8_cnn_improvements.ipynb

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  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4",
      "toc_visible": true,
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/prachisinhaa/Misinformation-Video-Detection/blob/main/improvements.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Downsampling"
      ],
      "metadata": {
        "id": "KyfuKnPu6jRV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "unique_results_df = pd.read_csv('uniqueResults.csv')"
      ],
      "metadata": {
        "id": "ArLo7bDBbfJp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Number of samples in unique_results_df:\", unique_results_df.shape[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YsDT6k4BbZbD",
        "outputId": "0fd64835-9966-4804-a102-a7d678c8ae54"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of samples in unique_results_df: 2943\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check the number of fields in each downsampled DataFrame\n",
        "#print(\"Number of fields in all_results_sample:\", all_results_sample.shape[1])\n",
        "print(\"Number of fields in unique_results_df:\", unique_results_df.shape[1])\n",
        "#print(\"Number of fields in all_Top10_SERP_MM_sample:\", all_Top10_SERP_MM_sample.shape[1])\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u9skeP2YgBvv",
        "outputId": "f656c1f5-3033-420c-ef2e-31a83c43e06e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of fields in unique_results_df: 15\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Preprocessing\n",
        "\n",
        "Basically getting the thumbnail using the Youtube API\n",
        "\n"
      ],
      "metadata": {
        "id": "S44SMIGA6meY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install requests"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r04-DLZW6ZV0",
        "outputId": "5a735ac3-4300-4190-f720-88c8f6021116"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (2.31.0)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests) (3.6)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests) (2024.2.2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import os\n",
        "import requests"
      ],
      "metadata": {
        "id": "47DGQNNy6e7j"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def extract_video_id(url):\n",
        "    if 'v=' in url:\n",
        "        return url.split('v=')[1]\n",
        "    else:\n",
        "        print(f\"URL format not recognized: {url}\")\n",
        "        return None\n",
        "\n",
        "def download_thumbnail(url, output_dir, video_id):\n",
        "    response = requests.get(url)\n",
        "    if response.status_code == 200:\n",
        "        thumbnail_filename = os.path.join(output_dir, f\"{video_id}.jpg\")\n",
        "        with open(thumbnail_filename, 'wb') as f:\n",
        "            f.write(response.content)\n",
        "        return thumbnail_filename\n",
        "    else:\n",
        "        print(f\"Failed to download thumbnail from {url}\")\n"
      ],
      "metadata": {
        "id": "fHXr535E6hm6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "output_dir = 'thumbnails'\n",
        "os.makedirs(output_dir, exist_ok=True)"
      ],
      "metadata": {
        "id": "R3nTObTU6tLL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for index, row in unique_results_df.iterrows():\n",
        "    youtube_url = row['vid_url']\n",
        "    video_id = extract_video_id(youtube_url)\n",
        "    if video_id:\n",
        "        # API request to get thumbnails ->\n",
        "        api_key = \"REDACTED\"\n",
        "        api_url = f\"https://www.googleapis.com/youtube/v3/videos?id={video_id}&key={api_key}&part=snippet\"\n",
        "        response = requests.get(api_url)\n",
        "        if response.status_code == 200:\n",
        "            data = response.json()\n",
        "            if 'items' in data and data['items']:\n",
        "                thumbnail_url = data['items'][0]['snippet']['thumbnails']['medium']['url']\n",
        "                # Downloading image\n",
        "                thumbnail_filename = download_thumbnail(thumbnail_url, output_dir, video_id)\n",
        "                if thumbnail_filename:\n",
        "                    print(f\"Thumbnail image downloaded for video ID {video_id}: {thumbnail_filename}\")\n",
        "            else:\n",
        "                print(f\"No video information found for video ID {video_id}\")\n",
        "        else:\n",
        "            print(f\"Failed to fetch video information from YouTube API for video ID {video_id}\")\n",
        "    else:\n",
        "        print(f\"No video ID found in URL: {youtube_url}\")\n",
        "\n",
        "print(\"Thumbnail images downloaded for all videos.\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u1pgbM5p6OqL",
        "outputId": "a4a74c6c-995a-4273-9451-68edca89357f"
      },
      "execution_count": null,
      "outputs": [
        {
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          "name": "stdout",
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            "No video information found for video ID 2Jfo_t57cQI\n",
            "No video information found for video ID seqKlU3rTrA\n",
            "URL format not recognized: https://www.youtube.com/playlist?list=PLIsie7qEw2AimRDF9LoDpRFCTt2cCc1di\n",
            "No video ID found in URL: https://www.youtube.com/playlist?list=PLIsie7qEw2AimRDF9LoDpRFCTt2cCc1di\n",
            "URL format not recognized: https://www.youtube.com/playlist?list=PLlH8o7mO6L-u39zXSnrpzvLDJ36lwBRa_\n",
            "No video ID found in URL: https://www.youtube.com/playlist?list=PLlH8o7mO6L-u39zXSnrpzvLDJ36lwBRa_\n",
            "Thumbnail image downloaded for video ID W9W1zJ0BMro: thumbnails/W9W1zJ0BMro.jpg\n",
            "Thumbnail image downloaded for video ID bfWm3Tbica0: thumbnails/bfWm3Tbica0.jpg\n",
            "Thumbnail image downloaded for video ID 2bwGsOBTlhE: thumbnails/2bwGsOBTlhE.jpg\n",
            "Thumbnail image downloaded for video ID pz2p4EQtEXs: thumbnails/pz2p4EQtEXs.jpg\n",
            "Thumbnail image downloaded for video ID Rh2-xXFKxUg: thumbnails/Rh2-xXFKxUg.jpg\n",
            "No video information found for video ID lb52oT1kA0Q\n",
            "Thumbnail image downloaded for video ID jnZTTRxf2UE: thumbnails/jnZTTRxf2UE.jpg\n",
            "Thumbnail image downloaded for video ID Ffhi1CPzT48: thumbnails/Ffhi1CPzT48.jpg\n",
            "No video information found for video ID 2QavHDgrzzk\n",
            "Thumbnail image downloaded for video ID eQT5RNVf4GM: thumbnails/eQT5RNVf4GM.jpg\n",
            "No video information found for video ID TzmAdpruCTg\n",
            "Thumbnail image downloaded for video ID M3C6bp9FNGQ: thumbnails/M3C6bp9FNGQ.jpg\n",
            "Thumbnail image downloaded for video ID kKtCcEd3xIs: thumbnails/kKtCcEd3xIs.jpg\n",
            "Thumbnail image downloaded for video ID 5WV4VLGUeQk: thumbnails/5WV4VLGUeQk.jpg\n",
            "Thumbnail image downloaded for video ID Pu13Mso2VEA: thumbnails/Pu13Mso2VEA.jpg\n",
            "Thumbnail image downloaded for video ID vWNfFlXOF58: thumbnails/vWNfFlXOF58.jpg\n",
            "Thumbnail image downloaded for video ID xdk9Mre_KaI: thumbnails/xdk9Mre_KaI.jpg\n",
            "No video information found for video ID Wcn2GVmouWM\n",
            "No video information found for video ID KLwBil5Tngk\n",
            "Thumbnail image downloaded for video ID 2dNvtMzKEw8: thumbnails/2dNvtMzKEw8.jpg\n",
            "Thumbnail image downloaded for video ID xrmOtfZllrU: thumbnails/xrmOtfZllrU.jpg\n",
            "Thumbnail image downloaded for video ID 4vBe_OeEbH8: thumbnails/4vBe_OeEbH8.jpg\n",
            "Thumbnail image downloaded for video ID LXRm0SUUxfo: thumbnails/LXRm0SUUxfo.jpg\n",
            "Thumbnail image downloaded for video ID URG2UYOyypc: thumbnails/URG2UYOyypc.jpg\n",
            "No video information found for video ID o0_J0gDX6Q4\n",
            "Thumbnail image downloaded for video ID tncjsUkqNyY: thumbnails/tncjsUkqNyY.jpg\n",
            "No video information found for video ID GVwudh63ZRs\n",
            "Thumbnail image downloaded for video ID Hv6n7tah8hA: thumbnails/Hv6n7tah8hA.jpg\n",
            "No video information found for video ID VGhvX84BIio\n",
            "No video information found for video ID uVP6Spe9f1Q\n",
            "No video information found for video ID bsEVvigosyo\n",
            "Thumbnail image downloaded for video ID XtSqSfv1_D0: thumbnails/XtSqSfv1_D0.jpg\n",
            "Thumbnail image downloaded for video ID WQptarOLSBU&list=RDWQptarOLSBU&index=1: thumbnails/WQptarOLSBU&list=RDWQptarOLSBU&index=1.jpg\n",
            "Thumbnail image downloaded for video ID W8c327UcjLQ: thumbnails/W8c327UcjLQ.jpg\n",
            "Thumbnail image downloaded for video ID 0vpqO0HrFwc: thumbnails/0vpqO0HrFwc.jpg\n",
            "Thumbnail image downloaded for video ID ffwVNyrTSUQ: thumbnails/ffwVNyrTSUQ.jpg\n",
            "Thumbnail image downloaded for video ID U8K9bbMo-M4: thumbnails/U8K9bbMo-M4.jpg\n",
            "Thumbnail image downloaded for video ID duOLIS0Pkew: thumbnails/duOLIS0Pkew.jpg\n",
            "Thumbnail image downloaded for video ID u8lVmtYHNL4: thumbnails/u8lVmtYHNL4.jpg\n",
            "Thumbnail image downloaded for video ID _tc6lD6XTaI: thumbnails/_tc6lD6XTaI.jpg\n",
            "No video information found for video ID 3pxVqSNk-D8\n",
            "No video information found for video ID 9sX8-G-STAM\n",
            "Thumbnail image downloaded for video ID u7fo3qjLS0E: thumbnails/u7fo3qjLS0E.jpg\n",
            "Thumbnail image downloaded for video ID fbCncZewNy8: thumbnails/fbCncZewNy8.jpg\n",
            "Thumbnail image downloaded for video ID 25m5x0wguZI: thumbnails/25m5x0wguZI.jpg\n",
            "Thumbnail image downloaded for video ID MgwWycuvx-0: thumbnails/MgwWycuvx-0.jpg\n",
            "Thumbnail image downloaded for video ID 0_DfT6Ft5Ik: thumbnails/0_DfT6Ft5Ik.jpg\n",
            "Thumbnail image downloaded for video ID MYtii-u_VGw: thumbnails/MYtii-u_VGw.jpg\n",
            "Thumbnail image downloaded for video ID FHXN0u30vDM: thumbnails/FHXN0u30vDM.jpg\n",
            "No video information found for video ID i2nxKMmYJXs\n",
            "No video information found for video ID EBn9qcJKik4\n",
            "No video information found for video ID 2hJJBClQVc0\n",
            "Thumbnail image downloaded for video ID YKwV00Bs7Cw: thumbnails/YKwV00Bs7Cw.jpg\n",
            "Thumbnail image downloaded for video ID NAC7QF1yp-g: thumbnails/NAC7QF1yp-g.jpg\n",
            "Thumbnail image downloaded for video ID KzJXqeGU5A8: thumbnails/KzJXqeGU5A8.jpg\n",
            "Thumbnail image downloaded for video ID h5y1JlgCCwg: thumbnails/h5y1JlgCCwg.jpg\n",
            "Thumbnail image downloaded for video ID BxdBFCwqlGw: thumbnails/BxdBFCwqlGw.jpg\n",
            "Thumbnail image downloaded for video ID wMcT3S0vGeY: thumbnails/wMcT3S0vGeY.jpg\n",
            "Thumbnail image downloaded for video ID Wvtlx_rS_OM: thumbnails/Wvtlx_rS_OM.jpg\n",
            "Thumbnail image downloaded for video ID LY8y46g9xNo: thumbnails/LY8y46g9xNo.jpg\n",
            "Thumbnail image downloaded for video ID mXLhqVivMPI: thumbnails/mXLhqVivMPI.jpg\n",
            "Thumbnail image downloaded for video ID zqrPQZM9HFM: thumbnails/zqrPQZM9HFM.jpg\n",
            "Thumbnail image downloaded for video ID BI_ZehPOMwI: thumbnails/BI_ZehPOMwI.jpg\n",
            "Thumbnail image downloaded for video ID XyLQjMC2Q38: thumbnails/XyLQjMC2Q38.jpg\n",
            "Thumbnail image downloaded for video ID x90tf8JzFVs: thumbnails/x90tf8JzFVs.jpg\n",
            "Thumbnail image downloaded for video ID gA9Pzf12BIw: thumbnails/gA9Pzf12BIw.jpg\n",
            "Thumbnail image downloaded for video ID wWxZGux3l3k: thumbnails/wWxZGux3l3k.jpg\n",
            "Thumbnail image downloaded for video ID XkKPt7XGMns: thumbnails/XkKPt7XGMns.jpg\n",
            "Thumbnail image downloaded for video ID ZNkmhY_ju8o: thumbnails/ZNkmhY_ju8o.jpg\n",
            "Thumbnail image downloaded for video ID -LvMHRT6H24: thumbnails/-LvMHRT6H24.jpg\n",
            "Thumbnail image downloaded for video ID -RIfYNZrwco: thumbnails/-RIfYNZrwco.jpg\n",
            "Thumbnail image downloaded for video ID OM5-BhMNXOI: thumbnails/OM5-BhMNXOI.jpg\n",
            "No video information found for video ID yxocMGS1clU\n",
            "Thumbnail image downloaded for video ID 1Uv39j-6Rvo: thumbnails/1Uv39j-6Rvo.jpg\n",
            "Thumbnail image downloaded for video ID f4ZHdzl6ZWg: thumbnails/f4ZHdzl6ZWg.jpg\n",
            "Thumbnail image downloaded for video ID xbZN7QLjy2U: thumbnails/xbZN7QLjy2U.jpg\n",
            "Thumbnail image downloaded for video ID ig6Ncb39aM4: thumbnails/ig6Ncb39aM4.jpg\n",
            "Thumbnail image downloaded for video ID GX8Hg6kWQYI: thumbnails/GX8Hg6kWQYI.jpg\n",
            "Thumbnail image downloaded for video ID xuZ3M7UghIM: thumbnails/xuZ3M7UghIM.jpg\n",
            "Thumbnail image downloaded for video ID DhTagWzkjHk: thumbnails/DhTagWzkjHk.jpg\n",
            "Thumbnail image downloaded for video ID YXjdGI33qrE: thumbnails/YXjdGI33qrE.jpg\n",
            "Thumbnail image downloaded for video ID f9pILzCwtwg: thumbnails/f9pILzCwtwg.jpg\n",
            "Thumbnail image downloaded for video ID WTsmIbNku5g: thumbnails/WTsmIbNku5g.jpg\n",
            "No video information found for video ID JgnxNjNla30\n",
            "Thumbnail image downloaded for video ID kMb7PY2GxtA: thumbnails/kMb7PY2GxtA.jpg\n",
            "Thumbnail image downloaded for video ID BmzSQJ42G94: thumbnails/BmzSQJ42G94.jpg\n",
            "No video information found for video ID 4-BNZiGy9yM\n",
            "No video information found for video ID Sc6R5o8Fj1E\n",
            "Thumbnail image downloaded for video ID SNE2oCZH_4k: thumbnails/SNE2oCZH_4k.jpg\n",
            "No video information found for video ID _WYnTKS_L1c\n",
            "Thumbnail image downloaded for video ID Vb2ZXRh74WU: thumbnails/Vb2ZXRh74WU.jpg\n",
            "No video information found for video ID qb5jdZaGLRE\n",
            "Thumbnail image downloaded for video ID At0-Gp_LU8c: thumbnails/At0-Gp_LU8c.jpg\n",
            "Thumbnail image downloaded for video ID 0A-zi4Vist8: thumbnails/0A-zi4Vist8.jpg\n",
            "Thumbnail image downloaded for video ID RFIdo2mr6Jg: thumbnails/RFIdo2mr6Jg.jpg\n",
            "Thumbnail image downloaded for video ID VEiBL6LnD00: thumbnails/VEiBL6LnD00.jpg\n",
            "Thumbnail image downloaded for video ID APdHkY3kOkQ: thumbnails/APdHkY3kOkQ.jpg\n",
            "Thumbnail image downloaded for video ID wWdsGfzdReU: thumbnails/wWdsGfzdReU.jpg\n",
            "No video information found for video ID 8Jyr37aOPGE\n",
            "No video information found for video ID abeKZuYpC7w\n",
            "Thumbnail image downloaded for video ID bivXt0hVufk: thumbnails/bivXt0hVufk.jpg\n",
            "Thumbnail image downloaded for video ID K3YfLAAi3Vc: thumbnails/K3YfLAAi3Vc.jpg\n",
            "Thumbnail image downloaded for video ID BvPRb_FuP_Y: thumbnails/BvPRb_FuP_Y.jpg\n",
            "Thumbnail image downloaded for video ID R-boOqOGPy4: thumbnails/R-boOqOGPy4.jpg\n",
            "Thumbnail image downloaded for video ID qzJv39W0PLY: thumbnails/qzJv39W0PLY.jpg\n",
            "Thumbnail image downloaded for video ID whPWlwM47z8: thumbnails/whPWlwM47z8.jpg\n",
            "No video information found for video ID K5FqdrTBXPI\n",
            "Thumbnail image downloaded for video ID p_0KXiau6HU: thumbnails/p_0KXiau6HU.jpg\n",
            "Thumbnail image downloaded for video ID 7cnh_uFkv1g: thumbnails/7cnh_uFkv1g.jpg\n",
            "Thumbnail image downloaded for video ID Pgw5Vk2ICQM: thumbnails/Pgw5Vk2ICQM.jpg\n",
            "Thumbnail image downloaded for video ID Zz9Bzi_GyD0: thumbnails/Zz9Bzi_GyD0.jpg\n",
            "No video information found for video ID yo5w0pm24ic\n",
            "Thumbnail image downloaded for video ID wYnIvrDlhb4: thumbnails/wYnIvrDlhb4.jpg\n",
            "Thumbnail image downloaded for video ID 6zm7LfuDCu8: thumbnails/6zm7LfuDCu8.jpg\n",
            "Thumbnail image downloaded for video ID c43_hfbFr3g: thumbnails/c43_hfbFr3g.jpg\n",
            "Thumbnail image downloaded for video ID zoqkmiRy3w8: thumbnails/zoqkmiRy3w8.jpg\n",
            "Thumbnail image downloaded for video ID DHt842u_Yw4: thumbnails/DHt842u_Yw4.jpg\n",
            "No video information found for video ID B9fMrhVvA50\n",
            "Thumbnail image downloaded for video ID _Tud1Y1pMkc: thumbnails/_Tud1Y1pMkc.jpg\n",
            "Thumbnail images downloaded for all videos.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!zip -r thumbnails.zip thumbnails"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a5qOGO3QrxZ2",
        "outputId": "e10607eb-ab8b-4645-9bcc-d2a28c5a0944"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  adding: thumbnails/ (stored 0%)\n",
            "  adding: thumbnails/agx3jbF4Nyo.jpg (deflated 0%)\n",
            "  adding: thumbnails/v2MkhDtmuIQ.jpg (deflated 0%)\n",
            "  adding: thumbnails/BYI9lORP44s.jpg (deflated 1%)\n",
            "  adding: thumbnails/slwgXXVXM3I.jpg (deflated 0%)\n",
            "  adding: thumbnails/xrmOtfZllrU.jpg (deflated 0%)\n",
            "  adding: thumbnails/9czoezm2vqw.jpg (deflated 2%)\n",
            "  adding: thumbnails/bgobWEK_Ny4.jpg (deflated 0%)\n",
            "  adding: thumbnails/MwZ9C-FHJcg.jpg (deflated 0%)\n",
            "  adding: thumbnails/tDo8se89dbQ.jpg (deflated 2%)\n",
            "  adding: thumbnails/EQZYW2oBYr8.jpg (deflated 2%)\n",
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "id": "0zqRiXEBlu3O",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ff6de773-b22d-40b9-ecb2-ae54eced4165"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Dataset\n"
      ],
      "metadata": {
        "id": "Th40qf0QjPae"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import os\n",
        "\n",
        "csv_file = 'uniqueResults.csv'\n",
        "image_folder = 'drive/MyDrive/thumbnails'\n",
        "\n",
        "annotations_df = pd.read_csv(csv_file)\n",
        "\n",
        "annotations_df['video_id'] = annotations_df['vid_url'].str.extract(r'(?<=v=)([\\w-]+)')\n",
        "\n",
        "# Create a new DataFrame 'id_df' with just the 'video_id' column\n",
        "# Drop all columns except 'video_id' and 'annotations'\n",
        "annotations_df = annotations_df[['video_id', 'normalized_annotation']].copy()\n",
        "\n",
        "# Get a list of image names from the folder\n",
        "image_names = os.listdir(image_folder)\n",
        "\n",
        "# # Remove the '.jpg' extension from image names\n",
        "image_names_without_extension = [name.replace('.jpg', '') for name in image_names]\n",
        "print(len(image_names_without_extension))\n",
        "\n",
        "filtered_annotations_df = annotations_df[annotations_df['video_id'].isin(image_names_without_extension)]\n",
        "filtered_annotations_df\n",
        "\n",
        "# Keep only unique values of 'video_id' in filtered_annotations_df\n",
        "filtered_annotations_df = filtered_annotations_df.drop_duplicates(subset=['video_id'])\n",
        "\n",
        "# Get the list of unique video IDs in filtered_annotations_df\n",
        "unique_video_ids = filtered_annotations_df['video_id'].unique()\n",
        "\n",
        "# Now filtered_annotations_df contains only unique values of 'video_id'\n",
        "# Get the list of image names to delete\n",
        "images_to_delete = [name + '.jpg' for name in image_names_without_extension if name not in unique_video_ids]\n",
        "\n",
        "# Delete the images from the folder\n",
        "for image_name in images_to_delete:\n",
        "    image_path = os.path.join(image_folder, image_name)\n",
        "    os.remove(image_path)\n",
        "\n",
        "print(f\"Deleted {len(images_to_delete)} images from the folder.\")"
      ],
      "metadata": {
        "id": "ZPWEo2WXhIvN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a68d3f45-63d5-4a86-f48b-fb3ab64094cf"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2469\n",
            "Deleted 0 images from the folder.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "filtered_annotations_df.shape"
      ],
      "metadata": {
        "id": "FkDbNORNh5-C",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a2f385b8-a22f-4588-cf9d-d429783223be"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2469, 2)"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "output_csv_file = 'filtered_unique_results.csv'\n",
        "filtered_annotations_df.to_csv(output_csv_file, index=False)\n",
        "\n",
        "print(f\"Filtered annotations saved to {output_csv_file}.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GB1n0RgUh2Q7",
        "outputId": "7ba94624-8746-4ce9-b1a7-000a4506fb86"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Filtered annotations saved to filtered_unique_results.csv.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "filtered_annotations_df = pd.read_csv('filtered_unique_results.csv')"
      ],
      "metadata": {
        "id": "IwqKii-LB4Dj"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import cv2\n",
        "image_folder = 'drive/MyDrive/thumbnails'\n",
        "data = []\n",
        "for index, row in filtered_annotations_df.iterrows():\n",
        "    video_id = row['video_id']\n",
        "    annotation = row['normalized_annotation']\n",
        "    image_index = image_names_without_extension.index(video_id)\n",
        "    image_path = os.path.join(image_folder, image_names[image_index])\n",
        "    image = cv2.imread(image_path)  # Read image using OpenCV\n",
        "    data.append((image, annotation))\n",
        "\n",
        "\n",
        "data_size = len(data)\n",
        "print(\"Size of the data:\", data_size)\n",
        "\n",
        "# Separate X and Y from data\n",
        "X = [item[0] for item in data]  # Extract images from the tuples\n",
        "Y = [item[1] for item in data]  # Extract annotations from the tuples\n",
        "\n",
        "\n",
        "# Convert labels from -1, 0, 1 to 0, 1, 2\n",
        "Y = [label + 1 for label in Y]\n",
        "\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# Split data into train and test sets\n",
        "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=42)\n",
        "\n",
        "# Further split the training set into training and validation sets\n",
        "X_train, X_val, Y_train, Y_val = train_test_split(X_train, Y_train, test_size=0.2, random_state=42)\n",
        "\n",
        "\n",
        "import numpy as np\n",
        "\n",
        "# Convert lists to NumPy arrays\n",
        "X_train = np.array(X_train)\n",
        "X_val = np.array(X_val)\n",
        "X_test = np.array(X_test)\n",
        "\n",
        "Y_train = np.array(Y_train)\n",
        "Y_val = np.array(Y_val)\n",
        "Y_test = np.array(Y_test)"
      ],
      "metadata": {
        "id": "qBG1l-c7l7bV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e1c02c2b-82ad-494a-bb05-874898e3e73a"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Size of the data: 2469\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# CNN using EfficientNetB0"
      ],
      "metadata": {
        "id": "gZ7pzHfctx0m"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow.keras.applications import EfficientNetB0\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense, GlobalAveragePooling2D\n",
        "\n",
        "# Load pre-trained EfficientNetB0 model without the top (classification) layer\n",
        "base_model = EfficientNetB0(weights='imagenet', include_top=False, input_shape=(180, 320, 3))\n",
        "\n",
        "# Freeze the pre-trained weights\n",
        "base_model.trainable = False\n",
        "\n",
        "# Create a new model on top\n",
        "model = Sequential([\n",
        "    base_model,\n",
        "    GlobalAveragePooling2D(),\n",
        "    Dense(256, activation='relu'),\n",
        "    Dense(3, activation='softmax')  # Assuming 3 classes: neutral, promoting, debunking\n",
        "])\n",
        "\n",
        "# Compile the model\n",
        "model.compile(optimizer='adam',\n",
        "              loss='sparse_categorical_crossentropy',\n",
        "              metrics=['accuracy'])\n",
        "\n",
        "# Print model summary\n",
        "model.summary()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ayyk3sHvghVr",
        "outputId": "7d4ab5c7-d7b4-40e5-a9b4-aab2ca602bb2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " efficientnetb0 (Functional  (None, 6, 10, 1280)       4049571   \n",
            " )                                                               \n",
            "                                                                 \n",
            " global_average_pooling2d_1  (None, 1280)              0         \n",
            "  (GlobalAveragePooling2D)                                       \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 256)               327936    \n",
            "                                                                 \n",
            " dense_3 (Dense)             (None, 3)                 771       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 4378278 (16.70 MB)\n",
            "Trainable params: 328707 (1.25 MB)\n",
            "Non-trainable params: 4049571 (15.45 MB)\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Training the model\n",
        "history = model.fit(\n",
        "    X_train, Y_train,\n",
        "    epochs=10,\n",
        "    batch_size=32,\n",
        "    validation_data=(X_val, Y_val)\n",
        ")\n",
        "\n",
        "# Evaluation\n",
        "test_loss, test_acc = model.evaluate(X_test, Y_test)\n",
        "print(\"Test Accuracy:\", test_acc)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RdTI-zBLl6if",
        "outputId": "da1773da-2a47-467b-e5b2-3934c089fe93"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "50/50 [==============================] - 176s 3s/step - loss: 0.8164 - accuracy: 0.6861 - val_loss: 0.7908 - val_accuracy: 0.7190\n",
            "Epoch 2/10\n",
            "50/50 [==============================] - 166s 3s/step - loss: 0.5961 - accuracy: 0.7646 - val_loss: 0.7712 - val_accuracy: 0.7089\n",
            "Epoch 3/10\n",
            "50/50 [==============================] - 155s 3s/step - loss: 0.4643 - accuracy: 0.8291 - val_loss: 0.7844 - val_accuracy: 0.7291\n",
            "Epoch 4/10\n",
            "50/50 [==============================] - 166s 3s/step - loss: 0.3504 - accuracy: 0.8842 - val_loss: 0.8525 - val_accuracy: 0.7139\n",
            "Epoch 5/10\n",
            "50/50 [==============================] - 166s 3s/step - loss: 0.2575 - accuracy: 0.9247 - val_loss: 0.8898 - val_accuracy: 0.6835\n",
            "Epoch 6/10\n",
            "50/50 [==============================] - 167s 3s/step - loss: 0.1747 - accuracy: 0.9544 - val_loss: 0.9611 - val_accuracy: 0.7063\n",
            "Epoch 7/10\n",
            "50/50 [==============================] - 168s 3s/step - loss: 0.1210 - accuracy: 0.9778 - val_loss: 1.0310 - val_accuracy: 0.6962\n",
            "Epoch 8/10\n",
            "50/50 [==============================] - 165s 3s/step - loss: 0.0950 - accuracy: 0.9829 - val_loss: 1.1077 - val_accuracy: 0.6759\n",
            "Epoch 9/10\n",
            "50/50 [==============================] - 156s 3s/step - loss: 0.0689 - accuracy: 0.9873 - val_loss: 1.0674 - val_accuracy: 0.6810\n",
            "Epoch 10/10\n",
            "50/50 [==============================] - 162s 3s/step - loss: 0.0404 - accuracy: 0.9968 - val_loss: 1.2582 - val_accuracy: 0.6962\n",
            "16/16 [==============================] - 39s 2s/step - loss: 1.0654 - accuracy: 0.7227\n",
            "Test Accuracy: 0.7226720452308655\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## with regularisation"
      ],
      "metadata": {
        "id": "LC_sJaUzuUt5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.layers import Dropout\n",
        "\n",
        "# Define and compile the model with dropout\n",
        "model_with_dropout = Sequential([\n",
        "    base_model,\n",
        "    GlobalAveragePooling2D(),\n",
        "    Dense(256, activation='relu'),\n",
        "    Dropout(0.5),  # Adding dropout layer with dropout rate of 0.5\n",
        "    Dense(3, activation='softmax')  # Assuming 3 classes: neutral, promoting, debunking\n",
        "])\n",
        "\n",
        "model_with_dropout.compile(optimizer='adam',\n",
        "                           loss='sparse_categorical_crossentropy',\n",
        "                           metrics=['accuracy'])\n",
        "\n",
        "# Train the model with dropout\n",
        "history_dropout = model_with_dropout.fit(\n",
        "    X_train, Y_train,\n",
        "    epochs=10,\n",
        "    batch_size=32,\n",
        "    validation_data=(X_val, Y_val)\n",
        ")\n",
        "\n",
        "# Evaluate the model with dropout on test data\n",
        "test_loss_dropout, test_acc_dropout = model_with_dropout.evaluate(X_test, Y_test)\n",
        "print(\"Test Accuracy with Dropout:\", test_acc_dropout)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_MAgCjwGk0Zo",
        "outputId": "48eaf3a5-1d8e-49f4-db6b-4064157550c1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "50/50 [==============================] - 173s 3s/step - loss: 0.8539 - accuracy: 0.6968 - val_loss: 0.7813 - val_accuracy: 0.7215\n",
            "Epoch 2/10\n",
            "50/50 [==============================] - 164s 3s/step - loss: 0.6555 - accuracy: 0.7494 - val_loss: 0.7856 - val_accuracy: 0.6861\n",
            "Epoch 3/10\n",
            "50/50 [==============================] - 162s 3s/step - loss: 0.5949 - accuracy: 0.7772 - val_loss: 0.7730 - val_accuracy: 0.7139\n",
            "Epoch 4/10\n",
            "50/50 [==============================] - 154s 3s/step - loss: 0.5141 - accuracy: 0.8013 - val_loss: 0.7984 - val_accuracy: 0.7291\n",
            "Epoch 5/10\n",
            "50/50 [==============================] - 162s 3s/step - loss: 0.4567 - accuracy: 0.8310 - val_loss: 0.7776 - val_accuracy: 0.7291\n",
            "Epoch 6/10\n",
            "50/50 [==============================] - 164s 3s/step - loss: 0.4008 - accuracy: 0.8487 - val_loss: 0.8473 - val_accuracy: 0.7190\n",
            "Epoch 7/10\n",
            "50/50 [==============================] - 163s 3s/step - loss: 0.3350 - accuracy: 0.8886 - val_loss: 0.9144 - val_accuracy: 0.7316\n",
            "Epoch 8/10\n",
            "50/50 [==============================] - 152s 3s/step - loss: 0.3114 - accuracy: 0.8937 - val_loss: 0.8552 - val_accuracy: 0.6987\n",
            "Epoch 9/10\n",
            "50/50 [==============================] - 163s 3s/step - loss: 0.2763 - accuracy: 0.9095 - val_loss: 0.9348 - val_accuracy: 0.7190\n",
            "Epoch 10/10\n",
            "50/50 [==============================] - 150s 3s/step - loss: 0.2346 - accuracy: 0.9196 - val_loss: 0.8838 - val_accuracy: 0.6835\n",
            "16/16 [==============================] - 38s 2s/step - loss: 0.8494 - accuracy: 0.7227\n",
            "Test Accuracy with Dropout: 0.7226720452308655\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## complex model with fine tuning"
      ],
      "metadata": {
        "id": "J1mFMCMV7SCY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow.keras.applications import EfficientNetB0\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import GlobalAveragePooling2D, Dense, Dropout\n",
        "\n",
        "# Load pre-trained EfficientNetB0 model without the top (classification) layer\n",
        "base_model = EfficientNetB0(weights='imagenet', include_top=False, input_shape=(180, 320, 3))\n",
        "\n",
        "# Freeze the pre-trained weights\n",
        "base_model.trainable = False\n",
        "\n",
        "# Create a new model with additional layers\n",
        "complex_model = Sequential([\n",
        "    base_model,\n",
        "    GlobalAveragePooling2D(),\n",
        "    Dense(512, activation='relu'),  # Increase the number of units in the dense layer\n",
        "    Dropout(0.5),  # Add dropout layer\n",
        "    Dense(256, activation='relu'),  # Add another dense layer\n",
        "    Dropout(0.5),  # Add dropout layer\n",
        "    Dense(3, activation='softmax')\n",
        "])\n",
        "\n",
        "complex_model.compile(optimizer='adam',\n",
        "              loss='sparse_categorical_crossentropy',\n",
        "              metrics=['accuracy'])\n",
        "\n",
        "complex_model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7Xi_h8Pd7bCy",
        "outputId": "c127798b-0ee6-482c-8379-0794ecbb764a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_5\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " efficientnetb0 (Functional  (None, 6, 10, 1280)       4049571   \n",
            " )                                                               \n",
            "                                                                 \n",
            " global_average_pooling2d_5  (None, 1280)              0         \n",
            "  (GlobalAveragePooling2D)                                       \n",
            "                                                                 \n",
            " dense_12 (Dense)            (None, 512)               655872    \n",
            "                                                                 \n",
            " dropout_5 (Dropout)         (None, 512)               0         \n",
            "                                                                 \n",
            " dense_13 (Dense)            (None, 256)               131328    \n",
            "                                                                 \n",
            " dropout_6 (Dropout)         (None, 256)               0         \n",
            "                                                                 \n",
            " dense_14 (Dense)            (None, 3)                 771       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 4837542 (18.45 MB)\n",
            "Trainable params: 787971 (3.01 MB)\n",
            "Non-trainable params: 4049571 (15.45 MB)\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "history = complex_model.fit(\n",
        "    X_train, Y_train,\n",
        "    epochs=10,\n",
        "    batch_size=32,\n",
        "    validation_data=(X_val, Y_val)\n",
        ")\n",
        "\n",
        "test_loss, test_acc = complex_model.evaluate(X_test, Y_test)\n",
        "print(\"Test Accuracy:\", test_acc)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t8k7yYIi8F-c",
        "outputId": "f781bdb7-370e-44aa-eddb-dfc328bf18dc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "50/50 [==============================] - 174s 3s/step - loss: 0.8755 - accuracy: 0.6829 - val_loss: 0.7828 - val_accuracy: 0.7190\n",
            "Epoch 2/10\n",
            "50/50 [==============================] - 163s 3s/step - loss: 0.7407 - accuracy: 0.7234 - val_loss: 0.7831 - val_accuracy: 0.7266\n",
            "Epoch 3/10\n",
            "50/50 [==============================] - 164s 3s/step - loss: 0.7054 - accuracy: 0.7228 - val_loss: 0.7821 - val_accuracy: 0.7190\n",
            "Epoch 4/10\n",
            "50/50 [==============================] - 153s 3s/step - loss: 0.6298 - accuracy: 0.7620 - val_loss: 0.7754 - val_accuracy: 0.7241\n",
            "Epoch 5/10\n",
            "50/50 [==============================] - 163s 3s/step - loss: 0.5758 - accuracy: 0.7816 - val_loss: 0.7798 - val_accuracy: 0.7165\n",
            "Epoch 6/10\n",
            "50/50 [==============================] - 162s 3s/step - loss: 0.5119 - accuracy: 0.7956 - val_loss: 0.8751 - val_accuracy: 0.6962\n",
            "Epoch 7/10\n",
            "50/50 [==============================] - 155s 3s/step - loss: 0.4432 - accuracy: 0.8323 - val_loss: 0.8571 - val_accuracy: 0.6810\n",
            "Epoch 8/10\n",
            "50/50 [==============================] - 162s 3s/step - loss: 0.3725 - accuracy: 0.8576 - val_loss: 0.9241 - val_accuracy: 0.6911\n",
            "Epoch 9/10\n",
            "50/50 [==============================] - 165s 3s/step - loss: 0.3298 - accuracy: 0.8791 - val_loss: 1.0925 - val_accuracy: 0.7165\n",
            "Epoch 10/10\n",
            "50/50 [==============================] - 163s 3s/step - loss: 0.3523 - accuracy: 0.8741 - val_loss: 0.9321 - val_accuracy: 0.7089\n",
            "16/16 [==============================] - 38s 2s/step - loss: 0.8670 - accuracy: 0.7348\n",
            "Test Accuracy: 0.7348178029060364\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## unfreezing some layers"
      ],
      "metadata": {
        "id": "ZqSWOPp-Nj-P"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow.keras.applications import EfficientNetB0\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import GlobalAveragePooling2D, Dense, Dropout\n",
        "\n",
        "# Load pre-trained EfficientNetB0 model without the top (classification) layer\n",
        "basemodel = EfficientNetB0(weights='imagenet', include_top=False, input_shape=(180, 320, 3))\n",
        "\n",
        "# Unfreeze some layers for fine-tuning\n",
        "# Here we unfreeze the last few layers\n",
        "for layer in base_model.layers[-20:]:\n",
        "    layer.trainable = True\n",
        "\n",
        "# Create a new model with additional layers\n",
        "unfrozen_model = Sequential([\n",
        "    base_model,\n",
        "    GlobalAveragePooling2D(),\n",
        "    Dense(512, activation='relu'),\n",
        "    Dropout(0.5),\n",
        "    Dense(256, activation='relu'),\n",
        "    Dropout(0.5),\n",
        "    Dense(3, activation='softmax')\n",
        "])\n",
        "\n",
        "# Compile the model\n",
        "unfrozen_model.compile(optimizer='adam',\n",
        "              loss='sparse_categorical_crossentropy',\n",
        "              metrics=['accuracy'])\n",
        "\n",
        "# Print model summary\n",
        "unfrozen_model.summary()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JjXZ8_kXNiTe",
        "outputId": "65efe125-d7ea-42ba-91e5-4a4e7cffc623"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_6\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " efficientnetb0 (Functional  (None, 6, 10, 1280)       4049571   \n",
            " )                                                               \n",
            "                                                                 \n",
            " global_average_pooling2d_6  (None, 1280)              0         \n",
            "  (GlobalAveragePooling2D)                                       \n",
            "                                                                 \n",
            " dense_15 (Dense)            (None, 512)               655872    \n",
            "                                                                 \n",
            " dropout_7 (Dropout)         (None, 512)               0         \n",
            "                                                                 \n",
            " dense_16 (Dense)            (None, 256)               131328    \n",
            "                                                                 \n",
            " dropout_8 (Dropout)         (None, 256)               0         \n",
            "                                                                 \n",
            " dense_17 (Dense)            (None, 3)                 771       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 4837542 (18.45 MB)\n",
            "Trainable params: 787971 (3.01 MB)\n",
            "Non-trainable params: 4049571 (15.45 MB)\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "history = unfrozen_model.fit(\n",
        "    X_train, Y_train,\n",
        "    epochs=10,\n",
        "    batch_size=32,\n",
        "    validation_data=(X_val, Y_val)\n",
        ")\n",
        "\n",
        "test_loss, test_acc = unfrozen_model.evaluate(X_test, Y_test)\n",
        "print(\"Test Accuracy:\", test_acc)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GIZdPWSHN-FE",
        "outputId": "7b904916-0163-43d6-ea98-74df067adcd4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "50/50 [==============================] - 175s 3s/step - loss: 0.8707 - accuracy: 0.6842 - val_loss: 0.8098 - val_accuracy: 0.7190\n",
            "Epoch 2/10\n",
            "50/50 [==============================] - 166s 3s/step - loss: 0.7294 - accuracy: 0.7234 - val_loss: 0.7665 - val_accuracy: 0.7241\n",
            "Epoch 3/10\n",
            "50/50 [==============================] - 164s 3s/step - loss: 0.6699 - accuracy: 0.7361 - val_loss: 0.8085 - val_accuracy: 0.7291\n",
            "Epoch 4/10\n",
            "50/50 [==============================] - 165s 3s/step - loss: 0.6002 - accuracy: 0.7582 - val_loss: 0.8357 - val_accuracy: 0.7291\n",
            "Epoch 5/10\n",
            "50/50 [==============================] - 163s 3s/step - loss: 0.5006 - accuracy: 0.7937 - val_loss: 0.9389 - val_accuracy: 0.7316\n",
            "Epoch 6/10\n",
            "50/50 [==============================] - 165s 3s/step - loss: 0.4330 - accuracy: 0.8367 - val_loss: 0.8967 - val_accuracy: 0.6886\n",
            "Epoch 7/10\n",
            "50/50 [==============================] - 163s 3s/step - loss: 0.3394 - accuracy: 0.8753 - val_loss: 1.0584 - val_accuracy: 0.6785\n",
            "Epoch 8/10\n",
            "50/50 [==============================] - 165s 3s/step - loss: 0.3099 - accuracy: 0.8873 - val_loss: 1.1243 - val_accuracy: 0.6911\n",
            "Epoch 9/10\n",
            "50/50 [==============================] - 165s 3s/step - loss: 0.2775 - accuracy: 0.8968 - val_loss: 1.2797 - val_accuracy: 0.7241\n",
            "Epoch 10/10\n",
            "50/50 [==============================] - 164s 3s/step - loss: 0.2349 - accuracy: 0.9120 - val_loss: 1.1344 - val_accuracy: 0.6684\n",
            "16/16 [==============================] - 38s 2s/step - loss: 1.1246 - accuracy: 0.6599\n",
            "Test Accuracy: 0.659919023513794\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "cGGikqjSYPUV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## freezing all layers except last one"
      ],
      "metadata": {
        "id": "WMQNoaTKZGI6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow.keras.applications import EfficientNetB0\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import GlobalAveragePooling2D, Dense, Dropout\n",
        "\n",
        "# Load pre-trained EfficientNetB0 model without the top (classification) layer\n",
        "base_model = EfficientNetB0(weights='imagenet', include_top=False, input_shape=(180, 320, 3))\n",
        "\n",
        "# Freeze all layers except for the last one\n",
        "for layer in base_model.layers[:-1]:\n",
        "    layer.trainable = False\n",
        "\n",
        "last_model = Sequential([\n",
        "    base_model,\n",
        "    GlobalAveragePooling2D(),\n",
        "    Dense(1024, activation='relu'),\n",
        "    Dropout(0.5),  # Add dropout layer for regularization\n",
        "    Dense(512, activation='relu'),  # Another dense layer\n",
        "    Dropout(0.5),  # Add dropout layer\n",
        "    Dense(256, activation='relu'),  # Another dense layer\n",
        "    Dropout(0.5),  # Add dropout layer\n",
        "    Dense(3, activation='softmax')  # Assuming 3 classes: neutral, promoting, debunking\n",
        "])\n",
        "last_model.compile(optimizer='adam',\n",
        "              loss='sparse_categorical_crossentropy',\n",
        "              metrics=['accuracy'])\n",
        "\n",
        "last_model.summary()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t-hcuOw-ZLYJ",
        "outputId": "04f6c83b-f8ad-4ef0-fbbc-660167f1be91"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/keras-applications/efficientnetb0_notop.h5\n",
            "16705208/16705208 [==============================] - 2s 0us/step\n",
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " efficientnetb0 (Functional  (None, 6, 10, 1280)       4049571   \n",
            " )                                                               \n",
            "                                                                 \n",
            " global_average_pooling2d (  (None, 1280)              0         \n",
            " GlobalAveragePooling2D)                                         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 1024)              1311744   \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 1024)              0         \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 512)               524800    \n",
            "                                                                 \n",
            " dropout_1 (Dropout)         (None, 512)               0         \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 256)               131328    \n",
            "                                                                 \n",
            " dropout_2 (Dropout)         (None, 256)               0         \n",
            "                                                                 \n",
            " dense_3 (Dense)             (None, 3)                 771       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 6018214 (22.96 MB)\n",
            "Trainable params: 1968643 (7.51 MB)\n",
            "Non-trainable params: 4049571 (15.45 MB)\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "last_history = last_model.fit(\n",
        "    X_train, Y_train,\n",
        "    epochs=10,\n",
        "    batch_size=32,\n",
        "    validation_data=(X_val, Y_val)\n",
        ")\n",
        "\n",
        "test_loss, test_acc = last_model.evaluate(X_test, Y_test)\n",
        "print(\"Test Accuracy:\", test_acc)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JdZWmhWIZfKe",
        "outputId": "9fdb0deb-974f-488a-a4c7-23ff3a5b3505"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "50/50 [==============================] - 22s 208ms/step - loss: 0.9230 - accuracy: 0.6715 - val_loss: 0.8153 - val_accuracy: 0.7190\n",
            "Epoch 2/10\n",
            "50/50 [==============================] - 4s 82ms/step - loss: 0.8268 - accuracy: 0.6981 - val_loss: 0.7846 - val_accuracy: 0.7190\n",
            "Epoch 3/10\n",
            "50/50 [==============================] - 4s 85ms/step - loss: 0.7764 - accuracy: 0.7127 - val_loss: 0.8070 - val_accuracy: 0.7215\n",
            "Epoch 4/10\n",
            "50/50 [==============================] - 5s 95ms/step - loss: 0.7352 - accuracy: 0.7266 - val_loss: 0.7674 - val_accuracy: 0.7165\n",
            "Epoch 5/10\n",
            "50/50 [==============================] - 5s 92ms/step - loss: 0.6567 - accuracy: 0.7335 - val_loss: 0.7989 - val_accuracy: 0.7266\n",
            "Epoch 6/10\n",
            "50/50 [==============================] - 4s 84ms/step - loss: 0.6286 - accuracy: 0.7570 - val_loss: 0.7986 - val_accuracy: 0.7089\n",
            "Epoch 7/10\n",
            "50/50 [==============================] - 4s 86ms/step - loss: 0.5614 - accuracy: 0.7791 - val_loss: 0.8356 - val_accuracy: 0.6785\n",
            "Epoch 8/10\n",
            "50/50 [==============================] - 5s 93ms/step - loss: 0.5012 - accuracy: 0.7968 - val_loss: 0.9166 - val_accuracy: 0.7165\n",
            "Epoch 9/10\n",
            "50/50 [==============================] - 5s 94ms/step - loss: 0.4369 - accuracy: 0.8342 - val_loss: 0.9705 - val_accuracy: 0.6684\n",
            "Epoch 10/10\n",
            "50/50 [==============================] - 5s 95ms/step - loss: 0.3835 - accuracy: 0.8551 - val_loss: 0.9478 - val_accuracy: 0.6684\n",
            "16/16 [==============================] - 2s 140ms/step - loss: 0.9157 - accuracy: 0.7227\n",
            "Test Accuracy: 0.7226720452308655\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## lowering learning rate"
      ],
      "metadata": {
        "id": "YdEFohcqukWt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow.keras.applications import EfficientNetB0\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import GlobalAveragePooling2D, Dense, BatchNormalization, Dropout\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "\n",
        "# Load pre-trained EfficientNetB0 model without the top (classification) layer\n",
        "base_model = EfficientNetB0(weights='imagenet', include_top=False, input_shape=(180, 320, 3))\n",
        "\n",
        "# Freeze all layers except for the last one\n",
        "for layer in base_model.layers[:-1]:\n",
        "    layer.trainable = False\n",
        "\n",
        "# Add more layers to the model for deeper feature extraction\n",
        "low_rate_model = Sequential([\n",
        "    base_model,\n",
        "    GlobalAveragePooling2D(),\n",
        "    Dense(1024, activation='relu'),\n",
        "    Dropout(0.5),\n",
        "    BatchNormalization(),  # Add batch normalization\n",
        "    Dense(512, activation='relu'),\n",
        "    Dropout(0.5),\n",
        "    BatchNormalization(),  # Add batch normalization\n",
        "    Dense(256, activation='relu'),\n",
        "    Dropout(0.5),\n",
        "    Dense(3, activation='softmax')\n",
        "])\n",
        "\n",
        "# Adjust the learning rate\n",
        "optimizer = Adam(learning_rate=0.0001)\n",
        "\n",
        "# Compile the model with the new optimizer\n",
        "low_rate_model.compile(optimizer=optimizer,\n",
        "              loss='sparse_categorical_crossentropy',\n",
        "              metrics=['accuracy'])\n",
        "\n",
        "# Print model summary\n",
        "low_rate_model.summary()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hrhf8lEBuj8Y",
        "outputId": "0a1217f4-4cd7-47d0-bab5-c6d625e648c3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_3\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " efficientnetb0 (Functional  (None, 6, 10, 1280)       4049571   \n",
            " )                                                               \n",
            "                                                                 \n",
            " global_average_pooling2d_3  (None, 1280)              0         \n",
            "  (GlobalAveragePooling2D)                                       \n",
            "                                                                 \n",
            " dense_12 (Dense)            (None, 1024)              1311744   \n",
            "                                                                 \n",
            " dropout_9 (Dropout)         (None, 1024)              0         \n",
            "                                                                 \n",
            " batch_normalization_4 (Bat  (None, 1024)              4096      \n",
            " chNormalization)                                                \n",
            "                                                                 \n",
            " dense_13 (Dense)            (None, 512)               524800    \n",
            "                                                                 \n",
            " dropout_10 (Dropout)        (None, 512)               0         \n",
            "                                                                 \n",
            " batch_normalization_5 (Bat  (None, 512)               2048      \n",
            " chNormalization)                                                \n",
            "                                                                 \n",
            " dense_14 (Dense)            (None, 256)               131328    \n",
            "                                                                 \n",
            " dropout_11 (Dropout)        (None, 256)               0         \n",
            "                                                                 \n",
            " dense_15 (Dense)            (None, 3)                 771       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 6024358 (22.98 MB)\n",
            "Trainable params: 1971715 (7.52 MB)\n",
            "Non-trainable params: 4052643 (15.46 MB)\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "low_rate_history = low_rate_model.fit(\n",
        "    X_train, Y_train,\n",
        "    epochs=10,\n",
        "    batch_size=32,\n",
        "    validation_data=(X_val, Y_val)\n",
        ")\n",
        "\n",
        "# Evaluate the model on the test set\n",
        "test_loss, test_accuracy = low_rate_model.evaluate(X_test, Y_test)\n",
        "print(\"Test Accuracy:\", test_accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fBMo8lVQvDHw",
        "outputId": "a7182f95-f10e-4eed-84c1-0d8ae178b928"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "50/50 [==============================] - 16s 129ms/step - loss: 1.7783 - accuracy: 0.3006 - val_loss: 1.0879 - val_accuracy: 0.3949\n",
            "Epoch 2/10\n",
            "50/50 [==============================] - 4s 88ms/step - loss: 1.3618 - accuracy: 0.4272 - val_loss: 0.9978 - val_accuracy: 0.5671\n",
            "Epoch 3/10\n",
            "50/50 [==============================] - 4s 86ms/step - loss: 1.1636 - accuracy: 0.5070 - val_loss: 0.9377 - val_accuracy: 0.6329\n",
            "Epoch 4/10\n",
            "50/50 [==============================] - 4s 85ms/step - loss: 1.0758 - accuracy: 0.5778 - val_loss: 0.8863 - val_accuracy: 0.6911\n",
            "Epoch 5/10\n",
            "50/50 [==============================] - 4s 88ms/step - loss: 1.0701 - accuracy: 0.5975 - val_loss: 0.8559 - val_accuracy: 0.7063\n",
            "Epoch 6/10\n",
            "50/50 [==============================] - 4s 85ms/step - loss: 0.9998 - accuracy: 0.6203 - val_loss: 0.8408 - val_accuracy: 0.6937\n",
            "Epoch 7/10\n",
            "50/50 [==============================] - 4s 85ms/step - loss: 1.0037 - accuracy: 0.6348 - val_loss: 0.8194 - val_accuracy: 0.7114\n",
            "Epoch 8/10\n",
            "50/50 [==============================] - 4s 87ms/step - loss: 0.9688 - accuracy: 0.6487 - val_loss: 0.8138 - val_accuracy: 0.7063\n",
            "Epoch 9/10\n",
            "50/50 [==============================] - 5s 96ms/step - loss: 0.9489 - accuracy: 0.6468 - val_loss: 0.8144 - val_accuracy: 0.6987\n",
            "Epoch 10/10\n",
            "50/50 [==============================] - 5s 94ms/step - loss: 0.9736 - accuracy: 0.6361 - val_loss: 0.8239 - val_accuracy: 0.6886\n",
            "16/16 [==============================] - 1s 66ms/step - loss: 0.8032 - accuracy: 0.6984\n",
            "Test Accuracy: 0.6983805894851685\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## increasing epochs of complex efficient net model with fine tuning\n"
      ],
      "metadata": {
        "id": "qh0xi5U5BM5y"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow.keras.applications import EfficientNetB0\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import GlobalAveragePooling2D, Dense, Dropout\n",
        "\n",
        "# Load pre-trained EfficientNetB0 model without the top (classification) layer\n",
        "base_model = EfficientNetB0(weights='imagenet', include_top=False, input_shape=(180, 320, 3))\n",
        "\n",
        "# Freeze the pre-trained weights\n",
        "base_model.trainable = False\n",
        "\n",
        "# Create a new model with additional layers\n",
        "epoch_model = Sequential([\n",
        "    base_model,\n",
        "    GlobalAveragePooling2D(),\n",
        "    Dense(512, activation='relu'),  # Increase the number of units in the dense layer\n",
        "    Dropout(0.5),  # Add dropout layer\n",
        "    Dense(256, activation='relu'),  # Add another dense layer\n",
        "    Dropout(0.5),  # Add dropout layer\n",
        "    Dense(3, activation='softmax')\n",
        "])\n",
        "\n",
        "epoch_model.compile(optimizer='adam',\n",
        "              loss='sparse_categorical_crossentropy',\n",
        "              metrics=['accuracy'])\n",
        "\n",
        "epoch_model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-TglKli6BMM6",
        "outputId": "8d62a0b7-402e-43f7-8a89-87010b6b8de4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_4\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " efficientnetb0 (Functional  (None, 6, 10, 1280)       4049571   \n",
            " )                                                               \n",
            "                                                                 \n",
            " global_average_pooling2d_5  (None, 1280)              0         \n",
            "  (GlobalAveragePooling2D)                                       \n",
            "                                                                 \n",
            " dense_20 (Dense)            (None, 512)               655872    \n",
            "                                                                 \n",
            " dropout_15 (Dropout)        (None, 512)               0         \n",
            "                                                                 \n",
            " dense_21 (Dense)            (None, 256)               131328    \n",
            "                                                                 \n",
            " dropout_16 (Dropout)        (None, 256)               0         \n",
            "                                                                 \n",
            " dense_22 (Dense)            (None, 3)                 771       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 4837542 (18.45 MB)\n",
            "Trainable params: 787971 (3.01 MB)\n",
            "Non-trainable params: 4049571 (15.45 MB)\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "epoch_history = epoch_model.fit(\n",
        "    X_train, Y_train,\n",
        "    epochs=20,\n",
        "    batch_size=32,\n",
        "    validation_data=(X_val, Y_val)\n",
        ")\n",
        "\n",
        "test_loss, test_acc = epoch_model.evaluate(X_test, Y_test)\n",
        "print(\"Test Accuracy:\", test_acc)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d_I_Ta_TBlMg",
        "outputId": "36fc6518-1b4a-427f-c6d4-142d67d2f02b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "50/50 [==============================] - 6s 125ms/step - loss: 0.8027 - accuracy: 0.7146 - val_loss: 0.7612 - val_accuracy: 0.7190\n",
            "Epoch 2/20\n",
            "50/50 [==============================] - 4s 84ms/step - loss: 0.7202 - accuracy: 0.7380 - val_loss: 0.7846 - val_accuracy: 0.7190\n",
            "Epoch 3/20\n",
            "50/50 [==============================] - 4s 86ms/step - loss: 0.6570 - accuracy: 0.7570 - val_loss: 0.7706 - val_accuracy: 0.7013\n",
            "Epoch 4/20\n",
            "50/50 [==============================] - 4s 85ms/step - loss: 0.6262 - accuracy: 0.7525 - val_loss: 0.7799 - val_accuracy: 0.7139\n",
            "Epoch 5/20\n",
            "50/50 [==============================] - 4s 89ms/step - loss: 0.5279 - accuracy: 0.7994 - val_loss: 0.7726 - val_accuracy: 0.7190\n",
            "Epoch 6/20\n",
            "50/50 [==============================] - 4s 87ms/step - loss: 0.4931 - accuracy: 0.8025 - val_loss: 0.8182 - val_accuracy: 0.7063\n",
            "Epoch 7/20\n",
            "50/50 [==============================] - 4s 86ms/step - loss: 0.4303 - accuracy: 0.8297 - val_loss: 0.8493 - val_accuracy: 0.6886\n",
            "Epoch 8/20\n",
            "50/50 [==============================] - 5s 93ms/step - loss: 0.3672 - accuracy: 0.8639 - val_loss: 1.0065 - val_accuracy: 0.7342\n",
            "Epoch 9/20\n",
            "50/50 [==============================] - 4s 85ms/step - loss: 0.3297 - accuracy: 0.8759 - val_loss: 1.0083 - val_accuracy: 0.5949\n",
            "Epoch 10/20\n",
            "50/50 [==============================] - 4s 86ms/step - loss: 0.2972 - accuracy: 0.8873 - val_loss: 1.0072 - val_accuracy: 0.6987\n",
            "Epoch 11/20\n",
            "50/50 [==============================] - 5s 94ms/step - loss: 0.2683 - accuracy: 0.9044 - val_loss: 1.0142 - val_accuracy: 0.6861\n",
            "Epoch 12/20\n",
            "50/50 [==============================] - 4s 85ms/step - loss: 0.2278 - accuracy: 0.9196 - val_loss: 1.0884 - val_accuracy: 0.6532\n",
            "Epoch 13/20\n",
            "50/50 [==============================] - 4s 87ms/step - loss: 0.2168 - accuracy: 0.9228 - val_loss: 1.1241 - val_accuracy: 0.6785\n",
            "Epoch 14/20\n",
            "50/50 [==============================] - 4s 84ms/step - loss: 0.1634 - accuracy: 0.9430 - val_loss: 1.2114 - val_accuracy: 0.6709\n",
            "Epoch 15/20\n",
            "50/50 [==============================] - 5s 94ms/step - loss: 0.1619 - accuracy: 0.9443 - val_loss: 1.1355 - val_accuracy: 0.6785\n",
            "Epoch 16/20\n",
            "50/50 [==============================] - 4s 86ms/step - loss: 0.1385 - accuracy: 0.9544 - val_loss: 1.4358 - val_accuracy: 0.7063\n",
            "Epoch 17/20\n",
            "50/50 [==============================] - 5s 95ms/step - loss: 0.1394 - accuracy: 0.9468 - val_loss: 1.4222 - val_accuracy: 0.7013\n",
            "Epoch 18/20\n",
            "50/50 [==============================] - 5s 94ms/step - loss: 0.1176 - accuracy: 0.9551 - val_loss: 1.4061 - val_accuracy: 0.6835\n",
            "Epoch 19/20\n",
            "50/50 [==============================] - 4s 86ms/step - loss: 0.1436 - accuracy: 0.9506 - val_loss: 1.3109 - val_accuracy: 0.6911\n",
            "Epoch 20/20\n",
            "50/50 [==============================] - 4s 84ms/step - loss: 0.1115 - accuracy: 0.9595 - val_loss: 1.3532 - val_accuracy: 0.7089\n",
            "16/16 [==============================] - 1s 70ms/step - loss: 1.2612 - accuracy: 0.7287\n",
            "Test Accuracy: 0.7287449240684509\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## making model more complex"
      ],
      "metadata": {
        "id": "4stkamLOAd4u"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow.keras.applications import EfficientNetB0\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import GlobalAveragePooling2D, Dense, Dropout, BatchNormalization\n",
        "\n",
        "# Load pre-trained EfficientNetB0 model without the top (classification) layer\n",
        "base_model = EfficientNetB0(weights='imagenet', include_top=False, input_shape=(180, 320, 3))\n",
        "\n",
        "# Freeze the pre-trained weights\n",
        "base_model.trainable = False\n",
        "\n",
        "# Create a new model with additional layers\n",
        "dense_model = Sequential([\n",
        "    base_model,\n",
        "    GlobalAveragePooling2D(),\n",
        "    Dense(2048, activation='relu'),\n",
        "    Dropout(0.5),\n",
        "    BatchNormalization(),\n",
        "    Dense(1024, activation='relu'),  # dense layer\n",
        "    Dropout(0.5),                     # Add dropout layer\n",
        "    Dense(512, activation='relu'),  # Add another dense layer\n",
        "    Dropout(0.5),                    # Add dropout layer\n",
        "    Dense(256, activation='relu'),  # Add another dense layer\n",
        "    Dropout(0.5),                    # Add dropout layer\n",
        "    Dense(3, activation='softmax')\n",
        "])\n",
        "\n",
        "dense_model.compile(optimizer='adam',\n",
        "              loss='sparse_categorical_crossentropy',\n",
        "              metrics=['accuracy'])\n",
        "\n",
        "dense_model.summary()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aUkWq0WDAgpx",
        "outputId": "90a08e2a-443e-415a-a3c1-572ab7b7fb1f"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " efficientnetb0 (Functional  (None, 6, 10, 1280)       4049571   \n",
            " )                                                               \n",
            "                                                                 \n",
            " global_average_pooling2d_3  (None, 1280)              0         \n",
            "  (GlobalAveragePooling2D)                                       \n",
            "                                                                 \n",
            " dense_10 (Dense)            (None, 2048)              2623488   \n",
            "                                                                 \n",
            " dropout_8 (Dropout)         (None, 2048)              0         \n",
            "                                                                 \n",
            " batch_normalization_1 (Bat  (None, 2048)              8192      \n",
            " chNormalization)                                                \n",
            "                                                                 \n",
            " dense_11 (Dense)            (None, 1024)              2098176   \n",
            "                                                                 \n",
            " dropout_9 (Dropout)         (None, 1024)              0         \n",
            "                                                                 \n",
            " dense_12 (Dense)            (None, 512)               524800    \n",
            "                                                                 \n",
            " dropout_10 (Dropout)        (None, 512)               0         \n",
            "                                                                 \n",
            " dense_13 (Dense)            (None, 256)               131328    \n",
            "                                                                 \n",
            " dropout_11 (Dropout)        (None, 256)               0         \n",
            "                                                                 \n",
            " dense_14 (Dense)            (None, 3)                 771       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 9436326 (36.00 MB)\n",
            "Trainable params: 5382659 (20.53 MB)\n",
            "Non-trainable params: 4053667 (15.46 MB)\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dense_history = dense_model.fit(\n",
        "    X_train, Y_train,\n",
        "    epochs=20,\n",
        "    batch_size=32,\n",
        "    validation_data=(X_val, Y_val)\n",
        ")\n",
        "\n",
        "test_loss, test_acc = dense_model.evaluate(X_test, Y_test)\n",
        "print(\"Test Accuracy:\", test_acc)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PsQz8ueIArmY",
        "outputId": "8476c56b-b95a-40e2-cb7c-8cd8331313bd"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "50/50 [==============================] - 233s 5s/step - loss: 0.3473 - accuracy: 0.8753 - val_loss: 1.0521 - val_accuracy: 0.6835\n",
            "Epoch 2/20\n",
            "50/50 [==============================] - 202s 4s/step - loss: 0.3023 - accuracy: 0.8905 - val_loss: 1.2897 - val_accuracy: 0.7063\n",
            "Epoch 3/20\n",
            "50/50 [==============================] - 202s 4s/step - loss: 0.2789 - accuracy: 0.8924 - val_loss: 1.4120 - val_accuracy: 0.6203\n",
            "Epoch 4/20\n",
            "50/50 [==============================] - 200s 4s/step - loss: 0.2957 - accuracy: 0.8987 - val_loss: 1.1945 - val_accuracy: 0.6785\n",
            "Epoch 5/20\n",
            "50/50 [==============================] - 194s 4s/step - loss: 0.3211 - accuracy: 0.8854 - val_loss: 1.1280 - val_accuracy: 0.6861\n",
            "Epoch 6/20\n",
            "50/50 [==============================] - 195s 4s/step - loss: 0.2741 - accuracy: 0.8981 - val_loss: 1.4158 - val_accuracy: 0.6177\n",
            "Epoch 7/20\n",
            "50/50 [==============================] - 194s 4s/step - loss: 0.2970 - accuracy: 0.8892 - val_loss: 1.2432 - val_accuracy: 0.6405\n",
            "Epoch 8/20\n",
            "50/50 [==============================] - 195s 4s/step - loss: 0.2589 - accuracy: 0.9025 - val_loss: 1.2924 - val_accuracy: 0.6278\n",
            "Epoch 9/20\n",
            "50/50 [==============================] - 192s 4s/step - loss: 0.2644 - accuracy: 0.9101 - val_loss: 1.2293 - val_accuracy: 0.6506\n",
            "Epoch 10/20\n",
            "50/50 [==============================] - 196s 4s/step - loss: 0.2675 - accuracy: 0.9108 - val_loss: 1.3420 - val_accuracy: 0.6532\n",
            "Epoch 11/20\n",
            "50/50 [==============================] - 195s 4s/step - loss: 0.2402 - accuracy: 0.9114 - val_loss: 1.3877 - val_accuracy: 0.6886\n",
            "Epoch 12/20\n",
            "50/50 [==============================] - 193s 4s/step - loss: 0.2284 - accuracy: 0.9222 - val_loss: 1.3070 - val_accuracy: 0.6228\n",
            "Epoch 13/20\n",
            "50/50 [==============================] - 196s 4s/step - loss: 0.2185 - accuracy: 0.9177 - val_loss: 1.7700 - val_accuracy: 0.6886\n",
            "Epoch 14/20\n",
            "50/50 [==============================] - 197s 4s/step - loss: 0.2248 - accuracy: 0.9184 - val_loss: 1.3982 - val_accuracy: 0.6658\n",
            "Epoch 15/20\n",
            "50/50 [==============================] - 183s 4s/step - loss: 0.2287 - accuracy: 0.9203 - val_loss: 1.4600 - val_accuracy: 0.6810\n",
            "Epoch 16/20\n",
            "50/50 [==============================] - 182s 4s/step - loss: 0.1931 - accuracy: 0.9253 - val_loss: 1.4882 - val_accuracy: 0.6987\n",
            "Epoch 17/20\n",
            "50/50 [==============================] - 189s 4s/step - loss: 0.2101 - accuracy: 0.9272 - val_loss: 1.4553 - val_accuracy: 0.7089\n",
            "Epoch 18/20\n",
            "50/50 [==============================] - 189s 4s/step - loss: 0.1894 - accuracy: 0.9297 - val_loss: 1.7484 - val_accuracy: 0.6937\n",
            "Epoch 19/20\n",
            "50/50 [==============================] - 191s 4s/step - loss: 0.2395 - accuracy: 0.9184 - val_loss: 1.3703 - val_accuracy: 0.6835\n",
            "Epoch 20/20\n",
            "50/50 [==============================] - 189s 4s/step - loss: 0.1913 - accuracy: 0.9323 - val_loss: 1.3816 - val_accuracy: 0.6810\n",
            "16/16 [==============================] - 45s 3s/step - loss: 1.3279 - accuracy: 0.6984\n",
            "Test Accuracy: 0.6983805894851685\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score, precision_score, fbeta_score, classification_report, confusion_matrix\n",
        "import seaborn as sns\n",
        "\n",
        "y_pred_prob = dense_model.predict(X_test)\n",
        "\n",
        "# Get predicted class labels by finding the index of the maximum probability\n",
        "y_pred = np.argmax(y_pred_prob, axis=1)\n",
        "\n",
        "\n",
        "# Calculate accuracy\n",
        "accuracy = accuracy_score(Y_test, y_pred)\n",
        "print(\"Accuracy:\", accuracy)\n",
        "\n",
        "# Calculate precision\n",
        "precision = precision_score(Y_test, y_pred, average='weighted')\n",
        "print(\"Precision:\", precision)\n",
        "\n",
        "# Calculate F2 score (assuming binary classification)\n",
        "f2_score = fbeta_score(Y_test, y_pred, beta=2, average='weighted')\n",
        "print(\"F2 Score:\", f2_score)\n",
        "\n",
        "# Generate classification report\n",
        "print(\"\\nClassification Report:\")\n",
        "print(classification_report(Y_test, y_pred))\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uU_Y0PB5Q0ow",
        "outputId": "9342b0b5-e20a-45a9-cac4-44c38b81563e"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "16/16 [==============================] - 55s 3s/step\n",
            "Accuracy: 0.13157894736842105\n",
            "Precision: 0.6540652877048653\n",
            "F2 Score: 0.09121984046888777\n",
            "\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.14      0.11      0.12        81\n",
            "           1       0.84      0.04      0.08       365\n",
            "           2       0.10      0.83      0.18        48\n",
            "\n",
            "    accuracy                           0.13       494\n",
            "   macro avg       0.36      0.33      0.13       494\n",
            "weighted avg       0.65      0.13      0.10       494\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## b1"
      ],
      "metadata": {
        "id": "3xrrmKG2UpSs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow.keras.applications import EfficientNetB1\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense, GlobalAveragePooling2D\n",
        "\n",
        "# Load pre-trained EfficientNetB0 model without the top (classification) layer\n",
        "base_model_b1 = EfficientNetB1(weights='imagenet', include_top=False, input_shape=(180, 320, 3))\n",
        "\n",
        "# Freeze the pre-trained weights\n",
        "base_model_b1.trainable = False\n",
        "\n",
        "# Create a new model on top\n",
        "b1_model = Sequential([\n",
        "    base_model,\n",
        "    GlobalAveragePooling2D(),\n",
        "    Dense(256, activation='relu'),\n",
        "    Dense(3, activation='softmax')\n",
        "])\n",
        "\n",
        "# Compile the model\n",
        "b1_model.compile(optimizer='adam',\n",
        "              loss='sparse_categorical_crossentropy',\n",
        "              metrics=['accuracy'])\n",
        "\n",
        "b1_model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eWQnF7YwUq5g",
        "outputId": "1ecd5d04-6bbb-497d-8c8c-d5ae599916c5"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/keras-applications/efficientnetb1_notop.h5\n",
            "27018416/27018416 [==============================] - 0s 0us/step\n",
            "Model: \"sequential_3\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " efficientnetb0 (Functional  (None, 6, 10, 1280)       4049571   \n",
            " )                                                               \n",
            "                                                                 \n",
            " global_average_pooling2d_4  (None, 1280)              0         \n",
            "  (GlobalAveragePooling2D)                                       \n",
            "                                                                 \n",
            " dense_15 (Dense)            (None, 256)               327936    \n",
            "                                                                 \n",
            " dense_16 (Dense)            (None, 3)                 771       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 4378278 (16.70 MB)\n",
            "Trainable params: 328707 (1.25 MB)\n",
            "Non-trainable params: 4049571 (15.45 MB)\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "b1_history = b1_model.fit(\n",
        "    X_train, Y_train,\n",
        "    epochs=10,\n",
        "    batch_size=32,\n",
        "    validation_data=(X_val, Y_val)\n",
        ")\n",
        "\n",
        "test_loss, test_acc = b1_model.evaluate(X_test, Y_test)\n",
        "print(\"Test Accuracy:\", test_acc)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4WJ5nyDBVJXH",
        "outputId": "f8920fe3-a570-44d2-917e-4553d3e1e23a"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "50/50 [==============================] - 193s 4s/step - loss: 0.8353 - accuracy: 0.6924 - val_loss: 0.7884 - val_accuracy: 0.7139\n",
            "Epoch 2/10\n",
            "50/50 [==============================] - 185s 4s/step - loss: 0.5930 - accuracy: 0.7652 - val_loss: 0.7549 - val_accuracy: 0.7241\n",
            "Epoch 3/10\n",
            "50/50 [==============================] - 187s 4s/step - loss: 0.4763 - accuracy: 0.8259 - val_loss: 0.7964 - val_accuracy: 0.7266\n",
            "Epoch 4/10\n",
            "50/50 [==============================] - 189s 4s/step - loss: 0.3587 - accuracy: 0.8741 - val_loss: 0.7992 - val_accuracy: 0.7190\n",
            "Epoch 5/10\n",
            "50/50 [==============================] - 182s 4s/step - loss: 0.2622 - accuracy: 0.9222 - val_loss: 0.8553 - val_accuracy: 0.6835\n",
            "Epoch 6/10\n",
            "50/50 [==============================] - 177s 4s/step - loss: 0.1837 - accuracy: 0.9563 - val_loss: 0.9464 - val_accuracy: 0.6911\n",
            "Epoch 7/10\n",
            "50/50 [==============================] - 182s 4s/step - loss: 0.1232 - accuracy: 0.9785 - val_loss: 0.9723 - val_accuracy: 0.6962\n",
            "Epoch 8/10\n",
            "50/50 [==============================] - 190s 4s/step - loss: 0.0944 - accuracy: 0.9829 - val_loss: 1.0414 - val_accuracy: 0.7038\n",
            "Epoch 9/10\n",
            "50/50 [==============================] - 229s 5s/step - loss: 0.0628 - accuracy: 0.9899 - val_loss: 1.0265 - val_accuracy: 0.6835\n",
            "Epoch 10/10\n",
            "50/50 [==============================] - 189s 4s/step - loss: 0.0443 - accuracy: 0.9962 - val_loss: 1.1783 - val_accuracy: 0.6911\n",
            "16/16 [==============================] - 45s 3s/step - loss: 1.0779 - accuracy: 0.7206\n",
            "Test Accuracy: 0.7206477522850037\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## fine tuned b1\n"
      ],
      "metadata": {
        "id": "4LmDhrbt7dPu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow.keras.applications import EfficientNetB1\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import GlobalAveragePooling2D, Dense, Dropout\n",
        "\n",
        "# Load pre-trained EfficientNetB0 model without the top (classification) layer\n",
        "base_model_b1 = EfficientNetB1(weights='imagenet', include_top=False, input_shape=(180, 320, 3))\n",
        "\n",
        "# Freeze the pre-trained weights\n",
        "base_model_b1.trainable = False\n",
        "\n",
        "# Create a new model with additional layers\n",
        "complex_model_b1 = Sequential([\n",
        "    base_model_b1,\n",
        "    GlobalAveragePooling2D(),\n",
        "    Dense(512, activation='relu'),  # Increase the number of units in the dense layer\n",
        "    Dropout(0.5),  # Add dropout layer\n",
        "    Dense(256, activation='relu'),  # Add another dense layer\n",
        "    Dropout(0.5),  # Add dropout layer\n",
        "    Dense(3, activation='softmax')\n",
        "])\n",
        "\n",
        "complex_model_b1.compile(optimizer='adam',\n",
        "              loss='sparse_categorical_crossentropy',\n",
        "              metrics=['accuracy'])\n",
        "\n",
        "complex_model_b1.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Hq9QdrQy7fzw",
        "outputId": "2544d59f-b78f-4141-ef47-b7c02075b330"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_4\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " efficientnetb1 (Functional  (None, 6, 10, 1280)       6575239   \n",
            " )                                                               \n",
            "                                                                 \n",
            " global_average_pooling2d_5  (None, 1280)              0         \n",
            "  (GlobalAveragePooling2D)                                       \n",
            "                                                                 \n",
            " dense_17 (Dense)            (None, 512)               655872    \n",
            "                                                                 \n",
            " dropout_12 (Dropout)        (None, 512)               0         \n",
            "                                                                 \n",
            " dense_18 (Dense)            (None, 256)               131328    \n",
            "                                                                 \n",
            " dropout_13 (Dropout)        (None, 256)               0         \n",
            "                                                                 \n",
            " dense_19 (Dense)            (None, 3)                 771       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 7363210 (28.09 MB)\n",
            "Trainable params: 787971 (3.01 MB)\n",
            "Non-trainable params: 6575239 (25.08 MB)\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "complex_b1_history = complex_model_b1.fit(\n",
        "    X_train, Y_train,\n",
        "    epochs=10,\n",
        "    batch_size=32,\n",
        "    validation_data=(X_val, Y_val)\n",
        ")\n",
        "\n",
        "test_loss, test_acc = complex_model_b1.evaluate(X_test, Y_test)\n",
        "print(\"Test Accuracy:\", test_acc)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "moX3WmCU74o0",
        "outputId": "593c9959-0a84-4b56-a38a-13b0bc7ee306"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "50/50 [==============================] - 314s 6s/step - loss: 0.8760 - accuracy: 0.6772 - val_loss: 0.7720 - val_accuracy: 0.7190\n",
            "Epoch 2/10\n",
            "50/50 [==============================] - 257s 5s/step - loss: 0.7626 - accuracy: 0.7051 - val_loss: 0.7550 - val_accuracy: 0.7241\n",
            "Epoch 3/10\n",
            "50/50 [==============================] - 286s 6s/step - loss: 0.6925 - accuracy: 0.7278 - val_loss: 0.7617 - val_accuracy: 0.7291\n",
            "Epoch 4/10\n",
            "50/50 [==============================] - 288s 6s/step - loss: 0.6342 - accuracy: 0.7481 - val_loss: 0.7995 - val_accuracy: 0.7316\n",
            "Epoch 5/10\n",
            "50/50 [==============================] - 287s 6s/step - loss: 0.5777 - accuracy: 0.7785 - val_loss: 0.7964 - val_accuracy: 0.7266\n",
            "Epoch 6/10\n",
            "50/50 [==============================] - 292s 6s/step - loss: 0.5307 - accuracy: 0.7918 - val_loss: 0.7981 - val_accuracy: 0.7190\n",
            "Epoch 7/10\n",
            "50/50 [==============================] - 256s 5s/step - loss: 0.4445 - accuracy: 0.8266 - val_loss: 0.8641 - val_accuracy: 0.7165\n",
            "Epoch 8/10\n",
            "50/50 [==============================] - 258s 5s/step - loss: 0.4301 - accuracy: 0.8310 - val_loss: 0.9225 - val_accuracy: 0.7165\n",
            "Epoch 9/10\n",
            "50/50 [==============================] - 291s 6s/step - loss: 0.3651 - accuracy: 0.8639 - val_loss: 0.8931 - val_accuracy: 0.7215\n",
            "Epoch 10/10\n",
            "50/50 [==============================] - 289s 6s/step - loss: 0.3234 - accuracy: 0.8696 - val_loss: 1.0189 - val_accuracy: 0.6835\n",
            "16/16 [==============================] - 66s 4s/step - loss: 0.9649 - accuracy: 0.7105\n",
            "Test Accuracy: 0.7105262875556946\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# resnet"
      ],
      "metadata": {
        "id": "rDS-a_M2v__o"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow.keras.applications import ResNet50\n",
        "from tensorflow.keras.models import Model\n",
        "from tensorflow.keras.layers import GlobalAveragePooling2D, Dense, Dropout\n",
        "\n",
        "# Load pre-trained ResNet50 model without the top (classification) layer\n",
        "base_model = ResNet50(weights='imagenet', include_top=False, input_shape=(180, 320, 3))\n",
        "\n",
        "# Freeze all layers of the base model\n",
        "for layer in base_model.layers:\n",
        "    layer.trainable = False\n",
        "\n",
        "# Add more layers to the model for classification\n",
        "x = base_model.output\n",
        "x = GlobalAveragePooling2D()(x)\n",
        "x = Dense(1024, activation='relu')(x)\n",
        "x = Dropout(0.5)(x)\n",
        "x = Dense(512, activation='relu')(x)\n",
        "x = Dropout(0.5)(x)\n",
        "x = Dense(256, activation='relu')(x)\n",
        "x = Dropout(0.5)(x)\n",
        "predictions = Dense(3, activation='softmax')(x)  # Assuming 3 classes: neutral, promoting, debunking\n",
        "\n",
        "# Create the final model\n",
        "resnet_model = Model(inputs=base_model.input, outputs=predictions)\n",
        "\n",
        "# Compile the model\n",
        "resnet_model.compile(optimizer='adam',\n",
        "              loss='sparse_categorical_crossentropy',\n",
        "              metrics=['accuracy'])\n",
        "\n",
        "# Print model summary\n",
        "resnet_model.summary()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5-tqyKTWv_wL",
        "outputId": "daec7931-7935-4357-c37a-a9c4a1753a02"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/resnet/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
            "94765736/94765736 [==============================] - 5s 0us/step\n",
            "Model: \"model\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                Output Shape                 Param #   Connected to                  \n",
            "==================================================================================================\n",
            " input_5 (InputLayer)        [(None, 180, 320, 3)]        0         []                            \n",
            "                                                                                                  \n",
            " conv1_pad (ZeroPadding2D)   (None, 186, 326, 3)          0         ['input_5[0][0]']             \n",
            "                                                                                                  \n",
            " conv1_conv (Conv2D)         (None, 90, 160, 64)          9472      ['conv1_pad[0][0]']           \n",
            "                                                                                                  \n",
            " conv1_bn (BatchNormalizati  (None, 90, 160, 64)          256       ['conv1_conv[0][0]']          \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            " conv1_relu (Activation)     (None, 90, 160, 64)          0         ['conv1_bn[0][0]']            \n",
            "                                                                                                  \n",
            " pool1_pad (ZeroPadding2D)   (None, 92, 162, 64)          0         ['conv1_relu[0][0]']          \n",
            "                                                                                                  \n",
            " pool1_pool (MaxPooling2D)   (None, 45, 80, 64)           0         ['pool1_pad[0][0]']           \n",
            "                                                                                                  \n",
            " conv2_block1_1_conv (Conv2  (None, 45, 80, 64)           4160      ['pool1_pool[0][0]']          \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv2_block1_1_bn (BatchNo  (None, 45, 80, 64)           256       ['conv2_block1_1_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv2_block1_1_relu (Activ  (None, 45, 80, 64)           0         ['conv2_block1_1_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv2_block1_2_conv (Conv2  (None, 45, 80, 64)           36928     ['conv2_block1_1_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv2_block1_2_bn (BatchNo  (None, 45, 80, 64)           256       ['conv2_block1_2_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv2_block1_2_relu (Activ  (None, 45, 80, 64)           0         ['conv2_block1_2_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv2_block1_0_conv (Conv2  (None, 45, 80, 256)          16640     ['pool1_pool[0][0]']          \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv2_block1_3_conv (Conv2  (None, 45, 80, 256)          16640     ['conv2_block1_2_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv2_block1_0_bn (BatchNo  (None, 45, 80, 256)          1024      ['conv2_block1_0_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv2_block1_3_bn (BatchNo  (None, 45, 80, 256)          1024      ['conv2_block1_3_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv2_block1_add (Add)      (None, 45, 80, 256)          0         ['conv2_block1_0_bn[0][0]',   \n",
            "                                                                     'conv2_block1_3_bn[0][0]']   \n",
            "                                                                                                  \n",
            " conv2_block1_out (Activati  (None, 45, 80, 256)          0         ['conv2_block1_add[0][0]']    \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            " conv2_block2_1_conv (Conv2  (None, 45, 80, 64)           16448     ['conv2_block1_out[0][0]']    \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv2_block2_1_bn (BatchNo  (None, 45, 80, 64)           256       ['conv2_block2_1_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv2_block2_1_relu (Activ  (None, 45, 80, 64)           0         ['conv2_block2_1_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv2_block2_2_conv (Conv2  (None, 45, 80, 64)           36928     ['conv2_block2_1_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv2_block2_2_bn (BatchNo  (None, 45, 80, 64)           256       ['conv2_block2_2_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv2_block2_2_relu (Activ  (None, 45, 80, 64)           0         ['conv2_block2_2_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv2_block2_3_conv (Conv2  (None, 45, 80, 256)          16640     ['conv2_block2_2_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv2_block2_3_bn (BatchNo  (None, 45, 80, 256)          1024      ['conv2_block2_3_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv2_block2_add (Add)      (None, 45, 80, 256)          0         ['conv2_block1_out[0][0]',    \n",
            "                                                                     'conv2_block2_3_bn[0][0]']   \n",
            "                                                                                                  \n",
            " conv2_block2_out (Activati  (None, 45, 80, 256)          0         ['conv2_block2_add[0][0]']    \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            " conv2_block3_1_conv (Conv2  (None, 45, 80, 64)           16448     ['conv2_block2_out[0][0]']    \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv2_block3_1_bn (BatchNo  (None, 45, 80, 64)           256       ['conv2_block3_1_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv2_block3_1_relu (Activ  (None, 45, 80, 64)           0         ['conv2_block3_1_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv2_block3_2_conv (Conv2  (None, 45, 80, 64)           36928     ['conv2_block3_1_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv2_block3_2_bn (BatchNo  (None, 45, 80, 64)           256       ['conv2_block3_2_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv2_block3_2_relu (Activ  (None, 45, 80, 64)           0         ['conv2_block3_2_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv2_block3_3_conv (Conv2  (None, 45, 80, 256)          16640     ['conv2_block3_2_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv2_block3_3_bn (BatchNo  (None, 45, 80, 256)          1024      ['conv2_block3_3_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv2_block3_add (Add)      (None, 45, 80, 256)          0         ['conv2_block2_out[0][0]',    \n",
            "                                                                     'conv2_block3_3_bn[0][0]']   \n",
            "                                                                                                  \n",
            " conv2_block3_out (Activati  (None, 45, 80, 256)          0         ['conv2_block3_add[0][0]']    \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            " conv3_block1_1_conv (Conv2  (None, 23, 40, 128)          32896     ['conv2_block3_out[0][0]']    \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv3_block1_1_bn (BatchNo  (None, 23, 40, 128)          512       ['conv3_block1_1_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv3_block1_1_relu (Activ  (None, 23, 40, 128)          0         ['conv3_block1_1_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv3_block1_2_conv (Conv2  (None, 23, 40, 128)          147584    ['conv3_block1_1_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv3_block1_2_bn (BatchNo  (None, 23, 40, 128)          512       ['conv3_block1_2_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv3_block1_2_relu (Activ  (None, 23, 40, 128)          0         ['conv3_block1_2_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv3_block1_0_conv (Conv2  (None, 23, 40, 512)          131584    ['conv2_block3_out[0][0]']    \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv3_block1_3_conv (Conv2  (None, 23, 40, 512)          66048     ['conv3_block1_2_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv3_block1_0_bn (BatchNo  (None, 23, 40, 512)          2048      ['conv3_block1_0_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv3_block1_3_bn (BatchNo  (None, 23, 40, 512)          2048      ['conv3_block1_3_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv3_block1_add (Add)      (None, 23, 40, 512)          0         ['conv3_block1_0_bn[0][0]',   \n",
            "                                                                     'conv3_block1_3_bn[0][0]']   \n",
            "                                                                                                  \n",
            " conv3_block1_out (Activati  (None, 23, 40, 512)          0         ['conv3_block1_add[0][0]']    \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            " conv3_block2_1_conv (Conv2  (None, 23, 40, 128)          65664     ['conv3_block1_out[0][0]']    \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv3_block2_1_bn (BatchNo  (None, 23, 40, 128)          512       ['conv3_block2_1_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv3_block2_1_relu (Activ  (None, 23, 40, 128)          0         ['conv3_block2_1_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv3_block2_2_conv (Conv2  (None, 23, 40, 128)          147584    ['conv3_block2_1_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv3_block2_2_bn (BatchNo  (None, 23, 40, 128)          512       ['conv3_block2_2_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv3_block2_2_relu (Activ  (None, 23, 40, 128)          0         ['conv3_block2_2_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv3_block2_3_conv (Conv2  (None, 23, 40, 512)          66048     ['conv3_block2_2_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv3_block2_3_bn (BatchNo  (None, 23, 40, 512)          2048      ['conv3_block2_3_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv3_block2_add (Add)      (None, 23, 40, 512)          0         ['conv3_block1_out[0][0]',    \n",
            "                                                                     'conv3_block2_3_bn[0][0]']   \n",
            "                                                                                                  \n",
            " conv3_block2_out (Activati  (None, 23, 40, 512)          0         ['conv3_block2_add[0][0]']    \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            " conv3_block3_1_conv (Conv2  (None, 23, 40, 128)          65664     ['conv3_block2_out[0][0]']    \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv3_block3_1_bn (BatchNo  (None, 23, 40, 128)          512       ['conv3_block3_1_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv3_block3_1_relu (Activ  (None, 23, 40, 128)          0         ['conv3_block3_1_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv3_block3_2_conv (Conv2  (None, 23, 40, 128)          147584    ['conv3_block3_1_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv3_block3_2_bn (BatchNo  (None, 23, 40, 128)          512       ['conv3_block3_2_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv3_block3_2_relu (Activ  (None, 23, 40, 128)          0         ['conv3_block3_2_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv3_block3_3_conv (Conv2  (None, 23, 40, 512)          66048     ['conv3_block3_2_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv3_block3_3_bn (BatchNo  (None, 23, 40, 512)          2048      ['conv3_block3_3_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv3_block3_add (Add)      (None, 23, 40, 512)          0         ['conv3_block2_out[0][0]',    \n",
            "                                                                     'conv3_block3_3_bn[0][0]']   \n",
            "                                                                                                  \n",
            " conv3_block3_out (Activati  (None, 23, 40, 512)          0         ['conv3_block3_add[0][0]']    \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            " conv3_block4_1_conv (Conv2  (None, 23, 40, 128)          65664     ['conv3_block3_out[0][0]']    \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv3_block4_1_bn (BatchNo  (None, 23, 40, 128)          512       ['conv3_block4_1_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv3_block4_1_relu (Activ  (None, 23, 40, 128)          0         ['conv3_block4_1_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv3_block4_2_conv (Conv2  (None, 23, 40, 128)          147584    ['conv3_block4_1_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv3_block4_2_bn (BatchNo  (None, 23, 40, 128)          512       ['conv3_block4_2_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv3_block4_2_relu (Activ  (None, 23, 40, 128)          0         ['conv3_block4_2_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv3_block4_3_conv (Conv2  (None, 23, 40, 512)          66048     ['conv3_block4_2_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv3_block4_3_bn (BatchNo  (None, 23, 40, 512)          2048      ['conv3_block4_3_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv3_block4_add (Add)      (None, 23, 40, 512)          0         ['conv3_block3_out[0][0]',    \n",
            "                                                                     'conv3_block4_3_bn[0][0]']   \n",
            "                                                                                                  \n",
            " conv3_block4_out (Activati  (None, 23, 40, 512)          0         ['conv3_block4_add[0][0]']    \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            " conv4_block1_1_conv (Conv2  (None, 12, 20, 256)          131328    ['conv3_block4_out[0][0]']    \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block1_1_bn (BatchNo  (None, 12, 20, 256)          1024      ['conv4_block1_1_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv4_block1_1_relu (Activ  (None, 12, 20, 256)          0         ['conv4_block1_1_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv4_block1_2_conv (Conv2  (None, 12, 20, 256)          590080    ['conv4_block1_1_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block1_2_bn (BatchNo  (None, 12, 20, 256)          1024      ['conv4_block1_2_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv4_block1_2_relu (Activ  (None, 12, 20, 256)          0         ['conv4_block1_2_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv4_block1_0_conv (Conv2  (None, 12, 20, 1024)         525312    ['conv3_block4_out[0][0]']    \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block1_3_conv (Conv2  (None, 12, 20, 1024)         263168    ['conv4_block1_2_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block1_0_bn (BatchNo  (None, 12, 20, 1024)         4096      ['conv4_block1_0_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv4_block1_3_bn (BatchNo  (None, 12, 20, 1024)         4096      ['conv4_block1_3_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv4_block1_add (Add)      (None, 12, 20, 1024)         0         ['conv4_block1_0_bn[0][0]',   \n",
            "                                                                     'conv4_block1_3_bn[0][0]']   \n",
            "                                                                                                  \n",
            " conv4_block1_out (Activati  (None, 12, 20, 1024)         0         ['conv4_block1_add[0][0]']    \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            " conv4_block2_1_conv (Conv2  (None, 12, 20, 256)          262400    ['conv4_block1_out[0][0]']    \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block2_1_bn (BatchNo  (None, 12, 20, 256)          1024      ['conv4_block2_1_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv4_block2_1_relu (Activ  (None, 12, 20, 256)          0         ['conv4_block2_1_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv4_block2_2_conv (Conv2  (None, 12, 20, 256)          590080    ['conv4_block2_1_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block2_2_bn (BatchNo  (None, 12, 20, 256)          1024      ['conv4_block2_2_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv4_block2_2_relu (Activ  (None, 12, 20, 256)          0         ['conv4_block2_2_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv4_block2_3_conv (Conv2  (None, 12, 20, 1024)         263168    ['conv4_block2_2_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block2_3_bn (BatchNo  (None, 12, 20, 1024)         4096      ['conv4_block2_3_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv4_block2_add (Add)      (None, 12, 20, 1024)         0         ['conv4_block1_out[0][0]',    \n",
            "                                                                     'conv4_block2_3_bn[0][0]']   \n",
            "                                                                                                  \n",
            " conv4_block2_out (Activati  (None, 12, 20, 1024)         0         ['conv4_block2_add[0][0]']    \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            " conv4_block3_1_conv (Conv2  (None, 12, 20, 256)          262400    ['conv4_block2_out[0][0]']    \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block3_1_bn (BatchNo  (None, 12, 20, 256)          1024      ['conv4_block3_1_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv4_block3_1_relu (Activ  (None, 12, 20, 256)          0         ['conv4_block3_1_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv4_block3_2_conv (Conv2  (None, 12, 20, 256)          590080    ['conv4_block3_1_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block3_2_bn (BatchNo  (None, 12, 20, 256)          1024      ['conv4_block3_2_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv4_block3_2_relu (Activ  (None, 12, 20, 256)          0         ['conv4_block3_2_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv4_block3_3_conv (Conv2  (None, 12, 20, 1024)         263168    ['conv4_block3_2_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block3_3_bn (BatchNo  (None, 12, 20, 1024)         4096      ['conv4_block3_3_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv4_block3_add (Add)      (None, 12, 20, 1024)         0         ['conv4_block2_out[0][0]',    \n",
            "                                                                     'conv4_block3_3_bn[0][0]']   \n",
            "                                                                                                  \n",
            " conv4_block3_out (Activati  (None, 12, 20, 1024)         0         ['conv4_block3_add[0][0]']    \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            " conv4_block4_1_conv (Conv2  (None, 12, 20, 256)          262400    ['conv4_block3_out[0][0]']    \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block4_1_bn (BatchNo  (None, 12, 20, 256)          1024      ['conv4_block4_1_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv4_block4_1_relu (Activ  (None, 12, 20, 256)          0         ['conv4_block4_1_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv4_block4_2_conv (Conv2  (None, 12, 20, 256)          590080    ['conv4_block4_1_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block4_2_bn (BatchNo  (None, 12, 20, 256)          1024      ['conv4_block4_2_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv4_block4_2_relu (Activ  (None, 12, 20, 256)          0         ['conv4_block4_2_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv4_block4_3_conv (Conv2  (None, 12, 20, 1024)         263168    ['conv4_block4_2_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block4_3_bn (BatchNo  (None, 12, 20, 1024)         4096      ['conv4_block4_3_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv4_block4_add (Add)      (None, 12, 20, 1024)         0         ['conv4_block3_out[0][0]',    \n",
            "                                                                     'conv4_block4_3_bn[0][0]']   \n",
            "                                                                                                  \n",
            " conv4_block4_out (Activati  (None, 12, 20, 1024)         0         ['conv4_block4_add[0][0]']    \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            " conv4_block5_1_conv (Conv2  (None, 12, 20, 256)          262400    ['conv4_block4_out[0][0]']    \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block5_1_bn (BatchNo  (None, 12, 20, 256)          1024      ['conv4_block5_1_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv4_block5_1_relu (Activ  (None, 12, 20, 256)          0         ['conv4_block5_1_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv4_block5_2_conv (Conv2  (None, 12, 20, 256)          590080    ['conv4_block5_1_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block5_2_bn (BatchNo  (None, 12, 20, 256)          1024      ['conv4_block5_2_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv4_block5_2_relu (Activ  (None, 12, 20, 256)          0         ['conv4_block5_2_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv4_block5_3_conv (Conv2  (None, 12, 20, 1024)         263168    ['conv4_block5_2_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block5_3_bn (BatchNo  (None, 12, 20, 1024)         4096      ['conv4_block5_3_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv4_block5_add (Add)      (None, 12, 20, 1024)         0         ['conv4_block4_out[0][0]',    \n",
            "                                                                     'conv4_block5_3_bn[0][0]']   \n",
            "                                                                                                  \n",
            " conv4_block5_out (Activati  (None, 12, 20, 1024)         0         ['conv4_block5_add[0][0]']    \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            " conv4_block6_1_conv (Conv2  (None, 12, 20, 256)          262400    ['conv4_block5_out[0][0]']    \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block6_1_bn (BatchNo  (None, 12, 20, 256)          1024      ['conv4_block6_1_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv4_block6_1_relu (Activ  (None, 12, 20, 256)          0         ['conv4_block6_1_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv4_block6_2_conv (Conv2  (None, 12, 20, 256)          590080    ['conv4_block6_1_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block6_2_bn (BatchNo  (None, 12, 20, 256)          1024      ['conv4_block6_2_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv4_block6_2_relu (Activ  (None, 12, 20, 256)          0         ['conv4_block6_2_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv4_block6_3_conv (Conv2  (None, 12, 20, 1024)         263168    ['conv4_block6_2_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block6_3_bn (BatchNo  (None, 12, 20, 1024)         4096      ['conv4_block6_3_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv4_block6_add (Add)      (None, 12, 20, 1024)         0         ['conv4_block5_out[0][0]',    \n",
            "                                                                     'conv4_block6_3_bn[0][0]']   \n",
            "                                                                                                  \n",
            " conv4_block6_out (Activati  (None, 12, 20, 1024)         0         ['conv4_block6_add[0][0]']    \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            " conv5_block1_1_conv (Conv2  (None, 6, 10, 512)           524800    ['conv4_block6_out[0][0]']    \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv5_block1_1_bn (BatchNo  (None, 6, 10, 512)           2048      ['conv5_block1_1_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv5_block1_1_relu (Activ  (None, 6, 10, 512)           0         ['conv5_block1_1_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv5_block1_2_conv (Conv2  (None, 6, 10, 512)           2359808   ['conv5_block1_1_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv5_block1_2_bn (BatchNo  (None, 6, 10, 512)           2048      ['conv5_block1_2_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv5_block1_2_relu (Activ  (None, 6, 10, 512)           0         ['conv5_block1_2_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv5_block1_0_conv (Conv2  (None, 6, 10, 2048)          2099200   ['conv4_block6_out[0][0]']    \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv5_block1_3_conv (Conv2  (None, 6, 10, 2048)          1050624   ['conv5_block1_2_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv5_block1_0_bn (BatchNo  (None, 6, 10, 2048)          8192      ['conv5_block1_0_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv5_block1_3_bn (BatchNo  (None, 6, 10, 2048)          8192      ['conv5_block1_3_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv5_block1_add (Add)      (None, 6, 10, 2048)          0         ['conv5_block1_0_bn[0][0]',   \n",
            "                                                                     'conv5_block1_3_bn[0][0]']   \n",
            "                                                                                                  \n",
            " conv5_block1_out (Activati  (None, 6, 10, 2048)          0         ['conv5_block1_add[0][0]']    \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            " conv5_block2_1_conv (Conv2  (None, 6, 10, 512)           1049088   ['conv5_block1_out[0][0]']    \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv5_block2_1_bn (BatchNo  (None, 6, 10, 512)           2048      ['conv5_block2_1_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv5_block2_1_relu (Activ  (None, 6, 10, 512)           0         ['conv5_block2_1_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv5_block2_2_conv (Conv2  (None, 6, 10, 512)           2359808   ['conv5_block2_1_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv5_block2_2_bn (BatchNo  (None, 6, 10, 512)           2048      ['conv5_block2_2_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv5_block2_2_relu (Activ  (None, 6, 10, 512)           0         ['conv5_block2_2_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv5_block2_3_conv (Conv2  (None, 6, 10, 2048)          1050624   ['conv5_block2_2_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv5_block2_3_bn (BatchNo  (None, 6, 10, 2048)          8192      ['conv5_block2_3_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv5_block2_add (Add)      (None, 6, 10, 2048)          0         ['conv5_block1_out[0][0]',    \n",
            "                                                                     'conv5_block2_3_bn[0][0]']   \n",
            "                                                                                                  \n",
            " conv5_block2_out (Activati  (None, 6, 10, 2048)          0         ['conv5_block2_add[0][0]']    \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            " conv5_block3_1_conv (Conv2  (None, 6, 10, 512)           1049088   ['conv5_block2_out[0][0]']    \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv5_block3_1_bn (BatchNo  (None, 6, 10, 512)           2048      ['conv5_block3_1_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv5_block3_1_relu (Activ  (None, 6, 10, 512)           0         ['conv5_block3_1_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv5_block3_2_conv (Conv2  (None, 6, 10, 512)           2359808   ['conv5_block3_1_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv5_block3_2_bn (BatchNo  (None, 6, 10, 512)           2048      ['conv5_block3_2_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv5_block3_2_relu (Activ  (None, 6, 10, 512)           0         ['conv5_block3_2_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv5_block3_3_conv (Conv2  (None, 6, 10, 2048)          1050624   ['conv5_block3_2_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv5_block3_3_bn (BatchNo  (None, 6, 10, 2048)          8192      ['conv5_block3_3_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv5_block3_add (Add)      (None, 6, 10, 2048)          0         ['conv5_block2_out[0][0]',    \n",
            "                                                                     'conv5_block3_3_bn[0][0]']   \n",
            "                                                                                                  \n",
            " conv5_block3_out (Activati  (None, 6, 10, 2048)          0         ['conv5_block3_add[0][0]']    \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            " global_average_pooling2d_4  (None, 2048)                 0         ['conv5_block3_out[0][0]']    \n",
            "  (GlobalAveragePooling2D)                                                                        \n",
            "                                                                                                  \n",
            " dense_16 (Dense)            (None, 1024)                 2098176   ['global_average_pooling2d_4[0\n",
            "                                                                    ][0]']                        \n",
            "                                                                                                  \n",
            " dropout_12 (Dropout)        (None, 1024)                 0         ['dense_16[0][0]']            \n",
            "                                                                                                  \n",
            " dense_17 (Dense)            (None, 512)                  524800    ['dropout_12[0][0]']          \n",
            "                                                                                                  \n",
            " dropout_13 (Dropout)        (None, 512)                  0         ['dense_17[0][0]']            \n",
            "                                                                                                  \n",
            " dense_18 (Dense)            (None, 256)                  131328    ['dropout_13[0][0]']          \n",
            "                                                                                                  \n",
            " dropout_14 (Dropout)        (None, 256)                  0         ['dense_18[0][0]']            \n",
            "                                                                                                  \n",
            " dense_19 (Dense)            (None, 3)                    771       ['dropout_14[0][0]']          \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 26342787 (100.49 MB)\n",
            "Trainable params: 2755075 (10.51 MB)\n",
            "Non-trainable params: 23587712 (89.98 MB)\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## training"
      ],
      "metadata": {
        "id": "3gA1MdFQxqUs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "resnet_history = resnet_model.fit(\n",
        "    X_train, Y_train,\n",
        "    epochs=10,\n",
        "    batch_size=32,\n",
        "    validation_data=(X_val, Y_val)\n",
        ")\n",
        "\n",
        "# Evaluate the model on the test set\n",
        "test_loss, test_accuracy = resnet_model.evaluate(X_test, Y_test)\n",
        "print(\"Test Accuracy:\", test_accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vd88Wrp4wPPn",
        "outputId": "d3d990c7-20f1-4f84-b5a2-5861e08dcbb2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "50/50 [==============================] - 20s 263ms/step - loss: 1.4652 - accuracy: 0.5867 - val_loss: 0.7922 - val_accuracy: 0.7190\n",
            "Epoch 2/10\n",
            "50/50 [==============================] - 6s 130ms/step - loss: 0.9002 - accuracy: 0.6753 - val_loss: 0.8668 - val_accuracy: 0.7190\n",
            "Epoch 3/10\n",
            "50/50 [==============================] - 7s 132ms/step - loss: 0.8386 - accuracy: 0.7019 - val_loss: 0.8728 - val_accuracy: 0.7190\n",
            "Epoch 4/10\n",
            "50/50 [==============================] - 6s 129ms/step - loss: 0.8228 - accuracy: 0.7038 - val_loss: 0.8102 - val_accuracy: 0.7190\n",
            "Epoch 5/10\n",
            "50/50 [==============================] - 7s 131ms/step - loss: 0.7935 - accuracy: 0.7101 - val_loss: 0.8218 - val_accuracy: 0.7190\n",
            "Epoch 6/10\n",
            "50/50 [==============================] - 6s 127ms/step - loss: 0.8004 - accuracy: 0.7114 - val_loss: 0.8073 - val_accuracy: 0.7190\n",
            "Epoch 7/10\n",
            "50/50 [==============================] - 6s 130ms/step - loss: 0.7672 - accuracy: 0.7234 - val_loss: 0.8587 - val_accuracy: 0.7241\n",
            "Epoch 8/10\n",
            "50/50 [==============================] - 6s 127ms/step - loss: 0.7416 - accuracy: 0.7215 - val_loss: 0.7775 - val_accuracy: 0.7215\n",
            "Epoch 9/10\n",
            "50/50 [==============================] - 6s 129ms/step - loss: 0.7233 - accuracy: 0.7285 - val_loss: 0.8001 - val_accuracy: 0.7215\n",
            "Epoch 10/10\n",
            "50/50 [==============================] - 6s 127ms/step - loss: 0.7074 - accuracy: 0.7285 - val_loss: 0.8101 - val_accuracy: 0.7190\n",
            "16/16 [==============================] - 3s 209ms/step - loss: 0.7746 - accuracy: 0.7348\n",
            "Test Accuracy: 0.7348178029060364\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## results"
      ],
      "metadata": {
        "id": "rEjyGBpJxtKr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Plot training & validation accuracy values\n",
        "plt.plot(resnet_history.history['accuracy'])\n",
        "plt.plot(resnet_history.history['val_accuracy'])\n",
        "plt.title('Model accuracy')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.legend(['Train', 'Validation'], loc='upper left')\n",
        "plt.show()\n",
        "\n",
        "# Plot training & validation loss values\n",
        "plt.plot(resnet_history.history['loss'])\n",
        "plt.plot(resnet_history.history['val_loss'])\n",
        "plt.title('Model loss')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend(['Train', 'Validation'], loc='upper left')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 927
        },
        "id": "DuHQfIm3xMQv",
        "outputId": "7e60ec63-459f-415a-c4ab-61d220b2c9ed"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import roc_curve, auc, roc_auc_score\n",
        "from sklearn.preprocessing import label_binarize\n",
        "\n",
        "# Binarize the labels\n",
        "y_val_binarized = label_binarize(Y_val, classes=[0, 1, 2])  # Assuming 3 classes: 0, 1, 2\n",
        "\n",
        "# Compute ROC curve and ROC area for each class\n",
        "fpr = dict()\n",
        "tpr = dict()\n",
        "roc_auc = dict()\n",
        "num_classes = 3\n",
        "for i in range(num_classes):  # num_classes is the number of classes (3 in this case)\n",
        "    fpr[i], tpr[i], _ = roc_curve(y_val_binarized[:, i], y_pred_prob[:, i])\n",
        "    roc_auc[i] = auc(fpr[i], tpr[i])\n",
        "\n",
        "# Compute micro-average ROC curve and ROC area\n",
        "fpr[\"micro\"], tpr[\"micro\"], _ = roc_curve(y_val_binarized.ravel(), y_pred_prob.ravel())\n",
        "roc_auc[\"micro\"] = auc(fpr[\"micro\"], tpr[\"micro\"])\n",
        "\n",
        "# Compute macro-average ROC curve and ROC area\n",
        "all_fpr = np.unique(np.concatenate([fpr[i] for i in range(num_classes)]))\n",
        "mean_tpr = np.zeros_like(all_fpr)\n",
        "for i in range(num_classes):\n",
        "    mean_tpr += np.interp(all_fpr, fpr[i], tpr[i])\n",
        "mean_tpr /= num_classes\n",
        "fpr[\"macro\"] = all_fpr\n",
        "tpr[\"macro\"] = mean_tpr\n",
        "roc_auc[\"macro\"] = auc(fpr[\"macro\"], tpr[\"macro\"])\n",
        "\n",
        "# Plot ROC curve for each class\n",
        "plt.figure()\n",
        "plt.plot(fpr[\"micro\"], tpr[\"micro\"],\n",
        "         label=f'micro-average ROC curve (area = {roc_auc[\"micro\"]:0.2f})',\n",
        "         color='deeppink', linestyle=':', linewidth=4)\n",
        "\n",
        "plt.plot(fpr[\"macro\"], tpr[\"macro\"],\n",
        "         label=f'macro-average ROC curve (area = {roc_auc[\"macro\"]:0.2f})',\n",
        "         color='navy', linestyle=':', linewidth=4)\n",
        "\n",
        "colors = ['aqua', 'darkorange', 'cornflowerblue']\n",
        "for i, color in zip(range(num_classes), colors):\n",
        "    plt.plot(fpr[i], tpr[i], color=color, lw=2,\n",
        "             label=f'ROC curve of class {i} (area = {roc_auc[i]:0.2f})')\n",
        "\n",
        "plt.plot([0, 1], [0, 1], 'k--', lw=2)\n",
        "plt.xlim([0.0, 1.0])\n",
        "plt.ylim([0.0, 1.05])\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate')\n",
        "plt.title('ROC Curve for Multiclass Classification')\n",
        "plt.legend(loc=\"lower right\")\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 471
        },
        "id": "1nbDzJ6zyI7-",
        "outputId": "50e5c9ed-605a-4e22-d433-38018e873dcb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Get predicted probabilities for the validation set\n",
        "y_pred_prob = resnet_model.predict(X_val)\n",
        "\n",
        "# Get predicted classes by taking the class with the highest probability\n",
        "y_pred = np.argmax(y_pred_prob, axis=1)\n",
        "\n",
        "# Compute confusion matrix\n",
        "cm = confusion_matrix(Y_val, y_pred)\n",
        "\n",
        "# Plot confusion matrix\n",
        "plt.figure(figsize=(8, 6))\n",
        "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', annot_kws={\"size\": 12})\n",
        "plt.title('Confusion Matrix')\n",
        "plt.xlabel('Predicted labels')\n",
        "plt.ylabel('True labels')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 580
        },
        "id": "rXDTW5M0x9VW",
        "outputId": "08f5413e-4c53-4435-80f6-1997bb3c4a5b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "13/13 [==============================] - 1s 102ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}