MultiClassPyTorch_Wine_Dataset.ipynb

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        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import tqdm\n",
        "\n",
        "import copy\n",
        "\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import confusion_matrix\n",
        "\n",
        "from sklearn.preprocessing import OneHotEncoder\n",
        "\n",
        "from torch.optim.lr_scheduler import StepLR\n",
        "from torch.utils.data import DataLoader, TensorDataset"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install ucimlrepo\n",
        "\n",
        "from ucimlrepo import fetch_ucirepo\n",
        "\n",
        "# fetch dataset\n",
        "wine = fetch_ucirepo(id=109)\n",
        "\n",
        "# data (as pandas dataframes)\n",
        "X = wine.data.features\n",
        "y = wine.data.targets\n",
        "\n",
        "# metadata\n",
        "print(wine.metadata)\n",
        "\n",
        "# variable information\n",
        "print(wine.variables)"
      ],
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          "text": [
            "Requirement already satisfied: ucimlrepo in /usr/local/lib/python3.10/dist-packages (0.0.3)\n",
            "{'uci_id': 109, 'name': 'Wine', 'repository_url': 'https://archive.ics.uci.edu/dataset/109/wine', 'data_url': 'https://archive.ics.uci.edu/static/public/109/data.csv', 'abstract': 'Using chemical analysis to determine the origin of wines', 'area': 'Physics and Chemistry', 'tasks': ['Classification'], 'characteristics': ['Tabular'], 'num_instances': 178, 'num_features': 13, 'feature_types': ['Integer', 'Real'], 'demographics': [], 'target_col': ['class'], 'index_col': None, 'has_missing_values': 'no', 'missing_values_symbol': None, 'year_of_dataset_creation': 1992, 'last_updated': 'Mon Aug 28 2023', 'dataset_doi': '10.24432/C5PC7J', 'creators': ['Stefan Aeberhard', 'M. Forina'], 'intro_paper': {'title': 'Comparative analysis of statistical pattern recognition methods in high dimensional settings', 'authors': 'S. Aeberhard, D. Coomans, O. Vel', 'published_in': 'Pattern Recognition', 'year': 1994, 'url': 'https://www.semanticscholar.org/paper/83dc3e4030d7b9fbdbb4bde03ce12ab70ca10528', 'doi': '10.1016/0031-3203(94)90145-7'}, 'additional_info': {'summary': 'These data are the results of a chemical analysis of wines grown in the same region in Italy but derived from three different cultivars. The analysis determined the quantities of 13 constituents found in each of the three types of wines. \\r\\n\\r\\nI think that the initial data set had around 30 variables, but for some reason I only have the 13 dimensional version. I had a list of what the 30 or so variables were, but a.)  I lost it, and b.), I would not know which 13 variables are included in the set.\\r\\n\\r\\nThe attributes are (dontated by Riccardo Leardi, riclea@anchem.unige.it )\\r\\n1) Alcohol\\r\\n2) Malic acid\\r\\n3) Ash\\r\\n4) Alcalinity of ash  \\r\\n5) Magnesium\\r\\n6) Total phenols\\r\\n7) Flavanoids\\r\\n8) Nonflavanoid phenols\\r\\n9) Proanthocyanins\\r\\n10)Color intensity\\r\\n11)Hue\\r\\n12)OD280/OD315 of diluted wines\\r\\n13)Proline \\r\\n\\r\\nIn a classification context, this is a well posed problem with \"well behaved\" class structures. A good data set for first testing of a new classifier, but not very challenging.           ', 'purpose': 'test', 'funded_by': None, 'instances_represent': None, 'recommended_data_splits': None, 'sensitive_data': None, 'preprocessing_description': None, 'variable_info': 'All attributes are continuous\\r\\n\\t\\r\\nNo statistics available, but suggest to standardise variables for certain uses (e.g. for us with classifiers which are NOT scale invariant)\\r\\n\\r\\nNOTE: 1st attribute is class identifier (1-3)', 'citation': None}}\n",
            "                            name     role         type demographic  \\\n",
            "0                          class   Target  Categorical        None   \n",
            "1                        Alcohol  Feature   Continuous        None   \n",
            "2                      Malicacid  Feature   Continuous        None   \n",
            "3                            Ash  Feature   Continuous        None   \n",
            "4              Alcalinity_of_ash  Feature   Continuous        None   \n",
            "5                      Magnesium  Feature      Integer        None   \n",
            "6                  Total_phenols  Feature   Continuous        None   \n",
            "7                     Flavanoids  Feature   Continuous        None   \n",
            "8           Nonflavanoid_phenols  Feature   Continuous        None   \n",
            "9                Proanthocyanins  Feature   Continuous        None   \n",
            "10               Color_intensity  Feature   Continuous        None   \n",
            "11                           Hue  Feature   Continuous        None   \n",
            "12  0D280_0D315_of_diluted_wines  Feature   Continuous        None   \n",
            "13                       Proline  Feature      Integer        None   \n",
            "\n",
            "   description units missing_values  \n",
            "0         None  None             no  \n",
            "1         None  None             no  \n",
            "2         None  None             no  \n",
            "3         None  None             no  \n",
            "4         None  None             no  \n",
            "5         None  None             no  \n",
            "6         None  None             no  \n",
            "7         None  None             no  \n",
            "8         None  None             no  \n",
            "9         None  None             no  \n",
            "10        None  None             no  \n",
            "11        None  None             no  \n",
            "12        None  None             no  \n",
            "13        None  None             no  \n"
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        "ohe = OneHotEncoder(handle_unknown='ignore', sparse_output=False).fit(y)\n",
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              "   Alcohol  Malicacid   Ash  Alcalinity_of_ash  Magnesium  Total_phenols  \\\n",
              "0    14.23       1.71  2.43               15.6        127           2.80   \n",
              "1    13.20       1.78  2.14               11.2        100           2.65   \n",
              "2    13.16       2.36  2.67               18.6        101           2.80   \n",
              "3    14.37       1.95  2.50               16.8        113           3.85   \n",
              "4    13.24       2.59  2.87               21.0        118           2.80   \n",
              "\n",
              "   Flavanoids  Nonflavanoid_phenols  Proanthocyanins  Color_intensity   Hue  \\\n",
              "0        3.06                  0.28             2.29             5.64  1.04   \n",
              "1        2.76                  0.26             1.28             4.38  1.05   \n",
              "2        3.24                  0.30             2.81             5.68  1.03   \n",
              "3        3.49                  0.24             2.18             7.80  0.86   \n",
              "4        2.69                  0.39             1.82             4.32  1.04   \n",
              "\n",
              "   0D280_0D315_of_diluted_wines  Proline  \n",
              "0                          3.92     1065  \n",
              "1                          3.40     1050  \n",
              "2                          3.17     1185  \n",
              "3                          3.45     1480  \n",
              "4                          2.93      735  "
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              "      <th>Alcalinity_of_ash</th>\n",
              "      <th>Magnesium</th>\n",
              "      <th>Total_phenols</th>\n",
              "      <th>Flavanoids</th>\n",
              "      <th>Nonflavanoid_phenols</th>\n",
              "      <th>Proanthocyanins</th>\n",
              "      <th>Color_intensity</th>\n",
              "      <th>Hue</th>\n",
              "      <th>0D280_0D315_of_diluted_wines</th>\n",
              "      <th>Proline</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>14.23</td>\n",
              "      <td>1.71</td>\n",
              "      <td>2.43</td>\n",
              "      <td>15.6</td>\n",
              "      <td>127</td>\n",
              "      <td>2.80</td>\n",
              "      <td>3.06</td>\n",
              "      <td>0.28</td>\n",
              "      <td>2.29</td>\n",
              "      <td>5.64</td>\n",
              "      <td>1.04</td>\n",
              "      <td>3.92</td>\n",
              "      <td>1065</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>13.20</td>\n",
              "      <td>1.78</td>\n",
              "      <td>2.14</td>\n",
              "      <td>11.2</td>\n",
              "      <td>100</td>\n",
              "      <td>2.65</td>\n",
              "      <td>2.76</td>\n",
              "      <td>0.26</td>\n",
              "      <td>1.28</td>\n",
              "      <td>4.38</td>\n",
              "      <td>1.05</td>\n",
              "      <td>3.40</td>\n",
              "      <td>1050</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>13.16</td>\n",
              "      <td>2.36</td>\n",
              "      <td>2.67</td>\n",
              "      <td>18.6</td>\n",
              "      <td>101</td>\n",
              "      <td>2.80</td>\n",
              "      <td>3.24</td>\n",
              "      <td>0.30</td>\n",
              "      <td>2.81</td>\n",
              "      <td>5.68</td>\n",
              "      <td>1.03</td>\n",
              "      <td>3.17</td>\n",
              "      <td>1185</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>14.37</td>\n",
              "      <td>1.95</td>\n",
              "      <td>2.50</td>\n",
              "      <td>16.8</td>\n",
              "      <td>113</td>\n",
              "      <td>3.85</td>\n",
              "      <td>3.49</td>\n",
              "      <td>0.24</td>\n",
              "      <td>2.18</td>\n",
              "      <td>7.80</td>\n",
              "      <td>0.86</td>\n",
              "      <td>3.45</td>\n",
              "      <td>1480</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>13.24</td>\n",
              "      <td>2.59</td>\n",
              "      <td>2.87</td>\n",
              "      <td>21.0</td>\n",
              "      <td>118</td>\n",
              "      <td>2.80</td>\n",
              "      <td>2.69</td>\n",
              "      <td>0.39</td>\n",
              "      <td>1.82</td>\n",
              "      <td>4.32</td>\n",
              "      <td>1.04</td>\n",
              "      <td>2.93</td>\n",
              "      <td>735</td>\n",
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        "y = torch.tensor(y, dtype=torch.float32)"
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      "execution_count": 44,
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            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# splitting to form train and test sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.7, shuffle=True)\n",
        "input_size = X_train.shape[1]  # Assuming X_train is a DataFrame\n",
        "output_size = y_train.shape[1]  # Assuming one-hot encoded labels"
      ],
      "metadata": {
        "id": "qaU7Mznfy2yH"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class Multiclass(nn.Module):\n",
        "    def __init__(self):\n",
        "        super().__init__()\n",
        "        self.hidden = nn.Linear(input_size, 20)\n",
        "        self.act = nn.ReLU()\n",
        "        self.output = nn.Linear(20, output_size)\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = self.act(self.hidden(x))\n",
        "        x = self.output(x)\n",
        "        return x"
      ],
      "metadata": {
        "id": "GVwBq55Yy6k3"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# initializing the loss metric and optimizer\n",
        "\n",
        "model = Multiclass()\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "optimizer = optim.Adam(model.parameters(), lr=0.001)"
      ],
      "metadata": {
        "id": "1NEPP41IzXXS"
      },
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# preparing the  model and training parameters\n",
        "num_epochs = 300\n",
        "batch_size = 5\n",
        "batches_per_epoch = len(X_train) // batch_size"
      ],
      "metadata": {
        "id": "pNvG2KPJzZ_k"
      },
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy_highest = - np.inf  #intialized to negative infinity\n",
        "weights_best = None\n",
        "history_of_training_loss = []\n",
        "history_accuracy_of_training = []\n",
        "history_of_test_loss = []\n",
        "history_accuracy_of_test = []"
      ],
      "metadata": {
        "id": "h1_igbpazhMw"
      },
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# training loop\n",
        "for epoch in range(num_epochs):\n",
        "    loss_in_epoch = []\n",
        "    accuracy_in_epoch = []\n",
        "    # set model in training mode and run through each batch\n",
        "    model.train()\n",
        "    with tqdm.trange(batches_per_epoch, unit=\"batch\", mininterval=0) as bar:\n",
        "        bar.set_description(f\"No. {epoch} Epoch\")\n",
        "        for i in bar:\n",
        "            # take a batch\n",
        "            start = i * batch_size\n",
        "            X_batch = X_train[start:start+batch_size]\n",
        "            y_batch = y_train[start:start+batch_size]\n",
        "            # model carries out forward pass\n",
        "            y_pred = model(X_batch)\n",
        "            loss = criterion(y_pred, y_batch) # we determine the cross entropy loss\n",
        "\n",
        "            # model now does backward pass\n",
        "            optimizer.zero_grad()\n",
        "            loss.backward()\n",
        "            # updating weights of the model\n",
        "            optimizer.step()\n",
        "\n",
        "            # computing and storing metrics from this batch\n",
        "            accuracy = (torch.argmax(y_pred, 1) == torch.argmax(y_batch, 1)).float().mean()\n",
        "\n",
        "            #all batch losses in the epoch are stored\n",
        "            loss_in_epoch.append(float(loss))\n",
        "\n",
        "            #all batch accuracies in the epoch are stored\n",
        "            accuracy_in_epoch.append(float(accuracy))\n",
        "\n",
        "            bar.set_postfix(\n",
        "                loss=float(loss),\n",
        "                accuracy=float(accuracy)\n",
        "            )\n",
        "\n",
        "    #Evaluating the model on the test set\n",
        "    model.eval()\n",
        "\n",
        "    with torch.no_grad(): #gradient tracking is turned off\n",
        "        y_pred = model(X_test.float()) # generating the results when hte model is used on the test set\n",
        "        # we comepare the model test results (y_pred) with the actual values (y_test)\n",
        "        accuracy=(torch.argmax(y_pred, 1) == torch.argmax(y_test, 1)).float().mean() #accuracy = correct / total\n",
        "        accuracy=float(accuracy)\n",
        "        loss = criterion(y_pred, torch.max(y_test, 1)[1])\n",
        "        # calculatinfg the cross entropy loss\n",
        "        #criterion is the loss function criterion = nn.CrossEntropyLoss()\n",
        "\n",
        "        history_of_training_loss.append(np.mean(loss_in_epoch))\n",
        "        history_accuracy_of_training.append(np.mean(accuracy_in_epoch))\n",
        "        history_of_test_loss.append(loss)\n",
        "        history_accuracy_of_test.append(accuracy)\n",
        "\n",
        "        if accuracy>accuracy_highest:\n",
        "            # will store highest accuracy reocrded in any of the epochs\n",
        "            accuracy_highest=accuracy\n",
        "            #now we will store the weights of the best model to the weights_best variable\n",
        "            weights_best=copy.deepcopy(model.state_dict())\n",
        "        print(f\"Epoch No. {epoch} validation: Cross-entropy loss={loss:.2f}, Accuracy={accuracy*100:.1f}%\")\n",
        "\n",
        "          # Check if epoch is greater than 20 and accuracy is decreasing\n",
        "        if epoch > 20:\n",
        "            last_15_accuracies = history_accuracy_of_test[-15:]\n",
        "            if accuracy <= min(last_15_accuracies):\n",
        "                print(f\"Early stopping at epoch {epoch} due to decreasing accuracy.\")\n",
        "                break\n",
        "\n",
        "# Restoring best model using the best weights identified during evaluation\n",
        "model.load_state_dict(weights_best)\n",
        "\n",
        "# Plot the loss and accuracy\n",
        "plt.plot(history_of_training_loss, label=\"train\")\n",
        "plt.plot(history_of_test_loss, label=\"test\")\n",
        "plt.xlabel(\"epochs\")\n",
        "plt.ylabel(\"cross entropy\")\n",
        "plt.legend()\n",
        "plt.show()\n",
        "\n",
        "plt.plot(history_accuracy_of_training, label=\"train\")\n",
        "plt.plot(history_accuracy_of_test, label=\"test\")\n",
        "plt.xlabel(\"epochs\")\n",
        "plt.ylabel(\"accuracy\")\n",
        "plt.legend()\n",
        "plt.show()\n",
        "\n",
        "\n",
        "model.eval()\n",
        "with torch.no_grad():\n",
        "    test_outputs = model(X_test.float())\n",
        "    predicted = torch.argmax(test_outputs, dim=1)\n",
        "    y_true = torch.max(y_test, 1)[1]\n",
        "    # Computing and printing the confusion matrix\n",
        "    cm = confusion_matrix(y_true, predicted)\n",
        "    print(\"Confusion Matrix:\")\n",
        "    print(cm)\n",
        "\n",
        "    # Visualizing the confusion matrix\n",
        "    plt.figure(figsize=(10, 8))\n",
        "    sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Blues\", xticklabels=range(output_size), yticklabels=range(output_size))\n",
        "    plt.xlabel(\"Predicted\")\n",
        "    plt.ylabel(\"True\")\n",
        "    plt.title(\"Confusion Matrix\")\n",
        "    plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "L-nmtz2vzjdP",
        "outputId": "11c46359-f3f6-4f14-eb33-b62ba0e548cd"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "No. 0 Epoch: 100%|██████████| 24/24 [00:00<00:00, 64.89batch/s, accuracy=0.6, loss=4.11]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch No. 0 validation: Cross-entropy loss=11.46, Accuracy=24.1%\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "No. 1 Epoch: 100%|██████████| 24/24 [00:00<00:00, 98.20batch/s, accuracy=0, loss=2.51] \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch No. 1 validation: Cross-entropy loss=2.03, Accuracy=35.2%\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "No. 2 Epoch: 100%|██████████| 24/24 [00:00<00:00, 118.70batch/s, accuracy=0.2, loss=1.13]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch No. 2 validation: Cross-entropy loss=0.80, Accuracy=57.4%\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "No. 3 Epoch: 100%|██████████| 24/24 [00:00<00:00, 103.27batch/s, accuracy=0.2, loss=1.14]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch No. 3 validation: Cross-entropy loss=0.61, Accuracy=68.5%\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "No. 4 Epoch: 100%|██████████| 24/24 [00:00<00:00, 67.11batch/s, accuracy=0.2, loss=1.09]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch No. 4 validation: Cross-entropy loss=0.56, Accuracy=85.2%\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "No. 5 Epoch: 100%|██████████| 24/24 [00:00<00:00, 68.18batch/s, accuracy=0.2, loss=1.06]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch No. 5 validation: Cross-entropy loss=0.53, Accuracy=83.3%\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "No. 6 Epoch: 100%|██████████| 24/24 [00:00<00:00, 125.19batch/s, accuracy=0.2, loss=1.06]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch No. 6 validation: Cross-entropy loss=0.51, Accuracy=87.0%\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "No. 7 Epoch: 100%|██████████| 24/24 [00:00<00:00, 135.95batch/s, accuracy=0.2, loss=1.05]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch No. 7 validation: Cross-entropy loss=0.49, Accuracy=90.7%\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "No. 8 Epoch: 100%|██████████| 24/24 [00:00<00:00, 39.96batch/s, accuracy=0.2, loss=1.03]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch No. 8 validation: Cross-entropy loss=0.47, Accuracy=90.7%\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "No. 9 Epoch: 100%|██████████| 24/24 [00:00<00:00, 80.32batch/s, accuracy=0.4, loss=1.01]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch No. 9 validation: Cross-entropy loss=0.46, Accuracy=90.7%\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "No. 10 Epoch: 100%|██████████| 24/24 [00:00<00:00, 109.14batch/s, accuracy=0.4, loss=0.983]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch No. 10 validation: Cross-entropy loss=0.44, Accuracy=90.7%\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "No. 11 Epoch: 100%|██████████| 24/24 [00:00<00:00, 59.17batch/s, accuracy=0.4, loss=0.961]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch No. 11 validation: Cross-entropy loss=0.43, Accuracy=88.9%\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "No. 12 Epoch: 100%|██████████| 24/24 [00:00<00:00, 62.88batch/s, accuracy=0.4, loss=0.937]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch No. 12 validation: Cross-entropy loss=0.41, Accuracy=90.7%\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "No. 13 Epoch: 100%|██████████| 24/24 [00:00<00:00, 66.98batch/s, accuracy=0.4, loss=0.912]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch No. 13 validation: Cross-entropy loss=0.40, Accuracy=90.7%\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "No. 14 Epoch: 100%|██████████| 24/24 [00:00<00:00, 77.82batch/s, accuracy=0.4, loss=0.876]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch No. 14 validation: Cross-entropy loss=0.39, Accuracy=92.6%\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "No. 15 Epoch: 100%|██████████| 24/24 [00:00<00:00, 55.71batch/s, accuracy=0.4, loss=0.859]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch No. 15 validation: Cross-entropy loss=0.38, Accuracy=92.6%\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "No. 16 Epoch: 100%|██████████| 24/24 [00:00<00:00, 72.11batch/s, accuracy=0.4, loss=0.848]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch No. 16 validation: Cross-entropy loss=0.37, Accuracy=92.6%\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "No. 17 Epoch: 100%|██████████| 24/24 [00:00<00:00, 65.64batch/s, accuracy=0.6, loss=0.81]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch No. 17 validation: Cross-entropy loss=0.36, Accuracy=94.4%\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "No. 18 Epoch: 100%|██████████| 24/24 [00:00<00:00, 49.03batch/s, accuracy=0.8, loss=0.798]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch No. 18 validation: Cross-entropy loss=0.34, Accuracy=94.4%\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "No. 19 Epoch: 100%|██████████| 24/24 [00:00<00:00, 70.88batch/s, accuracy=0.8, loss=0.775]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch No. 19 validation: Cross-entropy loss=0.33, Accuracy=96.3%\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "No. 20 Epoch: 100%|██████████| 24/24 [00:00<00:00, 63.56batch/s, accuracy=0.8, loss=0.759]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch No. 20 validation: Cross-entropy loss=0.32, Accuracy=96.3%\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "No. 21 Epoch: 100%|██████████| 24/24 [00:00<00:00, 47.22batch/s, accuracy=0.8, loss=0.738]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch No. 21 validation: Cross-entropy loss=0.32, Accuracy=96.3%\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "No. 22 Epoch: 100%|██████████| 24/24 [00:00<00:00, 70.20batch/s, accuracy=0.8, loss=0.707]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch No. 22 validation: Cross-entropy loss=0.31, Accuracy=96.3%\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "No. 23 Epoch: 100%|██████████| 24/24 [00:00<00:00, 77.79batch/s, accuracy=0.8, loss=0.697]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch No. 23 validation: Cross-entropy loss=0.30, Accuracy=96.3%\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "No. 24 Epoch: 100%|██████████| 24/24 [00:00<00:00, 72.85batch/s, accuracy=0.8, loss=0.676]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch No. 24 validation: Cross-entropy loss=0.29, Accuracy=96.3%\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "No. 25 Epoch: 100%|██████████| 24/24 [00:00<00:00, 54.07batch/s, accuracy=0.8, loss=0.655]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch No. 25 validation: Cross-entropy loss=0.28, Accuracy=96.3%\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "No. 26 Epoch: 100%|██████████| 24/24 [00:00<00:00, 70.96batch/s, accuracy=0.8, loss=0.647]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch No. 26 validation: Cross-entropy loss=0.28, Accuracy=96.3%\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "No. 27 Epoch: 100%|██████████| 24/24 [00:00<00:00, 74.97batch/s, accuracy=0.8, loss=0.612]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch No. 27 validation: Cross-entropy loss=0.27, Accuracy=96.3%\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "No. 28 Epoch: 100%|██████████| 24/24 [00:00<00:00, 71.31batch/s, accuracy=0.8, loss=0.608]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch No. 28 validation: Cross-entropy loss=0.27, Accuracy=98.1%\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "No. 29 Epoch: 100%|██████████| 24/24 [00:00<00:00, 75.31batch/s, accuracy=0.8, loss=0.586]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch No. 29 validation: Cross-entropy loss=0.26, Accuracy=98.1%\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "No. 30 Epoch: 100%|██████████| 24/24 [00:00<00:00, 59.02batch/s, accuracy=0.8, loss=0.579]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch No. 30 validation: Cross-entropy loss=0.25, Accuracy=98.1%\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "No. 31 Epoch: 100%|██████████| 24/24 [00:00<00:00, 71.40batch/s, accuracy=0.8, loss=0.551]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch No. 31 validation: Cross-entropy loss=0.25, Accuracy=98.1%\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "No. 32 Epoch: 100%|██████████| 24/24 [00:00<00:00, 58.57batch/s, accuracy=0.8, loss=0.541]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch No. 32 validation: Cross-entropy loss=0.24, Accuracy=98.1%\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "No. 33 Epoch: 100%|██████████| 24/24 [00:00<00:00, 71.07batch/s, accuracy=0.8, loss=0.524]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch No. 33 validation: Cross-entropy loss=0.24, Accuracy=98.1%\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "No. 34 Epoch: 100%|██████████| 24/24 [00:00<00:00, 66.48batch/s, accuracy=0.8, loss=0.511]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch No. 34 validation: Cross-entropy loss=0.23, Accuracy=98.1%\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "No. 35 Epoch: 100%|██████████| 24/24 [00:00<00:00, 82.95batch/s, accuracy=0.8, loss=0.498]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch No. 35 validation: Cross-entropy loss=0.23, Accuracy=98.1%\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "No. 36 Epoch: 100%|██████████| 24/24 [00:00<00:00, 58.77batch/s, accuracy=0.8, loss=0.484]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch No. 36 validation: Cross-entropy loss=0.22, Accuracy=98.1%\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "No. 37 Epoch: 100%|██████████| 24/24 [00:00<00:00, 40.69batch/s, accuracy=0.8, loss=0.472]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch No. 37 validation: Cross-entropy loss=0.22, Accuracy=98.1%\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "No. 38 Epoch: 100%|██████████| 24/24 [00:00<00:00, 73.26batch/s, accuracy=0.8, loss=0.458] \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch No. 38 validation: Cross-entropy loss=0.22, Accuracy=98.1%\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "No. 39 Epoch: 100%|██████████| 24/24 [00:00<00:00, 85.01batch/s, accuracy=0.8, loss=0.455]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch No. 39 validation: Cross-entropy loss=0.21, Accuracy=98.1%\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "No. 40 Epoch: 100%|██████████| 24/24 [00:00<00:00, 78.59batch/s, accuracy=0.8, loss=0.434]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch No. 40 validation: Cross-entropy loss=0.21, Accuracy=98.1%\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "No. 41 Epoch: 100%|██████████| 24/24 [00:00<00:00, 61.24batch/s, accuracy=0.8, loss=0.427]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch No. 41 validation: Cross-entropy loss=0.20, Accuracy=98.1%\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "No. 42 Epoch: 100%|██████████| 24/24 [00:00<00:00, 60.61batch/s, accuracy=0.8, loss=0.416]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch No. 42 validation: Cross-entropy loss=0.20, Accuracy=98.1%\n",
            "Early stopping at epoch 42 due to decreasing accuracy.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Confusion Matrix:\n",
            "[[13  0  0]\n",
            " [ 1 25  0]\n",
            " [ 0  0 15]]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}