02-Diabetic_Retinopathy_Augmented-96x96,64,128-LaptopCPUOnly.ipynb

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  {
   "cell_type": "code",
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   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Diabetic Retinopathy Detection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This Jupyter Notebook uses neural networks to TBD.\n",
    "\n",
    "<!--\n",
    "\n",
    "## Approach\n",
    "* Evaluations of classification performed were performed using 2 sample datasets:\n",
    "    * sklearn's Iris sample dataset, consisting of 150 sample data points for 3 varieties of iris flowers\n",
    "    * voice dataset, containing 3168 sample data points of male and female speakers\n",
    "\n",
    "* Hyper-parameter tuning was performed using a GridSearch estimator for each classification model:\n",
    "    \n",
    "* Parameters providing the best scores and associated scores were plotted\n",
    "    \n",
    "## Results\n",
    "* Refer to the Figures and Table of results provided below\n",
    "* Hyperparameter tuning provided by sklearn GridSearchCV provided a convenient method of evaluating many classification model parameter sets in an efficient way.  The blue dots represent the optimal parameter sets selected for each Dataset/Classification Model, and the grey dots represent suboptimal parameter sets.\n",
    "* For the Iris dataset, the Support Vector Machine SVC model provided the  combination of best score of 0.964 and Mean Fit Time of 0.200 ms, with Random Forest and Decision Tree classifiers providing close performance.\n",
    "* For the Voice dataset, the SVC classifer also had the hightest Best Score at 0.980, which was slightly above the performance of the Random Forest classifier.  With SVC, the fit time was singificantly higher for the Voice dataset (40.6 ms) than for the Iris dataset (0.2 ms), which is expected given the larger number of features associated with the Voice dataset (20) vs. the Iris dataset (4)\n",
    "* It's interesting to note that the K-Nearest Neighbors classifier, while performing lower amongst these models, operated with fast Mean Fit Time (3.2 ms) for Voice, which might make using KNN a good choice vs. other more calculation-intensive options in cases where lower fit time is more important than optimum accuracy.\n",
    "\n",
    "-->"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "TBD\n",
    "\n",
    "<!--\n",
    "\n",
    "| Figure: Neural Network Performance: Best Score vs. Mean Fit Time (ms) | \n",
    "| :----------: |\n",
    "| ![Figure: Neural Network Performance: Best Score vs. Mean Fit Time (ms) is Loading...](docs/Figure-Neural_Network_Performance-A.png \"Figure: Neural Network Performance: Best Score vs. Mean Fit Time (ms)\") |\n",
    "\n",
    "\n",
    "| Figure: Tuned Classifier Performance: Best Score vs. F1 Score - All Datasets/Classifiers | Figure: Tuned Classifier Performance: Precision vs. Recall - All Datasets/Classifiers |\n",
    "| :----------: | :----------: |\n",
    "| ![Figure: Tuned Classifier Performance: Best Score vs. F1 Score - All Datasets/Classifiers is Loading...](docs/Figure-Hyper_Parameter_Tuning-BestScore_vs_F1-Combined.png \"Figure: uned Classifier Performance: Best Score vs. F1 Score - All Datasets/Classifiers\") | ![Figure: Tuned Classifier Performance: Precision vs. Recall - All Datasets/Classifiers is Loading...](docs/Figure-Hyper_Parameter_Tuning-Precision_vs_Recall-Combined.png \"Figure: Tuned Classifier Performance: Precision vs. Recall - All Datasets/Classifiers\") |\n",
    "\n",
    "| Figure: Tuned Classifier Performance - Subplots |\n",
    "| :----------: |\n",
    "| ![Figure: Tuned Classifier Performance - Subplots is Loading...](docs/Figure-Hyper_Parameter_Tuning-Subplots.png \"Figure: Tuned Classifier Performance - Subplots\") |\n",
    "\n",
    "| Table: Tuned Classifier Performance |\n",
    "| :----------: |\n",
    "| ![Table: Tuned Classifier Performance is Loading...](docs/Table-Hyper_Parameter_Tuning.png \"Table: Tuned Classifier Performance\") |\n",
    "\n",
    "-->"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "nbpresent": {
     "id": "828c2e12-b1c6-4994-8f55-ce86373b6c97"
    },
    "scrolled": true,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "# %matplotlib notebook\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.mplot3d import Axes3D    # Support 3D graphing\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pandas.plotting import table\n",
    "import math\n",
    "import random\n",
    "import shutil\n",
    "import os\n",
    "\n",
    "# from imutils import paths\n",
    "# import argparse\n",
    "# import pickle\n",
    "# import cv2\n",
    "\n",
    "from pprint import pprint\n",
    "\n",
    "# Visualization\n",
    "import graphviz\n",
    "import pydotplus\n",
    "from IPython.display import Image\n",
    "\n",
    "# Machine Learning - Data Preparation and Pre-Processing\n",
    "from sklearn.model_selection import train_test_split # Split data into training and testing samples\n",
    "from sklearn.model_selection import cross_val_score  # Score a model using k-fold or other cross validation\n",
    "\n",
    "from sklearn.preprocessing import OneHotEncoder   # Convert categorical integer features (X) to One-Hot encoded values\n",
    "from sklearn.preprocessing import LabelEncoder    # Convert categorical labeled values to categorical integer values\n",
    "from sklearn.preprocessing import LabelBinarizer  # Convert categorical labeled values to Binary encoded values\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler  # Scale numerical features to standard normal distribution\n",
    "from sklearn.preprocessing import MinMaxScaler    # Scale numerical values based upon mix/max values\n",
    "\n",
    "# Machine Learning - Sci-Kit Learn - Models - Regression\n",
    "from sklearn.linear_model import LinearRegression  # TBD\n",
    "from sklearn.linear_model import Lasso             # TBD\n",
    "from sklearn.linear_model import Ridge             # TBD\n",
    "from sklearn.linear_model import ElasticNet        # TBD\n",
    "\n",
    "# Machine Learning - Sci-Kit Learn - Models - Classification\n",
    "from sklearn.linear_model import LogisticRegression   # Logistic Regression Classifier\n",
    "from sklearn import tree                              # Decision Tree Classifier\n",
    "from sklearn.ensemble import RandomForestClassifier   # Random Forest Classifier\n",
    "from sklearn import svm                               # Support Vector Machine Classifier\n",
    "from sklearn.neighbors import KNeighborsClassifier    # K-Nearest Neighbors (KNN)\n",
    "\n",
    "# Machine Learning - GridSearch for Hyper-Parameter tuning\n",
    "from sklearn.model_selection import GridSearchCV      # Grid Search\n",
    "\n",
    "# Machine Learning - Quantify Model Performance\n",
    "from sklearn.metrics import mean_squared_error    # Mean Squared Error (MSE) metric\n",
    "from sklearn.metrics import r2_score              # R-squared (Coefficient of Determination) metric\n",
    "from sklearn.metrics import confusion_matrix      # Generate a confusion matrix (actual vs. predicted counts)\n",
    "from sklearn.metrics import classification_report # Calculate metrics for prediction performance\n",
    "from sklearn.metrics import precision_score       # Calculate the precision: Tp / (Tp + Fp) => Ability to avoid false negatives\n",
    "from sklearn.metrics import recall_score          # Calculate the recall: Tp / (Tp + Fn) => Ability to find all positive samples\n",
    "from sklearn.metrics import f1_score              # Calculate the F1 score: 2*(precision*recall)/(precision+recall)\n",
    "\n",
    "# Machine Learning - Dataset Generation\n",
    "from sklearn.datasets import make_regression     # Generate linear data\n",
    "from sklearn.datasets import make_s_curve        # Generate nonlinear data\n",
    "from sklearn.datasets import make_blobs          # Generate blobs for classification\n",
    "from sklearn.datasets import make_circles        # Generate circles for classification\n",
    "from sklearn.datasets import load_iris           # Sample multi-class dataset for classification\n",
    "from sklearn.datasets import make_classification # Generate datasets for classification\n",
    "\n",
    "# Machine Learning - Keras (Tensorflow) - Models\n",
    "from keras.models import Sequential               # Sequential model serving as foundation for neural network\n",
    "# from keras.layers import Dense                    # Nodes for specifying input, hidden, and output layers\n",
    "from keras.layers.core import Activation\n",
    "from keras.layers.core import Flatten\n",
    "from keras.layers.core import Dropout\n",
    "from keras.layers.core import Dense\n",
    "\n",
    "# Machine Learning - Keras (Tensorflow) - Convolutional Neural Networks (Image Classification)\n",
    "from keras.layers.normalization import BatchNormalization\n",
    "from keras.layers.convolutional import Conv2D\n",
    "from keras.layers.convolutional import MaxPooling2D\n",
    "from keras import backend as K\n",
    "\n",
    "# Machine Learning - Keras (Tensorflow) - Optimizers\n",
    "from keras.optimizers import SGD\n",
    "from keras.optimizers import Adam\n",
    "\n",
    "# Machine Learning - Keras (Tensorflow) - Encoding\n",
    "from keras.utils import to_categorical            # One-Hot Encoder provided through Keras\n",
    "\n",
    "# Machine Learning - Keras (Tensorflow) - Other related Tools\n",
    "from keras.utils import plot_model                # Plot a neural network model\n",
    "from keras.models import load_model               # Load a saved machine learning model\n",
    "from keras.preprocessing import image             # Loads an image for application of machine learning\n",
    "from keras.preprocessing.image import img_to_array # Converts an image to a numpy array\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "\n",
    "# Machine Learning - Keras (Tensorflow) -  Dataset Generation\n",
    "from keras.datasets import mnist                  # Images: Handwritten digits 0-9 (28x28 grayscale, 60K train, 10K test)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Locate the Training and Testing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data folders/subfolders\n",
    "\n",
    "# data/train_images - Source of training images in a single folder\n",
    "dir_src_train = \"./data/train_images/\"\n",
    "\n",
    "# data/train - Split of data from dir_src_train for training, segmented in subfolders by label\n",
    "dir_train = \"./data/train/\"\n",
    "\n",
    "# data/valid - Split of data from dir_src_train for validation, segmented in subfolders by label\n",
    "dir_valid = \"./data/valid/\"\n",
    "\n",
    "# data/test - Split of data from dir_src_train for testing, consolidated under a single subfolder \"test_subfolder\"\n",
    "dir_test = \"./data/test/\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# List of all training images, with labels\n",
    "labels_train_file = \"data/train.csv\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>id_code</th>\n",
       "      <th>diagnosis</th>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>3662 rows × 2 columns</p>\n",
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      "text/plain": [
       "           id_code  diagnosis\n",
       "0     000c1434d8d7          2\n",
       "1     001639a390f0          4\n",
       "2     0024cdab0c1e          1\n",
       "3     002c21358ce6          0\n",
       "4     005b95c28852          0\n",
       "...            ...        ...\n",
       "3657  ffa47f6a7bf4          2\n",
       "3658  ffc04fed30e6          0\n",
       "3659  ffcf7b45f213          2\n",
       "3660  ffd97f8cd5aa          0\n",
       "3661  ffec9a18a3ce          2\n",
       "\n",
       "[3662 rows x 2 columns]"
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     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "# Load into a dataframe\n",
    "all_train_labels_df = pd.read_csv(labels_train_file)\n",
    "\n",
    "# Limit subsequent pre-processing to only 15 images initially\n",
    "# all_train_labels_df = all_train_labels_df[0:100]\n",
    "\n",
    "# Process all training data\n",
    "all_train_labels_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>diagnosis</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>count</td>\n",
       "      <td>3662.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>1.126980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>1.298409</td>\n",
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       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>50%</td>\n",
       "      <td>1.000000</td>\n",
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       "      <td>75%</td>\n",
       "      <td>2.000000</td>\n",
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       "      <td>max</td>\n",
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       "         diagnosis\n",
       "count  3662.000000\n",
       "mean      1.126980\n",
       "std       1.298409\n",
       "min       0.000000\n",
       "25%       0.000000\n",
       "50%       1.000000\n",
       "75%       2.000000\n",
       "max       4.000000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_train_labels_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Look for any entries where the labels are not a number\n",
    "invalid_list = all_train_labels_df['diagnosis'].map(math.isnan)\n",
    "if sum(invalid_list) > 0:\n",
    "    print(f\"WARNING: Dropping {sum(invalid_list)} rows with labels having value NaN\")\n",
    "    print(all_train_labels_df[ invalid_list ])\n",
    "\n",
    "    # Drop rows with invalid labels\n",
    "    all_train_labels_df = all_train_labels_df[ invalid_list == False ].reset_index(drop=True)\n",
    "    all_train_labels_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Split the Training Data into Training, Validation, and Test Subsets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2197,) (2197,)\n",
      "(732,) (732,)\n",
      "(733,) (733,)\n"
     ]
    }
   ],
   "source": [
    "# Perform test_train_split twice to create training (60%), validation (20%), and testing (20%) subsets\n",
    "X_train, X_remain, y_train, y_remain = train_test_split(\n",
    "                        all_train_labels_df['id_code'],\n",
    "                        all_train_labels_df['diagnosis'],\n",
    "                        test_size = 0.4,\n",
    "                        random_state=1\n",
    "                        )\n",
    "\n",
    "X_valid, X_test, y_valid, y_test = train_test_split(\n",
    "                        X_remain,\n",
    "                        y_remain,\n",
    "                        test_size = 0.5,\n",
    "                        random_state=1\n",
    "                        )\n",
    "\n",
    "print( X_train.shape, y_train.shape )\n",
    "print( X_valid.shape, y_valid.shape )\n",
    "print( X_test.shape, y_test.shape )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Function to Copy Images to Proper Training, Validation, and Testing Subfolders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to copy the training, validation, or testing data\n",
    "# from the source training image folder to the appopriate destination folder\n",
    "def copy_images_to_folders( a_X = None, a_y = None, a_dest_type = None):\n",
    "    # a_X: Image id code, used to identify the file name of each image\n",
    "    # a_y: Label associated with each image\n",
    "    # a_dest_type:\n",
    "    #   'train': Each image will be copied to a training subfolder, under a subfolder based upon it's label\n",
    "    #   'valid': Each image will be copied to a validation subfolder, under a subfolder based upon it's label\n",
    "    #   'test': Each image will be copied to a testing subfolder, under a common subfolder \"test_subfolder\"\n",
    "    #\n",
    "    # IMPT: Depends upon dir_train, dir_valid, dir_test,\n",
    "    #       which must be set externally before this function is called\n",
    "    \n",
    "    # Basic argument checking\n",
    "    if (a_X is None) or (a_y is None) or (a_dest_type is None):\n",
    "        print(f\"Error: Mandatory argument is missing - X: '{a_X}', y: '{a_y}', Destination Flag: '{a_dest_type}'\")\n",
    "        return False\n",
    "        \n",
    "    # Dictionary to mapping of flag values to destination main folder\n",
    "    dest_folder_map = {\n",
    "        'train': dir_train,\n",
    "        'valid': dir_valid,\n",
    "        'test': dir_test\n",
    "    }\n",
    "    \n",
    "    # Check to ensure the destination flag is valid\n",
    "    if a_dest_type not in dest_folder_map.keys():\n",
    "        print(f\"Error: Invalid destination flag - Destination Flag: '{a_dest_type}' must be 'train', 'valid', or 'test'\")\n",
    "        return False\n",
    "            \n",
    "    # Create the destination folder if it doesn't already exist\n",
    "    if not os.path.exists(dest_folder_map[a_dest_type]):\n",
    "            os.mkdir(dest_folder_map[a_dest_type])\n",
    "\n",
    "    # Loop through each of the images in the dataframe\n",
    "    for X_val, y_val in zip(a_X, a_y):\n",
    "        # Source path of the image file, which is the set of all training images\n",
    "        src_file = dir_src_train + X_val + \".png\"\n",
    "\n",
    "        # Set the subfolder based upon the destination of this image\n",
    "        if a_dest_type == 'test':\n",
    "            # Destination path of the image file\n",
    "            # dest_subfolder = dest_folder_map[a_dest_type]\n",
    "            dest_subfolder = dest_folder_map[a_dest_type]  + \"test_subfolder/\"\n",
    "            \n",
    "        else:\n",
    "            # Assume it's either 'train' or 'valid' and set the subfolder based upon the label\n",
    "            # Subfolder of the image file uses the label for this image\n",
    "            dest_subfolder = dest_folder_map[a_dest_type] + str(y_val) + \"/\" \n",
    "\n",
    "        # Create the destination subfolder if it doesn't already exist\n",
    "        if not os.path.exists(dest_subfolder):\n",
    "                os.mkdir(dest_subfolder)\n",
    "\n",
    "        # Destination path of the image file\n",
    "        dest_file = dest_subfolder + X_val + \".png\"\n",
    "\n",
    "        # Copy the file\n",
    "        print(f\"Copying image {src_file} to {dest_file}... \", end='')\n",
    "        try:\n",
    "            shutil.copy2(src_file, dest_file)\n",
    "            print(f\"done.\")\n",
    "\n",
    "        except FileNotFoundError:\n",
    "            print(f\"error while attempting to copy.\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Copying image ./data/train_images/8bed09514c3b.png to ./data/train/4/8bed09514c3b.png... done.\n",
      "Copying image ./data/train_images/3710ff45299c.png to ./data/train/0/3710ff45299c.png... done.\n",
      "Copying image ./data/train_images/d88c4843aec3.png to ./data/train/0/d88c4843aec3.png... done.\n",
      "Copying image ./data/train_images/a247961a5cd9.png to ./data/train/0/a247961a5cd9.png... done.\n",
      "Copying image ./data/train_images/ae49cc60f251.png to ./data/train/0/ae49cc60f251.png... done.\n",
      "Copying image ./data/train_images/f66c4ee86629.png to ./data/train/0/f66c4ee86629.png... done.\n",
      "Copying image ./data/train_images/96a9706b8534.png to ./data/train/1/96a9706b8534.png... done.\n",
      "Copying image ./data/train_images/65f69234c8a7.png to ./data/train/0/65f69234c8a7.png... done.\n",
      "Copying image ./data/train_images/b549af91bd30.png to ./data/train/0/b549af91bd30.png... done.\n",
      "Copying image ./data/train_images/80ca40196225.png to ./data/train/3/80ca40196225.png... done.\n",
      "Copying image ./data/train_images/86e7f98f73f1.png to ./data/train/2/86e7f98f73f1.png... done.\n",
      "Copying image ./data/train_images/9cf7c1349673.png to ./data/train/0/9cf7c1349673.png... done.\n",
      "Copying image ./data/train_images/1d11794057ff.png to ./data/train/1/1d11794057ff.png... done.\n",
      "Copying image ./data/train_images/a987aa7aac37.png to ./data/train/0/a987aa7aac37.png... done.\n",
      "Copying image ./data/train_images/3cdda8b3df19.png to ./data/train/2/3cdda8b3df19.png... done.\n",
      "Copying image ./data/train_images/d1f1ea894da1.png to ./data/train/2/d1f1ea894da1.png... done.\n",
      "Copying image ./data/train_images/7ee6de71c140.png to ./data/train/0/7ee6de71c140.png... done.\n",
      "Copying image ./data/train_images/c5ad60521f8c.png to ./data/train/2/c5ad60521f8c.png... done.\n",
      "Copying image ./data/train_images/5ed6dc419e4d.png to ./data/train/2/5ed6dc419e4d.png... done.\n",
      "Copying image ./data/train_images/badb5ff8d3c7.png to ./data/train/0/badb5ff8d3c7.png... done.\n",
      "Copying image ./data/train_images/d5ad3362424c.png to ./data/train/0/d5ad3362424c.png... done.\n",
      "Copying image ./data/train_images/726dff37edc0.png to ./data/train/0/726dff37edc0.png... done.\n",
      "Copying image ./data/train_images/d74ccc796517.png to ./data/train/0/d74ccc796517.png... done.\n",
      "Copying image ./data/train_images/bb783d8e496f.png to ./data/train/1/bb783d8e496f.png... done.\n",
      "Copying image ./data/train_images/9033f1493da1.png to ./data/train/2/9033f1493da1.png... done.\n",
      "Copying image ./data/train_images/28f98cfe3858.png to ./data/train/1/28f98cfe3858.png... done.\n",
      "Copying image ./data/train_images/314862758acf.png to ./data/train/2/314862758acf.png... done.\n",
      "Copying image ./data/train_images/beb2ad14fd2d.png to ./data/train/0/beb2ad14fd2d.png... done.\n",
      "Copying image ./data/train_images/09c8323c612e.png to ./data/train/0/09c8323c612e.png... done.\n",
      "Copying image ./data/train_images/f02057c41256.png to ./data/train/0/f02057c41256.png... done.\n",
      "Copying image ./data/train_images/b0b3b16fc305.png to ./data/train/0/b0b3b16fc305.png... done.\n",
      "Copying image ./data/train_images/2ecbc2e3f239.png to ./data/train/1/2ecbc2e3f239.png... done.\n",
      "Copying image ./data/train_images/1f4bf8e28b41.png to ./data/train/2/1f4bf8e28b41.png... done.\n",
      "Copying image ./data/train_images/7b9d519cbd66.png to ./data/train/0/7b9d519cbd66.png... done.\n",
      "Copying image ./data/train_images/4eaf2f81819d.png to ./data/train/0/4eaf2f81819d.png... done.\n",
      "Copying image ./data/train_images/76c0c7e1b6cb.png to ./data/train/2/76c0c7e1b6cb.png... done.\n",
      "Copying image ./data/train_images/54dc6e8107cd.png to ./data/train/2/54dc6e8107cd.png... done.\n",
      "Copying image ./data/train_images/992b9a07b25f.png to ./data/train/0/992b9a07b25f.png... done.\n",
      "Copying image ./data/train_images/fd0a70082e7c.png to ./data/train/0/fd0a70082e7c.png... done.\n",
      "Copying image ./data/train_images/6d454444f17c.png to ./data/train/2/6d454444f17c.png... done.\n",
      "Copying image ./data/train_images/fed5bb685832.png to ./data/train/0/fed5bb685832.png... done.\n",
      "Copying image ./data/train_images/9e5ec293267c.png to ./data/train/2/9e5ec293267c.png... done.\n",
      "Copying image ./data/train_images/2994f17f58a5.png to ./data/train/1/2994f17f58a5.png... done.\n",
      "Copying image ./data/train_images/3de8ad4151e1.png to ./data/train/1/3de8ad4151e1.png... done.\n",
      "Copying image ./data/train_images/2221cf5c7935.png to ./data/train/0/2221cf5c7935.png... done.\n",
      "Copying image ./data/train_images/4ce74e5eb51d.png to ./data/train/2/4ce74e5eb51d.png... done.\n",
      "Copying image ./data/train_images/c280730cc211.png to ./data/train/2/c280730cc211.png... done.\n",
      "Copying image ./data/train_images/6cfb7b44ef6f.png to ./data/train/4/6cfb7b44ef6f.png... done.\n",
      "Copying image ./data/train_images/2a93334f663a.png to ./data/train/0/2a93334f663a.png... done.\n",
      "Copying image ./data/train_images/196e6a186452.png to ./data/train/2/196e6a186452.png... done.\n",
      "Copying image ./data/train_images/8ffa608170d3.png to ./data/train/0/8ffa608170d3.png... done.\n",
      "Copying image ./data/train_images/7e5a76c4e103.png to ./data/train/0/7e5a76c4e103.png... done.\n",
      "Copying image ./data/train_images/dad71ba27a9b.png to ./data/train/4/dad71ba27a9b.png... done.\n",
      "Copying image ./data/train_images/2b10f138e67d.png to ./data/train/0/2b10f138e67d.png... done.\n",
      "Copying image ./data/train_images/8ff2733f6aef.png to ./data/train/2/8ff2733f6aef.png... done.\n",
      "Copying image ./data/train_images/ec57cc20d776.png to ./data/train/0/ec57cc20d776.png... done.\n",
      "Copying image ./data/train_images/28824d12d31d.png to ./data/train/2/28824d12d31d.png... done.\n",
      "Copying image ./data/train_images/e9129ce55fd7.png to ./data/train/2/e9129ce55fd7.png... done.\n",
      "Copying image ./data/train_images/3580a545016d.png to ./data/train/0/3580a545016d.png... done.\n",
      "Copying image ./data/train_images/3254e48c8aa0.png to ./data/train/0/3254e48c8aa0.png... done.\n",
      "Copying image ./data/train_images/0da321efbce6.png to ./data/train/0/0da321efbce6.png... done.\n",
      "Copying image ./data/train_images/bf7b4eae7ad0.png to ./data/train/0/bf7b4eae7ad0.png... done.\n",
      "Copying image ./data/train_images/11d8e5eaee5b.png to ./data/train/0/11d8e5eaee5b.png... done.\n",
      "Copying image ./data/train_images/edceb0657d77.png to ./data/train/0/edceb0657d77.png... done.\n",
      "Copying image ./data/train_images/61bbc11fe503.png to ./data/train/4/61bbc11fe503.png... done.\n",
      "Copying image ./data/train_images/8241e43408a8.png to ./data/train/0/8241e43408a8.png... done.\n",
      "Copying image ./data/train_images/8e3b79e1f1f7.png to ./data/train/2/8e3b79e1f1f7.png... done.\n",
      "Copying image ./data/train_images/2e79041ef722.png to ./data/train/2/2e79041ef722.png... done.\n",
      "Copying image ./data/train_images/0ae2dd2e09ea.png to ./data/train/0/0ae2dd2e09ea.png... done.\n",
      "Copying image ./data/train_images/32ed318235b8.png to ./data/train/0/32ed318235b8.png... done.\n",
      "Copying image ./data/train_images/c8d2d32f7f29.png to ./data/train/4/c8d2d32f7f29.png... done.\n",
      "Copying image ./data/train_images/01c7808d901d.png to ./data/train/2/01c7808d901d.png... done.\n",
      "Copying image ./data/train_images/58184d6fd087.png to ./data/train/1/58184d6fd087.png... done.\n",
      "Copying image ./data/train_images/599498e9e4bc.png to ./data/train/0/599498e9e4bc.png... done.\n",
      "Copying image ./data/train_images/da3a2275c850.png to ./data/train/0/da3a2275c850.png... done.\n",
      "Copying image ./data/train_images/6c250a30593b.png to ./data/train/4/6c250a30593b.png... done.\n",
      "Copying image ./data/train_images/650104ede84c.png to ./data/train/0/650104ede84c.png... done.\n",
      "Copying image ./data/train_images/42a67337fa8e.png to ./data/train/2/42a67337fa8e.png... done.\n",
      "Copying image ./data/train_images/f47a2a4a0411.png to ./data/train/1/f47a2a4a0411.png... done.\n",
      "Copying image ./data/train_images/a96ff96bbae5.png to ./data/train/0/a96ff96bbae5.png... done.\n",
      "Copying image ./data/train_images/d16e59a2b33a.png to ./data/train/0/d16e59a2b33a.png... done.\n",
      "Copying image ./data/train_images/f6f433f3306f.png to ./data/train/3/f6f433f3306f.png... done.\n",
      "Copying image ./data/train_images/ffd97f8cd5aa.png to ./data/train/0/ffd97f8cd5aa.png... done.\n",
      "Copying image ./data/train_images/8dfa629ca74e.png to ./data/train/2/8dfa629ca74e.png... done.\n",
      "Copying image ./data/train_images/b46b09a45f39.png to ./data/train/0/b46b09a45f39.png... done.\n",
      "Copying image ./data/train_images/e4d3d437b0a8.png to ./data/train/0/e4d3d437b0a8.png... done.\n",
      "Copying image ./data/train_images/c3789c1dab96.png to ./data/train/0/c3789c1dab96.png... done.\n",
      "Copying image ./data/train_images/79059d0592c4.png to ./data/train/2/79059d0592c4.png... done.\n",
      "Copying image ./data/train_images/6a2642131e4a.png to ./data/train/0/6a2642131e4a.png... done.\n",
      "Copying image ./data/train_images/57f5ad4b5b29.png to ./data/train/0/57f5ad4b5b29.png... done.\n",
      "Copying image ./data/train_images/82d364726a58.png to ./data/train/0/82d364726a58.png... done.\n",
      "Copying image ./data/train_images/bfda2fd0533a.png to ./data/train/2/bfda2fd0533a.png... done.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Copying image ./data/train_images/3323fd59782e.png to ./data/train/0/3323fd59782e.png... done.\n",
      "Copying image ./data/train_images/658ad9f09f5d.png to ./data/train/0/658ad9f09f5d.png... done.\n",
      "Copying image ./data/train_images/2d870833c0c9.png to ./data/train/2/2d870833c0c9.png... done.\n",
      "Copying image ./data/train_images/f920ccd926db.png to ./data/train/2/f920ccd926db.png... done.\n",
      "Copying image ./data/train_images/0182152c50de.png to ./data/train/0/0182152c50de.png... done.\n",
      "Copying image ./data/train_images/8906c9ed54a2.png to ./data/train/0/8906c9ed54a2.png... done.\n",
      "Copying image ./data/train_images/91b6ebaa3678.png to ./data/train/3/91b6ebaa3678.png... done.\n",
      "Copying image ./data/train_images/2b3a4a81d748.png to ./data/train/0/2b3a4a81d748.png... done.\n",
      "Copying image ./data/train_images/2fb3a8606a77.png to ./data/train/2/2fb3a8606a77.png... done.\n",
      "Copying image ./data/train_images/e251bdf05b85.png to ./data/train/0/e251bdf05b85.png... done.\n",
      "Copying image ./data/train_images/b09101adb478.png to ./data/train/0/b09101adb478.png... done.\n",
      "Copying image ./data/train_images/b91ef82e723a.png to ./data/train/2/b91ef82e723a.png... done.\n",
      "Copying image ./data/train_images/4134b290f5f3.png to ./data/train/0/4134b290f5f3.png... done.\n",
      "Copying image ./data/train_images/83517eaeccb9.png to ./data/train/0/83517eaeccb9.png... done.\n",
      "Copying image ./data/train_images/a07d571bf7ba.png to ./data/train/0/a07d571bf7ba.png... done.\n",
      "Copying image ./data/train_images/f06e7a9df795.png to ./data/train/0/f06e7a9df795.png... done.\n",
      "Copying image ./data/train_images/a8dea22ef903.png to ./data/train/0/a8dea22ef903.png... done.\n",
      "Copying image ./data/train_images/4689b739d240.png to ./data/train/4/4689b739d240.png... done.\n",
      "Copying image ./data/train_images/b21abe5d9722.png to ./data/train/0/b21abe5d9722.png... done.\n",
      "Copying image ./data/train_images/a14fcf84bfe1.png to ./data/train/3/a14fcf84bfe1.png... done.\n",
      "Copying image ./data/train_images/8114d6a160df.png to ./data/train/1/8114d6a160df.png... done.\n",
      "Copying image ./data/train_images/0f6e645466a2.png to ./data/train/0/0f6e645466a2.png... done.\n",
      "Copying image ./data/train_images/c3b15bf9b4bc.png to ./data/train/0/c3b15bf9b4bc.png... done.\n",
      "Copying image ./data/train_images/0232dfea7547.png to ./data/train/0/0232dfea7547.png... done.\n",
      "Copying image ./data/train_images/13ab8db8c700.png to ./data/train/1/13ab8db8c700.png... done.\n",
      "Copying image ./data/train_images/b574d229ec4c.png to ./data/train/2/b574d229ec4c.png... done.\n",
      "Copying image ./data/train_images/54bdcdecd8f3.png to ./data/train/0/54bdcdecd8f3.png... done.\n",
      "Copying image ./data/train_images/87b671c6d4c5.png to ./data/train/0/87b671c6d4c5.png... done.\n",
      "Copying image ./data/train_images/de730033c683.png to ./data/train/0/de730033c683.png... done.\n",
      "Copying image ./data/train_images/0ceb222f6629.png to ./data/train/4/0ceb222f6629.png... done.\n",
      "Copying image ./data/train_images/7b87b0015282.png to ./data/train/0/7b87b0015282.png... done.\n",
      "Copying image ./data/train_images/bda7ff3b1562.png to ./data/train/2/bda7ff3b1562.png... done.\n",
      "Copying image ./data/train_images/f233638e0e90.png to ./data/train/0/f233638e0e90.png... done.\n",
      "Copying image ./data/train_images/5293576816aa.png to ./data/train/0/5293576816aa.png... done.\n",
      "Copying image ./data/train_images/3dec415b188a.png to ./data/train/0/3dec415b188a.png... done.\n",
      "Copying image ./data/train_images/75238d945315.png to ./data/train/0/75238d945315.png... done.\n",
      "Copying image ./data/train_images/9c088d2d1559.png to ./data/train/2/9c088d2d1559.png... done.\n",
      "Copying image ./data/train_images/1f3f32efaf20.png to ./data/train/0/1f3f32efaf20.png... done.\n",
      "Copying image ./data/train_images/a76b69e443ce.png to ./data/train/4/a76b69e443ce.png... done.\n",
      "Copying image ./data/train_images/3a1d3ce00f0c.png to ./data/train/2/3a1d3ce00f0c.png... done.\n",
      "Copying image ./data/train_images/f2f569a64949.png to ./data/train/0/f2f569a64949.png... done.\n",
      "Copying image ./data/train_images/8693ab1fd2be.png to ./data/train/0/8693ab1fd2be.png... done.\n",
      "Copying image ./data/train_images/49386d603494.png to ./data/train/1/49386d603494.png... done.\n",
      "Copying image ./data/train_images/7f84284598f5.png to ./data/train/2/7f84284598f5.png... done.\n",
      "Copying image ./data/train_images/93d6d20a5ee3.png to ./data/train/0/93d6d20a5ee3.png... done.\n",
      "Copying image ./data/train_images/50915e2329a1.png to ./data/train/0/50915e2329a1.png... done.\n",
      "Copying image ./data/train_images/232549883508.png to ./data/train/2/232549883508.png... done.\n",
      "Copying image ./data/train_images/80c67efc8101.png to ./data/train/0/80c67efc8101.png... done.\n",
      "Copying image ./data/train_images/af3b0115aad1.png to ./data/train/3/af3b0115aad1.png... done.\n",
      "Copying image ./data/train_images/4818672273af.png to ./data/train/2/4818672273af.png... done.\n",
      "Copying image ./data/train_images/d9e58e4d8689.png to ./data/train/0/d9e58e4d8689.png... done.\n",
      "Copying image ./data/train_images/dc3c0d8ee20b.png to ./data/train/2/dc3c0d8ee20b.png... done.\n",
      "Copying image ./data/train_images/8329e80c10ac.png to ./data/train/0/8329e80c10ac.png... done.\n",
      "Copying image ./data/train_images/8bdb891661a8.png to ./data/train/2/8bdb891661a8.png... done.\n",
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      "Copying image ./data/train_images/5b1c4cefeb24.png to ./data/train/0/5b1c4cefeb24.png... done.\n",
      "Copying image ./data/train_images/18b7e34eab8f.png to ./data/train/0/18b7e34eab8f.png... done.\n",
      "Copying image ./data/train_images/54bbe3da103e.png to ./data/train/2/54bbe3da103e.png... done.\n",
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      "Copying image ./data/train_images/1f15ca672675.png to ./data/train/0/1f15ca672675.png... done.\n",
      "Copying image ./data/train_images/7a12f49e29df.png to ./data/train/0/7a12f49e29df.png... done.\n",
      "Copying image ./data/train_images/b2b79b37d314.png to ./data/train/0/b2b79b37d314.png... done.\n",
      "Copying image ./data/train_images/3132556f5352.png to ./data/train/0/3132556f5352.png... done.\n",
      "Copying image ./data/train_images/36a1e3c780a0.png to ./data/train/2/36a1e3c780a0.png... done.\n",
      "Copying image ./data/train_images/88e5051f65bd.png to ./data/train/0/88e5051f65bd.png... done.\n",
      "Copying image ./data/train_images/80a02014b418.png to ./data/train/1/80a02014b418.png... done.\n",
      "Copying image ./data/train_images/d56d32a1d62d.png to ./data/train/0/d56d32a1d62d.png... done.\n",
      "Copying image ./data/train_images/cec299c2a2d5.png to ./data/train/0/cec299c2a2d5.png... done.\n",
      "Copying image ./data/train_images/8bc6716c2238.png to ./data/train/1/8bc6716c2238.png... done.\n",
      "Copying image ./data/train_images/c62cef02efa2.png to ./data/train/0/c62cef02efa2.png... done.\n",
      "Copying image ./data/train_images/5a179c123fd8.png to ./data/train/0/5a179c123fd8.png... done.\n",
      "Copying image ./data/train_images/012a242ac6ff.png to ./data/train/2/012a242ac6ff.png... done.\n",
      "Copying image ./data/train_images/b6304c545f95.png to ./data/train/2/b6304c545f95.png... done.\n",
      "Copying image ./data/train_images/d18aea8238a0.png to ./data/train/2/d18aea8238a0.png... done.\n",
      "Copying image ./data/train_images/2d07162a13b1.png to ./data/train/0/2d07162a13b1.png... done.\n",
      "Copying image ./data/train_images/9b32e8ef0ca0.png to ./data/train/2/9b32e8ef0ca0.png... done.\n",
      "Copying image ./data/train_images/d271d3a2b552.png to ./data/train/4/d271d3a2b552.png... done.\n",
      "Copying image ./data/train_images/8676427e4625.png to ./data/train/1/8676427e4625.png... done.\n",
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     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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    },
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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      "Copying image ./data/train_images/5077cdb88aed.png to ./data/test/test_subfolder/5077cdb88aed.png... done.\n",
      "Copying image ./data/train_images/19545647508e.png to ./data/test/test_subfolder/19545647508e.png... done.\n",
      "Copying image ./data/train_images/05a5183c92d0.png to ./data/test/test_subfolder/05a5183c92d0.png... done.\n",
      "Copying image ./data/train_images/4409965eb2a4.png to ./data/test/test_subfolder/4409965eb2a4.png... done.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Copying image ./data/train_images/10a5026eb8e6.png to ./data/test/test_subfolder/10a5026eb8e6.png... done.\n",
      "Copying image ./data/train_images/910bfd38e2f5.png to ./data/test/test_subfolder/910bfd38e2f5.png... done.\n",
      "Copying image ./data/train_images/4731e708ede3.png to ./data/test/test_subfolder/4731e708ede3.png... done.\n",
      "Copying image ./data/train_images/2c2aa057afc5.png to ./data/test/test_subfolder/2c2aa057afc5.png... done.\n",
      "Copying image ./data/train_images/a6d45de20e4d.png to ./data/test/test_subfolder/a6d45de20e4d.png... done.\n",
      "Copying image ./data/train_images/6d3d1fe6c32a.png to ./data/test/test_subfolder/6d3d1fe6c32a.png... done.\n",
      "Copying image ./data/train_images/cfb17a7cc8d4.png to ./data/test/test_subfolder/cfb17a7cc8d4.png... done.\n",
      "Copying image ./data/train_images/af87d48ffe01.png to ./data/test/test_subfolder/af87d48ffe01.png... done.\n",
      "Copying image ./data/train_images/fca931da5c5e.png to ./data/test/test_subfolder/fca931da5c5e.png... done.\n",
      "Copying image ./data/train_images/2927665214e1.png to ./data/test/test_subfolder/2927665214e1.png... done.\n",
      "Copying image ./data/train_images/ae58ccb5905e.png to ./data/test/test_subfolder/ae58ccb5905e.png... done.\n",
      "Copying image ./data/train_images/762d6e5d5068.png to ./data/test/test_subfolder/762d6e5d5068.png... done.\n",
      "Copying image ./data/train_images/10de500cf0c5.png to ./data/test/test_subfolder/10de500cf0c5.png... done.\n",
      "Copying image ./data/train_images/3810040096cb.png to ./data/test/test_subfolder/3810040096cb.png... done.\n",
      "Copying image ./data/train_images/8f10e41a2f02.png to ./data/test/test_subfolder/8f10e41a2f02.png... done.\n",
      "Copying image ./data/train_images/45369821d6a3.png to ./data/test/test_subfolder/45369821d6a3.png... done.\n",
      "Copying image ./data/train_images/47e51065b819.png to ./data/test/test_subfolder/47e51065b819.png... done.\n",
      "Copying image ./data/train_images/7e70344b0c25.png to ./data/test/test_subfolder/7e70344b0c25.png... done.\n",
      "Copying image ./data/train_images/b4f41b5bf0ef.png to ./data/test/test_subfolder/b4f41b5bf0ef.png... done.\n",
      "Copying image ./data/train_images/4dd9d29eae5d.png to ./data/test/test_subfolder/4dd9d29eae5d.png... done.\n",
      "Copying image ./data/train_images/97bf61736b86.png to ./data/test/test_subfolder/97bf61736b86.png... done.\n",
      "Copying image ./data/train_images/913490237ad4.png to ./data/test/test_subfolder/913490237ad4.png... done.\n",
      "Copying image ./data/train_images/f69400b316a7.png to ./data/test/test_subfolder/f69400b316a7.png... done.\n",
      "Copying image ./data/train_images/0f96c358a250.png to ./data/test/test_subfolder/0f96c358a250.png... done.\n",
      "Copying image ./data/train_images/5188a8afa879.png to ./data/test/test_subfolder/5188a8afa879.png... done.\n",
      "Copying image ./data/train_images/bf811911acf9.png to ./data/test/test_subfolder/bf811911acf9.png... done.\n",
      "Copying image ./data/train_images/61a62b1dcc36.png to ./data/test/test_subfolder/61a62b1dcc36.png... done.\n",
      "Copying image ./data/train_images/5eb8fb1aad41.png to ./data/test/test_subfolder/5eb8fb1aad41.png... done.\n",
      "Copying image ./data/train_images/84b79243e430.png to ./data/test/test_subfolder/84b79243e430.png... done.\n",
      "Copying image ./data/train_images/613bacb35c05.png to ./data/test/test_subfolder/613bacb35c05.png... done.\n",
      "Copying image ./data/train_images/005b95c28852.png to ./data/test/test_subfolder/005b95c28852.png... done.\n",
      "Copying image ./data/train_images/a0d04a19cf40.png to ./data/test/test_subfolder/a0d04a19cf40.png... done.\n",
      "Copying image ./data/train_images/750e0168399d.png to ./data/test/test_subfolder/750e0168399d.png... done.\n",
      "Copying image ./data/train_images/ad1f7445b1a8.png to ./data/test/test_subfolder/ad1f7445b1a8.png... done.\n",
      "Copying image ./data/train_images/46acc506fa61.png to ./data/test/test_subfolder/46acc506fa61.png... done.\n",
      "Copying image ./data/train_images/a2b97d98f130.png to ./data/test/test_subfolder/a2b97d98f130.png... done.\n"
     ]
    }
   ],
   "source": [
    "# Copy the source training images to their proper subfolders for training, validation, and testing\n",
    "copy_images_to_folders( X_train, y_train, 'train' )\n",
    "copy_images_to_folders( X_valid, y_valid, 'valid')\n",
    "copy_images_to_folders( X_test, y_test, 'test' )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define the Convolutional Neural Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the classification model for the Convolutional Neural Network\n",
    "# REFERENCE: PyImageSearch (Adrian Rosebrock)\n",
    "#            https://www.pyimagesearch.com/2018/04/16/keras-and-convolutional-neural-networks-cnns/\n",
    "class SmallerVGGNet:\n",
    "    @staticmethod\n",
    "    def build(width, height, depth, classes):\n",
    "        # initialize the model along with the input shape to be\n",
    "        # \"channels last\" and the channels dimension itself\n",
    "        model = Sequential()\n",
    "        inputShape = (height, width, depth)\n",
    "        chanDim = -1\n",
    "\n",
    "        # if we are using \"channels first\", update the input shape\n",
    "        # and channels dimension\n",
    "        if K.image_data_format() == \"channels_first\":\n",
    "            inputShape = (depth, height, width)\n",
    "            chanDim = 1\n",
    "            \n",
    "        # CONV => RELU => POOL\n",
    "        model.add(Conv2D(32, (3, 3), padding=\"same\", input_shape=inputShape ) )\n",
    "        \n",
    "        model.add(Activation(\"relu\"))\n",
    "        model.add(BatchNormalization(axis=chanDim))\n",
    "        \n",
    "        model.add(MaxPooling2D(pool_size=(3, 3)))\n",
    "        model.add(Dropout(0.25))\n",
    "        \n",
    "        # (CONV => RELU) * 2 => POOL\n",
    "        model.add(Conv2D(64, (3, 3), padding=\"same\"))\n",
    "        model.add(Activation(\"relu\"))\n",
    "        model.add(BatchNormalization(axis=chanDim))\n",
    "        model.add(Conv2D(64, (3, 3), padding=\"same\"))\n",
    "        model.add(Activation(\"relu\"))\n",
    "        model.add(BatchNormalization(axis=chanDim))\n",
    "        model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "        model.add(Dropout(0.25))\n",
    "        \n",
    "        # (CONV => RELU) * 2 => POOL\n",
    "        model.add(Conv2D(128, (3, 3), padding=\"same\"))\n",
    "        model.add(Activation(\"relu\"))\n",
    "        model.add(BatchNormalization(axis=chanDim))\n",
    "        model.add(Conv2D(128, (3, 3), padding=\"same\"))\n",
    "        model.add(Activation(\"relu\"))\n",
    "        model.add(BatchNormalization(axis=chanDim))\n",
    "        model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "        model.add(Dropout(0.25))\n",
    "        \n",
    "        # first (and only) set of FC => RELU layers\n",
    "        model.add(Flatten())\n",
    "        model.add(Dense(1024))\n",
    "        model.add(Activation(\"relu\"))\n",
    "        model.add(BatchNormalization())\n",
    "        model.add(Dropout(0.5))\n",
    "\n",
    "        # softmax classifier\n",
    "        model.add(Dense(classes))\n",
    "        model.add(Activation(\"softmax\"))\n",
    "\n",
    "        # return the constructed network architecture\n",
    "        return model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create Image Generators linked to the Image Training, Validation, and Testing subfolders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Covert the images from -\n",
    "# Source Images: Width 3216 x Height 2136 x Depth 3 (RGB)\n",
    "# Analysis Images: Width 96 x Height 96 x Depth 3 (RGB)     -- Just to get this to run initially\n",
    "\n",
    "# image_dims = (2136, 3216, 3)   # Height, Width, Depth (RGB = 3, Grayscale = 1)\n",
    "image_dims = (96, 96, 3)         # Height, Width, Depth (RGB = 3, Grayscale = 1)\n",
    "n_classes = all_train_labels_df['diagnosis'].nunique()  # Number of classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 2197 images belonging to 5 classes.\n"
     ]
    }
   ],
   "source": [
    "# Create a Image Data Generator for the training data\n",
    "train_datagen = ImageDataGenerator(\n",
    "        rotation_range=20,\n",
    "        zoom_range=0.15,\n",
    "        width_shift_range=0.2,\n",
    "        height_shift_range=0.2,\n",
    "        shear_range=0.15,\n",
    "        horizontal_flip=True,\n",
    "        fill_mode=\"nearest\")\n",
    "\n",
    "# Define generator batch size and step size\n",
    "train_n_images = len(X_train)\n",
    "train_n_batch_size = 32\n",
    "train_n_step_size = train_n_images // train_n_batch_size\n",
    "\n",
    "# Associate the generator with the training images\n",
    "train_generator = train_datagen.flow_from_directory(\n",
    "    directory=dir_train,            # Folder containing training images\n",
    "    target_size=(image_dims[1], image_dims[0]),       # (Width, Height)\n",
    "    color_mode=\"rgb\",               # Color images\n",
    "    batch_size=train_n_batch_size,  # Batch size of 4 (for prototyping), 32 later\n",
    "    class_mode=\"categorical\",       # Number of classes > 2, so using \"categorical\" vs \"binary\"\n",
    "    shuffle=True,                   # Shuffle the training samples randomly\n",
    "    seed=1                          # Random number seed to use for shuffle\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 732 images belonging to 5 classes.\n"
     ]
    }
   ],
   "source": [
    "# Create a Image Data Generator for the validation data\n",
    "valid_datagen = ImageDataGenerator()\n",
    "\n",
    "# Define generator batch size and step size\n",
    "valid_n_images = len(X_valid)\n",
    "valid_n_batch_size = train_n_batch_size\n",
    "valid_n_step_size = valid_n_images // valid_n_batch_size\n",
    "\n",
    "# Associate the generator with the training images\n",
    "valid_generator = valid_datagen.flow_from_directory(\n",
    "    directory=dir_valid,            # Folder containing training images\n",
    "    target_size=(image_dims[1], image_dims[0]),       # (Width, Height)\n",
    "    color_mode=\"rgb\",               # Color images\n",
    "    batch_size=valid_n_batch_size,  # Batch size of 4 (for prototyping), 32 later\n",
    "    class_mode=\"categorical\",       # Number of classes > 2, so using \"categorical\" vs \"binary\"\n",
    "    shuffle=True,                   # Shuffle the training samples randomly\n",
    "    seed=1                          # Random number seed to use for shuffle\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 733 images belonging to 1 classes.\n"
     ]
    }
   ],
   "source": [
    "# Create a Image Data Generator for the testing data\n",
    "test_datagen = ImageDataGenerator()\n",
    "\n",
    "# Define generator batch size and step size\n",
    "test_n_images = len(X_test)\n",
    "test_n_batch_size = 1\n",
    "test_n_step_size = test_n_images // test_n_batch_size\n",
    "\n",
    "# Associate the generator with the training images\n",
    "test_generator = test_datagen.flow_from_directory(\n",
    "    directory=dir_test,             # Folder containing training images\n",
    "    target_size=(image_dims[1], image_dims[0]),       # (Width, Height)\n",
    "    color_mode=\"rgb\",               # Color images\n",
    "    batch_size=test_n_batch_size,   # Batch size of 4 (for prototyping), 32 later\n",
    "    class_mode=None,                # Number of classes > 2, so using \"categorical\" vs \"binary\"\n",
    "    shuffle=False,                   # Shuffle the training samples randomly\n",
    "    seed=1                          # Random number seed to use for shuffle\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set parameters needed to fit the CNN model\n",
    "# initialize the number of epochs to train for, initial learning rate,\n",
    "# batch size, and image dimensions\n",
    "n_epochs = 10\n",
    "init_learning_rate = 1e-3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: Logging before flag parsing goes to stderr.\n",
      "W0908 12:32:39.896201 13916 deprecation_wrapper.py:119] From C:\\Users\\dadja\\Anaconda3\\envs\\PythonTF\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:74: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
      "\n",
      "W0908 12:32:39.973283 13916 deprecation_wrapper.py:119] From C:\\Users\\dadja\\Anaconda3\\envs\\PythonTF\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:517: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
      "\n",
      "W0908 12:32:39.988937 13916 deprecation_wrapper.py:119] From C:\\Users\\dadja\\Anaconda3\\envs\\PythonTF\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:4138: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n",
      "\n",
      "W0908 12:32:40.074891 13916 deprecation_wrapper.py:119] From C:\\Users\\dadja\\Anaconda3\\envs\\PythonTF\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:174: The name tf.get_default_session is deprecated. Please use tf.compat.v1.get_default_session instead.\n",
      "\n",
      "W0908 12:32:40.076887 13916 deprecation_wrapper.py:119] From C:\\Users\\dadja\\Anaconda3\\envs\\PythonTF\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:181: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.\n",
      "\n",
      "W0908 12:32:40.227512 13916 deprecation_wrapper.py:119] From C:\\Users\\dadja\\Anaconda3\\envs\\PythonTF\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:1834: The name tf.nn.fused_batch_norm is deprecated. Please use tf.compat.v1.nn.fused_batch_norm instead.\n",
      "\n",
      "W0908 12:32:40.388087 13916 deprecation_wrapper.py:119] From C:\\Users\\dadja\\Anaconda3\\envs\\PythonTF\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:3976: The name tf.nn.max_pool is deprecated. Please use tf.nn.max_pool2d instead.\n",
      "\n",
      "W0908 12:32:40.410026 13916 deprecation.py:506] From C:\\Users\\dadja\\Anaconda3\\envs\\PythonTF\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n",
      "W0908 12:32:41.547749 13916 deprecation_wrapper.py:119] From C:\\Users\\dadja\\Anaconda3\\envs\\PythonTF\\lib\\site-packages\\keras\\optimizers.py:790: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Initialize the model\n",
    "model = SmallerVGGNet.build(\n",
    "    width=image_dims[1],\n",
    "    height=image_dims[0],\n",
    "    depth=image_dims[2],\n",
    "    classes=n_classes)\n",
    "\n",
    "opt = Adam( lr=init_learning_rate, decay=init_learning_rate / n_epochs )\n",
    "\n",
    "model.compile(loss=\"categorical_crossentropy\", optimizer=opt, metrics=[\"accuracy\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "W0908 12:32:41.887840 13916 deprecation.py:323] From C:\\Users\\dadja\\Anaconda3\\envs\\PythonTF\\lib\\site-packages\\tensorflow\\python\\ops\\math_grad.py:1250: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.where in 2.0, which has the same broadcast rule as np.where\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      " - 446s - loss: 1.8239 - acc: 0.4761 - val_loss: 2.4057 - val_acc: 0.6207\n",
      "Epoch 2/10\n",
      " - 350s - loss: 1.2884 - acc: 0.5849 - val_loss: 1.2245 - val_acc: 0.6114\n",
      "Epoch 3/10\n",
      " - 342s - loss: 1.1539 - acc: 0.6040 - val_loss: 1.3770 - val_acc: 0.5743\n",
      "Epoch 4/10\n",
      " - 338s - loss: 1.0422 - acc: 0.6465 - val_loss: 0.9148 - val_acc: 0.7057\n",
      "Epoch 5/10\n",
      " - 338s - loss: 1.0014 - acc: 0.6617 - val_loss: 1.0289 - val_acc: 0.7000\n",
      "Epoch 6/10\n",
      " - 322s - loss: 0.9756 - acc: 0.6640 - val_loss: 1.0601 - val_acc: 0.6686\n",
      "Epoch 7/10\n",
      " - 324s - loss: 0.9230 - acc: 0.6736 - val_loss: 1.0470 - val_acc: 0.6257\n",
      "Epoch 8/10\n",
      " - 330s - loss: 0.8938 - acc: 0.6762 - val_loss: 0.8801 - val_acc: 0.7114\n",
      "Epoch 9/10\n",
      " - 333s - loss: 0.8500 - acc: 0.6966 - val_loss: 0.9327 - val_acc: 0.7014\n",
      "Epoch 10/10\n",
      " - 318s - loss: 0.8764 - acc: 0.6952 - val_loss: 0.8292 - val_acc: 0.7014\n"
     ]
    }
   ],
   "source": [
    "# Fit/Train the model (using the train_generator)\n",
    "hist = model.fit_generator(\n",
    "    generator=train_generator,\n",
    "    steps_per_epoch=train_n_step_size,\n",
    "    validation_data=valid_generator,\n",
    "    validation_steps=valid_n_step_size,\n",
    "    epochs=n_epochs,\n",
    "    verbose=2\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Evaluate the model (using the valid_generator)\n",
    "loss_info = model.evaluate_generator(\n",
    "    generator=valid_generator,\n",
    "    steps=valid_n_step_size\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss: 0.9485\n",
      "Acc: 0.6786\n"
     ]
    }
   ],
   "source": [
    "for m,v in zip(model.metrics_names, loss_info):\n",
    "    print(f\"{m.title()}: {v:0.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'val_loss': [2.405654316598719,\n",
       "  1.2244887127195085,\n",
       "  1.3770318378720965,\n",
       "  0.9148429410798209,\n",
       "  1.0288523857934135,\n",
       "  1.060097190652575,\n",
       "  1.0470026527132308,\n",
       "  0.8800856426783971,\n",
       "  0.9327349581037249,\n",
       "  0.8291861217362541],\n",
       " 'val_acc': [0.6207386363636364,\n",
       "  0.6114285710879734,\n",
       "  0.5742857136045183,\n",
       "  0.7057142863954816,\n",
       "  0.7000000006811959,\n",
       "  0.6685714278902326,\n",
       "  0.6257142850330898,\n",
       "  0.7114285724503654,\n",
       "  0.7014285721097674,\n",
       "  0.7014285710879734],\n",
       " 'loss': [1.823877776370329,\n",
       "  1.2873365984777931,\n",
       "  1.1554392526111206,\n",
       "  1.0413785219743126,\n",
       "  1.0019114998967213,\n",
       "  0.9755777406637443,\n",
       "  0.9223082056221731,\n",
       "  0.8935215487766486,\n",
       "  0.8499207937414597,\n",
       "  0.8761800179305308],\n",
       " 'acc': [0.47610294117647056,\n",
       "  0.5847575059939202,\n",
       "  0.6036951503081905,\n",
       "  0.6471131642475965,\n",
       "  0.661431870834932,\n",
       "  0.6642032334765992,\n",
       "  0.6743648963492131,\n",
       "  0.6762124713243568,\n",
       "  0.6969976907514259,\n",
       "  0.6951501156386272]}"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hist.history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Model Fitting History')"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot Epoch history\n",
    "# fig1 = plt.figure(figsize=(10,10))\n",
    "fig1 = plt.figure(figsize=(10,10))\n",
    "\n",
    "# Create a single plot of all results\n",
    "ax1 = fig1.add_subplot( 2,1,1 )\n",
    "ax2 = fig1.add_subplot( 2,1,2 )\n",
    "\n",
    "# X-axis\n",
    "x_vals = range(len(hist.history['acc']))\n",
    "\n",
    "# Plots\n",
    "ax1.plot( x_vals, hist.history['acc'],\n",
    "          label='Accuracy (Training)', c='b', marker='o', linestyle=':')\n",
    "\n",
    "ax2.plot( x_vals, hist.history['loss'],\n",
    "          label='Loss (Training)', c='b', marker='s', linestyle='-')\n",
    "\n",
    "ax1.plot( x_vals, hist.history['val_acc'],\n",
    "          label='Accuracy (Validation)', c='r', marker='o', linestyle=':')\n",
    "\n",
    "ax2.plot( x_vals, hist.history['val_loss'],\n",
    "          label='Loss (Validation)', c='r', marker='s', linestyle='-')\n",
    "\n",
    "ax1.legend()\n",
    "ax1.set_xlabel(\"Epoch\")\n",
    "ax1.set_title(\"Model Fitting History\")\n",
    "\n",
    "ax2.legend()\n",
    "ax2.set_xlabel(\"Epoch\")\n",
    "ax2.set_title(\"Model Fitting History\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "733/733 [==============================] - 102s 139ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([0, 0, 2, 0, 2, 1, 2, 0, 2, 1, 2, 2, 2, 0, 0, 0, 2, 2, 2, 0, 2, 2,\n",
       "       2, 0, 2, 2, 0, 2, 0, 2, 2, 2, 0, 2, 0, 2, 2, 2, 2, 0, 0, 2, 0, 0,\n",
       "       2, 1, 0, 2, 0, 2, 0, 2, 2, 2, 2, 2, 0, 2, 0, 2, 2, 0, 2, 0, 2, 0,\n",
       "       2, 2, 0, 0, 1, 0, 0, 0, 0, 0, 0, 2, 0, 2, 2, 0, 2, 2, 2, 0, 0, 2,\n",
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       "       0, 0, 2, 0, 2, 2, 0, 0, 2, 1, 0, 0, 2, 0, 2, 2, 2, 0, 0, 0, 2, 2,\n",
       "       0, 0, 1, 2, 0, 0, 2, 2, 0, 0, 0, 0, 0, 2, 2, 1, 0, 0, 0, 0, 2, 2,\n",
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       "       0, 0, 0, 2, 2, 2, 0, 0, 2, 0, 2, 0, 1, 2, 2, 0, 0, 0, 2, 2, 2, 0,\n",
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       "       2, 2, 0, 2, 2, 0, 0, 0, 2, 2, 2, 2, 2, 0, 0, 1, 0, 2, 2, 2, 0, 0,\n",
       "       2, 0, 0, 0, 2, 2, 2, 0, 2, 0, 0, 1, 0, 0, 2, 2, 0, 2, 0, 0, 2, 2,\n",
       "       0, 2, 2, 0, 0, 0, 2, 0, 2, 2, 1, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0,\n",
       "       0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 2, 2, 0, 0, 2, 0, 0, 0,\n",
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       "       0, 2, 0, 0, 0, 0, 0, 1, 2, 0, 2, 0, 2, 0, 2, 2, 0, 2, 2, 0, 0, 0,\n",
       "       2, 2, 2, 2, 0, 0, 0, 2, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 0, 2, 0, 0,\n",
       "       2, 2, 2, 0, 2, 0, 2, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 0, 0, 0,\n",
       "       0, 0, 2, 0, 2, 0, 0, 0, 2, 0, 0, 2, 2, 0, 0, 0, 2, 2, 2, 2, 2, 0,\n",
       "       2, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 2, 0, 2, 2, 0, 0, 0, 0, 2, 2,\n",
       "       2, 2, 2, 2, 0, 2, 2, 0, 2, 2, 0, 2, 0, 2, 0, 1, 2, 0, 0, 2, 0, 0,\n",
       "       0, 2, 0, 0, 0, 0, 2, 0, 2, 0, 2, 2, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       2, 0, 2, 0, 2, 2, 0, 1, 2, 2, 1, 2, 2, 1, 2, 2, 0, 1, 0, 2, 2, 0,\n",
       "       0, 0, 1, 0, 0, 2, 0], dtype=int64)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Predict the output (using the test_generator)\n",
    "test_generator.reset()\n",
    "pred_onehot = model.predict_generator(test_generator,\n",
    "                             steps=test_n_step_size,\n",
    "                             verbose=1)\n",
    "\n",
    "predicted_class = np.argmax(pred_onehot, axis=1)\n",
    "predicted_class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "733"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_n_step_size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save the prediction results to a file\n",
    "\n",
    "# Get the list of test image id codes used by the test generator to make the predictions\n",
    "test_id_code_list = [ (x.split(\"\\\\\"))[1].replace(\".png\",\"\") for x in test_generator.filenames ]\n",
    "\n",
    "# Associate the test image id codes and the preoductions in a dataframe\n",
    "results_df = pd.DataFrame({\"id_code\": test_id_code_list, \"diagnosis\": predicted_class})\n",
    "results_df.to_csv(\"./data/prediction_results.csv\",index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save the model to a file\n",
    "model.save(\"./saved_model.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predict the Class for Official Test Images"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load the Saved Convolutional Neural Network Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load a saved model\n",
    "model = load_model(\"./saved_model.h5\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Configure the Image Generator for the Official Test Images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1928"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Count how many test images are populated\n",
    "# test_tally_file = \"../input/aptos2019-blindness-detection/test.csv\"\n",
    "test_tally_file = \"./data/test.csv\"\n",
    "\n",
    "# Load test counts into a dataframe\n",
    "test_tally_df = pd.read_csv(test_tally_file)\n",
    "len(test_tally_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 1928 images belonging to 1 classes.\n"
     ]
    }
   ],
   "source": [
    "# data/test_images - Source of official testing images in a single folder (used only to submit official results)\n",
    "# dir_src_test = \"./data/official_test/\"\n",
    "dir_src_test = \"./official_test/\"\n",
    "\n",
    "# Create a Image Data Generator for the official testing data\n",
    "official_test_datagen = ImageDataGenerator()\n",
    "\n",
    "# Define generator batch size and step size\n",
    "official_test_n_images = len(test_tally_df)\n",
    "official_test_n_batch_size = 1\n",
    "official_test_n_step_size = official_test_n_images // official_test_n_batch_size\n",
    "\n",
    "# Associate the generator with the training images\n",
    "official_test_generator = official_test_datagen.flow_from_directory(\n",
    "    directory=dir_src_test,             # Folder containing official training images\n",
    "    target_size=(image_dims[1], image_dims[0]),       # (Width, Height)\n",
    "    color_mode=\"rgb\",               # Color images\n",
    "    batch_size=official_test_n_batch_size,   # Batch size\n",
    "    class_mode=None,                # Number of classes > 2, so using \"categorical\" vs \"binary\"\n",
    "    shuffle=False,                   # Shuffle the training samples randomly\n",
    "    seed=1                          # Random number seed to use for shuffle\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predict the Classifications based upon the Images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1928/1928 [==============================] - 106s 55ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([2, 2, 2, ..., 2, 2, 0], dtype=int64)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Predict the output (using the test_generator)\n",
    "official_test_generator.reset()\n",
    "official_pred_onehot = model.predict_generator(official_test_generator,\n",
    "                             steps=len(official_test_generator),\n",
    "                             verbose=1)\n",
    "\n",
    "official_predicted_class = np.argmax(official_pred_onehot, axis=1)\n",
    "official_predicted_class"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Save the Predictions from the Official Test Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "           id_code  diagnosis\n",
       "0     0005cfc8afb6          2\n",
       "1     003f0afdcd15          2\n",
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     "metadata": {},
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   "source": [
    "# Save the prediction results to a file\n",
    "\n",
    "# Get the list of test image id codes used by the test generator to make the predictions\n",
    "official_test_id_code_list = [ (x.split(\"\\\\\"))[1].replace(\".png\",\"\") for x in official_test_generator.filenames ]\n",
    "\n",
    "# Associate the test image id codes and the preoductions in a dataframe\n",
    "official_results_df = pd.DataFrame({\"id_code\": official_test_id_code_list, \"diagnosis\": official_predicted_class})\n",
    "official_results_df.to_csv(\"./submission.csv\",index=False)\n",
    "official_results_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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