DL_LAB1_B21BB007.ipynb

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    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "import torch.nn.functional as F\n",
        "import torchvision\n",
        "import torchvision.transforms as transforms\n",
        "import numpy as np\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')"
      ],
      "metadata": {
        "id": "2jatbx8QZLIY"
      },
      "execution_count": 7,
      "outputs": []
    },
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        "batch_size = 100\n",
        "num_classes = 7\n",
        "input = 16\n",
        "hidden = 100\n",
        "num_epochs = 10"
      ],
      "metadata": {
        "id": "5CDD40pvgomd"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_excel(\"/content/Dry_Bean_Dataset.xlsx\")"
      ],
      "metadata": {
        "id": "Oqazk9hijWVS"
      },
      "execution_count": 8,
      "outputs": []
    },
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      "source": [
        "df"
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        "outputId": "fbacfad9-51a1-49f5-9f97-ab37ac02fb17"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Area  Perimeter  MajorAxisLength  MinorAxisLength  AspectRation  \\\n",
              "0      28395    610.291       208.178117       173.888747      1.197191   \n",
              "1      28734    638.018       200.524796       182.734419      1.097356   \n",
              "2      29380    624.110       212.826130       175.931143      1.209713   \n",
              "3      30008    645.884       210.557999       182.516516      1.153638   \n",
              "4      30140    620.134       201.847882       190.279279      1.060798   \n",
              "...      ...        ...              ...              ...           ...   \n",
              "13606  42097    759.696       288.721612       185.944705      1.552728   \n",
              "13607  42101    757.499       281.576392       190.713136      1.476439   \n",
              "13608  42139    759.321       281.539928       191.187979      1.472582   \n",
              "13609  42147    763.779       283.382636       190.275731      1.489326   \n",
              "13610  42159    772.237       295.142741       182.204716      1.619841   \n",
              "\n",
              "       Eccentricity  ConvexArea  EquivDiameter    Extent  Solidity  roundness  \\\n",
              "0          0.549812       28715     190.141097  0.763923  0.988856   0.958027   \n",
              "1          0.411785       29172     191.272750  0.783968  0.984986   0.887034   \n",
              "2          0.562727       29690     193.410904  0.778113  0.989559   0.947849   \n",
              "3          0.498616       30724     195.467062  0.782681  0.976696   0.903936   \n",
              "4          0.333680       30417     195.896503  0.773098  0.990893   0.984877   \n",
              "...             ...         ...            ...       ...       ...        ...   \n",
              "13606      0.765002       42508     231.515799  0.714574  0.990331   0.916603   \n",
              "13607      0.735702       42494     231.526798  0.799943  0.990752   0.922015   \n",
              "13608      0.734065       42569     231.631261  0.729932  0.989899   0.918424   \n",
              "13609      0.741055       42667     231.653248  0.705389  0.987813   0.907906   \n",
              "13610      0.786693       42600     231.686223  0.788962  0.989648   0.888380   \n",
              "\n",
              "       Compactness  ShapeFactor1  ShapeFactor2  ShapeFactor3  ShapeFactor4  \\\n",
              "0         0.913358      0.007332      0.003147      0.834222      0.998724   \n",
              "1         0.953861      0.006979      0.003564      0.909851      0.998430   \n",
              "2         0.908774      0.007244      0.003048      0.825871      0.999066   \n",
              "3         0.928329      0.007017      0.003215      0.861794      0.994199   \n",
              "4         0.970516      0.006697      0.003665      0.941900      0.999166   \n",
              "...            ...           ...           ...           ...           ...   \n",
              "13606     0.801865      0.006858      0.001749      0.642988      0.998385   \n",
              "13607     0.822252      0.006688      0.001886      0.676099      0.998219   \n",
              "13608     0.822730      0.006681      0.001888      0.676884      0.996767   \n",
              "13609     0.817457      0.006724      0.001852      0.668237      0.995222   \n",
              "13610     0.784997      0.007001      0.001640      0.616221      0.998180   \n",
              "\n",
              "          Class  \n",
              "0         SEKER  \n",
              "1         SEKER  \n",
              "2         SEKER  \n",
              "3         SEKER  \n",
              "4         SEKER  \n",
              "...         ...  \n",
              "13606  DERMASON  \n",
              "13607  DERMASON  \n",
              "13608  DERMASON  \n",
              "13609  DERMASON  \n",
              "13610  DERMASON  \n",
              "\n",
              "[13611 rows x 17 columns]"
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Area</th>\n",
              "      <th>Perimeter</th>\n",
              "      <th>MajorAxisLength</th>\n",
              "      <th>MinorAxisLength</th>\n",
              "      <th>AspectRation</th>\n",
              "      <th>Eccentricity</th>\n",
              "      <th>ConvexArea</th>\n",
              "      <th>EquivDiameter</th>\n",
              "      <th>Extent</th>\n",
              "      <th>Solidity</th>\n",
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              "      <th>Compactness</th>\n",
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              "      <th>ShapeFactor2</th>\n",
              "      <th>ShapeFactor3</th>\n",
              "      <th>ShapeFactor4</th>\n",
              "      <th>Class</th>\n",
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              "      <th>0</th>\n",
              "      <td>28395</td>\n",
              "      <td>610.291</td>\n",
              "      <td>208.178117</td>\n",
              "      <td>173.888747</td>\n",
              "      <td>1.197191</td>\n",
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              "      <td>0.763923</td>\n",
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              "      <td>0.007332</td>\n",
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              "      <td>SEKER</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>28734</td>\n",
              "      <td>638.018</td>\n",
              "      <td>200.524796</td>\n",
              "      <td>182.734419</td>\n",
              "      <td>1.097356</td>\n",
              "      <td>0.411785</td>\n",
              "      <td>29172</td>\n",
              "      <td>191.272750</td>\n",
              "      <td>0.783968</td>\n",
              "      <td>0.984986</td>\n",
              "      <td>0.887034</td>\n",
              "      <td>0.953861</td>\n",
              "      <td>0.006979</td>\n",
              "      <td>0.003564</td>\n",
              "      <td>0.909851</td>\n",
              "      <td>0.998430</td>\n",
              "      <td>SEKER</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>29380</td>\n",
              "      <td>624.110</td>\n",
              "      <td>212.826130</td>\n",
              "      <td>175.931143</td>\n",
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              "      <td>0.947849</td>\n",
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              "      <td>0.007244</td>\n",
              "      <td>0.003048</td>\n",
              "      <td>0.825871</td>\n",
              "      <td>0.999066</td>\n",
              "      <td>SEKER</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>30008</td>\n",
              "      <td>645.884</td>\n",
              "      <td>210.557999</td>\n",
              "      <td>182.516516</td>\n",
              "      <td>1.153638</td>\n",
              "      <td>0.498616</td>\n",
              "      <td>30724</td>\n",
              "      <td>195.467062</td>\n",
              "      <td>0.782681</td>\n",
              "      <td>0.976696</td>\n",
              "      <td>0.903936</td>\n",
              "      <td>0.928329</td>\n",
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              "      <td>0.003215</td>\n",
              "      <td>0.861794</td>\n",
              "      <td>0.994199</td>\n",
              "      <td>SEKER</td>\n",
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              "      <th>4</th>\n",
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              "      <td>0.003665</td>\n",
              "      <td>0.941900</td>\n",
              "      <td>0.999166</td>\n",
              "      <td>SEKER</td>\n",
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              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-d7c15748-77f5-4184-b8b7-98794f45762d button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Building a Neural Network"
      ],
      "metadata": {
        "id": "zKxE2W1hZKeK"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "pV6SYT1oYpIV"
      },
      "outputs": [],
      "source": [
        "class MLP(nn.Module):\n",
        "    def __init__(self, input , hidden , num_classes):\n",
        "        super(MLP, self).__init__()\n",
        "\n",
        "        self.l1 = nn.Linear(input , hidden )\n",
        "        self.relu = nn.ReLU()\n",
        "        self.l2 = nn.Linear(hidden, num_classes)\n",
        "\n",
        "\n",
        "    def forward(self, x) -> torch.Tensor:\n",
        "        out1 = self.l1(x)\n",
        "        out2 = self.relu(out1)\n",
        "        outf = self.l2(out2)\n",
        "\n",
        "        return outf\n",
        "\n",
        "model = MLP(input, hidden, num_classes)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(model.parameters())\n",
        "\n",
        "model.state_dict()"
      ],
      "metadata": {
        "id": "TywTK_wtQo38"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Loss and Optimizer\n"
      ],
      "metadata": {
        "id": "nko41tsalbvS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "Loss = nn.CrossEntropyLoss()\n",
        "\n",
        "optimizer = torch.optim.Adam(params = model.parameters() , lr= 0.001)"
      ],
      "metadata": {
        "id": "JkhvV3FAlgbj"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Custom dataset and Training, vallidation, test split"
      ],
      "metadata": {
        "id": "QlzvcRVXeXMB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import preprocessing\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "scaler = StandardScaler()\n",
        "\n",
        "label_encoder = preprocessing.LabelEncoder()\n"
      ],
      "metadata": {
        "id": "WCF8ocVQIbzD"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class custom_dataset(Dataset):\n",
        "    def __init__(self):\n",
        "        super(custom_dataset, self).__init__()\n",
        "        self.df = pd.read_excel(\"/content/Dry_Bean_Dataset.xlsx\")\n",
        "        self.scaled_data = scaler.fit_transform(self.df.iloc[:, 0:16].values)\n",
        "        self.labels = label_encoder.fit_transform(df['Class'].values)\n",
        "\n",
        "\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.df)\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        X = torch.tensor(self.scaled_data[idx] , dtype= torch.float32)\n",
        "        Y = torch.tensor(self.labels[idx], dtype = torch.long)\n",
        "\n",
        "        return X , Y"
      ],
      "metadata": {
        "id": "qZ9YOT190lKa"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = custom_dataset()"
      ],
      "metadata": {
        "id": "ItO7E2is50G5"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "len(dataset)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dI8A71LZ6Ofv",
        "outputId": "76128e66-ff85-498b-d2c6-0ec0727bfacc"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "13611"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_set, test_set, val_set = torch.utils.data.random_split(dataset, [0.6 , 0.2 , 0.2])"
      ],
      "metadata": {
        "id": "vdzmk63C511G"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_loader = torch.utils.data.DataLoader(\n",
        "      train_set,batch_size=batch_size, shuffle=True\n",
        "   )\n",
        "\n",
        "test_loader = torch.utils.data.DataLoader(\n",
        "      test_set, batch_size=batch_size, shuffle=False\n",
        "     )\n",
        "\n",
        "val_loader = torch.utils.data.DataLoader(\n",
        "      val_set, batch_size=batch_size, shuffle=False\n",
        "     )"
      ],
      "metadata": {
        "id": "U0FAC-y_BGsC"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Training Loop"
      ],
      "metadata": {
        "id": "QANAYyl4lgr4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "for epoch in range(10):\n",
        "    loss = 0\n",
        "    for i, (features, labels) in enumerate(train_loader):\n",
        "\n",
        "        features = features.to(device)\n",
        "        labels = labels.to(device)\n",
        "\n",
        "\n",
        "        # reset the gradients back to zero\n",
        "        # PyTorch accumulates gradients on subsequent backward passes\n",
        "        optimizer.zero_grad()\n",
        "\n",
        "        #forward pass\n",
        "        outputs = model(features)\n",
        "\n",
        "        # compute loss\n",
        "        train_loss = Loss(outputs, labels)\n",
        "        print(train_loss,\"lol\")\n",
        "\n",
        "        # compute accumulated gradients\n",
        "        train_loss.backward()\n",
        "\n",
        "        # parameter update based on current gradients\n",
        "        optimizer.step()\n",
        "\n",
        "        # add the mini-batch training loss to epoch loss\n",
        "        loss += train_loss.item()\n",
        "        print(loss,\" huu\")\n",
        "\n",
        "    # compute the epoch training loss\n",
        "    loss = loss / len(train_loader)\n",
        "\n",
        "    # display the epoch training loss\n",
        "    print(\"epoch : {}/{}, recon loss = {:.8f}\".format(epoch + 1, 10, loss))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LXqeJPKWjDtz",
        "outputId": "17f14744-377f-4d64-d61d-1b9dfaabd21a"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor(1.8852, grad_fn=<NllLossBackward0>) lol\n",
            "1.885239601135254  huu\n",
            "tensor(1.8167, grad_fn=<NllLossBackward0>) lol\n",
            "3.7019662857055664  huu\n",
            "tensor(1.8693, grad_fn=<NllLossBackward0>) lol\n",
            "5.571231245994568  huu\n",
            "tensor(1.8619, grad_fn=<NllLossBackward0>) lol\n",
            "7.4331135749816895  huu\n",
            "tensor(1.7985, grad_fn=<NllLossBackward0>) lol\n",
            "9.231640458106995  huu\n",
            "tensor(1.8359, grad_fn=<NllLossBackward0>) lol\n",
            "11.067496061325073  huu\n",
            "tensor(1.7470, grad_fn=<NllLossBackward0>) lol\n",
            "12.814459562301636  huu\n",
            "tensor(1.7851, grad_fn=<NllLossBackward0>) lol\n",
            "14.599597454071045  huu\n",
            "tensor(1.7092, grad_fn=<NllLossBackward0>) lol\n",
            "16.30878436565399  huu\n",
            "tensor(1.6878, grad_fn=<NllLossBackward0>) lol\n",
            "17.996574759483337  huu\n",
            "tensor(1.6589, grad_fn=<NllLossBackward0>) lol\n",
            "19.65548086166382  huu\n",
            "tensor(1.6029, grad_fn=<NllLossBackward0>) lol\n",
            "21.258384704589844  huu\n",
            "tensor(1.6809, grad_fn=<NllLossBackward0>) lol\n",
            "22.939246654510498  huu\n",
            "tensor(1.5621, grad_fn=<NllLossBackward0>) lol\n",
            "24.501323223114014  huu\n",
            "tensor(1.5361, grad_fn=<NllLossBackward0>) lol\n",
            "26.037466049194336  huu\n",
            "tensor(1.5515, grad_fn=<NllLossBackward0>) lol\n",
            "27.588935375213623  huu\n",
            "tensor(1.4639, grad_fn=<NllLossBackward0>) lol\n",
            "29.05279815196991  huu\n",
            "tensor(1.5075, grad_fn=<NllLossBackward0>) lol\n",
            "30.560327887535095  huu\n",
            "tensor(1.5093, grad_fn=<NllLossBackward0>) lol\n",
            "32.06958520412445  huu\n",
            "tensor(1.4865, grad_fn=<NllLossBackward0>) lol\n",
            "33.556102871894836  huu\n",
            "tensor(1.4573, grad_fn=<NllLossBackward0>) lol\n",
            "35.01338303089142  huu\n",
            "tensor(1.4247, grad_fn=<NllLossBackward0>) lol\n",
            "36.43812274932861  huu\n",
            "tensor(1.4091, grad_fn=<NllLossBackward0>) lol\n",
            "37.84726917743683  huu\n",
            "tensor(1.3569, grad_fn=<NllLossBackward0>) lol\n",
            "39.204147815704346  huu\n",
            "tensor(1.3163, grad_fn=<NllLossBackward0>) lol\n",
            "40.52041947841644  huu\n",
            "tensor(1.3207, grad_fn=<NllLossBackward0>) lol\n",
            "41.841092586517334  huu\n",
            "tensor(1.3144, grad_fn=<NllLossBackward0>) lol\n",
            "43.155490040779114  huu\n",
            "tensor(1.4348, grad_fn=<NllLossBackward0>) lol\n",
            "44.59033787250519  huu\n",
            "tensor(1.3473, grad_fn=<NllLossBackward0>) lol\n",
            "45.937593936920166  huu\n",
            "tensor(1.3314, grad_fn=<NllLossBackward0>) lol\n",
            "47.269028067588806  huu\n",
            "tensor(1.2131, grad_fn=<NllLossBackward0>) lol\n",
            "48.48217523097992  huu\n",
            "tensor(1.1705, grad_fn=<NllLossBackward0>) lol\n",
            "49.65268695354462  huu\n",
            "tensor(1.2602, grad_fn=<NllLossBackward0>) lol\n",
            "50.91285765171051  huu\n",
            "tensor(1.2286, grad_fn=<NllLossBackward0>) lol\n",
            "52.14145648479462  huu\n",
            "tensor(1.2235, grad_fn=<NllLossBackward0>) lol\n",
            "53.36491870880127  huu\n",
            "tensor(1.1910, grad_fn=<NllLossBackward0>) lol\n",
            "54.55592882633209  huu\n",
            "tensor(1.1837, grad_fn=<NllLossBackward0>) lol\n",
            "55.73965299129486  huu\n",
            "tensor(1.1543, grad_fn=<NllLossBackward0>) lol\n",
            "56.893961787223816  huu\n",
            "tensor(1.1270, grad_fn=<NllLossBackward0>) lol\n",
            "58.020986914634705  huu\n",
            "tensor(1.0943, grad_fn=<NllLossBackward0>) lol\n",
            "59.115267634391785  huu\n",
            "tensor(1.0858, grad_fn=<NllLossBackward0>) lol\n",
            "60.201035380363464  huu\n",
            "tensor(1.0808, grad_fn=<NllLossBackward0>) lol\n",
            "61.281858801841736  huu\n",
            "tensor(1.0764, grad_fn=<NllLossBackward0>) lol\n",
            "62.35823655128479  huu\n",
            "tensor(1.0642, grad_fn=<NllLossBackward0>) lol\n",
            "63.42244207859039  huu\n",
            "tensor(1.0057, grad_fn=<NllLossBackward0>) lol\n",
            "64.42814898490906  huu\n",
            "tensor(1.1092, grad_fn=<NllLossBackward0>) lol\n",
            "65.53735744953156  huu\n",
            "tensor(1.0529, grad_fn=<NllLossBackward0>) lol\n",
            "66.590301156044  huu\n",
            "tensor(0.9863, grad_fn=<NllLossBackward0>) lol\n",
            "67.57658207416534  huu\n",
            "tensor(0.9335, grad_fn=<NllLossBackward0>) lol\n",
            "68.51006591320038  huu\n",
            "tensor(0.9733, grad_fn=<NllLossBackward0>) lol\n",
            "69.4833356142044  huu\n",
            "tensor(0.9237, grad_fn=<NllLossBackward0>) lol\n",
            "70.40698766708374  huu\n",
            "tensor(0.9685, grad_fn=<NllLossBackward0>) lol\n",
            "71.37544697523117  huu\n",
            "tensor(0.9567, grad_fn=<NllLossBackward0>) lol\n",
            "72.33217614889145  huu\n",
            "tensor(0.9465, grad_fn=<NllLossBackward0>) lol\n",
            "73.27864760160446  huu\n",
            "tensor(0.8835, grad_fn=<NllLossBackward0>) lol\n",
            "74.16211730241776  huu\n",
            "tensor(0.9369, grad_fn=<NllLossBackward0>) lol\n",
            "75.09899473190308  huu\n",
            "tensor(1.0625, grad_fn=<NllLossBackward0>) lol\n",
            "76.16149485111237  huu\n",
            "tensor(0.8361, grad_fn=<NllLossBackward0>) lol\n",
            "76.99754625558853  huu\n",
            "tensor(0.9126, grad_fn=<NllLossBackward0>) lol\n",
            "77.91012889146805  huu\n",
            "tensor(0.8499, grad_fn=<NllLossBackward0>) lol\n",
            "78.7600411772728  huu\n",
            "tensor(0.9184, grad_fn=<NllLossBackward0>) lol\n",
            "79.6784496307373  huu\n",
            "tensor(0.8581, grad_fn=<NllLossBackward0>) lol\n",
            "80.53650665283203  huu\n",
            "tensor(0.8234, grad_fn=<NllLossBackward0>) lol\n",
            "81.35987490415573  huu\n",
            "tensor(0.9062, grad_fn=<NllLossBackward0>) lol\n",
            "82.2661075592041  huu\n",
            "tensor(0.8522, grad_fn=<NllLossBackward0>) lol\n",
            "83.11832290887833  huu\n",
            "tensor(0.8400, grad_fn=<NllLossBackward0>) lol\n",
            "83.95832985639572  huu\n",
            "tensor(0.8144, grad_fn=<NllLossBackward0>) lol\n",
            "84.77275323867798  huu\n",
            "tensor(0.7429, grad_fn=<NllLossBackward0>) lol\n",
            "85.5156900882721  huu\n",
            "tensor(0.7733, grad_fn=<NllLossBackward0>) lol\n",
            "86.28894197940826  huu\n",
            "tensor(0.8275, grad_fn=<NllLossBackward0>) lol\n",
            "87.11640483140945  huu\n",
            "tensor(0.7511, grad_fn=<NllLossBackward0>) lol\n",
            "87.86745846271515  huu\n",
            "tensor(0.7419, grad_fn=<NllLossBackward0>) lol\n",
            "88.60936087369919  huu\n",
            "tensor(0.6482, grad_fn=<NllLossBackward0>) lol\n",
            "89.25752770900726  huu\n",
            "tensor(0.7424, grad_fn=<NllLossBackward0>) lol\n",
            "89.99995142221451  huu\n",
            "tensor(0.6838, grad_fn=<NllLossBackward0>) lol\n",
            "90.68373513221741  huu\n",
            "tensor(0.7328, grad_fn=<NllLossBackward0>) lol\n",
            "91.41654425859451  huu\n",
            "tensor(0.8131, grad_fn=<NllLossBackward0>) lol\n",
            "92.22959578037262  huu\n",
            "tensor(0.7914, grad_fn=<NllLossBackward0>) lol\n",
            "93.02100437879562  huu\n",
            "tensor(0.7177, grad_fn=<NllLossBackward0>) lol\n",
            "93.73874914646149  huu\n",
            "tensor(0.7264, grad_fn=<NllLossBackward0>) lol\n",
            "94.46513456106186  huu\n",
            "tensor(0.7416, grad_fn=<NllLossBackward0>) lol\n",
            "95.20675963163376  huu\n",
            "tensor(0.8438, grad_fn=<NllLossBackward0>) lol\n",
            "96.05058085918427  huu\n",
            "epoch : 1/10, recon loss = 1.17134855\n",
            "tensor(0.7418, grad_fn=<NllLossBackward0>) lol\n",
            "0.7417999505996704  huu\n",
            "tensor(0.6500, grad_fn=<NllLossBackward0>) lol\n",
            "1.3918415904045105  huu\n",
            "tensor(0.6123, grad_fn=<NllLossBackward0>) lol\n",
            "2.0041500329971313  huu\n",
            "tensor(0.6793, grad_fn=<NllLossBackward0>) lol\n",
            "2.683471143245697  huu\n",
            "tensor(0.6623, grad_fn=<NllLossBackward0>) lol\n",
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            "42.028806269168854  huu\n",
            "epoch : 2/10, recon loss = 0.51254642\n",
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            "27.659262880682945  huu\n",
            "epoch : 3/10, recon loss = 0.33730808\n",
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            "22.32817178964615  huu\n",
            "epoch : 4/10, recon loss = 0.27229478\n",
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            "20.124340891838074  huu\n",
            "epoch : 5/10, recon loss = 0.24541879\n",
            "tensor(0.2032, grad_fn=<NllLossBackward0>) lol\n",
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            "18.977462366223335  huu\n",
            "epoch : 6/10, recon loss = 0.23143247\n",
            "tensor(0.2731, grad_fn=<NllLossBackward0>) lol\n",
            "0.2730812728404999  huu\n",
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            "18.333898603916168  huu\n",
            "epoch : 7/10, recon loss = 0.22358413\n",
            "tensor(0.1701, grad_fn=<NllLossBackward0>) lol\n",
            "0.17009639739990234  huu\n",
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            "17.865259028971195  huu\n",
            "epoch : 8/10, recon loss = 0.21786901\n",
            "tensor(0.2078, grad_fn=<NllLossBackward0>) lol\n",
            "0.20782332122325897  huu\n",
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            "17.53364124894142  huu\n",
            "epoch : 9/10, recon loss = 0.21382489\n",
            "tensor(0.3399, grad_fn=<NllLossBackward0>) lol\n",
            "0.33985453844070435  huu\n",
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            "tensor(0.2045, grad_fn=<NllLossBackward0>) lol\n",
            "13.295583628118038  huu\n",
            "tensor(0.1322, grad_fn=<NllLossBackward0>) lol\n",
            "13.42774634808302  huu\n",
            "tensor(0.1531, grad_fn=<NllLossBackward0>) lol\n",
            "13.580848895013332  huu\n",
            "tensor(0.2268, grad_fn=<NllLossBackward0>) lol\n",
            "13.807636342942715  huu\n",
            "tensor(0.1068, grad_fn=<NllLossBackward0>) lol\n",
            "13.9144257158041  huu\n",
            "tensor(0.2516, grad_fn=<NllLossBackward0>) lol\n",
            "14.166020467877388  huu\n",
            "tensor(0.1626, grad_fn=<NllLossBackward0>) lol\n",
            "14.328625336289406  huu\n",
            "tensor(0.1477, grad_fn=<NllLossBackward0>) lol\n",
            "14.476369112730026  huu\n",
            "tensor(0.1699, grad_fn=<NllLossBackward0>) lol\n",
            "14.646219804883003  huu\n",
            "tensor(0.1938, grad_fn=<NllLossBackward0>) lol\n",
            "14.840037882328033  huu\n",
            "tensor(0.2981, grad_fn=<NllLossBackward0>) lol\n",
            "15.1381134390831  huu\n",
            "tensor(0.3933, grad_fn=<NllLossBackward0>) lol\n",
            "15.531370997428894  huu\n",
            "tensor(0.2226, grad_fn=<NllLossBackward0>) lol\n",
            "15.75401271879673  huu\n",
            "tensor(0.3127, grad_fn=<NllLossBackward0>) lol\n",
            "16.06668208539486  huu\n",
            "tensor(0.1958, grad_fn=<NllLossBackward0>) lol\n",
            "16.262497827410698  huu\n",
            "tensor(0.1771, grad_fn=<NllLossBackward0>) lol\n",
            "16.439635545015335  huu\n",
            "tensor(0.1697, grad_fn=<NllLossBackward0>) lol\n",
            "16.6093278080225  huu\n",
            "tensor(0.1434, grad_fn=<NllLossBackward0>) lol\n",
            "16.752743989229202  huu\n",
            "tensor(0.2030, grad_fn=<NllLossBackward0>) lol\n",
            "16.955732330679893  huu\n",
            "tensor(0.2147, grad_fn=<NllLossBackward0>) lol\n",
            "17.170460432767868  huu\n",
            "tensor(0.0957, grad_fn=<NllLossBackward0>) lol\n",
            "17.26612113416195  huu\n",
            "epoch : 10/10, recon loss = 0.21056245\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Training Loop and Validation testing"
      ],
      "metadata": {
        "id": "gK01JNrnoivl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "n_total_steps = len(train_loader)"
      ],
      "metadata": {
        "id": "awXBdVe1haqH"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(train_loader)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Kx29PUYxqyOs",
        "outputId": "013d9d62-ab54-4102-f057-f3acf6b5e6d2"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<torch.utils.data.dataloader.DataLoader object at 0x7da7fbb4b0a0>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(n_total_steps)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DWt4JzxBozoI",
        "outputId": "e0ad821c-6fe3-4e8a-8c5a-9a6a02a0a633"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "82\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "l1 = []\n",
        "l2 = []\n",
        "\n",
        "\n",
        "for epoch in range(10):\n",
        "    loss = 0\n",
        "    for i, (features, labels) in enumerate(train_loader):\n",
        "\n",
        "\n",
        "        features = features.to(device)\n",
        "\n",
        "        labels = labels.to(device)\n",
        "\n",
        "\n",
        "        # reset the gradients back to zero\n",
        "        # PyTorch accumulates gradients on subsequent backward passes\n",
        "        optimizer.zero_grad()\n",
        "\n",
        "        # compute reconstructions\n",
        "        outputs = model(features)\n",
        "\n",
        "        # compute training reconstruction loss\n",
        "        train_loss = Loss(outputs, labels)\n",
        "\n",
        "        # compute accumulated gradients\n",
        "        train_loss.backward()\n",
        "\n",
        "        # perform parameter update based on current gradients\n",
        "        optimizer.step()\n",
        "\n",
        "        # add the mini-batch training loss to epoch loss\n",
        "        train_loss += train_loss.item()\n",
        "\n",
        "\n",
        "    # compute the epoch training loss\n",
        "    loss = train_loss / len(train_loader)\n",
        "    l1.append(loss)\n",
        "\n",
        "    # display the epoch training loss\n",
        "    print(\"epoch : {}/{}, recon loss = {:.8f}\".format(epoch + 1, 10, loss))\n",
        "\n",
        "\n",
        "    min_valid_loss = np.inf\n",
        "    valid_loss = 0.0\n",
        "    model.eval()     # Optional when not using Model Specific layer\n",
        "    for data, labels in val_loader:\n",
        "        # Transfer Data to GPU if available\n",
        "        data, labels = data.to(device), labels.to(device)\n",
        "\n",
        "\n",
        "        # Forward Pass\n",
        "        target = model(data)\n",
        "        # Find the Loss\n",
        "        loss = Loss(target,labels)\n",
        "        # Calculate Loss\n",
        "        valid_loss += loss.item()\n",
        "    l2.append(valid_loss / len(val_loader))\n",
        "\n",
        "    print(f'Epoch {epoch+1} \\n \\t\\t Training Loss: { train_loss / len(train_loader)} \\t\\t Validation Loss: { valid_loss / len(val_loader)}')\n",
        "\n",
        "    if min_valid_loss > valid_loss:\n",
        "        print(f'Validation Loss Decreased({min_valid_loss:.6f}--->{valid_loss:.6f}) \\t Saving The Model')\n",
        "        min_valid_loss = valid_loss\n",
        "\n",
        "\n",
        "\n",
        "    min_test_loss = np.inf\n",
        "    test_loss = 0.0\n",
        "    model.eval()     # Optional when not using Model Specific layer\n",
        "\n",
        "    correct = 0\n",
        "    for data, labels in test_loader:\n",
        "        # Transfer Data to GPU if available\n",
        "        data, labels = data.to(device), labels.to(device)\n",
        "\n",
        "\n",
        "        # Forward Pass\n",
        "        target = model(data)\n",
        "        # Find the Loss\n",
        "        loss = Loss(target,labels)\n",
        "        # Calculate Loss\n",
        "        test_loss += loss.item()\n",
        "\n",
        "        correct += (target.argmax(1) == labels).type(torch.float).sum().item()\n",
        "\n",
        "    l2.append(test_loss / len(test_loader))\n",
        "\n",
        "    print(f'Epoch {epoch+1} \\n \\t\\t Training Loss: { train_loss / len(train_loader)} \\t\\t Validation Loss: { valid_loss / len(val_loader)} \\t\\t Test Loss: {test_loss/len(test_loader)} \\t\\t Accuracy: {((correct/len(test_loader.dataset))*100):>8f}%')\n",
        "\n",
        "    if min_valid_loss > valid_loss:\n",
        "        print(f'Validation Loss Decreased({min_valid_loss:.6f}--->{valid_loss:.6f}) \\t Saving The Model')\n",
        "        min_valid_loss = valid_loss\n",
        "        # Saving State Dict\n",
        "        torch.save(model.state_dict(), 'saved_model.pth')\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dszj_TYhpAsf",
        "outputId": "cf876fd4-2e6c-4c7c-ef9e-2b10a7cf2823"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch : 1/10, recon loss = 0.00332869\n",
            "Epoch 1 \n",
            " \t\t Training Loss: 0.0033286912366747856 \t\t Validation Loss: 0.1967395166201251\n",
            "Validation Loss Decreased(inf--->5.508706) \t Saving The Model\n",
            "Epoch 1 \n",
            " \t\t Training Loss: 0.0033286912366747856 \t\t Validation Loss: 0.1967395166201251 \t\t Test Loss: 0.1826850221093212 \t\t Accuracy: 92.983101%\n",
            "epoch : 2/10, recon loss = 0.00351050\n",
            "Epoch 2 \n",
            " \t\t Training Loss: 0.0035105037968605757 \t\t Validation Loss: 0.19866518356970378\n",
            "Validation Loss Decreased(inf--->5.562625) \t Saving The Model\n",
            "Epoch 2 \n",
            " \t\t Training Loss: 0.0035105037968605757 \t\t Validation Loss: 0.19866518356970378 \t\t Test Loss: 0.1849589587322303 \t\t Accuracy: 92.946363%\n",
            "epoch : 3/10, recon loss = 0.00303766\n",
            "Epoch 3 \n",
            " \t\t Training Loss: 0.003037660149857402 \t\t Validation Loss: 0.19622792463217462\n",
            "Validation Loss Decreased(inf--->5.494382) \t Saving The Model\n",
            "Epoch 3 \n",
            " \t\t Training Loss: 0.003037660149857402 \t\t Validation Loss: 0.19622792463217462 \t\t Test Loss: 0.18147033586033753 \t\t Accuracy: 92.946363%\n",
            "epoch : 4/10, recon loss = 0.00314127\n",
            "Epoch 4 \n",
            " \t\t Training Loss: 0.0031412735115736723 \t\t Validation Loss: 0.1974002274551562\n",
            "Validation Loss Decreased(inf--->5.527206) \t Saving The Model\n",
            "Epoch 4 \n",
            " \t\t Training Loss: 0.0031412735115736723 \t\t Validation Loss: 0.1974002274551562 \t\t Test Loss: 0.18178926594555378 \t\t Accuracy: 92.946363%\n",
            "epoch : 5/10, recon loss = 0.00457642\n",
            "Epoch 5 \n",
            " \t\t Training Loss: 0.004576424136757851 \t\t Validation Loss: 0.19693176953920297\n",
            "Validation Loss Decreased(inf--->5.514090) \t Saving The Model\n",
            "Epoch 5 \n",
            " \t\t Training Loss: 0.004576424136757851 \t\t Validation Loss: 0.19693176953920297 \t\t Test Loss: 0.1824033542403153 \t\t Accuracy: 92.836150%\n",
            "epoch : 6/10, recon loss = 0.00488818\n",
            "Epoch 6 \n",
            " \t\t Training Loss: 0.004888184368610382 \t\t Validation Loss: 0.19751945271023683\n",
            "Validation Loss Decreased(inf--->5.530545) \t Saving The Model\n",
            "Epoch 6 \n",
            " \t\t Training Loss: 0.004888184368610382 \t\t Validation Loss: 0.19751945271023683 \t\t Test Loss: 0.18158048604215896 \t\t Accuracy: 92.909625%\n",
            "epoch : 7/10, recon loss = 0.00564183\n",
            "Epoch 7 \n",
            " \t\t Training Loss: 0.005641832947731018 \t\t Validation Loss: 0.1976245048322848\n",
            "Validation Loss Decreased(inf--->5.533486) \t Saving The Model\n",
            "Epoch 7 \n",
            " \t\t Training Loss: 0.005641832947731018 \t\t Validation Loss: 0.1976245048322848 \t\t Test Loss: 0.18244685578559125 \t\t Accuracy: 93.019838%\n",
            "epoch : 8/10, recon loss = 0.00306492\n",
            "Epoch 8 \n",
            " \t\t Training Loss: 0.0030649162363260984 \t\t Validation Loss: 0.1985619908996991\n",
            "Validation Loss Decreased(inf--->5.559736) \t Saving The Model\n",
            "Epoch 8 \n",
            " \t\t Training Loss: 0.0030649162363260984 \t\t Validation Loss: 0.1985619908996991 \t\t Test Loss: 0.1839943788945675 \t\t Accuracy: 92.762675%\n",
            "epoch : 9/10, recon loss = 0.00435638\n",
            "Epoch 9 \n",
            " \t\t Training Loss: 0.004356383811682463 \t\t Validation Loss: 0.19851502749536717\n",
            "Validation Loss Decreased(inf--->5.558421) \t Saving The Model\n",
            "Epoch 9 \n",
            " \t\t Training Loss: 0.004356383811682463 \t\t Validation Loss: 0.19851502749536717 \t\t Test Loss: 0.18179063898112094 \t\t Accuracy: 92.799412%\n",
            "epoch : 10/10, recon loss = 0.00778912\n",
            "Epoch 10 \n",
            " \t\t Training Loss: 0.0077891224063932896 \t\t Validation Loss: 0.19452645177287714\n",
            "Validation Loss Decreased(inf--->5.446741) \t Saving The Model\n",
            "Epoch 10 \n",
            " \t\t Training Loss: 0.0077891224063932896 \t\t Validation Loss: 0.19452645177287714 \t\t Test Loss: 0.1787783285336835 \t\t Accuracy: 92.983101%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Assuming l1 is your list or array of loss values\n",
        "l1_np = [item.detach().numpy() for item in l1]\n",
        "\n",
        "plt.plot(l1_np)\n",
        "plt.xlabel(\"epoch\")\n",
        "plt.ylabel(\"loss\")\n",
        "plt.title(\"training loss\")\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "L_FEQjpdXY4e",
        "outputId": "13c1959e-81f6-497b-d709-e37aa2823b57"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Assuming l1 is your list or array of loss values\n",
        "# l2_np = [item.detach().numpy() for item in l2]\n",
        "\n",
        "plt.plot(l2)\n",
        "plt.xlabel(\"epoch\")\n",
        "plt.ylabel(\"loss\")\n",
        "plt.title(\"Validation loss\")\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "--wk7iNyXm_1",
        "outputId": "1c63d0fe-24bf-45fb-dc7a-69414a30494a"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}