project.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FFCtVfTCrPQH"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "import numpy as np \n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "import cv2\n",
        "from glob import glob\n",
        "import sklearn\n",
        "from sklearn.model_selection import train_test_split\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from keras.utils.np_utils import to_categorical\n",
        "from keras.models import Sequential,load_model\n",
        "from keras.layers import Activation, Dropout, Dense, Flatten, BatchNormalization, Conv2D, MaxPooling2D\n",
        "from keras.optimizers import RMSprop\n",
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "\n",
        "import json\n",
        "from PIL import Image"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F6d6iAkwrT1O",
        "outputId": "5166ad14-7f73-44b5-b417-903c5f73a323"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jM4q_wKYrmUW",
        "outputId": "749fc975-5428-49aa-f109-78d0fa56106a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
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            "   creating: Number/IM_Train/train/9/\n",
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            " extracting: Number/IM_Train/train/9/10841.1.png  \n",
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            " extracting: Number/IM_Train/train/9/10870.3.png  \n",
            " extracting: Number/IM_Train/train/9/1088.1.png  \n",
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            " extracting: Number/IM_Train/train/9/10912.1.png  \n",
            "  inflating: Number/IM_Train/train/9/10914.0.png  \n",
            " extracting: Number/IM_Train/train/9/10922.1.png  \n",
            " extracting: Number/IM_Train/train/9/10932.2.png  \n",
            " extracting: Number/IM_Train/train/9/10947.1.png  \n",
            " extracting: Number/IM_Train/train/9/10966.0.png  \n",
            " extracting: Number/IM_Train/train/9/10975.2.png  \n",
            " extracting: Number/IM_Train/train/9/10983.1.png  \n",
            "  inflating: Number/IM_Train/train/9/10985.0.png  \n",
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            " extracting: Number/IM_Train/train/9/10988.2.png  \n",
            "  inflating: Number/IM_Train/train/9/10992.2.png  \n",
            " extracting: Number/IM_Train/train/9/10993.1.png  \n",
            " extracting: Number/IM_Train/train/9/10995.0.png  \n",
            "  inflating: Number/IM_Train/train/9/10999.0.png  \n",
            " extracting: Number/IM_Train/train/9/11000.1.png  \n",
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            " extracting: Number/IM_Train/train/9/1102.1.png  \n",
            " extracting: Number/IM_Train/train/9/11022.3.png  \n",
            " extracting: Number/IM_Train/train/9/11029.1.png  \n",
            " extracting: Number/IM_Train/train/9/11049.1.png  \n",
            "  inflating: Number/IM_Train/train/9/11051.1.png  \n",
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            " extracting: Number/IM_Train/train/9/1106.1.png  \n",
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            " extracting: Number/IM_Train/train/9/11079.0.png  \n",
            " extracting: Number/IM_Train/train/9/11097.1.png  \n",
            "  inflating: Number/IM_Train/train/9/11099.0.png  \n",
            " extracting: Number/IM_Train/train/9/111.0.png  \n",
            " extracting: Number/IM_Train/train/9/11100.0.png  \n",
            " extracting: Number/IM_Train/train/9/11115.1.png  \n",
            " extracting: Number/IM_Train/train/9/11119.2.png  \n",
            " extracting: Number/IM_Train/train/9/11120.0.png  \n",
            " extracting: Number/IM_Train/train/9/11123.1.png  \n",
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            " extracting: Number/IM_Train/train/9/11137.0.png  \n",
            " extracting: Number/IM_Train/train/9/11156.0.png  \n",
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            " extracting: Number/IM_Train/train/9/11163.0.png  \n",
            "  inflating: Number/IM_Train/train/9/11171.1.png  \n",
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            " extracting: Number/IM_Train/train/9/11202.1.png  \n",
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            " extracting: Number/IM_Train/train/9/11213.0.png  \n",
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            " extracting: Number/IM_Train/train/9/11235.0.png  \n",
            " extracting: Number/IM_Train/train/9/11242.1.png  \n",
            " extracting: Number/IM_Train/train/9/11249.1.png  \n",
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            " extracting: Number/IM_Train/train/9/11261.1.png  \n",
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            " extracting: Number/IM_Train/train/9/11285.0.png  \n",
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            " extracting: Number/IM_Train/train/9/11297.2.png  \n",
            "  inflating: Number/IM_Train/train/9/11307.0.png  \n",
            " extracting: Number/IM_Train/train/9/11329.2.png  \n",
            " extracting: Number/IM_Train/train/9/11340.1.png  \n",
            " extracting: Number/IM_Train/train/9/11343.1.png  \n",
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            " extracting: Number/IM_Train/train/9/11351.1.png  \n",
            " extracting: Number/IM_Train/train/9/11354.1.png  \n",
            " extracting: Number/IM_Train/train/9/11355.2.png  \n",
            " extracting: Number/IM_Train/train/9/11366.0.png  \n",
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            " extracting: Number/IM_Train/train/9/1137.1.png  \n",
            " extracting: Number/IM_Train/train/9/11381.2.png  \n",
            " extracting: Number/IM_Train/train/9/114.1.png  \n",
            " extracting: Number/IM_Train/train/9/11426.2.png  \n",
            " extracting: Number/IM_Train/train/9/11431.1.png  \n",
            " extracting: Number/IM_Train/train/9/11435.1.png  \n",
            " extracting: Number/IM_Train/train/9/11437.1.png  \n",
            " extracting: Number/IM_Train/train/9/11449.1.png  \n",
            " extracting: Number/IM_Train/train/9/11451.2.png  \n",
            " extracting: Number/IM_Train/train/9/11457.0.png  \n",
            " extracting: Number/IM_Train/train/9/11468.0.png  \n",
            " extracting: Number/IM_Train/train/9/1147.1.png  \n",
            " extracting: Number/IM_Train/train/9/11473.0.png  \n",
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            " extracting: Number/IM_Train/train/9/11485.2.png  \n",
            "  inflating: Number/IM_Train/train/9/11492.1.png  \n",
            " extracting: Number/IM_Train/train/9/11493.0.png  \n",
            " extracting: Number/IM_Train/train/9/11497.1.png  \n",
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            " extracting: Number/IM_Train/train/9/11507.0.png  \n",
            " extracting: Number/IM_Train/train/9/1151.0.png  \n",
            " extracting: Number/IM_Train/train/9/11514.1.png  \n",
            "  inflating: Number/IM_Train/train/9/11515.1.png  \n",
            "  inflating: Number/IM_Train/train/9/11518.1.png  \n",
            " extracting: Number/IM_Train/train/9/11520.2.png  \n",
            " extracting: Number/IM_Train/train/9/11526.1.png  \n",
            "  inflating: Number/IM_Train/train/9/1153.0.png  \n",
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            "  inflating: Number/IM_Train/train/9/1292.2.png  \n",
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            " extracting: Number/IM_Train/train/9/13041.2.png  \n",
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            "  inflating: Number/IM_Train/train/9/132.0.png  \n",
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            "  inflating: Number/IM_Train/train/9/13380.0.png  \n",
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            " extracting: Number/IM_Train/train/9/13396.0.png  \n",
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            " extracting: Number/IM_Train/train/9/134.0.png  \n",
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            " extracting: Number/IM_Train/train/9/13416.1.png  \n",
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            " extracting: Number/IM_Train/train/9/13447.1.png  \n",
            "  inflating: Number/IM_Train/train/9/13448.1.png  \n",
            " extracting: Number/IM_Train/train/9/13450.1.png  \n",
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            " extracting: Number/IM_Train/train/9/13466.2.png  \n",
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            " extracting: Number/IM_Train/train/9/13541.2.png  \n",
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            " extracting: Number/IM_Train/train/9/13552.2.png  \n",
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            " extracting: Number/IM_Train/train/9/13618.2.png  \n",
            " extracting: Number/IM_Train/train/9/13621.0.png  \n",
            " extracting: Number/IM_Train/train/9/13631.1.png  \n",
            " extracting: Number/IM_Train/train/9/13640.1.png  \n",
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            "  inflating: Number/IM_Train/train/9/1395.1.png  \n",
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            "  inflating: Number/IM_Train/train/9/1512.2.png  \n",
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            " extracting: Number/IM_Train/train/9/15255.0.png  \n",
            "  inflating: Number/IM_Train/train/9/15261.0.png  \n",
            " extracting: Number/IM_Train/train/9/153.1.png  \n",
            "  inflating: Number/IM_Train/train/9/1530.0.png  \n",
            " extracting: Number/IM_Train/train/9/15311.0.png  \n",
            " extracting: Number/IM_Train/train/9/15314.2.png  \n",
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            " extracting: Number/IM_Train/train/9/15413.2.png  \n",
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            " extracting: Number/IM_Train/train/9/15423.1.png  \n",
            "  inflating: Number/IM_Train/train/9/15445.0.png  \n",
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            " extracting: Number/IM_Train/train/9/15483.1.png  \n",
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            " extracting: Number/IM_Train/train/9/15492.1.png  \n",
            " extracting: Number/IM_Train/train/9/15519.0.png  \n",
            " extracting: Number/IM_Train/train/9/15525.1.png  \n",
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            " extracting: Number/IM_Train/train/9/15546.1.png  \n",
            " extracting: Number/IM_Train/train/9/15553.0.png  \n",
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            " extracting: Number/IM_Train/train/9/15581.1.png  \n",
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            " extracting: Number/IM_Train/train/9/15602.0.png  \n",
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            " extracting: Number/IM_Train/train/9/15613.2.png  \n",
            " extracting: Number/IM_Train/train/9/15638.1.png  \n",
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            " extracting: Number/IM_Train/train/9/15641.3.png  \n",
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            " extracting: Number/IM_Train/train/9/15650.0.png  \n",
            "  inflating: Number/IM_Train/train/9/15652.0.png  \n",
            " extracting: Number/IM_Train/train/9/15657.2.png  \n",
            " extracting: Number/IM_Train/train/9/1567.0.png  \n",
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            "  inflating: Number/IM_Train/train/9/17438.1.png  \n",
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            " extracting: Number/IM_Train/train/9/17641.2.png  \n",
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            " extracting: Number/IM_Train/train/9/17660.2.png  \n",
            "  inflating: Number/IM_Train/train/9/17661.0.png  \n",
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            " extracting: Number/IM_Train/train/9/17744.1.png  \n",
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            " extracting: Number/IM_Train/train/9/19420.1.png  \n",
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            " extracting: Number/IM_Train/train/9/19463.2.png  \n",
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            " extracting: Number/IM_Train/train/9/19474.0.png  \n",
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            " extracting: Number/IM_Train/train/9/19527.2.png  \n",
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            " extracting: Number/IM_Train/train/9/19547.1.png  \n",
            " extracting: Number/IM_Train/train/9/19553.3.png  \n",
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            "  inflating: Number/IM_Train/train/9/19561.0.png  \n",
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            " extracting: Number/IM_Train/train/9/19584.1.png  \n",
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            "  inflating: Number/IM_Train/train/9/19600.1.png  \n",
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            " extracting: Number/IM_Train/train/9/19604.1.png  \n",
            " extracting: Number/IM_Train/train/9/19612.0.png  \n",
            " extracting: Number/IM_Train/train/9/19618.1.png  \n",
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            " extracting: Number/IM_Train/train/9/19629.1.png  \n",
            " extracting: Number/IM_Train/train/9/19634.0.png  \n",
            " extracting: Number/IM_Train/train/9/19645.0.png  \n",
            " extracting: Number/IM_Train/train/9/19645.2.png  \n",
            "  inflating: Number/IM_Train/train/9/19655.0.png  \n",
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            " extracting: Number/IM_Train/train/9/19661.1.png  \n",
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            " extracting: Number/IM_Train/train/9/19677.1.png  \n",
            " extracting: Number/IM_Train/train/9/19683.0.png  \n",
            " extracting: Number/IM_Train/train/9/19688.0.png  \n",
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            " extracting: Number/IM_Train/train/9/19716.0.png  \n",
            "  inflating: Number/IM_Train/train/9/19725.0.png  \n",
            " extracting: Number/IM_Train/train/9/19737.1.png  \n",
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            " extracting: Number/IM_Train/train/9/1975.1.png  \n",
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            " extracting: Number/IM_Train/train/9/19793.2.png  \n",
            " extracting: Number/IM_Train/train/9/19794.1.png  \n",
            " extracting: Number/IM_Train/train/9/19798.2.png  \n",
            " extracting: Number/IM_Train/train/9/198.1.png  \n",
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            " extracting: Number/IM_Train/train/9/19811.2.png  \n",
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            " extracting: Number/IM_Train/train/9/19816.3.png  \n",
            " extracting: Number/IM_Train/train/9/19831.1.png  \n",
            " extracting: Number/IM_Train/train/9/19843.1.png  \n",
            " extracting: Number/IM_Train/train/9/19865.1.png  \n",
            " extracting: Number/IM_Train/train/9/19884.1.png  \n",
            "  inflating: Number/IM_Train/train/9/19889.0.png  \n",
            " extracting: Number/IM_Train/train/9/19892.2.png  \n",
            " extracting: Number/IM_Train/train/9/19893.1.png  \n",
            " extracting: Number/IM_Train/train/9/19905.1.png  \n",
            "  inflating: Number/IM_Train/train/9/19905.3.png  \n",
            " extracting: Number/IM_Train/train/9/1992.0.png  \n",
            " extracting: Number/IM_Train/train/9/19930.1.png  \n",
            " extracting: Number/IM_Train/train/9/19934.0.png  \n",
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            " extracting: Number/IM_Train/train/9/19960.0.png  \n",
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            " extracting: Number/IM_Train/train/9/19971.1.png  \n",
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            " extracting: Number/IM_Train/train/9/20004.2.png  \n",
            " extracting: Number/IM_Train/train/9/20006.1.png  \n",
            " extracting: Number/IM_Train/train/9/20014.1.png  \n",
            " extracting: Number/IM_Train/train/9/20020.2.png  \n",
            " extracting: Number/IM_Train/train/9/20026.0.png  \n",
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            " extracting: Number/IM_Train/train/9/20034.1.png  \n",
            " extracting: Number/IM_Train/train/9/20037.0.png  \n",
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            "  inflating: Number/IM_Train/train/9/20458.0.png  \n",
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            "  inflating: Number/IM_Train/train/9/20864.0.png  \n",
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            "  inflating: Number/IM_Train/train/9/21211.0.png  \n",
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            "  inflating: Number/IM_Train/train/9/21451.0.png  \n",
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            " extracting: Number/IM_Train/train/9/21531.2.png  \n",
            "  inflating: Number/IM_Train/train/9/21540.0.png  \n",
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            " extracting: Number/IM_Train/train/9/21607.2.png  \n",
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            " extracting: Number/IM_Train/train/9/21632.1.png  \n",
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            " extracting: Number/IM_Train/train/9/21668.0.png  \n",
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            " extracting: Number/IM_Train/train/9/21671.1.png  \n",
            " extracting: Number/IM_Train/train/9/21683.1.png  \n",
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            " extracting: Number/IM_Train/train/9/21701.2.png  \n",
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            " extracting: Number/IM_Train/train/9/21712.1.png  \n",
            " extracting: Number/IM_Train/train/9/21713.1.png  \n",
            "  inflating: Number/IM_Train/train/9/21716.1.png  \n",
            " extracting: Number/IM_Train/train/9/21736.0.png  \n",
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            " extracting: Number/IM_Train/train/9/21745.0.png  \n",
            " extracting: Number/IM_Train/train/9/21751.0.png  \n",
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            " extracting: Number/IM_Train/train/9/21765.0.png  \n",
            " extracting: Number/IM_Train/train/9/21776.2.png  \n",
            " extracting: Number/IM_Train/train/9/21780.2.png  \n",
            " extracting: Number/IM_Train/train/9/21784.2.png  \n",
            "  inflating: Number/IM_Train/train/9/21789.0.png  \n",
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            " extracting: Number/IM_Train/train/9/21818.2.png  \n",
            " extracting: Number/IM_Train/train/9/2182.1.png  \n",
            " extracting: Number/IM_Train/train/9/21820.1.png  \n",
            " extracting: Number/IM_Train/train/9/21824.2.png  \n",
            " extracting: Number/IM_Train/train/9/2183.1.png  \n",
            " extracting: Number/IM_Train/train/9/21837.0.png  \n",
            " extracting: Number/IM_Train/train/9/21847.0.png  \n",
            " extracting: Number/IM_Train/train/9/21850.2.png  \n",
            " extracting: Number/IM_Train/train/9/21850.3.png  \n",
            " extracting: Number/IM_Train/train/9/21861.1.png  \n",
            "  inflating: Number/IM_Train/train/9/21882.0.png  \n",
            " extracting: Number/IM_Train/train/9/21885.0.png  \n",
            " extracting: Number/IM_Train/train/9/21888.1.png  \n",
            " extracting: Number/IM_Train/train/9/21893.1.png  \n",
            "  inflating: Number/IM_Train/train/9/21902.0.png  \n",
            " extracting: Number/IM_Train/train/9/21915.0.png  \n",
            " extracting: Number/IM_Train/train/9/21922.1.png  \n",
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            " extracting: Number/IM_Train/train/9/21929.0.png  \n",
            " extracting: Number/IM_Train/train/9/21953.1.png  \n",
            " extracting: Number/IM_Train/train/9/21959.3.png  \n",
            " extracting: Number/IM_Train/train/9/21963.1.png  \n",
            " extracting: Number/IM_Train/train/9/21969.1.png  \n",
            " extracting: Number/IM_Train/train/9/21974.1.png  \n",
            " extracting: Number/IM_Train/train/9/21977.0.png  \n",
            " extracting: Number/IM_Train/train/9/21982.1.png  \n",
            " extracting: Number/IM_Train/train/9/21988.1.png  \n",
            " extracting: Number/IM_Train/train/9/21990.0.png  \n",
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            " extracting: Number/IM_Train/train/9/21994.1.png  \n",
            " extracting: Number/IM_Train/train/9/2200.1.png  \n",
            " extracting: Number/IM_Train/train/9/22002.0.png  \n",
            " extracting: Number/IM_Train/train/9/22008.0.png  \n",
            " extracting: Number/IM_Train/train/9/22009.0.png  \n",
            " extracting: Number/IM_Train/train/9/22011.0.png  \n",
            "  inflating: Number/IM_Train/train/9/22027.2.png  \n",
            "  inflating: Number/IM_Train/train/9/22036.1.png  \n",
            " extracting: Number/IM_Train/train/9/22057.1.png  \n",
            " extracting: Number/IM_Train/train/9/22060.0.png  \n",
            " extracting: Number/IM_Train/train/9/22063.2.png  \n",
            " extracting: Number/IM_Train/train/9/22065.2.png  \n",
            " extracting: Number/IM_Train/train/9/22067.1.png  \n",
            " extracting: Number/IM_Train/train/9/22068.0.png  \n",
            "  inflating: Number/IM_Train/train/9/22075.0.png  \n",
            " extracting: Number/IM_Train/train/9/22081.0.png  \n",
            " extracting: Number/IM_Train/train/9/22083.1.png  \n",
            " extracting: Number/IM_Train/train/9/22086.1.png  \n",
            " extracting: Number/IM_Train/train/9/22091.1.png  \n",
            " extracting: Number/IM_Train/train/9/22098.1.png  \n",
            " extracting: Number/IM_Train/train/9/22099.1.png  \n",
            " extracting: Number/IM_Train/train/9/22102.1.png  \n",
            "  inflating: Number/IM_Train/train/9/22112.1.png  \n",
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            "  inflating: Number/IM_Train/train/9/22193.0.png  \n",
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            " extracting: Number/IM_Train/train/9/23651.0.png  \n",
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            " extracting: Number/IM_Train/train/9/23732.2.png  \n",
            "  inflating: Number/IM_Train/train/9/23741.1.png  \n",
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            " extracting: Number/IM_Train/train/9/23773.1.png  \n",
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            " extracting: Number/IM_Train/train/9/23783.1.png  \n",
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            " extracting: Number/IM_Train/train/9/23792.0.png  \n",
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            " extracting: Number/IM_Train/train/9/23803.1.png  \n",
            "  inflating: Number/IM_Train/train/9/23819.0.png  \n",
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            " extracting: Number/IM_Train/train/9/23832.1.png  \n",
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            " extracting: Number/IM_Train/train/9/23834.1.png  \n",
            " extracting: Number/IM_Train/train/9/23837.1.png  \n",
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            " extracting: Number/IM_Train/train/9/23857.2.png  \n",
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            " extracting: Number/IM_Train/train/9/23863.2.png  \n",
            " extracting: Number/IM_Train/train/9/23874.2.png  \n",
            " extracting: Number/IM_Train/train/9/23883.1.png  \n",
            " extracting: Number/IM_Train/train/9/23891.1.png  \n",
            " extracting: Number/IM_Train/train/9/23895.1.png  \n",
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            " extracting: Number/IM_Train/train/9/23916.0.png  \n",
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            " extracting: Number/IM_Train/train/9/23926.0.png  \n",
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            " extracting: Number/IM_Train/train/9/23930.1.png  \n",
            " extracting: Number/IM_Train/train/9/23931.1.png  \n",
            " extracting: Number/IM_Train/train/9/23947.1.png  \n",
            " extracting: Number/IM_Train/train/9/23948.2.png  \n",
            " extracting: Number/IM_Train/train/9/23957.1.png  \n",
            "  inflating: Number/IM_Train/train/9/2396.0.png  \n",
            "  inflating: Number/IM_Train/train/9/23961.0.png  \n",
            " extracting: Number/IM_Train/train/9/23964.1.png  \n",
            " extracting: Number/IM_Train/train/9/23979.0.png  \n",
            " extracting: Number/IM_Train/train/9/23988.2.png  \n",
            " extracting: Number/IM_Train/train/9/23992.3.png  \n",
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            " extracting: Number/IM_Train/train/9/24.1.png  \n",
            " extracting: Number/IM_Train/train/9/240.1.png  \n",
            " extracting: Number/IM_Train/train/9/24001.1.png  \n",
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            " extracting: Number/IM_Train/train/9/24015.0.png  \n",
            " extracting: Number/IM_Train/train/9/24030.0.png  \n",
            " extracting: Number/IM_Train/train/9/24031.1.png  \n",
            " extracting: Number/IM_Train/train/9/24043.3.png  \n",
            " extracting: Number/IM_Train/train/9/24069.1.png  \n",
            " extracting: Number/IM_Train/train/9/24071.2.png  \n",
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            " extracting: Number/IM_Train/train/9/24078.0.png  \n",
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            " extracting: Number/IM_Train/train/9/255.0.png  \n",
            " extracting: Number/IM_Train/train/9/2550.1.png  \n",
            "  inflating: Number/IM_Train/train/9/25501.1.png  \n",
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            " extracting: Number/IM_Train/train/9/25510.1.png  \n",
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            " extracting: Number/IM_Train/train/9/25532.1.png  \n",
            "  inflating: Number/IM_Train/train/9/2555.0.png  \n",
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            "  inflating: Number/IM_Train/train/9/25597.0.png  \n",
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            " extracting: Number/IM_Train/train/9/25613.1.png  \n",
            " extracting: Number/IM_Train/train/9/25629.0.png  \n",
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            "  inflating: Number/IM_Train/train/9/25648.0.png  \n",
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            " extracting: Number/IM_Train/train/9/25659.0.png  \n",
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            " extracting: Number/IM_Train/train/9/25687.0.png  \n",
            " extracting: Number/IM_Train/train/9/25690.1.png  \n",
            " extracting: Number/IM_Train/train/9/25700.1.png  \n",
            " extracting: Number/IM_Train/train/9/25735.1.png  \n",
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            "  inflating: Number/IM_Train/train/9/25739.2.png  \n",
            " extracting: Number/IM_Train/train/9/2575.1.png  \n",
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            "  inflating: Number/IM_Train/train/9/25759.0.png  \n",
            " extracting: Number/IM_Train/train/9/25767.0.png  \n",
            " extracting: Number/IM_Train/train/9/25770.1.png  \n",
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            "  inflating: Number/IM_Train/train/9/25805.0.png  \n",
            " extracting: Number/IM_Train/train/9/25817.3.png  \n",
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            " extracting: Number/IM_Train/train/9/2583.2.png  \n",
            " extracting: Number/IM_Train/train/9/25833.0.png  \n",
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            " extracting: Number/IM_Train/train/9/25845.0.png  \n",
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            " extracting: Number/IM_Train/train/9/25890.0.png  \n",
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            " extracting: Number/IM_Train/train/9/25902.2.png  \n",
            " extracting: Number/IM_Train/train/9/25904.1.png  \n",
            " extracting: Number/IM_Train/train/9/25910.3.png  \n",
            " extracting: Number/IM_Train/train/9/2592.1.png  \n",
            " extracting: Number/IM_Train/train/9/25938.1.png  \n",
            " extracting: Number/IM_Train/train/9/25954.0.png  \n",
            " extracting: Number/IM_Train/train/9/2596.2.png  \n",
            " extracting: Number/IM_Train/train/9/25964.0.png  \n",
            " extracting: Number/IM_Train/train/9/25981.1.png  \n",
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            "  inflating: Number/IM_Train/train/9/26531.1.png  \n",
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            " extracting: Number/IM_Train/train/9/27444.2.png  \n",
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            " extracting: Number/IM_Train/train/9/27570.1.png  \n",
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            " extracting: Number/IM_Train/train/9/27591.1.png  \n",
            "  inflating: Number/IM_Train/train/9/27596.1.png  \n",
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            " extracting: Number/IM_Train/train/9/27648.0.png  \n",
            " extracting: Number/IM_Train/train/9/27671.2.png  \n",
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            " extracting: Number/IM_Train/train/9/27680.2.png  \n",
            "  inflating: Number/IM_Train/train/9/27689.0.png  \n",
            " extracting: Number/IM_Train/train/9/2770.1.png  \n",
            " extracting: Number/IM_Train/train/9/27708.1.png  \n",
            " extracting: Number/IM_Train/train/9/27711.1.png  \n",
            " extracting: Number/IM_Train/train/9/27718.1.png  \n",
            " extracting: Number/IM_Train/train/9/27721.1.png  \n",
            " extracting: Number/IM_Train/train/9/27722.1.png  \n",
            "  inflating: Number/IM_Train/train/9/27735.0.png  \n",
            " extracting: Number/IM_Train/train/9/27741.0.png  \n",
            " extracting: Number/IM_Train/train/9/27758.1.png  \n",
            " extracting: Number/IM_Train/train/9/27764.1.png  \n",
            " extracting: Number/IM_Train/train/9/27790.1.png  \n",
            " extracting: Number/IM_Train/train/9/27795.2.png  \n",
            " extracting: Number/IM_Train/train/9/27797.1.png  \n",
            " extracting: Number/IM_Train/train/9/27803.0.png  \n",
            " extracting: Number/IM_Train/train/9/27820.0.png  \n",
            " extracting: Number/IM_Train/train/9/27822.1.png  \n",
            " extracting: Number/IM_Train/train/9/27827.0.png  \n",
            " extracting: Number/IM_Train/train/9/27836.2.png  \n",
            " extracting: Number/IM_Train/train/9/27837.3.png  \n",
            "  inflating: Number/IM_Train/train/9/27839.0.png  \n",
            " extracting: Number/IM_Train/train/9/27853.1.png  \n",
            "  inflating: Number/IM_Train/train/9/27861.1.png  \n",
            " extracting: Number/IM_Train/train/9/27864.1.png  \n",
            " extracting: Number/IM_Train/train/9/27865.0.png  \n",
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            " extracting: Number/IM_Train/train/9/27877.0.png  \n",
            " extracting: Number/IM_Train/train/9/27879.1.png  \n",
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            " extracting: Number/IM_Train/train/9/27886.1.png  \n",
            "  inflating: Number/IM_Train/train/9/27891.1.png  \n",
            " extracting: Number/IM_Train/train/9/27894.1.png  \n",
            " extracting: Number/IM_Train/train/9/27895.1.png  \n",
            " extracting: Number/IM_Train/train/9/27912.0.png  \n",
            " extracting: Number/IM_Train/train/9/27913.1.png  \n",
            " extracting: Number/IM_Train/train/9/27915.0.png  \n",
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            " extracting: Number/IM_Train/train/9/27917.0.png  \n",
            " extracting: Number/IM_Train/train/9/27918.0.png  \n",
            " extracting: Number/IM_Train/train/9/27918.1.png  \n",
            " extracting: Number/IM_Train/train/9/27925.0.png  \n",
            " extracting: Number/IM_Train/train/9/27927.0.png  \n",
            " extracting: Number/IM_Train/train/9/27935.1.png  \n",
            " extracting: Number/IM_Train/train/9/2794.0.png  \n",
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            " extracting: Number/IM_Train/train/9/2794.2.png  \n",
            " extracting: Number/IM_Train/train/9/27951.0.png  \n",
            " extracting: Number/IM_Train/train/9/27964.1.png  \n",
            "  inflating: Number/IM_Train/train/9/27976.1.png  \n",
            " extracting: Number/IM_Train/train/9/27983.2.png  \n",
            " extracting: Number/IM_Train/train/9/27984.0.png  \n",
            " extracting: Number/IM_Train/train/9/27997.2.png  \n",
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            "  inflating: Number/IM_Train/train/9/28274.2.png  \n",
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            " extracting: Number/IM_Train/train/9/29691.1.png  \n",
            "  inflating: Number/IM_Train/train/9/29705.0.png  \n",
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            " extracting: Number/IM_Train/train/9/29723.2.png  \n",
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            " extracting: Number/IM_Train/train/9/2974.0.png  \n",
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            " extracting: Number/IM_Train/train/9/29757.1.png  \n",
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            " extracting: Number/IM_Train/train/9/2980.3.png  \n",
            " extracting: Number/IM_Train/train/9/29805.0.png  \n",
            " extracting: Number/IM_Train/train/9/29815.0.png  \n",
            "  inflating: Number/IM_Train/train/9/29816.0.png  \n",
            " extracting: Number/IM_Train/train/9/2982.1.png  \n",
            " extracting: Number/IM_Train/train/9/29843.1.png  \n",
            " extracting: Number/IM_Train/train/9/29857.1.png  \n",
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            " extracting: Number/IM_Train/train/9/29866.1.png  \n",
            "  inflating: Number/IM_Train/train/9/29867.0.png  \n",
            " extracting: Number/IM_Train/train/9/29870.0.png  \n",
            " extracting: Number/IM_Train/train/9/29876.0.png  \n",
            " extracting: Number/IM_Train/train/9/29876.1.png  \n",
            " extracting: Number/IM_Train/train/9/29883.1.png  \n",
            " extracting: Number/IM_Train/train/9/29898.0.png  \n",
            " extracting: Number/IM_Train/train/9/299.1.png  \n",
            " extracting: Number/IM_Train/train/9/29909.0.png  \n",
            " extracting: Number/IM_Train/train/9/29918.0.png  \n",
            " extracting: Number/IM_Train/train/9/29920.0.png  \n",
            " extracting: Number/IM_Train/train/9/29923.1.png  \n",
            " extracting: Number/IM_Train/train/9/29923.2.png  \n",
            " extracting: Number/IM_Train/train/9/2993.1.png  \n",
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            " extracting: Number/IM_Train/train/9/29955.1.png  \n",
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            " extracting: Number/IM_Train/train/9/29959.1.png  \n",
            " extracting: Number/IM_Train/train/9/29963.1.png  \n",
            " extracting: Number/IM_Train/train/9/29969.2.png  \n",
            " extracting: Number/IM_Train/train/9/29987.2.png  \n",
            "  inflating: Number/IM_Train/train/9/2999.0.png  \n",
            " extracting: Number/IM_Train/train/9/29991.0.png  \n",
            " extracting: Number/IM_Train/train/9/29994.1.png  \n",
            "  inflating: Number/IM_Train/train/9/29996.0.png  \n",
            "  inflating: Number/IM_Train/train/9/300.0.png  \n",
            " extracting: Number/IM_Train/train/9/30007.0.png  \n",
            " extracting: Number/IM_Train/train/9/3001.1.png  \n",
            " extracting: Number/IM_Train/train/9/30022.0.png  \n",
            " extracting: Number/IM_Train/train/9/30034.1.png  \n",
            " extracting: Number/IM_Train/train/9/3005.2.png  \n",
            " extracting: Number/IM_Train/train/9/30084.2.png  \n",
            " extracting: Number/IM_Train/train/9/30084.3.png  \n",
            " extracting: Number/IM_Train/train/9/3009.0.png  \n",
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            " extracting: Number/IM_Train/train/9/30109.0.png  \n",
            " extracting: Number/IM_Train/train/9/30111.2.png  \n",
            " extracting: Number/IM_Train/train/9/30113.2.png  \n",
            " extracting: Number/IM_Train/train/9/30115.1.png  \n",
            " extracting: Number/IM_Train/train/9/3012.2.png  \n",
            " extracting: Number/IM_Train/train/9/30123.2.png  \n",
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            " extracting: Number/IM_Train/train/9/30132.1.png  \n",
            "  inflating: Number/IM_Train/train/9/30136.0.png  \n",
            " extracting: Number/IM_Train/train/9/3014.1.png  \n",
            " extracting: Number/IM_Train/train/9/30144.1.png  \n",
            " extracting: Number/IM_Train/train/9/3015.1.png  \n",
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            "  inflating: Number/IM_Train/train/9/31802.0.png  \n",
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            "  inflating: Number/IM_Train/train/9/31895.0.png  \n",
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            "  inflating: Number/IM_Train/train/9/31949.0.png  \n",
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            "  inflating: Number/IM_Train/train/9/32014.0.png  \n",
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            " extracting: Number/IM_Train/train/9/32029.0.png  \n",
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            " extracting: Number/IM_Train/train/9/32055.1.png  \n",
            " extracting: Number/IM_Train/train/9/32056.1.png  \n",
            "  inflating: Number/IM_Train/train/9/32066.2.png  \n",
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            "  inflating: Number/IM_Train/train/9/3214.0.png  \n",
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            "  inflating: Number/IM_Train/train/9/32181.0.png  \n",
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            "  inflating: Number/IM_Train/train/9/32361.0.png  \n",
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            "  inflating: Number/IM_Train/train/9/32857.0.png  \n",
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            "  inflating: Number/IM_Train/train/9/3357.2.png  \n",
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            "  inflating: Number/IM_Train/train/9/3541.2.png  \n",
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            " extracting: Number/IM_Train/train/9/3566.0.png  \n",
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            " extracting: Number/IM_Train/train/9/3607.0.png  \n",
            " extracting: Number/IM_Train/train/9/3611.0.png  \n",
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            " extracting: Number/IM_Train/train/9/3615.1.png  \n",
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            " extracting: Number/IM_Train/train/9/3633.0.png  \n",
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            " extracting: Number/IM_Train/train/9/3694.1.png  \n",
            " extracting: Number/IM_Train/train/9/3699.1.png  \n",
            " extracting: Number/IM_Train/train/9/3710.1.png  \n",
            " extracting: Number/IM_Train/train/9/3717.1.png  \n",
            " extracting: Number/IM_Train/train/9/3722.1.png  \n",
            " extracting: Number/IM_Train/train/9/3723.1.png  \n",
            " extracting: Number/IM_Train/train/9/3727.1.png  \n",
            " extracting: Number/IM_Train/train/9/3729.1.png  \n",
            " extracting: Number/IM_Train/train/9/374.0.png  \n",
            " extracting: Number/IM_Train/train/9/3740.2.png  \n",
            " extracting: Number/IM_Train/train/9/3744.2.png  \n",
            "  inflating: Number/IM_Train/train/9/3745.0.png  \n",
            " extracting: Number/IM_Train/train/9/3765.3.png  \n",
            " extracting: Number/IM_Train/train/9/3770.0.png  \n",
            " extracting: Number/IM_Train/train/9/3781.1.png  \n",
            " extracting: Number/IM_Train/train/9/3787.2.png  \n",
            " extracting: Number/IM_Train/train/9/3793.1.png  \n",
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            " extracting: Number/IM_Train/train/9/380.1.png  \n",
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            " extracting: Number/IM_Train/train/9/3814.0.png  \n",
            "  inflating: Number/IM_Train/train/9/3817.0.png  \n",
            " extracting: Number/IM_Train/train/9/3825.0.png  \n",
            " extracting: Number/IM_Train/train/9/3829.1.png  \n",
            " extracting: Number/IM_Train/train/9/3836.1.png  \n",
            "  inflating: Number/IM_Train/train/9/3839.0.png  \n",
            " extracting: Number/IM_Train/train/9/3843.1.png  \n",
            " extracting: Number/IM_Train/train/9/3848.0.png  \n",
            " extracting: Number/IM_Train/train/9/3850.1.png  \n",
            " extracting: Number/IM_Train/train/9/3853.0.png  \n",
            " extracting: Number/IM_Train/train/9/3864.1.png  \n",
            " extracting: Number/IM_Train/train/9/3867.0.png  \n",
            " extracting: Number/IM_Train/train/9/387.0.png  \n",
            "  inflating: Number/IM_Train/train/9/387.1.png  \n",
            " extracting: Number/IM_Train/train/9/3874.1.png  \n",
            " extracting: Number/IM_Train/train/9/3879.0.png  \n",
            " extracting: Number/IM_Train/train/9/3890.1.png  \n",
            " extracting: Number/IM_Train/train/9/3902.1.png  \n",
            " extracting: Number/IM_Train/train/9/3906.0.png  \n",
            " extracting: Number/IM_Train/train/9/3912.1.png  \n",
            " extracting: Number/IM_Train/train/9/3917.1.png  \n",
            " extracting: Number/IM_Train/train/9/392.1.png  \n",
            " extracting: Number/IM_Train/train/9/3923.1.png  \n",
            "  inflating: Number/IM_Train/train/9/3925.0.png  \n",
            " extracting: Number/IM_Train/train/9/3933.1.png  \n",
            "  inflating: Number/IM_Train/train/9/3934.2.png  \n",
            " extracting: Number/IM_Train/train/9/3939.0.png  \n",
            " extracting: Number/IM_Train/train/9/3945.1.png  \n",
            "  inflating: Number/IM_Train/train/9/395.2.png  \n",
            " extracting: Number/IM_Train/train/9/396.2.png  \n",
            " extracting: Number/IM_Train/train/9/3965.0.png  \n",
            " extracting: Number/IM_Train/train/9/3968.1.png  \n",
            "  inflating: Number/IM_Train/train/9/3981.0.png  \n",
            " extracting: Number/IM_Train/train/9/3982.1.png  \n",
            " extracting: Number/IM_Train/train/9/3987.0.png  \n",
            "  inflating: Number/IM_Train/train/9/3995.0.png  \n",
            " extracting: Number/IM_Train/train/9/4.0.png  \n",
            " extracting: Number/IM_Train/train/9/4000.1.png  \n",
            " extracting: Number/IM_Train/train/9/4004.1.png  \n",
            "  inflating: Number/IM_Train/train/9/4005.0.png  \n",
            "  inflating: Number/IM_Train/train/9/4016.0.png  \n",
            " extracting: Number/IM_Train/train/9/4030.2.png  \n",
            " extracting: Number/IM_Train/train/9/4033.1.png  \n",
            "  inflating: Number/IM_Train/train/9/404.1.png  \n",
            " extracting: Number/IM_Train/train/9/4044.1.png  \n",
            " extracting: Number/IM_Train/train/9/4050.2.png  \n",
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            " extracting: Number/IM_Train/train/9/4060.1.png  \n",
            "  inflating: Number/IM_Train/train/9/4066.0.png  \n",
            " extracting: Number/IM_Train/train/9/4067.2.png  \n",
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            "  inflating: Number/IM_Train/train/9/5639.1.png  \n",
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            " extracting: Number/IM_Train/train/9/5662.2.png  \n",
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            "  inflating: Number/IM_Train/train/9/5784.0.png  \n",
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            " extracting: Number/IM_Train/train/9/5911.1.png  \n",
            "  inflating: Number/IM_Train/train/9/5916.0.png  \n",
            " extracting: Number/IM_Train/train/9/5918.1.png  \n",
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            " extracting: Number/IM_Train/train/9/5926.0.png  \n",
            "  inflating: Number/IM_Train/train/9/5928.1.png  \n",
            " extracting: Number/IM_Train/train/9/5939.1.png  \n",
            " extracting: Number/IM_Train/train/9/5939.2.png  \n",
            " extracting: Number/IM_Train/train/9/594.0.png  \n",
            "  inflating: Number/IM_Train/train/9/5940.0.png  \n",
            " extracting: Number/IM_Train/train/9/5947.1.png  \n",
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            "  inflating: Number/IM_Train/train/9/6423.1.png  \n",
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            "  inflating: Number/IM_Train/train/9/7070.0.png  \n",
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            "  inflating: Number/IM_Train/train/9/7359.0.png  \n",
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            " extracting: Number/IM_Train/train/9/7366.1.png  \n",
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            "  inflating: Number/IM_Train/train/9/7451.0.png  \n",
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            "  inflating: Number/IM_Train/train/9/7614.0.png  \n",
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            " extracting: Number/IM_Train/train/9/7717.1.png  \n",
            "  inflating: Number/IM_Train/train/9/7728.2.png  \n",
            " extracting: Number/IM_Train/train/9/7735.0.png  \n",
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            " extracting: Number/IM_Train/train/9/7780.0.png  \n",
            "  inflating: Number/IM_Train/train/9/7782.0.png  \n",
            " extracting: Number/IM_Train/train/9/7787.1.png  \n",
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            " extracting: Number/IM_Train/train/9/7806.0.png  \n",
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            " extracting: Number/IM_Train/train/9/781.0.png  \n",
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            " extracting: Number/IM_Train/train/9/7812.1.png  \n",
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            " extracting: Number/IM_Train/train/9/784.1.png  \n",
            " extracting: Number/IM_Train/train/9/7840.1.png  \n",
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            " extracting: Number/IM_Train/train/9/926.0.png  \n",
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            " extracting: Number/IM_Train/train/9/9392.1.png  \n",
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            " extracting: Number/IM_Train/train/9/9467.1.png  \n",
            "  inflating: Number/IM_Train/train/9/9476.1.png  \n",
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            " extracting: Number/IM_Train/train/9/9517.1.png  \n",
            " extracting: Number/IM_Train/train/9/9520.2.png  \n",
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            " extracting: Number/IM_Train/train/9/9529.2.png  \n",
            " extracting: Number/IM_Train/train/9/9531.1.png  \n",
            " extracting: Number/IM_Train/train/9/9533.1.png  \n",
            " extracting: Number/IM_Train/train/9/9543.1.png  \n",
            " extracting: Number/IM_Train/train/9/9550.2.png  \n",
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            " extracting: Number/IM_Train/train/9/9593.1.png  \n",
            " extracting: Number/IM_Train/train/9/9606.1.png  \n",
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            " extracting: Number/IM_Train/train/9/9616.0.png  \n",
            "  inflating: Number/IM_Train/train/9/9618.0.png  \n",
            " extracting: Number/IM_Train/train/9/9619.2.png  \n",
            " extracting: Number/IM_Train/train/9/9621.1.png  \n",
            " extracting: Number/IM_Train/train/9/9636.0.png  \n",
            " extracting: Number/IM_Train/train/9/9640.0.png  \n",
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            " extracting: Number/IM_Train/train/9/965.0.png  \n",
            "  inflating: Number/IM_Train/train/9/9659.0.png  \n",
            " extracting: Number/IM_Train/train/9/9662.3.png  \n",
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            " extracting: Number/IM_Train/train/9/967.1.png  \n",
            " extracting: Number/IM_Train/train/9/9675.1.png  \n",
            " extracting: Number/IM_Train/train/9/9675.3.png  \n",
            " extracting: Number/IM_Train/train/9/9676.1.png  \n",
            " extracting: Number/IM_Train/train/9/9677.1.png  \n",
            "  inflating: Number/IM_Train/train/9/9682.1.png  \n",
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            " extracting: Number/IM_Train/train/9/9686.2.png  \n",
            " extracting: Number/IM_Train/train/9/9712.1.png  \n",
            " extracting: Number/IM_Train/train/9/9714.0.png  \n",
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            " extracting: Number/IM_Train/train/9/973.1.png  \n",
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            " extracting: Number/IM_Train/train/9/9733.2.png  \n",
            " extracting: Number/IM_Train/train/9/9733.3.png  \n",
            " extracting: Number/IM_Train/train/9/9734.2.png  \n",
            " extracting: Number/IM_Train/train/9/9735.1.png  \n",
            "  inflating: Number/IM_Train/train/9/9744.0.png  \n",
            "  inflating: Number/IM_Train/train/9/9749.0.png  \n",
            " extracting: Number/IM_Train/train/9/9750.1.png  \n",
            " extracting: Number/IM_Train/train/9/9753.1.png  \n",
            " extracting: Number/IM_Train/train/9/9766.1.png  \n",
            " extracting: Number/IM_Train/train/9/9775.2.png  \n",
            "  inflating: Number/IM_Train/train/9/9780.0.png  \n",
            " extracting: Number/IM_Train/train/9/9785.2.png  \n",
            " extracting: Number/IM_Train/train/9/979.1.png  \n",
            " extracting: Number/IM_Train/train/9/9791.2.png  \n",
            " extracting: Number/IM_Train/train/9/9799.2.png  \n",
            " extracting: Number/IM_Train/train/9/9804.1.png  \n",
            " extracting: Number/IM_Train/train/9/9807.1.png  \n",
            " extracting: Number/IM_Train/train/9/9821.1.png  \n",
            " extracting: Number/IM_Train/train/9/9824.2.png  \n",
            " extracting: Number/IM_Train/train/9/983.1.png  \n",
            " extracting: Number/IM_Train/train/9/9832.1.png  \n",
            " extracting: Number/IM_Train/train/9/9836.2.png  \n",
            " extracting: Number/IM_Train/train/9/9845.1.png  \n",
            " extracting: Number/IM_Train/train/9/9848.2.png  \n",
            " extracting: Number/IM_Train/train/9/9852.1.png  \n",
            " extracting: Number/IM_Train/train/9/9858.1.png  \n",
            "  inflating: Number/IM_Train/train/9/9865.0.png  \n",
            " extracting: Number/IM_Train/train/9/9870.1.png  \n",
            " extracting: Number/IM_Train/train/9/9873.1.png  \n",
            " extracting: Number/IM_Train/train/9/9881.1.png  \n",
            " extracting: Number/IM_Train/train/9/9885.2.png  \n",
            " extracting: Number/IM_Train/train/9/9886.1.png  \n",
            " extracting: Number/IM_Train/train/9/9888.1.png  \n",
            " extracting: Number/IM_Train/train/9/989.2.png  \n",
            " extracting: Number/IM_Train/train/9/9891.2.png  \n",
            " extracting: Number/IM_Train/train/9/9907.1.png  \n",
            " extracting: Number/IM_Train/train/9/9913.1.png  \n",
            " extracting: Number/IM_Train/train/9/9917.1.png  \n",
            " extracting: Number/IM_Train/train/9/9925.1.png  \n",
            " extracting: Number/IM_Train/train/9/9926.3.png  \n",
            " extracting: Number/IM_Train/train/9/9932.1.png  \n",
            " extracting: Number/IM_Train/train/9/9946.1.png  \n",
            " extracting: Number/IM_Train/train/9/9966.1.png  \n",
            "  inflating: Number/IM_Train/train/9/9967.0.png  \n",
            " extracting: Number/IM_Train/train/9/9971.1.png  \n",
            " extracting: Number/IM_Train/train/9/9977.1.png  \n",
            " extracting: Number/IM_Train/train/9/9980.1.png  \n",
            " extracting: Number/img_crop_save.py  \n",
            "  inflating: Number/project.ipynb    \n",
            "  inflating: Number/sample_submission.csv  \n",
            "   creating: Number/save_model/\n",
            "   creating: Number/save_model/saved_model_1/\n",
            "   creating: Number/save_model/saved_model_1/assets/\n",
            "  inflating: Number/save_model/saved_model_1/keras_metadata.pb  \n",
            "  inflating: Number/save_model/saved_model_1/saved_model.pb  \n",
            "   creating: Number/save_model/saved_model_1/variables/\n",
            "  inflating: Number/save_model/saved_model_1/variables/variables.data-00000-of-00001  \n",
            "  inflating: Number/save_model/saved_model_1/variables/variables.index  \n",
            "   creating: Number/save_model/saved_model_2/\n",
            "  inflating: Number/tempCodeRunnerFile.py  \n",
            "  inflating: Number/test_sub.csv     \n",
            "  inflating: Number/test_submit.ipynb  \n"
          ]
        }
      ],
      "source": [
        "!unzip /content/drive/MyDrive/Number.zip"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qGI-PpCVrPQL"
      },
      "source": [
        "Clean Data (It is not used because the data has been overly cleaned.It is not used because the data has been overly cleaned.)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "p0x9av1xrPQO"
      },
      "outputs": [],
      "source": [
        "# import cv2 \n",
        "# import pytesseract\n",
        "# import os\n",
        "\n",
        "\n",
        "# f_path =  r\"E:\\SuperAi\\Number\\IM_Train\\train\\7\"\n",
        "# path = r\"E:\\SuperAi\\Number\\IM_Train\\train\\0\\506.2.png\"\n",
        "\n",
        "# pytesseract.pytesseract.tesseract_cmd = \"C:\\Program Files\\Tesseract-OCR/tesseract.exe\"\n",
        "\n",
        "\n",
        "# # img = cv2.imread(path)\n",
        "# # img = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)\n",
        "# # img = cv2.resize(img,(64,64))\n",
        "# # digit = pytesseract.image_to_string(img,lang='eng', config='--psm 10 --oem 3 -c tessedit_char_whitelist=0123456789')\n",
        "# # print(digit[0])\n",
        "\n",
        "# # cv2.imshow('re',img)\n",
        "# # cv2.waitKey(0)\n",
        "\n",
        "# img = cv2.imread(f_path)\n",
        "# count = 0\n",
        "# digit_img = []\n",
        "# def load_images_from_folder(folder,count):\n",
        "    \n",
        "#     for filename in os.listdir(folder):\n",
        "#         count = count +1\n",
        "#         img = cv2.imread(os.path.join(folder,filename))\n",
        "#         img = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)\n",
        "#         img = cv2.resize(img,(64,64))\n",
        "#         digit = pytesseract.image_to_string(img,lang='eng', config='--psm 10 --oem 3 -c tessedit_char_whitelist=0123456789')\n",
        "#         try :\n",
        "#             if digit[0] == '7':\n",
        "#                 digit_img.append(digit[0])\n",
        "#         except:\n",
        "#             pass\n",
        "#         if count %50 == 0:\n",
        "#             print(f'Process : {count}')\n",
        "#             print(len(digit_img))\n",
        "\n",
        "        \n",
        "#     return digit\n",
        "\n",
        "# load_images_from_folder(f_path,count)\n",
        "# print(digit_img)\n",
        "# print(f'len : {digit_img}')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MZRvNZuRrPQR"
      },
      "source": [
        "Get Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2TFg4CsvrPQR",
        "outputId": "2c0a5420-8f62-4eeb-fd7a-118027d2e328"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0 : done\n",
            "1 : done\n",
            "2 : done\n",
            "3 : done\n",
            "4 : done\n",
            "5 : done\n",
            "6 : done\n",
            "7 : done\n",
            "8 : done\n",
            "9 : done\n",
            "Total Dataponits =  73018\n"
          ]
        }
      ],
      "source": [
        "current_dir = '/content/Number'\n",
        "data = os.listdir(current_dir+r'/IM_Train/train' )\n",
        "data_X = []     \n",
        "data_y = []  \n",
        "data_classes = len(data)\n",
        "for i in range (0,data_classes):\n",
        "    data_list = os.listdir(current_dir+r'/IM_Train/train' +\"/\"+str(i))\n",
        "    for j in data_list:\n",
        "        pic = cv2.imread(current_dir+r'/IM_Train/train' +\"/\"+str(i)+\"/\"+j)\n",
        "        pic = cv2.resize(pic,(32,32))\n",
        "        data_X.append(pic)\n",
        "        data_y.append(i)\n",
        "    print(f'{i} : done')\n",
        "\n",
        "         \n",
        "if len(data_X) == len(data_y) :\n",
        "    print(\"Total Dataponits = \",len(data_X))\n",
        "\n",
        "# Labels and images\n",
        "data_X = np.array(data_X)\n",
        "data_y = np.array(data_y)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BmuQvTRfrPQU",
        "outputId": "db574cb8-05be-4e93-c8eb-2c23c19a60c8"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([0, 0, 0, ..., 9, 9, 9])"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data_y"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QjwaT5lArPQV",
        "outputId": "4844f3f5-2d46-49ba-e229-8e0e940cd329"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Training Set Shape =  (59144, 32, 32, 3)\n",
            "Validation Set Shape =  (6572, 32, 32, 3)\n",
            "Test Set Shape =  (7302, 32, 32, 3)\n"
          ]
        }
      ],
      "source": [
        "train_X, test_X, train_y, test_y = train_test_split(data_X,data_y,test_size=0.1)\n",
        "train_X, valid_X, train_y, valid_y = train_test_split(train_X,train_y,test_size=0.1)\n",
        "print(\"Training Set Shape = \",train_X.shape)\n",
        "print(\"Validation Set Shape = \",valid_X.shape)\n",
        "print(\"Test Set Shape = \",test_X.shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Pre_processing"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "d3k1gkQirPQW"
      },
      "outputs": [],
      "source": [
        "def Prep(img):\n",
        "    img = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) #making image grayscale\n",
        "    img = cv2.equalizeHist(img) #Histogram equalization to enhance contrast\n",
        "    img = img/255 #normalizing\n",
        "    return img\n",
        "train_X = np.array(list(map(Prep, train_X)))\n",
        "test_X = np.array(list(map(Prep, test_X)))\n",
        "valid_X= np.array(list(map(Prep, valid_X)))\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9PUc-_wArPQW"
      },
      "outputs": [],
      "source": [
        "#Reshaping the images\n",
        "train_X = train_X.reshape(train_X.shape[0], train_X.shape[1], train_X.shape[2],1)\n",
        "test_X = test_X.reshape(test_X.shape[0], test_X.shape[1], test_X.shape[2],1)\n",
        "valid_X = valid_X.reshape(valid_X.shape[0], valid_X.shape[1], valid_X.shape[2],1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fWYGzSmyrPQX",
        "outputId": "f6d3d06c-9d3e-498d-e37a-16f1a41cc8ce"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(6572, 32, 32, 1)"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "valid_X.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nRD3rAGArPQX"
      },
      "outputs": [],
      "source": [
        "train_y = to_categorical(train_y, data_classes)\n",
        "test_y = to_categorical(test_y, data_classes)\n",
        "valid_y = to_categorical(valid_y, data_classes)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rHXt651zrPQX"
      },
      "outputs": [],
      "source": [
        "#Augmentation\n",
        "datagen = ImageDataGenerator(width_shift_range=0.1, height_shift_range=0.1, shear_range=0.1,zoom_range=0.1, rotation_range=10)\n",
        "datagen.fit(train_X)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "wtYTqrD9rPQg"
      },
      "outputs": [],
      "source": [
        "def prepare_data(img_input):\n",
        "    img_input = cv2.resize(img_input,(32,32))\n",
        "    img_input = np.array(img_input)\n",
        "\n",
        "    img_input = cv2.cvtColor(img_input,cv2.COLOR_BGR2GRAY) #making image grayscale\n",
        "    img_input = cv2.equalizeHist(img_input) #Histogram equalization to enhance contrast\n",
        "    img_input = img_input/255 #normalizing\n",
        "\n",
        "    # train_X = train_X.reshape(train_X.shape[0], train_X.shape[1], train_X.shape[2],1)\n",
        "    img_input = img_input.reshape(1,32,32,1)\n",
        "    return img_input\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Training"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QbRR8c4urPQY",
        "outputId": "e1da0d67-0d08-4404-dcdf-5bf52c718f5e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d (Conv2D)             (None, 32, 32, 64)        1664      \n",
            "                                                                 \n",
            " conv2d_1 (Conv2D)           (None, 32, 32, 64)        102464    \n",
            "                                                                 \n",
            " max_pooling2d (MaxPooling2D  (None, 16, 16, 64)       0         \n",
            " )                                                               \n",
            "                                                                 \n",
            " conv2d_2 (Conv2D)           (None, 16, 16, 32)        18464     \n",
            "                                                                 \n",
            " conv2d_3 (Conv2D)           (None, 16, 16, 32)        9248      \n",
            "                                                                 \n",
            " conv2d_4 (Conv2D)           (None, 16, 16, 16)        4624      \n",
            "                                                                 \n",
            " max_pooling2d_1 (MaxPooling  (None, 8, 8, 16)         0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 1024)              0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 512)               524800    \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 512)               0         \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 256)               131328    \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 10)                2570      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 795,162\n",
            "Trainable params: 795,162\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "model = Sequential()\n",
        "\n",
        "model.add((Conv2D(64,(5,5),input_shape=(32, 32, 1) ,padding = 'Same' ,activation='relu')))\n",
        "model.add((Conv2D(64, (5,5),padding=\"same\",activation='relu')))\n",
        "model.add(MaxPooling2D(pool_size=(2,2)))\n",
        "\n",
        "\n",
        "model.add((Conv2D(32, (3,3),padding=\"same\", activation='relu')))\n",
        "model.add((Conv2D(32, (3,3), padding=\"same\", activation='relu')))\n",
        "\n",
        "model.add((Conv2D(16, (3,3), padding=\"same\", activation='relu')))\n",
        "model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2)))\n",
        "\n",
        "\n",
        "model.add(Flatten())\n",
        "model.add(Dense(512,activation='relu'))\n",
        "model.add(Dropout(0.5))\n",
        "model.add(Dense(256,activation='relu'))\n",
        "\n",
        "model.add(Dense(10, activation='softmax'))\n",
        "\n",
        "model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ja4Nr6L5rPQb",
        "outputId": "8150ce12-1202-4365-86bd-477f13de7e7b"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/keras/optimizers/optimizer_v2/rmsprop.py:135: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
            "  super(RMSprop, self).__init__(name, **kwargs)\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "200/200 - 19s - loss: 1.8643 - accuracy: 0.3524 - val_loss: 0.9916 - val_accuracy: 0.6894 - 19s/epoch - 94ms/step\n",
            "Epoch 2/100\n",
            "200/200 - 6s - loss: 1.1078 - accuracy: 0.6444 - val_loss: 0.6880 - val_accuracy: 0.7839 - 6s/epoch - 29ms/step\n",
            "Epoch 3/100\n",
            "200/200 - 6s - loss: 0.8755 - accuracy: 0.7188 - val_loss: 0.5700 - val_accuracy: 0.8188 - 6s/epoch - 32ms/step\n",
            "Epoch 4/100\n",
            "200/200 - 6s - loss: 0.7937 - accuracy: 0.7444 - val_loss: 0.6025 - val_accuracy: 0.8142 - 6s/epoch - 29ms/step\n",
            "Epoch 5/100\n",
            "200/200 - 6s - loss: 0.7210 - accuracy: 0.7738 - val_loss: 0.4931 - val_accuracy: 0.8471 - 6s/epoch - 29ms/step\n",
            "Epoch 6/100\n",
            "200/200 - 6s - loss: 0.6875 - accuracy: 0.7837 - val_loss: 0.5033 - val_accuracy: 0.8462 - 6s/epoch - 29ms/step\n",
            "Epoch 7/100\n",
            "200/200 - 6s - loss: 0.6505 - accuracy: 0.7929 - val_loss: 0.5023 - val_accuracy: 0.8428 - 6s/epoch - 32ms/step\n",
            "Epoch 8/100\n",
            "200/200 - 6s - loss: 0.6243 - accuracy: 0.8013 - val_loss: 0.4649 - val_accuracy: 0.8541 - 6s/epoch - 29ms/step\n",
            "Epoch 9/100\n",
            "200/200 - 6s - loss: 0.6210 - accuracy: 0.8024 - val_loss: 0.5139 - val_accuracy: 0.8390 - 6s/epoch - 32ms/step\n",
            "Epoch 10/100\n",
            "200/200 - 9s - loss: 0.6038 - accuracy: 0.8138 - val_loss: 0.5142 - val_accuracy: 0.8478 - 9s/epoch - 46ms/step\n",
            "Epoch 11/100\n",
            "200/200 - 6s - loss: 0.6004 - accuracy: 0.8067 - val_loss: 0.4471 - val_accuracy: 0.8596 - 6s/epoch - 29ms/step\n",
            "Epoch 12/100\n",
            "200/200 - 6s - loss: 0.5842 - accuracy: 0.8188 - val_loss: 0.4823 - val_accuracy: 0.8533 - 6s/epoch - 29ms/step\n",
            "Epoch 13/100\n",
            "200/200 - 6s - loss: 0.5795 - accuracy: 0.8166 - val_loss: 0.4461 - val_accuracy: 0.8632 - 6s/epoch - 29ms/step\n",
            "Epoch 14/100\n",
            "200/200 - 6s - loss: 0.5628 - accuracy: 0.8202 - val_loss: 0.4216 - val_accuracy: 0.8669 - 6s/epoch - 32ms/step\n",
            "Epoch 15/100\n",
            "200/200 - 6s - loss: 0.5662 - accuracy: 0.8240 - val_loss: 0.4290 - val_accuracy: 0.8670 - 6s/epoch - 28ms/step\n",
            "Epoch 16/100\n",
            "200/200 - 6s - loss: 0.5540 - accuracy: 0.8263 - val_loss: 0.4551 - val_accuracy: 0.8628 - 6s/epoch - 29ms/step\n",
            "Epoch 17/100\n",
            "200/200 - 6s - loss: 0.5515 - accuracy: 0.8272 - val_loss: 0.4555 - val_accuracy: 0.8580 - 6s/epoch - 28ms/step\n",
            "Epoch 18/100\n",
            "200/200 - 6s - loss: 0.5523 - accuracy: 0.8265 - val_loss: 0.4052 - val_accuracy: 0.8790 - 6s/epoch - 32ms/step\n",
            "Epoch 19/100\n",
            "200/200 - 6s - loss: 0.5304 - accuracy: 0.8305 - val_loss: 0.4452 - val_accuracy: 0.8705 - 6s/epoch - 29ms/step\n",
            "Epoch 20/100\n",
            "200/200 - 6s - loss: 0.5483 - accuracy: 0.8316 - val_loss: 0.4405 - val_accuracy: 0.8637 - 6s/epoch - 28ms/step\n",
            "Epoch 21/100\n",
            "200/200 - 6s - loss: 0.5286 - accuracy: 0.8334 - val_loss: 0.4239 - val_accuracy: 0.8748 - 6s/epoch - 29ms/step\n",
            "Epoch 22/100\n",
            "200/200 - 6s - loss: 0.5376 - accuracy: 0.8350 - val_loss: 0.4220 - val_accuracy: 0.8729 - 6s/epoch - 31ms/step\n",
            "Epoch 23/100\n",
            "200/200 - 6s - loss: 0.5441 - accuracy: 0.8296 - val_loss: 0.4262 - val_accuracy: 0.8728 - 6s/epoch - 32ms/step\n",
            "Epoch 24/100\n",
            "200/200 - 6s - loss: 0.5338 - accuracy: 0.8345 - val_loss: 0.4109 - val_accuracy: 0.8742 - 6s/epoch - 31ms/step\n",
            "Epoch 25/100\n",
            "200/200 - 6s - loss: 0.5236 - accuracy: 0.8376 - val_loss: 0.4124 - val_accuracy: 0.8777 - 6s/epoch - 32ms/step\n",
            "Epoch 26/100\n",
            "200/200 - 6s - loss: 0.5270 - accuracy: 0.8358 - val_loss: 0.4228 - val_accuracy: 0.8757 - 6s/epoch - 32ms/step\n",
            "Epoch 27/100\n",
            "200/200 - 6s - loss: 0.5332 - accuracy: 0.8355 - val_loss: 0.4231 - val_accuracy: 0.8713 - 6s/epoch - 32ms/step\n",
            "Epoch 28/100\n",
            "200/200 - 6s - loss: 0.5067 - accuracy: 0.8439 - val_loss: 0.4100 - val_accuracy: 0.8813 - 6s/epoch - 32ms/step\n",
            "Epoch 29/100\n",
            "200/200 - 6s - loss: 0.5224 - accuracy: 0.8441 - val_loss: 0.4199 - val_accuracy: 0.8739 - 6s/epoch - 28ms/step\n",
            "Epoch 30/100\n",
            "200/200 - 6s - loss: 0.5305 - accuracy: 0.8411 - val_loss: 0.4286 - val_accuracy: 0.8751 - 6s/epoch - 28ms/step\n",
            "Epoch 31/100\n",
            "200/200 - 6s - loss: 0.5170 - accuracy: 0.8444 - val_loss: 0.4297 - val_accuracy: 0.8740 - 6s/epoch - 28ms/step\n",
            "Epoch 32/100\n",
            "200/200 - 6s - loss: 0.5190 - accuracy: 0.8365 - val_loss: 0.4600 - val_accuracy: 0.8780 - 6s/epoch - 28ms/step\n",
            "Epoch 33/100\n",
            "200/200 - 6s - loss: 0.5285 - accuracy: 0.8407 - val_loss: 0.4503 - val_accuracy: 0.8736 - 6s/epoch - 28ms/step\n",
            "Epoch 34/100\n",
            "200/200 - 6s - loss: 0.5288 - accuracy: 0.8366 - val_loss: 0.4196 - val_accuracy: 0.8764 - 6s/epoch - 31ms/step\n",
            "Epoch 35/100\n",
            "200/200 - 6s - loss: 0.5253 - accuracy: 0.8388 - val_loss: 0.4056 - val_accuracy: 0.8822 - 6s/epoch - 31ms/step\n",
            "Epoch 36/100\n",
            "200/200 - 6s - loss: 0.5331 - accuracy: 0.8377 - val_loss: 0.4149 - val_accuracy: 0.8790 - 6s/epoch - 31ms/step\n",
            "Epoch 37/100\n",
            "200/200 - 6s - loss: 0.5208 - accuracy: 0.8398 - val_loss: 0.3988 - val_accuracy: 0.8792 - 6s/epoch - 29ms/step\n",
            "Epoch 38/100\n",
            "200/200 - 6s - loss: 0.5290 - accuracy: 0.8402 - val_loss: 0.3999 - val_accuracy: 0.8801 - 6s/epoch - 32ms/step\n",
            "Epoch 39/100\n",
            "200/200 - 6s - loss: 0.5470 - accuracy: 0.8341 - val_loss: 0.4164 - val_accuracy: 0.8745 - 6s/epoch - 29ms/step\n",
            "Epoch 40/100\n",
            "200/200 - 6s - loss: 0.5274 - accuracy: 0.8345 - val_loss: 0.4157 - val_accuracy: 0.8780 - 6s/epoch - 31ms/step\n",
            "Epoch 41/100\n",
            "200/200 - 8s - loss: 0.5173 - accuracy: 0.8413 - val_loss: 0.4186 - val_accuracy: 0.8824 - 8s/epoch - 38ms/step\n",
            "Epoch 42/100\n",
            "200/200 - 6s - loss: 0.5250 - accuracy: 0.8419 - val_loss: 0.4451 - val_accuracy: 0.8705 - 6s/epoch - 31ms/step\n",
            "Epoch 43/100\n",
            "200/200 - 6s - loss: 0.5333 - accuracy: 0.8361 - val_loss: 0.4301 - val_accuracy: 0.8719 - 6s/epoch - 31ms/step\n",
            "Epoch 44/100\n",
            "200/200 - 6s - loss: 0.5271 - accuracy: 0.8399 - val_loss: 0.4304 - val_accuracy: 0.8720 - 6s/epoch - 31ms/step\n",
            "Epoch 45/100\n",
            "200/200 - 6s - loss: 0.5408 - accuracy: 0.8357 - val_loss: 0.4247 - val_accuracy: 0.8755 - 6s/epoch - 31ms/step\n",
            "Epoch 46/100\n",
            "200/200 - 6s - loss: 0.5124 - accuracy: 0.8455 - val_loss: 0.4315 - val_accuracy: 0.8813 - 6s/epoch - 31ms/step\n",
            "Epoch 47/100\n",
            "200/200 - 6s - loss: 0.5119 - accuracy: 0.8412 - val_loss: 0.4178 - val_accuracy: 0.8743 - 6s/epoch - 31ms/step\n",
            "Epoch 48/100\n",
            "200/200 - 6s - loss: 0.5181 - accuracy: 0.8416 - val_loss: 0.4359 - val_accuracy: 0.8796 - 6s/epoch - 32ms/step\n",
            "Epoch 49/100\n",
            "200/200 - 6s - loss: 0.5172 - accuracy: 0.8408 - val_loss: 0.4493 - val_accuracy: 0.8780 - 6s/epoch - 32ms/step\n",
            "Epoch 50/100\n",
            "200/200 - 6s - loss: 0.5126 - accuracy: 0.8477 - val_loss: 0.4674 - val_accuracy: 0.8684 - 6s/epoch - 28ms/step\n",
            "Epoch 51/100\n",
            "200/200 - 6s - loss: 0.5357 - accuracy: 0.8376 - val_loss: 0.4170 - val_accuracy: 0.8780 - 6s/epoch - 28ms/step\n",
            "Epoch 52/100\n",
            "200/200 - 6s - loss: 0.5279 - accuracy: 0.8456 - val_loss: 0.4126 - val_accuracy: 0.8815 - 6s/epoch - 31ms/step\n",
            "Epoch 53/100\n",
            "200/200 - 6s - loss: 0.5426 - accuracy: 0.8369 - val_loss: 0.4155 - val_accuracy: 0.8815 - 6s/epoch - 31ms/step\n",
            "Epoch 54/100\n",
            "200/200 - 6s - loss: 0.5158 - accuracy: 0.8387 - val_loss: 0.4358 - val_accuracy: 0.8733 - 6s/epoch - 31ms/step\n",
            "Epoch 55/100\n",
            "200/200 - 6s - loss: 0.5387 - accuracy: 0.8397 - val_loss: 0.4373 - val_accuracy: 0.8822 - 6s/epoch - 31ms/step\n",
            "Epoch 56/100\n",
            "200/200 - 6s - loss: 0.5221 - accuracy: 0.8405 - val_loss: 0.4741 - val_accuracy: 0.8764 - 6s/epoch - 31ms/step\n",
            "Epoch 57/100\n",
            "200/200 - 6s - loss: 0.5330 - accuracy: 0.8391 - val_loss: 0.4330 - val_accuracy: 0.8760 - 6s/epoch - 28ms/step\n",
            "Epoch 58/100\n",
            "200/200 - 6s - loss: 0.5188 - accuracy: 0.8430 - val_loss: 0.4316 - val_accuracy: 0.8789 - 6s/epoch - 31ms/step\n",
            "Epoch 59/100\n",
            "200/200 - 8s - loss: 0.5286 - accuracy: 0.8402 - val_loss: 0.4734 - val_accuracy: 0.8745 - 8s/epoch - 39ms/step\n",
            "Epoch 60/100\n",
            "200/200 - 6s - loss: 0.5230 - accuracy: 0.8429 - val_loss: 0.4242 - val_accuracy: 0.8746 - 6s/epoch - 28ms/step\n",
            "Epoch 61/100\n",
            "200/200 - 6s - loss: 0.5282 - accuracy: 0.8431 - val_loss: 0.4179 - val_accuracy: 0.8818 - 6s/epoch - 28ms/step\n",
            "Epoch 62/100\n",
            "200/200 - 6s - loss: 0.5312 - accuracy: 0.8384 - val_loss: 0.4299 - val_accuracy: 0.8767 - 6s/epoch - 28ms/step\n",
            "Epoch 63/100\n",
            "200/200 - 12s - loss: 0.5399 - accuracy: 0.8349 - val_loss: 0.4312 - val_accuracy: 0.8772 - 12s/epoch - 59ms/step\n",
            "Epoch 64/100\n",
            "200/200 - 6s - loss: 0.5247 - accuracy: 0.8422 - val_loss: 0.4811 - val_accuracy: 0.8609 - 6s/epoch - 28ms/step\n",
            "Epoch 65/100\n",
            "200/200 - 6s - loss: 0.5155 - accuracy: 0.8419 - val_loss: 0.4297 - val_accuracy: 0.8742 - 6s/epoch - 28ms/step\n",
            "Epoch 66/100\n",
            "200/200 - 6s - loss: 0.5229 - accuracy: 0.8409 - val_loss: 0.4400 - val_accuracy: 0.8830 - 6s/epoch - 28ms/step\n",
            "Epoch 67/100\n",
            "200/200 - 6s - loss: 0.5234 - accuracy: 0.8389 - val_loss: 0.4541 - val_accuracy: 0.8658 - 6s/epoch - 28ms/step\n",
            "Epoch 68/100\n",
            "200/200 - 6s - loss: 0.5418 - accuracy: 0.8327 - val_loss: 0.4607 - val_accuracy: 0.8789 - 6s/epoch - 31ms/step\n",
            "Epoch 69/100\n",
            "200/200 - 6s - loss: 0.5145 - accuracy: 0.8435 - val_loss: 0.5762 - val_accuracy: 0.8376 - 6s/epoch - 28ms/step\n",
            "Epoch 70/100\n",
            "200/200 - 6s - loss: 0.5453 - accuracy: 0.8386 - val_loss: 0.4435 - val_accuracy: 0.8698 - 6s/epoch - 32ms/step\n",
            "Epoch 71/100\n",
            "200/200 - 6s - loss: 0.5132 - accuracy: 0.8466 - val_loss: 0.4346 - val_accuracy: 0.8751 - 6s/epoch - 31ms/step\n",
            "Epoch 72/100\n",
            "200/200 - 7s - loss: 0.5382 - accuracy: 0.8391 - val_loss: 0.4405 - val_accuracy: 0.8819 - 7s/epoch - 35ms/step\n",
            "Epoch 73/100\n",
            "200/200 - 6s - loss: 0.5332 - accuracy: 0.8344 - val_loss: 0.4343 - val_accuracy: 0.8723 - 6s/epoch - 31ms/step\n",
            "Epoch 74/100\n",
            "200/200 - 6s - loss: 0.5260 - accuracy: 0.8423 - val_loss: 0.4269 - val_accuracy: 0.8731 - 6s/epoch - 31ms/step\n",
            "Epoch 75/100\n",
            "200/200 - 7s - loss: 0.5250 - accuracy: 0.8376 - val_loss: 0.4267 - val_accuracy: 0.8772 - 7s/epoch - 36ms/step\n",
            "Epoch 76/100\n",
            "200/200 - 6s - loss: 0.5427 - accuracy: 0.8351 - val_loss: 0.4213 - val_accuracy: 0.8778 - 6s/epoch - 31ms/step\n",
            "Epoch 77/100\n",
            "200/200 - 6s - loss: 0.5442 - accuracy: 0.8368 - val_loss: 0.4754 - val_accuracy: 0.8742 - 6s/epoch - 31ms/step\n",
            "Epoch 78/100\n",
            "200/200 - 6s - loss: 0.5547 - accuracy: 0.8343 - val_loss: 0.4336 - val_accuracy: 0.8804 - 6s/epoch - 28ms/step\n",
            "Epoch 79/100\n",
            "200/200 - 6s - loss: 0.5609 - accuracy: 0.8319 - val_loss: 0.4725 - val_accuracy: 0.8731 - 6s/epoch - 31ms/step\n",
            "Epoch 80/100\n",
            "200/200 - 6s - loss: 0.5373 - accuracy: 0.8392 - val_loss: 0.5643 - val_accuracy: 0.8702 - 6s/epoch - 32ms/step\n",
            "Epoch 81/100\n",
            "200/200 - 6s - loss: 0.5472 - accuracy: 0.8332 - val_loss: 0.4419 - val_accuracy: 0.8731 - 6s/epoch - 28ms/step\n",
            "Epoch 82/100\n",
            "200/200 - 6s - loss: 0.5639 - accuracy: 0.8322 - val_loss: 0.4912 - val_accuracy: 0.8577 - 6s/epoch - 30ms/step\n",
            "Epoch 83/100\n",
            "200/200 - 5s - loss: 0.5537 - accuracy: 0.8327 - val_loss: 0.4280 - val_accuracy: 0.8723 - 5s/epoch - 27ms/step\n",
            "Epoch 84/100\n",
            "200/200 - 6s - loss: 0.5409 - accuracy: 0.8344 - val_loss: 0.4622 - val_accuracy: 0.8685 - 6s/epoch - 28ms/step\n",
            "Epoch 85/100\n",
            "200/200 - 6s - loss: 0.5610 - accuracy: 0.8317 - val_loss: 0.4734 - val_accuracy: 0.8792 - 6s/epoch - 31ms/step\n",
            "Epoch 86/100\n",
            "200/200 - 6s - loss: 0.5517 - accuracy: 0.8307 - val_loss: 0.4727 - val_accuracy: 0.8684 - 6s/epoch - 31ms/step\n",
            "Epoch 87/100\n",
            "200/200 - 6s - loss: 0.5549 - accuracy: 0.8332 - val_loss: 0.5038 - val_accuracy: 0.8576 - 6s/epoch - 31ms/step\n",
            "Epoch 88/100\n",
            "200/200 - 6s - loss: 0.5697 - accuracy: 0.8309 - val_loss: 0.4389 - val_accuracy: 0.8731 - 6s/epoch - 32ms/step\n",
            "Epoch 89/100\n",
            "200/200 - 6s - loss: 0.5571 - accuracy: 0.8322 - val_loss: 0.4238 - val_accuracy: 0.8752 - 6s/epoch - 28ms/step\n",
            "Epoch 90/100\n",
            "200/200 - 7s - loss: 0.5500 - accuracy: 0.8351 - val_loss: 0.4521 - val_accuracy: 0.8774 - 7s/epoch - 34ms/step\n",
            "Epoch 91/100\n",
            "200/200 - 6s - loss: 0.5521 - accuracy: 0.8357 - val_loss: 0.4368 - val_accuracy: 0.8707 - 6s/epoch - 29ms/step\n",
            "Epoch 92/100\n",
            "200/200 - 6s - loss: 0.5471 - accuracy: 0.8341 - val_loss: 0.4329 - val_accuracy: 0.8726 - 6s/epoch - 31ms/step\n",
            "Epoch 93/100\n",
            "200/200 - 6s - loss: 0.5409 - accuracy: 0.8393 - val_loss: 0.4912 - val_accuracy: 0.8763 - 6s/epoch - 28ms/step\n",
            "Epoch 94/100\n",
            "200/200 - 6s - loss: 0.5702 - accuracy: 0.8263 - val_loss: 0.4698 - val_accuracy: 0.8702 - 6s/epoch - 31ms/step\n",
            "Epoch 95/100\n",
            "200/200 - 6s - loss: 0.5638 - accuracy: 0.8309 - val_loss: 0.4550 - val_accuracy: 0.8719 - 6s/epoch - 29ms/step\n",
            "Epoch 96/100\n",
            "200/200 - 6s - loss: 0.5453 - accuracy: 0.8359 - val_loss: 0.4558 - val_accuracy: 0.8678 - 6s/epoch - 28ms/step\n",
            "Epoch 97/100\n",
            "200/200 - 6s - loss: 0.5602 - accuracy: 0.8316 - val_loss: 0.4687 - val_accuracy: 0.8638 - 6s/epoch - 28ms/step\n",
            "Epoch 98/100\n",
            "200/200 - 6s - loss: 0.5619 - accuracy: 0.8281 - val_loss: 0.5184 - val_accuracy: 0.8664 - 6s/epoch - 31ms/step\n",
            "Epoch 99/100\n",
            "200/200 - 6s - loss: 0.5545 - accuracy: 0.8341 - val_loss: 0.4320 - val_accuracy: 0.8717 - 6s/epoch - 29ms/step\n",
            "Epoch 100/100\n",
            "200/200 - 6s - loss: 0.5743 - accuracy: 0.8272 - val_loss: 0.4928 - val_accuracy: 0.8585 - 6s/epoch - 28ms/step\n",
            "Test Score =  0.48217225074768066\n",
            "Test Accuracy = 0.8530539870262146\n"
          ]
        }
      ],
      "source": [
        "#Compiling the model\n",
        "\n",
        "optimizer = RMSprop(lr=0.001, rho=0.9, epsilon = 1e-08, decay=0.0)\n",
        "model.compile(optimizer=optimizer,loss='categorical_crossentropy',metrics=['accuracy'])\n",
        "\n",
        "#Fit the model\n",
        "\n",
        "history = model.fit(datagen.flow(train_X, train_y, batch_size=64),\n",
        "                              epochs = 100, validation_data = (valid_X, valid_y),\n",
        "                              verbose = 2, steps_per_epoch= 200)\n",
        "\n",
        "# Testing the model on the test set\n",
        "\n",
        "score = model.evaluate(test_X, test_y, verbose=0)\n",
        "print('Test Score = ',score[0])\n",
        "print('Test Accuracy =', score[1])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "ipIHI_mbrPQc",
        "outputId": "ee4b0cbf-a84c-4d21-ddbe-8f4d77cbe34a"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f43bb74bb90>]"
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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qi+HqZ2Hkd8ysxeTxEHcaXP/y8YU5mHZQCXPRE5LPNIHiqDVt1Bnn9HSJWguJOLEwBxOSo644dcMczFyBbjry7huBXl9uhjH5k7ceVvw/GDzJjDFd1+LcFd8uMjMHL/hDc+dSdLKZOffTNaf2B0ccl71HqimoaONz0lsknWk6Rr953dTUT6XmFtFpvT/Qyw7AY8PgT4Pgzxlmks4XfzITdurLzcSemBT44ftmNMrqJ5tPvHRgJXz4SzMde8ZdrZ/7WG3mFuNya5wut9/HqhusdxIkp8vNy2ty2Xm4Y2d9fGtjHpc/tYrvLfiaI1W+J9damW3jvU353VHMky95vPm99mlAwZDmUSArdhcz84kv+XRHkf//Fae8TpxQwSL2fWFml834pRlWWLrXzFRb9ThEp5ha+U8+hbAY0z6+YCqs/IuZBbdoLsRlmKFHXTBl91Tz/OoD/H15NnUOFy63JiQwgJmjBnDNhBSmDI1j2fYiFm3IY3NeBb+9dAQ/v+Dkn8jI5dZU1TdSWd9ISFAAybFtTMVv4aGPdvLK2oMAfOf0JO6+aDinJUZid7qpsTuJCg0iLDgQl1vz6NJdPLvqAFMy4theUMltr27izfnTCAsO5L9bCrj7rS24NVTWN/KTszNOeF2+ySvn462FnJEay8xRA4gICcLt1nxzqJwvdhdTXteIvdFNo8vNzFEDmH1GMqqNQ/OCinqyi6oJDQ4gLDiQ2PBgkmPDCQtu53PaPwNncD+CKg5SGz+WSM+8iE+2F3Hnos0oFLe/ton/++44bpia1uZ6pMdH0j/Sz6xb0aN6f6DnrjbjQy96sLndqiLPTAve9jZc+kjziXcSMs2szqwXzCy2iHgzLb/l9OVe4IXVB3j4o52cPSyB01NiCA0KpLTWzsdbC1m6vbmGNjQxknMyE3hs2R7qHS5+fclwlFKs21/KG+vzODM1lhumpvkEidutUQqfMCqvdbAxt4yCinrqHC5q7U5GDOrXZmg1utz86eNdvLI2F7en314pmH/OUH51yXBCg8zrFVU2sDqnhPNHJJIQZc6B8vKaXF5Ze5B5Z6XTLyyI51cfYMm2QoICFI2u5kEAMeHBRIYEcriygZumD+H+K0azfNcRbn9tM3/4YDvThsbxu/e2MjUjjpjwYB76aCdRoUFcP7n5Erv1DhcFFXXkl9dzuKIBW7UdW00DdXYX6QmRDB8YRWhQIM+vPsDqnBKUMt0y4cGBTBsax47DVRRX2wkKUMRGhBAaFECjy83ibw/z0ppc7r9iNBPSfJv1Vmbb+Olrm6h1tD4NQv+IYIYmRjElI46pGXFMTo8jMrT5a15c4+CgcwiT2c7rR4aw660tTBjSnwcX7+CMlBie+cFE7nlvK/d9sI3i6gbumpnps33+/dU+Hlm6m+iwIO66aDg/nDaEkKATO1K1O11UNziPbrdTTa3dSZ3DRWK/U7N8/vTuUS5am/NyDz0frnm2Y/9TU2xmcIZEmjPX+TsvyinOVm3nv1sKsNXYOTMllvFp/RkU03wOi1fX5nL/f3dw2dhBPDV3PMGBzV/IRpebldk2NuaWc8GIRKZkxKE1/OE/21i04RDfn5RKfkUdX+eUEhkSSK3DRUJUKLeck4EC1uwrJSu3DICU/hEkx4ZRWNnA7qJqnzIGKHBruHzcIB65+nRiwps7livqHPz8jc18nVPKdRNTGJUUTUx4MFkHy1m0IY9RSdH87tIRLN1eyAffFNDo0kSEBHLTWemMHNSPu9/awoUjB/LvH04kMEBRUmPnjfV51De6iAoNIio0iBq7kyNVJoAvGDHAJ6T/9lk2T35uLqxxTmYCC384iYAAuPWVTazea+O2806jqLKBb/LKyS2ta/X+x0WGEBYUwOHK5qabhKhQ5p+bwZwpaew8XMVHWw+zam8Jo5OimTV2EBeMHEB0mHkP3G7Ne5vz+cuyPdiq7Vw0aiDzzkpnxrB43t2Uz73vb2PYgCgenD3GDH1udFFe56CwsoGCinp2F1axNb8Sp1sTEx7MX68/g5mjBuJ0ubnhufVcWrCAmwM+5N2Rf+W+bck4XG6mZMTxwrzJRIUG0ehyc+/723h3Uz5TM+J44MrRjE6K5u/L9/Lk53uZNWYQtQ4nq/aWMDQhkp+cncHMUQNIijFHTw2NLnYVVtEvLIjTEqP87rCLqxqY9+JG9tlquP+K0dw4Na3No5GTTWvNR1sL+d8Pd+Jyu/nsV+edUjudvjv135YNCybDlU/BxJs6/n9HdppZadHJ3Ve24+R0ucmx1bAtv5JdhdVMGBLLd8Yl+XwJVu8t4aU1B1ixx4bLrQkObK6RRoeZIAsPCWSfrZaLRg3knzdO6HDtSmvNQx/t5MWvc0mICuH2807jB9OG8O2hCv7xRQ6rc0oAGDYgimlD4wgKCKCgop6C8nrio0KYmhHH1KHxDEuMIjI0iKAAxb9X7ueJT/cwMDqMuy8257eotTt58esDHK5o4P9dPY5rJ/qe+/vzXUf43btbKa11EBYcwPWTUrlsbBKLNuTx4dbDaA2jk6J55/bpPjXT4+F2a+59fxv1jS7+cu3pR48+6h0ubnpxAxsOlJHYL5QJabGMTY4hNS6ClP7hJMeGkxAVevQ9rXM4ySmuobjKztmZCe03h7RQa3eycOV+Xlt3kNJaByn9w8kvr+eczAT+eeME+oW1PbqqzuEkK7ecR5fuZmdhFbedNxStYeHK/bx4seKCQ/+EG94itwqW7zrCjVOHEB7SXD6tNW9syOPxZXuoqG9k0pD+bMwt57qJKTx6zekEKPhyj41Hl+5mzxGzsx6TbGZ07imqxuk5rEqLi2DmqAFcNGogk9PjCAkKIKe4hpte2EB5nYOxyTFsyC3jsrGDePTq04mJ8L9ODY0udhZWcaisjqkZ8T4VlOOhteabQxV8vLWQ/bYayusaKa9zEBUaxJjkaEYnRfNlto0v99gYnRTN3uJqLhubxFNzx7d6rqqGRv7+2V6+yi7mukmp/Gj6ECJCWn/mtNbsOFzFyEH9CArsfL9b3w30jc/Bx78254LoihNbnQRaa0pqHOSV1ZJbUsf2w5Vsza9kx+FKGhpNp2VQgMLp1lw0agB/+t44nG7Nwx/u5JMdRST2C+XqCYO5bmIqqXHh7CqsZvPBcnJLa6lzuKh3uEiKCeO3s0YcbbY4nrJlHSxndFJ0q7DMKa4mOiyYAdHH90X7Jq+cX7z5DYfKmkeXJPYL5V8/mMDEIf6bumzVdlbn2Dg3M5F4r5pT9pFqPvimgJump5/wF749bremtNZBQlTISatRNjS6+HhrIW9syGPEoH787+wxPkdV7f3vQx/t5I315vJ4c6ek8cjV4zr82pV1jTz5+V5eWZvLjVPT+OOVYwgIaF5vrTX7bDUs31XMit3FBAcGcHpKDKenxFBS42D5riOs2VeKw+kmKjSIGcPiWX+gjKAAxYvzpjAmOZpnV+3nsWV7CA0KIDUugsGx4cRGhFBrd1Jtb8RWbWefrRaXuzmrJqTFcumYQQwf1I/BseH0jwhhY24Zy3cdYe2+UlLjIjhveCLnZibicLnZe6Sa3UXVLN91hPzyekICA8gcGEVcZAixESFU1DnYebiK0loHkSGB/PqSEdx0VjpPfW6OSl788WQuGDHg6Dq/v7mAR5buprTWzpjkaLYXVJEQFcJPzx/GnMmpR78fFXUO7nlvG5/sKOKczASenjuhzZ1WR/XdQH9nnrkyz907TvkZl5V1jbzw9QFeXptLRV3zqJKw4ADGJsdwekosp6fEMHZwDEPiI3h5TS6PLdtDSFAATpdGo7nzwkxuOSfjuIO6pzU0ujhYWkdESCDhIYHEhAd3OLBExyz+9jBr95XwxyvHHPeRApijE+8a/PGoczj5OqeUL3YfYcVuG7ERwSz84STS4ptPx7w1v4J3svI5XFHP4coGKjy15ujwYPpHBDMqKZqxg2NIigljZbaNpduL2OFnBFNMeDAzhsVzsLSu1eOhQQFMHRrP7DOSuWTMwKNNXE201hypshMeHHg0dO1OF995ajX1Dhef3n0uuwqrePjjXXx7qIIzU2N5+KqxjEuJISu3jCc+zWbt/lL6hQVx/aRUJqf356EPd1Jcbed74wfzny0FpPaP4Pl5k8lI6MDVt9rQNwNda3NtxtMuhKsXdt/rdKgomuwjNWQdLKOwooGiqgbKax1EhwcTHxmCS2vezcqn2u7k4tEDOXtYAmlxEaTGRZAeH9HmYdqBkloe+O92osOCuffykaT0j/C7nBCnCq11lx3ZFFc3cKisjoKKBoqrGhg3OIaJQ/of/b4UVzewdl8pESFBDB8YRUr/CAIDjv+1N+aWcd2/1pIeH0FuaR0Do0P5zSUjuGZCis/RCsCmg+W8tCaXpdsKcbo1Q+IjeGrOeM5IjWX9/lJ++vpmXG7NMzdO4KxhCW284rH1zUAv3m1OMzr7aZjww+57nWPYdLCcd7IO8VW2jUJPB1lggCIxKpS4yBCqGhopqbFjd7q5bOwg7rwwk1FJnTyvtxCiyz24eAdvZx3itnNP49ZzM/y2lXsrqmxg/YFSZo4aSJRX8+Shsjpue3UT910+irMzJdA7bsOzsOQ38IstZiz5SeJ2az7bdYSFK/ez6WA5UaFBnD0sgfNHJDJjWALJseGtaglOl7tLOkuEEN1Da43D5e6S5kyXW5/QkUKTYwV67xmH3lBlLq827loT4LmrzcShts4V3Unr95ey/kAZUzLiGJ8Wi8Pp5t1N+by0JpeDpXWk9A/nwStHc92k1HZHW0iYC3FqU0p1Wd9UZ8K8Pb0n0Le9DSv+D776s7k6UO5qc1HbbugMLa5q4LbXNh3tvAwNCiAoQFHrcDEhLZbfXjqCWWMGSVALIU6q3hPoB9dC5ABz4dj1z5gT5Kef3emnfXblfgAzcUYptNb8/r2t1DtcfPCzsyipcbB2Xyl1DidzpqRxZmpsp19TCCFORO8IdK3NqUDTZ5irsUy9Hba/D6Ov6tTTfrK9iD8tMZfr2n64kr9cezrvby5gxR4bD145mvGeKdkXjx7Y6VUQQojO6lCgK6VmAU8CgcBzWutHWzyeBrwMxHqWuUdrvaSLy9q2ykPm+ptpnnMoDxxjfjrhcEU9v39vK+MGx3DJ6IE88Vk2eWV1ZBdVc9Zp8fxoenrnyy2EEF2o3UBXSgUCC4CLgXxgo1JqsdZ6p9di/wO8rbV+Rik1GlgCpHdDef07uNb8TpvWJU/ncmt++eYWnC43T80dT0ZCJOkJkfz6nW8JDQzgsevOaDX+VAghelpHauhTgByt9X4ApdSbwFWAd6BroGkAdQxwuCsL2a68NeZaiZ2olTc0ujhQUkteWR2f7zrChtwy/nr9GUdndF15RjIjBvXD6dIM7uApXIUQ4mTqSKAPBg553c4HprZY5kHgU6XUnUAkcFGXlK6j8tZB6tQTPmf53iPV3PDcemzV9qP3zZ2SxtUTfE8MNXxgv04VUwghulNXdYrOBV7SWj+hlJoOvKqUGqu19rkEjlJqPjAfIC3N/8nzj1tdGdh2w7jrTujfc4prmPvsepSCJ+ecydCEKFLjzMmBhBDCSjoS6AVAqtftFM993m4GZgFordcqpcKABKDYeyGt9UJgIZiZoidYZl95niuWn8BFZQ+U1HLDs+sAzaJbpzNsQFSXFEkIIXpCR2a+bAQylVIZSqkQYA6wuMUyecBMAKXUKCAMsHVlQduUtwYCQyB5wnH9W0FFPTc8uw6nW/PGrdMkzIUQltduoGutncAdwDJgF2Y0yw6l1ENKqdmexX4N3KqU+hZYBMzTJ+skMQfXmjAP7vj5ryvqHNz0wgZq7E5eu3mqtI0LIXqFDrWhe8aUL2lx3wNef+8EZrT8v27nqIPCLXDWnR3+l4ZGFze/nEVeaR2v3DyF0clydkMhRO9g7ZmiBVngdkLa9A4t7nJr7lz0DZvzyllwwwSmDY3v5gIKIcTJY+2zR9n2mN9JZ3Zo8T9/spvPdh7hj1eM5vJxSd1YMCGEOPmsHei1NkBBZPsnin93Uz4LV+7nR9OHMG/GyTs/uhBCnCzWDvSaYoiIb3dC0aaDZdz3/jbOOi2e+68YfZIKJ4QQJ5e1A73WBlEDjrlIZX0jt726maTYMP5543TA/6AAABCoSURBVAS5+LAQoteydrrVlrTb3PLlnmJKauw8du0ZMvtTCNGrWTzQiyEy8ZiLfJVto39EMBOH9D9JhRJCiJ5h8UAvMVcpaoPbrVmZXcI5mYndeh0/IYQ4FVg30BsbwF51zCaXnYVVlNTYOW/4sWvxQgjRG1g30Gs9p4o5RpPLV9lmmXOGtz+sUQghrM76gX6MUS5fZdsYnRTNgH4dP8+LEEJYlfUDvY0aelVDI5sPlnPeCGluEUL0Db0g0P03p6zJKcXp1tJ+LoToM3pBoPsP7K+ybUSFBjEhTYYrCiH6BusGeo0NgiMhJLLVQ1prVmbbOOu0eEKCrLuKQghxPKybdrW2Nptb9tlqKKiol/ZzIUSfYu1Ab2OEy7IdRwC4YMSxz/MihBC9ibUDvY3280+2F3FmaizJseEnuVBCCNFzLB7orZtcDpXVsa2gklljB/VAoYQQoudYM9Dd7jbP47JsRxEAl0mgCyH6GGsGen05aJffJpel24sYlRTNkPjWo1+EEKI3s2agtzGpqLiqgU0Hy6V2LoTokywa6MXmd4tRLk3NLdJ+LoToiywa6P5niS7dXsTQxEgyB0T1QKGEEKJnWTTQS8xvr0Avq3Ww/kAZl40dhFJyMQshRN9jzUCvKQYVAOFxR+/acKAUl1szc9TAHiyYEEL0HGsGeq0NIhIgoLn4BRUNAGTI6BYhRB9l0UAvadV+XlhRT2hQALERwT1UKCGE6FkWDfRiiGoR6FUNJMeGS/u5EKLP6lCgK6VmKaX2KKVylFL3+Hn8b0qpLZ6fbKVURdcX1Yuf87gUVTYwKFouNSeE6LuC2ltAKRUILAAuBvKBjUqpxVrrnU3LaK3v9lr+TmB8N5S1WU3rQC+sqGfaafHd+rJCCHEq60gNfQqQo7Xer7V2AG8CVx1j+bnAoq4onF+OWmis9Ql0l1tzpNpOUozU0IUQfVdHAn0wcMjrdr7nvlaUUkOADOCLNh6fr5TKUkpl2Wy24y2r4WcMuq3ajsutGRQjp8sVQvRdXd0pOgd4V2vt8veg1nqh1nqS1npSYuIJXk3IzyzRwsp6AJKlhi6E6MM6EugFQKrX7RTPff7MoTubW6A50L1GuRRVmjHogyTQhRB9WEcCfSOQqZTKUEqFYEJ7ccuFlFIjgf7A2q4tYgt+auiHPYGeJE0uQog+rN1A11o7gTuAZcAu4G2t9Q6l1ENKqdlei84B3tRa6+4pqkeN50yLkd41dDOpqL9MKhJC9GHtDlsE0FovAZa0uO+BFrcf7LpiHcP0O2DcdRDcXBsvrGwgKSZMJhUJIfo0680UDQ6D/kN87iqsbJD2cyFEn2e9QPejqLKBZGk/F0L0cZYPdJdbc6RKauhCCGH5QC+pseN0a5JipYYuhOjbLB/ohU1DFuXEXEKIPs76gV5hZolKk4sQoq+zfqB7aujJ0uQihOjjLB/oRVUNhMikIiGEsH6gH66ol0lFQghBLwj0Is8sUSGE6OssH+hm2r+0nwshhKUDvWlSkdTQhRDC4oFe2jSpSAJdCCGsHeiHj17YQppchBDC0oFe5Ln0nNTQhRDC4oFeVtsIQFxkSA+XRAghep6lA93hNNeiDgsO7OGSCCFEz7N2oLvcAIQEWXo1hBCiS1g6Ce2NnkAPtPRqCCFEl7B0EjbV0IMDZdq/EEJYO9CdbkKCAuQ8LkIIgcUD3e50Eyrt50IIAVg80B0uCXQhhGhi6TS0N7qlQ1QIITwsnYYOl1uGLAohhIel09DhdBEaJJOKhBACLB/oUkMXQogmlk5DuwS6EEIc1aE0VErNUkrtUUrlKKXuaWOZ65VSO5VSO5RSb3RtMf1zOKVTVAghmgS1t4BSKhBYAFwM5AMblVKLtdY7vZbJBO4FZmity5VSA7qrwN4cLjdRYe2ughBC9Akdqd5OAXK01vu11g7gTeCqFsvcCizQWpcDaK2Lu7aY/kkNXQghmnUkDQcDh7xu53vu8zYcGK6U+loptU4pNaurCngs0oYuhBDNuqq9IgjIBM4HUoCVSqlxWusK74WUUvOB+QBpaWmdflEZ5SKEEM06koYFQKrX7RTPfd7ygcVa60at9QEgGxPwPrTWC7XWk7TWkxITE0+0zEeZc7nIOHQhhICOBfpGIFMplaGUCgHmAItbLPMfTO0cpVQCpglmfxeW0y8zsUhq6EIIAR0IdK21E7gDWAbsAt7WWu9QSj2klJrtWWwZUKqU2gmsAH6rtS7trkI3kan/QgjRrENt6FrrJcCSFvc94PW3Bn7l+TkptNamU1RGuQghBGDhmaJOt0ZruZ6oEEI0sWwaOpzm8nPShi6EEIZl07Ap0KWGLoQQhmXT0C6BLoQQPiybhkdr6NIpKoQQgJUD3eUCIDRYJhYJIQRYONDtUkMXQggflk1DGeUihBC+LJuG0ikqhBC+LJuGUkMXQghflk1DGYcuhBC+LJuGDpcEuhBCeLNsGtqdZtiijHIRQgjDsmkoTS5CCOHLsmnY3CkqE4uEEAIsHOgybFEIIXxZNg3tMmxRCCF8WDYN5eRcQgjhy7Jp6HC5CQ5UBASoni6KEEKcEqwb6HI9USGE8GHZRHQ43dIhKoQQXiybiHanSwJdCCG8WDYRHU63jEEXQggv1g10lzS5CCGEN8smonSKCiGEL8smol06RYUQwodlE9HudMssUSGE8GLZRJRhi0II4atDiaiUmqWU2qOUylFK3ePn8XlKKZtSaovn55auL6ovh9TQhRDCR1B7CyilAoEFwMVAPrBRKbVYa72zxaJvaa3v6IYy+iXj0IUQwldHEnEKkKO13q+1dgBvAld1b7Ha53DJKBchhPDWkUQcDBzyup3vua+la5RSW5VS7yqlUrukdMcgE4uEEMJXV1VxPwTStdanA58BL/tbSCk1XymVpZTKstlsnXpB6RQVQghfHUnEAsC7xp3iue8orXWp1truufkcMNHfE2mtF2qtJ2mtJyUmJp5IeY+SQBdCCF8dScSNQKZSKkMpFQLMARZ7L6CUSvK6ORvY1XVF9E8mFgkhhK92R7lorZ1KqTuAZUAg8ILWeodS6iEgS2u9GPiFUmo24ATKgHndWGbcbo3TrWXYohBCeGk30AG01kuAJS3ue8Dr73uBe7u2aG1zuOQC0UII0ZIlE9Eu1xMVQohWLJmIdqcLQJpchBDCiyUT0eGpocs4dCGEaGbpQJc2dCGEaGbJRJROUSGEaM2SiWhvlE5RIYRoyZKJKDV0IYRozZKJ2NwpasniCyFEt7BkIkqnqBBCtGbJRLRLoAshRCuWTESZWCSEEK1ZMhFlYpEQQrRmzUCXUS5CCNGKJRPRISfnEkKIViyZiNIpKoQQrVkyEWUcuhBCtGbJRHQ43QQoCJImFyGEOMqSiehwyfVEhRCiJUumor3RJR2iQgjRgiVT0eFyExosY9CFEMKbJQPd7nRLDV0IIVqwZCo6nG4Z4SKEEC1YMhUdTukUFUKIliyZinYJdCGEaMWSqShNLkII0ZolU1HGoQshRGuWTEWHjHIRQohWLJmKdqdLauhCCNFCh1JRKTVLKbVHKZWjlLrnGMtdo5TSSqlJXVfE1kwbukwsEkIIb+0GulIqEFgAXAaMBuYqpUb7Wa4fcBewvqsL2ZIMWxRCiNY6kopTgByt9X6ttQN4E7jKz3IPA38GGrqwfH5Jp6gQQrTWkVQcDBzyup3vue8opdQEIFVr/XEXlq1NMvVfCCFa63QqKqUCgL8Cv+7AsvOVUllKqSybzXbCr2l3ugkNlkAXQghvHUnFAiDV63aK574m/YCxwJdKqVxgGrDYX8eo1nqh1nqS1npSYmLiCRVYa206RaWGLoQQPjqSihuBTKVUhlIqBJgDLG56UGtdqbVO0Fqna63TgXXAbK11VncUuNGlAbmeqBBCtNRuKmqtncAdwDJgF/C21nqHUuohpdTs7i5gSw6XXCBaCCH8CerIQlrrJcCSFvc90May53e+WG2zN7oApFNUCCFasFwqNtXQ5YpFQgjhy3qB7vQ0uUgNXQghfFguFY8GurShCyGED8ulol0CXQgh/LJcKjYFulzgQgghfFkuFaXJRQgh/LNcKh4d5SKBLoQQPiyXis2jXGTYohBCeLNcoNudZmKRnJxLCCF8WS4VZRy6EEL4Z7lUlE5RIYTwz3KpKCfnEkII/yyXivZGGeUihBD+WC4Vh8RHcNnYQYQGySgXIYTw1qHT555KLhkziEvGDOrpYgghxCnHcjV0IYQQ/kmgCyFELyGBLoQQvYQEuhBC9BIS6EII0UtIoAshRC8hgS6EEL2EBLoQQvQSSmvdMy+slA04eIL/ngCUdGFxrKIvrndfXGfom+vdF9cZjn+9h2itE/090GOB3hlKqSyt9aSeLsfJ1hfXuy+uM/TN9e6L6wxdu97S5CKEEL2EBLoQQvQSVg30hT1dgB7SF9e7L64z9M317ovrDF243pZsQxdCCNGaVWvoQgghWrBcoCulZiml9iilcpRS9/R0ebqDUipVKbVCKbVTKbVDKXWX5/44pdRnSqm9nt/9e7qsXU0pFaiU+kYp9ZHndoZSar1ne7+llArp6TJ2NaVUrFLqXaXUbqXULqXU9D6yre/2fL63K6UWKaXCetv2Vkq9oJQqVkpt97rP77ZVxlOedd+qlJpwvK9nqUBXSgUCC4DLgNHAXKXU6J4tVbdwAr/WWo8GpgE/96znPcDnWutM4HPP7d7mLmCX1+0/A3/TWg8DyoGbe6RU3etJ4BOt9UjgDMz69+ptrZQaDPwCmKS1HgsEAnPofdv7JWBWi/va2raXAZmen/nAM8f7YpYKdGAKkKO13q+1dgBvAlf1cJm6nNa6UGu92fN3NeYLPhizri97FnsZ+G7PlLB7KKVSgO8Az3luK+BC4F3PIr1xnWOAc4HnAbTWDq11Bb18W3sEAeFKqSAgAiikl21vrfVKoKzF3W1t26uAV7SxDohVSiUdz+tZLdAHA4e8bud77uu1lFLpwHhgPTBQa13oeagIGNhDxeoufwd+B7g9t+OBCq2103O7N27vDMAGvOhpanpOKRVJL9/WWusC4HEgDxPklcAmev/2hra3bafzzWqB3qcopaKA94Bfaq2rvB/TZnhSrxmipJS6AijWWm/q6bKcZEHABOAZrfV4oJYWzSu9bVsDeNqNr8Ls0JKBSFo3TfR6Xb1trRboBUCq1+0Uz329jlIqGBPmr2ut3/fcfaTpEMzzu7inytcNZgCzlVK5mKa0CzFty7GeQ3Londs7H8jXWq/33H4XE/C9eVsDXAQc0FrbtNaNwPuYz0Bv397Q9rbtdL5ZLdA3ApmenvAQTCfK4h4uU5fztB0/D+zSWv/V66HFwE2ev28C/nuyy9ZdtNb3aq1TtNbpmO36hdb6RmAFcK1nsV61zgBa6yLgkFJqhOeumcBOevG29sgDpimlIjyf96b17tXb26OtbbsY+JFntMs0oNKraaZjtNaW+gEuB7KBfcAfero83bSOZ2MOw7YCWzw/l2PalD8H9gLLgbieLms3rf/5wEeev4cCG4Ac4B0gtKfL1w3reyaQ5dne/wH694VtDfwvsBvYDrwKhPa27Q0swvQRNGKOxm5ua9sCCjOKbx+wDTMC6LheT2aKCiFEL2G1JhchhBBtkEAXQoheQgJdCCF6CQl0IYToJSTQhRCil5BAF0KIXkICXQghegkJdCGE6CX+P4//mFR3lN3vAAAAAElFTkSuQmCC",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "from matplotlib import pyplot as plt\n",
        "plt.plot(history.history['accuracy'])\n",
        "plt.plot(history.history['val_accuracy'])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Save & load model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 165
        },
        "id": "Mnp3OfgHrPQd",
        "outputId": "3c0d7017-3e05-4d3f-8dbf-81548e787b29"
      },
      "outputs": [],
      "source": [
        "model.save('./content/Number/save_model/saved_model_3/sa3.h5')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Zyx-lbLrrPQd"
      },
      "outputs": [],
      "source": [
        "model = load_model('/content/Number/save_model/saved_model_3/sa3.h5')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Model summary"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RBIuUpeORbjG",
        "outputId": "39d7573f-de3d-4565-eaf7-d98699148500"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d (Conv2D)             (None, 32, 32, 64)        1664      \n",
            "                                                                 \n",
            " conv2d_1 (Conv2D)           (None, 32, 32, 64)        102464    \n",
            "                                                                 \n",
            " max_pooling2d (MaxPooling2D  (None, 16, 16, 64)       0         \n",
            " )                                                               \n",
            "                                                                 \n",
            " conv2d_2 (Conv2D)           (None, 16, 16, 32)        18464     \n",
            "                                                                 \n",
            " conv2d_3 (Conv2D)           (None, 16, 16, 32)        9248      \n",
            "                                                                 \n",
            " conv2d_4 (Conv2D)           (None, 16, 16, 16)        4624      \n",
            "                                                                 \n",
            " max_pooling2d_1 (MaxPooling  (None, 8, 8, 16)         0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 1024)              0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 512)               524800    \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 512)               0         \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 256)               131328    \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 10)                2570      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 795,162\n",
            "Trainable params: 795,162\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "model.summary()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y67mK0ZTrPQd"
      },
      "source": [
        "Crop_test_data\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "aq49s700rPQe"
      },
      "outputs": [],
      "source": [
        "current_dir = os.getcwd()\n",
        "\n",
        "img_folder = os.listdir(current_dir+'\\IM_Test\\IM_Test')\n",
        "\n",
        "json_files = current_dir+r'\\IM_Test\\IM_Test.json'\n",
        "\n",
        "if not os.path.exists('out'):\n",
        "    os.makedirs('out')\n",
        "\n",
        "if not os.path.exists('saved'):\n",
        "    os.makedirs('saved')\n",
        "\n",
        "f= json_files\n",
        "print('trying to read:',f)\n",
        "fl = open(f,'r')\n",
        "data = json.load(fl)\n",
        "\n",
        "for i in range(len(img_folder)):\n",
        "\tfor j in range(len(data[i]['bboxes'])):\n",
        "\t\tboxes = data[i]['bboxes'][j]\n",
        "\t\tcoor = list(boxes.values())\n",
        "\t\tcoor.pop(0)\n",
        "\t\timg = Image.open(f\"E:\\SuperAi\\\\Number\\IM_Test\\IM_Test\\\\{i+1}.png\")\n",
        "\t\timg2 = img.crop((coor[0],coor[2],coor[1],coor[3]))\n",
        "\t\timg2.save(f'E:\\SuperAi\\\\Number\\IM_Test\\New Folder\\IM_Test_crop\\\\{i+1}_{j}.png')\n",
        "\t\tif i % 100 ==0:\n",
        "\t\t\tprint(f'{i} cropped')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Test Prediction"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q2CBU7lWrPQh",
        "outputId": "1f1fb235-6030-4b4d-ab92-ebab771ba8ea"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 36ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 27ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 36ms/step\n",
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            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "Predicting done : 21100\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 37ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
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            "1/1 [==============================] - 0s 20ms/step\n",
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            "Predicting done : 21200\n",
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            "Predicting done : 21300\n",
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            "Predicting done : 21400\n",
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            "Predicting done : 21500\n",
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            "Predicting done : 21600\n",
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            "Predicting done : 21700\n",
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            "Predicting done : 21900\n",
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            "Predicting done : 22000\n",
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            "Predicting done : 22100\n",
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            "Predicting done : 22200\n",
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            "Predicting done : 22300\n",
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            "Predicting done : 22400\n",
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            "Predicting done : 22700\n",
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            "Predicting done : 22800\n",
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            "Predicting done : 22900\n",
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            "Predicting done : 23000\n",
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            "Predicting done : 23100\n",
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            "Predicting done : 23200\n",
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            "Predicting done : 23300\n",
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            "Predicting done : 23400\n",
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            "Predicting done : 23500\n",
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            "Predicting done : 23600\n",
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            "Predicting done : 23700\n",
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            "Predicting done : 23800\n",
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            "Predicting done : 23900\n",
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            "Predicting done : 24000\n",
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            "Predicting done : 24100\n",
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            "Predicting done : 24200\n",
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            "Predicting done : 24300\n",
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            "Predicting done : 24400\n",
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            "Predicting done : 24500\n",
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            "Predicting done : 24600\n",
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            "Predicting done : 24700\n",
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            "Predicting done : 24800\n",
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            "Predicting done : 24900\n",
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            "Predicting done : 25000\n",
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            "Predicting done : 25100\n",
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            "Predicting done : 25200\n",
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            "Predicting done : 25300\n",
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            "Predicting done : 25400\n",
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            "Predicting done : 25500\n",
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            "Predicting done : 25600\n",
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            "Predicting done : 25700\n",
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            "Predicting done : 25800\n",
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            "Predicting done : 25900\n",
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            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 29ms/step\n",
            "1/1 [==============================] - 0s 31ms/step\n",
            "1/1 [==============================] - 0s 29ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "Predicting done : 26000\n",
            "1/1 [==============================] - 0s 26ms/step\n",
            "1/1 [==============================] - 0s 53ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 31ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 28ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 31ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "[5,\n",
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              " 2,\n",
              " 1,\n",
              " 7,\n",
              " 7]"
            ]
          },
          "execution_count": 19,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "all_result = []\n",
        "test_folder = os.listdir('/content/Number/IM_Test/New folder/IM_Test_crop')\n",
        "test_folder.sort(key=lambda f: int(''. join(filter(str. isdigit, f))))\n",
        "img_input = cv2.imread('/content/Number/IM_Test/New folder/IM_Test_crop/'+str(i))\n",
        "for i in test_folder:\n",
        "    img_input = cv2.imread('/content/Number/IM_Test/New folder/IM_Test_crop/'+str(i))\n",
        "    img_input = prepare_data(img_input)\n",
        "    predictions = model.predict(img_input)\n",
        "    result = np.argmax(predictions,axis=1)\n",
        "    all_result.append(result[0])\n",
        "    if len(all_result) % 100 ==0:\n",
        "        print(f'Predicting done : {len(all_result)}')\n",
        "all_result[:100]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Bua1QCE7rPQi"
      },
      "source": [
        "**********************"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6Z9LEj8SrPQj"
      },
      "outputs": [],
      "source": [
        "#test by folder\n",
        "img_folder = '/content/Number/IM_Test/New folder/'\n",
        "img_generator = tf.keras.preprocessing.image_dataset_from_directory(\n",
        "    img_folder,\n",
        "    color_mode='grayscale',\n",
        "    image_size=(32,32),\n",
        "    shuffle=False\n",
        ")\n",
        "\n",
        "prob = model.predict(prepare_data(img_generator),2000)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m2IGWd1vAYLs",
        "outputId": "f868332b-21ae-4c37-fccf-deb0de22c846"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "814/814 [==============================] - 8s 10ms/step\n"
          ]
        }
      ],
      "source": [
        "predict_2 = model.predict(img_generator,2000)\n",
        "predict_2_class = np.argmax(predict_2,axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LAnvVk-wBgWo",
        "outputId": "94354bf6-4eb8-403c-8215-13d40601937e"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([1, 3, 3, ..., 3, 7, 3])"
            ]
          },
          "execution_count": 49,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "predict_2_class"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Save result to CSV"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "KzhGPNL3CEp5"
      },
      "outputs": [],
      "source": [
        "test_raw = pd.read_csv('/content/Number/IM_Submit.csv')\n",
        "test_raw['class']=all_result\n",
        "test_raw\n",
        "test_raw.to_csv('/content/Number/IM_Submit_2.csv',index=False)"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "collapsed_sections": [],
      "provenance": []
    },
    "gpuClass": "standard",
    "kernelspec": {
      "display_name": "Python 3.10.6 64-bit",
      "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": "3.10.6"
    },
    "orig_nbformat": 4,
    "vscode": {
      "interpreter": {
        "hash": "954ca36c1dc5874706b3eaa250ea91b30568c53426f4712f0ffe31e1a7cc271c"
      }
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  },
  "nbformat": 4,
  "nbformat_minor": 0
}