simple_neural_network.ipynb

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      "cell_type": "markdown",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/hiitsmocha/learn-pytorch/blob/main/simple_neural_network.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": 1,
      "metadata": {
        "id": "bgtJ2YQNGKGW"
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      "source": [
        "import torch\n",
        "import numpy as np"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch.nn as nn\n",
        "import torch.nn.functional as F"
      ],
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        "id": "uN1bFX2Mv33e"
      },
      "execution_count": 2,
      "outputs": []
    },
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      "source": [
        "# Create a Model Class that inherits nn.Module"
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      "execution_count": 3,
      "outputs": []
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      "source": [
        "class Model(nn.Module):\n",
        "  # Input layer (4 features of the flower) --> Hidden layer1 (number of neurons) --> H2 (n)\n",
        "  # --> Output (3 classes of iris flowers)\n",
        "  def __init__(self, in_features = 4, h1 = 8, h2 = 9, out_features = 3):\n",
        "    super().__init__() # instantiate our nn.Module\n",
        "    self.fc1 = nn.Linear(in_features, h1)\n",
        "    self.fc2 = nn.Linear(h1, h2)\n",
        "    self.out = nn.Linear(h2, out_features)\n",
        "\n",
        "  def forward(self, x):\n",
        "    x = F.relu(self.fc1(x))\n",
        "    x = F.relu(self.fc2(x))\n",
        "    x = self.out(x)\n",
        "    return x"
      ],
      "metadata": {
        "id": "fxzJPABGwwCz"
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      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Pick a manual seed for randomization\n",
        "torch.manual_seed(41)\n",
        "# create an instance of model\n",
        "model = Model()"
      ],
      "metadata": {
        "id": "UQGtolWnyh1I"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Load data and train model (as CSV file)\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "%matplotlib inline"
      ],
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        "id": "EOFEXIOEzB76"
      },
      "execution_count": 6,
      "outputs": []
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      "source": [
        "url = 'https://gist.githubusercontent.com/curran/a08a1080b88344b0c8a7/raw/0e7a9b0a5d22642a06d3d5b9bcbad9890c8ee534/iris.csv'\n",
        "my_df = pd.read_csv(url)\n"
      ],
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      "execution_count": 7,
      "outputs": []
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      "source": [
        "my_df"
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        "outputId": "51fd2d1a-ff5a-4583-d910-bacc0742173a"
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      "execution_count": 8,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "     sepal_length  sepal_width  petal_length  petal_width    species\n",
              "0             5.1          3.5           1.4          0.2     setosa\n",
              "1             4.9          3.0           1.4          0.2     setosa\n",
              "2             4.7          3.2           1.3          0.2     setosa\n",
              "3             4.6          3.1           1.5          0.2     setosa\n",
              "4             5.0          3.6           1.4          0.2     setosa\n",
              "..            ...          ...           ...          ...        ...\n",
              "145           6.7          3.0           5.2          2.3  virginica\n",
              "146           6.3          2.5           5.0          1.9  virginica\n",
              "147           6.5          3.0           5.2          2.0  virginica\n",
              "148           6.2          3.4           5.4          2.3  virginica\n",
              "149           5.9          3.0           5.1          1.8  virginica\n",
              "\n",
              "[150 rows x 5 columns]"
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              "      <th>0</th>\n",
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              "      <th>2</th>\n",
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              "      <th>145</th>\n",
              "      <td>6.7</td>\n",
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            }
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "my_df.head()"
      ],
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        "colab": {
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        "id": "pDP10rQozu_f",
        "outputId": "e0b80ee5-1ae2-449d-8f31-411b692e277b"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   sepal_length  sepal_width  petal_length  petal_width species\n",
              "0           5.1          3.5           1.4          0.2  setosa\n",
              "1           4.9          3.0           1.4          0.2  setosa\n",
              "2           4.7          3.2           1.3          0.2  setosa\n",
              "3           4.6          3.1           1.5          0.2  setosa\n",
              "4           5.0          3.6           1.4          0.2  setosa"
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "my_df",
              "summary": "{\n  \"name\": \"my_df\",\n  \"rows\": 150,\n  \"fields\": [\n    {\n      \"column\": \"sepal_length\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.8280661279778629,\n        \"min\": 4.3,\n        \"max\": 7.9,\n        \"num_unique_values\": 35,\n        \"samples\": [\n          6.2,\n          4.5,\n          5.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sepal_width\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4335943113621737,\n        \"min\": 2.0,\n        \"max\": 4.4,\n        \"num_unique_values\": 23,\n        \"samples\": [\n          2.3,\n          4.0,\n          3.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"petal_length\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.7644204199522617,\n        \"min\": 1.0,\n        \"max\": 6.9,\n        \"num_unique_values\": 43,\n        \"samples\": [\n          6.7,\n          3.8,\n          3.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"petal_width\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7631607417008414,\n        \"min\": 0.1,\n        \"max\": 2.5,\n        \"num_unique_values\": 22,\n        \"samples\": [\n          0.2,\n          1.2,\n          1.3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"species\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"setosa\",\n          \"versicolor\",\n          \"virginica\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "my_df['species'] = my_df['species'].replace('setosa', 0.0)\n",
        "my_df['species'] = my_df['species'].replace('versicolor', 1.0)\n",
        "my_df['species'] = my_df['species'].replace('virginica', 2.0)\n",
        "my_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 478
        },
        "id": "7Vu5fXRMzy46",
        "outputId": "7027d138-13f7-416e-d6b0-580a8ca637ce"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-10-31d2ab34cca2>:3: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
            "  my_df['species'] = my_df['species'].replace('virginica', 2.0)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     sepal_length  sepal_width  petal_length  petal_width  species\n",
              "0             5.1          3.5           1.4          0.2      0.0\n",
              "1             4.9          3.0           1.4          0.2      0.0\n",
              "2             4.7          3.2           1.3          0.2      0.0\n",
              "3             4.6          3.1           1.5          0.2      0.0\n",
              "4             5.0          3.6           1.4          0.2      0.0\n",
              "..            ...          ...           ...          ...      ...\n",
              "145           6.7          3.0           5.2          2.3      2.0\n",
              "146           6.3          2.5           5.0          1.9      2.0\n",
              "147           6.5          3.0           5.2          2.0      2.0\n",
              "148           6.2          3.4           5.4          2.3      2.0\n",
              "149           5.9          3.0           5.1          1.8      2.0\n",
              "\n",
              "[150 rows x 5 columns]"
            ],
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              "      <th></th>\n",
              "      <th>sepal_length</th>\n",
              "      <th>sepal_width</th>\n",
              "      <th>petal_length</th>\n",
              "      <th>petal_width</th>\n",
              "      <th>species</th>\n",
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              "      <th>0</th>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4.7</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.6</td>\n",
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              "      <td>0.2</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
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              "      <td>3.6</td>\n",
              "      <td>1.4</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
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              "      <td>2.0</td>\n",
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              "    <tr>\n",
              "      <th>146</th>\n",
              "      <td>6.3</td>\n",
              "      <td>2.5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.9</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>147</th>\n",
              "      <td>6.5</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>148</th>\n",
              "      <td>6.2</td>\n",
              "      <td>3.4</td>\n",
              "      <td>5.4</td>\n",
              "      <td>2.3</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>149</th>\n",
              "      <td>5.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.1</td>\n",
              "      <td>1.8</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>150 rows × 5 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-086e4fc0-94e3-4428-9375-536685b4cf09')\"\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",
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              "      border-radius: 50%;\n",
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              "      height: 32px;\n",
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              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
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              "      fill: #174EA6;\n",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
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              "      fill: #D2E3FC;\n",
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              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "      fill: #FFFFFF;\n",
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              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-086e4fc0-94e3-4428-9375-536685b4cf09 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-086e4fc0-94e3-4428-9375-536685b4cf09');\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",
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              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
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              "      --disabled-fill-color: #AAA;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
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              "      --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",
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              "    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",
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              "  }\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",
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              "    20% {\n",
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              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "    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-10d065ac-98cf-460c-b96e-e9e482e31b69 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_9c4b2d38-0bea-4c0a-9701-ecc1e7811348\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate: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",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('my_df')\"\n",
              "            title=\"Generate code using this dataframe.\"\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",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_9c4b2d38-0bea-4c0a-9701-ecc1e7811348 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('my_df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "my_df",
              "summary": "{\n  \"name\": \"my_df\",\n  \"rows\": 150,\n  \"fields\": [\n    {\n      \"column\": \"sepal_length\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.8280661279778629,\n        \"min\": 4.3,\n        \"max\": 7.9,\n        \"num_unique_values\": 35,\n        \"samples\": [\n          6.2,\n          4.5,\n          5.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sepal_width\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4335943113621737,\n        \"min\": 2.0,\n        \"max\": 4.4,\n        \"num_unique_values\": 23,\n        \"samples\": [\n          2.3,\n          4.0,\n          3.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"petal_length\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.7644204199522617,\n        \"min\": 1.0,\n        \"max\": 6.9,\n        \"num_unique_values\": 43,\n        \"samples\": [\n          6.7,\n          3.8,\n          3.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"petal_width\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7631607417008414,\n        \"min\": 0.1,\n        \"max\": 2.5,\n        \"num_unique_values\": 22,\n        \"samples\": [\n          0.2,\n          1.2,\n          1.3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"species\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.8192319205190405,\n        \"min\": 0.0,\n        \"max\": 2.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.0,\n          1.0,\n          2.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Train Test Split! Set X, y\n",
        "X = my_df.drop('species', axis = 1)\n",
        "y = my_df['species']"
      ],
      "metadata": {
        "id": "A556hQyY0aAp"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Convert these to numpy arrays\n",
        "X = X.values\n",
        "y = y.values"
      ],
      "metadata": {
        "id": "0_DrF86O0toz"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split"
      ],
      "metadata": {
        "id": "-Z1o8TQZ01Ws"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Train test split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 41)"
      ],
      "metadata": {
        "id": "wsRQ3BkH08Lm"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Convert X features to float tensors\n",
        "X_train = torch.FloatTensor(X_train)\n",
        "X_test = torch.FloatTensor(X_test)\n",
        "# Convert y lables to tensors long\n",
        "y_train = torch.LongTensor(y_train)\n",
        "y_test = torch.LongTensor(y_test)"
      ],
      "metadata": {
        "id": "tq8bwwW81MlG"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Set the criterion of model to measure the error, how far off the predicitons are from the results\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "# Choose Adam Optimizer, lr = learning rate(if error doesnt go down after a bunch of iterations(epochs))\n",
        "optimizer = torch.optim.Adam(model.parameters(), lr = 0.01)"
      ],
      "metadata": {
        "id": "KqQX0vROZcKj"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Train our model!\n",
        "# Epochs? (one run through all the training data in our network)\n",
        "epochs = 100\n",
        "losses = []\n",
        "for i in range(epochs):\n",
        "  # Go forward and get a prediction\n",
        "  y_pred = model.forward(X_train) # Get predicted results\n",
        "  # Measure the loss/error, gonna be high at first\n",
        "  loss = criterion(y_pred, y_train) # predicted values vs the y_train\n",
        "\n",
        "  # Keep track of our losses\n",
        "  losses.append(loss.detach().numpy())\n",
        "\n",
        "  # Print very 10 epochs\n",
        "  if i % 10 == 0:\n",
        "    print(f\"Epoch: {i} and loss: {loss}\")\n",
        "\n",
        "  # Do some back propagation: take the error rate of forward propagationa and feed it back through the network\n",
        "  # to fine tune the weights\n",
        "  optimizer.zero_grad()\n",
        "  loss.backward()\n",
        "  optimizer.step()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "prkyLEk0Z0rW",
        "outputId": "eaad3a23-a90d-4814-c7db-8095c6bb5988"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 0 and loss: 1.1251550912857056\n",
            "Epoch: 10 and loss: 1.0096259117126465\n",
            "Epoch: 20 and loss: 0.8157405853271484\n",
            "Epoch: 30 and loss: 0.585706353187561\n",
            "Epoch: 40 and loss: 0.3999636471271515\n",
            "Epoch: 50 and loss: 0.2676880657672882\n",
            "Epoch: 60 and loss: 0.17942363023757935\n",
            "Epoch: 70 and loss: 0.12151690572500229\n",
            "Epoch: 80 and loss: 0.0860118418931961\n",
            "Epoch: 90 and loss: 0.06520850956439972\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Graph it out\n",
        "plt.plot(range(epochs), losses)\n",
        "plt.ylabel('losses/error')\n",
        "plt.xlabel('Epoch')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "452-43dDbZtH",
        "outputId": "b2b9ec82-7597-44d1-d328-bf46464d02a8"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 0, 'Epoch')"
            ]
          },
          "metadata": {},
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}