InceptionV3model.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyNtCH3+83xxEeRMAbIvSwi+",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/09aryan/MEME_CLASSIFICATION_USING_SENTIMENT_ANALYSIS/blob/main/InceptionV3model.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "\n",
        "# Mount Google Drive\n",
        "drive.mount('/content/drive')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CanEuQgdmMZd",
        "outputId": "eae416d9-18d9-4b47-a629-ad23724b7e34"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r4caUTjFiKyA",
        "outputId": "e96c94ad-27c2-40b3-c206-ff8d1badc0c0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
            "1/1 [==============================] - 0s 207ms/step\n",
            "1/1 [==============================] - 0s 209ms/step\n",
            "Processed 2144 images in 869.09 seconds\n",
            "1/1 [==============================] - 0s 215ms/step\n",
            "Error processing image /content/drive/MyDrive/images/image_5119.png: image file is truncated\n",
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            "Processed 2176 images in 880.97 seconds\n",
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            "Processed 2208 images in 893.24 seconds\n",
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            "Processed 2240 images in 906.68 seconds\n",
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            "Processed 2272 images in 920.16 seconds\n",
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            "Processed 2304 images in 933.14 seconds\n",
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            "Processed 2336 images in 946.99 seconds\n",
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            "Processed 2368 images in 958.68 seconds\n",
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            "Processed 2400 images in 971.42 seconds\n",
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            "Processed 2432 images in 984.40 seconds\n",
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            "Processed 2464 images in 997.69 seconds\n",
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            "Processed 2496 images in 1010.98 seconds\n",
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            "Processed 2528 images in 1024.21 seconds\n",
            "1/1 [==============================] - 0s 216ms/step\n",
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            "1/1 [==============================] - 0s 361ms/step\n",
            "Processed 2560 images in 1036.67 seconds\n",
            "1/1 [==============================] - 0s 383ms/step\n",
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            "1/1 [==============================] - 0s 372ms/step\n",
            "Processed 2592 images in 1047.99 seconds\n",
            "1/1 [==============================] - 0s 394ms/step\n",
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            "1/1 [==============================] - 0s 216ms/step\n",
            "1/1 [==============================] - 0s 229ms/step\n",
            "1/1 [==============================] - 0s 219ms/step\n",
            "Processed 2624 images in 1060.76 seconds\n",
            "1/1 [==============================] - 0s 222ms/step\n",
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            "Processed 2656 images in 1073.89 seconds\n",
            "1/1 [==============================] - 0s 220ms/step\n",
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            "Processed 2688 images in 1086.51 seconds\n",
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            "Processed 2720 images in 1099.34 seconds\n",
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            "1/1 [==============================] - 0s 365ms/step\n",
            "Processed 2752 images in 1110.79 seconds\n",
            "1/1 [==============================] - 0s 404ms/step\n",
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            "Processed 2784 images in 1121.78 seconds\n",
            "1/1 [==============================] - 0s 215ms/step\n",
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            "1/1 [==============================] - 0s 209ms/step\n",
            "Processed 2816 images in 1134.78 seconds\n",
            "1/1 [==============================] - 0s 215ms/step\n",
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            "Processed 2848 images in 1147.83 seconds\n",
            "1/1 [==============================] - 0s 217ms/step\n",
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            "Processed 2880 images in 1160.87 seconds\n",
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            "Processed 2912 images in 1173.73 seconds\n",
            "1/1 [==============================] - 0s 219ms/step\n",
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            "1/1 [==============================] - 0s 344ms/step\n",
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            "1/1 [==============================] - 0s 377ms/step\n",
            "Processed 2944 images in 1185.39 seconds\n",
            "1/1 [==============================] - 0s 370ms/step\n",
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            "1/1 [==============================] - 0s 212ms/step\n",
            "Processed 2976 images in 1197.89 seconds\n",
            "1/1 [==============================] - 0s 219ms/step\n",
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            "1/1 [==============================] - 0s 222ms/step\n",
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            "1/1 [==============================] - 0s 212ms/step\n",
            "Processed 3008 images in 1211.09 seconds\n",
            "1/1 [==============================] - 0s 233ms/step\n",
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            "Processed 3040 images in 1224.14 seconds\n",
            "1/1 [==============================] - 0s 217ms/step\n",
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            "Processed 3072 images in 1237.50 seconds\n",
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            "Processed 3104 images in 1250.42 seconds\n",
            "1/1 [==============================] - 0s 230ms/step\n",
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            "1/1 [==============================] - 0s 377ms/step\n",
            "Processed 3136 images in 1263.83 seconds\n",
            "1/1 [==============================] - 0s 228ms/step\n",
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            "Processed 3168 images in 1275.58 seconds\n",
            "1/1 [==============================] - 0s 368ms/step\n",
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            "1/1 [==============================] - 0s 215ms/step\n",
            "Processed 3200 images in 1288.09 seconds\n",
            "1/1 [==============================] - 0s 223ms/step\n",
            "1/1 [==============================] - 0s 213ms/step\n",
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            "Processed 3232 images in 1301.27 seconds\n",
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            "Processed 3264 images in 1314.77 seconds\n",
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            "Processed 3296 images in 1327.70 seconds\n",
            "1/1 [==============================] - 0s 221ms/step\n",
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            "Processed 3328 images in 1340.85 seconds\n",
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            "1/1 [==============================] - 0s 347ms/step\n",
            "Processed 3360 images in 1353.12 seconds\n",
            "1/1 [==============================] - 0s 392ms/step\n",
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            "1/1 [==============================] - 0s 262ms/step\n",
            "Processed 3392 images in 1365.20 seconds\n",
            "1/1 [==============================] - 0s 369ms/step\n",
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            "1/1 [==============================] - 0s 213ms/step\n",
            "Processed 3424 images in 1378.27 seconds\n",
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            "Processed 3456 images in 1391.27 seconds\n",
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            "Processed 3488 images in 1404.02 seconds\n",
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            "Processed 3520 images in 1417.44 seconds\n",
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            "Processed 3552 images in 1430.27 seconds\n",
            "1/1 [==============================] - 0s 348ms/step\n",
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            "Processed 3584 images in 1443.42 seconds\n",
            "1/1 [==============================] - 0s 418ms/step\n",
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            "Processed 3616 images in 1455.83 seconds\n",
            "1/1 [==============================] - 0s 349ms/step\n",
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            "Processed 3648 images in 1469.14 seconds\n",
            "1/1 [==============================] - 0s 221ms/step\n",
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            "Processed 3680 images in 1482.05 seconds\n",
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            "Processed 3712 images in 1495.55 seconds\n",
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            "Processed 3744 images in 1508.67 seconds\n",
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            "1/1 [==============================] - 0s 389ms/step\n",
            "1/1 [==============================] - 0s 374ms/step\n",
            "Processed 3776 images in 1520.94 seconds\n",
            "1/1 [==============================] - 0s 358ms/step\n",
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            "Processed 3808 images in 1531.72 seconds\n",
            "1/1 [==============================] - 0s 217ms/step\n",
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            "Processed 3840 images in 1545.07 seconds\n",
            "1/1 [==============================] - 0s 224ms/step\n",
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            "Processed 3872 images in 1558.20 seconds\n",
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            "Processed 3904 images in 1571.43 seconds\n",
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            "Processed 3936 images in 1584.91 seconds\n",
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            "Processed 3968 images in 1598.32 seconds\n",
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            "1/1 [==============================] - 0s 394ms/step\n",
            "Processed 4000 images in 1609.91 seconds\n",
            "1/1 [==============================] - 0s 395ms/step\n",
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            "Processed 4032 images in 1622.30 seconds\n",
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            "Processed 4064 images in 1635.75 seconds\n",
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            "1/1 [==============================] - 0s 214ms/step\n",
            "Processed 4096 images in 1649.29 seconds\n",
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            "1/1 [==============================] - 0s 209ms/step\n",
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            "1/1 [==============================] - 0s 210ms/step\n",
            "Processed 4128 images in 1662.37 seconds\n",
            "1/1 [==============================] - 0s 221ms/step\n",
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            "1/1 [==============================] - 0s 220ms/step\n",
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            "1/1 [==============================] - 0s 211ms/step\n",
            "Processed 4160 images in 1675.35 seconds\n",
            "1/1 [==============================] - 0s 234ms/step\n",
            "1/1 [==============================] - 0s 211ms/step\n",
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            "1/1 [==============================] - 0s 407ms/step\n",
            "1/1 [==============================] - 0s 356ms/step\n",
            "1/1 [==============================] - 0s 391ms/step\n",
            "Processed 4192 images in 1687.64 seconds\n",
            "1/1 [==============================] - 0s 360ms/step\n",
            "1/1 [==============================] - 0s 364ms/step\n",
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            "1/1 [==============================] - 0s 213ms/step\n",
            "1/1 [==============================] - 0s 212ms/step\n",
            "1/1 [==============================] - 0s 258ms/step\n",
            "Processed 4224 images in 1699.08 seconds\n",
            "1/1 [==============================] - 0s 362ms/step\n",
            "1/1 [==============================] - 0s 372ms/step\n",
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            "1/1 [==============================] - 0s 213ms/step\n",
            "1/1 [==============================] - 0s 213ms/step\n",
            "1/1 [==============================] - 0s 212ms/step\n",
            "Processed 4256 images in 1712.39 seconds\n",
            "1/1 [==============================] - 0s 225ms/step\n",
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            "Processed 4288 images in 1725.55 seconds\n",
            "1/1 [==============================] - 0s 219ms/step\n",
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            "Processed 4320 images in 1738.69 seconds\n",
            "1/1 [==============================] - 0s 219ms/step\n",
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            "1/1 [==============================] - 0s 216ms/step\n",
            "1/1 [==============================] - 0s 227ms/step\n",
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            "Processed 4352 images in 1752.70 seconds\n",
            "1/1 [==============================] - 0s 218ms/step\n",
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            "1/1 [==============================] - 0s 358ms/step\n",
            "1/1 [==============================] - 0s 370ms/step\n",
            "1/1 [==============================] - 0s 229ms/step\n",
            "Processed 4384 images in 1766.09 seconds\n",
            "1/1 [==============================] - 0s 220ms/step\n",
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            "1/1 [==============================] - 0s 212ms/step\n",
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            "1/1 [==============================] - 0s 377ms/step\n",
            "1/1 [==============================] - 0s 398ms/step\n",
            "1/1 [==============================] - 0s 377ms/step\n",
            "Processed 4416 images in 1778.45 seconds\n",
            "1/1 [==============================] - 0s 385ms/step\n",
            "1/1 [==============================] - 0s 376ms/step\n",
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            "1/1 [==============================] - 0s 229ms/step\n",
            "1/1 [==============================] - 0s 212ms/step\n",
            "Processed 4448 images in 1789.89 seconds\n",
            "1/1 [==============================] - 0s 226ms/step\n",
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            "1/1 [==============================] - 0s 212ms/step\n",
            "Processed 4480 images in 1803.24 seconds\n",
            "1/1 [==============================] - 0s 222ms/step\n",
            "1/1 [==============================] - 0s 223ms/step\n",
            "1/1 [==============================] - 0s 213ms/step\n",
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            "1/1 [==============================] - 0s 207ms/step\n",
            "1/1 [==============================] - 0s 212ms/step\n",
            "1/1 [==============================] - 0s 210ms/step\n",
            "Processed 4512 images in 1817.13 seconds\n",
            "1/1 [==============================] - 0s 225ms/step\n",
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            "1/1 [==============================] - 0s 218ms/step\n",
            "1/1 [==============================] - 0s 218ms/step\n",
            "1/1 [==============================] - 0s 212ms/step\n",
            "1/1 [==============================] - 0s 228ms/step\n",
            "Processed 4544 images in 1830.89 seconds\n",
            "1/1 [==============================] - 0s 220ms/step\n",
            "1/1 [==============================] - 0s 209ms/step\n",
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            "1/1 [==============================] - 0s 386ms/step\n",
            "1/1 [==============================] - 0s 408ms/step\n",
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            "1/1 [==============================] - 0s 298ms/step\n",
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            "1/1 [==============================] - 0s 231ms/step\n",
            "1/1 [==============================] - 0s 219ms/step\n",
            "1/1 [==============================] - 0s 220ms/step\n",
            "1/1 [==============================] - 0s 227ms/step\n",
            "Processed 4576 images in 1844.10 seconds\n",
            "1/1 [==============================] - 0s 224ms/step\n",
            "1/1 [==============================] - 0s 219ms/step\n",
            "1/1 [==============================] - 0s 220ms/step\n",
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            "1/1 [==============================] - 0s 217ms/step\n",
            "1/1 [==============================] - 0s 330ms/step\n",
            "1/1 [==============================] - 0s 413ms/step\n",
            "1/1 [==============================] - 0s 382ms/step\n",
            "1/1 [==============================] - 0s 382ms/step\n",
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            "1/1 [==============================] - 0s 419ms/step\n",
            "1/1 [==============================] - 0s 387ms/step\n",
            "1/1 [==============================] - 0s 395ms/step\n",
            "1/1 [==============================] - 0s 383ms/step\n",
            "Processed 4608 images in 1857.85 seconds\n",
            "1/1 [==============================] - 0s 227ms/step\n",
            "1/1 [==============================] - 0s 217ms/step\n",
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            "1/1 [==============================] - 0s 364ms/step\n",
            "1/1 [==============================] - 0s 369ms/step\n",
            "1/1 [==============================] - 0s 400ms/step\n",
            "Processed 4640 images in 1869.48 seconds\n",
            "1/1 [==============================] - 0s 390ms/step\n",
            "1/1 [==============================] - 0s 390ms/step\n",
            "1/1 [==============================] - 0s 374ms/step\n",
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            "1/1 [==============================] - 0s 212ms/step\n",
            "1/1 [==============================] - 0s 219ms/step\n",
            "1/1 [==============================] - 0s 206ms/step\n",
            "Processed 4672 images in 1882.36 seconds\n",
            "1/1 [==============================] - 0s 217ms/step\n",
            "1/1 [==============================] - 0s 351ms/step\n",
            "1/1 [==============================] - 0s 353ms/step\n",
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            "1/1 [==============================] - 0s 210ms/step\n",
            "1/1 [==============================] - 0s 210ms/step\n",
            "1/1 [==============================] - 0s 211ms/step\n",
            "Processed 4704 images in 1895.49 seconds\n",
            "1/1 [==============================] - 0s 215ms/step\n",
            "1/1 [==============================] - 0s 215ms/step\n",
            "1/1 [==============================] - 0s 217ms/step\n",
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            "1/1 [==============================] - 0s 207ms/step\n",
            "1/1 [==============================] - 0s 219ms/step\n",
            "1/1 [==============================] - 0s 206ms/step\n",
            "Processed 4736 images in 1908.71 seconds\n",
            "1/1 [==============================] - 0s 221ms/step\n",
            "1/1 [==============================] - 0s 228ms/step\n",
            "1/1 [==============================] - 0s 207ms/step\n",
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            "1/1 [==============================] - 0s 213ms/step\n",
            "1/1 [==============================] - 0s 216ms/step\n",
            "1/1 [==============================] - 0s 224ms/step\n",
            "Processed 4768 images in 1921.88 seconds\n",
            "1/1 [==============================] - 0s 219ms/step\n",
            "1/1 [==============================] - 0s 212ms/step\n",
            "1/1 [==============================] - 0s 223ms/step\n",
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            "1/1 [==============================] - 0s 254ms/step\n",
            "1/1 [==============================] - 0s 232ms/step\n",
            "1/1 [==============================] - 0s 210ms/step\n",
            "1/1 [==============================] - 0s 215ms/step\n",
            "Processed 4800 images in 1935.17 seconds\n",
            "1/1 [==============================] - 0s 226ms/step\n",
            "1/1 [==============================] - 0s 213ms/step\n",
            "1/1 [==============================] - 0s 207ms/step\n",
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            "1/1 [==============================] - 0s 345ms/step\n",
            "1/1 [==============================] - 0s 384ms/step\n",
            "1/1 [==============================] - 0s 366ms/step\n",
            "Processed 4832 images in 1948.12 seconds\n",
            "1/1 [==============================] - 0s 360ms/step\n",
            "1/1 [==============================] - 0s 225ms/step\n",
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            "1/1 [==============================] - 0s 336ms/step\n",
            "Processed 4864 images in 1959.62 seconds\n",
            "1/1 [==============================] - 0s 401ms/step\n",
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            "1/1 [==============================] - 0s 219ms/step\n",
            "1/1 [==============================] - 0s 210ms/step\n",
            "1/1 [==============================] - 0s 211ms/step\n",
            "1/1 [==============================] - 0s 225ms/step\n",
            "Processed 4896 images in 1972.49 seconds\n",
            "1/1 [==============================] - 0s 222ms/step\n",
            "1/1 [==============================] - 0s 213ms/step\n",
            "1/1 [==============================] - 0s 224ms/step\n",
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            "1/1 [==============================] - 0s 208ms/step\n",
            "1/1 [==============================] - 0s 207ms/step\n",
            "1/1 [==============================] - 0s 211ms/step\n",
            "1/1 [==============================] - 0s 213ms/step\n",
            "1/1 [==============================] - 0s 212ms/step\n",
            "Processed 4928 images in 1985.78 seconds\n",
            "1/1 [==============================] - 0s 234ms/step\n",
            "1/1 [==============================] - 0s 213ms/step\n",
            "1/1 [==============================] - 0s 225ms/step\n",
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            "1/1 [==============================] - 0s 357ms/step\n",
            "1/1 [==============================] - 0s 378ms/step\n",
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            "1/1 [==============================] - 0s 397ms/step\n",
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            "1/1 [==============================] - 0s 216ms/step\n",
            "1/1 [==============================] - 0s 208ms/step\n",
            "1/1 [==============================] - 0s 212ms/step\n",
            "1/1 [==============================] - 0s 216ms/step\n",
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            "1/1 [==============================] - 0s 210ms/step\n",
            "1/1 [==============================] - 0s 208ms/step\n",
            "1/1 [==============================] - 0s 221ms/step\n",
            "1/1 [==============================] - 0s 214ms/step\n",
            "Processed 4960 images in 1998.84 seconds\n",
            "1/1 [==============================] - 0s 223ms/step\n",
            "1/1 [==============================] - 0s 221ms/step\n",
            "1/1 [==============================] - 0s 211ms/step\n",
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            "1/1 [==============================] - 0s 228ms/step\n",
            "1/1 [==============================] - 0s 345ms/step\n",
            "1/1 [==============================] - 0s 357ms/step\n",
            "1/1 [==============================] - 0s 372ms/step\n",
            "1/1 [==============================] - 0s 394ms/step\n",
            "1/1 [==============================] - 0s 377ms/step\n",
            "1/1 [==============================] - 0s 360ms/step\n",
            "1/1 [==============================] - 0s 386ms/step\n",
            "1/1 [==============================] - 0s 379ms/step\n",
            "1/1 [==============================] - 0s 362ms/step\n",
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            "1/1 [==============================] - 0s 214ms/step\n",
            "1/1 [==============================] - 0s 212ms/step\n",
            "1/1 [==============================] - 0s 226ms/step\n",
            "1/1 [==============================] - 0s 213ms/step\n",
            "Processed 4992 images in 2012.22 seconds\n",
            "1/1 [==============================] - 0s 235ms/step\n",
            "1/1 [==============================] - 0s 226ms/step\n",
            "1/1 [==============================] - 0s 214ms/step\n",
            "1/1 [==============================] - 0s 210ms/step\n",
            "1/1 [==============================] - 0s 231ms/step\n",
            "1/1 [==============================] - 0s 210ms/step\n",
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            "1/1 [==============================] - 0s 236ms/step\n",
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            "1/1 [==============================] - 0s 226ms/step\n",
            "1/1 [==============================] - 0s 212ms/step\n",
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            "1/1 [==============================] - 0s 223ms/step\n",
            "1/1 [==============================] - 0s 212ms/step\n",
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            "1/1 [==============================] - 0s 224ms/step\n",
            "1/1 [==============================] - 0s 217ms/step\n",
            "1/1 [==============================] - 0s 214ms/step\n",
            "1/1 [==============================] - 0s 327ms/step\n",
            "1/1 [==============================] - 0s 404ms/step\n",
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            "1/1 [==============================] - 0s 364ms/step\n",
            "1/1 [==============================] - 0s 364ms/step\n",
            "1/1 [==============================] - 0s 406ms/step\n",
            "1/1 [==============================] - 0s 382ms/step\n",
            "Processed 5024 images in 2025.18 seconds\n",
            "1/1 [==============================] - 0s 376ms/step\n",
            "1/1 [==============================] - 0s 403ms/step\n",
            "1/1 [==============================] - 0s 211ms/step\n",
            "1/1 [==============================] - 0s 220ms/step\n",
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            "1/1 [==============================] - 0s 212ms/step\n",
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            "1/1 [==============================] - 0s 210ms/step\n",
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            "1/1 [==============================] - 0s 226ms/step\n",
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            "1/1 [==============================] - 0s 214ms/step\n",
            "1/1 [==============================] - 0s 211ms/step\n",
            "1/1 [==============================] - 0s 220ms/step\n",
            "1/1 [==============================] - 0s 370ms/step\n",
            "Processed 5056 images in 2036.74 seconds\n",
            "1/1 [==============================] - 0s 402ms/step\n",
            "1/1 [==============================] - 0s 356ms/step\n",
            "1/1 [==============================] - 0s 376ms/step\n",
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            "1/1 [==============================] - 0s 233ms/step\n",
            "1/1 [==============================] - 0s 211ms/step\n",
            "1/1 [==============================] - 0s 211ms/step\n",
            "1/1 [==============================] - 0s 234ms/step\n",
            "Processed 5088 images in 2050.01 seconds\n",
            "1/1 [==============================] - 0s 225ms/step\n",
            "1/1 [==============================] - 0s 214ms/step\n",
            "1/1 [==============================] - 0s 224ms/step\n",
            "1/1 [==============================] - 0s 211ms/step\n",
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            "1/1 [==============================] - 0s 211ms/step\n",
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            "1/1 [==============================] - 0s 221ms/step\n",
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            "1/1 [==============================] - 0s 209ms/step\n",
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            "1/1 [==============================] - 0s 221ms/step\n",
            "1/1 [==============================] - 0s 214ms/step\n",
            "1/1 [==============================] - 0s 213ms/step\n",
            "1/1 [==============================] - 0s 225ms/step\n",
            "Processed 5120 images in 2063.35 seconds\n",
            "1/1 [==============================] - 0s 231ms/step\n",
            "1/1 [==============================] - 0s 210ms/step\n",
            "1/1 [==============================] - 0s 217ms/step\n",
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            "1/1 [==============================] - 0s 364ms/step\n",
            "1/1 [==============================] - 0s 385ms/step\n",
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            "1/1 [==============================] - 0s 215ms/step\n",
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            "1/1 [==============================] - 0s 215ms/step\n",
            "1/1 [==============================] - 0s 208ms/step\n",
            "1/1 [==============================] - 0s 212ms/step\n",
            "1/1 [==============================] - 0s 210ms/step\n",
            "Processed 5152 images in 2076.83 seconds\n",
            "1/1 [==============================] - 0s 216ms/step\n",
            "1/1 [==============================] - 0s 205ms/step\n",
            "1/1 [==============================] - 0s 220ms/step\n",
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            "1/1 [==============================] - 0s 385ms/step\n",
            "1/1 [==============================] - 0s 377ms/step\n",
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            "1/1 [==============================] - 0s 350ms/step\n",
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            "1/1 [==============================] - 0s 211ms/step\n",
            "1/1 [==============================] - 0s 213ms/step\n",
            "1/1 [==============================] - 0s 218ms/step\n",
            "1/1 [==============================] - 0s 206ms/step\n",
            "Processed 5184 images in 2090.28 seconds\n",
            "1/1 [==============================] - 0s 211ms/step\n",
            "1/1 [==============================] - 0s 211ms/step\n",
            "1/1 [==============================] - 0s 218ms/step\n",
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            "1/1 [==============================] - 0s 365ms/step\n",
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            "1/1 [==============================] - 0s 209ms/step\n",
            "1/1 [==============================] - 0s 207ms/step\n",
            "1/1 [==============================] - 0s 214ms/step\n",
            "1/1 [==============================] - 0s 217ms/step\n",
            "Processed 5216 images in 2104.00 seconds\n",
            "1/1 [==============================] - 0s 218ms/step\n",
            "1/1 [==============================] - 0s 208ms/step\n",
            "1/1 [==============================] - 0s 230ms/step\n",
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            "1/1 [==============================] - 0s 394ms/step\n",
            "1/1 [==============================] - 0s 340ms/step\n",
            "1/1 [==============================] - 0s 365ms/step\n",
            "Processed 5248 images in 2117.42 seconds\n",
            "1/1 [==============================] - 0s 298ms/step\n",
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            "1/1 [==============================] - 0s 438ms/step\n",
            "Processed 5280 images in 2128.81 seconds\n",
            "1/1 [==============================] - 0s 384ms/step\n",
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            "Processed 5312 images in 2142.01 seconds\n",
            "1/1 [==============================] - 0s 223ms/step\n",
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            "1/1 [==============================] - 0s 223ms/step\n",
            "Processed 5344 images in 2154.89 seconds\n",
            "1/1 [==============================] - 0s 221ms/step\n",
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            "1/1 [==============================] - 0s 215ms/step\n",
            "Processed 5376 images in 2168.53 seconds\n",
            "1/1 [==============================] - 0s 230ms/step\n",
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            "1/1 [==============================] - 0s 216ms/step\n",
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            "1/1 [==============================] - 0s 218ms/step\n",
            "Processed 5408 images in 2181.81 seconds\n",
            "1/1 [==============================] - 0s 219ms/step\n",
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            "Processed 5440 images in 2195.44 seconds\n",
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            "Processed 5472 images in 2207.61 seconds\n",
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            "Processed 5504 images in 2219.79 seconds\n",
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            "Processed 5536 images in 2232.97 seconds\n",
            "1/1 [==============================] - 0s 227ms/step\n",
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            "Processed 5568 images in 2246.83 seconds\n",
            "1/1 [==============================] - 0s 248ms/step\n",
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            "Processed 5600 images in 2260.85 seconds\n",
            "1/1 [==============================] - 0s 220ms/step\n",
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            "Processed 5632 images in 2274.43 seconds\n",
            "1/1 [==============================] - 0s 222ms/step\n",
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            "Processed 5664 images in 2288.14 seconds\n",
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            "Processed 5696 images in 2301.64 seconds\n",
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            "Processed 5728 images in 2313.48 seconds\n",
            "1/1 [==============================] - 0s 421ms/step\n",
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            "Processed 5760 images in 2326.26 seconds\n",
            "1/1 [==============================] - 0s 219ms/step\n",
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            "1/1 [==============================] - 0s 235ms/step\n",
            "Processed 5792 images in 2339.55 seconds\n",
            "1/1 [==============================] - 0s 214ms/step\n",
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            "Processed 5824 images in 2352.42 seconds\n",
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            "Processed 5856 images in 2365.66 seconds\n",
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            "Processed 5888 images in 2379.01 seconds\n",
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            "Processed 5920 images in 2391.79 seconds\n",
            "1/1 [==============================] - 0s 371ms/step\n",
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            "1/1 [==============================] - 0s 357ms/step\n",
            "Processed 5952 images in 2403.35 seconds\n",
            "1/1 [==============================] - 0s 405ms/step\n",
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            "1/1 [==============================] - 0s 213ms/step\n",
            "Processed 5984 images in 2416.54 seconds\n",
            "1/1 [==============================] - 0s 233ms/step\n",
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            "Processed 6016 images in 2429.96 seconds\n",
            "1/1 [==============================] - 0s 216ms/step\n",
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            "Processed 6048 images in 2442.78 seconds\n",
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            "Processed 6080 images in 2455.86 seconds\n",
            "1/1 [==============================] - 0s 215ms/step\n",
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            "1/1 [==============================] - 0s 399ms/step\n",
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            "Processed 6112 images in 2468.08 seconds\n",
            "1/1 [==============================] - 0s 364ms/step\n",
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            "1/1 [==============================] - 0s 221ms/step\n",
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            "Processed 6144 images in 2479.25 seconds\n",
            "1/1 [==============================] - 0s 217ms/step\n",
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            "Processed 6176 images in 2492.57 seconds\n",
            "1/1 [==============================] - 0s 217ms/step\n",
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            "Processed 6208 images in 2505.51 seconds\n",
            "1/1 [==============================] - 0s 220ms/step\n",
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            "Processed 6240 images in 2518.50 seconds\n",
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            "Processed 6272 images in 2531.83 seconds\n",
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            "Processed 6304 images in 2543.38 seconds\n",
            "1/1 [==============================] - 0s 359ms/step\n",
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            "Processed 6336 images in 2555.78 seconds\n",
            "1/1 [==============================] - 0s 220ms/step\n",
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            "1/1 [==============================] - 0s 223ms/step\n",
            "Processed 6368 images in 2568.98 seconds\n",
            "1/1 [==============================] - 0s 218ms/step\n",
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            "Processed 6400 images in 2582.55 seconds\n",
            "1/1 [==============================] - 0s 217ms/step\n",
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            "Processed 6432 images in 2595.78 seconds\n",
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            "Processed 6464 images in 2609.10 seconds\n",
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            "1/1 [==============================] - 0s 356ms/step\n",
            "Processed 6496 images in 2621.58 seconds\n",
            "1/1 [==============================] - 0s 390ms/step\n",
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            "Processed 6528 images in 2633.64 seconds\n",
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            "Processed 6560 images in 2647.04 seconds\n",
            "1/1 [==============================] - 0s 222ms/step\n",
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            "Processed 6592 images in 2660.99 seconds\n",
            "1/1 [==============================] - 0s 239ms/step\n",
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            "Processed 6624 images in 2674.91 seconds\n",
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            "Processed 6656 images in 2688.04 seconds\n",
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            "Processed 6688 images in 2701.31 seconds\n",
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            "Processed 6720 images in 2712.46 seconds\n",
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            "Processed 6752 images in 2724.70 seconds\n",
            "1/1 [==============================] - 0s 219ms/step\n",
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            "Processed 6784 images in 2737.63 seconds\n",
            "1/1 [==============================] - 0s 219ms/step\n",
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            "Processed 6816 images in 2750.64 seconds\n",
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            "Processed 6848 images in 2763.63 seconds\n",
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            "1/1 [==============================] - 0s 371ms/step\n",
            "1/1 [==============================] - 0s 365ms/step\n",
            "1/1 [==============================] - 0s 386ms/step\n",
            "Processed 6880 images in 2775.78 seconds\n",
            "1/1 [==============================] - 0s 374ms/step\n",
            "1/1 [==============================] - 0s 388ms/step\n",
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            "1/1 [==============================] - 0s 209ms/step\n",
            "1/1 [==============================] - 0s 222ms/step\n",
            "1/1 [==============================] - 0s 211ms/step\n",
            "Processed 6912 images in 2787.53 seconds\n",
            "1/1 [==============================] - 0s 220ms/step\n",
            "1/1 [==============================] - 0s 207ms/step\n",
            "1/1 [==============================] - 0s 229ms/step\n",
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            "1/1 [==============================] - 0s 208ms/step\n",
            "1/1 [==============================] - 0s 218ms/step\n",
            "1/1 [==============================] - 0s 227ms/step\n",
            "1/1 [==============================] - 0s 216ms/step\n",
            "Processed 6944 images in 2801.22 seconds\n",
            "1/1 [==============================] - 0s 226ms/step\n",
            "1/1 [==============================] - 0s 212ms/step\n",
            "1/1 [==============================] - 0s 209ms/step\n",
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            "1/1 [==============================] - 0s 209ms/step\n",
            "1/1 [==============================] - 0s 223ms/step\n",
            "1/1 [==============================] - 0s 209ms/step\n",
            "Processed 6976 images in 2814.63 seconds\n",
            "1/1 [==============================] - 0s 213ms/step\n",
            "1/1 [==============================] - 0s 225ms/step\n",
            "1/1 [==============================] - 0s 215ms/step\n",
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            "1/1 [==============================] - 0s 256ms/step\n",
            "1/1 [==============================] - 0s 369ms/step\n"
          ]
        }
      ],
      "source": [
        "import os\n",
        "import csv\n",
        "import time\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.preprocessing import image\n",
        "from tensorflow.keras.applications.inception_v3 import InceptionV3, preprocess_input\n",
        "from tensorflow.keras.layers import GlobalAveragePooling2D, Dense\n",
        "from tensorflow.keras.models import Model\n",
        "from google.colab import drive\n",
        "\n",
        "def process_image(img_path, model, target_size=(299, 299)):\n",
        "    img = image.load_img(img_path, target_size=target_size)\n",
        "    img_array = image.img_to_array(img)\n",
        "    img_array = preprocess_input(img_array)\n",
        "    img_array = tf.image.resize(img_array, (299, 299))\n",
        "    img_array = tf.expand_dims(img_array, 0)  # Create a batch\n",
        "\n",
        "    features = model.predict(img_array)\n",
        "\n",
        "    if tf.rank(features) == 4:\n",
        "        features = tf.reduce_mean(features, axis=(1, 2))\n",
        "    else:\n",
        "        # Handle the case where the features have only 2 dimensions\n",
        "        features = tf.reduce_mean(features, axis=1)\n",
        "\n",
        "    return features.numpy().tolist()\n",
        "\n",
        "def process_images_in_colab(model, folder_path, target_size=(299, 299), batch_size=32):\n",
        "    image_data = []\n",
        "    img_paths = [os.path.join(folder_path, filename) for filename in os.listdir(folder_path)\n",
        "                 if filename.endswith((\".jpg\", \".jpeg\", \".png\"))]\n",
        "\n",
        "    start_time = time.time()\n",
        "\n",
        "    for i, img_path in enumerate(img_paths):\n",
        "        try:\n",
        "            features = process_image(img_path, model, target_size=target_size)\n",
        "\n",
        "            image_data.append({\n",
        "                'ImageName': os.path.basename(img_path),\n",
        "                'Features': features\n",
        "            })\n",
        "\n",
        "            if (i + 1) % batch_size == 0:\n",
        "                elapsed_time = time.time() - start_time\n",
        "                print(f\"Processed {i + 1} images in {elapsed_time:.2f} seconds\")\n",
        "\n",
        "        except Exception as e:\n",
        "            print(f\"Error processing image {img_path}: {str(e)}\")\n",
        "\n",
        "    return image_data\n",
        "\n",
        "def save_to_csv(image_data, csv_file_name):\n",
        "    with open(csv_file_name, mode='w', newline='') as csvfile:\n",
        "        fieldnames = ['ImageName', 'Features']\n",
        "        writer = csv.DictWriter(csvfile, fieldnames=fieldnames)\n",
        "        writer.writeheader()\n",
        "        for data in image_data:\n",
        "            writer.writerow(data)\n",
        "\n",
        "drive.mount('/content/drive')\n",
        "\n",
        "base_model = InceptionV3(weights='imagenet', include_top=False)\n",
        "\n",
        "x = base_model.output\n",
        "x = GlobalAveragePooling2D()(x)\n",
        "x = Dense(1024, activation='relu')(x)\n",
        "x = Dense(512, activation='relu')(x)\n",
        "output = Dense(2048, activation='linear')(x)\n",
        "\n",
        "model = Model(inputs=base_model.input, outputs=output)\n",
        "\n",
        "folder_path = '/content/drive/MyDrive/images'\n",
        "image_data = process_images_in_colab(model, folder_path, target_size=(299, 299), batch_size=32)\n",
        "csv_file_name = '/content/drive/MyDrive/image_data_fine_tuned.csv'\n",
        "save_to_csv(image_data, csv_file_name)\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "# Load the CSV files into pandas DataFrames\n",
        "labels_df = pd.read_csv('/content/drive/MyDrive/labels.csv')\n",
        "image_data_df = pd.read_csv('/content/drive/MyDrive/image_data_fine_tuned.csv')\n",
        "\n",
        "# Merge the DataFrames based on the 'image_name' and 'ImageName' columns\n",
        "combined_df = pd.merge(labels_df, image_data_df, left_on='image_name', right_on='ImageName', how='inner')\n",
        "\n",
        "# Select only the columns of interest\n",
        "result_df = combined_df[['image_name', 'text_ocr', 'text_corrected', 'overall_sentiment', 'Features']]\n",
        "\n",
        "# Save the result to a new CSV file\n",
        "result_df.to_csv('/content/drive/MyDrive/combined_data.csv', index=False)\n"
      ],
      "metadata": {
        "id": "Jk2I0w3hz5Te"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import LabelEncoder, StandardScaler\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense, Dropout\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from tensorflow.keras.utils import to_categorical\n",
        "\n",
        "# Load the combined dataset\n",
        "df = pd.read_csv('/content/drive/MyDrive/combined_data.csv')\n",
        "\n",
        "# Preprocess the sentiment labels for multi-class classification\n",
        "le = LabelEncoder()\n",
        "df['overall_sentiment'] = le.fit_transform(df['overall_sentiment'])\n",
        "\n",
        "# Split the dataset into features (X) and labels (y)\n",
        "X = np.vstack(df['Features'].apply(eval).values)\n",
        "y = df['overall_sentiment'].values\n",
        "\n",
        "# Split the data into training and testing sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "\n",
        "# Feature scaling\n",
        "scaler = StandardScaler()\n",
        "X_train_scaled = scaler.fit_transform(X_train)\n",
        "X_test_scaled = scaler.transform(X_test)\n",
        "\n",
        "# Build a sentiment analysis model for multi-class classification\n",
        "num_classes = len(le.classes_)\n",
        "model = Sequential([\n",
        "    Dense(512, activation='relu', input_dim=X_train_scaled.shape[1]),\n",
        "    Dropout(0.5),\n",
        "    Dense(256, activation='relu'),\n",
        "    Dropout(0.3),\n",
        "    Dense(128, activation='relu'),\n",
        "    Dropout(0.2),\n",
        "    Dense(num_classes, activation='softmax')\n",
        "])\n",
        "\n",
        "model.compile(optimizer=Adam(learning_rate=0.00001), loss='categorical_crossentropy', metrics=['accuracy'])\n",
        "\n",
        "# Convert labels to one-hot encoding for multi-class classification\n",
        "y_train_one_hot = to_categorical(y_train, num_classes)\n",
        "y_test_one_hot = to_categorical(y_test, num_classes)\n",
        "\n",
        "# Train the model\n",
        "model.fit(X_train_scaled, y_train_one_hot, epochs=50, batch_size=100, validation_split=0.2)\n",
        "\n",
        "# Evaluate the model\n",
        "accuracy = model.evaluate(X_test_scaled, y_test_one_hot)[1]\n",
        "print(f\"Test Accuracy: {accuracy * 100:.2f}%\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x7sdDBaB4jkj",
        "outputId": "9f15b593-1690-46e3-a4a5-8a07221be844"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n",
            "45/45 [==============================] - 2s 14ms/step - loss: 1.6136 - accuracy: 0.2146 - val_loss: 1.6003 - val_accuracy: 0.4350\n",
            "Epoch 2/50\n",
            "45/45 [==============================] - 0s 9ms/step - loss: 1.6008 - accuracy: 0.3185 - val_loss: 1.5871 - val_accuracy: 0.4350\n",
            "Epoch 3/50\n",
            "45/45 [==============================] - 0s 9ms/step - loss: 1.5880 - accuracy: 0.3925 - val_loss: 1.5751 - val_accuracy: 0.4350\n",
            "Epoch 4/50\n",
            "45/45 [==============================] - 0s 9ms/step - loss: 1.5757 - accuracy: 0.4272 - val_loss: 1.5642 - val_accuracy: 0.4350\n",
            "Epoch 5/50\n",
            "45/45 [==============================] - 0s 8ms/step - loss: 1.5629 - accuracy: 0.4364 - val_loss: 1.5529 - val_accuracy: 0.4350\n",
            "Epoch 6/50\n",
            "45/45 [==============================] - 0s 9ms/step - loss: 1.5507 - accuracy: 0.4382 - val_loss: 1.5411 - val_accuracy: 0.4350\n",
            "Epoch 7/50\n",
            "45/45 [==============================] - 0s 8ms/step - loss: 1.5379 - accuracy: 0.4440 - val_loss: 1.5287 - val_accuracy: 0.4350\n",
            "Epoch 8/50\n",
            "45/45 [==============================] - 0s 8ms/step - loss: 1.5260 - accuracy: 0.4420 - val_loss: 1.5164 - val_accuracy: 0.4350\n",
            "Epoch 9/50\n",
            "45/45 [==============================] - 0s 9ms/step - loss: 1.5148 - accuracy: 0.4407 - val_loss: 1.5042 - val_accuracy: 0.4350\n",
            "Epoch 10/50\n",
            "45/45 [==============================] - 0s 9ms/step - loss: 1.4983 - accuracy: 0.4418 - val_loss: 1.4921 - val_accuracy: 0.4350\n",
            "Epoch 11/50\n",
            "45/45 [==============================] - 0s 9ms/step - loss: 1.4879 - accuracy: 0.4405 - val_loss: 1.4801 - val_accuracy: 0.4350\n",
            "Epoch 12/50\n",
            "45/45 [==============================] - 0s 10ms/step - loss: 1.4735 - accuracy: 0.4420 - val_loss: 1.4690 - val_accuracy: 0.4350\n",
            "Epoch 13/50\n",
            "45/45 [==============================] - 0s 10ms/step - loss: 1.4609 - accuracy: 0.4427 - val_loss: 1.4581 - val_accuracy: 0.4350\n",
            "Epoch 14/50\n",
            "45/45 [==============================] - 0s 9ms/step - loss: 1.4512 - accuracy: 0.4423 - val_loss: 1.4479 - val_accuracy: 0.4350\n",
            "Epoch 15/50\n",
            "45/45 [==============================] - 0s 9ms/step - loss: 1.4375 - accuracy: 0.4420 - val_loss: 1.4379 - val_accuracy: 0.4350\n",
            "Epoch 16/50\n",
            "45/45 [==============================] - 0s 9ms/step - loss: 1.4280 - accuracy: 0.4414 - val_loss: 1.4292 - val_accuracy: 0.4350\n",
            "Epoch 17/50\n",
            "45/45 [==============================] - 0s 9ms/step - loss: 1.4183 - accuracy: 0.4423 - val_loss: 1.4205 - val_accuracy: 0.4350\n",
            "Epoch 18/50\n",
            "45/45 [==============================] - 0s 8ms/step - loss: 1.4100 - accuracy: 0.4416 - val_loss: 1.4127 - val_accuracy: 0.4350\n",
            "Epoch 19/50\n",
            "45/45 [==============================] - 0s 9ms/step - loss: 1.4002 - accuracy: 0.4425 - val_loss: 1.4051 - val_accuracy: 0.4350\n",
            "Epoch 20/50\n",
            "45/45 [==============================] - 1s 13ms/step - loss: 1.3905 - accuracy: 0.4429 - val_loss: 1.3986 - val_accuracy: 0.4350\n",
            "Epoch 21/50\n",
            "45/45 [==============================] - 1s 13ms/step - loss: 1.3892 - accuracy: 0.4425 - val_loss: 1.3923 - val_accuracy: 0.4350\n",
            "Epoch 22/50\n",
            "45/45 [==============================] - 1s 13ms/step - loss: 1.3781 - accuracy: 0.4407 - val_loss: 1.3866 - val_accuracy: 0.4350\n",
            "Epoch 23/50\n",
            "45/45 [==============================] - 1s 13ms/step - loss: 1.3734 - accuracy: 0.4407 - val_loss: 1.3814 - val_accuracy: 0.4350\n",
            "Epoch 24/50\n",
            "45/45 [==============================] - 1s 14ms/step - loss: 1.3665 - accuracy: 0.4411 - val_loss: 1.3766 - val_accuracy: 0.4350\n",
            "Epoch 25/50\n",
            "45/45 [==============================] - 1s 13ms/step - loss: 1.3629 - accuracy: 0.4416 - val_loss: 1.3722 - val_accuracy: 0.4350\n",
            "Epoch 26/50\n",
            "45/45 [==============================] - 1s 13ms/step - loss: 1.3587 - accuracy: 0.4409 - val_loss: 1.3680 - val_accuracy: 0.4350\n",
            "Epoch 27/50\n",
            "45/45 [==============================] - 1s 13ms/step - loss: 1.3551 - accuracy: 0.4418 - val_loss: 1.3642 - val_accuracy: 0.4350\n",
            "Epoch 28/50\n",
            "45/45 [==============================] - 1s 14ms/step - loss: 1.3502 - accuracy: 0.4402 - val_loss: 1.3608 - val_accuracy: 0.4350\n",
            "Epoch 29/50\n",
            "45/45 [==============================] - 1s 13ms/step - loss: 1.3494 - accuracy: 0.4405 - val_loss: 1.3576 - val_accuracy: 0.4350\n",
            "Epoch 30/50\n",
            "45/45 [==============================] - 1s 13ms/step - loss: 1.3419 - accuracy: 0.4389 - val_loss: 1.3546 - val_accuracy: 0.4350\n",
            "Epoch 31/50\n",
            "45/45 [==============================] - 1s 14ms/step - loss: 1.3409 - accuracy: 0.4414 - val_loss: 1.3517 - val_accuracy: 0.4350\n",
            "Epoch 32/50\n",
            "45/45 [==============================] - 1s 11ms/step - loss: 1.3355 - accuracy: 0.4382 - val_loss: 1.3491 - val_accuracy: 0.4350\n",
            "Epoch 33/50\n",
            "45/45 [==============================] - 0s 8ms/step - loss: 1.3361 - accuracy: 0.4396 - val_loss: 1.3468 - val_accuracy: 0.4350\n",
            "Epoch 34/50\n",
            "45/45 [==============================] - 0s 9ms/step - loss: 1.3325 - accuracy: 0.4402 - val_loss: 1.3447 - val_accuracy: 0.4350\n",
            "Epoch 35/50\n",
            "45/45 [==============================] - 0s 9ms/step - loss: 1.3295 - accuracy: 0.4310 - val_loss: 1.3426 - val_accuracy: 0.4350\n",
            "Epoch 36/50\n",
            "45/45 [==============================] - 0s 8ms/step - loss: 1.3225 - accuracy: 0.4393 - val_loss: 1.3405 - val_accuracy: 0.4350\n",
            "Epoch 37/50\n",
            "45/45 [==============================] - 0s 9ms/step - loss: 1.3319 - accuracy: 0.4317 - val_loss: 1.3388 - val_accuracy: 0.4350\n",
            "Epoch 38/50\n",
            "45/45 [==============================] - 0s 9ms/step - loss: 1.3273 - accuracy: 0.4369 - val_loss: 1.3372 - val_accuracy: 0.4350\n",
            "Epoch 39/50\n",
            "45/45 [==============================] - 0s 9ms/step - loss: 1.3219 - accuracy: 0.4360 - val_loss: 1.3356 - val_accuracy: 0.4350\n",
            "Epoch 40/50\n",
            "45/45 [==============================] - 0s 9ms/step - loss: 1.3224 - accuracy: 0.4340 - val_loss: 1.3343 - val_accuracy: 0.4350\n",
            "Epoch 41/50\n",
            "45/45 [==============================] - 0s 9ms/step - loss: 1.3200 - accuracy: 0.4391 - val_loss: 1.3328 - val_accuracy: 0.4350\n",
            "Epoch 42/50\n",
            "45/45 [==============================] - 0s 9ms/step - loss: 1.3163 - accuracy: 0.4400 - val_loss: 1.3314 - val_accuracy: 0.4350\n",
            "Epoch 43/50\n",
            "45/45 [==============================] - 0s 9ms/step - loss: 1.3185 - accuracy: 0.4328 - val_loss: 1.3301 - val_accuracy: 0.4350\n",
            "Epoch 44/50\n",
            "45/45 [==============================] - 0s 9ms/step - loss: 1.3171 - accuracy: 0.4349 - val_loss: 1.3290 - val_accuracy: 0.4350\n",
            "Epoch 45/50\n",
            "45/45 [==============================] - 0s 8ms/step - loss: 1.3169 - accuracy: 0.4335 - val_loss: 1.3281 - val_accuracy: 0.4350\n",
            "Epoch 46/50\n",
            "45/45 [==============================] - 0s 8ms/step - loss: 1.3155 - accuracy: 0.4295 - val_loss: 1.3270 - val_accuracy: 0.4350\n",
            "Epoch 47/50\n",
            "45/45 [==============================] - 0s 9ms/step - loss: 1.3152 - accuracy: 0.4288 - val_loss: 1.3260 - val_accuracy: 0.4350\n",
            "Epoch 48/50\n",
            "45/45 [==============================] - 0s 8ms/step - loss: 1.3122 - accuracy: 0.4346 - val_loss: 1.3251 - val_accuracy: 0.4350\n",
            "Epoch 49/50\n",
            "45/45 [==============================] - 0s 8ms/step - loss: 1.3100 - accuracy: 0.4353 - val_loss: 1.3242 - val_accuracy: 0.4350\n",
            "Epoch 50/50\n",
            "45/45 [==============================] - 0s 8ms/step - loss: 1.3084 - accuracy: 0.4366 - val_loss: 1.3234 - val_accuracy: 0.4350\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 1.2992 - accuracy: 0.4713\n",
            "Test Accuracy: 47.13%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import LabelEncoder, StandardScaler\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense, Dropout, BatchNormalization\n",
        "from tensorflow.keras.optimizers import RMSprop\n",
        "from tensorflow.keras.utils import to_categorical\n",
        "from tensorflow.keras.callbacks import LearningRateScheduler, EarlyStopping\n",
        "\n",
        "# Load the combined dataset\n",
        "df = pd.read_csv('/content/drive/MyDrive/combined_data.csv')\n",
        "\n",
        "# Preprocess the sentiment labels for multi-class classification\n",
        "le = LabelEncoder()\n",
        "df['overall_sentiment'] = le.fit_transform(df['overall_sentiment'])\n",
        "\n",
        "# Split the dataset into features (X) and labels (y)\n",
        "X = np.vstack(df['Features'].apply(eval).values)\n",
        "y = df['overall_sentiment'].values\n",
        "\n",
        "# Split the data into training and testing sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "\n",
        "# Feature scaling\n",
        "scaler = StandardScaler()\n",
        "X_train_scaled = scaler.fit_transform(X_train)\n",
        "X_test_scaled = scaler.transform(X_test)\n",
        "\n",
        "# Learning rate scheduler\n",
        "def lr_schedule(epoch):\n",
        "    return 0.00001 * 0.9**epoch\n",
        "\n",
        "lr_scheduler = LearningRateScheduler(lr_schedule)\n",
        "\n",
        "# Early stopping\n",
        "early_stopping = EarlyStopping(monitor='val_accuracy', patience=10, restore_best_weights=True)\n",
        "\n",
        "# Build a sentiment analysis model for multi-class classification\n",
        "num_classes = len(le.classes_)\n",
        "model = Sequential([\n",
        "    Dense(512, activation='relu', input_dim=X_train_scaled.shape[1]),\n",
        "    BatchNormalization(),\n",
        "    Dropout(0.5),\n",
        "    Dense(256, activation='relu'),\n",
        "    BatchNormalization(),\n",
        "    Dropout(0.3),\n",
        "    Dense(128, activation='relu'),\n",
        "    BatchNormalization(),\n",
        "    Dropout(0.2),\n",
        "    Dense(num_classes, activation='softmax')\n",
        "])\n",
        "\n",
        "# Compile the model with RMSprop optimizer and categorical crossentropy loss\n",
        "model.compile(optimizer=RMSprop(learning_rate=0.00001), loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n",
        "\n",
        "# Train the model with learning rate scheduler and early stopping\n",
        "model.fit(X_train_scaled, y_train, epochs=100, batch_size=100, validation_split=0.2, callbacks=[lr_scheduler, early_stopping])\n",
        "\n",
        "# Evaluate the model\n",
        "accuracy = model.evaluate(X_test_scaled, y_test)[1]\n",
        "print(f\"Test Accuracy: {accuracy * 100:.2f}%\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "orvNvEFgCcoK",
        "outputId": "ccb07b7c-d672-44c0-ddc9-4f7a70630164"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "45/45 [==============================] - 3s 27ms/step - loss: 2.2637 - accuracy: 0.2088 - val_loss: 1.6054 - val_accuracy: 0.2735 - lr: 1.0000e-05\n",
            "Epoch 2/100\n",
            "45/45 [==============================] - 0s 10ms/step - loss: 2.3064 - accuracy: 0.1944 - val_loss: 1.5981 - val_accuracy: 0.3130 - lr: 9.0000e-06\n",
            "Epoch 3/100\n",
            "45/45 [==============================] - 0s 10ms/step - loss: 2.2701 - accuracy: 0.2034 - val_loss: 1.5981 - val_accuracy: 0.3022 - lr: 8.1000e-06\n",
            "Epoch 4/100\n",
            "45/45 [==============================] - 1s 14ms/step - loss: 2.2289 - accuracy: 0.2119 - val_loss: 1.6058 - val_accuracy: 0.2771 - lr: 7.2900e-06\n",
            "Epoch 5/100\n",
            "45/45 [==============================] - 1s 16ms/step - loss: 2.2518 - accuracy: 0.2079 - val_loss: 1.6142 - val_accuracy: 0.2502 - lr: 6.5610e-06\n",
            "Epoch 6/100\n",
            "45/45 [==============================] - 1s 15ms/step - loss: 2.2333 - accuracy: 0.2054 - val_loss: 1.6227 - val_accuracy: 0.2430 - lr: 5.9049e-06\n",
            "Epoch 7/100\n",
            "45/45 [==============================] - 1s 14ms/step - loss: 2.2319 - accuracy: 0.2137 - val_loss: 1.6314 - val_accuracy: 0.2350 - lr: 5.3144e-06\n",
            "Epoch 8/100\n",
            "45/45 [==============================] - 1s 14ms/step - loss: 2.1943 - accuracy: 0.2128 - val_loss: 1.6473 - val_accuracy: 0.2045 - lr: 4.7830e-06\n",
            "Epoch 9/100\n",
            "45/45 [==============================] - 1s 14ms/step - loss: 2.2202 - accuracy: 0.2057 - val_loss: 1.6638 - val_accuracy: 0.1919 - lr: 4.3047e-06\n",
            "Epoch 10/100\n",
            "45/45 [==============================] - 1s 14ms/step - loss: 2.2119 - accuracy: 0.2005 - val_loss: 1.6757 - val_accuracy: 0.1839 - lr: 3.8742e-06\n",
            "Epoch 11/100\n",
            "45/45 [==============================] - 1s 14ms/step - loss: 2.1934 - accuracy: 0.2193 - val_loss: 1.6827 - val_accuracy: 0.1821 - lr: 3.4868e-06\n",
            "Epoch 12/100\n",
            "45/45 [==============================] - 1s 15ms/step - loss: 2.1951 - accuracy: 0.2059 - val_loss: 1.6930 - val_accuracy: 0.1794 - lr: 3.1381e-06\n",
            "44/44 [==============================] - 0s 2ms/step - loss: 1.5937 - accuracy: 0.3343\n",
            "Test Accuracy: 33.43%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split, GridSearchCV\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.metrics import accuracy_score, classification_report\n",
        "from imblearn.over_sampling import SMOTE\n",
        "from nltk.corpus import stopwords\n",
        "\n",
        "# Load your dataset\n",
        "# Assuming df is your DataFrame with columns: 'Features' and 'overall_sentiment'\n",
        "df = pd.read_csv('/content/drive/MyDrive/combined_data.csv')\n",
        "\n",
        "# Convert 'overall_sentiment' to one-hot encoding\n",
        "df_one_hot = pd.get_dummies(df['overall_sentiment'], prefix='sentiment')\n",
        "\n",
        "# Concatenate the one-hot encoding columns with the original DataFrame\n",
        "df = pd.concat([df, df_one_hot], axis=1)\n",
        "\n",
        "# Extract features and labels\n",
        "X = df['Features']\n",
        "y = df['overall_sentiment']\n",
        "\n",
        "# Split the data into training and testing sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "\n",
        "# Text cleaning and preprocessing\n",
        "stop_words = set(stopwords.words('english'))\n",
        "X_train = X_train.apply(lambda x: ' '.join([word for word in x.split() if word.lower() not in stop_words]))\n",
        "X_test = X_test.apply(lambda x: ' '.join([word for word in x.split() if word.lower() not in stop_words]))\n",
        "\n",
        "# Convert text data to numerical features using TF-IDF vectorization\n",
        "vectorizer = TfidfVectorizer(max_features=5000)\n",
        "X_train_tfidf = vectorizer.fit_transform(X_train)\n",
        "X_test_tfidf = vectorizer.transform(X_test)\n",
        "\n",
        "# Use SMOTE to address class imbalance on the training set\n",
        "smote = SMOTE(random_state=42)\n",
        "X_train_smote, y_train_smote = smote.fit_resample(X_train_tfidf, y_train)\n",
        "\n",
        "# Initialize a Support Vector Machine (SVM) classifier and perform grid search\n",
        "param_grid = {'C': [0.1, 1, 10, 100], 'kernel': ['linear', 'rbf', 'poly', 'sigmoid']}\n",
        "svm_classifier = SVC()\n",
        "grid_search = GridSearchCV(svm_classifier, param_grid, cv=5)\n",
        "grid_search.fit(X_train_smote, y_train_smote)\n",
        "\n",
        "# Get the best parameters\n",
        "best_params = grid_search.best_params_\n",
        "print(f'Best Parameters: {best_params}')\n",
        "\n",
        "# Train the SVM classifier with the best parameters\n",
        "svm_classifier = SVC(**best_params)\n",
        "svm_classifier.fit(X_train_smote, y_train_smote)\n",
        "\n",
        "# Make predictions on the test set\n",
        "svm_predictions = svm_classifier.predict(X_test_tfidf)\n",
        "\n",
        "# Evaluate the SVM model\n",
        "svm_accuracy = accuracy_score(y_test, svm_predictions)\n",
        "svm_classification_rep = classification_report(y_test, svm_predictions)\n",
        "\n",
        "print('SVM Model:')\n",
        "print(f'Accuracy: {svm_accuracy}')\n",
        "print('Classification Report:')\n",
        "print(svm_classification_rep)\n",
        "\n",
        "# Try a Random Forest classifier as well\n",
        "rf_classifier = RandomForestClassifier(n_estimators=100, random_state=42)\n",
        "rf_classifier.fit(X_train_smote, y_train_smote)\n",
        "rf_predictions = rf_classifier.predict(X_test_tfidf)\n",
        "rf_accuracy = accuracy_score(y_test, rf_predictions)\n",
        "rf_classification_rep = classification_report(y_test, rf_predictions)\n",
        "\n",
        "print('Random Forest Model:')\n",
        "print(f'Accuracy: {rf_accuracy}')\n",
        "print('Classification Report:')\n",
        "print(rf_classification_rep)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K067mr8Y6m4c",
        "outputId": "d636e01b-01e0-4598-d30c-9dab0113391f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best Parameters: {'C': 10, 'kernel': 'rbf'}\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "SVM Model:\n",
            "Accuracy: 0.07890961262553801\n",
            "Classification Report:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "     negative       0.07      0.98      0.14       103\n",
            "      neutral       0.00      0.00      0.00       405\n",
            "     positive       0.41      0.01      0.03       657\n",
            "very_negative       0.00      0.00      0.00        23\n",
            "very_positive       0.00      0.00      0.00       206\n",
            "\n",
            "     accuracy                           0.08      1394\n",
            "    macro avg       0.10      0.20      0.03      1394\n",
            " weighted avg       0.20      0.08      0.02      1394\n",
            "\n",
            "Random Forest Model:\n",
            "Accuracy: 0.02295552367288379\n",
            "Classification Report:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "     negative       0.00      0.00      0.00       103\n",
            "      neutral       0.00      0.00      0.00       405\n",
            "     positive       0.41      0.01      0.03       657\n",
            "very_negative       0.02      1.00      0.03        23\n",
            "very_positive       0.00      0.00      0.00       206\n",
            "\n",
            "     accuracy                           0.02      1394\n",
            "    macro avg       0.09      0.20      0.01      1394\n",
            " weighted avg       0.19      0.02      0.01      1394\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import nltk\n",
        "nltk.download('stopwords', download_dir='/usr/share/nltk_data')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oRVZKGvd-Twy",
        "outputId": "700ffc6c-10cd-473d-f9a3-0830d1b1581a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package stopwords to /usr/share/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    }
  ]
}