cnn-with-93-accuracy-on-hindi-mnist.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "97136881",
   "metadata": {
    "papermill": {
     "duration": 0.00894,
     "end_time": "2022-08-22T14:21:23.250582",
     "exception": false,
     "start_time": "2022-08-22T14:21:23.241642",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Importing the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "aef6b326",
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
    "execution": {
     "iopub.execute_input": "2022-08-22T14:21:23.268046Z",
     "iopub.status.busy": "2022-08-22T14:21:23.267311Z",
     "iopub.status.idle": "2022-08-22T14:21:23.278451Z",
     "shell.execute_reply": "2022-08-22T14:21:23.277631Z"
    },
    "papermill": {
     "duration": 0.022169,
     "end_time": "2022-08-22T14:21:23.280502",
     "exception": false,
     "start_time": "2022-08-22T14:21:23.258333",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "    # This Python 3 environment comes with many helpful analytics libraries installed\n",
    "# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n",
    "# For example, here's several helpful packages to load\n",
    "\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "\n",
    "# Input data files are available in the read-only \"../input/\" directory\n",
    "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
    "\n",
    "import os\n",
    "# for dirname, _, filenames in os.walk('/kaggle/input'):\n",
    "#     for filename in filenames:\n",
    "#         print(os.path.join(dirname, filename))\n",
    "\n",
    "# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n",
    "# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "cb18e0f6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-22T14:21:23.296992Z",
     "iopub.status.busy": "2022-08-22T14:21:23.296210Z",
     "iopub.status.idle": "2022-08-22T14:21:24.214312Z",
     "shell.execute_reply": "2022-08-22T14:21:24.213327Z"
    },
    "papermill": {
     "duration": 0.92862,
     "end_time": "2022-08-22T14:21:24.216780",
     "exception": false,
     "start_time": "2022-08-22T14:21:23.288160",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# importing some essential libraries\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.image import imread"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "24028c1d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-22T14:21:24.233017Z",
     "iopub.status.busy": "2022-08-22T14:21:24.232711Z",
     "iopub.status.idle": "2022-08-22T14:21:24.250938Z",
     "shell.execute_reply": "2022-08-22T14:21:24.250094Z"
    },
    "papermill": {
     "duration": 0.028581,
     "end_time": "2022-08-22T14:21:24.253047",
     "exception": false,
     "start_time": "2022-08-22T14:21:24.224466",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['test', 'train']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_data_dir = 'Hindi-MNIST'\n",
    "os.listdir(my_data_dir) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8a03008b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-22T14:21:24.269376Z",
     "iopub.status.busy": "2022-08-22T14:21:24.269080Z",
     "iopub.status.idle": "2022-08-22T14:21:24.272791Z",
     "shell.execute_reply": "2022-08-22T14:21:24.271909Z"
    },
    "papermill": {
     "duration": 0.014225,
     "end_time": "2022-08-22T14:21:24.274844",
     "exception": false,
     "start_time": "2022-08-22T14:21:24.260619",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "test_path = my_data_dir+'/test/'\n",
    "train_path = my_data_dir+'/train/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "81d14476",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-22T14:21:24.290998Z",
     "iopub.status.busy": "2022-08-22T14:21:24.290727Z",
     "iopub.status.idle": "2022-08-22T14:21:24.301798Z",
     "shell.execute_reply": "2022-08-22T14:21:24.300870Z"
    },
    "papermill": {
     "duration": 0.021621,
     "end_time": "2022-08-22T14:21:24.303742",
     "exception": false,
     "start_time": "2022-08-22T14:21:24.282121",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['9', '0', '7', '6', '1', '8', '4', '3', '2', '5']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.listdir(test_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f947fe1e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-22T14:21:24.320338Z",
     "iopub.status.busy": "2022-08-22T14:21:24.319728Z",
     "iopub.status.idle": "2022-08-22T14:21:24.335911Z",
     "shell.execute_reply": "2022-08-22T14:21:24.332067Z"
    },
    "papermill": {
     "duration": 0.027418,
     "end_time": "2022-08-22T14:21:24.338800",
     "exception": false,
     "start_time": "2022-08-22T14:21:24.311382",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['9', '0', '7', '6', '1', '8', '4', '3', '2', '5']"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.listdir(train_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "326c93f6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-22T14:21:24.369264Z",
     "iopub.status.busy": "2022-08-22T14:21:24.368872Z",
     "iopub.status.idle": "2022-08-22T14:21:25.415149Z",
     "shell.execute_reply": "2022-08-22T14:21:25.414008Z"
    },
    "papermill": {
     "duration": 1.06403,
     "end_time": "2022-08-22T14:21:25.417396",
     "exception": false,
     "start_time": "2022-08-22T14:21:24.353366",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fddcf14bc10>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sample_path= train_path+'/4/'+ os.listdir(train_path+'/4')[0]\n",
    "sample_img=imread(sample_path)\n",
    "plt.imshow(sample_img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "0f3cebc0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-22T14:21:25.435588Z",
     "iopub.status.busy": "2022-08-22T14:21:25.435271Z",
     "iopub.status.idle": "2022-08-22T14:21:25.441098Z",
     "shell.execute_reply": "2022-08-22T14:21:25.440164Z"
    },
    "papermill": {
     "duration": 0.016913,
     "end_time": "2022-08-22T14:21:25.443094",
     "exception": false,
     "start_time": "2022-08-22T14:21:25.426181",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(32, 32)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_img.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b90170c4",
   "metadata": {
    "papermill": {
     "duration": 0.007803,
     "end_time": "2022-08-22T14:21:25.458982",
     "exception": false,
     "start_time": "2022-08-22T14:21:25.451179",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### Let's check how many images there are and what their dimensions are"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6d0b8f38",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-22T14:21:25.476199Z",
     "iopub.status.busy": "2022-08-22T14:21:25.475925Z",
     "iopub.status.idle": "2022-08-22T14:21:32.122271Z",
     "shell.execute_reply": "2022-08-22T14:21:32.120179Z"
    },
    "papermill": {
     "duration": 6.658205,
     "end_time": "2022-08-22T14:21:32.125182",
     "exception": false,
     "start_time": "2022-08-22T14:21:25.466977",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total images in 0 : 1700\n",
      "Dimension of images in 0 : (32, 32)\n",
      "Total images in 1 : 1700\n",
      "Dimension of images in 1 : (32, 32)\n",
      "Total images in 2 : 1700\n",
      "Dimension of images in 2 : (32, 32)\n",
      "Total images in 3 : 1700\n",
      "Dimension of images in 3 : (32, 32)\n",
      "Total images in 4 : 1700\n",
      "Dimension of images in 4 : (32, 32)\n",
      "Total images in 5 : 1700\n",
      "Dimension of images in 5 : (32, 32)\n",
      "Total images in 6 : 1700\n",
      "Dimension of images in 6 : (32, 32)\n",
      "Total images in 7 : 1700\n",
      "Dimension of images in 7 : (32, 32)\n",
      "Total images in 8 : 1700\n",
      "Dimension of images in 8 : (32, 32)\n",
      "Total images in 9 : 1700\n",
      "Dimension of images in 9 : (32, 32)\n"
     ]
    }
   ],
   "source": [
    "# for train data\n",
    "for i in range (10):\n",
    "    print('Total images in '+str(i)+' : '+str(len(os.listdir(train_path+str(i)))))\n",
    "    print('Dimension of images in '+str(i)+\" : \" + str(imread(train_path+'/'+str(i)+'/'+ os.listdir(train_path+'/'+str(i)+'/')[0]).shape) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9413fdb5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-22T14:21:32.145344Z",
     "iopub.status.busy": "2022-08-22T14:21:32.144980Z",
     "iopub.status.idle": "2022-08-22T14:21:34.025395Z",
     "shell.execute_reply": "2022-08-22T14:21:34.023809Z"
    },
    "papermill": {
     "duration": 1.894173,
     "end_time": "2022-08-22T14:21:34.028558",
     "exception": false,
     "start_time": "2022-08-22T14:21:32.134385",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total images in 0 : 300\n",
      "Dimension of images in 0 : (32, 32)\n",
      "Total images in 1 : 300\n",
      "Dimension of images in 1 : (32, 32)\n",
      "Total images in 2 : 300\n",
      "Dimension of images in 2 : (32, 32)\n",
      "Total images in 3 : 300\n",
      "Dimension of images in 3 : (32, 32)\n",
      "Total images in 4 : 300\n",
      "Dimension of images in 4 : (32, 32)\n",
      "Total images in 5 : 300\n",
      "Dimension of images in 5 : (32, 32)\n",
      "Total images in 6 : 300\n",
      "Dimension of images in 6 : (32, 32)\n",
      "Total images in 7 : 300\n",
      "Dimension of images in 7 : (32, 32)\n",
      "Total images in 8 : 300\n",
      "Dimension of images in 8 : (32, 32)\n",
      "Total images in 9 : 300\n",
      "Dimension of images in 9 : (32, 32)\n"
     ]
    }
   ],
   "source": [
    "# for test data\n",
    "for i in range (10):\n",
    "    print('Total images in '+str(i)+' : '+str(len(os.listdir(test_path+str(i)))))\n",
    "    print('Dimension of images in '+str(i)+\" : \" + str(imread(test_path+'/'+str(i)+'/'+ os.listdir(test_path+'/'+str(i)+'/')[0]).shape) )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c804f9e6",
   "metadata": {
    "papermill": {
     "duration": 0.008737,
     "end_time": "2022-08-22T14:21:34.046732",
     "exception": false,
     "start_time": "2022-08-22T14:21:34.037995",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Preparing the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "08112c35",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-22T14:21:34.065584Z",
     "iopub.status.busy": "2022-08-22T14:21:34.065257Z",
     "iopub.status.idle": "2022-08-22T14:21:39.010983Z",
     "shell.execute_reply": "2022-08-22T14:21:39.009996Z"
    },
    "papermill": {
     "duration": 4.958146,
     "end_time": "2022-08-22T14:21:39.013573",
     "exception": false,
     "start_time": "2022-08-22T14:21:34.055427",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "8d102dda",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-22T14:21:39.033848Z",
     "iopub.status.busy": "2022-08-22T14:21:39.032693Z",
     "iopub.status.idle": "2022-08-22T14:21:39.038974Z",
     "shell.execute_reply": "2022-08-22T14:21:39.038076Z"
    },
    "papermill": {
     "duration": 0.018155,
     "end_time": "2022-08-22T14:21:39.040919",
     "exception": false,
     "start_time": "2022-08-22T14:21:39.022764",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "image_gen = ImageDataGenerator(rotation_range=20, # rotate the image 20 degrees\n",
    "                               width_shift_range=0.10, # Shift the pic width by a max of 5%\n",
    "                               height_shift_range=0.10, # Shift the pic height by a max of 5%\n",
    "                               rescale=1/255, # Rescale the image by normalzing it.\n",
    "                               shear_range=0.1, # Shear means cutting away part of the image (max 10%)\n",
    "                               zoom_range=0.1, # Zoom in by 10% max\n",
    "                               horizontal_flip=True, # Allo horizontal flipping\n",
    "                               fill_mode='nearest' # Fill in missing pixels with the nearest filled value\n",
    "                              )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b83d8aa6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-22T14:21:39.060033Z",
     "iopub.status.busy": "2022-08-22T14:21:39.059777Z",
     "iopub.status.idle": "2022-08-22T14:21:39.242997Z",
     "shell.execute_reply": "2022-08-22T14:21:39.241997Z"
    },
    "papermill": {
     "duration": 0.194995,
     "end_time": "2022-08-22T14:21:39.245243",
     "exception": false,
     "start_time": "2022-08-22T14:21:39.050248",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fddd63be3a0>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPsAAAD5CAYAAADhukOtAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAUvUlEQVR4nO3de3BUVZ4H8O8vTUOAkEDkFV6CDPLwBZoFHWYdFddi0C2lRimZql1q1jXujO6q5ZRjae2qUzWW7giW4+6wgyMObuEDx+c6jKvF6uKDBQIiRHEQECUSE56GEEzS3b/9oy9VgTm/m0533+5kzvdTlUrn/HJyD5f8crvvr885oqogoj9/JcUeABEVBpOdyBNMdiJPMNmJPMFkJ/IEk53IE31y6SwicwE8CiAG4Deq+mDY9/eVflqKgbkckohCfINjaNc2ccUk2zq7iMQA7ADwVwDqAWwEsFBVP7b6lEulzpI5WR2PiLq2XtegWQ85kz2Xp/EzAexU1d2q2g7gWQBX5/DziChCuST7aAB7O31dH7QRUQ+Uy2t211OFP3lNICI1AGoAoBQDcjgcEeUilyt7PYCxnb4eA2Dfqd+kqstUtVpVq+Pol8PhiCgXuST7RgCTRGSCiPQFcD2AV/MzLCLKt6yfxqtqQkRuAfDfSJfelqvqR3kbGRHlVU51dlVdDWB1nsZCRBHiO+iIPMFkJ/IEk53IE0x2Ik8w2Yk8kdPdeAqIc94BACBWUW7GUhPHmLGvJw8yY2ofDmX72p3t8f2tZh9pPGjGUocP2+NIJOyBUI/DKzuRJ5jsRJ5gshN5gslO5AkmO5EneDe+G0oGutfPO3b5WWafIz88asYePnuVGftuf/vueUnI3+j6xHFn+8cdQ80+j31hLxXW+LuZZqzqhZ1mLNnYZMaoOHhlJ/IEk53IE0x2Ik8w2Yk8wWQn8gSTncgTLL2domSAvdz1vprznO2P/eOvzD4Xl2Y7knhWvSbEy4z2b8w+V075vRn77KctZuzq/neasVG/POJs1w73RB2KHq/sRJ5gshN5gslO5AkmO5EnmOxEnmCyE3kip9KbiOwBcBRAEkBCVavzMajIhawZl5w+yYzd86OVzvZsy2u7Ouyy1uoWeybds3svMGMt37g3z1xXvcLsM6CkrxmzSnkA0D7LntEnfd2lQ5beiicfdfZLVfVAHn4OEUWIT+OJPJFrsiuAN0Rkk4jU5GNARBSNXJ/Gz1bVfSIyHMCbIvKJqq7t/A3BH4EaACiF/VZUIopWTld2Vd0XfG4C8BKAP1nDSFWXqWq1qlbH4b55RETRyzrZRWSgiAw68RjAFQDq8jUwIsqvXJ7GjwDwkqTLWH0APK2qr+dlVBEr6d/fjO1YYMcWlH3d7WNtb7cXjly42J41NnJdsxkbfNAuecXOdW8bdeB8u+Q1LqT0FibREfLrU8J7vz1N1smuqrsBuOd8ElGPwz+/RJ5gshN5gslO5AkmO5EnmOxEnvBywcmSIYPN2GUXbcvrseZvuMmMTXx+lxlLhOyVlozFzFjrnFHO9soS+786qSkzdsdX9l5vY562f6Yed+85R8XDKzuRJ5jsRJ5gshN5gslO5AkmO5EnvLwbnxo22Iz9deXb3f55h5P2ZJchLw80Y4mmkNW8VO1Qyo4N3tXmbL90y9+afQ7sd0+eAYDJ/2ZvG1X64QdmTBMJM9YjhKxDGNotpBIifex0kpDJV1LuXucv8fnezAeWAV7ZiTzBZCfyBJOdyBNMdiJPMNmJPMFkJ/KEl6W3MDGxJ4VYvgmZSBJrs8tkCOkXKpU0Q33eda/5OWyrvY3T0PYG+1At9hZVYeVBq7QlfdzbQgGAlNqrD5dUlJuxZEgptXWcu/R5dIz9q99uHwrfjLD/zwacbq8beNbwr8zYJUM2O9tfmjbMHkgWeGUn8gSTncgTTHYiTzDZiTzBZCfyBJOdyBNdlt5EZDmAqwA0qerZQVslgOcAjAewB8ACVT0c3TDzq+SwvX3S281TzdiVA9yzvKr62GWtg2fZs6TKXrXLUNphb9cUxuqXPGT/94SVw2JDh5qxxCT3encAcOA89469R6bapatRZ+43Y/NGfWTGvlu23YxNi7tn7Q2J9Zwdha01AF9C4UtvvwUw95S2uwCsUdVJANYEXxNRD9Zlsgf7rR86pflqACuCxysAXJPfYRFRvmX7mn2EqjYAQPB5eP6GRERRiPztsiJSA6AGAErRc14nEfkm2yt7o4hUAUDw2dzRQFWXqWq1qlbHYb/3mYiilW2yvwpgUfB4EYBX8jMcIopKJqW3ZwBcAmCoiNQDuBfAgwBWicgNAL4AcF2Ug8w3/dqenfSHPdPM2C9G2gssWibPsbd46njSvtWRqP/SjJX0s58hyZgqZ/uxqXYZp+Eiuzw4ZfZnZuyesU+asZn93DPi4mIfK3th1yz3S8c27TB71Cfci3YCwJY2u9z4QevpZmz1F/bvVUtdpbN9AtaZfbLRZbKr6kIjNCevIyGiSPEddESeYLITeYLJTuQJJjuRJ5jsRJ7wcsHJZLO9iGL5c/a+Z7sucPebGLdnvS2d8IIZ+978O81Y1Vp71cOd11eYsR/O+x9n+8KKTWafcX3sdzbGJOx60P1rRUvK3juuts0ex/+2TDFju1rtmXnbmtylstRb7nIXAJxWZ5feSr+0y7bSfMyMjTwasqhnq7s8G7KcZ1Z4ZSfyBJOdyBNMdiJPMNmJPMFkJ/IEk53IE6Jh+3XlWblU6izp2fNnYkNPM2M7HxvjbN9+sT37K6x0tea4PQPsk5DZVX9fsduM9RN78chCer3VPTPv9qduMPucvtoua8UO2DE9dtweSJu7jJZssctkYXvp9XTrdQ2a9ZBzoz1e2Yk8wWQn8gSTncgTTHYiTzDZiTzh5USYMMlDR8zY2N+Md7a/N8v+m3lxqX2sOf3tu75z+u+1O6L7d9xXHrWrDPdvvsqMpertySmlB503fQEAIza474KP37jNPtZRe1uuhBmhTPHKTuQJJjuRJ5jsRJ5gshN5gslO5AkmO5EnMtn+aTmAqwA0qerZQdt9AG4EsD/4trtVdXVUg8w7sUtGfcaNNmM7rnVPXDkn3hpysMLuXLu13b3G26/+2d6h61uvf2zGUq32v01TIZOoNOX+eQWceEUny+TK/lsAcx3tj6jq9OCj9yQ6kae6THZVXQvgUAHGQkQRyuU1+y0islVElovIkLyNiIgikW2yLwUwEcB0AA0AFlvfKCI1IlIrIrUdsNfjJqJoZZXsqtqoqklVTQF4HMDMkO9dpqrVqlodh72vOBFFK6tkF5GqTl/OB1CXn+EQUVQyKb09A+ASAENFpB7AvQAuEZHpSO9QswfATdENMUsl9vpuJeecacaafm7PRNsxY6mzPS6FLa+FOcP4H200n3sBFW/Zz7i02V77jXqXLpNdVRc6mp+IYCxEFCG+g47IE0x2Ik8w2Yk8wWQn8gSTncgTvXvByZDympw3xY4tOWzG/u/M18xYTOzjZaM+0WLGvk7Zx5oSt0tlZSXuFS7fWfCw2eeyljvN2IQl9rsekyzL9Sq8shN5gslO5AkmO5EnmOxEnmCyE3mCyU7kiV5deot9a7wZO/yAe+FFAFh75n/ZPzOL8lrSWFwRAG7dd5EZ2/TIDDPW/4C9u9nx2+3S4bvnPu9sr+pTZvZZsehRM3bz5/9kxk57erMZ0zYuVNLT8MpO5AkmO5EnmOxEnmCyE3mCyU7kiV5xNz5WXu5s/+TmYWafunN+acbi0jercbSm2p3tc7a5Vu5KG/Qz+y54xcZaM6ZJey28fvsmm7EfL5/tbP/1mHVmn5n94mbsxjtfMWNPN11pxvr9wbhTn7L/XRQtXtmJPMFkJ/IEk53IE0x2Ik8w2Yk8wWQn8kQm2z+NBfAUgJEAUgCWqeqjIlIJ4DkA45HeAmqBqtozNHLQOttdavqPK+2NaQaUZFde29Vhrwt3+Ru3O9un/sLevj65094GT7MsQ6U+/tSMffxgtbN9w+K1Zp+w0ltNxT4z9tqde81Ysm6Usz3xud2HopXJlT0B4A5VnQrgQgA3i8g0AHcBWKOqkwCsCb4moh6qy2RX1QZV3Rw8PgpgO4DRAK4GsCL4thUArolojESUB916zS4i4wHMALAewAhVbQDSfxAADM/76IgobzJOdhEpA/ACgNtUNeMFw0WkRkRqRaS2A1zQgKhYMkp2EYkjnegrVfXFoLlRRKqCeBWAJldfVV2mqtWqWh2HvbkBEUWry2QXEUF6P/btqrqkU+hVAIuCx4sA2DMmiKjoRFXDv0HkOwDeAbAN6dIbANyN9Ov2VQDGAfgCwHWqategAJRLpc6SOd0eZMPLU53tH/zFSrNPTOy/Y2vt5elw+0M/MmMjXt7lbE827bd/YBfnN9+sGYJ//Nk0s8+2a+0ZgmElzMPJVjM254E7nO3Dfr3B7MMZcblbr2vQrIfEFeuyzq6q7wJwdgbQ/cwloqLgO+iIPMFkJ/IEk53IE0x2Ik8w2Yk80SsWnHy/+klne0xKs/p5D+yxF0oc+cpuM5ZodL5vqEdJNrvf3Dj53+2x33rRpWbs8bHvmbEhsQFmrHx+g7M9tqrC7JM8GFq5pRzxyk7kCSY7kSeY7ESeYLITeYLJTuQJJjuRJ3pF6a2spPsltqSmzNhn748zYxMObur2sXqD1J56M7b2jQvMWPLv3jFjYTML7z7j9872R8Zca/YBS2+R4pWdyBNMdiJPMNmJPMFkJ/IEk53IE73ibnw2DqaOm7HBO+x+muiIYDTFF/bvGvil3a9F7eW/K6S/GTunr3snsLZh9uQZexMqygde2Yk8wWQn8gSTncgTTHYiTzDZiTzBZCfyRJelNxEZC+ApACOR3v5pmao+KiL3AbgRwIm9j+5W1dVRDbS72kO2XYq1FXZLpoIS9+Y9fUaPMru0XHLMjFWU2OW1MI1JdyGtz7FEVj+PcpdJnT0B4A5V3SwigwBsEpE3g9gjqvpwdMMjonzJZK+3BgANweOjIrIdwOioB0ZE+dWt1+wiMh7ADKR3cAWAW0Rkq4gsF5Eh+R4cEeVPxskuImUAXgBwm6o2A1gKYCKA6Uhf+Rcb/WpEpFZEajtgv/WSiKKVUbKLSBzpRF+pqi8CgKo2qmpSVVMAHgcw09VXVZeparWqVsfRL1/jJqJu6jLZRUQAPAFgu6ou6dRe1enb5gOoy//wiChfMrkbPxvA3wDYJiJbgra7ASwUkekAFMAeADdFMD4AwOFkq7M9bPuhYTH7WUTDZUkzNnjTeDOW2rPX2a6JCMpJJTEzFKsoN2NtM85wtu/9sf0S6v1ZS0MGMjAkZvvp7u872+OffWX2YVEuWpncjX8XgKt422Nq6kTUNb6DjsgTTHYiTzDZiTzBZCfyBJOdyBO9YsHJbz/xE2f7/T9Yafb5/kD3gocAsHneo2ZsVsU/mLEJS6Y620vqdpl9pJ9dAkxMsbeh+uoiu+RVfoVdvnr4zGXO9gtL7VJeWHktbButhw66zwcAtC2ucgeaNoeMg6LEKzuRJ5jsRJ5gshN5gslO5AkmO5EnmOxEnhANWZgx38qlUmfJnG73KxnoLg11zJxs9tm1wK4qXn7+R2Zs99HTzFj9OvdqXLF29yKPAIDzms3QkunPm7GLS4+asQElfe3jZaFN7X3g/nLLD8zY4J/bsw5LNm53tmtHe+YDo25br2vQrIecv5C8shN5gslO5AkmO5EnmOxEnmCyE3mCyU7kiV4x6y11zL0XWeztD8w+UzbYZaF9w4eascRZI8zYgBvdM+neu+Apu0/WZbL8ltcaEi1m7Nuv327Gpj58yIwlP91qxgpZ0qXM8MpO5AkmO5EnmOxEnmCyE3mCyU7kiS4nwohIKYC1APohfff+d6p6r4hUAngOwHikt39aoKr2wm/IfiIMEWUm14kwbQAuU9XzkN6eea6IXAjgLgBrVHUSgDXB10TUQ3WZ7Jp2okgbDz4UwNUAVgTtKwBcE8UAiSg/Mt2fPRbs4NoE4E1VXQ9ghKo2AEDweXhkoySinGWU7KqaVNXpAMYAmCkiZ2d6ABGpEZFaEantgL1tMBFFq1t341X1CIC3AcwF0CgiVQAQfG4y+ixT1WpVrY7D3jCBiKLVZbKLyDARGRw87g/gcgCfAHgVwKLg2xYBeCWiMRJRHmQyEaYKwAoRiSH9x2GVqr4mIusArBKRGwB8AeC6CMdJRDnqMtlVdSuAGY72gwBYNCfqJfgOOiJPMNmJPMFkJ/IEk53IE0x2Ik8UdPsnEdkP4PPgy6EADhTs4DaO42Qcx8l62zhOV9VhrkBBk/2kA4vUqmp1UQ7OcXAcHo6DT+OJPMFkJ/JEMZN9WRGP3RnHcTKO42R/NuMo2mt2IiosPo0n8kRRkl1E5orIH0Vkp4gUbe06EdkjIttEZIuI1BbwuMtFpElE6jq1VYrImyLyafB5SJHGcZ+IfBmcky0iMq8A4xgrIm+JyHYR+UhEbg3aC3pOQsZR0HMiIqUiskFEPgzGcX/Qntv5UNWCfgCIAdgF4AykNzT7EMC0Qo8jGMseAEOLcNyLAZwPoK5T278CuCt4fBeAh4o0jvsA/KTA56MKwPnB40EAdgCYVuhzEjKOgp4TAAKgLHgcB7AewIW5no9iXNlnAtipqrtVtR3As0gvXukNVV0L4NQdEwu+gKcxjoJT1QZV3Rw8PgpgO4DRKPA5CRlHQWla3hd5LUayjwawt9PX9SjCCQ0ogDdEZJOI1BRpDCf0pAU8bxGRrcHT/MhfTnQmIuORXj+hqIuanjIOoMDnJIpFXouR7K4F7ItVEpitqucD+B6Am0Xk4iKNoydZCmAi0nsENABYXKgDi0gZgBcA3KaqzYU6bgbjKPg50RwWebUUI9nrAYzt9PUYAPuKMA6o6r7gcxOAl5B+iVEsGS3gGTVVbQx+0VIAHkeBzomIxJFOsJWq+mLQXPBz4hpHsc5JcOwj6OYir5ZiJPtGAJNEZIKI9AVwPdKLVxaUiAwUkUEnHgO4AkBdeK9I9YgFPE/8MgXmowDnREQEwBMAtqvqkk6hgp4TaxyFPieRLfJaqDuMp9xtnIf0nc5dAO4p0hjOQLoS8CGAjwo5DgDPIP10sAPpZzo3ADgN6W20Pg0+VxZpHP8JYBuArcEvV1UBxvEdpF/KbQWwJfiYV+hzEjKOgp4TAOcC+CA4Xh2AfwnaczoffAcdkSf4DjoiTzDZiTzBZCfyBJOdyBNMdiJPMNmJPMFkJ/IEk53IE/8PeOyYM4WKpY8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(sample_img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b66da4d8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-22T14:21:39.265344Z",
     "iopub.status.busy": "2022-08-22T14:21:39.264460Z",
     "iopub.status.idle": "2022-08-22T14:21:39.447868Z",
     "shell.execute_reply": "2022-08-22T14:21:39.447013Z"
    },
    "papermill": {
     "duration": 0.195496,
     "end_time": "2022-08-22T14:21:39.450038",
     "exception": false,
     "start_time": "2022-08-22T14:21:39.254542",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fddd642ce20>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(image_gen.random_transform(np.expand_dims(sample_img,axis=2)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "97e969aa",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-22T14:21:39.470129Z",
     "iopub.status.busy": "2022-08-22T14:21:39.469842Z",
     "iopub.status.idle": "2022-08-22T14:21:59.986647Z",
     "shell.execute_reply": "2022-08-22T14:21:59.985174Z"
    },
    "papermill": {
     "duration": 20.529721,
     "end_time": "2022-08-22T14:21:59.989210",
     "exception": false,
     "start_time": "2022-08-22T14:21:39.459489",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 17000 images belonging to 10 classes.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.preprocessing.image.DirectoryIterator at 0x7fddd6471370>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image_gen.flow_from_directory(train_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "957b1d85",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-22T14:22:00.011150Z",
     "iopub.status.busy": "2022-08-22T14:22:00.010056Z",
     "iopub.status.idle": "2022-08-22T14:22:00.230656Z",
     "shell.execute_reply": "2022-08-22T14:22:00.229808Z"
    },
    "papermill": {
     "duration": 0.233124,
     "end_time": "2022-08-22T14:22:00.232597",
     "exception": false,
     "start_time": "2022-08-22T14:21:59.999473",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 3000 images belonging to 10 classes.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.preprocessing.image.DirectoryIterator at 0x7fddd6495130>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image_gen.flow_from_directory(test_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f48048c8",
   "metadata": {
    "papermill": {
     "duration": 0.010107,
     "end_time": "2022-08-22T14:22:00.252821",
     "exception": false,
     "start_time": "2022-08-22T14:22:00.242714",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Creating the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d7df4573",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-22T14:22:00.275189Z",
     "iopub.status.busy": "2022-08-22T14:22:00.273555Z",
     "iopub.status.idle": "2022-08-22T14:22:00.280222Z",
     "shell.execute_reply": "2022-08-22T14:22:00.279369Z"
    },
    "papermill": {
     "duration": 0.019352,
     "end_time": "2022-08-22T14:22:00.282249",
     "exception": false,
     "start_time": "2022-08-22T14:22:00.262897",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Activation, Dropout, Flatten, Dense, Conv2D, MaxPooling2D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "fc746a82",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-22T14:22:00.303580Z",
     "iopub.status.busy": "2022-08-22T14:22:00.302531Z",
     "iopub.status.idle": "2022-08-22T14:22:03.061278Z",
     "shell.execute_reply": "2022-08-22T14:22:03.060288Z"
    },
    "papermill": {
     "duration": 2.771974,
     "end_time": "2022-08-22T14:22:03.063984",
     "exception": false,
     "start_time": "2022-08-22T14:22:00.292010",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-08-16 12:51:26.796753: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  SSE4.1 SSE4.2 AVX AVX2 AVX512F AVX512_VNNI FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
     ]
    }
   ],
   "source": [
    "image_shape=(32,32,1)\n",
    "model= Sequential()\n",
    "model.add(Conv2D(filters=32, kernel_size=(3,3),input_shape=image_shape, activation='relu',))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "\n",
    "model.add(Conv2D(filters=64, kernel_size=(3,3),input_shape=image_shape, activation='relu',))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "\n",
    "model.add(Conv2D(filters=64, kernel_size=(3,3),input_shape=image_shape, activation='relu',))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "\n",
    "\n",
    "model.add(Flatten())\n",
    "\n",
    "\n",
    "model.add(Dense(128))\n",
    "model.add(Activation('relu'))\n",
    "\n",
    "# Dropouts help reduce overfitting by randomly turning neurons off during training.\n",
    "# Here we say randomly turn off 50% of neurons.\n",
    "model.add(Dropout(0.5))\n",
    "\n",
    "# Last layer, remember its multi-class classification so we use sigmoid\n",
    "model.add(Dense(10))\n",
    "model.add(Activation('softmax'))\n",
    "\n",
    "model.compile(loss='categorical_crossentropy',\n",
    "              optimizer='adam',\n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "f1ff820c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-22T14:22:03.085527Z",
     "iopub.status.busy": "2022-08-22T14:22:03.085032Z",
     "iopub.status.idle": "2022-08-22T14:22:03.091687Z",
     "shell.execute_reply": "2022-08-22T14:22:03.090769Z"
    },
    "papermill": {
     "duration": 0.020297,
     "end_time": "2022-08-22T14:22:03.094293",
     "exception": false,
     "start_time": "2022-08-22T14:22:03.073996",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d (Conv2D)             (None, 30, 30, 32)        320       \n",
      "                                                                 \n",
      " max_pooling2d (MaxPooling2D  (None, 15, 15, 32)       0         \n",
      " )                                                               \n",
      "                                                                 \n",
      " conv2d_1 (Conv2D)           (None, 13, 13, 64)        18496     \n",
      "                                                                 \n",
      " max_pooling2d_1 (MaxPooling  (None, 6, 6, 64)         0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " conv2d_2 (Conv2D)           (None, 4, 4, 64)          36928     \n",
      "                                                                 \n",
      " max_pooling2d_2 (MaxPooling  (None, 2, 2, 64)         0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " flatten (Flatten)           (None, 256)               0         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 128)               32896     \n",
      "                                                                 \n",
      " activation (Activation)     (None, 128)               0         \n",
      "                                                                 \n",
      " dropout (Dropout)           (None, 128)               0         \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 10)                1290      \n",
      "                                                                 \n",
      " activation_1 (Activation)   (None, 10)                0         \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 89,930\n",
      "Trainable params: 89,930\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15eb8b3a",
   "metadata": {
    "papermill": {
     "duration": 0.00972,
     "end_time": "2022-08-22T14:22:03.114024",
     "exception": false,
     "start_time": "2022-08-22T14:22:03.104304",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Early Stopping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "03f93117",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-22T14:22:03.136599Z",
     "iopub.status.busy": "2022-08-22T14:22:03.134798Z",
     "iopub.status.idle": "2022-08-22T14:22:03.140963Z",
     "shell.execute_reply": "2022-08-22T14:22:03.140144Z"
    },
    "papermill": {
     "duration": 0.01907,
     "end_time": "2022-08-22T14:22:03.143153",
     "exception": false,
     "start_time": "2022-08-22T14:22:03.124083",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from tensorflow.keras.callbacks import EarlyStopping\n",
    "early_stop = EarlyStopping(patience=2, monitor='val_loss')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c2f820d",
   "metadata": {
    "papermill": {
     "duration": 0.009965,
     "end_time": "2022-08-22T14:22:03.162883",
     "exception": false,
     "start_time": "2022-08-22T14:22:03.152918",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Training the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "c82b293b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-22T14:22:03.184186Z",
     "iopub.status.busy": "2022-08-22T14:22:03.183907Z",
     "iopub.status.idle": "2022-08-22T14:22:06.289121Z",
     "shell.execute_reply": "2022-08-22T14:22:06.287583Z"
    },
    "papermill": {
     "duration": 3.118597,
     "end_time": "2022-08-22T14:22:06.291578",
     "exception": false,
     "start_time": "2022-08-22T14:22:03.172981",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 17000 images belonging to 10 classes.\n"
     ]
    }
   ],
   "source": [
    "batch_size = 16\n",
    "image_shape=(32,32,1)\n",
    "train_image_gen = image_gen.flow_from_directory(train_path,\n",
    "                                               target_size=image_shape[:2],\n",
    "                                                color_mode='grayscale',\n",
    "                                               batch_size=batch_size,\n",
    "                                               class_mode='categorical')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "945d13c0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-22T14:22:06.313620Z",
     "iopub.status.busy": "2022-08-22T14:22:06.313058Z",
     "iopub.status.idle": "2022-08-22T14:22:06.530607Z",
     "shell.execute_reply": "2022-08-22T14:22:06.529424Z"
    },
    "papermill": {
     "duration": 0.23079,
     "end_time": "2022-08-22T14:22:06.532705",
     "exception": false,
     "start_time": "2022-08-22T14:22:06.301915",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 3000 images belonging to 10 classes.\n"
     ]
    }
   ],
   "source": [
    "test_image_gen = image_gen.flow_from_directory(test_path,\n",
    "                                               target_size=image_shape[:2],\n",
    "                                               color_mode='grayscale',\n",
    "                                               batch_size=batch_size,\n",
    "                                               class_mode='categorical',shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "42c5705e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-22T14:22:06.554042Z",
     "iopub.status.busy": "2022-08-22T14:22:06.553770Z",
     "iopub.status.idle": "2022-08-22T14:22:06.560263Z",
     "shell.execute_reply": "2022-08-22T14:22:06.559312Z"
    },
    "papermill": {
     "duration": 0.019327,
     "end_time": "2022-08-22T14:22:06.562185",
     "exception": false,
     "start_time": "2022-08-22T14:22:06.542858",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'0': 0,\n",
       " '1': 1,\n",
       " '2': 2,\n",
       " '3': 3,\n",
       " '4': 4,\n",
       " '5': 5,\n",
       " '6': 6,\n",
       " '7': 7,\n",
       " '8': 8,\n",
       " '9': 9}"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_image_gen.class_indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "0af8d09a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-22T14:22:06.583589Z",
     "iopub.status.busy": "2022-08-22T14:22:06.583306Z",
     "iopub.status.idle": "2022-08-22T14:30:35.698084Z",
     "shell.execute_reply": "2022-08-22T14:30:35.697119Z"
    },
    "papermill": {
     "duration": 509.425948,
     "end_time": "2022-08-22T14:30:35.998329",
     "exception": false,
     "start_time": "2022-08-22T14:22:06.572381",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "1063/1063 [==============================] - 38s 35ms/step - loss: 1.0192 - accuracy: 0.6476 - val_loss: 0.4060 - val_accuracy: 0.8717\n",
      "Epoch 2/20\n",
      "1063/1063 [==============================] - 28s 27ms/step - loss: 0.4665 - accuracy: 0.8452 - val_loss: 0.2800 - val_accuracy: 0.9040\n",
      "Epoch 3/20\n",
      "1063/1063 [==============================] - 32s 30ms/step - loss: 0.3513 - accuracy: 0.8846 - val_loss: 0.2502 - val_accuracy: 0.9070\n",
      "Epoch 4/20\n",
      "1063/1063 [==============================] - 35s 32ms/step - loss: 0.2927 - accuracy: 0.9021 - val_loss: 0.2052 - val_accuracy: 0.9377\n",
      "Epoch 5/20\n",
      "1063/1063 [==============================] - 35s 33ms/step - loss: 0.2511 - accuracy: 0.9168 - val_loss: 0.1587 - val_accuracy: 0.9513\n",
      "Epoch 6/20\n",
      "1063/1063 [==============================] - 35s 33ms/step - loss: 0.2312 - accuracy: 0.9246 - val_loss: 0.1300 - val_accuracy: 0.9590\n",
      "Epoch 7/20\n",
      "1063/1063 [==============================] - 55s 52ms/step - loss: 0.2081 - accuracy: 0.9314 - val_loss: 0.1511 - val_accuracy: 0.9470\n",
      "Epoch 8/20\n",
      "1063/1063 [==============================] - 44s 41ms/step - loss: 0.1963 - accuracy: 0.9371 - val_loss: 0.1470 - val_accuracy: 0.9553\n"
     ]
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "results = model.fit_generator(train_image_gen,epochs=20,\n",
    "                              validation_data=test_image_gen,\n",
    "                             callbacks=[early_stop])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7c39315",
   "metadata": {
    "papermill": {
     "duration": 0.315853,
     "end_time": "2022-08-22T14:30:36.874124",
     "exception": false,
     "start_time": "2022-08-22T14:30:36.558271",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Evaluate the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "9b4377c2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-22T14:30:37.449981Z",
     "iopub.status.busy": "2022-08-22T14:30:37.449604Z",
     "iopub.status.idle": "2022-08-22T14:30:37.468836Z",
     "shell.execute_reply": "2022-08-22T14:30:37.467716Z"
    },
    "papermill": {
     "duration": 0.310952,
     "end_time": "2022-08-22T14:30:37.471254",
     "exception": false,
     "start_time": "2022-08-22T14:30:37.160302",
     "status": "completed"
    },
    "tags": []
   },
   "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>loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>val_loss</th>\n",
       "      <th>val_accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.986384</td>\n",
       "      <td>0.660000</td>\n",
       "      <td>0.388864</td>\n",
       "      <td>0.864000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.455899</td>\n",
       "      <td>0.844882</td>\n",
       "      <td>0.272579</td>\n",
       "      <td>0.907667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.338803</td>\n",
       "      <td>0.887706</td>\n",
       "      <td>0.209032</td>\n",
       "      <td>0.934333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.290055</td>\n",
       "      <td>0.908412</td>\n",
       "      <td>0.185424</td>\n",
       "      <td>0.942000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.239679</td>\n",
       "      <td>0.920294</td>\n",
       "      <td>0.151393</td>\n",
       "      <td>0.948667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       loss  accuracy  val_loss  val_accuracy\n",
       "0  0.986384  0.660000  0.388864      0.864000\n",
       "1  0.455899  0.844882  0.272579      0.907667\n",
       "2  0.338803  0.887706  0.209032      0.934333\n",
       "3  0.290055  0.908412  0.185424      0.942000\n",
       "4  0.239679  0.920294  0.151393      0.948667"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.metrics_names\n",
    "losses = pd.DataFrame(model.history.history)\n",
    "losses.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "ff031a75",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-22T14:30:38.089462Z",
     "iopub.status.busy": "2022-08-22T14:30:38.089049Z",
     "iopub.status.idle": "2022-08-22T14:30:38.315275Z",
     "shell.execute_reply": "2022-08-22T14:30:38.313492Z"
    },
    "papermill": {
     "duration": 0.560832,
     "end_time": "2022-08-22T14:30:38.318073",
     "exception": false,
     "start_time": "2022-08-22T14:30:37.757241",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "losses[['accuracy','val_accuracy']].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "5bd500f0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-22T14:30:38.894523Z",
     "iopub.status.busy": "2022-08-22T14:30:38.894172Z",
     "iopub.status.idle": "2022-08-22T14:30:39.089387Z",
     "shell.execute_reply": "2022-08-22T14:30:39.088412Z"
    },
    "papermill": {
     "duration": 0.482529,
     "end_time": "2022-08-22T14:30:39.091599",
     "exception": false,
     "start_time": "2022-08-22T14:30:38.609070",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "losses[['loss','val_loss']].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "05de0cf3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-22T14:30:39.665685Z",
     "iopub.status.busy": "2022-08-22T14:30:39.665327Z",
     "iopub.status.idle": "2022-08-22T14:30:42.907467Z",
     "shell.execute_reply": "2022-08-22T14:30:42.906247Z"
    },
    "papermill": {
     "duration": 3.532764,
     "end_time": "2022-08-22T14:30:42.910259",
     "exception": false,
     "start_time": "2022-08-22T14:30:39.377495",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 0 ... 9 9 9]\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras.preprocessing import image\n",
    "y_pred = np.argmax(model.predict(test_image_gen), axis=-1)\n",
    "# y_pred = np.round(y_pred).astype(int)\n",
    "# pred_probabilities = model.predict_classes(test_image_gen)\n",
    "print(y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "6c404fb6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-22T14:30:43.530882Z",
     "iopub.status.busy": "2022-08-22T14:30:43.530476Z",
     "iopub.status.idle": "2022-08-22T14:30:43.536855Z",
     "shell.execute_reply": "2022-08-22T14:30:43.535839Z"
    },
    "papermill": {
     "duration": 0.295371,
     "end_time": "2022-08-22T14:30:43.540621",
     "exception": false,
     "start_time": "2022-08-22T14:30:43.245250",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3000"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "39024609",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-22T14:30:44.127777Z",
     "iopub.status.busy": "2022-08-22T14:30:44.127419Z",
     "iopub.status.idle": "2022-08-22T14:30:44.282733Z",
     "shell.execute_reply": "2022-08-22T14:30:44.280751Z"
    },
    "papermill": {
     "duration": 0.458821,
     "end_time": "2022-08-22T14:30:44.285675",
     "exception": false,
     "start_time": "2022-08-22T14:30:43.826854",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      1.00      0.98       300\n",
      "           1       0.98      0.98      0.98       300\n",
      "           2       0.96      0.96      0.96       300\n",
      "           3       0.99      0.83      0.91       300\n",
      "           4       0.99      0.99      0.99       300\n",
      "           5       0.98      0.97      0.98       300\n",
      "           6       0.86      0.97      0.91       300\n",
      "           7       0.97      0.96      0.97       300\n",
      "           8       0.99      0.99      0.99       300\n",
      "           9       0.96      0.98      0.97       300\n",
      "\n",
      "    accuracy                           0.96      3000\n",
      "   macro avg       0.97      0.96      0.96      3000\n",
      "weighted avg       0.97      0.96      0.96      3000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report,confusion_matrix\n",
    "print(classification_report(test_image_gen.classes,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "a7d2ae73",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-22T14:30:44.862073Z",
     "iopub.status.busy": "2022-08-22T14:30:44.861714Z",
     "iopub.status.idle": "2022-08-22T14:30:45.393138Z",
     "shell.execute_reply": "2022-08-22T14:30:45.392240Z"
    },
    "papermill": {
     "duration": 0.820749,
     "end_time": "2022-08-22T14:30:45.395330",
     "exception": false,
     "start_time": "2022-08-22T14:30:44.574581",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "plt.figure(figsize=(10,6))\n",
    "sns.heatmap(confusion_matrix(test_image_gen.classes,y_pred),annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b89a2692",
   "metadata": {
    "papermill": {
     "duration": 0.291367,
     "end_time": "2022-08-22T14:30:46.020174",
     "exception": false,
     "start_time": "2022-08-22T14:30:45.728807",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Predicting an image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "ed2ff1ae",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-22T14:30:46.601299Z",
     "iopub.status.busy": "2022-08-22T14:30:46.600914Z",
     "iopub.status.idle": "2022-08-22T14:30:46.783713Z",
     "shell.execute_reply": "2022-08-22T14:30:46.782846Z"
    },
    "papermill": {
     "duration": 0.474932,
     "end_time": "2022-08-22T14:30:46.785806",
     "exception": false,
     "start_time": "2022-08-22T14:30:46.310874",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fb6d0132250>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(sample_img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "17f9e2e5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-22T14:30:47.665182Z",
     "iopub.status.busy": "2022-08-22T14:30:47.663878Z",
     "iopub.status.idle": "2022-08-22T14:30:47.794960Z",
     "shell.execute_reply": "2022-08-22T14:30:47.792417Z"
    },
    "papermill": {
     "duration": 0.583281,
     "end_time": "2022-08-22T14:30:47.797547",
     "exception": false,
     "start_time": "2022-08-22T14:30:47.214266",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4\n"
     ]
    }
   ],
   "source": [
    "pred=model.predict(sample_img.reshape(1,32,32,1))\n",
    "pred= np.argmax(pred)\n",
    "print(pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "a1d98e13",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "ce9d6dbd",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op while saving (showing 3 of 3). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: ram://81052ebe-4567-4e60-8fe8-e1c482b9d57d/assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: ram://81052ebe-4567-4e60-8fe8-e1c482b9d57d/assets\n"
     ]
    }
   ],
   "source": [
    "pickle.dump(model,open('model.pickle','wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "349e8a35",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.7.0"
  },
  "papermill": {
   "default_parameters": {},
   "duration": 575.562382,
   "end_time": "2022-08-22T14:30:51.211092",
   "environment_variables": {},
   "exception": null,
   "input_path": "__notebook__.ipynb",
   "output_path": "__notebook__.ipynb",
   "parameters": {},
   "start_time": "2022-08-22T14:21:15.648710",
   "version": "2.3.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}