kaggle_titanic.ipynb

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        "<a href=\"https://colab.research.google.com/github/homayun-chaab/kaggle-titanic/blob/main/kaggle_titanic.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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          "text": [
            "Collecting opendatasets\n",
            "  Downloading opendatasets-0.1.22-py3-none-any.whl (15 kB)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from opendatasets) (4.66.1)\n",
            "Requirement already satisfied: kaggle in /usr/local/lib/python3.10/dist-packages (from opendatasets) (1.5.16)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.10/dist-packages (from opendatasets) (8.1.7)\n",
            "Requirement already satisfied: six>=1.10 in /usr/local/lib/python3.10/dist-packages (from kaggle->opendatasets) (1.16.0)\n",
            "Requirement already satisfied: certifi in /usr/local/lib/python3.10/dist-packages (from kaggle->opendatasets) (2023.11.17)\n",
            "Requirement already satisfied: python-dateutil in /usr/local/lib/python3.10/dist-packages (from kaggle->opendatasets) (2.8.2)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from kaggle->opendatasets) (2.31.0)\n",
            "Requirement already satisfied: python-slugify in /usr/local/lib/python3.10/dist-packages (from kaggle->opendatasets) (8.0.1)\n",
            "Requirement already satisfied: urllib3 in /usr/local/lib/python3.10/dist-packages (from kaggle->opendatasets) (2.0.7)\n",
            "Requirement already satisfied: bleach in /usr/local/lib/python3.10/dist-packages (from kaggle->opendatasets) (6.1.0)\n",
            "Requirement already satisfied: webencodings in /usr/local/lib/python3.10/dist-packages (from bleach->kaggle->opendatasets) (0.5.1)\n",
            "Requirement already satisfied: text-unidecode>=1.3 in /usr/local/lib/python3.10/dist-packages (from python-slugify->kaggle->opendatasets) (1.3)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->kaggle->opendatasets) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->kaggle->opendatasets) (3.6)\n",
            "Installing collected packages: opendatasets\n",
            "Successfully installed opendatasets-0.1.22\n"
          ]
        }
      ],
      "source": [
        "pip install opendatasets"
      ]
    },
    {
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        "id": "YEoQNKynreP9",
        "outputId": "603c1ad0-fb1b-44ee-e0a1-60115cfe5e2a"
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        {
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          "text": [
            "Please provide your Kaggle credentials to download this dataset. Learn more: http://bit.ly/kaggle-creds\n",
            "Your Kaggle username: homayunchaab\n",
            "Your Kaggle Key: ··········\n",
            "Downloading titanic.zip to ./titanic\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 34.1k/34.1k [00:00<00:00, 19.7MB/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Extracting archive ./titanic/titanic.zip to ./titanic\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        }
      ],
      "source": [
        "import opendatasets as od\n",
        "import pandas as pd\n",
        "\n",
        "od.download(\n",
        "    \"https://www.kaggle.com/competitions/titanic/data\")"
      ]
    },
    {
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      "outputs": [
        {
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              "   PassengerId  Pclass                              Name     Sex   Age  SibSp  \\\n",
              "0          892       3                  Kelly, Mr. James    male  34.5      0   \n",
              "1          893       3  Wilkes, Mrs. James (Ellen Needs)  female  47.0      1   \n",
              "\n",
              "   Parch  Ticket    Fare Cabin Embarked  \n",
              "0      0  330911  7.8292   NaN        Q  \n",
              "1      0  363272  7.0000   NaN        S  "
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          },
          "metadata": {},
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      ],
      "source": [
        "# reading the XLSX file\n",
        "file =('titanic/\\\n",
        "test.csv')\n",
        "train_file =('titanic/\\\n",
        "test.csv')\n",
        "nd = pd.read_csv(file)\n",
        "test_pd = pd.read_csv(train_file)\n",
        "# displaying the contents of the XLSX file\n",
        "\n",
        "test_pd.head(2)"
      ]
    },
    {
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      "execution_count": null,
      "metadata": {
        "id": "dJy41C6zewr-"
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      "source": [
        "def clean(data):\n",
        "  data = data.drop([\"PassengerId\", \"Name\", \"Ticket\", \"Cabin\"], axis=1)\n",
        "\n",
        "  cols = ['SibSp', 'Parch', 'Fare', 'Age']\n",
        "  for col in cols:\n",
        "    data[col].fillna(data[col].median(), inplace=True)\n",
        "\n",
        "  data.Embarked.fillna(\"U\", inplace=True)\n",
        "  return data\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6YK2zWQ6fk5z"
      },
      "outputs": [],
      "source": [
        "data = clean(nd)\n",
        "test = clean(test_pd)\n"
      ]
    },
    {
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        "outputId": "a3d49c33-5a76-4b9c-93da-be3612747180"
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Pclass     Sex   Age  SibSp  Parch     Fare Embarked\n",
              "0       3    male  34.5      0      0   7.8292        Q\n",
              "1       3  female  47.0      1      0   7.0000        S\n",
              "2       2    male  62.0      0      0   9.6875        Q\n",
              "3       3    male  27.0      0      0   8.6625        S\n",
              "4       3  female  22.0      1      1  12.2875        S"
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              "      <td>female</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>12.2875</td>\n",
              "      <td>S</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-606c45d9-d326-41b7-8ba5-632cb395d6e7')\"\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",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </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-606c45d9-d326-41b7-8ba5-632cb395d6e7 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-606c45d9-d326-41b7-8ba5-632cb395d6e7');\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-14af8571-c58a-41ce-9501-f8c2e88a9edd\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-14af8571-c58a-41ce-9501-f8c2e88a9edd')\"\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-14af8571-c58a-41ce-9501-f8c2e88a9edd 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": [
        "test.head(5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "oBd8XIZ4mjmt",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        },
        "outputId": "f2687372-913a-4598-b224-9b5c71647751"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['female' 'male']\n",
            "['C' 'Q' 'S']\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Pclass  Sex   Age  SibSp  Parch     Fare  Embarked\n",
              "0       3    1  34.5      0      0   7.8292         1\n",
              "1       3    0  47.0      1      0   7.0000         2\n",
              "2       2    1  62.0      0      0   9.6875         1\n",
              "3       3    1  27.0      0      0   8.6625         2\n",
              "4       3    0  22.0      1      1  12.2875         2"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-1484e148-eae7-4224-a076-05592e6562ea\" 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",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Embarked</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>34.5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.8292</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>47.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>7.0000</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>62.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>9.6875</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>27.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8.6625</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>12.2875</td>\n",
              "      <td>2</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-1484e148-eae7-4224-a076-05592e6562ea')\"\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",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </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-1484e148-eae7-4224-a076-05592e6562ea 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-1484e148-eae7-4224-a076-05592e6562ea');\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-770dcec3-6791-42aa-986d-08cd91565a8a\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-770dcec3-6791-42aa-986d-08cd91565a8a')\"\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-770dcec3-6791-42aa-986d-08cd91565a8a 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
        }
      ],
      "source": [
        "from sklearn import preprocessing\n",
        "le = preprocessing.LabelEncoder()\n",
        "\n",
        "cols = [\"Sex\", \"Embarked\"]\n",
        "for col in cols:\n",
        "  data[col] = le.fit_transform(data[col])\n",
        "  test[col] = le.fit_transform(test[col])\n",
        "  print(le.classes_)\n",
        "\n",
        "data.head(5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2bStKcHBnboN"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "train = torch.tensor(data.values)\n",
        "test = torch.tensor(test.values)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zNP-uhqtow2k",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8fbd1646-23cd-4bd6-b27e-0e142d1effd0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([418, 7])"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "train.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zbvIye3Tozpa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "1dff1b85-718d-4523-e8db-d173fe3e0fa6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'cuda'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
        "device"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OElT-UUp53MJ"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IK6eCJuC6AGA"
      },
      "outputs": [],
      "source": [
        "train = train.type(torch.float)\n",
        "test = test.type(torch.float)\n",
        "train = train.to(device)\n",
        "test = test.to(device)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XmIryunS6Ps_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "121921cf-28ba-428c-8923-5b1f2d396626"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.float32"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "train.dtype"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qeVPwEOo7Lpt"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(train,\n",
        "                                                    data,\n",
        "                                                    test_size=0.2,\n",
        "                                                    random_state=42)\n",
        "y_train = torch.tensor(y_train.values)\n",
        "y_test = torch.tensor(y_test.values)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gw03zEfHKbLY"
      },
      "outputs": [],
      "source": [
        "y_train = y_train.type(torch.float)\n",
        "y_test = y_test.type(torch.float)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jINEccvg7u5u"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZE7IjZ4h75Ow"
      },
      "outputs": [],
      "source": [
        "from torch import nn\n",
        "class titanikV1(nn.Module):\n",
        "  def __init__(self,\n",
        "               input_shape: int,\n",
        "               output_shape: int,\n",
        "               hidden_units: int):\n",
        "    super().__init__()\n",
        "    self.linear_layer_stack = nn.Sequential(\n",
        "        nn.Linear(in_features=input_shape, out_features=hidden_units),\n",
        "        nn.ReLU(),\n",
        "        nn.Linear(in_features=hidden_units, out_features=hidden_units),\n",
        "        nn.ReLU(),\n",
        "        nn.Linear(in_features=hidden_units, out_features=output_shape),\n",
        "    )\n",
        "  def forward(self, x):\n",
        "    return self.linear_layer_stack(x)\n",
        "\n",
        "\n",
        "model_0 = titanikV1(input_shape=7,\n",
        "                    output_shape=2,\n",
        "                    hidden_units=10).to(device)\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "a82_DV5A-aAe",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b8690bad-f3af-4982-8ad1-2b549f83d50d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "titanikV1(\n",
              "  (linear_layer_stack): Sequential(\n",
              "    (0): Linear(in_features=7, out_features=10, bias=True)\n",
              "    (1): ReLU()\n",
              "    (2): Linear(in_features=10, out_features=10, bias=True)\n",
              "    (3): ReLU()\n",
              "    (4): Linear(in_features=10, out_features=2, bias=True)\n",
              "  )\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 96
        }
      ],
      "source": [
        "model_0"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "89yXN6VGBxOi",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1168d861-d71f-435b-a924-f405d4440568"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "OrderedDict([('linear_layer_stack.0.weight',\n",
              "              tensor([[ 0.2890,  0.3137, -0.0885,  0.3472, -0.0828,  0.0763, -0.1840],\n",
              "                      [ 0.2220,  0.3332, -0.2773,  0.3285,  0.0707,  0.2792,  0.0512],\n",
              "                      [ 0.1822, -0.0534,  0.2914,  0.0559, -0.1764,  0.0963, -0.1741],\n",
              "                      [-0.0443, -0.1535,  0.2507, -0.2984, -0.1742, -0.1067, -0.2273],\n",
              "                      [ 0.0357, -0.3733,  0.3413, -0.3211,  0.2918,  0.0629, -0.1227],\n",
              "                      [ 0.2336,  0.0589,  0.3054,  0.0413, -0.1192,  0.1016, -0.1025],\n",
              "                      [ 0.1591,  0.3375,  0.2185, -0.1652,  0.2182,  0.0676,  0.1919],\n",
              "                      [-0.2304, -0.3742, -0.1460, -0.2899,  0.3101,  0.1089,  0.1566],\n",
              "                      [ 0.1195, -0.0066,  0.2958, -0.2685,  0.0238, -0.2580,  0.1165],\n",
              "                      [-0.1302,  0.1158, -0.0787,  0.3135, -0.2240, -0.2254, -0.2254]],\n",
              "                     device='cuda:0')),\n",
              "             ('linear_layer_stack.0.bias',\n",
              "              tensor([ 0.3400,  0.1260,  0.3637, -0.3119, -0.3749, -0.2957, -0.2543,  0.1531,\n",
              "                       0.1353,  0.3141], device='cuda:0')),\n",
              "             ('linear_layer_stack.2.weight',\n",
              "              tensor([[-0.1633, -0.2156,  0.1678, -0.1278,  0.1919, -0.0750,  0.1809, -0.2457,\n",
              "                       -0.1596,  0.0964],\n",
              "                      [ 0.0669, -0.0806,  0.1885,  0.2150, -0.2293, -0.1688,  0.2896, -0.1067,\n",
              "                       -0.1121, -0.3060],\n",
              "                      [-0.1811,  0.0790, -0.0417, -0.2295,  0.0074, -0.2160, -0.2683, -0.1741,\n",
              "                       -0.2768, -0.2014],\n",
              "                      [ 0.3161,  0.0597,  0.0974, -0.2949, -0.2077, -0.1053,  0.0494, -0.2783,\n",
              "                       -0.1363, -0.1893],\n",
              "                      [ 0.0009, -0.1177, -0.0219, -0.2143, -0.2171, -0.1845, -0.1082, -0.2496,\n",
              "                        0.2651, -0.0628],\n",
              "                      [ 0.2721,  0.0985, -0.2678,  0.2188, -0.0870, -0.1212, -0.2625, -0.3144,\n",
              "                        0.0905, -0.0691],\n",
              "                      [ 0.1231, -0.2595,  0.2348, -0.2321, -0.0546,  0.0661,  0.1633,  0.2553,\n",
              "                        0.2881, -0.2507],\n",
              "                      [ 0.0796, -0.1360, -0.0347, -0.2367,  0.2880, -0.2321,  0.1690,  0.1111,\n",
              "                        0.1028, -0.1710],\n",
              "                      [ 0.2874,  0.0695,  0.0407, -0.2787,  0.1327, -0.0474, -0.1449,  0.2716,\n",
              "                        0.0705, -0.1750],\n",
              "                      [-0.1601, -0.0151,  0.1766, -0.0808, -0.1804, -0.1083, -0.2362,  0.1128,\n",
              "                        0.2448, -0.2977]], device='cuda:0')),\n",
              "             ('linear_layer_stack.2.bias',\n",
              "              tensor([ 0.0734,  0.1634,  0.0573, -0.1126,  0.1651,  0.1662,  0.1182, -0.0556,\n",
              "                      -0.0837,  0.0338], device='cuda:0')),\n",
              "             ('linear_layer_stack.4.weight',\n",
              "              tensor([[-0.0559, -0.0942,  0.2021,  0.2718, -0.0313, -0.0708,  0.0046, -0.0189,\n",
              "                        0.0760,  0.0886],\n",
              "                      [-0.2872, -0.1167,  0.2663,  0.1232, -0.0157, -0.1907, -0.1935, -0.2833,\n",
              "                       -0.1031,  0.1068]], device='cuda:0')),\n",
              "             ('linear_layer_stack.4.bias',\n",
              "              tensor([0.2016, 0.1460], device='cuda:0'))])"
            ]
          },
          "metadata": {},
          "execution_count": 97
        }
      ],
      "source": [
        "model_0.state_dict()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lTD-JCmTB5g9"
      },
      "outputs": [],
      "source": [
        "loss_fn = nn.CrossEntropyLoss()\n",
        "optimizer = torch.optim.Adam(model_0.parameters(),\n",
        "                             lr=0.1)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8Kt-i0yaCWuR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bd90bef0-9ac0-4e2f-9ec6-1a0c1968299f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[-3.4156e-02, -1.9911e+00],\n",
              "        [ 1.3935e-02, -1.4657e+00],\n",
              "        [-5.2601e-05, -1.6492e+00],\n",
              "        [ 5.0560e-01, -8.7561e-01],\n",
              "        [ 2.8143e-02, -1.2959e+00],\n",
              "        [ 1.8064e-02, -1.6369e+00],\n",
              "        [-5.9661e-02, -1.6932e+00],\n",
              "        [ 5.0525e-01, -2.3329e-02],\n",
              "        [-8.3012e-03, -2.8988e+00],\n",
              "        [ 5.2886e-02, -1.0609e+00],\n",
              "        [ 1.9931e-02, -1.5609e+00],\n",
              "        [ 6.2330e-01, -2.5202e-01],\n",
              "        [-5.4873e-02, -2.0248e+00],\n",
              "        [ 3.1010e-01, -7.6437e-01],\n",
              "        [ 1.5768e-02, -1.6122e+00],\n",
              "        [-3.9023e-02, -2.0795e+00],\n",
              "        [ 2.4464e-02, -1.5084e+00],\n",
              "        [ 1.5768e-02, -1.6122e+00],\n",
              "        [ 3.2520e-02, -1.4419e+00],\n",
              "        [ 8.1849e-03, -1.6765e+00],\n",
              "        [ 2.6251e-02, -1.6048e+00],\n",
              "        [-8.0623e-02, -2.2539e+00],\n",
              "        [ 4.9494e-02, -1.1719e+00],\n",
              "        [ 1.3687e-02, -1.4909e+00],\n",
              "        [ 2.0977e-02, -1.5575e+00],\n",
              "        [ 4.5189e-02, -1.3416e+00],\n",
              "        [ 1.2882e-02, -1.6576e+00],\n",
              "        [-1.3299e-01, -3.3471e+00],\n",
              "        [ 1.0829e-01, -7.4360e-01],\n",
              "        [ 6.5862e-02, -1.3924e+00],\n",
              "        [ 8.8554e-01, -1.0014e+00],\n",
              "        [ 2.5802e-02, -1.5381e+00],\n",
              "        [ 3.5912e-01, -1.2005e+00],\n",
              "        [-6.1503e-02, -1.7843e+00],\n",
              "        [ 3.9447e-01, -1.0081e-01],\n",
              "        [-2.6282e-02, -1.6053e+00],\n",
              "        [ 7.8785e-03, -1.6777e+00],\n",
              "        [-6.8590e-02, -1.8677e+00],\n",
              "        [ 4.1126e-02, -1.2393e+00],\n",
              "        [-5.2747e-02, -1.8126e+00],\n",
              "        [-8.5474e-03, -1.9492e+00],\n",
              "        [ 5.6777e-01, -9.5978e-01],\n",
              "        [ 1.9601e-01, -2.2461e+00],\n",
              "        [ 1.5768e-02, -1.6122e+00],\n",
              "        [ 1.0341e+00, -2.7149e+00],\n",
              "        [ 3.9281e-02, -1.3146e+00],\n",
              "        [-1.9872e-01, -3.2761e+00],\n",
              "        [ 3.8791e-02, -1.4353e+00],\n",
              "        [-1.4837e-01, -3.4520e+00],\n",
              "        [ 7.5738e-01, -1.4397e+00],\n",
              "        [ 1.4144e-03, -1.8068e+00],\n",
              "        [ 7.9560e-01, -1.3505e+00],\n",
              "        [ 9.1272e-01, -1.6334e+00],\n",
              "        [ 1.5768e-02, -1.6122e+00],\n",
              "        [ 1.5044e-02, -1.3344e+00],\n",
              "        [ 1.4683e-01, -1.2879e+00],\n",
              "        [-1.9892e-02, -1.7120e+00],\n",
              "        [ 1.0642e+00, -2.3087e+00],\n",
              "        [ 3.3839e-02, -1.4552e+00],\n",
              "        [ 1.2882e-02, -1.6576e+00],\n",
              "        [ 2.0952e-02, -1.5576e+00],\n",
              "        [-6.8356e-02, -2.1124e+00],\n",
              "        [ 1.1393e+00, -2.0782e+00],\n",
              "        [-1.1047e-01, -2.2556e+00],\n",
              "        [-8.4200e-02, -2.0775e+00],\n",
              "        [-2.9473e-02, -2.1358e+00],\n",
              "        [ 2.6991e-01, -2.2559e+00],\n",
              "        [ 2.6966e-02, -1.5860e+00],\n",
              "        [-2.1218e-02, -3.0887e+00],\n",
              "        [-3.4927e-01, -4.1356e+00],\n",
              "        [-3.3176e-01, -3.7213e+00],\n",
              "        [-1.1848e-01, -2.3368e+00],\n",
              "        [ 2.3135e-02, -1.6352e+00],\n",
              "        [-2.0063e-01, -3.0694e+00],\n",
              "        [ 3.4332e-01,  4.2780e-02],\n",
              "        [-6.1075e-03, -1.5865e+00],\n",
              "        [ 1.1937e-02, -1.6614e+00],\n",
              "        [-1.4652e-02, -1.7774e+00],\n",
              "        [ 1.3999e+00, -2.4714e+00],\n",
              "        [ 2.6787e-02, -1.5867e+00],\n",
              "        [ 3.0147e-01, -2.2289e-01],\n",
              "        [ 3.3572e-01, -2.7814e-01],\n",
              "        [ 1.6864e-02, -1.7293e+00],\n",
              "        [ 3.5800e-01, -1.8558e+00],\n",
              "        [ 1.1937e-02, -1.6614e+00],\n",
              "        [-1.5101e-01, -2.7756e+00],\n",
              "        [ 5.3200e-01, -9.7334e-01],\n",
              "        [ 1.2882e-02, -1.6576e+00],\n",
              "        [ 4.6844e-02, -1.2184e+00],\n",
              "        [ 1.7236e-02, -1.6059e+00],\n",
              "        [ 9.5325e-01, -3.0643e+00],\n",
              "        [-4.8612e-02, -2.1861e+00],\n",
              "        [-8.0389e-02, -2.2236e+00],\n",
              "        [ 1.7701e-01, -9.9265e-01],\n",
              "        [ 1.0933e-01, -9.2992e-01],\n",
              "        [-1.6037e-02, -1.4056e+00],\n",
              "        [ 2.6885e-02, -1.3362e+00],\n",
              "        [ 5.5988e-03, -1.4547e+00],\n",
              "        [ 1.3741e+00, -2.1972e+00],\n",
              "        [ 4.4210e-01, -2.1668e-02],\n",
              "        [ 3.1890e-02, -1.2476e+00],\n",
              "        [ 1.8370e-02, -1.6357e+00],\n",
              "        [-6.6797e-02, -2.0459e+00],\n",
              "        [ 3.0008e-02, -1.4177e+00],\n",
              "        [ 1.5001e-02, -1.6492e+00],\n",
              "        [ 1.7746e-01, -1.8285e+00],\n",
              "        [ 1.2989e+00, -2.9090e+00],\n",
              "        [ 2.6966e-02, -1.5860e+00],\n",
              "        [ 1.3224e-02, -1.6905e+00],\n",
              "        [-5.4454e-01, -5.3878e+00],\n",
              "        [-8.8106e-02, -2.0375e+00],\n",
              "        [ 1.3551e+00, -2.6286e+00],\n",
              "        [ 5.4751e-03, -1.7559e+00],\n",
              "        [ 3.1692e-02, -1.4109e+00],\n",
              "        [-1.1760e-01, -2.1984e+00],\n",
              "        [ 3.7141e-01, -1.0290e+00],\n",
              "        [ 4.1067e-02, -1.3577e+00],\n",
              "        [-1.5101e-01, -2.7756e+00],\n",
              "        [ 3.2025e-02, -2.1595e+00],\n",
              "        [ 3.5179e-02, -1.3627e+00],\n",
              "        [ 1.1573e+00, -2.3633e+00],\n",
              "        [ 3.6058e-01, -7.3562e-01],\n",
              "        [-2.6618e-01, -3.4085e+00],\n",
              "        [ 2.0110e-02, -1.5601e+00],\n",
              "        [ 1.4968e-02, -1.5465e+00],\n",
              "        [ 2.6744e-01, -3.0498e+00],\n",
              "        [-6.9738e-02, -2.0556e+00],\n",
              "        [ 1.6317e-02, -1.3589e+00],\n",
              "        [ 1.0716e+00, -2.3330e+00],\n",
              "        [ 2.3052e-02, -1.3089e+00],\n",
              "        [ 1.5014e-01, -1.7039e+00],\n",
              "        [-1.2471e-02, -1.6985e+00],\n",
              "        [ 2.6174e-02, -1.5891e+00],\n",
              "        [ 4.8936e-01, -7.2643e-01],\n",
              "        [-8.0044e-02, -2.4927e+00],\n",
              "        [-2.5305e-01, -3.3233e+00],\n",
              "        [-1.9092e-01, -3.0098e+00],\n",
              "        [-2.1858e-01, -3.0260e+00],\n",
              "        [ 1.3137e-02, -1.6566e+00],\n",
              "        [ 1.5768e-02, -1.6122e+00],\n",
              "        [ 5.9088e-03, -1.7541e+00],\n",
              "        [-3.1722e-02, -1.9341e+00],\n",
              "        [ 9.7735e-02, -2.3714e+00],\n",
              "        [-5.2747e-02, -1.8126e+00],\n",
              "        [ 2.2779e-02, -1.5152e+00],\n",
              "        [ 1.0282e-02, -1.5995e+00],\n",
              "        [-1.6209e-01, -2.9571e+00],\n",
              "        [ 4.0435e-01, -3.5833e-02],\n",
              "        [-8.1448e-02, -2.4547e+00],\n",
              "        [-9.2619e-03, -1.6833e+00],\n",
              "        [-1.4556e-01, -2.4990e+00],\n",
              "        [-3.9408e-01, -4.3981e+00],\n",
              "        [ 5.0891e-01, -2.0179e-01],\n",
              "        [-2.2047e-01, -3.8744e+00],\n",
              "        [ 1.8373e-01, -4.9917e-01],\n",
              "        [ 2.0977e-02, -1.5575e+00],\n",
              "        [ 4.0426e-01,  9.1582e-03],\n",
              "        [ 4.2242e-01, -6.5204e-01],\n",
              "        [ 2.1025e-02, -1.7126e+00],\n",
              "        [ 3.3523e-01, -1.2433e+00],\n",
              "        [-1.2643e-01, -2.8035e+00],\n",
              "        [ 1.4977e-02, -1.6154e+00],\n",
              "        [ 6.4151e-01, -2.3486e-02],\n",
              "        [-1.8267e-01, -2.9838e+00],\n",
              "        [-6.6682e-02, -2.4299e+00],\n",
              "        [-6.8356e-02, -2.1124e+00],\n",
              "        [ 2.2784e-02, -1.5331e+00],\n",
              "        [ 4.2180e-01, -3.3743e-01],\n",
              "        [ 2.2752e-02, -1.6367e+00],\n",
              "        [ 5.7346e-02, -1.1896e+00],\n",
              "        [ 2.6966e-02, -1.5860e+00],\n",
              "        [-1.2029e-01, -3.3305e+00],\n",
              "        [ 4.5750e-02, -1.3044e+00],\n",
              "        [ 2.4642e-02, -1.1990e+00],\n",
              "        [ 7.6847e-02, -7.9197e-01],\n",
              "        [-1.8107e-01, -3.2239e+00],\n",
              "        [-5.7458e-02, -1.6557e+00],\n",
              "        [ 4.4668e-01,  6.9117e-02],\n",
              "        [-5.0589e-02, -1.8389e+00],\n",
              "        [-1.2471e-02, -1.6985e+00],\n",
              "        [ 1.5768e-02, -1.6122e+00],\n",
              "        [ 1.4847e-02, -1.6498e+00],\n",
              "        [ 2.8153e-02, -1.5109e+00],\n",
              "        [-7.7524e-02, -2.5761e+00],\n",
              "        [-2.4337e-01, -3.0356e+00],\n",
              "        [-1.5320e-02, -1.7442e+00],\n",
              "        [-5.3899e-02, -1.9174e+00],\n",
              "        [-1.9867e-02, -1.7119e+00],\n",
              "        [ 5.8743e-01, -2.2534e+00],\n",
              "        [-1.8849e-02, -1.5807e+00],\n",
              "        [-1.5333e-02, -1.8865e+00],\n",
              "        [ 5.1790e-01, -8.8240e-01],\n",
              "        [ 5.2490e-02, -1.1463e+00],\n",
              "        [-1.7159e-01, -2.8238e+00],\n",
              "        [ 1.5001e-02, -1.6492e+00],\n",
              "        [ 1.2860e+00, -2.8993e+00],\n",
              "        [ 1.2882e-02, -1.6576e+00],\n",
              "        [ 1.6839e-02, -1.6418e+00],\n",
              "        [ 3.3622e-02, -1.3689e+00],\n",
              "        [ 2.0723e-02, -1.5577e+00],\n",
              "        [-2.4557e-02, -1.7135e+00],\n",
              "        [-1.8998e-01, -3.3282e+00],\n",
              "        [ 3.8719e-01, -3.4230e+00],\n",
              "        [ 1.7236e-02, -1.6059e+00],\n",
              "        [-1.6938e-01, -2.7511e+00],\n",
              "        [-8.1469e-02, -1.9320e+00],\n",
              "        [ 3.5485e-01, -3.6512e-01],\n",
              "        [-1.9465e-03, -1.9760e+00],\n",
              "        [ 2.6966e-02, -1.5860e+00],\n",
              "        [-1.6823e-02, -1.9652e+00],\n",
              "        [-7.6051e-03, -2.0676e+00],\n",
              "        [ 1.4265e-02, -1.5486e+00],\n",
              "        [ 1.5768e-02, -1.6122e+00],\n",
              "        [-1.5682e-01, -3.5630e+00],\n",
              "        [-1.9234e-01, -2.7701e+00],\n",
              "        [ 2.7951e-02, -1.4602e+00],\n",
              "        [ 1.2984e-02, -1.6572e+00],\n",
              "        [-2.0144e-01, -3.6218e+00],\n",
              "        [ 2.0977e-02, -1.5575e+00],\n",
              "        [ 4.9633e-02, -1.2891e+00],\n",
              "        [ 2.0952e-02, -1.5576e+00],\n",
              "        [-2.2742e-01, -3.0069e+00],\n",
              "        [-3.7219e-02, -2.0033e+00],\n",
              "        [ 1.5768e-02, -1.6122e+00],\n",
              "        [-7.9941e-02, -2.3674e+00],\n",
              "        [ 1.5001e-02, -1.6492e+00],\n",
              "        [-2.0514e-02, -1.7762e+00],\n",
              "        [ 8.8927e-03, -1.3472e+00],\n",
              "        [ 3.7390e-02, -1.3386e+00],\n",
              "        [ 1.7570e-02, -1.3923e+00],\n",
              "        [ 4.6491e-01, -5.9378e-01],\n",
              "        [-7.7328e-02, -2.3269e+00],\n",
              "        [ 2.0952e-02, -1.5576e+00],\n",
              "        [-4.3370e-03, -1.6845e+00],\n",
              "        [-1.7655e-01, -3.1521e+00],\n",
              "        [ 3.7591e-02, -1.4402e+00],\n",
              "        [ 4.6585e-02, -1.2164e+00],\n",
              "        [ 2.3979e-02, -1.5104e+00],\n",
              "        [-7.0296e-02, -2.4786e+00],\n",
              "        [ 6.4986e-02, -1.0937e+00],\n",
              "        [ 1.5210e+00, -2.0307e+00],\n",
              "        [-4.4481e-03, -1.8985e+00],\n",
              "        [-2.3277e-01, -3.0940e+00],\n",
              "        [ 1.2147e+00, -2.0362e+00],\n",
              "        [-6.3828e-02, -2.3743e+00],\n",
              "        [ 1.5920e-02, -1.3487e+00],\n",
              "        [ 9.3298e-01, -3.1930e-01],\n",
              "        [ 2.6174e-02, -1.5891e+00],\n",
              "        [ 1.7301e-02, -1.4421e+00],\n",
              "        [ 6.1286e-02, -1.5289e+00],\n",
              "        [-4.2064e-01, -4.2712e+00],\n",
              "        [ 1.0073e-02, -1.5396e+00],\n",
              "        [ 4.9434e-01, -7.0896e-01],\n",
              "        [ 1.6496e-02, -1.6089e+00],\n",
              "        [ 1.8591e-02, -1.5666e+00],\n",
              "        [ 3.8001e-02, -1.3171e+00],\n",
              "        [ 2.7145e-02, -1.5852e+00],\n",
              "        [-1.6381e-02, -1.7935e+00],\n",
              "        [ 1.9027e-02, -1.5302e+00],\n",
              "        [ 4.4907e-02, -1.1912e+00],\n",
              "        [ 3.5953e-01,  8.4488e-03],\n",
              "        [ 3.2058e-01, -2.3051e-01],\n",
              "        [-1.5730e-02, -1.8673e+00],\n",
              "        [ 6.2604e-01, -3.0064e+00],\n",
              "        [-1.1833e-01, -2.3818e+00],\n",
              "        [-7.6683e-02, -2.6342e+00],\n",
              "        [ 3.5306e-02, -1.3621e+00],\n",
              "        [-7.9619e-02, -2.0684e+00],\n",
              "        [ 1.4501e+00, -2.2898e+00],\n",
              "        [-4.0363e-02, -2.5572e+00],\n",
              "        [-6.4492e-02, -2.4262e+00],\n",
              "        [ 3.6374e-01, -1.2888e-01],\n",
              "        [-7.8846e-02, -1.8832e+00],\n",
              "        [ 1.6335e-01, -1.7787e+00],\n",
              "        [ 3.7161e-01,  3.0757e-02],\n",
              "        [ 1.3622e-02, -1.6547e+00],\n",
              "        [ 2.0952e-02, -1.5576e+00],\n",
              "        [ 2.2770e-02, -1.3111e+00],\n",
              "        [ 6.5351e-02, -1.0891e+00],\n",
              "        [-2.1334e-02, -1.8034e+00],\n",
              "        [-1.8110e-01, -3.1755e+00],\n",
              "        [-2.2348e-01, -3.7943e+00],\n",
              "        [ 6.3411e-02, -1.0988e+00],\n",
              "        [ 2.6966e-02, -1.5860e+00],\n",
              "        [-1.7859e-02, -1.7501e+00],\n",
              "        [-2.8345e-01, -3.3406e+00],\n",
              "        [-8.7805e-02, -2.0527e+00],\n",
              "        [ 3.2995e-01, -2.1495e+00],\n",
              "        [-4.4943e-02, -2.4377e+00],\n",
              "        [ 8.4830e-01, -1.5148e+00],\n",
              "        [-1.0250e-01, -2.4527e+00],\n",
              "        [ 6.0060e-02, -1.3174e+00],\n",
              "        [ 1.2933e-02, -1.6574e+00],\n",
              "        [-2.7846e-02, -1.7271e+00],\n",
              "        [ 8.1849e-03, -1.6765e+00],\n",
              "        [ 3.0410e-01, -1.1830e+00],\n",
              "        [-1.9319e-01, -3.2397e+00],\n",
              "        [-6.3068e-02, -2.3811e+00],\n",
              "        [-2.4723e-02, -2.2025e+00],\n",
              "        [ 5.4584e-02, -1.1263e+00],\n",
              "        [ 3.5615e-02, -1.4138e+00],\n",
              "        [-3.4394e-02, -1.8342e+00],\n",
              "        [-3.6643e-01, -4.0135e+00],\n",
              "        [-2.8482e-02, -1.8732e+00],\n",
              "        [ 3.8435e-02, -1.3153e+00],\n",
              "        [ 7.8330e-01, -1.3437e+00],\n",
              "        [ 1.0435e-01, -7.4755e-01],\n",
              "        [-9.1746e-03, -2.0735e+00],\n",
              "        [ 2.7068e-02, -1.5855e+00],\n",
              "        [-4.3835e-03, -1.7347e+00],\n",
              "        [-8.1405e-02, -1.9428e+00],\n",
              "        [ 3.5866e-01, -1.2830e+00],\n",
              "        [-1.4316e-01, -3.4638e+00],\n",
              "        [ 2.6004e+00, -4.7130e+00],\n",
              "        [ 8.3232e-03, -1.7102e+00],\n",
              "        [-3.0694e-02, -3.5695e+00],\n",
              "        [-9.9586e-02, -2.2130e+00],\n",
              "        [-6.1329e-03, -1.9052e+00],\n",
              "        [ 1.6868e-02, -1.5739e+00],\n",
              "        [-3.1305e-02, -1.9383e+00],\n",
              "        [-9.7470e-03, -1.9540e+00],\n",
              "        [-1.3647e-02, -2.4651e+00],\n",
              "        [ 5.5661e-02, -1.1964e+00],\n",
              "        [-2.3329e-01, -3.4875e+00],\n",
              "        [-1.2541e-02, -2.2151e+00],\n",
              "        [ 1.8119e-02, -1.5849e+00],\n",
              "        [-6.4909e-02, -1.9445e+00],\n",
              "        [-3.4182e-01, -3.7363e+00],\n",
              "        [ 5.2937e-01, -8.8567e-01],\n",
              "        [ 3.4566e-02, -1.3651e+00],\n",
              "        [ 3.7158e-02, -1.3220e+00],\n",
              "        [-6.6543e-02, -2.9107e+00],\n",
              "        [-8.3066e-03, -1.6133e+00],\n",
              "        [ 1.5768e-02, -1.6122e+00]], device='cuda:0', grad_fn=<AddmmBackward0>)"
            ]
          },
          "metadata": {},
          "execution_count": 99
        }
      ],
      "source": [
        "model_0(X_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IG7AkLHJCiDp",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "469860c5-5e31-4361-d5eb-ade52d0a3632"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[ 0.0282, -1.4601],\n",
              "        [ 1.1104, -2.1338],\n",
              "        [ 0.0376, -1.3202],\n",
              "        [-0.0160, -2.0477],\n",
              "        [-0.0417, -2.3350],\n",
              "        [-0.1765, -3.1605],\n",
              "        [ 0.0167, -1.7296],\n",
              "        [-0.1729, -3.1034],\n",
              "        [ 0.0240, -1.3310],\n",
              "        [ 0.0119, -1.6614]], device='cuda:0')"
            ]
          },
          "metadata": {},
          "execution_count": 100
        }
      ],
      "source": [
        "model_0.eval()\n",
        "with torch.inference_mode():\n",
        "  y_logits = model_0(X_test).to(device)\n",
        "\n",
        "y_logits[:10]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "16dd1YOLFV--",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3ae6c5bf-183e-4926-f8db-dcc13a5cf09a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor([[ 0.0282, -1.4601],\n",
            "        [ 1.1104, -2.1338],\n",
            "        [ 0.0376, -1.3202],\n",
            "        [-0.0160, -2.0477],\n",
            "        [-0.0417, -2.3350]], device='cuda:0')\n",
            "tensor([[0.5070, 0.1885],\n",
            "        [0.7522, 0.1059],\n",
            "        [0.5094, 0.2108],\n",
            "        [0.4960, 0.1143],\n",
            "        [0.4896, 0.0883]], device='cuda:0')\n"
          ]
        }
      ],
      "source": [
        "y_pred_probs = torch.sigmoid(y_logits)\n",
        "print(y_logits[:5])\n",
        "print(y_pred_probs[:5])\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RhTXQRPmFlkZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6f3be852-acd9-4d97-d0fb-83da294910be"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], device='cuda:0')"
            ]
          },
          "metadata": {},
          "execution_count": 102
        }
      ],
      "source": [
        "y_peds = torch.argmax(y_pred_probs, dim=1)\n",
        "y_peds"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Nf0RQx8FI0bK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d8004f5c-d371-4a0d-ee1f-3621572ca5e3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "helper_function.py already exist, skiping download...\n"
          ]
        }
      ],
      "source": [
        "import requests\n",
        "from pathlib import Path\n",
        "\n",
        "if Path(\"helper_functions.py\").is_file():\n",
        "  print(f\"helper_function.py already exist, skiping download...\")\n",
        "else:\n",
        "  print(\"Downloading...\")\n",
        "  request = requests.get(\"https://raw.githubusercontent.com/mrdbourke/pytorch-deep-learning/main/helper_functions.py\")\n",
        "  with open(\"helper_functions.py\", \"wb\") as f:\n",
        "    f.write(request.content)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "omgF6pGMIoVa"
      },
      "outputs": [],
      "source": [
        "from helper_functions import accuracy_fn"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_train.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ox556XXJbE3_",
        "outputId": "4178978b-3c6b-4c14-a34a-7e760913a897"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([334, 7])"
            ]
          },
          "metadata": {},
          "execution_count": 105
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tb1DcgSzFvlu",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 408
        },
        "outputId": "7f40ae4e-f9c6-475d-dac9-6380d50bd629"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "logits:torch.Size([334, 2]) | y_Train:torch.Size([334, 7])\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "RuntimeError",
          "evalue": "0D or 1D target tensor expected, multi-target not supported",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-116-2de7ee252fff>\u001b[0m in \u001b[0;36m<cell line: 8>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     14\u001b[0m   \u001b[0;31m#y_logits = torch.t(y_logits)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m   \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"logits:{y_logits.shape} | y_Train:{y_train.shape}\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 16\u001b[0;31m   \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mloss_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_logits\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     17\u001b[0m   \u001b[0;31m#acc = accuracy_fn(y_true=X_train,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     18\u001b[0m   \u001b[0;31m#                  y_pred=y_pred)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1516\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compiled_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# type: ignore[misc]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1517\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1518\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1519\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1520\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1525\u001b[0m                 \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1526\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1527\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1528\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1529\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/loss.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input, target)\u001b[0m\n\u001b[1;32m   1177\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1178\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1179\u001b[0;31m         return F.cross_entropy(input, target, weight=self.weight,\n\u001b[0m\u001b[1;32m   1180\u001b[0m                                \u001b[0mignore_index\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mignore_index\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreduction\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreduction\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1181\u001b[0m                                label_smoothing=self.label_smoothing)\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/functional.py\u001b[0m in \u001b[0;36mcross_entropy\u001b[0;34m(input, target, weight, size_average, ignore_index, reduce, reduction, label_smoothing)\u001b[0m\n\u001b[1;32m   3051\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0msize_average\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mreduce\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3052\u001b[0m         \u001b[0mreduction\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_Reduction\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlegacy_get_string\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msize_average\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreduce\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3053\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_C\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_nn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcross_entropy_loss\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mweight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_Reduction\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_enum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreduction\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mignore_index\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel_smoothing\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3054\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3055\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mRuntimeError\u001b[0m: 0D or 1D target tensor expected, multi-target not supported"
          ]
        }
      ],
      "source": [
        "torch.manual_seed(42)\n",
        "torch.cuda.manual_seed(42)\n",
        "\n",
        "\n",
        "epochs = 1000\n",
        "X_tran, y_train = X_train.to(device), y_train.to(device)\n",
        "X_test, y_test = X_test.to(device), y_test.to(device)\n",
        "for epoch in range(epochs):\n",
        "  model_0.train()\n",
        "\n",
        "  y_logits = model_0(X_train)\n",
        "  y_pred = torch.softmax(y_logits, dim=1).argmax(dim=1)\n",
        "\n",
        "  #y_logits = torch.t(y_logits)\n",
        "  print(f\"logits:{y_logits.shape} | y_Train:{y_train.shape}\")\n",
        "  loss = loss_fn(y_logits, y_train)\n",
        "  #acc = accuracy_fn(y_true=X_train,\n",
        "  #                  y_pred=y_pred)\n",
        "\n",
        "\n",
        "  optimizer.zero_grad()\n",
        "  loss.backward()\n",
        "  optimizer.step()\n",
        "\n",
        "  model_0.eval()\n",
        "  with torch.inference_mode():\n",
        "    test_logits = model_0(X_test).squeeze()\n",
        "    test_preds = torch.softmax(test_logits, dim=1).argmax(dim=1)\n",
        "    #print(f\"test LOgits:{test_logits.shape} | y_train:{y_train.shape}\")\n",
        "    test_loss = loss_fn(test_logits, y_test)\n",
        "\n",
        "  if epoch % 10 == 0:\n",
        "    print(f\"Epoch: {epoch} | Loss: {loss:.4f} | TEst Loss: {test_loss}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LdNvcetlKCVU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0190b250-7b9c-4b94-f638-421185d6f07a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[ 0.0282, -1.4601],\n",
              "        [ 1.1104, -2.1338],\n",
              "        [ 0.0376, -1.3202],\n",
              "        [-0.0160, -2.0477],\n",
              "        [-0.0417, -2.3350],\n",
              "        [-0.1765, -3.1605],\n",
              "        [ 0.0167, -1.7296],\n",
              "        [-0.1729, -3.1034],\n",
              "        [ 0.0240, -1.3310],\n",
              "        [ 0.0119, -1.6614]], device='cuda:0')"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ],
      "source": [
        "model_0.eval()\n",
        "with torch.inference_mode():\n",
        "  y_logits = model_0(X_test).to(device)\n",
        "\n",
        "y_logits[:10]\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_probs = torch.softmax(y_logits, dim=1)\n",
        "print(y_logits[:5])\n",
        "print(y_pred_probs[:5])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xiMBbUFVsiFY",
        "outputId": "525027f0-13d7-4a9f-e0c6-a743edf8d0ce"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor([[ 0.0282, -1.4601],\n",
            "        [ 1.1104, -2.1338],\n",
            "        [ 0.0376, -1.3202],\n",
            "        [-0.0160, -2.0477],\n",
            "        [-0.0417, -2.3350]], device='cuda:0')\n",
            "tensor([[0.8158, 0.1842],\n",
            "        [0.9625, 0.0375],\n",
            "        [0.7954, 0.2046],\n",
            "        [0.8841, 0.1159],\n",
            "        [0.9083, 0.0917]], device='cuda:0')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "y_peds = torch.argmax(y_pred_probs, dim=1)\n",
        "y_peds\n",
        "Pclass\tSex\tAge\tSibSp\tParch\tFare\tEmbarked"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 141
        },
        "id": "zIMc0NJrsh4v",
        "outputId": "c0cf7653-2b65-4671-c7a9-76001b6ad7cb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "SyntaxError",
          "evalue": "invalid syntax (<ipython-input-62-e1f240c134f1>, line 3)",
          "traceback": [
            "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-62-e1f240c134f1>\"\u001b[0;36m, line \u001b[0;32m3\u001b[0m\n\u001b[0;31m    Pclass\tSex\tAge\tSibSp\tParch\tFare\tEmbarked\u001b[0m\n\u001b[0m          \t^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class titanikV2(nn.Module):\n",
        "  def __init__(self):\n",
        "    super().__init__()\n",
        "    self.pclass = nn.Parameter(torch.randn(1,\n",
        "                                           requires_grad=True,\n",
        "                                           dtype=torch.float))\n",
        "    self.Sex = nn.Parameter(torch.randn(1,\n",
        "                                           requires_grad=True,\n",
        "                                           dtype=torch.float))\n",
        "    self.Age = nn.Parameter(torch.randn(1,\n",
        "                                           requires_grad=True,\n",
        "                                           dtype=torch.float))\n",
        "    self.SibSp = nn.Parameter(torch.randn(1,\n",
        "                                           requires_grad=True,\n",
        "                                           dtype=torch.float))\n",
        "    self.Parch = nn.Parameter(torch.randn(1,\n",
        "                                           requires_grad=True,\n",
        "                                           dtype=torch.float))\n",
        "    self.Fare = nn.Parameter(torch.randn(1,\n",
        "                                           requires_grad=True,\n",
        "                                           dtype=torch.float))\n",
        "    self.Embarked = nn.Parameter(torch.randn(1,\n",
        "                                           requires_grad=True,\n",
        "                                           dtype=torch.float))\n",
        "  def forward(self, x):\n",
        "    return self.weights"
      ],
      "metadata": {
        "id": "fWhkKX81sllf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "3v217-N4wao2"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyM4L1gC5ZdKvfVGzdj98n59",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}