Tabular_PyTorch_Fastai_AdultDS.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/moarshy/PyTorch-Projects/blob/main/Tabular_PyTorch_Fastai_AdultDS.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uV_5ABpplFK2"
      },
      "source": [
        "\n",
        "\n",
        "*   In this colab notebook, we practice applying deep learning to tabular data\n",
        "*   We use both PyTorch to implement from the basics and use the amazing fastai library\n",
        "\n",
        "The following amzing blogs/notebooks were very helpful\n",
        "*   https://walkwithfastai.com/tab.clas.binary\n",
        "*   https://jovian.ai/aakanksha-ns/shelter-outcome\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TjOuNyKt26W1",
        "outputId": "5c54d705-cd6d-439b-f4f9-8b19dfc81398",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive', force_remount=True)"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kUfiVWHG3A9l"
      },
      "source": [
        "#importing the necessary libraries\n",
        "from pathlib import Path\n",
        "import zipfile\n",
        "from tqdm import tqdm_notebook\n",
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "from torch.utils.data import Dataset, DataLoader"
      ],
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kP00quZr6-_Z",
        "outputId": "2117336b-e5b6-4636-9bb7-7980b309a61d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# installing the fastai library. Must be done everytime the notebook is open\n",
        "!pip install -U fastai -q"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[K     |████████████████████████████████| 194kB 8.4MB/s \n",
            "\u001b[K     |████████████████████████████████| 51kB 8.1MB/s \n",
            "\u001b[?25h"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ASE5ilv57BAK"
      },
      "source": [
        "from fastai.tabular.all import *"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CuMr9VMu4AEQ"
      },
      "source": [
        "# Download the data\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "We9-aGWe3uSJ"
      },
      "source": [
        "base_path = Path('/content/drive/My Drive/Colab Notebooks/Portfolio/Tabular/Adult')\n",
        "#path = untar_data(URLs.ADULT_SAMPLE, dest=base_path)"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D5TRe6FF3SxK"
      },
      "source": [
        "# Dataset class, model class and training function using PyTorch"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HVuka3IP3CYN"
      },
      "source": [
        "#we build a dataset class\n",
        "class AdultDataset():\n",
        "    def __init__(self, x, y, catcols, contcols):\n",
        "        \n",
        "        self.x = x.copy()\n",
        "        self.y = y.copy()\n",
        "        \n",
        "        self.cat_features = self.x.loc[:, catcols].values\n",
        "        self.cont_features = self.x.loc[:, contcols].values\n",
        "        self.y = self.y.values\n",
        "        \n",
        "    def __len__(self):\n",
        "        return self.x.shape[0]\n",
        "    \n",
        "    def __getitem__(self, idx):\n",
        "        to_return = {\n",
        "            'x_cat' : torch.tensor(self.cat_features[idx, :], dtype=torch.long),\n",
        "            'x_cont' : torch.tensor(self.cont_features[idx, :], dtype=torch.float),\n",
        "            'y' : torch.tensor(self.y[idx], dtype=torch.float)            \n",
        "        }\n",
        "        return to_return"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UvcMCzj93NQX"
      },
      "source": [
        "#this is our model. \n",
        "#It uses embedding layer for categorical columns and then concate both the embedding layer and continuous features to be used in the subsequent layers\n",
        "#we initialise the layers following - embedding --> uniform, linear --> kaiming uniform, batchnorm --> constant\n",
        "class Model(nn.Module):\n",
        "    def __init__(self, n_cont, num_targets, hidden_size, embedding_size):\n",
        "        super(Model, self).__init__()\n",
        "        \n",
        "        self.embeddings = nn.ModuleList([nn.Embedding(categories, size) for categories, size in embedding_size])\n",
        "        n_emb = sum(e.embedding_dim for e in self.embeddings) \n",
        "        self.n_emb, self.n_cont = n_emb, n_cont\n",
        "        \n",
        "        self.bn0 = nn.BatchNorm1d(self.n_cont)\n",
        "        self.lin1 = nn.Linear(in_features=self.n_emb + self.n_cont, out_features=hidden_size[0])\n",
        "        self.bn1 = nn.BatchNorm1d(hidden_size[0])\n",
        "        self.dropout1 = nn.Dropout(0.4)\n",
        "        self.relu1 = nn.PReLU()\n",
        "        self.emb_drop = nn.Dropout(0.6)\n",
        "        \n",
        "        self.hidden = nn.ModuleList()\n",
        "        for k in range(1, len(hidden_size)):\n",
        "            self.hidden.append(nn.Linear(in_features=hidden_size[k-1], out_features=hidden_size[k]))\n",
        "            self.hidden.append(nn.BatchNorm1d(hidden_size[k]))\n",
        "            self.hidden.append(nn.Dropout(0.5))\n",
        "            self.hidden.append(nn.PReLU())\n",
        "    \n",
        "        self.out = nn.Linear(hidden_size[-1], num_targets)\n",
        "        self.initialize_weights()\n",
        "        \n",
        "    def forward(self, x_cat, x_cont):\n",
        "        x = [e(x_cat[:,i]) for i,e in enumerate(self.embeddings)]\n",
        "        x = torch.cat(x, 1)\n",
        "        x = self.emb_drop(x)\n",
        "        x2 = self.bn0(x_cont)\n",
        "        x = torch.cat([x, x2], 1)\n",
        "        x = self.lin1(x)\n",
        "        x = self.bn1(x)\n",
        "        x = self.dropout1(x)\n",
        "        x = self.relu1(x)\n",
        "        \n",
        "        for l in self.hidden:\n",
        "            x = l(x)\n",
        "        \n",
        "        x = self.out(x)\n",
        "    \n",
        "        return x\n",
        "\n",
        "    def initialize_weights(self):\n",
        "        for m in self.modules():\n",
        "            if isinstance(m, nn.Linear):\n",
        "                nn.init.kaiming_uniform_(m.weight)\n",
        "                nn.init.constant_(m.bias, 0)\n",
        "            \n",
        "            elif isinstance(m, nn.BatchNorm2d):\n",
        "                nn.init.constant_(m.weight, 1)\n",
        "                nn.init.constant_(m.bias, 0)\n",
        "\n",
        "            elif isinstance(m, nn.Embedding):\n",
        "                nn.init.uniform_(m.weight, -1., 1.)"
      ],
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LL_pJVAh3OWA"
      },
      "source": [
        "#we build a training function\n",
        "def training(epochs, trainloader, validloader, loss_fn, optimizer, scheduler):\n",
        "  history = []\n",
        "  for epoch in range(epochs):\n",
        "    model.train()\n",
        "\n",
        "    train_loss = 0\n",
        "    loop = tqdm_notebook(enumerate(trainloader), leave=False, total=len(trainloader))\n",
        "    for i, data in loop:\n",
        "        \n",
        "        x_cat, x_cont, targets = data['x_cat'].to(device), data['x_cont'].to(device), data['y']\n",
        "        targets = targets.type(torch.LongTensor).to(device)\n",
        "\n",
        "        output = model(x_cat, x_cont)\n",
        "        loss = loss_fn(output, targets)\n",
        "\n",
        "        optimizer.zero_grad()\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "        scheduler.step()\n",
        "\n",
        "        train_loss += loss.item()\n",
        "\n",
        "        loop.set_description(f'Epoch {epoch}/{epochs}')\n",
        "        loop.set_postfix(loss=loss.item())\n",
        "\n",
        "    train_loss /= len(trainloader)\n",
        "\n",
        "    model.eval()\n",
        "    valid_loss = 0\n",
        "    valid_preds = []\n",
        "    correct, total, acc  = [0]*3\n",
        "\n",
        "    with torch.no_grad():\n",
        "      for data in validloader:\n",
        "          x_cat, x_cont, targets = data['x_cat'].to(device), data['x_cont'].to(device), data['y']\n",
        "          targets = targets.type(torch.LongTensor).to(device)\n",
        "\n",
        "          output = model(x_cat, x_cont)\n",
        "          loss = loss_fn(output, targets)\n",
        "          \n",
        "          valid_loss += loss.item()\n",
        "\n",
        "          output = torch.log_softmax(output, dim=1)\n",
        "          _, pred = torch.max(output, 1)\n",
        "          correct += (pred == targets).sum().item()\n",
        "          total += len(pred)\n",
        "\n",
        "          valid_preds.append(output.detach().cpu().numpy())\n",
        "\n",
        "    valid_loss /= len(validloader)\n",
        "    acc = correct/total\n",
        "    print(f'Epoch: {epoch}, Train Loss: {train_loss}, Valid Loss: {valid_loss}, Accuracy: {acc:.2f}')\n",
        "    history.append((epoch, train_loss, valid_loss, acc))\n",
        "\n",
        "  return history, valid_preds"
      ],
      "execution_count": 87,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N9vBmv3a4KRT"
      },
      "source": [
        "# Exploring the data\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v12DcYMN4Sqa"
      },
      "source": [
        "df = pd.read_csv(base_path/'adult_sample/adult.csv')"
      ],
      "execution_count": 88,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K0tTzF5-6snw",
        "outputId": "f43730e6-ce10-4f8c-ed30-10c460ab42b1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        }
      },
      "source": [
        "df.head(5)"
      ],
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>age</th>\n",
              "      <th>workclass</th>\n",
              "      <th>fnlwgt</th>\n",
              "      <th>education</th>\n",
              "      <th>education-num</th>\n",
              "      <th>marital-status</th>\n",
              "      <th>occupation</th>\n",
              "      <th>relationship</th>\n",
              "      <th>race</th>\n",
              "      <th>sex</th>\n",
              "      <th>capital-gain</th>\n",
              "      <th>capital-loss</th>\n",
              "      <th>hours-per-week</th>\n",
              "      <th>native-country</th>\n",
              "      <th>salary</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>49</td>\n",
              "      <td>Private</td>\n",
              "      <td>101320</td>\n",
              "      <td>Assoc-acdm</td>\n",
              "      <td>12.0</td>\n",
              "      <td>Married-civ-spouse</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Wife</td>\n",
              "      <td>White</td>\n",
              "      <td>Female</td>\n",
              "      <td>0</td>\n",
              "      <td>1902</td>\n",
              "      <td>40</td>\n",
              "      <td>United-States</td>\n",
              "      <td>&gt;=50k</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>44</td>\n",
              "      <td>Private</td>\n",
              "      <td>236746</td>\n",
              "      <td>Masters</td>\n",
              "      <td>14.0</td>\n",
              "      <td>Divorced</td>\n",
              "      <td>Exec-managerial</td>\n",
              "      <td>Not-in-family</td>\n",
              "      <td>White</td>\n",
              "      <td>Male</td>\n",
              "      <td>10520</td>\n",
              "      <td>0</td>\n",
              "      <td>45</td>\n",
              "      <td>United-States</td>\n",
              "      <td>&gt;=50k</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>38</td>\n",
              "      <td>Private</td>\n",
              "      <td>96185</td>\n",
              "      <td>HS-grad</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Divorced</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Unmarried</td>\n",
              "      <td>Black</td>\n",
              "      <td>Female</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>32</td>\n",
              "      <td>United-States</td>\n",
              "      <td>&lt;50k</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>38</td>\n",
              "      <td>Self-emp-inc</td>\n",
              "      <td>112847</td>\n",
              "      <td>Prof-school</td>\n",
              "      <td>15.0</td>\n",
              "      <td>Married-civ-spouse</td>\n",
              "      <td>Prof-specialty</td>\n",
              "      <td>Husband</td>\n",
              "      <td>Asian-Pac-Islander</td>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>40</td>\n",
              "      <td>United-States</td>\n",
              "      <td>&gt;=50k</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>42</td>\n",
              "      <td>Self-emp-not-inc</td>\n",
              "      <td>82297</td>\n",
              "      <td>7th-8th</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Married-civ-spouse</td>\n",
              "      <td>Other-service</td>\n",
              "      <td>Wife</td>\n",
              "      <td>Black</td>\n",
              "      <td>Female</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>50</td>\n",
              "      <td>United-States</td>\n",
              "      <td>&lt;50k</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   age          workclass  fnlwgt  ... hours-per-week  native-country salary\n",
              "0   49            Private  101320  ...             40   United-States  >=50k\n",
              "1   44            Private  236746  ...             45   United-States  >=50k\n",
              "2   38            Private   96185  ...             32   United-States   <50k\n",
              "3   38       Self-emp-inc  112847  ...             40   United-States  >=50k\n",
              "4   42   Self-emp-not-inc   82297  ...             50   United-States   <50k\n",
              "\n",
              "[5 rows x 15 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rug3dTyQ8LrY",
        "outputId": "e1ddc97a-7174-4857-c0a9-feb08d1eda4e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "df.info()"
      ],
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 32561 entries, 0 to 32560\n",
            "Data columns (total 15 columns):\n",
            " #   Column          Non-Null Count  Dtype  \n",
            "---  ------          --------------  -----  \n",
            " 0   age             32561 non-null  int64  \n",
            " 1   workclass       32561 non-null  object \n",
            " 2   fnlwgt          32561 non-null  int64  \n",
            " 3   education       32561 non-null  object \n",
            " 4   education-num   32074 non-null  float64\n",
            " 5   marital-status  32561 non-null  object \n",
            " 6   occupation      32049 non-null  object \n",
            " 7   relationship    32561 non-null  object \n",
            " 8   race            32561 non-null  object \n",
            " 9   sex             32561 non-null  object \n",
            " 10  capital-gain    32561 non-null  int64  \n",
            " 11  capital-loss    32561 non-null  int64  \n",
            " 12  hours-per-week  32561 non-null  int64  \n",
            " 13  native-country  32561 non-null  object \n",
            " 14  salary          32561 non-null  object \n",
            "dtypes: float64(1), int64(5), object(9)\n",
            "memory usage: 3.7+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p7z8wjwY9bnJ"
      },
      "source": [
        "#we fillna the education-num with the mode\n",
        "df['education-num'] = df['education-num'].fillna(df['education-num'].value_counts().index[0])"
      ],
      "execution_count": 91,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5EcPPIoD9yQy"
      },
      "source": [
        "#we fillna the occupation with the mode\n",
        "df['occupation'] = df['occupation'].fillna(df['occupation'].value_counts().index[0])"
      ],
      "execution_count": 92,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wCE91SFs8XK6"
      },
      "source": [
        "# Preprocessing \n",
        "def preprocess(df, catcols, contcols):\n",
        "    lbl_enc = {}\n",
        "    for c in catcols:\n",
        "        lbl_enc[c] = LabelEncoder()\n",
        "        df[c] = lbl_enc[c].fit_transform(df[c].values)\n",
        "      \n",
        "    mms = {}\n",
        "    for c in contcols:\n",
        "      mms[c] = MinMaxScaler()\n",
        "      df[c] = mms[c].fit_transform(df[c].values.reshape(-1,1))\n",
        "    \n",
        "    return df, lbl_enc, mms\n",
        "\n",
        "def preprocess_target(df, target_column):\n",
        "    le = LabelEncoder()\n",
        "    df[target_column] = le.fit_transform(df[target_column].values)\n",
        "\n",
        "    return df, le"
      ],
      "execution_count": 93,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9e1PFACB_YNj"
      },
      "source": [
        "#setting the device\n",
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "\n",
        "#the column lists\n",
        "in_features = len(features.columns)\n",
        "classes = df['salary'].nunique()\n",
        "cat_names = ['workclass', 'education', 'marital-status', 'occupation', 'relationship', 'race', 'sex', 'native-country']\n",
        "cont_names = ['age', 'fnlwgt', 'education-num', 'capital-gain', 'capital-loss', 'hours-per-week']\n",
        "target_col = 'salary'\n",
        "ncont = len(cont_names)\n",
        "\n",
        "#preprocess and prepare the train/test split\n",
        "df, *_ = preprocess(df, cat_names, cont_names)\n",
        "df, _ = preprocess_target(df, target_col)\n",
        "features = df.drop('salary', axis=1)\n",
        "target = df['salary']\n",
        "X_train, X_test, y_train, y_test = train_test_split(features, target, test_size=0.33)\n",
        "\n",
        "#the embedding sizes\n",
        "embedding = []\n",
        "for c in cat_names:\n",
        "  embedding.append((df[c].nunique(), int((df[c].nunique()*1.6)**0.56)))\n",
        "\n",
        "#hyperparameters\n",
        "learningrate = 1e-3\n",
        "batchsize = 512\n",
        "epochs = 30\n",
        "\n",
        "#dataset and dataloader\n",
        "train_ds = AdultDataset(X_train, y_train, cat_names, cont_names)\n",
        "valid_ds = AdultDataset(X_test, y_test, cat_names, cont_names)\n",
        "\n",
        "train_dl = DataLoader(train_ds, batch_size=batchsize, shuffle=True)\n",
        "valid_dl = DataLoader(valid_ds, batch_size=batchsize*2, shuffle=False)\n",
        "\n",
        "#Model\n",
        "model = Model(n_cont=ncont, num_targets=classes, hidden_size=[200,100], embedding_size=embedding)\n",
        "model.to(device)\n",
        "\n",
        "#criterion, opt, scheduler\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "opt = torch.optim.Adam(model.parameters(), lr=learningrate)\n",
        "scheduler = torch.optim.lr_scheduler.OneCycleLR(opt, max_lr=1e-1, epochs=epochs, steps_per_epoch=len(train_dl))"
      ],
      "execution_count": 96,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Fcqoi0jhAbrt",
        "outputId": "5b48afb1-4a27-49b4-e2a6-05536c45f571",
        "colab": {
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          "height": 604,
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      },
      "source": [
        "hist, _ = training(epochs=epochs, trainloader=train_dl, validloader=valid_dl, loss_fn=criterion, optimizer=opt, scheduler=scheduler)"
      ],
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:8: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
            "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n",
            "  \n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
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              "HBox(children=(FloatProgress(value=0.0, max=43.0), HTML(value='')))"
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          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch: 0, Train Loss: 0.5196486364963443, Valid Loss: 0.38697515563531354, Accuracy: 0.83\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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          },
          "metadata": {
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        },
        {
          "output_type": "stream",
          "text": [
            "Epoch: 1, Train Loss: 0.38248428286508074, Valid Loss: 0.33907401832667267, Accuracy: 0.84\n"
          ],
          "name": "stdout"
        },
        {
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          "metadata": {
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        },
        {
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          "text": [
            "Epoch: 2, Train Loss: 0.3566991347213124, Valid Loss: 0.32374312660910864, Accuracy: 0.85\n"
          ],
          "name": "stdout"
        },
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        {
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          "text": [
            "Epoch: 3, Train Loss: 0.3494107251943544, Valid Loss: 0.3209684057669206, Accuracy: 0.85\n"
          ],
          "name": "stdout"
        },
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          "text": [
            "Epoch: 4, Train Loss: 0.34807287537774373, Valid Loss: 0.3190750696442344, Accuracy: 0.85\n"
          ],
          "name": "stdout"
        },
        {
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          "text": [
            "Epoch: 5, Train Loss: 0.3467837634474732, Valid Loss: 0.32207068529996, Accuracy: 0.85\n"
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          "text": [
            "Epoch: 6, Train Loss: 0.34404380584872046, Valid Loss: 0.3190893232822418, Accuracy: 0.85\n"
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            "Epoch: 7, Train Loss: 0.34761900333471074, Valid Loss: 0.32183942469683563, Accuracy: 0.85\n"
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            "Epoch: 8, Train Loss: 0.3469194333220637, Valid Loss: 0.3189391006122936, Accuracy: 0.85\n"
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            "Epoch: 9, Train Loss: 0.34523449456968974, Valid Loss: 0.3339819068258459, Accuracy: 0.84\n"
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            "Epoch: 10, Train Loss: 0.34524710095205974, Valid Loss: 0.3321759375658902, Accuracy: 0.84\n"
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          "text": [
            "Epoch: 12, Train Loss: 0.3437835109788318, Valid Loss: 0.3206509162079204, Accuracy: 0.85\n"
          ],
          "name": "stdout"
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          "output_type": "stream",
          "text": [
            "Epoch: 13, Train Loss: 0.3400458485581154, Valid Loss: 0.3411160138520328, Accuracy: 0.82\n"
          ],
          "name": "stdout"
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          "text": [
            "Epoch: 14, Train Loss: 0.3407954234023427, Valid Loss: 0.31507301330566406, Accuracy: 0.85\n"
          ],
          "name": "stdout"
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          "text": [
            "Epoch: 15, Train Loss: 0.33334229297416157, Valid Loss: 0.317174339836294, Accuracy: 0.85\n"
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            "Epoch: 16, Train Loss: 0.34001947627511137, Valid Loss: 0.31733701445839624, Accuracy: 0.85\n"
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            "Epoch: 17, Train Loss: 0.3347700993682063, Valid Loss: 0.3185238160870292, Accuracy: 0.85\n"
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          "text": [
            "Epoch: 18, Train Loss: 0.33242073031358943, Valid Loss: 0.32158619978211145, Accuracy: 0.85\n"
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          "output_type": "stream",
          "text": [
            "Epoch: 19, Train Loss: 0.3322255278742591, Valid Loss: 0.32233683358539233, Accuracy: 0.85\n"
          ],
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          "text": [
            "Epoch: 20, Train Loss: 0.3336727432040281, Valid Loss: 0.31968815760179, Accuracy: 0.85\n"
          ],
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          "output_type": "stream",
          "text": [
            "Epoch: 21, Train Loss: 0.3297048412090124, Valid Loss: 0.31396534768017853, Accuracy: 0.85\n"
          ],
          "name": "stdout"
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        {
          "output_type": "stream",
          "text": [
            "Epoch: 22, Train Loss: 0.3292164476804955, Valid Loss: 0.31713676994497125, Accuracy: 0.85\n"
          ],
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          "text": [
            "Epoch: 23, Train Loss: 0.33147115693535917, Valid Loss: 0.3105173707008362, Accuracy: 0.86\n"
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        {
          "output_type": "stream",
          "text": [
            "Epoch: 24, Train Loss: 0.3264614073343055, Valid Loss: 0.3114251467314633, Accuracy: 0.85\n"
          ],
          "name": "stdout"
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        {
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        },
        {
          "output_type": "stream",
          "text": [
            "Epoch: 25, Train Loss: 0.3252730265606281, Valid Loss: 0.31095298853787506, Accuracy: 0.85\n"
          ],
          "name": "stdout"
        },
        {
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          "metadata": {
            "tags": []
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        },
        {
          "output_type": "stream",
          "text": [
            "Epoch: 26, Train Loss: 0.32373936924823493, Valid Loss: 0.31093217026103626, Accuracy: 0.85\n"
          ],
          "name": "stdout"
        },
        {
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          "metadata": {
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        },
        {
          "output_type": "stream",
          "text": [
            "Epoch: 27, Train Loss: 0.322884086952653, Valid Loss: 0.3106734563003887, Accuracy: 0.85\n"
          ],
          "name": "stdout"
        },
        {
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          },
          "metadata": {
            "tags": []
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        },
        {
          "output_type": "stream",
          "text": [
            "Epoch: 28, Train Loss: 0.32604894873707796, Valid Loss: 0.3112430057742379, Accuracy: 0.85\n"
          ],
          "name": "stdout"
        },
        {
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          "data": {
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              "HBox(children=(FloatProgress(value=0.0, max=43.0), HTML(value='')))"
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          },
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        {
          "output_type": "stream",
          "text": [
            "Epoch: 29, Train Loss: 0.32115016219227815, Valid Loss: 0.31089692495085974, Accuracy: 0.85\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4vyZYgF-k_MM"
      },
      "source": [
        "# Training with fastai"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bHwyZcgnsvBa"
      },
      "source": [
        "#the following was taken from the awesome fastai blog - [walkwithfastai](https://walkwithfastai.com/tab.clas.binary)\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FqWygR7LsHfi"
      },
      "source": [
        "df = pd.read_csv(base_path/'adult_sample/adult.csv') "
      ],
      "execution_count": 98,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1MfgqTg3lAxA"
      },
      "source": [
        "cat_names = ['workclass', 'education', 'marital-status', 'occupation', 'relationship', 'race', 'sex', 'native-country']\n",
        "cont_names = ['age', 'fnlwgt', 'education-num', 'capital-gain', 'capital-loss', 'hours-per-week']\n",
        "target_col = 'salary'\n",
        "procs = [Categorify, FillMissing, Normalize]\n",
        "y_block = CategoryBlock()\n",
        "splits = RandomSplitter()(range_of(df))"
      ],
      "execution_count": 99,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vXzECJvald8N"
      },
      "source": [
        "to = TabularPandas(df, procs=procs, cat_names=cat_names, cont_names=cont_names,\n",
        "                   y_names=target_col, y_block=y_block, splits=splits)"
      ],
      "execution_count": 100,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q31LAcF9mYV2"
      },
      "source": [
        "trn_dl = TabDataLoader(to.train, bs=256, shuffle=True, drop_last=True)\n",
        "val_dl = TabDataLoader(to.valid, bs=512)\n",
        "dls = DataLoaders(trn_dl, val_dl)"
      ],
      "execution_count": 101,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "35d7ry_-mdDN",
        "outputId": "1fc8d7fb-e376-41f6-8bb9-8a04ab2fcdc7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 678
        }
      },
      "source": [
        "dls.show_batch()"
      ],
      "execution_count": 102,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>workclass</th>\n",
              "      <th>education</th>\n",
              "      <th>marital-status</th>\n",
              "      <th>occupation</th>\n",
              "      <th>relationship</th>\n",
              "      <th>race</th>\n",
              "      <th>sex</th>\n",
              "      <th>native-country</th>\n",
              "      <th>education-num_na</th>\n",
              "      <th>age</th>\n",
              "      <th>fnlwgt</th>\n",
              "      <th>education-num</th>\n",
              "      <th>capital-gain</th>\n",
              "      <th>capital-loss</th>\n",
              "      <th>hours-per-week</th>\n",
              "      <th>salary</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Private</td>\n",
              "      <td>Masters</td>\n",
              "      <td>Married-civ-spouse</td>\n",
              "      <td>Exec-managerial</td>\n",
              "      <td>Husband</td>\n",
              "      <td>White</td>\n",
              "      <td>Male</td>\n",
              "      <td>Japan</td>\n",
              "      <td>False</td>\n",
              "      <td>47.000000</td>\n",
              "      <td>586657.000414</td>\n",
              "      <td>14.0</td>\n",
              "      <td>-0.000004</td>\n",
              "      <td>-4.371089e-07</td>\n",
              "      <td>40.0</td>\n",
              "      <td>&gt;=50k</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Local-gov</td>\n",
              "      <td>7th-8th</td>\n",
              "      <td>Married-spouse-absent</td>\n",
              "      <td>Other-service</td>\n",
              "      <td>Not-in-family</td>\n",
              "      <td>White</td>\n",
              "      <td>Female</td>\n",
              "      <td>Puerto-Rico</td>\n",
              "      <td>False</td>\n",
              "      <td>42.000000</td>\n",
              "      <td>193537.000186</td>\n",
              "      <td>4.0</td>\n",
              "      <td>-0.000004</td>\n",
              "      <td>-4.371089e-07</td>\n",
              "      <td>35.0</td>\n",
              "      <td>&lt;50k</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Private</td>\n",
              "      <td>HS-grad</td>\n",
              "      <td>Married-civ-spouse</td>\n",
              "      <td>Transport-moving</td>\n",
              "      <td>Husband</td>\n",
              "      <td>White</td>\n",
              "      <td>Male</td>\n",
              "      <td>United-States</td>\n",
              "      <td>False</td>\n",
              "      <td>61.000000</td>\n",
              "      <td>198078.000308</td>\n",
              "      <td>9.0</td>\n",
              "      <td>-0.000004</td>\n",
              "      <td>-4.371089e-07</td>\n",
              "      <td>40.0</td>\n",
              "      <td>&gt;=50k</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Local-gov</td>\n",
              "      <td>Bachelors</td>\n",
              "      <td>Divorced</td>\n",
              "      <td>Prof-specialty</td>\n",
              "      <td>Unmarried</td>\n",
              "      <td>Black</td>\n",
              "      <td>Female</td>\n",
              "      <td>United-States</td>\n",
              "      <td>False</td>\n",
              "      <td>43.000000</td>\n",
              "      <td>598995.001651</td>\n",
              "      <td>13.0</td>\n",
              "      <td>-0.000004</td>\n",
              "      <td>-4.371089e-07</td>\n",
              "      <td>42.0</td>\n",
              "      <td>&lt;50k</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Private</td>\n",
              "      <td>Bachelors</td>\n",
              "      <td>Never-married</td>\n",
              "      <td>Machine-op-inspct</td>\n",
              "      <td>Not-in-family</td>\n",
              "      <td>White</td>\n",
              "      <td>Male</td>\n",
              "      <td>United-States</td>\n",
              "      <td>False</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>203709.999886</td>\n",
              "      <td>13.0</td>\n",
              "      <td>-0.000004</td>\n",
              "      <td>-4.371089e-07</td>\n",
              "      <td>40.0</td>\n",
              "      <td>&lt;50k</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>State-gov</td>\n",
              "      <td>Bachelors</td>\n",
              "      <td>Married-civ-spouse</td>\n",
              "      <td>Exec-managerial</td>\n",
              "      <td>Husband</td>\n",
              "      <td>White</td>\n",
              "      <td>Male</td>\n",
              "      <td>United-States</td>\n",
              "      <td>False</td>\n",
              "      <td>43.000000</td>\n",
              "      <td>139734.001264</td>\n",
              "      <td>13.0</td>\n",
              "      <td>15023.999754</td>\n",
              "      <td>-4.371089e-07</td>\n",
              "      <td>40.0</td>\n",
              "      <td>&gt;=50k</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Private</td>\n",
              "      <td>11th</td>\n",
              "      <td>Married-civ-spouse</td>\n",
              "      <td>Craft-repair</td>\n",
              "      <td>Husband</td>\n",
              "      <td>White</td>\n",
              "      <td>Male</td>\n",
              "      <td>United-States</td>\n",
              "      <td>False</td>\n",
              "      <td>22.000001</td>\n",
              "      <td>369386.999666</td>\n",
              "      <td>7.0</td>\n",
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              "      <td>-4.371089e-07</td>\n",
              "      <td>35.0</td>\n",
              "      <td>&lt;50k</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Local-gov</td>\n",
              "      <td>Masters</td>\n",
              "      <td>Never-married</td>\n",
              "      <td>Prof-specialty</td>\n",
              "      <td>Not-in-family</td>\n",
              "      <td>White</td>\n",
              "      <td>Female</td>\n",
              "      <td>United-States</td>\n",
              "      <td>False</td>\n",
              "      <td>33.000000</td>\n",
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              "      <td>14.0</td>\n",
              "      <td>-0.000004</td>\n",
              "      <td>-4.371089e-07</td>\n",
              "      <td>50.0</td>\n",
              "      <td>&lt;50k</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Private</td>\n",
              "      <td>HS-grad</td>\n",
              "      <td>Divorced</td>\n",
              "      <td>Tech-support</td>\n",
              "      <td>Own-child</td>\n",
              "      <td>White</td>\n",
              "      <td>Male</td>\n",
              "      <td>United-States</td>\n",
              "      <td>False</td>\n",
              "      <td>37.000000</td>\n",
              "      <td>206698.999482</td>\n",
              "      <td>9.0</td>\n",
              "      <td>-0.000004</td>\n",
              "      <td>-4.371089e-07</td>\n",
              "      <td>45.0</td>\n",
              "      <td>&lt;50k</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Private</td>\n",
              "      <td>1st-4th</td>\n",
              "      <td>Married-civ-spouse</td>\n",
              "      <td>Handlers-cleaners</td>\n",
              "      <td>Husband</td>\n",
              "      <td>White</td>\n",
              "      <td>Male</td>\n",
              "      <td>Mexico</td>\n",
              "      <td>False</td>\n",
              "      <td>33.000000</td>\n",
              "      <td>231825.999732</td>\n",
              "      <td>2.0</td>\n",
              "      <td>-0.000004</td>\n",
              "      <td>-4.371089e-07</td>\n",
              "      <td>40.0</td>\n",
              "      <td>&lt;50k</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8Wnpacdum4qM",
        "outputId": "d5336460-5cfc-4232-ff04-4a85f85cd761",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "emb_szs = get_emb_sz(to); emb_szs"
      ],
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[(10, 6), (17, 8), (8, 5), (16, 8), (7, 5), (6, 4), (3, 3), (43, 13), (3, 3)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 103
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t-PwqJdbnD3U",
        "outputId": "3e175392-688e-4b9e-b47f-679fd70aa484",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "to.cat_names\n"
      ],
      "execution_count": 104,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(#9) ['workclass','education','marital-status','occupation','relationship','race','sex','native-country','education-num_na']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 104
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PTlvEu6anGFH",
        "outputId": "0ba2618a-9a42-4288-ac95-b853e17ba760",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "to['workclass'].nunique()"
      ],
      "execution_count": 105,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "9"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 105
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ymii1cjAnIYa",
        "outputId": "ea4fc889-2a52-48f2-bade-c251164f2e55",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "cont_len = len(to.cont_names); cont_len"
      ],
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "6"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 106
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q9z98q2BnMIR",
        "outputId": "6d3ca526-8fd0-4dc8-b400-43203abc5b30",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "net = TabularModel(emb_szs, cont_len, 2, [200,100]); net"
      ],
      "execution_count": 107,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TabularModel(\n",
              "  (embeds): ModuleList(\n",
              "    (0): Embedding(10, 6)\n",
              "    (1): Embedding(17, 8)\n",
              "    (2): Embedding(8, 5)\n",
              "    (3): Embedding(16, 8)\n",
              "    (4): Embedding(7, 5)\n",
              "    (5): Embedding(6, 4)\n",
              "    (6): Embedding(3, 3)\n",
              "    (7): Embedding(43, 13)\n",
              "    (8): Embedding(3, 3)\n",
              "  )\n",
              "  (emb_drop): Dropout(p=0.0, inplace=False)\n",
              "  (bn_cont): BatchNorm1d(6, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "  (layers): Sequential(\n",
              "    (0): LinBnDrop(\n",
              "      (0): BatchNorm1d(61, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (1): Linear(in_features=61, out_features=200, bias=False)\n",
              "      (2): ReLU(inplace=True)\n",
              "    )\n",
              "    (1): LinBnDrop(\n",
              "      (0): BatchNorm1d(200, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (1): Linear(in_features=200, out_features=100, bias=False)\n",
              "      (2): ReLU(inplace=True)\n",
              "    )\n",
              "    (2): LinBnDrop(\n",
              "      (0): Linear(in_features=100, out_features=2, bias=True)\n",
              "    )\n",
              "  )\n",
              ")"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 107
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jO8nL1ejnTP0"
      },
      "source": [
        "learn = tabular_learner(dls, [200,100], metrics=accuracy)"
      ],
      "execution_count": 108,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aVzEAlkWnWvm",
        "outputId": "50cb5bab-395a-4371-8a4a-ad272d077430",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        }
      },
      "source": [
        "learn.lr_find()\n"
      ],
      "execution_count": 109,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              ""
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SuggestedLRs(lr_min=0.006918309628963471, lr_steep=0.00363078061491251)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 109
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vOatvVgindXt",
        "outputId": "b9b49b8e-5c46-4ee1-9bc6-0348055165f6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 959
        }
      },
      "source": [
        "learn.fit(epochs, 5e-2)"
      ],
      "execution_count": 110,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>epoch</th>\n",
              "      <th>train_loss</th>\n",
              "      <th>valid_loss</th>\n",
              "      <th>accuracy</th>\n",
              "      <th>time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>0.361784</td>\n",
              "      <td>0.335643</td>\n",
              "      <td>0.845977</td>\n",
              "      <td>00:01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.337509</td>\n",
              "      <td>0.323809</td>\n",
              "      <td>0.849969</td>\n",
              "      <td>00:01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.324518</td>\n",
              "      <td>0.322916</td>\n",
              "      <td>0.849355</td>\n",
              "      <td>00:01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.325411</td>\n",
              "      <td>0.321469</td>\n",
              "      <td>0.851966</td>\n",
              "      <td>00:01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>0.329693</td>\n",
              "      <td>0.325846</td>\n",
              "      <td>0.853194</td>\n",
              "      <td>00:01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>0.327428</td>\n",
              "      <td>0.370970</td>\n",
              "      <td>0.831388</td>\n",
              "      <td>00:01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>0.329880</td>\n",
              "      <td>0.349526</td>\n",
              "      <td>0.825860</td>\n",
              "      <td>00:01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>0.333474</td>\n",
              "      <td>0.341952</td>\n",
              "      <td>0.840448</td>\n",
              "      <td>00:01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>0.323300</td>\n",
              "      <td>0.344048</td>\n",
              "      <td>0.853194</td>\n",
              "      <td>00:01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>0.323360</td>\n",
              "      <td>0.324903</td>\n",
              "      <td>0.846898</td>\n",
              "      <td>00:02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>10</td>\n",
              "      <td>0.320458</td>\n",
              "      <td>0.340820</td>\n",
              "      <td>0.851198</td>\n",
              "      <td>00:01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>11</td>\n",
              "      <td>0.321513</td>\n",
              "      <td>0.323982</td>\n",
              "      <td>0.852426</td>\n",
              "      <td>00:01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>12</td>\n",
              "      <td>0.323084</td>\n",
              "      <td>0.418396</td>\n",
              "      <td>0.811579</td>\n",
              "      <td>00:01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>13</td>\n",
              "      <td>0.319306</td>\n",
              "      <td>0.328809</td>\n",
              "      <td>0.846284</td>\n",
              "      <td>00:01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>14</td>\n",
              "      <td>0.318252</td>\n",
              "      <td>0.332159</td>\n",
              "      <td>0.843059</td>\n",
              "      <td>00:01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>15</td>\n",
              "      <td>0.318714</td>\n",
              "      <td>0.321231</td>\n",
              "      <td>0.852119</td>\n",
              "      <td>00:01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>16</td>\n",
              "      <td>0.313041</td>\n",
              "      <td>0.339119</td>\n",
              "      <td>0.840141</td>\n",
              "      <td>00:01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>17</td>\n",
              "      <td>0.317149</td>\n",
              "      <td>0.322545</td>\n",
              "      <td>0.850430</td>\n",
              "      <td>00:01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>18</td>\n",
              "      <td>0.313669</td>\n",
              "      <td>0.325480</td>\n",
              "      <td>0.846591</td>\n",
              "      <td>00:01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>19</td>\n",
              "      <td>0.310896</td>\n",
              "      <td>0.328661</td>\n",
              "      <td>0.844134</td>\n",
              "      <td>00:01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>20</td>\n",
              "      <td>0.321504</td>\n",
              "      <td>0.322642</td>\n",
              "      <td>0.851351</td>\n",
              "      <td>00:01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>21</td>\n",
              "      <td>0.317414</td>\n",
              "      <td>0.337861</td>\n",
              "      <td>0.843520</td>\n",
              "      <td>00:01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>22</td>\n",
              "      <td>0.320331</td>\n",
              "      <td>0.331618</td>\n",
              "      <td>0.848280</td>\n",
              "      <td>00:01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>23</td>\n",
              "      <td>0.314488</td>\n",
              "      <td>0.356977</td>\n",
              "      <td>0.831542</td>\n",
              "      <td>00:02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>24</td>\n",
              "      <td>0.319688</td>\n",
              "      <td>0.348385</td>\n",
              "      <td>0.849048</td>\n",
              "      <td>00:01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>25</td>\n",
              "      <td>0.320428</td>\n",
              "      <td>0.327965</td>\n",
              "      <td>0.851658</td>\n",
              "      <td>00:01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>26</td>\n",
              "      <td>0.316824</td>\n",
              "      <td>0.345128</td>\n",
              "      <td>0.849662</td>\n",
              "      <td>00:01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>27</td>\n",
              "      <td>0.310488</td>\n",
              "      <td>0.338842</td>\n",
              "      <td>0.841677</td>\n",
              "      <td>00:01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>28</td>\n",
              "      <td>0.316684</td>\n",
              "      <td>0.327561</td>\n",
              "      <td>0.850584</td>\n",
              "      <td>00:01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>29</td>\n",
              "      <td>0.311111</td>\n",
              "      <td>0.330459</td>\n",
              "      <td>0.852887</td>\n",
              "      <td>00:01</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eSRTfnKJ0vz8"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}