Resnet50_Doodle.ipynb

{
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        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/Jyothiraditya135/Analytics135/blob/main/Resnet50_Doodle.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6u_cK5IPs37O"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import json\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.applications import ResNet50\n",
        "from tensorflow.keras.layers import Dense, GlobalAveragePooling2D\n",
        "from tensorflow.keras.preprocessing.image import img_to_array\n",
        "from tensorflow.keras.utils import to_categorical\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.utils import shuffle\n",
        "import torch.optim as optim\n",
        "import torchvision.transforms as transforms"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_HlhTNCnt0o4",
        "outputId": "c5ed4a8d-0857-4e20-e687-2b664254c5e7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/gdown/cli.py:121: FutureWarning: Option `--id` was deprecated in version 4.3.1 and will be removed in 5.0. You don't need to pass it anymore to use a file ID.\n",
            "  warnings.warn(\n",
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1peDYAB52fFNnMmrGDT7oyJJ8a3yZjJGg\n",
            "To: /content/doodle-detectives-aiclubiitm.zip\n",
            "100% 114M/114M [00:00<00:00, 161MB/s] \n"
          ]
        }
      ],
      "source": [
        "!gdown --id \"1peDYAB52fFNnMmrGDT7oyJJ8a3yZjJGg\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pqT3glwpuFWn",
        "outputId": "d44cdef6-f96e-4d61-f433-908635cd02ce"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  /content/doodle-detectives-aiclubiitm.zip\n",
            "  inflating: sample_submission.csv   \n",
            "  inflating: test.csv                \n",
            "  inflating: train.csv               \n"
          ]
        }
      ],
      "source": [
        "!unzip \"/content/doodle-detectives-aiclubiitm.zip\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mlobhyREs_Oe"
      },
      "outputs": [],
      "source": [
        "df = pd.read_csv(\"/content/train.csv\")"
      ]
    },
    {
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      "execution_count": null,
      "metadata": {
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        },
        "id": "Ot6rI0E1u9li",
        "outputId": "1330475c-35e2-4cd7-e783-bedacdb6d689"
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              "                                             drawing            key_id  \\\n",
              "0  [[[108, 91, 51, 5, 19, 49, 32, 4, 0, 3, 51, 11...  6351448545689600   \n",
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              "3  [[[79, 61, 59, 59, 65, 65, 56, 55, 83, 105, 12...  6629670151782400   \n",
              "4  [[[87, 108, 133, 169, 232, 242, 244, 255, 246,...  5793545258532864   \n",
              "\n",
              "        word  recognized  \n",
              "0       fish       False  \n",
              "1     potato       False  \n",
              "2       fish        True  \n",
              "3   flamingo       False  \n",
              "4  bulldozer        True  "
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          "metadata": {},
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      "source": [
        "df.head()"
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        "outputId": "85533bf6-a9b4-4da2-e6d3-9806dae79992"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[False  True]\n"
          ]
        }
      ],
      "source": [
        "unique_values = df['recognized'].unique()\n",
        "print(unique_values)"
      ]
    },
    {
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              "                                             drawing            key_id  \\\n",
              "0  [[[108, 91, 51, 5, 19, 49, 32, 4, 0, 3, 51, 11...  6351448545689600   \n",
              "1  [[[98, 89, 82, 71, 48, 16, 9, 5, 5, 12, 48, 56...  5623056384917504   \n",
              "2  [[[185, 217, 255, 255, 243, 243, 240, 235, 222...  4538232068898816   \n",
              "3  [[[79, 61, 59, 59, 65, 65, 56, 55, 83, 105, 12...  6629670151782400   \n",
              "4  [[[87, 108, 133, 169, 232, 242, 244, 255, 246,...  5793545258532864   \n",
              "\n",
              "        word  recognized  \n",
              "0       fish       False  \n",
              "1     potato       False  \n",
              "2       fish        True  \n",
              "3   flamingo       False  \n",
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              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-f59d7c0f-e02d-4fc8-a6ea-f542e779fe3c button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-f59d7c0f-e02d-4fc8-a6ea-f542e779fe3c');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-9ab83554-4939-4600-9200-2e3352e2ae46\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-9ab83554-4939-4600-9200-2e3352e2ae46')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-9ab83554-4939-4600-9200-2e3352e2ae46 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "df_true = df[df['recognized']==True]\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_false = df[df['recognized']==False]\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "T3J0njIiUXIJ",
        "outputId": "9e8fde4e-739a-452b-eacf-14632a969fbb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                             drawing            key_id  \\\n",
              "0  [[[108, 91, 51, 5, 19, 49, 32, 4, 0, 3, 51, 11...  6351448545689600   \n",
              "1  [[[98, 89, 82, 71, 48, 16, 9, 5, 5, 12, 48, 56...  5623056384917504   \n",
              "2  [[[185, 217, 255, 255, 243, 243, 240, 235, 222...  4538232068898816   \n",
              "3  [[[79, 61, 59, 59, 65, 65, 56, 55, 83, 105, 12...  6629670151782400   \n",
              "4  [[[87, 108, 133, 169, 232, 242, 244, 255, 246,...  5793545258532864   \n",
              "\n",
              "        word  recognized  \n",
              "0       fish       False  \n",
              "1     potato       False  \n",
              "2       fish        True  \n",
              "3   flamingo       False  \n",
              "4  bulldozer        True  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e77d866a-af34-4365-9bfa-3387c40d0492\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>drawing</th>\n",
              "      <th>key_id</th>\n",
              "      <th>word</th>\n",
              "      <th>recognized</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>[[[108, 91, 51, 5, 19, 49, 32, 4, 0, 3, 51, 11...</td>\n",
              "      <td>6351448545689600</td>\n",
              "      <td>fish</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>[[[98, 89, 82, 71, 48, 16, 9, 5, 5, 12, 48, 56...</td>\n",
              "      <td>5623056384917504</td>\n",
              "      <td>potato</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>[[[185, 217, 255, 255, 243, 243, 240, 235, 222...</td>\n",
              "      <td>4538232068898816</td>\n",
              "      <td>fish</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>[[[79, 61, 59, 59, 65, 65, 56, 55, 83, 105, 12...</td>\n",
              "      <td>6629670151782400</td>\n",
              "      <td>flamingo</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>[[[87, 108, 133, 169, 232, 242, 244, 255, 246,...</td>\n",
              "      <td>5793545258532864</td>\n",
              "      <td>bulldozer</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e77d866a-af34-4365-9bfa-3387c40d0492')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-e77d866a-af34-4365-9bfa-3387c40d0492 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-e77d866a-af34-4365-9bfa-3387c40d0492');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-84ef828b-4226-40e9-a040-556e16c5c16f\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-84ef828b-4226-40e9-a040-556e16c5c16f')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-84ef828b-4226-40e9-a040-556e16c5c16f button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "num_images_per_class = 200\n",
        "grouped = df.groupby('word')\n",
        "selected_df = pd.DataFrame()\n",
        "for label, group in grouped:\n",
        "    selected_samples = group.sample(n=num_images_per_class, random_state=42)\n",
        "    selected_df = pd.concat([selected_df, selected_samples])\n",
        "selected_df = selected_df.reset_index(drop=True)"
      ],
      "metadata": {
        "id": "9niRSBtpUehd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dff = pd.concat([selected_df, df_true], ignore_index=True)\n",
        "df = dff.sample(frac=1, random_state=42).reset_index(drop=True)"
      ],
      "metadata": {
        "id": "JiLrw2fRVBxm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def categorical_focal_loss(gamma=2., alpha=.25):\n",
        "    def categorical_focal_loss_fixed(y_true, y_pred):\n",
        "        y_pred /= tf.keras.backend.sum(y_pred, axis=-1, keepdims=True)\n",
        "        epsilon = tf.keras.backend.epsilon()\n",
        "        y_pred = tf.clip_by_value(y_pred, epsilon, 1. - epsilon)\n",
        "        cross_entropy = -y_true * tf.math.log(y_pred)\n",
        "        loss = alpha * tf.keras.backend.pow(1 - y_pred, gamma) * cross_entropy\n",
        "        return tf.keras.backend.sum(loss, axis=1)\n",
        "    return categorical_focal_loss_fixed"
      ],
      "metadata": {
        "id": "OVcf4aTvBxtu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fcwBnrJCwSnm",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c480104e-41e1-4256-fe8e-dca1f842e72f"
      },
      "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 [==============================] - 1s 0us/step\n"
          ]
        }
      ],
      "source": [
        "# Load the EfficientNetB0 model without the top classification layer\n",
        "base_model = ResNet50(weights='imagenet', include_top=False, input_shape=(224, 224, 3))\n",
        "\n",
        "# Freeze the layers of the base model\n",
        "for layer in base_model.layers:\n",
        "    layer.trainable = False\n",
        "\n",
        "# Get unique labels from the DataFrame\n",
        "unique_labels = df['word'].unique()\n",
        "num_classes = len(unique_labels)\n",
        "\n",
        "# Create a mapping from label strings to integers\n",
        "label_to_index = {label: index for index, label in enumerate(unique_labels)}\n",
        "\n",
        "# Add a new column with integer labels\n",
        "df['label_id'] = df['word'].map(label_to_index)\n",
        "\n",
        "model = tf.keras.Sequential([\n",
        "    base_model,\n",
        "    GlobalAveragePooling2D(),\n",
        "    Dense(num_classes, activation='softmax')\n",
        "])\n",
        "\n",
        "# Compile the model\n",
        "model.compile(optimizer='adam', loss=categorical_focal_loss(gamma=3., alpha=.81), metrics=['accuracy'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9ixVFZGgs9Vh"
      },
      "outputs": [],
      "source": [
        "# # Function to preprocess JSON representations and return an image array\n",
        "# def preprocess_json(json_data):\n",
        "#     image_data = json.loads(json_data)\n",
        "#     # Assuming JSON representation contains grayscale pixel values in the 'data' field\n",
        "#     image_array = np.array(image_data['data'])\n",
        "#     image_array = image_array.reshape(224, 224, 1)  # Add a single channel for grayscale\n",
        "#     image_array = np.repeat(image_array, 3, axis=2)  # Convert to RGB format\n",
        "#     image_array = img_to_array(image_array)\n",
        "#     image_array = np.expand_dims(image_array, axis=0)  # Add batch dimension\n",
        "#     return image_array\n",
        "\n",
        "from PIL import Image, ImageDraw\n",
        "import numpy as np\n",
        "import json\n",
        "\n",
        "def vector_to_numpy(drawing, side=256):\n",
        "    image = vector_to_image(drawing, side)\n",
        "    image_array = np.array(image)\n",
        "    return image_array\n",
        "\n",
        "transform = transforms.Compose([\n",
        "    transforms.Resize((224, 224)),  # Resize the image to the input size of ResNet-18\n",
        "    transforms.ToTensor(),  # Convert the image to a tensor\n",
        "    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),  # Normalize the image\n",
        "])\n",
        "\n",
        "def vector_to_image(drawing, side=256):\n",
        "    drawing = json.loads(drawing)\n",
        "    min_x, min_y, max_x, max_y = calculate_bounding_box(drawing)\n",
        "\n",
        "    # Calculate the offset to center the drawing within the canvas\n",
        "    offset_x = (side - (max_x - min_x + 1)) // 2\n",
        "    offset_y = (side - (max_y - min_y + 1)) // 2\n",
        "\n",
        "    # Create an RGB image with white background\n",
        "    image = Image.new('RGB', (side, side), color='white')\n",
        "    draw = ImageDraw.Draw(image)\n",
        "\n",
        "    for x, y in drawing:\n",
        "        xy = [(x0 - min_x + offset_x, y0 - min_y + offset_y) for x0, y0 in zip(x, y)]\n",
        "        draw.line(xy, fill='black', width=1)\n",
        "\n",
        "    # Resize the image to (224, 224)\n",
        "    image = image.resize((224, 224))\n",
        "\n",
        "    return image\n",
        "\n",
        "\n",
        "def calculate_bounding_box(drawing):\n",
        "    all_x = [x for x, _ in drawing]\n",
        "    all_y = [y for _, y in drawing]\n",
        "\n",
        "    min_x = min(min(x) for x in all_x)\n",
        "    min_y = min(min(y) for y in all_y)\n",
        "    max_x = max(max(x) for x in all_x)\n",
        "    max_y = max(max(y) for y in all_y)\n",
        "\n",
        "    return min_x, min_y, max_x, max_y\n",
        "\n",
        "def image_data_generator(df, batch_size, target_size=(224, 224)):\n",
        "    num_samples = len(df)\n",
        "    while True:\n",
        "        df_shuffled = shuffle(df)\n",
        "        for offset in range(0, num_samples, batch_size):\n",
        "            batch_samples = df_shuffled.iloc[offset:offset+batch_size]\n",
        "            X_batch = []\n",
        "            for json_data in batch_samples['drawing']:\n",
        "                image = vector_to_image(json_data, side=224)  # Ensure the image is 224x224\n",
        "                image_array = img_to_array(image)\n",
        "                X_batch.append(image_array)\n",
        "            X_batch = np.array(X_batch)\n",
        "            y_batch = to_categorical(batch_samples['label_id'], num_classes=num_classes)\n",
        "            yield X_batch, y_batch"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CEMy6bEqwXii",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2297d52e-a887-47f4-c38b-f721f9ece841"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/3\n",
            "1213/1213 [==============================] - 812s 656ms/step - loss: 1.1103 - accuracy: 0.5555\n",
            "Epoch 2/3\n",
            "1213/1213 [==============================] - 809s 663ms/step - loss: 0.7315 - accuracy: 0.6625\n",
            "Epoch 3/3\n",
            "1213/1213 [==============================] - 803s 662ms/step - loss: 0.6455 - accuracy: 0.6918\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3000: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n",
            "  saving_api.save_model(\n"
          ]
        }
      ],
      "source": [
        "# Define batch size and number of epochs\n",
        "batch_size = 200\n",
        "epochs = 3\n",
        "\n",
        "# Create the custom data generator\n",
        "train_generator = image_data_generator(df, batch_size=batch_size)\n",
        "steps_per_epoch = len(df) // batch_size\n",
        "\n",
        "# Train the model with contrastive loss using the custom data generator\n",
        "model.fit(train_generator, steps_per_epoch=steps_per_epoch, epochs=epochs)\n",
        "\n",
        "# Optionally, you can save the trained model for future use\n",
        "model.save('efficientnet_grayscale_contrastive_model.h5')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KOIob14f4izf"
      },
      "outputs": [],
      "source": [
        "# df = pd.read_csv(\"/content/train.csv\")\n",
        "# df = df[df['recognized']==False]\n",
        "# df.head()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# # Get unique labels from the DataFrame\n",
        "# unique_labels = df['word'].unique()\n",
        "# num_classes = len(unique_labels)\n",
        "\n",
        "# # Create a mapping from label strings to integers\n",
        "# label_to_index = {label: index for index, label in enumerate(unique_labels)}\n",
        "\n",
        "# # Add a new column with integer labels\n",
        "# df['label_id'] = df['word'].map(label_to_index)"
      ],
      "metadata": {
        "id": "aLl-0RRTaslU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "aeEMC-bS5h2S"
      },
      "outputs": [],
      "source": [
        "# # Define batch size and number of epochs\n",
        "# batch_size = 200\n",
        "# epochs = 2\n",
        "\n",
        "# # Create the custom data generator\n",
        "# train_generator = image_data_generator(df, batch_size=batch_size)\n",
        "# steps_per_epoch = len(df) // batch_size"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# from tensorflow.keras.models import load_model\n",
        "# pretrained_model = load_model('/content/efficientnet_grayscale_contrastive_model.h5')\n",
        "\n",
        "# # Freeze the layers of the pretrained_model model\n",
        "# for layer in pretrained_model.layers:\n",
        "#     layer.trainable = False\n",
        "\n",
        "# # Get unique labels from the DataFrame\n",
        "# unique_labels = df['word'].unique()\n",
        "# num_classes = len(unique_labels)\n",
        "\n",
        "# # Create a mapping from label strings to integers\n",
        "# label_to_index = {label: index for index, label in enumerate(unique_labels)}\n",
        "\n",
        "# # Add a new column with integer labels\n",
        "# df['label_id'] = df['word'].map(label_to_index)\n",
        "\n",
        "# full_model = tf.keras.Sequential([\n",
        "#      pretrained_model,\n",
        "#      Dense(num_classes, activation='softmax')\n",
        "#   ])\n",
        "\n",
        "# # Compile the model\n",
        "# full_model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "zif5YDNE3Wn7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# full_model.fit(train_generator, steps_per_epoch=steps_per_epoch, epochs=epochs)\n",
        "# full_model.save('updated_model.h5')"
      ],
      "metadata": {
        "id": "o5qi_18ibrI6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "i0LLTEIs2KSN"
      },
      "outputs": [],
      "source": [
        "# from google.colab import drive\n",
        "# drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QkbtpT3m23KI"
      },
      "outputs": [],
      "source": [
        "# # Assuming 'model' is your trained model\n",
        "# model.save('/content/drive/MyDrive/my_model.keras')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Mxmvljej2-cE"
      },
      "outputs": [],
      "source": [
        "# from tensorflow.keras.models import load_model\n",
        "\n",
        "# # Load the model from the local file\n",
        "# model = load_model('efficientnet_grayscale_contrastive_model.h5')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Get unique labels from the DataFrame\n",
        "unique_labels = df['word'].unique()\n",
        "num_classes = len(unique_labels)\n",
        "\n",
        "# Create a mapping from label strings to integers\n",
        "label_to_index = {label: index for index, label in enumerate(unique_labels)}\n",
        "\n",
        "# Add a new column with integer labels\n",
        "df['label_id'] = df['word'].map(label_to_index)"
      ],
      "metadata": {
        "id": "edW2aJovtFoZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "v2DUXa6uiIOM"
      },
      "outputs": [],
      "source": [
        "test_df = pd.read_csv(\"/content/test.csv\")\n",
        "ss = pd.read_csv(\"/content/sample_submission.csv\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8X03vCSB5Nzs",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "22f04518-713a-4621-f423-eedcf057c3c5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-3c3f8d21-d119-4199-a1cd-d2df774ce452 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ],
      "source": [
        "test_df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qdSJ3h6mU6JJ"
      },
      "outputs": [],
      "source": [
        "inv_map = {v: k for k, v in label_to_index.items()}"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def image_test_generator(df, batch_size, target_size=(224, 224)):\n",
        "    num_samples = len(df)\n",
        "    while True:\n",
        "        for offset in range(0, num_samples, batch_size):\n",
        "            batch_samples = df.iloc[offset:offset+batch_size]\n",
        "            X_batch = []\n",
        "            for json_data in batch_samples['drawing']:\n",
        "                image = vector_to_image(json_data, side=224)  # Ensure the image is 224x224\n",
        "                image_array = img_to_array(image)\n",
        "                X_batch.append(image_array)\n",
        "            X_batch = np.array(X_batch)\n",
        "            yield X_batch"
      ],
      "metadata": {
        "id": "JPfCZR6Iz_bl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Assuming you have a DataFrame 'test_df' with testing data\n",
        "\n",
        "# Define batch size for testing\n",
        "batch_size_test = 200\n",
        "\n",
        "# Create a custom data generator for testing data\n",
        "test_generator = image_test_generator(test_df, batch_size=batch_size_test)\n",
        "\n",
        "# Calculate the number of steps for testing\n",
        "steps_test = len(test_df) // batch_size_test\n",
        "\n",
        "# Generate predictions for testing data\n",
        "predictions = model.predict(test_generator, steps=steps_test)\n",
        "\n",
        "# 'predictions' will now contain the model's predicted outputs for the testing data."
      ],
      "metadata": {
        "id": "oU5JCCWy5WD1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "353b0c16-36d1-490c-ba3a-aa584caeb055"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "927/927 [==============================] - 611s 659ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "batch_samples = test_df.iloc[185400: 185439]\n",
        "X_batch = []\n",
        "for json_data in batch_samples['drawing']:\n",
        "    image = vector_to_image(json_data, side=224)  # Ensure the image is 224x224\n",
        "    image_array = img_to_array(image)\n",
        "    X_batch.append(image_array)\n",
        "X_batch = np.array(X_batch)\n",
        "preds = model.predict(X_batch)"
      ],
      "metadata": {
        "id": "BFNWJd57C8jR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7343ff3f-b87a-42f2-8e57-70e95e87519a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2/2 [==============================] - 2s 508ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predictions = np.concatenate((predictions, preds))"
      ],
      "metadata": {
        "id": "YcoJo94gDhIa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "predictions = predictions.reshape(-1, 101)\n",
        "print(predictions)\n",
        "print(len(predictions))"
      ],
      "metadata": {
        "id": "-9c78SQcG504",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0eebb63c-064e-4949-bcec-e1ea61f8b0a2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[1.2935013e-01 2.0552306e-03 5.7600500e-06 ... 1.9626461e-06\n",
            "  6.5969775e-04 1.9976518e-03]\n",
            " [3.0082148e-02 3.1879678e-04 8.7592233e-04 ... 4.9576460e-04\n",
            "  5.3420492e-05 1.5953374e-04]\n",
            " [1.2765985e-04 1.7610725e-04 6.2658884e-08 ... 1.1268155e-06\n",
            "  1.1073978e-02 1.2674040e-05]\n",
            " ...\n",
            " [3.6458734e-01 1.5622342e-04 3.0111158e-05 ... 1.4820631e-04\n",
            "  1.4898194e-02 5.1626773e-03]\n",
            " [1.9132325e-02 6.4228198e-07 5.7595476e-05 ... 5.6418063e-05\n",
            "  7.3491369e-04 1.1997583e-03]\n",
            " [1.8344552e-04 2.0924160e-04 3.8334462e-04 ... 1.6373773e-03\n",
            "  6.7597562e-06 5.3322998e-07]]\n",
            "185439\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def get_strings(soft_arr):\n",
        "  top_indices = np.argsort(-1*soft_arr, axis=1)[:, :3]\n",
        "  s1 = inv_map[top_indices[0][0]]\n",
        "  s2 = inv_map[top_indices[0][1]]\n",
        "  s3 = inv_map[top_indices[0][2]]\n",
        "  return f\"{s1} {s2} {s3}\""
      ],
      "metadata": {
        "id": "jVasgfjcEJlR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1LyyVTQqrk_B"
      },
      "outputs": [],
      "source": [
        "pr = [row.reshape(1, -1) for row in predictions]"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "result = [get_strings(row) for row in pr]"
      ],
      "metadata": {
        "id": "kgCBFGgBLLCM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zywtMMVKiMp2"
      },
      "outputs": [],
      "source": [
        "ss['word'] = result"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ss.to_csv(\"/content/sample_submission.csv\", index=False)"
      ],
      "metadata": {
        "id": "ryDuMxNfOc0k"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}