "\n",
    "AlexNet.add(Flatten())\n",
    "\n",
    "AlexNet.add(Dense(64, input_shape=(224,224,3,)))\n",
    "AlexNet.add(BatchNormalization())\n",
    "AlexNet.add(Activation('relu'))\n",
    "AlexNet.add(Dropout(0.4))\n",
    "\n",
    "AlexNet.add(Dense(64))\n",
    "AlexNet.add(BatchNormalization())\n",
    "AlexNet.add(Activation('relu'))\n",
    "AlexNet.add(Dropout(0.4))\n",
    "\n",
    "AlexNet.add(Dense(7))\n",
    "AlexNet.add(BatchNormalization())\n",
    "AlexNet.add(Activation('softmax'))\n",
    "\n",
    "AlexNet.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "AlexNet.compile(loss = keras.losses.categorical_crossentropy, optimizer= 'adam', metrics=['accuracy', 'Precision', 'Recall'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Specify the values for all arguments to data_generator_with_aug.\n",
    "data_generator_with_aug = ImageDataGenerator(preprocessing_function=preprocess_input,\n",
    "                                              horizontal_flip = True,\n",
    "                                              width_shift_range = 0.2,\n",
    "                                              height_shift_range = 0.2\n",
    "                                                )\n",
    "            \n",
    "data_generator_no_aug = ImageDataGenerator(preprocessing_function=preprocess_input)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 3340 images belonging to 7 classes.\n",
      "Found 2291 images belonging to 7 classes.\n"
     ]
    }
   ],
   "source": [
    "train_generator = data_generator_with_aug.flow_from_directory(\n",
    "                                        directory=TRAIN_DIR,\n",
    "                                        target_size=IMG_DIM,\n",
    "                                        batch_size=BATCH_SIZE,\n",
    "                                        class_mode='categorical')\n",
    "\n",
    "validation_generator = data_generator_no_aug.flow_from_directory(\n",
    "                                        directory=VAL_DIR,\n",
    "                                        target_size=IMG_DIM,batch_size=BATCH_SIZE,\n",
    "                                        class_mode='categorical')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.callbacks import ReduceLROnPlateau\n",
    "lrr= ReduceLROnPlateau(monitor='val_acc',   factor=.01,   patience=3,  min_lr=1e-5) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size= 100\n",
    "epochs=20\n",
    "learn_rate=.001"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "134/134 [==============================] - ETA: 0s - loss: 2.0600 - accuracy: 0.2326 - precision: 0.2687 - recall: 0.0269WARNING:tensorflow:Reduce LR on plateau conditioned on metric `val_acc` which is not available. Available metrics are: loss,accuracy,precision,recall,val_loss,val_accuracy,val_precision,val_recall,lr\n",
      "134/134 [==============================] - 535s 4s/step - loss: 2.0600 - accuracy: 0.2326 - precision: 0.2687 - recall: 0.0269 - val_loss: 4.5396 - val_accuracy: 0.1017 - val_precision: 0.0418 - val_recall: 0.0262\n",
      "Epoch 2/20\n",
      "134/134 [==============================] - ETA: 0s - loss: 1.8724 - accuracy: 0.3123 - precision: 0.3503 - recall: 0.0207WARNING:tensorflow:Reduce LR on plateau conditioned on metric `val_acc` which is not available. Available metrics are: loss,accuracy,precision,recall,val_loss,val_accuracy,val_precision,val_recall,lr\n",
      "134/134 [==============================] - 490s 4s/step - loss: 1.8724 - accuracy: 0.3123 - precision: 0.3503 - recall: 0.0207 - val_loss: 2.2200 - val_accuracy: 0.2213 - val_precision: 0.1166 - val_recall: 0.0113\n",
      "Epoch 3/20\n",
      "134/134 [==============================] - ETA: 0s - loss: 1.7449 - accuracy: 0.3341 - precision: 0.4231 - recall: 0.0165WARNING:tensorflow:Reduce LR on plateau conditioned on metric `val_acc` which is not available. Available metrics are: loss,accuracy,precision,recall,val_loss,val_accuracy,val_precision,val_recall,lr\n",
      "134/134 [==============================] - 486s 4s/step - loss: 1.7449 - accuracy: 0.3341 - precision: 0.4231 - recall: 0.0165 - val_loss: 1.7365 - val_accuracy: 0.3536 - val_precision: 0.2453 - val_recall: 0.0057\n",
      "Epoch 4/20\n",
      "134/134 [==============================] - ETA: 0s - loss: 1.6681 - accuracy: 0.3734 - precision: 0.4898 - recall: 0.0144WARNING:tensorflow:Reduce LR on plateau conditioned on metric `val_acc` which is not available. Available metrics are: loss,accuracy,precision,recall,val_loss,val_accuracy,val_precision,val_recall,lr\n",
      "134/134 [==============================] - 493s 4s/step - loss: 1.6681 - accuracy: 0.3734 - precision: 0.4898 - recall: 0.0144 - val_loss: 1.7082 - val_accuracy: 0.3828 - val_precision: 0.2171 - val_recall: 0.0122\n",
      "Epoch 5/20\n",
      "134/134 [==============================] - ETA: 0s - loss: 1.5978 - accuracy: 0.4024 - precision: 0.5306 - recall: 0.0156WARNING:tensorflow:Reduce LR on plateau conditioned on metric `val_acc` which is not available. Available metrics are: loss,accuracy,precision,recall,val_loss,val_accuracy,val_precision,val_recall,lr\n",
      "134/134 [==============================] - 504s 4s/step - loss: 1.5978 - accuracy: 0.4024 - precision: 0.5306 - recall: 0.0156 - val_loss: 1.7185 - val_accuracy: 0.3400 - val_precision: 0.3295 - val_recall: 0.0127\n",
      "Epoch 6/20\n",
      "134/134 [==============================] - ETA: 0s - loss: 1.5566 - accuracy: 0.4201 - precision: 0.6116 - recall: 0.0222WARNING:tensorflow:Reduce LR on plateau conditioned on metric `val_acc` which is not available. Available metrics are: loss,accuracy,precision,recall,val_loss,val_accuracy,val_precision,val_recall,lr\n",
      "134/134 [==============================] - 488s 4s/step - loss: 1.5566 - accuracy: 0.4201 - precision: 0.6116 - recall: 0.0222 - val_loss: 1.8926 - val_accuracy: 0.2759 - val_precision: 0.0755 - val_recall: 0.0162\n",
      "Epoch 7/20\n",
      "134/134 [==============================] - ETA: 0s - loss: 1.5296 - accuracy: 0.4353 - precision: 0.5685 - recall: 0.0249WARNING:tensorflow:Reduce LR on plateau conditioned on metric `val_acc` which is not available. Available metrics are: loss,accuracy,precision,recall,val_loss,val_accuracy,val_precision,val_recall,lr\n",
      "134/134 [==============================] - 473s 4s/step - loss: 1.5296 - accuracy: 0.4353 - precision: 0.5685 - recall: 0.0249 - val_loss: 1.4543 - val_accuracy: 0.4570 - val_precision: 0.8000 - val_recall: 0.0035\n",
      "Epoch 8/20\n",
      "134/134 [==============================] - ETA: 0s - loss: 1.4818 - accuracy: 0.4344 - precision: 0.5906 - recall: 0.0263WARNING:tensorflow:Reduce LR on plateau conditioned on metric `val_acc` which is not available. Available metrics are: loss,accuracy,precision,recall,val_loss,val_accuracy,val_precision,val_recall,lr\n",
      "134/134 [==============================] - 454s 3s/step - loss: 1.4818 - accuracy: 0.4344 - precision: 0.5906 - recall: 0.0263 - val_loss: 1.6221 - val_accuracy: 0.3972 - val_precision: 0.3117 - val_recall: 0.0441\n",
      "Epoch 9/20\n",
      "134/134 [==============================] - ETA: 0s - loss: 1.4618 - accuracy: 0.4500 - precision: 0.6205 - recall: 0.0416WARNING:tensorflow:Reduce LR on plateau conditioned on metric `val_acc` which is not available. Available metrics are: loss,accuracy,precision,recall,val_loss,val_accuracy,val_precision,val_recall,lr\n",
      "134/134 [==============================] - 454s 3s/step - loss: 1.4618 - accuracy: 0.4500 - precision: 0.6205 - recall: 0.0416 - val_loss: 1.5077 - val_accuracy: 0.4317 - val_precision: 0.3864 - val_recall: 0.0148\n",
      "Epoch 10/20\n",
      "134/134 [==============================] - ETA: 0s - loss: 1.4431 - accuracy: 0.4479 - precision: 0.6348 - recall: 0.0536WARNING:tensorflow:Reduce LR on plateau conditioned on metric `val_acc` which is not available. Available metrics are: loss,accuracy,precision,recall,val_loss,val_accuracy,val_precision,val_recall,lr\n",
      "134/134 [==============================] - 467s 3s/step - loss: 1.4431 - accuracy: 0.4479 - precision: 0.6348 - recall: 0.0536 - val_loss: 1.4503 - val_accuracy: 0.3632 - val_precision: 0.5769 - val_recall: 0.0262\n",
      "Epoch 11/20\n",
      "134/134 [==============================] - ETA: 0s - loss: 1.4296 - accuracy: 0.4527 - precision: 0.6116 - recall: 0.0665WARNING:tensorflow:Reduce LR on plateau conditioned on metric `val_acc` which is not available. Available metrics are: loss,accuracy,precision,recall,val_loss,val_accuracy,val_precision,val_recall,lr\n",
      "134/134 [==============================] - 447s 3s/step - loss: 1.4296 - accuracy: 0.4527 - precision: 0.6116 - recall: 0.0665 - val_loss: 1.7778 - val_accuracy: 0.3317 - val_precision: 0.1644 - val_recall: 0.0367\n",
      "Epoch 12/20\n",
      "134/134 [==============================] - ETA: 0s - loss: 1.4024 - accuracy: 0.4560 - precision: 0.6405 - recall: 0.0880WARNING:tensorflow:Reduce LR on plateau conditioned on metric `val_acc` which is not available. Available metrics are: loss,accuracy,precision,recall,val_loss,val_accuracy,val_precision,val_recall,lr\n",
      "134/134 [==============================] - 452s 3s/step - loss: 1.4024 - accuracy: 0.4560 - precision: 0.6405 - recall: 0.0880 - val_loss: 1.4662 - val_accuracy: 0.3994 - val_precision: 0.4881 - val_recall: 0.1606\n",
      "Epoch 13/20\n",
      "134/134 [==============================] - ETA: 0s - loss: 1.4031 - accuracy: 0.4632 - precision: 0.5813 - recall: 0.0856WARNING:tensorflow:Reduce LR on plateau conditioned on metric `val_acc` which is not available. Available metrics are: loss,accuracy,precision,recall,val_loss,val_accuracy,val_precision,val_recall,lr\n",
      "134/134 [==============================] - 454s 3s/step - loss: 1.4031 - accuracy: 0.4632 - precision: 0.5813 - recall: 0.0856 - val_loss: 1.4781 - val_accuracy: 0.4116 - val_precision: 0.4793 - val_recall: 0.0960\n",
      "Epoch 14/20\n",
      "134/134 [==============================] - ETA: 0s - loss: 1.3904 - accuracy: 0.4548 - precision: 0.6145 - recall: 0.1069WARNING:tensorflow:Reduce LR on plateau conditioned on metric `val_acc` which is not available. Available metrics are: loss,accuracy,precision,recall,val_loss,val_accuracy,val_precision,val_recall,lr\n",
      "134/134 [==============================] - 425s 3s/step - loss: 1.3904 - accuracy: 0.4548 - precision: 0.6145 - recall: 0.1069 - val_loss: 1.5103 - val_accuracy: 0.3566 - val_precision: 0.5039 - val_recall: 0.1419\n",
      "Epoch 15/20\n",
      "134/134 [==============================] - ETA: 0s - loss: 1.3818 - accuracy: 0.4650 - precision: 0.5780 - recall: 0.1009WARNING:tensorflow:Reduce LR on plateau conditioned on metric `val_acc` which is not available. Available metrics are: loss,accuracy,precision,recall,val_loss,val_accuracy,val_precision,val_recall,lr\n",
      "134/134 [==============================] - 421s 3s/step - loss: 1.3818 - accuracy: 0.4650 - precision: 0.5780 - recall: 0.1009 - val_loss: 1.4023 - val_accuracy: 0.4369 - val_precision: 0.6017 - val_recall: 0.0633\n",
      "Epoch 16/20\n",
      "134/134 [==============================] - ETA: 0s - loss: 1.3620 - accuracy: 0.4817 - precision: 0.6006 - recall: 0.1144WARNING:tensorflow:Reduce LR on plateau conditioned on metric `val_acc` which is not available. Available metrics are: loss,accuracy,precision,recall,val_loss,val_accuracy,val_precision,val_recall,lr\n",
      "134/134 [==============================] - 421s 3s/step - loss: 1.3620 - accuracy: 0.4817 - precision: 0.6006 - recall: 0.1144 - val_loss: 1.3938 - val_accuracy: 0.4265 - val_precision: 0.5886 - val_recall: 0.0450\n",
      "Epoch 17/20\n",
      "134/134 [==============================] - ETA: 0s - loss: 1.3559 - accuracy: 0.4563 - precision: 0.5803 - recall: 0.1147WARNING:tensorflow:Reduce LR on plateau conditioned on metric `val_acc` which is not available. Available metrics are: loss,accuracy,precision,recall,val_loss,val_accuracy,val_precision,val_recall,lr\n",
      "134/134 [==============================] - 461s 3s/step - loss: 1.3559 - accuracy: 0.4563 - precision: 0.5803 - recall: 0.1147 - val_loss: 1.5165 - val_accuracy: 0.3584 - val_precision: 0.3369 - val_recall: 0.2178\n",
      "Epoch 18/20\n",
      "134/134 [==============================] - ETA: 0s - loss: 1.3452 - accuracy: 0.4796 - precision: 0.5703 - recall: 0.1347WARNING:tensorflow:Reduce LR on plateau conditioned on metric `val_acc` which is not available. Available metrics are: loss,accuracy,precision,recall,val_loss,val_accuracy,val_precision,val_recall,lr\n",
      "134/134 [==============================] - 532s 4s/step - loss: 1.3452 - accuracy: 0.4796 - precision: 0.5703 - recall: 0.1347 - val_loss: 1.4251 - val_accuracy: 0.4347 - val_precision: 0.6411 - val_recall: 0.0585\n",
      "Epoch 19/20\n",
      "134/134 [==============================] - ETA: 0s - loss: 1.3476 - accuracy: 0.4728 - precision: 0.5746 - recall: 0.1533WARNING:tensorflow:Reduce LR on plateau conditioned on metric `val_acc` which is not available. Available metrics are: loss,accuracy,precision,recall,val_loss,val_accuracy,val_precision,val_recall,lr\n",
      "134/134 [==============================] - 526s 4s/step - loss: 1.3476 - accuracy: 0.4728 - precision: 0.5746 - recall: 0.1533 - val_loss: 1.6426 - val_accuracy: 0.3584 - val_precision: 0.3496 - val_recall: 0.2911\n",
      "Epoch 20/20\n",
      "134/134 [==============================] - ETA: 0s - loss: 1.3411 - accuracy: 0.4644 - precision: 0.5643 - recall: 0.1551WARNING:tensorflow:Reduce LR on plateau conditioned on metric `val_acc` which is not available. Available metrics are: loss,accuracy,precision,recall,val_loss,val_accuracy,val_precision,val_recall,lr\n",
      "134/134 [==============================] - 509s 4s/step - loss: 1.3411 - accuracy: 0.4644 - precision: 0.5643 - recall: 0.1551 - val_loss: 1.4098 - val_accuracy: 0.3811 - val_precision: 0.4583 - val_recall: 0.0912\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x1ad2484a370>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "AlexNet.fit_generator(train_generator, epochs = epochs, steps_per_epoch = np.ceil(float(3340) / float(BATCH_SIZE)), \n",
    "validation_data = validation_generator, validation_steps = np.ceil(float(2291) / float(BATCH_SIZE)), callbacks = [lrr], verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = AlexNet.predict_classes(validation_generator)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,7))\n",
    "for i in range(10):\n",
    "    samp = random.choice(range(len(validation_generator)))\n",
    "    image = validation_generator[samp][0][0]\n",
    "    category = y_test[samp]\n",
    "    pred_category = y_pred[samp]\n",
    "    if category == 0:\n",
    "        label = \"AMD\"\n",
    "    elif category == 1:\n",
    "        label = \"Cataracts\"\n",
    "    elif category == 2:\n",
    "        label = \"Diabetes\"\n",
    "    elif category == 3:\n",
    "        label = \"Glaucoma\"\n",
    "    elif category == 4:\n",
    "        label = \"Hypertension\"\n",
    "    elif category == 5:\n",
    "        label = \"Myopia\"\n",
    "    elif category == 6:\n",
    "        label = \"Normal\"\n",
    "    if pred_category == 0:\n",
    "        pred_label = \"AMD\"\n",
    "    elif pred_category == 1:\n",
    "        pred_label = \"Cataracts\"\n",
    "    elif pred_category == 2:\n",
    "        pred_label = \"Diabetes\"\n",
    "    elif pred_category == 3:\n",
    "        pred_label = \"Glaucoma\"\n",
    "    elif pred_category == 4:\n",
    "        pred_label = \"Hypertension\"\n",
    "    elif pred_category == 5:\n",
    "        pred_label = \"Myopia\"\n",
    "    elif pred_category == 6:\n",
    "        pred_label = \"Normal\"\n",
    "\n",
    "    plt.subplot(2,5,i+1)\n",
    "    plt.imshow(image, cmap = \"gray\")\n",
    "    plt.xlabel(\"Actual:{}\\nPrediction:{}\".format(label,pred_label))\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## MODEL 0: Individual Diabetes Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Patient Age</th>\n",
       "      <th>Patient Sex</th>\n",
       "      <th>Left-Fundus</th>\n",
       "      <th>Right-Fundus</th>\n",
       "      <th>Left-Diagnostic Keywords</th>\n",
       "      <th>Right-Diagnostic Keywords</th>\n",
       "      <th>N</th>\n",
       "      <th>D</th>\n",
       "      <th>G</th>\n",
       "      <th>C</th>\n",
       "      <th>A</th>\n",
       "      <th>H</th>\n",
       "      <th>M</th>\n",
       "      <th>O</th>\n",
       "      <th>filepath</th>\n",
       "      <th>labels</th>\n",
       "      <th>target</th>\n",
       "      <th>filename</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>69</td>\n",
       "      <td>Female</td>\n",
       "      <td>0_left.jpg</td>\n",
       "      <td>0_right.jpg</td>\n",
       "      <td>cataract</td>\n",
       "      <td>normal fundus</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>../input/ocular-disease-recognition-odir5k/ODI...</td>\n",
       "      <td>['N']</td>\n",
       "      <td>[1, 0, 0, 0, 0, 0, 0, 0]</td>\n",
       "      <td>0_right.jpg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>57</td>\n",
       "      <td>Male</td>\n",
       "      <td>1_left.jpg</td>\n",
       "      <td>1_right.jpg</td>\n",
       "      <td>normal fundus</td>\n",
       "      <td>normal fundus</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>../input/ocular-disease-recognition-odir5k/ODI...</td>\n",
       "      <td>['N']</td>\n",
       "      <td>[1, 0, 0, 0, 0, 0, 0, 0]</td>\n",
       "      <td>1_right.jpg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>42</td>\n",
       "      <td>Male</td>\n",
       "      <td>2_left.jpg</td>\n",
       "      <td>2_right.jpg</td>\n",
       "      <td>laser spot，moderate non proliferative retinopathy</td>\n",
       "      <td>moderate non proliferative retinopathy</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>../input/ocular-disease-recognition-odir5k/ODI...</td>\n",
       "      <td>['D']</td>\n",
       "      <td>[0, 1, 0, 0, 0, 0, 0, 0]</td>\n",
       "      <td>2_right.jpg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>53</td>\n",
       "      <td>Male</td>\n",
       "      <td>4_left.jpg</td>\n",
       "      <td>4_right.jpg</td>\n",
       "      <td>macular epiretinal membrane</td>\n",
       "      <td>mild nonproliferative retinopathy</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>../input/ocular-disease-recognition-odir5k/ODI...</td>\n",
       "      <td>['D']</td>\n",
       "      <td>[0, 1, 0, 0, 0, 0, 0, 0]</td>\n",
       "      <td>4_right.jpg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>50</td>\n",
       "      <td>Female</td>\n",
       "      <td>5_left.jpg</td>\n",
       "      <td>5_right.jpg</td>\n",
       "      <td>moderate non proliferative retinopathy</td>\n",
       "      <td>moderate non proliferative retinopathy</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>../input/ocular-disease-recognition-odir5k/ODI...</td>\n",
       "      <td>['D']</td>\n",
       "      <td>[0, 1, 0, 0, 0, 0, 0, 0]</td>\n",
       "      <td>5_right.jpg</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ID  Patient Age Patient Sex Left-Fundus Right-Fundus  \\\n",
       "0   0           69      Female  0_left.jpg  0_right.jpg   \n",
       "1   1           57        Male  1_left.jpg  1_right.jpg   \n",
       "2   2           42        Male  2_left.jpg  2_right.jpg   \n",
       "3   4           53        Male  4_left.jpg  4_right.jpg   \n",
       "4   5           50      Female  5_left.jpg  5_right.jpg   \n",
       "\n",
       "                            Left-Diagnostic Keywords  \\\n",
       "0                                           cataract   \n",
       "1                                      normal fundus   \n",
       "2  laser spot，moderate non proliferative retinopathy   \n",
       "3                        macular epiretinal membrane   \n",
       "4             moderate non proliferative retinopathy   \n",
       "\n",
       "                Right-Diagnostic Keywords  N  D  G  C  A  H  M  O  \\\n",
       "0                           normal fundus  0  0  0  1  0  0  0  0   \n",
       "1                           normal fundus  1  0  0  0  0  0  0  0   \n",
       "2  moderate non proliferative retinopathy  0  1  0  0  0  0  0  1   \n",
       "3       mild nonproliferative retinopathy  0  1  0  0  0  0  0  1   \n",
       "4  moderate non proliferative retinopathy  0  1  0  0  0  0  0  0   \n",
       "\n",
       "                                            filepath labels  \\\n",
       "0  ../input/ocular-disease-recognition-odir5k/ODI...  ['N']   \n",
       "1  ../input/ocular-disease-recognition-odir5k/ODI...  ['N']   \n",
       "2  ../input/ocular-disease-recognition-odir5k/ODI...  ['D']   \n",
       "3  ../input/ocular-disease-recognition-odir5k/ODI...  ['D']   \n",
       "4  ../input/ocular-disease-recognition-odir5k/ODI...  ['D']   \n",
       "\n",
       "                     target     filename  \n",
       "0  [1, 0, 0, 0, 0, 0, 0, 0]  0_right.jpg  \n",
       "1  [1, 0, 0, 0, 0, 0, 0, 0]  1_right.jpg  \n",
       "2  [0, 1, 0, 0, 0, 0, 0, 0]  2_right.jpg  \n",
       "3  [0, 1, 0, 0, 0, 0, 0, 0]  4_right.jpg  \n",
       "4  [0, 1, 0, 0, 0, 0, 0, 0]  5_right.jpg  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path = \"ocular_disease_fundus/\"\n",
    "df = pd.read_csv(path + \"full_df.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "file_names_diabetes = []\n",
    "labels_diabetes = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "for text, label, file_name in zip(df[\"Left-Diagnostic Keywords\"], df[\"D\"], df[\"Left-Fundus\"]):\n",
    "    \n",
    "    if((\"proliferative retinopathy\" in text) and (label == 1)):\n",
    "        file_names_diabetes.append(file_name)\n",
    "        labels_diabetes.append(1)\n",
    "    \n",
    "    elif((\"normal fundus\" in text) and (label == 0)):\n",
    "        file_names_diabetes.append(file_name)\n",
    "        labels_diabetes.append(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "for text, label, file_name in zip(df[\"Right-Diagnostic Keywords\"], df[\"D\"], df[\"Right-Fundus\"]):\n",
    "    \n",
    "    if((\"proliferative retinopathy\" in text) and (label == 1)):\n",
    "        file_names_diabetes.append(file_name)\n",
    "        labels_diabetes.append(1)\n",
    "    \n",
    "    elif((\"normal fundus\" in text) and (label == 0)):\n",
    "        file_names_diabetes.append(file_name)\n",
    "        labels_diabetes.append(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "ROW = 224\n",
    "COL = 224\n",
    "\n",
    "image_data_diabetes = []\n",
    "for idx, image_name in enumerate(file_names_diabetes):\n",
    "    \n",
    "    img = cv2.imread(os.path.join(path,\"preprocessed_images\",image_name))\n",
    "    try:\n",
    "        img = cv2.resize(img, (ROW, COL))\n",
    "        image_data_diabetes.append(img)\n",
    "    except:\n",
    "        del labels_diabetes[idx]\n",
    "    \n",
    "image_data_diabetes = np.array(image_data_diabetes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.utils.np_utils import to_categorical\n",
    "\n",
    "temp = []\n",
    "for idx, label in enumerate(labels_diabetes):\n",
    "    if label == 0:\n",
    "        temp.append(idx)\n",
    "\n",
    "temp = sample(temp, len([label for label in labels_diabetes if label == 1]))\n",
    "\n",
    "X_data = []\n",
    "y_data = []\n",
    "\n",
    "for idx in temp:\n",
    "    X_data.append(image_data_diabetes[idx])\n",
    "    y_data.append(labels_diabetes[idx])\n",
    "\n",
    "temp = []\n",
    "for idx, label in enumerate(labels_diabetes):\n",
    "    if label == 1:\n",
    "        temp.append(idx)\n",
    "        \n",
    "for idx in temp:\n",
    "    X_data.append(image_data_diabetes[idx])\n",
    "    y_data.append(labels_diabetes[idx])\n",
    "\n",
    "X_data = np.array(X_data)\n",
    "    \n",
    "y_data = np.array(y_data)\n",
    "y_data = np.expand_dims(y_data, axis = -1)\n",
    "y_data = to_categorical(y_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X_data, y_data, test_size=0.2, shuffle = True, random_state = 42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "vgg = VGG19(weights = \"imagenet\", include_top = False, input_shape=(ROW, COL, 3))\n",
    "for layer in vgg.layers:\n",
    "    layer.trainable = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "vgg19 (Functional)           (None, 7, 7, 512)         20024384  \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 25088)             0         \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 64)                1605696   \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 2)                 130       \n",
      "=================================================================\n",
      "Total params: 21,630,210\n",
      "Trainable params: 1,605,826\n",
      "Non-trainable params: 20,024,384\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "model.add(vgg)\n",
    "model.add(Flatten())\n",
    "model.add(Dense(64, activation = 'relu'))\n",
    "model.add(Dense(2,activation = \"softmax\"))\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/15\n",
      "81/81 [==============================] - 3511s 43s/step - loss: 3.7349 - accuracy: 0.5920 - precision: 0.5920 - recall: 0.5920 - val_loss: 0.8162 - val_accuracy: 0.6519 - val_precision: 0.6519 - val_recall: 0.6519\n",
      "Epoch 2/15\n",
      "81/81 [==============================] - 3418s 42s/step - loss: 0.5728 - accuracy: 0.7632 - precision: 0.7632 - recall: 0.7632 - val_loss: 0.6829 - val_accuracy: 0.7211 - val_precision: 0.7211 - val_recall: 0.7211\n",
      "Epoch 3/15\n",
      "81/81 [==============================] - 3192s 39s/step - loss: 0.3993 - accuracy: 0.8323 - precision: 0.8323 - recall: 0.8323 - val_loss: 0.6468 - val_accuracy: 0.7436 - val_precision: 0.7436 - val_recall: 0.7436\n",
      "Epoch 4/15\n",
      "81/81 [==============================] - 3215s 40s/step - loss: 0.3928 - accuracy: 0.8452 - precision: 0.8452 - recall: 0.8452 - val_loss: 0.6658 - val_accuracy: 0.7646 - val_precision: 0.7646 - val_recall: 0.7646\n",
      "Epoch 5/15\n",
      "81/81 [==============================] - 2882s 36s/step - loss: 0.3285 - accuracy: 0.8714 - precision: 0.8714 - recall: 0.8714 - val_loss: 0.7584 - val_accuracy: 0.7451 - val_precision: 0.7451 - val_recall: 0.7451\n",
      "Epoch 6/15\n",
      "81/81 [==============================] - 3058s 38s/step - loss: 0.2581 - accuracy: 0.9071 - precision: 0.9071 - recall: 0.9071 - val_loss: 0.6856 - val_accuracy: 0.7941 - val_precision: 0.7941 - val_recall: 0.7941\n",
      "Epoch 7/15\n",
      "81/81 [==============================] - 3325s 41s/step - loss: 0.2261 - accuracy: 0.9159 - precision: 0.9159 - recall: 0.9159 - val_loss: 0.7591 - val_accuracy: 0.7793 - val_precision: 0.7793 - val_recall: 0.7793\n",
      "Epoch 8/15\n",
      "81/81 [==============================] - 2970s 37s/step - loss: 0.2067 - accuracy: 0.9312 - precision: 0.9312 - recall: 0.9312 - val_loss: 0.9438 - val_accuracy: 0.7273 - val_precision: 0.7273 - val_recall: 0.7273\n",
      "Epoch 9/15\n",
      "81/81 [==============================] - 3189s 39s/step - loss: 0.2134 - accuracy: 0.9200 - precision: 0.9200 - recall: 0.9200 - val_loss: 0.7436 - val_accuracy: 0.7723 - val_precision: 0.7723 - val_recall: 0.7723\n",
      "Epoch 10/15\n",
      "81/81 [==============================] - 3408s 42s/step - loss: 0.1950 - accuracy: 0.9341 - precision: 0.9341 - recall: 0.9341 - val_loss: 0.8913 - val_accuracy: 0.7498 - val_precision: 0.7498 - val_recall: 0.7498\n",
      "Epoch 11/15\n",
      "81/81 [==============================] - 3178s 39s/step - loss: 0.1863 - accuracy: 0.9440 - precision: 0.9440 - recall: 0.9440 - val_loss: 0.7143 - val_accuracy: 0.8096 - val_precision: 0.8096 - val_recall: 0.8096\n",
      "Epoch 12/15\n",
      "81/81 [==============================] - 2873s 35s/step - loss: 0.1937 - accuracy: 0.9380 - precision: 0.9380 - recall: 0.9380 - val_loss: 0.7235 - val_accuracy: 0.8120 - val_precision: 0.8120 - val_recall: 0.8120\n",
      "Epoch 13/15\n",
      "81/81 [==============================] - 2680s 33s/step - loss: 0.1911 - accuracy: 0.9413 - precision: 0.9413 - recall: 0.9413 - val_loss: 0.9224 - val_accuracy: 0.7638 - val_precision: 0.7638 - val_recall: 0.7638\n",
      "Epoch 14/15\n",
      "81/81 [==============================] - 2594s 32s/step - loss: 0.1723 - accuracy: 0.9448 - precision: 0.9448 - recall: 0.9448 - val_loss: 0.7471 - val_accuracy: 0.8197 - val_precision: 0.8197 - val_recall: 0.8197\n",
      "Epoch 15/15\n",
      "81/81 [==============================] - 2594s 32s/step - loss: 0.1704 - accuracy: 0.9493 - precision: 0.9493 - recall: 0.9493 - val_loss: 0.9859 - val_accuracy: 0.7521 - val_precision: 0.7521 - val_recall: 0.7521\n"
     ]
    }
   ],
   "source": [
    "model.compile(optimizer = 'adam', \n",
    "              loss = 'categorical_crossentropy', \n",
    "              metrics=['accuracy', 'Precision', 'Recall'])\n",
    "\n",
    "history = model.fit(X_train, y_train, \n",
    "                    validation_data = (X_test, y_test), \n",
    "                    epochs = 15,\n",
    "                    batch_size = 64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From <ipython-input-31-fdaaf2360398>:1: Sequential.predict_classes (from tensorflow.python.keras.engine.sequential) is deprecated and will be removed after 2021-01-01.\n",
      "Instructions for updating:\n",
      "Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype(\"int32\")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).\n"
     ]
    }
   ],
   "source": [
    "y_pred = model.predict_classes(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test[][1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,7))\n",
    "import random \n",
    "for i in range(10):\n",
    "    \n",
    "    samp = random.choice(range(len(X_test)))\n",
    "    image = X_test[samp]\n",
    "    category = y_test[samp][1]\n",
    "    pred_category = y_pred[samp]\n",
    "    \n",
    "    if category == 0:\n",
    "        label = \"Normal\"\n",
    "    else:\n",
    "        label = \"Diabetes\"\n",
    "\n",
    "    if pred_category == 0:\n",
    "        pred_label = \"Normal\"\n",
    "    else:\n",
    "        pred_label = \"Diabetes\"\n",
    "\n",
    "    plt.subplot(2,5,i+1)\n",
    "    plt.imshow(image)\n",
    "    plt.xlabel(f\"Actual:{label} \\n Prediction:{pred_label}\")\n",
    "    \n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Model 3 - Resnet50 Multiclass (100 Epochs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import tensorflow_addons as tfa\n",
    "import pathlib\n",
    "from tensorflow.keras.applications import ResNet50\n",
    "\n",
    "from tensorflow.keras.applications import ResNet50\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, Flatten, GlobalAveragePooling2D, Dropout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "IMG_WIDTH=224\n",
    "IMG_HEIGHT=224\n",
    "IMG_DIM = (IMG_WIDTH, IMG_HEIGHT)\n",
    "BATCH_SIZE = 25\n",
    "IMG_DIR = pathlib.Path('ocular_disease_fundus/preprocessed_images')\n",
    "TRAIN_DIR = 'ocular_disease_fundus/ODIR-5K/ODIR-5K/Actual Training Images'\n",
    "VAL_DIR = 'ocular_disease_fundus/ODIR-5K/ODIR-5K/Actual Testing Images'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.applications.resnet50 import preprocess_input\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "\n",
    "# Specify the values for all arguments to data_generator_with_aug.\n",
    "data_generator_with_aug = ImageDataGenerator(preprocessing_function=preprocess_input,\n",
    "                                              horizontal_flip = True,\n",
    "                                              width_shift_range = 0.2,\n",
    "                                              height_shift_range = 0.2\n",
    "                                                )\n",
    "            \n",
    "data_generator_no_aug = ImageDataGenerator(preprocessing_function=preprocess_input)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 3340 images belonging to 7 classes.\n",
      "Found 2291 images belonging to 7 classes.\n"
     ]
    }
   ],
   "source": [
    "train_generator = data_generator_with_aug.flow_from_directory(\n",
    "                                        directory=TRAIN_DIR,\n",
    "                                        target_size=IMG_DIM,\n",
    "                                        batch_size=BATCH_SIZE,\n",
    "                                        class_mode='categorical')\n",
    "\n",
    "validation_generator = data_generator_no_aug.flow_from_directory(\n",
    "                                        directory=VAL_DIR,\n",
    "                                        target_size=IMG_DIM,batch_size=BATCH_SIZE,\n",
    "                                        class_mode='categorical')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.applications.resnet50 import ResNet50\n",
    "from tensorflow.keras.models import Model\n",
    "import tensorflow.keras as keras\n",
    "\n",
    "resnet = ResNet50(include_top=False, weights='imagenet', input_shape=(IMG_HEIGHT,IMG_WIDTH,3),pooling='max')\n",
    "\n",
    "output = resnet.layers[-1].output\n",
    "output = tf.keras.layers.Flatten()(output)\n",
    "resnet = Model(resnet.input, output)\n",
    "\n",
    "res_name = []\n",
    "for layer in resnet.layers:\n",
    "    res_name.append(layer.name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "set_trainable = False\n",
    "for layer in resnet.layers:\n",
    "    if layer.name in res_name[-22:]:\n",
    "        set_trainable = True\n",
    "    if set_trainable:\n",
    "        layer.trainable = True\n",
    "    else:\n",
    "        layer.trainable = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_3\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "functional_3 (Functional)    (None, 2048)              23587712  \n",
      "_________________________________________________________________\n",
      "dense_9 (Dense)              (None, 64)                131136    \n",
      "_________________________________________________________________\n",
      "dropout_6 (Dropout)          (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dense_10 (Dense)             (None, 64)                4160      \n",
      "_________________________________________________________________\n",
      "dropout_7 (Dropout)          (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dense_11 (Dense)             (None, 7)                 455       \n",
      "=================================================================\n",
      "Total params: 23,723,463\n",
      "Trainable params: 9,067,079\n",
      "Non-trainable params: 14,656,384\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "num_classes = 7\n",
    "\n",
    "model = Sequential()\n",
    "model.add(resnet)\n",
    "model.add(Dense(64, activation='relu'))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Dense(64, activation='relu'))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Dense(num_classes, activation='softmax'))\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "adam = tf.keras.optimizers.Adam(learning_rate=0.0001)\n",
    "\n",
    "reduce_lr = tf.keras.callbacks.ReduceLROnPlateau(monitor='val_loss',\n",
    "                                   factor=0.2,\n",
    "                                   patience=4,\n",
    "                                   verbose=1,\n",
    "                                   min_delta=5*1e-3,min_lr = 5*1e-7,\n",
    "                                   )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer = adam, loss = 'categorical_crossentropy', metrics=['accuracy',tfa.metrics.F1Score(num_classes=num_classes)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "134/134 [==============================] - 577s 4s/step - loss: 3.1743 - accuracy: 0.2662 - f1_score: 0.1772 - val_loss: 1.8539 - val_accuracy: 0.3400 - val_f1_score: 0.2237\n",
      "Epoch 2/100\n",
      "134/134 [==============================] - 565s 4s/step - loss: 1.8646 - accuracy: 0.3886 - f1_score: 0.2299 - val_loss: 1.6777 - val_accuracy: 0.4317 - val_f1_score: 0.2654\n",
      "Epoch 3/100\n",
      "134/134 [==============================] - 564s 4s/step - loss: 1.8031 - accuracy: 0.4144 - f1_score: 0.2443 - val_loss: 1.7277 - val_accuracy: 0.4260 - val_f1_score: 0.2845\n",
      "Epoch 4/100\n",
      "134/134 [==============================] - 567s 4s/step - loss: 1.7363 - accuracy: 0.4392 - f1_score: 0.2848 - val_loss: 1.5346 - val_accuracy: 0.4404 - val_f1_score: 0.2735\n",
      "Epoch 5/100\n",
      "134/134 [==============================] - 577s 4s/step - loss: 1.6705 - accuracy: 0.4626 - f1_score: 0.2991 - val_loss: 1.5025 - val_accuracy: 0.3767 - val_f1_score: 0.2334\n",
      "Epoch 6/100\n",
      "134/134 [==============================] - 564s 4s/step - loss: 1.6328 - accuracy: 0.4749 - f1_score: 0.3036 - val_loss: 1.6197 - val_accuracy: 0.3771 - val_f1_score: 0.2287\n",
      "Epoch 7/100\n",
      "134/134 [==============================] - 564s 4s/step - loss: 1.5957 - accuracy: 0.4814 - f1_score: 0.3131 - val_loss: 1.4787 - val_accuracy: 0.3819 - val_f1_score: 0.2410\n",
      "Epoch 8/100\n",
      "134/134 [==============================] - 564s 4s/step - loss: 1.5479 - accuracy: 0.4907 - f1_score: 0.3283 - val_loss: 1.5715 - val_accuracy: 0.3780 - val_f1_score: 0.2319\n",
      "Epoch 9/100\n",
      "134/134 [==============================] - 565s 4s/step - loss: 1.5105 - accuracy: 0.4904 - f1_score: 0.3298 - val_loss: 1.5467 - val_accuracy: 0.3802 - val_f1_score: 0.2326\n",
      "Epoch 10/100\n",
      "134/134 [==============================] - 566s 4s/step - loss: 1.4974 - accuracy: 0.4904 - f1_score: 0.3155 - val_loss: 1.4583 - val_accuracy: 0.3771 - val_f1_score: 0.2328\n",
      "Epoch 11/100\n",
      "134/134 [==============================] - 567s 4s/step - loss: 1.4819 - accuracy: 0.5084 - f1_score: 0.3296 - val_loss: 1.5479 - val_accuracy: 0.3850 - val_f1_score: 0.2505\n",
      "Epoch 12/100\n",
      "134/134 [==============================] - 567s 4s/step - loss: 1.4603 - accuracy: 0.5054 - f1_score: 0.3321 - val_loss: 1.7122 - val_accuracy: 0.3741 - val_f1_score: 0.2118\n",
      "Epoch 13/100\n",
      "134/134 [==============================] - 566s 4s/step - loss: 1.4457 - accuracy: 0.5075 - f1_score: 0.3249 - val_loss: 1.4568 - val_accuracy: 0.3867 - val_f1_score: 0.2393\n",
      "Epoch 14/100\n",
      "134/134 [==============================] - ETA: 0s - loss: 1.4401 - accuracy: 0.5132 - f1_score: 0.3312\n",
      "Epoch 00014: ReduceLROnPlateau reducing learning rate to 1.9999999494757503e-05.\n",
      "134/134 [==============================] - 566s 4s/step - loss: 1.4401 - accuracy: 0.5132 - f1_score: 0.3312 - val_loss: 1.5985 - val_accuracy: 0.3828 - val_f1_score: 0.2418\n",
      "Epoch 15/100\n",
      "134/134 [==============================] - 566s 4s/step - loss: 1.3704 - accuracy: 0.5195 - f1_score: 0.3499 - val_loss: 1.4275 - val_accuracy: 0.3854 - val_f1_score: 0.2514\n",
      "Epoch 16/100\n",
      "134/134 [==============================] - 569s 4s/step - loss: 1.3552 - accuracy: 0.5198 - f1_score: 0.3466 - val_loss: 1.4541 - val_accuracy: 0.3854 - val_f1_score: 0.2570\n",
      "Epoch 17/100\n",
      "134/134 [==============================] - 570s 4s/step - loss: 1.3330 - accuracy: 0.5198 - f1_score: 0.3492 - val_loss: 1.4428 - val_accuracy: 0.3854 - val_f1_score: 0.2539\n",
      "Epoch 18/100\n",
      "134/134 [==============================] - 569s 4s/step - loss: 1.3249 - accuracy: 0.5198 - f1_score: 0.3537 - val_loss: 1.4421 - val_accuracy: 0.3845 - val_f1_score: 0.2499\n",
      "Epoch 19/100\n",
      "134/134 [==============================] - ETA: 0s - loss: 1.3247 - accuracy: 0.5189 - f1_score: 0.3514\n",
      "Epoch 00019: ReduceLROnPlateau reducing learning rate to 3.999999898951501e-06.\n",
      "134/134 [==============================] - 570s 4s/step - loss: 1.3247 - accuracy: 0.5189 - f1_score: 0.3514 - val_loss: 1.4696 - val_accuracy: 0.3854 - val_f1_score: 0.2543\n",
      "Epoch 20/100\n",
      "134/134 [==============================] - 570s 4s/step - loss: 1.2933 - accuracy: 0.5246 - f1_score: 0.3641 - val_loss: 1.4625 - val_accuracy: 0.3850 - val_f1_score: 0.2526\n",
      "Epoch 21/100\n",
      "134/134 [==============================] - 569s 4s/step - loss: 1.3011 - accuracy: 0.5272 - f1_score: 0.3619 - val_loss: 1.4711 - val_accuracy: 0.3845 - val_f1_score: 0.2527\n",
      "Epoch 22/100\n",
      "134/134 [==============================] - 570s 4s/step - loss: 1.3017 - accuracy: 0.5213 - f1_score: 0.3568 - val_loss: 1.4614 - val_accuracy: 0.3845 - val_f1_score: 0.2528\n",
      "Epoch 23/100\n",
      "134/134 [==============================] - ETA: 0s - loss: 1.3081 - accuracy: 0.5201 - f1_score: 0.3552\n",
      "Epoch 00023: ReduceLROnPlateau reducing learning rate to 7.999999979801942e-07.\n",
      "134/134 [==============================] - 570s 4s/step - loss: 1.3081 - accuracy: 0.5201 - f1_score: 0.3552 - val_loss: 1.4639 - val_accuracy: 0.3845 - val_f1_score: 0.2535\n",
      "Epoch 24/100\n",
      "134/134 [==============================] - 569s 4s/step - loss: 1.2838 - accuracy: 0.5275 - f1_score: 0.3638 - val_loss: 1.4612 - val_accuracy: 0.3845 - val_f1_score: 0.2535\n",
      "Epoch 25/100\n",
      "134/134 [==============================] - 569s 4s/step - loss: 1.3007 - accuracy: 0.5257 - f1_score: 0.3615 - val_loss: 1.4595 - val_accuracy: 0.3845 - val_f1_score: 0.2532\n",
      "Epoch 26/100\n",
      "134/134 [==============================] - 606s 5s/step - loss: 1.2925 - accuracy: 0.5266 - f1_score: 0.3604 - val_loss: 1.4689 - val_accuracy: 0.3845 - val_f1_score: 0.2529\n",
      "Epoch 27/100\n",
      "134/134 [==============================] - ETA: 0s - loss: 1.3030 - accuracy: 0.5260 - f1_score: 0.3610\n",
      "Epoch 00027: ReduceLROnPlateau reducing learning rate to 5e-07.\n",
      "134/134 [==============================] - 601s 4s/step - loss: 1.3030 - accuracy: 0.5260 - f1_score: 0.3610 - val_loss: 1.4709 - val_accuracy: 0.3845 - val_f1_score: 0.2522\n",
      "Epoch 28/100\n",
      "134/134 [==============================] - 602s 4s/step - loss: 1.3050 - accuracy: 0.5174 - f1_score: 0.3565 - val_loss: 1.4687 - val_accuracy: 0.3841 - val_f1_score: 0.2506\n",
      "Epoch 29/100\n",
      "134/134 [==============================] - 588s 4s/step - loss: 1.3155 - accuracy: 0.5243 - f1_score: 0.3612 - val_loss: 1.4679 - val_accuracy: 0.3845 - val_f1_score: 0.2522\n",
      "Epoch 30/100\n",
      "134/134 [==============================] - 586s 4s/step - loss: 1.2933 - accuracy: 0.5216 - f1_score: 0.3559 - val_loss: 1.4651 - val_accuracy: 0.3841 - val_f1_score: 0.2512\n",
      "Epoch 31/100\n",
      "134/134 [==============================] - 618s 5s/step - loss: 1.2934 - accuracy: 0.5204 - f1_score: 0.3599 - val_loss: 1.4657 - val_accuracy: 0.3841 - val_f1_score: 0.2506\n",
      "Epoch 32/100\n",
      "134/134 [==============================] - 593s 4s/step - loss: 1.2814 - accuracy: 0.5308 - f1_score: 0.3708 - val_loss: 1.4689 - val_accuracy: 0.3841 - val_f1_score: 0.2506\n",
      "Epoch 33/100\n",
      "134/134 [==============================] - 622s 5s/step - loss: 1.3130 - accuracy: 0.5216 - f1_score: 0.3585 - val_loss: 1.4682 - val_accuracy: 0.3841 - val_f1_score: 0.2506\n",
      "Epoch 34/100\n",
      "134/134 [==============================] - 567s 4s/step - loss: 1.2924 - accuracy: 0.5281 - f1_score: 0.3627 - val_loss: 1.4628 - val_accuracy: 0.3841 - val_f1_score: 0.2512\n",
      "Epoch 35/100\n",
      "134/134 [==============================] - 568s 4s/step - loss: 1.3103 - accuracy: 0.5254 - f1_score: 0.3553 - val_loss: 1.4663 - val_accuracy: 0.3841 - val_f1_score: 0.2512\n",
      "Epoch 36/100\n",
      "134/134 [==============================] - 565s 4s/step - loss: 1.2869 - accuracy: 0.5326 - f1_score: 0.3653 - val_loss: 1.4649 - val_accuracy: 0.3837 - val_f1_score: 0.2518\n",
      "Epoch 37/100\n",
      "134/134 [==============================] - 562s 4s/step - loss: 1.2793 - accuracy: 0.5269 - f1_score: 0.3626 - val_loss: 1.4630 - val_accuracy: 0.3837 - val_f1_score: 0.2518\n",
      "Epoch 38/100\n",
      "134/134 [==============================] - 560s 4s/step - loss: 1.2937 - accuracy: 0.5275 - f1_score: 0.3587 - val_loss: 1.4653 - val_accuracy: 0.3837 - val_f1_score: 0.2512\n",
      "Epoch 39/100\n",
      "134/134 [==============================] - 562s 4s/step - loss: 1.2941 - accuracy: 0.5314 - f1_score: 0.3608 - val_loss: 1.4705 - val_accuracy: 0.3837 - val_f1_score: 0.2505\n",
      "Epoch 40/100\n",
      "134/134 [==============================] - 562s 4s/step - loss: 1.2877 - accuracy: 0.5326 - f1_score: 0.3723 - val_loss: 1.4684 - val_accuracy: 0.3837 - val_f1_score: 0.2518\n",
      "Epoch 41/100\n",
      "134/134 [==============================] - 566s 4s/step - loss: 1.2906 - accuracy: 0.5228 - f1_score: 0.3674 - val_loss: 1.4672 - val_accuracy: 0.3837 - val_f1_score: 0.2518\n",
      "Epoch 42/100\n",
      "134/134 [==============================] - 562s 4s/step - loss: 1.2803 - accuracy: 0.5296 - f1_score: 0.3673 - val_loss: 1.4704 - val_accuracy: 0.3841 - val_f1_score: 0.2520\n",
      "Epoch 43/100\n",
      "134/134 [==============================] - 563s 4s/step - loss: 1.2883 - accuracy: 0.5231 - f1_score: 0.3652 - val_loss: 1.4737 - val_accuracy: 0.3841 - val_f1_score: 0.2520\n",
      "Epoch 44/100\n",
      "134/134 [==============================] - 564s 4s/step - loss: 1.2839 - accuracy: 0.5243 - f1_score: 0.3595 - val_loss: 1.4727 - val_accuracy: 0.3841 - val_f1_score: 0.2520\n",
      "Epoch 45/100\n",
      "134/134 [==============================] - 572s 4s/step - loss: 1.2951 - accuracy: 0.5219 - f1_score: 0.3615 - val_loss: 1.4774 - val_accuracy: 0.3841 - val_f1_score: 0.2520\n",
      "Epoch 46/100\n",
      "134/134 [==============================] - 570s 4s/step - loss: 1.2968 - accuracy: 0.5159 - f1_score: 0.3607 - val_loss: 1.4733 - val_accuracy: 0.3837 - val_f1_score: 0.2525\n",
      "Epoch 47/100\n",
      "134/134 [==============================] - 577s 4s/step - loss: 1.2848 - accuracy: 0.5299 - f1_score: 0.3662 - val_loss: 1.4702 - val_accuracy: 0.3837 - val_f1_score: 0.2525\n",
      "Epoch 48/100\n",
      "134/134 [==============================] - 578s 4s/step - loss: 1.2825 - accuracy: 0.5251 - f1_score: 0.3668 - val_loss: 1.4719 - val_accuracy: 0.3845 - val_f1_score: 0.2549\n",
      "Epoch 49/100\n",
      "134/134 [==============================] - 597s 4s/step - loss: 1.2926 - accuracy: 0.5266 - f1_score: 0.3661 - val_loss: 1.4698 - val_accuracy: 0.3841 - val_f1_score: 0.2533\n",
      "Epoch 50/100\n",
      "134/134 [==============================] - 601s 4s/step - loss: 1.2911 - accuracy: 0.5344 - f1_score: 0.3717 - val_loss: 1.4713 - val_accuracy: 0.3841 - val_f1_score: 0.2533\n",
      "Epoch 51/100\n",
      "134/134 [==============================] - 598s 4s/step - loss: 1.3116 - accuracy: 0.5234 - f1_score: 0.3591 - val_loss: 1.4735 - val_accuracy: 0.3841 - val_f1_score: 0.2533\n",
      "Epoch 52/100\n",
      "134/134 [==============================] - 607s 5s/step - loss: 1.2714 - accuracy: 0.5338 - f1_score: 0.3767 - val_loss: 1.4728 - val_accuracy: 0.3845 - val_f1_score: 0.2540\n",
      "Epoch 53/100\n",
      "134/134 [==============================] - 565s 4s/step - loss: 1.2920 - accuracy: 0.5237 - f1_score: 0.3617 - val_loss: 1.4739 - val_accuracy: 0.3841 - val_f1_score: 0.2526\n",
      "Epoch 54/100\n",
      "134/134 [==============================] - 561s 4s/step - loss: 1.2931 - accuracy: 0.5144 - f1_score: 0.3623 - val_loss: 1.4726 - val_accuracy: 0.3845 - val_f1_score: 0.2540\n",
      "Epoch 55/100\n",
      "134/134 [==============================] - 560s 4s/step - loss: 1.2890 - accuracy: 0.5213 - f1_score: 0.3625 - val_loss: 1.4742 - val_accuracy: 0.3841 - val_f1_score: 0.2520\n",
      "Epoch 56/100\n",
      "134/134 [==============================] - 561s 4s/step - loss: 1.2903 - accuracy: 0.5290 - f1_score: 0.3703 - val_loss: 1.4777 - val_accuracy: 0.3845 - val_f1_score: 0.2534\n",
      "Epoch 57/100\n",
      "134/134 [==============================] - 561s 4s/step - loss: 1.2829 - accuracy: 0.5257 - f1_score: 0.3670 - val_loss: 1.4745 - val_accuracy: 0.3845 - val_f1_score: 0.2549\n",
      "Epoch 58/100\n",
      "134/134 [==============================] - 560s 4s/step - loss: 1.3070 - accuracy: 0.5219 - f1_score: 0.3644 - val_loss: 1.4756 - val_accuracy: 0.3841 - val_f1_score: 0.2548\n",
      "Epoch 59/100\n",
      "134/134 [==============================] - 560s 4s/step - loss: 1.2846 - accuracy: 0.5192 - f1_score: 0.3652 - val_loss: 1.4746 - val_accuracy: 0.3845 - val_f1_score: 0.2564\n",
      "Epoch 60/100\n",
      "134/134 [==============================] - 561s 4s/step - loss: 1.2876 - accuracy: 0.5251 - f1_score: 0.3650 - val_loss: 1.4777 - val_accuracy: 0.3845 - val_f1_score: 0.2558\n",
      "Epoch 61/100\n",
      "134/134 [==============================] - 560s 4s/step - loss: 1.2902 - accuracy: 0.5213 - f1_score: 0.3583 - val_loss: 1.4769 - val_accuracy: 0.3845 - val_f1_score: 0.2564\n",
      "Epoch 62/100\n",
      "134/134 [==============================] - 564s 4s/step - loss: 1.2778 - accuracy: 0.5368 - f1_score: 0.3741 - val_loss: 1.4742 - val_accuracy: 0.3841 - val_f1_score: 0.2554\n",
      "Epoch 63/100\n",
      "134/134 [==============================] - 563s 4s/step - loss: 1.2774 - accuracy: 0.5240 - f1_score: 0.3635 - val_loss: 1.4750 - val_accuracy: 0.3841 - val_f1_score: 0.2548\n",
      "Epoch 64/100\n",
      "134/134 [==============================] - 564s 4s/step - loss: 1.2844 - accuracy: 0.5246 - f1_score: 0.3616 - val_loss: 1.4715 - val_accuracy: 0.3837 - val_f1_score: 0.2544\n",
      "Epoch 65/100\n",
      "134/134 [==============================] - 564s 4s/step - loss: 1.2673 - accuracy: 0.5269 - f1_score: 0.3715 - val_loss: 1.4726 - val_accuracy: 0.3832 - val_f1_score: 0.2537\n",
      "Epoch 66/100\n",
      "134/134 [==============================] - 565s 4s/step - loss: 1.2751 - accuracy: 0.5266 - f1_score: 0.3728 - val_loss: 1.4723 - val_accuracy: 0.3832 - val_f1_score: 0.2553\n",
      "Epoch 67/100\n",
      "134/134 [==============================] - 564s 4s/step - loss: 1.2824 - accuracy: 0.5296 - f1_score: 0.3771 - val_loss: 1.4720 - val_accuracy: 0.3828 - val_f1_score: 0.2537\n",
      "Epoch 68/100\n",
      "134/134 [==============================] - 564s 4s/step - loss: 1.2729 - accuracy: 0.5278 - f1_score: 0.3737 - val_loss: 1.4706 - val_accuracy: 0.3832 - val_f1_score: 0.2556\n",
      "Epoch 69/100\n",
      "134/134 [==============================] - 564s 4s/step - loss: 1.2666 - accuracy: 0.5287 - f1_score: 0.3784 - val_loss: 1.4757 - val_accuracy: 0.3837 - val_f1_score: 0.2572\n",
      "Epoch 70/100\n",
      "134/134 [==============================] - 565s 4s/step - loss: 1.2791 - accuracy: 0.5299 - f1_score: 0.3756 - val_loss: 1.4768 - val_accuracy: 0.3837 - val_f1_score: 0.2566\n",
      "Epoch 71/100\n",
      "134/134 [==============================] - 564s 4s/step - loss: 1.2905 - accuracy: 0.5210 - f1_score: 0.3636 - val_loss: 1.4763 - val_accuracy: 0.3841 - val_f1_score: 0.2580\n",
      "Epoch 72/100\n",
      "134/134 [==============================] - 564s 4s/step - loss: 1.3006 - accuracy: 0.5281 - f1_score: 0.3699 - val_loss: 1.4784 - val_accuracy: 0.3841 - val_f1_score: 0.2592\n",
      "Epoch 73/100\n",
      "134/134 [==============================] - 564s 4s/step - loss: 1.2953 - accuracy: 0.5180 - f1_score: 0.3632 - val_loss: 1.4759 - val_accuracy: 0.3841 - val_f1_score: 0.2586\n",
      "Epoch 74/100\n",
      "134/134 [==============================] - 564s 4s/step - loss: 1.2699 - accuracy: 0.5219 - f1_score: 0.3668 - val_loss: 1.4724 - val_accuracy: 0.3832 - val_f1_score: 0.2568\n",
      "Epoch 75/100\n",
      "134/134 [==============================] - 565s 4s/step - loss: 1.2646 - accuracy: 0.5335 - f1_score: 0.3812 - val_loss: 1.4750 - val_accuracy: 0.3841 - val_f1_score: 0.2604\n",
      "Epoch 76/100\n",
      "134/134 [==============================] - 564s 4s/step - loss: 1.2799 - accuracy: 0.5308 - f1_score: 0.3753 - val_loss: 1.4766 - val_accuracy: 0.3841 - val_f1_score: 0.2603\n",
      "Epoch 77/100\n",
      "134/134 [==============================] - 564s 4s/step - loss: 1.2778 - accuracy: 0.5341 - f1_score: 0.3794 - val_loss: 1.4719 - val_accuracy: 0.3841 - val_f1_score: 0.2604\n",
      "Epoch 78/100\n",
      "134/134 [==============================] - 564s 4s/step - loss: 1.2748 - accuracy: 0.5305 - f1_score: 0.3750 - val_loss: 1.4760 - val_accuracy: 0.3841 - val_f1_score: 0.2604\n",
      "Epoch 79/100\n",
      "134/134 [==============================] - 563s 4s/step - loss: 1.2805 - accuracy: 0.5281 - f1_score: 0.3690 - val_loss: 1.4787 - val_accuracy: 0.3845 - val_f1_score: 0.2605\n",
      "Epoch 80/100\n",
      "134/134 [==============================] - 562s 4s/step - loss: 1.2796 - accuracy: 0.5272 - f1_score: 0.3675 - val_loss: 1.4745 - val_accuracy: 0.3845 - val_f1_score: 0.2605\n",
      "Epoch 81/100\n",
      "134/134 [==============================] - 564s 4s/step - loss: 1.2719 - accuracy: 0.5323 - f1_score: 0.3711 - val_loss: 1.4745 - val_accuracy: 0.3841 - val_f1_score: 0.2592\n",
      "Epoch 82/100\n",
      "134/134 [==============================] - 564s 4s/step - loss: 1.2656 - accuracy: 0.5257 - f1_score: 0.3709 - val_loss: 1.4760 - val_accuracy: 0.3845 - val_f1_score: 0.2605\n",
      "Epoch 83/100\n",
      "134/134 [==============================] - 564s 4s/step - loss: 1.2686 - accuracy: 0.5263 - f1_score: 0.3692 - val_loss: 1.4764 - val_accuracy: 0.3841 - val_f1_score: 0.2597\n",
      "Epoch 84/100\n",
      "134/134 [==============================] - 565s 4s/step - loss: 1.2863 - accuracy: 0.5251 - f1_score: 0.3683 - val_loss: 1.4753 - val_accuracy: 0.3841 - val_f1_score: 0.2597\n",
      "Epoch 85/100\n",
      "134/134 [==============================] - 565s 4s/step - loss: 1.2727 - accuracy: 0.5326 - f1_score: 0.3768 - val_loss: 1.4755 - val_accuracy: 0.3837 - val_f1_score: 0.2590\n",
      "Epoch 86/100\n",
      "134/134 [==============================] - 576s 4s/step - loss: 1.2847 - accuracy: 0.5308 - f1_score: 0.3695 - val_loss: 1.4807 - val_accuracy: 0.3845 - val_f1_score: 0.2621\n",
      "Epoch 87/100\n",
      "134/134 [==============================] - 561s 4s/step - loss: 1.2667 - accuracy: 0.5272 - f1_score: 0.3722 - val_loss: 1.4790 - val_accuracy: 0.3850 - val_f1_score: 0.2629\n",
      "Epoch 88/100\n",
      "134/134 [==============================] - 561s 4s/step - loss: 1.2824 - accuracy: 0.5317 - f1_score: 0.3726 - val_loss: 1.4728 - val_accuracy: 0.3837 - val_f1_score: 0.2602\n",
      "Epoch 89/100\n",
      "134/134 [==============================] - 562s 4s/step - loss: 1.2611 - accuracy: 0.5338 - f1_score: 0.3798 - val_loss: 1.4754 - val_accuracy: 0.3837 - val_f1_score: 0.2596\n",
      "Epoch 90/100\n",
      "134/134 [==============================] - 561s 4s/step - loss: 1.2667 - accuracy: 0.5272 - f1_score: 0.3773 - val_loss: 1.4755 - val_accuracy: 0.3832 - val_f1_score: 0.2577\n",
      "Epoch 91/100\n",
      "134/134 [==============================] - 562s 4s/step - loss: 1.2811 - accuracy: 0.5210 - f1_score: 0.3625 - val_loss: 1.4779 - val_accuracy: 0.3832 - val_f1_score: 0.2577\n",
      "Epoch 92/100\n",
      "134/134 [==============================] - 562s 4s/step - loss: 1.2744 - accuracy: 0.5257 - f1_score: 0.3684 - val_loss: 1.4780 - val_accuracy: 0.3841 - val_f1_score: 0.2579\n",
      "Epoch 93/100\n",
      "134/134 [==============================] - 564s 4s/step - loss: 1.2836 - accuracy: 0.5254 - f1_score: 0.3765 - val_loss: 1.4748 - val_accuracy: 0.3841 - val_f1_score: 0.2585\n",
      "Epoch 94/100\n",
      "134/134 [==============================] - 565s 4s/step - loss: 1.2764 - accuracy: 0.5311 - f1_score: 0.3675 - val_loss: 1.4735 - val_accuracy: 0.3841 - val_f1_score: 0.2592\n",
      "Epoch 95/100\n",
      "134/134 [==============================] - 565s 4s/step - loss: 1.2745 - accuracy: 0.5234 - f1_score: 0.3679 - val_loss: 1.4720 - val_accuracy: 0.3850 - val_f1_score: 0.2594\n",
      "Epoch 96/100\n",
      "134/134 [==============================] - 565s 4s/step - loss: 1.2719 - accuracy: 0.5338 - f1_score: 0.3778 - val_loss: 1.4791 - val_accuracy: 0.3845 - val_f1_score: 0.2593\n",
      "Epoch 97/100\n",
      "134/134 [==============================] - 566s 4s/step - loss: 1.2723 - accuracy: 0.5305 - f1_score: 0.3749 - val_loss: 1.4768 - val_accuracy: 0.3841 - val_f1_score: 0.2592\n",
      "Epoch 98/100\n",
      "134/134 [==============================] - 565s 4s/step - loss: 1.2696 - accuracy: 0.5326 - f1_score: 0.3732 - val_loss: 1.4788 - val_accuracy: 0.3845 - val_f1_score: 0.2608\n",
      "Epoch 99/100\n",
      "134/134 [==============================] - 564s 4s/step - loss: 1.2746 - accuracy: 0.5296 - f1_score: 0.3760 - val_loss: 1.4788 - val_accuracy: 0.3850 - val_f1_score: 0.2609\n",
      "Epoch 100/100\n",
      "134/134 [==============================] - 563s 4s/step - loss: 1.2682 - accuracy: 0.5287 - f1_score: 0.3710 - val_loss: 1.4789 - val_accuracy: 0.3850 - val_f1_score: 0.2594\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x1f5a462f670>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(train_generator,\n",
    "          steps_per_epoch=np.ceil(float(3340) / float(BATCH_SIZE)),\n",
    "          epochs = 100,callbacks=[reduce_lr],\n",
    "          validation_steps=np.ceil(float(2291) / float(BATCH_SIZE)),\n",
    "          validation_data = validation_generator)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = model.predict_classes(validation_generator)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,7))\n",
    "for i in range(10):\n",
    "    samp = random.choice(range(len(validation_generator)))\n",
    "    image = validation_generator[samp][0][0]\n",
    "    category = y_test[samp]\n",
    "    pred_category = y_pred[samp]\n",
    "    if category == 0:\n",
    "        label = \"AMD\"\n",
    "    elif category == 1:\n",
    "        label = \"Cataracts\"\n",
    "    elif category == 2:\n",
    "        label = \"Diabetes\"\n",
    "    elif category == 3:\n",
    "        label = \"Glaucoma\"\n",
    "    elif category == 4:\n",
    "        label = \"Hypertension\"\n",
    "    elif category == 5:\n",
    "        label = \"Myopia\"\n",
    "    elif category == 6:\n",
    "        label = \"Normal\"\n",
    "    if pred_category == 0:\n",
    "        pred_label = \"AMD\"\n",
    "    elif pred_category == 1:\n",
    "        pred_label = \"Cataracts\"\n",
    "    elif pred_category == 2:\n",
    "        pred_label = \"Diabetes\"\n",
    "    elif pred_category == 3:\n",
    "        pred_label = \"Glaucoma\"\n",
    "    elif pred_category == 4:\n",
    "        pred_label = \"Hypertension\"\n",
    "    elif pred_category == 5:\n",
    "        pred_label = \"Myopia\"\n",
    "    elif pred_category == 6:\n",
    "        pred_label = \"Normal\"\n",
    "\n",
    "    plt.subplot(2,5,i+1)\n",
    "    plt.imshow(image, cmap = \"gray\")\n",
    "    plt.xlabel(\"Actual:{}\\nPrediction:{}\".format(label,pred_label))\n",
    "plt.tight_layout()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}