{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xSssCyLJ5eEz"
      },
      "source": [
        "# Multiclass Image Classification"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Dh9ElYgm5iTP"
      },
      "source": [
        "Muhammad Hasnan Ramadhan 2502396111"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "E8pgOnXIvV1k"
      },
      "outputs": [],
      "source": [
        "# Import Library\n",
        "import os\n",
        "from torchvision import datasets\n",
        "import torchvision.transforms as transforms\n",
        "import collections\n",
        "from torchvision import utils\n",
        "from torch.utils.data import DataLoader\n",
        "from torchsummary import summary\n",
        "from torch import nn\n",
        "from torch import optim\n",
        "import torch\n",
        "import torchvision\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "from torch.utils.data import DataLoader, random_split, Dataset\n",
        "from PIL import Image"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hr-SUYiWvapm",
        "outputId": "546bd4f5-2072-4b9a-9933-23664645b259"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "device(type='cuda')"
            ]
          },
          "execution_count": 2,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# check device for cuda available\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "device"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "soI9RLN25bBV"
      },
      "source": [
        "## Data Preprocessing"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "JnIXwkY-0LQO"
      },
      "outputs": [],
      "source": [
        "class MultiLabelImageDataset(Dataset):\n",
        "    def __init__(self, csv_file, img_dir, transform, indices=None):\n",
        "        \"\"\"\n",
        "        Args:\n",
        "            csv_file (string): Path to the csv file with annotations.\n",
        "            img_dir (string): Directory with all the images.\n",
        "            transform (callable, optional): Optional transform to be applied on a sample.\n",
        "            indices (list, optional): List of indices to use in the subset.\n",
        "        \"\"\"\n",
        "        data = pd.read_csv(csv_file)\n",
        "        if indices is not None:\n",
        "            data = data.iloc[indices]\n",
        "        self.classes = [\"motorcycle, truck, train, bus, cycle, sitar, ektara, flutes, tabla, harmonium\"]\n",
        "        self.img_labels = data\n",
        "        self.img_dir = img_dir\n",
        "        self.transform = transform\n",
        "        self.label_columns = self.img_labels.columns[2:]\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.img_labels)\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        img_path = os.path.join(self.img_dir, self.img_labels.iloc[idx, 0].strip())\n",
        "        image = Image.open(img_path).convert('RGB')\n",
        "        labels = torch.tensor(self.img_labels.iloc[idx, 2:].values.astype(int))\n",
        "\n",
        "        if self.transform:\n",
        "            image = self.transform(image)\n",
        "\n",
        "        return image, labels\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {},
      "outputs": [],
      "source": [
        "data = pd.read_csv(\"D:\\Magister Program\\Master Computer Science\\Semester 2\\Deep Learning\\FINAL EXAM\\multilabel_class.csv\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(7943, 12)"
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "OrKabCyE2EgK"
      },
      "outputs": [],
      "source": [
        "# Define transformations\n",
        "transform = transforms.Compose([\n",
        "    transforms.Resize((256, 256)),\n",
        "    transforms.RandomHorizontalFlip(),\n",
        "    # transforms.CenterCrop(224),\n",
        "    transforms.RandAugment(),                      \n",
        "    transforms.ToTensor(),\n",
        "    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.5, 0.5, 0.5])\n",
        "])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "E80oSV9a0VfV"
      },
      "outputs": [],
      "source": [
        "# Set the random seed\n",
        "np.random.seed(42)\n",
        "\n",
        "# Load the full dataset\n",
        "csv_file = \"D:\\Magister Program\\Master Computer Science\\Semester 2\\Deep Learning\\FINAL EXAM\\multilabel_class.csv\"\n",
        "img_dir = \"D:\\Magister Program\\Master Computer Science\\Semester 2\\Deep Learning\\FINAL EXAM\\images\"\n",
        "full_dataset = pd.read_csv(csv_file)\n",
        "\n",
        "# Select 5000 random samples\n",
        "subset_indices = np.random.choice(full_dataset.index, 7000, replace=False)\n",
        "\n",
        "# Create the dataset instance\n",
        "dataset = MultiLabelImageDataset(csv_file, img_dir, transform, indices=subset_indices)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Index(['motorcycle', 'truck', 'boat', 'bus', 'cycle', 'sitar', 'ektara',\n",
              "       'flutes', 'tabla', 'harmonium'],\n",
              "      dtype='object')"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dataset.label_columns       "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Image shape: <built-in method size of Tensor object at 0x000002BE343D56D0>\n",
            "Labels: tensor([1, 0, 0, 0, 0, 0, 0, 0, 0, 0], dtype=torch.int32)\n"
          ]
        }
      ],
      "source": [
        "image, labels = dataset[0]  # Get the first sample\n",
        "\n",
        "print(\"Image shape:\", image.size)\n",
        "print(\"Labels:\", labels)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Batch 1 Image tensor: torch.Size([256, 256])\n",
            "Batch 1 Label: tensor([1, 0, 0, 0, 0, 0, 0, 0, 0, 0], dtype=torch.int32)\n",
            "Batch 2 Image tensor: torch.Size([256, 256])\n",
            "Batch 2 Label: tensor([0, 0, 0, 0, 1, 0, 0, 0, 0, 0], dtype=torch.int32)\n",
            "Batch 3 Image tensor: torch.Size([256, 256])\n",
            "Batch 3 Label: tensor([0, 0, 0, 0, 0, 0, 0, 0, 1, 1], dtype=torch.int32)\n",
            "Batch 4 Image tensor: torch.Size([256, 256])\n",
            "Batch 4 Label: tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 1], dtype=torch.int32)\n",
            "Batch 5 Image tensor: torch.Size([256, 256])\n",
            "Batch 5 Label: tensor([0, 0, 0, 0, 1, 0, 0, 0, 0, 0], dtype=torch.int32)\n"
          ]
        }
      ],
      "source": [
        "for i, (images, labels) in enumerate(dataset):\n",
        "    if i >= 5:  # Stop after the first 5\n",
        "        break\n",
        "    print(f\"Batch {i+1} Image tensor: {images[0].shape}\")  # Adjust indexing for batch\n",
        "    print(f\"Batch {i+1} Label:\", labels)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {},
      "outputs": [],
      "source": [
        "# from collections import defaultdict\n",
        "\n",
        "# # Initialize a defaultdict to store label counts\n",
        "# label_counts = defaultdict(int)\n",
        "\n",
        "# # Iterate through the dataset to count labels\n",
        "# for _, labels in dataset:\n",
        "#     for label_idx, label_value in enumerate(labels):\n",
        "#         if label_value.item() == 1:  # Assuming labels are binary (1 or 0)\n",
        "#             label_counts[label_idx] += 1\n",
        "\n",
        "# # Print label counts\n",
        "# print(\"Label Counts:\")\n",
        "# for label_idx, count in label_counts.items():\n",
        "#     print(f\"Label {label_idx}: {count}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "3yCDlKpH19yP"
      },
      "outputs": [],
      "source": [
        "# Define sizes for train, validation, and test sets\n",
        "train_size = int(0.8 * len(dataset))\n",
        "val_size = int(0.1 * len(dataset))\n",
        "test_size = len(dataset) - train_size - val_size\n",
        "\n",
        "# Split the dataset\n",
        "train_dataset, val_dataset, test_dataset = random_split(dataset, [train_size, val_size, test_size], generator=torch.Generator().manual_seed(42))\n",
        "\n",
        "train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)\n",
        "val_loader = DataLoader(val_dataset, batch_size=32, shuffle=False)\n",
        "test_loader = DataLoader(test_dataset, batch_size=32, shuffle=False)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Label Counts Training dataset:\n",
            "Label 4: 1006\n",
            "Label 8: 1059\n",
            "Label 9: 426\n",
            "Label 2: 1177\n",
            "Label 1: 723\n",
            "Label 3: 480\n",
            "Label 0: 938\n",
            "Label 6: 229\n",
            "Label 5: 270\n",
            "Label 7: 397\n"
          ]
        }
      ],
      "source": [
        "from collections import defaultdict\n",
        "\n",
        "# Initialize a defaultdict to store label counts\n",
        "label_counts = defaultdict(int)\n",
        "\n",
        "# Iterate through the dataset to count labels\n",
        "for _, labels in train_dataset:\n",
        "    for label_idx, label_value in enumerate(labels):\n",
        "        if label_value.item() == 1:  # Assuming labels are binary (1 or 0)\n",
        "            label_counts[label_idx] += 1\n",
        "\n",
        "# Print label counts\n",
        "print(\"Label Counts Training dataset:\")\n",
        "for label_idx, count in label_counts.items():\n",
        "    print(f\"Label {label_idx}: {count}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0XEg1CeN3IYb"
      },
      "source": [
        "## Exploration"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OKl5JKUV5G4y",
        "outputId": "a6b115c4-cd1a-400d-b61b-f88e68d11c51"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Train dataset size after split: 5600\n",
            "Validation dataset size after split: 700\n",
            "Test dataset size after split: 700\n"
          ]
        }
      ],
      "source": [
        "# Print sizes of each split\n",
        "print(f'Train dataset size after split: {len(train_dataset)}')\n",
        "print(f'Validation dataset size after split: {len(val_dataset)}')\n",
        "print(f'Test dataset size after split: {len(test_dataset)}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "42O-FS5BHSK6"
      },
      "outputs": [],
      "source": [
        "classes = ['motorcycle', 'truck', 'boat', 'bus', 'cycle', 'star', 'ekara', 'flutes', 'tabla', 'harmonium']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 887
        },
        "id": "YrWIZJ8t3KCK",
        "outputId": "469c240a-e7a7-488a-f2d3-65a563d38c26"
      },
      "outputs": [],
      "source": [
        "# def imshow(img):\n",
        "#     img = img / 2 + 0.5\n",
        "#     npimg = img.numpy()\n",
        "#     plt.imshow(np.transpose(npimg, (1, 2, 0)))\n",
        "#     plt.show()\n",
        "\n",
        "# # To visualize some images and labels\n",
        "# dataiter = iter(train_loader)\n",
        "# images, labels = next(dataiter)\n",
        "# imshow(torchvision.utils.make_grid(images))\n",
        "\n",
        "\n",
        "# for idx, label in enumerate(labels):\n",
        "#     present_classes = [classes[i] for i, is_present in enumerate(label) if is_present == 1]\n",
        "#     print(f\"Image {idx+1} has labels: {', '.join(present_classes)}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vqWAIiZc5RA9"
      },
      "source": [
        "## Modelling"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "3dFPV4qL34T2"
      },
      "outputs": [],
      "source": [
        "from torchvision import models\n",
        "\n",
        "vgg16 = models.vgg16(weights='IMAGENET1K_V1')\n",
        "\n",
        "# Freeze the convolutional base to prevent updating weights during training\n",
        "for param in vgg16.features.parameters():\n",
        "    param.requires_grad = False"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "rBbKwMS59a_T"
      },
      "outputs": [],
      "source": [
        "num_features = vgg16.classifier[6].in_features\n",
        "num_classes = 10\n",
        "vgg16.classifier[6] = torch.nn.Linear(num_features, num_classes)\n",
        "vgg16 = vgg16.to(device)\n",
        "optimizer = torch.optim.Adam(vgg16.parameters(), lr=0.0001)\n",
        "loss_fn = nn.BCEWithLogitsLoss()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "1bx9z9cQ-GN7"
      },
      "outputs": [],
      "source": [
        "def calculate_accuracy(outputs, labels, threshold=0.7):\n",
        "    probs = torch.sigmoid(outputs)\n",
        "    preds = (probs > threshold).float()\n",
        "    correct = (preds == labels).float()\n",
        "    acc = correct.sum() / (labels.size(0) * labels.size(1))\n",
        "    return acc\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pn7VIAl8-g0T",
        "outputId": "0c97902a-24ed-46b3-8c14-8c93e1632c12"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1, Train Loss: 0.084, Train Acc: 0.969, Val Loss: 0.046, Val Acc: 0.983\n",
            "Epoch 2, Train Loss: 0.040, Train Acc: 0.984, Val Loss: 0.040, Val Acc: 0.989\n",
            "Epoch 3, Train Loss: 0.036, Train Acc: 0.986, Val Loss: 0.034, Val Acc: 0.990\n",
            "Epoch 4, Train Loss: 0.035, Train Acc: 0.987, Val Loss: 0.037, Val Acc: 0.990\n",
            "Epoch 5, Train Loss: 0.028, Train Acc: 0.990, Val Loss: 0.041, Val Acc: 0.986\n"
          ]
        }
      ],
      "source": [
        "train_losses, val_losses, train_accs, val_accs = [], [], [], []\n",
        "\n",
        "for epoch in range(5):  # loop over the dataset multiple times\n",
        "    vgg16.train()\n",
        "    running_loss, running_acc = 0.0, 0.0\n",
        "    for inputs, labels in train_loader:\n",
        "        inputs, labels = inputs.to(device), labels.to(device).float()\n",
        "        optimizer.zero_grad()\n",
        "        outputs = vgg16(inputs)\n",
        "        loss = loss_fn(outputs, labels)\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "        running_loss += loss.item()\n",
        "        running_acc += calculate_accuracy(outputs, labels).item()  # Convert to scalar immediately if possible\n",
        "    train_losses.append(running_loss / len(train_loader))\n",
        "    train_accs.append(running_acc / len(train_loader))\n",
        "\n",
        "    vgg16.eval()\n",
        "    val_loss, val_acc = 0.0, 0.0\n",
        "    all_preds = []\n",
        "    all_labels = []\n",
        "    with torch.no_grad():\n",
        "        for inputs, labels in val_loader:\n",
        "            inputs, labels = inputs.to(device), labels.to(device).float()\n",
        "            outputs = vgg16(inputs)\n",
        "            loss = loss_fn(outputs, labels)\n",
        "            val_loss += loss.item()\n",
        "            val_acc += calculate_accuracy(outputs, labels).item()  # Convert to scalar immediately if possible\n",
        "\n",
        "            preds = (torch.sigmoid(outputs) > 0.7).float()  # Threshold the outputs\n",
        "            all_preds.extend(preds.cpu().numpy())\n",
        "            all_labels.extend(labels.cpu().numpy())\n",
        "    val_losses.append(val_loss / len(val_loader))\n",
        "    val_accs.append(val_acc / len(val_loader))\n",
        "\n",
        "    print(f'Epoch {epoch + 1}, Train Loss: {train_losses[-1]:.3f}, Train Acc: {train_accs[-1]:.3f}, Val Loss: {val_losses[-1]:.3f}, Val Acc: {val_accs[-1]:.3f}')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8gApF0Hb5rqW"
      },
      "source": [
        "## Evaluation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "2Og2SpwX5s1i",
        "outputId": "5e8ff955-fac7-48ff-bc68-597f39889949"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "# Plot training dan validation loss\n",
        "plt.figure(figsize=(10, 5))\n",
        "plt.plot(train_losses, label = 'training_loss')\n",
        "plt.plot(val_losses, label='Validation Loss')\n",
        "plt.title('Training and Validation Loss per Epoch')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "['motorcycle']\n"
          ]
        }
      ],
      "source": [
        "def predict(image):\n",
        "    input_tensor = transform(image).to(device)\n",
        "    input_batch = input_tensor.unsqueeze(0)\n",
        "    with torch.no_grad():\n",
        "        output = vgg16(input_batch)\n",
        "    probabilities = torch.sigmoid(output)\n",
        "    threshold = 0.7\n",
        "    preds = (probabilities > threshold).float()\n",
        "    # Assuming `classes` is a list containing the class labels\n",
        "    decoded_predictions = [classes[i] for i, pred in enumerate(preds[0]) if pred >= 0.7]\n",
        "    return decoded_predictions\n",
        "\n",
        "\n",
        "# Example usage\n",
        "image = Image.open(\"D:\\Magister Program\\Master Computer Science\\Semester 2\\Deep Learning\\FINAL EXAM\\images\\image1000.jpg\")\n",
        "predictions = predict(image)\n",
        "print(predictions)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/jpeg": 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",
            "image/png": 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",
            "text/plain": [
              "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=363x450>"
            ]
          },
          "execution_count": 23,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "image"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {},
      "outputs": [],
      "source": [
        "def predict_with_dataloader(test_dataloader, model, device, classes):\n",
        "    model.eval()  # Set the model to evaluation mode\n",
        "    all_predicted_classes = []\n",
        "    all_predicted_probabilities = []\n",
        "\n",
        "    with torch.no_grad():  # Disable gradient computation\n",
        "        for images, _ in test_dataloader:  # Assuming labels are not needed for prediction\n",
        "            images = images.to(device)\n",
        "            outputs = model(images)\n",
        "            probabilities = torch.sigmoid(outputs)  # Apply sigmoid to convert outputs to probabilities\n",
        "            predicted_labels = (probabilities > 0.5).float()  # Apply threshold\n",
        "            \n",
        "            # Loop through each item in the batch\n",
        "            for prob, labels in zip(probabilities, predicted_labels):\n",
        "                # Extract classes and probabilities for predicted labels\n",
        "                current_classes = [classes[i] for i, label in enumerate(labels) if label == 1]\n",
        "                current_probs = [p.item() * 100 for p, l in zip(prob, labels) if l == 1]\n",
        "                all_predicted_classes.append(current_classes)\n",
        "                all_predicted_probabilities.append(current_probs)\n",
        "\n",
        "    return all_predicted_classes, all_predicted_probabilities\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Image 1:\n",
            "  Predicted class: bus, with probability: 97.16%\n",
            "Image 2:\n",
            "  Predicted class: truck, with probability: 99.97%\n",
            "Image 3:\n",
            "  Predicted class: boat, with probability: 100.00%\n",
            "Image 4:\n",
            "  Predicted class: motorcycle, with probability: 99.65%\n",
            "Image 5:\n",
            "  Predicted class: tabla, with probability: 100.00%\n",
            "Image 6:\n",
            "  Predicted class: cycle, with probability: 100.00%\n",
            "Image 7:\n",
            "  Predicted class: cycle, with probability: 99.99%\n",
            "Image 8:\n",
            "  Predicted class: cycle, with probability: 100.00%\n",
            "Image 9:\n",
            "  Predicted class: cycle, with probability: 99.98%\n",
            "Image 10:\n",
            "  Predicted class: tabla, with probability: 100.00%\n",
            "  Predicted class: harmonium, with probability: 100.00%\n",
            "Image 11:\n",
            "  Predicted class: truck, with probability: 100.00%\n",
            "Image 12:\n",
            "  Predicted class: bus, with probability: 99.97%\n",
            "Image 13:\n",
            "  Predicted class: boat, with probability: 100.00%\n",
            "Image 14:\n",
            "  Predicted class: truck, with probability: 99.82%\n",
            "Image 15:\n",
            "  Predicted class: star, with probability: 100.00%\n",
            "  Predicted class: tabla, with probability: 100.00%\n",
            "  Predicted class: harmonium, with probability: 100.00%\n",
            "Image 16:\n",
            "  Predicted class: tabla, with probability: 100.00%\n",
            "Image 17:\n",
            "  Predicted class: star, with probability: 100.00%\n",
            "  Predicted class: tabla, with probability: 100.00%\n",
            "  Predicted class: harmonium, with probability: 100.00%\n",
            "Image 18:\n",
            "  Predicted class: motorcycle, with probability: 100.00%\n",
            "Image 19:\n",
            "  Predicted class: motorcycle, with probability: 99.92%\n",
            "Image 20:\n",
            "  Predicted class: motorcycle, with probability: 99.47%\n",
            "Image 21:\n",
            "  Predicted class: truck, with probability: 69.01%\n",
            "Image 22:\n",
            "  Predicted class: truck, with probability: 99.91%\n",
            "Image 23:\n",
            "  Predicted class: boat, with probability: 100.00%\n",
            "Image 24:\n",
            "  Predicted class: boat, with probability: 100.00%\n",
            "Image 25:\n",
            "  Predicted class: truck, with probability: 99.99%\n",
            "Image 26:\n",
            "  Predicted class: truck, with probability: 99.96%\n",
            "Image 27:\n",
            "  Predicted class: boat, with probability: 95.17%\n",
            "Image 28:\n",
            "  Predicted class: ekara, with probability: 99.99%\n",
            "Image 29:\n",
            "  Predicted class: motorcycle, with probability: 86.15%\n",
            "  Predicted class: truck, with probability: 80.54%\n",
            "Image 30:\n",
            "  Predicted class: star, with probability: 100.00%\n",
            "Image 31:\n",
            "  Predicted class: boat, with probability: 100.00%\n",
            "Image 32:\n",
            "  Predicted class: truck, with probability: 99.89%\n",
            "Image 33:\n",
            "  Predicted class: cycle, with probability: 100.00%\n",
            "Image 34:\n",
            "  Predicted class: boat, with probability: 100.00%\n",
            "Image 35:\n",
            "  Predicted class: star, with probability: 100.00%\n",
            "  Predicted class: tabla, with probability: 100.00%\n",
            "  Predicted class: harmonium, with probability: 100.00%\n",
            "Image 36:\n",
            "  Predicted class: flutes, with probability: 100.00%\n",
            "Image 37:\n",
            "  Predicted class: boat, with probability: 100.00%\n",
            "Image 38:\n",
            "  Predicted class: cycle, with probability: 100.00%\n",
            "Image 39:\n",
            "  Predicted class: motorcycle, with probability: 98.82%\n",
            "  Predicted class: truck, with probability: 85.75%\n",
            "  Predicted class: boat, with probability: 75.62%\n",
            "Image 40:\n",
            "  Predicted class: star, with probability: 100.00%\n",
            "Image 41:\n",
            "  Predicted class: boat, with probability: 100.00%\n",
            "Image 42:\n",
            "  Predicted class: ekara, with probability: 100.00%\n",
            "  Predicted class: flutes, with probability: 100.00%\n",
            "  Predicted class: tabla, with probability: 100.00%\n",
            "  Predicted class: harmonium, with probability: 100.00%\n",
            "Image 43:\n",
            "  Predicted class: boat, with probability: 100.00%\n",
            "Image 44:\n",
            "  Predicted class: truck, with probability: 100.00%\n",
            "Image 45:\n",
            "  Predicted class: cycle, with probability: 100.00%\n",
            "Image 46:\n",
            "  Predicted class: tabla, with probability: 85.25%\n",
            "Image 47:\n",
            "  Predicted class: boat, with probability: 100.00%\n",
            "Image 48:\n",
            "  Predicted class: truck, with probability: 77.30%\n",
            "Image 49:\n",
            "  Predicted class: boat, with probability: 100.00%\n",
            "Image 50:\n",
            "  Predicted class: boat, with probability: 99.96%\n",
            "Image 51:\n",
            "  Predicted class: motorcycle, with probability: 99.19%\n",
            "  Predicted class: truck, with probability: 51.78%\n",
            "  Predicted class: boat, with probability: 90.03%\n",
            "Image 52:\n",
            "  Predicted class: bus, with probability: 96.21%\n",
            "Image 53:\n",
            "  Predicted class: cycle, with probability: 99.99%\n",
            "Image 54:\n",
            "  Predicted class: cycle, with probability: 100.00%\n",
            "Image 55:\n",
            "  Predicted class: truck, with probability: 87.43%\n",
            "Image 56:\n",
            "  Predicted class: boat, with probability: 97.22%\n",
            "Image 57:\n",
            "  Predicted class: motorcycle, with probability: 98.09%\n",
            "Image 58:\n",
            "  Predicted class: cycle, with probability: 100.00%\n",
            "Image 59:\n",
            "  Predicted class: cycle, with probability: 99.99%\n",
            "Image 60:\n",
            "  Predicted class: bus, with probability: 99.55%\n",
            "Image 61:\n",
            "  Predicted class: flutes, with probability: 100.00%\n",
            "  Predicted class: tabla, with probability: 100.00%\n",
            "  Predicted class: harmonium, with probability: 100.00%\n",
            "Image 62:\n",
            "  Predicted class: truck, with probability: 99.40%\n",
            "Image 63:\n",
            "  Predicted class: boat, with probability: 100.00%\n",
            "Image 64:\n",
            "  Predicted class: truck, with probability: 100.00%\n",
            "Image 65:\n",
            "  Predicted class: tabla, with probability: 99.49%\n",
            "Image 66:\n",
            "  Predicted class: cycle, with probability: 99.84%\n",
            "Image 67:\n",
            "  Predicted class: flutes, with probability: 100.00%\n",
            "  Predicted class: tabla, with probability: 100.00%\n",
            "Image 68:\n",
            "  Predicted class: star, with probability: 100.00%\n",
            "  Predicted class: tabla, with probability: 100.00%\n",
            "Image 69:\n",
            "  Predicted class: truck, with probability: 72.69%\n",
            "  Predicted class: bus, with probability: 63.75%\n",
            "Image 70:\n",
            "  Predicted class: tabla, with probability: 100.00%\n",
            "Image 71:\n",
            "  Predicted class: ekara, with probability: 100.00%\n",
            "  Predicted class: flutes, with probability: 100.00%\n",
            "  Predicted class: tabla, with probability: 100.00%\n",
            "  Predicted class: harmonium, with probability: 100.00%\n",
            "Image 72:\n",
            "  Predicted class: truck, with probability: 62.34%\n",
            "Image 73:\n",
            "  Predicted class: truck, with probability: 98.83%\n",
            "Image 74:\n",
            "  Predicted class: ekara, with probability: 100.00%\n",
            "  Predicted class: flutes, with probability: 100.00%\n",
            "  Predicted class: tabla, with probability: 100.00%\n",
            "  Predicted class: harmonium, with probability: 100.00%\n",
            "Image 75:\n",
            "  Predicted class: ekara, with probability: 100.00%\n",
            "Image 76:\n",
            "  Predicted class: harmonium, with probability: 100.00%\n",
            "Image 77:\n",
            "  Predicted class: tabla, with probability: 100.00%\n",
            "Image 78:\n",
            "  Predicted class: cycle, with probability: 99.43%\n",
            "Image 79:\n",
            "  Predicted class: truck, with probability: 99.99%\n",
            "Image 80:\n",
            "  Predicted class: boat, with probability: 100.00%\n",
            "Image 81:\n",
            "  Predicted class: cycle, with probability: 100.00%\n",
            "Image 82:\n",
            "  Predicted class: truck, with probability: 99.95%\n",
            "Image 83:\n",
            "  Predicted class: cycle, with probability: 100.00%\n",
            "Image 84:\n",
            "  Predicted class: truck, with probability: 99.62%\n",
            "Image 85:\n",
            "  Predicted class: flutes, with probability: 100.00%\n",
            "Image 86:\n",
            "  Predicted class: truck, with probability: 99.85%\n",
            "Image 87:\n",
            "  Predicted class: bus, with probability: 99.99%\n",
            "Image 88:\n",
            "  Predicted class: truck, with probability: 99.79%\n",
            "Image 89:\n",
            "  Predicted class: cycle, with probability: 99.97%\n",
            "Image 90:\n",
            "  Predicted class: boat, with probability: 100.00%\n",
            "Image 91:\n",
            "  Predicted class: boat, with probability: 99.99%\n",
            "Image 92:\n",
            "  Predicted class: cycle, with probability: 100.00%\n",
            "Image 93:\n",
            "  Predicted class: flutes, with probability: 100.00%\n",
            "  Predicted class: tabla, with probability: 100.00%\n",
            "Image 94:\n",
            "  Predicted class: flutes, with probability: 99.99%\n",
            "  Predicted class: tabla, with probability: 100.00%\n",
            "Image 95:\n",
            "  Predicted class: harmonium, with probability: 99.58%\n",
            "Image 96:\n",
            "  Predicted class: cycle, with probability: 100.00%\n",
            "Image 97:\n",
            "  Predicted class: flutes, with probability: 100.00%\n",
            "  Predicted class: tabla, with probability: 100.00%\n",
            "  Predicted class: harmonium, with probability: 100.00%\n",
            "Image 98:\n",
            "  Predicted class: tabla, with probability: 100.00%\n",
            "  Predicted class: harmonium, with probability: 100.00%\n",
            "Image 99:\n",
            "  Predicted class: motorcycle, with probability: 97.44%\n",
            "Image 100:\n",
            "  Predicted class: truck, with probability: 100.00%\n",
            "Image 101:\n",
            "  Predicted class: bus, with probability: 99.68%\n",
            "Image 102:\n",
            "  Predicted class: motorcycle, with probability: 99.90%\n",
            "Image 103:\n",
            "  Predicted class: star, with probability: 100.00%\n",
            "Image 104:\n",
            "  Predicted class: bus, with probability: 96.96%\n",
            "Image 105:\n",
            "  Predicted class: boat, with probability: 100.00%\n",
            "Image 106:\n",
            "  Predicted class: flutes, with probability: 85.70%\n",
            "  Predicted class: tabla, with probability: 99.97%\n",
            "Image 107:\n",
            "  Predicted class: cycle, with probability: 100.00%\n",
            "Image 108:\n",
            "  Predicted class: motorcycle, with probability: 100.00%\n",
            "Image 109:\n",
            "  Predicted class: boat, with probability: 100.00%\n",
            "Image 110:\n",
            "  Predicted class: flutes, with probability: 100.00%\n",
            "  Predicted class: tabla, with probability: 100.00%\n",
            "  Predicted class: harmonium, with probability: 100.00%\n",
            "Image 111:\n",
            "  Predicted class: motorcycle, with probability: 100.00%\n",
            "Image 112:\n",
            "  Predicted class: tabla, with probability: 100.00%\n",
            "Image 113:\n",
            "  Predicted class: ekara, with probability: 100.00%\n",
            "  Predicted class: flutes, with probability: 100.00%\n",
            "  Predicted class: tabla, with probability: 100.00%\n",
            "  Predicted class: harmonium, with probability: 100.00%\n",
            "Image 114:\n",
            "  Predicted class: truck, with probability: 89.69%\n",
            "Image 115:\n",
            "  Predicted class: boat, with probability: 100.00%\n",
            "Image 116:\n",
            "  Predicted class: boat, with probability: 100.00%\n",
            "Image 117:\n",
            "  Predicted class: motorcycle, with probability: 100.00%\n",
            "Image 118:\n",
            "  Predicted class: motorcycle, with probability: 100.00%\n",
            "Image 119:\n",
            "  Predicted class: cycle, with probability: 50.86%\n",
            "Image 120:\n",
            "  Predicted class: harmonium, with probability: 100.00%\n",
            "Image 121:\n",
            "  Predicted class: cycle, with probability: 100.00%\n",
            "Image 122:\n",
            "  Predicted class: cycle, with probability: 100.00%\n",
            "Image 123:\n",
            "  Predicted class: boat, with probability: 100.00%\n",
            "Image 124:\n",
            "  Predicted class: boat, with probability: 100.00%\n",
            "Image 125:\n",
            "  Predicted class: harmonium, with probability: 100.00%\n",
            "Image 126:\n",
            "  Predicted class: truck, with probability: 100.00%\n",
            "Image 127:\n",
            "  Predicted class: motorcycle, with probability: 96.71%\n",
            "Image 128:\n",
            "  Predicted class: ekara, with probability: 97.24%\n",
            "Image 129:\n",
            "  Predicted class: tabla, with probability: 100.00%\n",
            "Image 130:\n",
            "  Predicted class: ekara, with probability: 100.00%\n",
            "  Predicted class: flutes, with probability: 100.00%\n",
            "  Predicted class: tabla, with probability: 100.00%\n",
            "  Predicted class: harmonium, with probability: 99.99%\n",
            "Image 131:\n",
            "  Predicted class: boat, with probability: 95.52%\n",
            "Image 132:\n",
            "  Predicted class: motorcycle, with probability: 99.15%\n",
            "  Predicted class: truck, with probability: 56.29%\n",
            "  Predicted class: boat, with probability: 90.81%\n",
            "Image 133:\n",
            "  Predicted class: truck, with probability: 100.00%\n",
            "Image 134:\n",
            "  Predicted class: cycle, with probability: 100.00%\n",
            "Image 135:\n",
            "  Predicted class: bus, with probability: 95.99%\n",
            "Image 136:\n",
            "  Predicted class: cycle, with probability: 100.00%\n",
            "Image 137:\n",
            "  Predicted class: tabla, with probability: 100.00%\n",
            "Image 138:\n",
            "  Predicted class: boat, with probability: 100.00%\n",
            "Image 139:\n",
            "  Predicted class: motorcycle, with probability: 99.60%\n",
            "Image 140:\n",
            "  Predicted class: motorcycle, with probability: 99.97%\n",
            "Image 141:\n",
            "  Predicted class: truck, with probability: 98.96%\n",
            "Image 142:\n",
            "  Predicted class: boat, with probability: 100.00%\n",
            "Image 143:\n",
            "  Predicted class: boat, with probability: 71.80%\n",
            "Image 144:\n",
            "  Predicted class: motorcycle, with probability: 99.10%\n",
            "  Predicted class: truck, with probability: 82.52%\n",
            "  Predicted class: boat, with probability: 78.87%\n",
            "Image 145:\n",
            "  Predicted class: motorcycle, with probability: 100.00%\n",
            "Image 146:\n",
            "  Predicted class: boat, with probability: 100.00%\n",
            "Image 147:\n",
            "  Predicted class: cycle, with probability: 78.67%\n",
            "Image 148:\n",
            "  Predicted class: cycle, with probability: 99.91%\n",
            "Image 149:\n",
            "  Predicted class: boat, with probability: 100.00%\n",
            "Image 150:\n",
            "  Predicted class: boat, with probability: 100.00%\n",
            "Image 151:\n",
            "  Predicted class: star, with probability: 100.00%\n",
            "Image 152:\n",
            "  Predicted class: ekara, with probability: 100.00%\n",
            "Image 153:\n",
            "  Predicted class: truck, with probability: 99.93%\n",
            "Image 154:\n",
            "  Predicted class: truck, with probability: 98.67%\n",
            "Image 155:\n",
            "  Predicted class: ekara, with probability: 100.00%\n",
            "  Predicted class: flutes, with probability: 100.00%\n",
            "  Predicted class: tabla, with probability: 100.00%\n",
            "  Predicted class: harmonium, with probability: 100.00%\n",
            "Image 156:\n",
            "  Predicted class: motorcycle, with probability: 100.00%\n",
            "Image 157:\n",
            "  Predicted class: tabla, with probability: 100.00%\n",
            "Image 158:\n",
            "  Predicted class: truck, with probability: 90.14%\n",
            "Image 159:\n",
            "  Predicted class: bus, with probability: 98.73%\n",
            "Image 160:\n",
            "  Predicted class: tabla, with probability: 99.93%\n",
            "  Predicted class: harmonium, with probability: 97.26%\n",
            "Image 161:\n",
            "  Predicted class: ekara, with probability: 100.00%\n",
            "  Predicted class: tabla, with probability: 100.00%\n",
            "Image 162:\n",
            "  Predicted class: cycle, with probability: 100.00%\n",
            "Image 163:\n",
            "  Predicted class: motorcycle, with probability: 100.00%\n",
            "Image 164:\n",
            "  Predicted class: cycle, with probability: 100.00%\n",
            "Image 165:\n",
            "  Predicted class: tabla, with probability: 100.00%\n",
            "Image 166:\n",
            "  Predicted class: boat, with probability: 100.00%\n",
            "Image 167:\n",
            "  Predicted class: boat, with probability: 100.00%\n",
            "Image 168:\n",
            "  Predicted class: ekara, with probability: 100.00%\n",
            "Image 169:\n",
            "  Predicted class: motorcycle, with probability: 100.00%\n",
            "Image 170:\n",
            "  Predicted class: harmonium, with probability: 100.00%\n",
            "Image 171:\n",
            "  Predicted class: bus, with probability: 99.97%\n",
            "Image 172:\n",
            "  Predicted class: bus, with probability: 99.97%\n",
            "Image 173:\n",
            "  Predicted class: boat, with probability: 100.00%\n",
            "Image 174:\n",
            "  Predicted class: motorcycle, with probability: 99.56%\n",
            "Image 175:\n",
            "  Predicted class: bus, with probability: 93.00%\n",
            "Image 176:\n",
            "  Predicted class: motorcycle, with probability: 99.64%\n",
            "Image 177:\n",
            "  Predicted class: cycle, with probability: 100.00%\n",
            "Image 178:\n",
            "  Predicted class: flutes, with probability: 99.92%\n",
            "Image 179:\n",
            "  Predicted class: motorcycle, with probability: 100.00%\n",
            "Image 180:\n",
            "  Predicted class: star, with probability: 100.00%\n",
            "  Predicted class: tabla, with probability: 100.00%\n",
            "  Predicted class: harmonium, with probability: 100.00%\n",
            "Image 181:\n",
            "  Predicted class: cycle, with probability: 100.00%\n",
            "Image 182:\n",
            "  Predicted class: boat, with probability: 99.99%\n",
            "Image 183:\n",
            "  Predicted class: motorcycle, with probability: 99.73%\n",
            "  Predicted class: truck, with probability: 81.15%\n",
            "  Predicted class: boat, with probability: 68.18%\n",
            "Image 184:\n",
            "  Predicted class: truck, with probability: 92.14%\n",
            "Image 185:\n",
            "  Predicted class: tabla, with probability: 100.00%\n",
            "Image 186:\n",
            "  Predicted class: truck, with probability: 99.91%\n",
            "Image 187:\n",
            "  Predicted class: cycle, with probability: 100.00%\n",
            "Image 188:\n",
            "  Predicted class: boat, with probability: 100.00%\n",
            "Image 189:\n",
            "  Predicted class: cycle, with probability: 100.00%\n",
            "Image 190:\n",
            "  Predicted class: ekara, with probability: 100.00%\n",
            "  Predicted class: flutes, with probability: 100.00%\n",
            "  Predicted class: tabla, with probability: 100.00%\n",
            "  Predicted class: harmonium, with probability: 99.99%\n",
            "Image 191:\n",
            "  Predicted class: cycle, with probability: 100.00%\n",
            "Image 192:\n",
            "  Predicted class: tabla, with probability: 100.00%\n",
            "Image 193:\n",
            "  Predicted class: cycle, with probability: 100.00%\n",
            "Image 194:\n",
            "  Predicted class: cycle, with probability: 99.99%\n",
            "Image 195:\n",
            "  Predicted class: cycle, with probability: 99.80%\n",
            "Image 196:\n",
            "  Predicted class: cycle, with probability: 100.00%\n",
            "Image 197:\n",
            "  Predicted class: flutes, with probability: 100.00%\n",
            "  Predicted class: tabla, with probability: 100.00%\n",
            "Image 198:\n",
            "  Predicted class: boat, with probability: 99.92%\n",
            "Image 199:\n",
            "  Predicted class: cycle, with probability: 97.88%\n",
            "Image 200:\n",
            "  Predicted class: star, with probability: 100.00%\n",
            "Image 201:\n",
            "  Predicted class: motorcycle, with probability: 100.00%\n",
            "Image 202:\n",
            "  Predicted class: motorcycle, with probability: 99.61%\n",
            "Image 203:\n",
            "  Predicted class: motorcycle, with probability: 100.00%\n",
            "Image 204:\n",
            "  Predicted class: truck, with probability: 99.97%\n",
            "Image 205:\n",
            "  Predicted class: truck, with probability: 99.91%\n",
            "Image 206:\n",
            "  Predicted class: boat, with probability: 99.98%\n",
            "Image 207:\n",
            "  Predicted class: harmonium, with probability: 99.58%\n",
            "Image 208:\n",
            "  Predicted class: tabla, with probability: 100.00%\n",
            "Image 209:\n",
            "  Predicted class: truck, with probability: 100.00%\n",
            "Image 210:\n",
            "  Predicted class: truck, with probability: 99.91%\n",
            "Image 211:\n",
            "  Predicted class: motorcycle, with probability: 99.96%\n",
            "Image 212:\n",
            "  Predicted class: truck, with probability: 99.92%\n",
            "Image 213:\n",
            "  Predicted class: motorcycle, with probability: 100.00%\n",
            "Image 214:\n",
            "  Predicted class: ekara, with probability: 100.00%\n",
            "Image 215:\n",
            "  Predicted class: harmonium, with probability: 100.00%\n",
            "Image 216:\n",
            "  Predicted class: cycle, with probability: 100.00%\n",
            "Image 217:\n",
            "  Predicted class: truck, with probability: 100.00%\n",
            "Image 218:\n",
            "  Predicted class: bus, with probability: 99.46%\n",
            "Image 219:\n",
            "  Predicted class: truck, with probability: 79.60%\n",
            "Image 220:\n",
            "  Predicted class: truck, with probability: 100.00%\n",
            "Image 221:\n",
            "  Predicted class: truck, with probability: 99.22%\n",
            "Image 222:\n",
            "  Predicted class: motorcycle, with probability: 99.99%\n",
            "Image 223:\n",
            "  Predicted class: cycle, with probability: 100.00%\n",
            "Image 224:\n",
            "  Predicted class: truck, with probability: 99.93%\n",
            "Image 225:\n",
            "  Predicted class: motorcycle, with probability: 100.00%\n",
            "Image 226:\n",
            "  Predicted class: cycle, with probability: 100.00%\n",
            "Image 227:\n",
            "  Predicted class: cycle, with probability: 100.00%\n",
            "Image 228:\n",
            "  Predicted class: motorcycle, with probability: 99.31%\n",
            "Image 229:\n",
            "  Predicted class: cycle, with probability: 99.51%\n",
            "Image 230:\n",
            "  Predicted class: truck, with probability: 99.93%\n",
            "Image 231:\n",
            "  Predicted class: cycle, with probability: 99.98%\n",
            "Image 232:\n",
            "  Predicted class: motorcycle, with probability: 100.00%\n",
            "Image 233:\n",
            "  Predicted class: truck, with probability: 100.00%\n",
            "Image 234:\n",
            "  Predicted class: boat, with probability: 100.00%\n",
            "Image 235:\n",
            "  Predicted class: boat, with probability: 88.88%\n",
            "Image 236:\n",
            "  Predicted class: cycle, with probability: 100.00%\n",
            "Image 237:\n",
            "  Predicted class: boat, with probability: 100.00%\n",
            "Image 238:\n",
            "  Predicted class: tabla, with probability: 100.00%\n",
            "Image 239:\n",
            "  Predicted class: boat, with probability: 99.99%\n",
            "Image 240:\n",
            "  Predicted class: boat, with probability: 100.00%\n",
            "Image 241:\n",
            "  Predicted class: tabla, with probability: 100.00%\n",
            "Image 242:\n",
            "  Predicted class: cycle, with probability: 100.00%\n",
            "Image 243:\n",
            "Image 244:\n",
            "  Predicted class: truck, with probability: 99.90%\n",
            "Image 245:\n",
            "  Predicted class: bus, with probability: 70.02%\n",
            "Image 246:\n",
            "  Predicted class: truck, with probability: 98.41%\n",
            "Image 247:\n",
            "  Predicted class: boat, with probability: 99.90%\n",
            "Image 248:\n",
            "  Predicted class: ekara, with probability: 100.00%\n",
            "Image 249:\n",
            "  Predicted class: motorcycle, with probability: 99.18%\n",
            "  Predicted class: truck, with probability: 63.17%\n",
            "  Predicted class: boat, with probability: 68.38%\n",
            "Image 250:\n",
            "  Predicted class: boat, with probability: 100.00%\n",
            "Image 251:\n",
            "  Predicted class: motorcycle, with probability: 96.01%\n",
            "Image 252:\n",
            "  Predicted class: motorcycle, with probability: 99.97%\n",
            "Image 253:\n",
            "  Predicted class: truck, with probability: 90.58%\n",
            "Image 254:\n",
            "  Predicted class: flutes, with probability: 100.00%\n",
            "Image 255:\n",
            "  Predicted class: boat, with probability: 100.00%\n",
            "Image 256:\n",
            "  Predicted class: tabla, with probability: 100.00%\n",
            "Image 257:\n",
            "  Predicted class: cycle, with probability: 100.00%\n",
            "Image 258:\n",
            "  Predicted class: motorcycle, with probability: 50.39%\n",
            "  Predicted class: boat, with probability: 95.79%\n",
            "Image 259:\n",
            "  Predicted class: motorcycle, with probability: 100.00%\n",
            "Image 260:\n",
            "  Predicted class: cycle, with probability: 100.00%\n",
            "Image 261:\n",
            "  Predicted class: truck, with probability: 99.96%\n",
            "Image 262:\n",
            "  Predicted class: tabla, with probability: 100.00%\n",
            "  Predicted class: harmonium, with probability: 100.00%\n",
            "Image 263:\n",
            "  Predicted class: motorcycle, with probability: 100.00%\n",
            "Image 264:\n",
            "  Predicted class: cycle, with probability: 100.00%\n",
            "Image 265:\n",
            "  Predicted class: truck, with probability: 99.98%\n",
            "Image 266:\n",
            "  Predicted class: motorcycle, with probability: 99.36%\n",
            "Image 267:\n",
            "  Predicted class: motorcycle, with probability: 100.00%\n",
            "Image 268:\n",
            "  Predicted class: ekara, with probability: 100.00%\n",
            "  Predicted class: flutes, with probability: 100.00%\n",
            "  Predicted class: tabla, with probability: 100.00%\n",
            "  Predicted class: harmonium, with probability: 100.00%\n",
            "Image 269:\n",
            "  Predicted class: boat, with probability: 100.00%\n",
            "Image 270:\n",
            "  Predicted class: tabla, with probability: 99.73%\n",
            "  Predicted class: harmonium, with probability: 99.67%\n",
            "Image 271:\n",
            "  Predicted class: truck, with probability: 99.97%\n",
            "Image 272:\n",
            "  Predicted class: flutes, with probability: 99.72%\n",
            "  Predicted class: tabla, with probability: 100.00%\n",
            "Image 273:\n",
            "  Predicted class: truck, with probability: 94.29%\n",
            "Image 274:\n",
            "  Predicted class: truck, with probability: 97.29%\n",
            "Image 275:\n",
            "  Predicted class: flutes, with probability: 98.40%\n",
            "Image 276:\n",
            "  Predicted class: cycle, with probability: 100.00%\n",
            "Image 277:\n",
            "  Predicted class: boat, with probability: 100.00%\n",
            "Image 278:\n",
            "  Predicted class: boat, with probability: 99.99%\n",
            "Image 279:\n",
            "  Predicted class: truck, with probability: 99.49%\n",
            "Image 280:\n",
            "  Predicted class: boat, with probability: 100.00%\n",
            "Image 281:\n",
            "  Predicted class: boat, with probability: 100.00%\n",
            "Image 282:\n",
            "  Predicted class: motorcycle, with probability: 99.13%\n",
            "  Predicted class: truck, with probability: 60.61%\n",
            "  Predicted class: boat, with probability: 67.41%\n",
            "Image 283:\n",
            "  Predicted class: bus, with probability: 99.97%\n",
            "Image 284:\n",
            "  Predicted class: boat, with probability: 100.00%\n",
            "Image 285:\n",
            "  Predicted class: boat, with probability: 100.00%\n",
            "Image 286:\n",
            "  Predicted class: cycle, with probability: 99.97%\n",
            "Image 287:\n",
            "  Predicted class: boat, with probability: 54.53%\n",
            "Image 288:\n",
            "  Predicted class: star, with probability: 100.00%\n",
            "Image 289:\n",
            "  Predicted class: bus, with probability: 99.97%\n",
            "Image 290:\n",
            "  Predicted class: tabla, with probability: 100.00%\n",
            "Image 291:\n",
            "  Predicted class: cycle, with probability: 100.00%\n",
            "Image 292:\n",
            "  Predicted class: boat, with probability: 100.00%\n",
            "Image 293:\n",
            "  Predicted class: ekara, with probability: 100.00%\n",
            "  Predicted class: flutes, with probability: 100.00%\n",
            "  Predicted class: tabla, with probability: 100.00%\n",
            "  Predicted class: harmonium, with probability: 100.00%\n",
            "Image 294:\n",
            "  Predicted class: boat, with probability: 100.00%\n",
            "Image 295:\n",
            "  Predicted class: flutes, with probability: 100.00%\n",
            "  Predicted class: tabla, with probability: 100.00%\n",
            "Image 296:\n",
            "  Predicted class: motorcycle, with probability: 94.98%\n",
            "  Predicted class: truck, with probability: 59.10%\n",
            "  Predicted class: boat, with probability: 82.54%\n",
            "Image 297:\n",
            "  Predicted class: cycle, with probability: 100.00%\n",
            "Image 298:\n",
            "  Predicted class: boat, with probability: 90.55%\n",
            "Image 299:\n",
            "  Predicted class: boat, with probability: 100.00%\n",
            "Image 300:\n",
            "  Predicted class: motorcycle, with probability: 100.00%\n",
            "Image 301:\n",
            "  Predicted class: boat, with probability: 100.00%\n",
            "Image 302:\n",
            "  Predicted class: bus, with probability: 98.47%\n",
            "Image 303:\n",
            "  Predicted class: boat, with probability: 100.00%\n",
            "Image 304:\n",
            "  Predicted class: truck, with probability: 99.60%\n",
            "Image 305:\n",
            "  Predicted class: tabla, with probability: 100.00%\n",
            "  Predicted class: harmonium, with probability: 100.00%\n",
            "Image 306:\n",
            "  Predicted class: truck, with probability: 89.65%\n",
            "Image 307:\n",
            "  Predicted class: motorcycle, with probability: 98.30%\n",
            "  Predicted class: boat, with probability: 90.78%\n",
            "Image 308:\n",
            "  Predicted class: boat, with probability: 100.00%\n",
            "Image 309:\n",
            "  Predicted class: cycle, with probability: 100.00%\n",
            "Image 310:\n",
            "  Predicted class: boat, with probability: 100.00%\n",
            "Image 311:\n",
            "  Predicted class: bus, with probability: 99.58%\n",
            "Image 312:\n",
            "  Predicted class: truck, with probability: 99.93%\n",
            "Image 313:\n",
            "  Predicted class: bus, with probability: 89.01%\n",
            "Image 314:\n",
            "  Predicted class: cycle, with probability: 100.00%\n",
            "Image 315:\n",
            "  Predicted class: star, with probability: 100.00%\n",
            "  Predicted class: tabla, with probability: 100.00%\n",
            "  Predicted class: harmonium, with probability: 100.00%\n",
            "Image 316:\n",
            "  Predicted class: cycle, with probability: 99.97%\n",
            "Image 317:\n",
            "  Predicted class: cycle, with probability: 100.00%\n",
            "Image 318:\n",
            "  Predicted class: cycle, with probability: 100.00%\n",
            "Image 319:\n",
            "  Predicted class: tabla, with probability: 100.00%\n",
            "Image 320:\n",
            "  Predicted class: cycle, with probability: 100.00%\n",
            "Image 321:\n",
            "  Predicted class: harmonium, with probability: 100.00%\n",
            "Image 322:\n",
            "  Predicted class: truck, with probability: 99.80%\n",
            "Image 323:\n",
            "  Predicted class: star, with probability: 87.37%\n",
            "Image 324:\n",
            "  Predicted class: truck, with probability: 93.25%\n",
            "Image 325:\n",
            "  Predicted class: tabla, with probability: 99.99%\n",
            "Image 326:\n",
            "  Predicted class: cycle, with probability: 100.00%\n",
            "Image 327:\n",
            "  Predicted class: motorcycle, with probability: 94.24%\n",
            "Image 328:\n",
            "  Predicted class: truck, with probability: 100.00%\n",
            "Image 329:\n",
            "  Predicted class: truck, with probability: 52.95%\n",
            "  Predicted class: bus, with probability: 68.15%\n",
            "Image 330:\n",
            "  Predicted class: truck, with probability: 99.99%\n",
            "Image 331:\n",
            "  Predicted class: cycle, with probability: 99.94%\n",
            "Image 332:\n",
            "  Predicted class: motorcycle, with probability: 100.00%\n",
            "Image 333:\n",
            "  Predicted class: truck, with probability: 99.54%\n",
            "Image 334:\n",
            "  Predicted class: motorcycle, with probability: 99.99%\n",
            "Image 335:\n",
            "  Predicted class: motorcycle, with probability: 64.28%\n",
            "  Predicted class: cycle, with probability: 56.91%\n",
            "Image 336:\n",
            "  Predicted class: boat, with probability: 99.92%\n",
            "Image 337:\n",
            "  Predicted class: truck, with probability: 81.20%\n",
            "Image 338:\n",
            "  Predicted class: cycle, with probability: 99.91%\n",
            "Image 339:\n",
            "  Predicted class: flutes, with probability: 100.00%\n",
            "  Predicted class: tabla, with probability: 100.00%\n",
            "  Predicted class: harmonium, with probability: 100.00%\n",
            "Image 340:\n",
            "  Predicted class: truck, with probability: 100.00%\n",
            "Image 341:\n",
            "  Predicted class: cycle, with probability: 100.00%\n",
            "Image 342:\n",
            "  Predicted class: harmonium, with probability: 100.00%\n",
            "Image 343:\n",
            "  Predicted class: motorcycle, with probability: 99.84%\n",
            "Image 344:\n",
            "  Predicted class: motorcycle, with probability: 100.00%\n",
            "Image 345:\n",
            "  Predicted class: truck, with probability: 99.95%\n",
            "Image 346:\n",
            "  Predicted class: truck, with probability: 99.94%\n",
            "Image 347:\n",
            "  Predicted class: boat, with probability: 100.00%\n",
            "Image 348:\n",
            "  Predicted class: truck, with probability: 94.67%\n",
            "Image 349:\n",
            "  Predicted class: ekara, with probability: 100.00%\n",
            "  Predicted class: tabla, with probability: 99.93%\n",
            "Image 350:\n",
            "  Predicted class: motorcycle, with probability: 100.00%\n",
            "Image 351:\n",
            "  Predicted class: truck, with probability: 99.99%\n",
            "Image 352:\n",
            "  Predicted class: cycle, with probability: 100.00%\n",
            "Image 353:\n",
            "  Predicted class: star, with probability: 100.00%\n",
            "  Predicted class: tabla, with probability: 100.00%\n",
            "  Predicted class: harmonium, with probability: 100.00%\n",
            "Image 354:\n",
            "  Predicted class: bus, with probability: 98.14%\n",
            "Image 355:\n",
            "  Predicted class: motorcycle, with probability: 91.62%\n",
            "Image 356:\n",
            "  Predicted class: boat, with probability: 100.00%\n",
            "Image 357:\n",
            "  Predicted class: cycle, with probability: 100.00%\n",
            "Image 358:\n",
            "  Predicted class: truck, with probability: 100.00%\n",
            "Image 359:\n",
            "  Predicted class: truck, with probability: 86.96%\n",
            "Image 360:\n",
            "  Predicted class: boat, with probability: 99.99%\n",
            "Image 361:\n",
            "  Predicted class: cycle, with probability: 100.00%\n",
            "Image 362:\n",
            "  Predicted class: flutes, with probability: 100.00%\n",
            "  Predicted class: tabla, with probability: 100.00%\n",
            "  Predicted class: harmonium, with probability: 100.00%\n",
            "Image 363:\n",
            "  Predicted class: boat, with probability: 100.00%\n",
            "Image 364:\n",
            "  Predicted class: truck, with probability: 99.97%\n",
            "Image 365:\n",
            "  Predicted class: cycle, with probability: 99.93%\n",
            "Image 366:\n",
            "  Predicted class: motorcycle, with probability: 100.00%\n",
            "Image 367:\n",
            "  Predicted class: motorcycle, with probability: 97.26%\n",
            "Image 368:\n",
            "  Predicted class: harmonium, with probability: 100.00%\n",
            "Image 369:\n",
            "  Predicted class: cycle, with probability: 100.00%\n",
            "Image 370:\n",
            "  Predicted class: truck, with probability: 99.01%\n",
            "Image 371:\n",
            "  Predicted class: motorcycle, with probability: 100.00%\n",
            "Image 372:\n",
            "  Predicted class: flutes, with probability: 99.97%\n",
            "  Predicted class: tabla, with probability: 100.00%\n",
            "  Predicted class: harmonium, with probability: 99.99%\n",
            "Image 373:\n",
            "  Predicted class: tabla, with probability: 99.96%\n",
            "Image 374:\n",
            "  Predicted class: boat, with probability: 100.00%\n",
            "Image 375:\n",
            "  Predicted class: truck, with probability: 100.00%\n",
            "Image 376:\n",
            "  Predicted class: bus, with probability: 99.78%\n",
            "Image 377:\n",
            "  Predicted class: ekara, with probability: 100.00%\n",
            "  Predicted class: tabla, with probability: 100.00%\n",
            "Image 378:\n",
            "  Predicted class: motorcycle, with probability: 100.00%\n",
            "Image 379:\n",
            "  Predicted class: boat, with probability: 99.20%\n",
            "Image 380:\n",
            "  Predicted class: motorcycle, with probability: 99.81%\n",
            "Image 381:\n",
            "  Predicted class: truck, with probability: 99.14%\n",
            "Image 382:\n",
            "  Predicted class: cycle, with probability: 100.00%\n",
            "Image 383:\n",
            "Image 384:\n",
            "  Predicted class: motorcycle, with probability: 100.00%\n",
            "Image 385:\n",
            "  Predicted class: boat, with probability: 100.00%\n",
            "Image 386:\n",
            "  Predicted class: ekara, with probability: 100.00%\n",
            "  Predicted class: flutes, with probability: 100.00%\n",
            "  Predicted class: tabla, with probability: 100.00%\n",
            "  Predicted class: harmonium, with probability: 100.00%\n",
            "Image 387:\n",
            "  Predicted class: star, with probability: 100.00%\n",
            "  Predicted class: tabla, with probability: 100.00%\n",
            "Image 388:\n",
            "  Predicted class: cycle, with probability: 100.00%\n",
            "Image 389:\n",
            "  Predicted class: bus, with probability: 100.00%\n",
            "Image 390:\n",
            "  Predicted class: motorcycle, with probability: 100.00%\n",
            "Image 391:\n",
            "  Predicted class: cycle, with probability: 100.00%\n",
            "Image 392:\n",
            "  Predicted class: truck, with probability: 87.34%\n",
            "Image 393:\n",
            "  Predicted class: truck, with probability: 100.00%\n",
            "Image 394:\n",
            "  Predicted class: star, with probability: 100.00%\n",
            "  Predicted class: tabla, with probability: 100.00%\n",
            "Image 395:\n",
            "  Predicted class: truck, with probability: 100.00%\n",
            "Image 396:\n",
            "  Predicted class: star, with probability: 100.00%\n",
            "  Predicted class: tabla, with probability: 100.00%\n",
            "  Predicted class: harmonium, with probability: 100.00%\n",
            "Image 397:\n",
            "  Predicted class: tabla, with probability: 100.00%\n",
            "Image 398:\n",
            "  Predicted class: star, with probability: 100.00%\n",
            "  Predicted class: tabla, with probability: 100.00%\n",
            "Image 399:\n",
            "  Predicted class: tabla, with probability: 93.18%\n",
            "Image 400:\n",
            "  Predicted class: boat, with probability: 98.56%\n",
            "Image 401:\n",
            "  Predicted class: bus, with probability: 50.19%\n",
            "Image 402:\n",
            "  Predicted class: cycle, with probability: 100.00%\n",
            "Image 403:\n",
            "  Predicted class: motorcycle, with probability: 100.00%\n",
            "Image 404:\n",
            "  Predicted class: star, with probability: 100.00%\n",
            "  Predicted class: tabla, with probability: 100.00%\n",
            "Image 405:\n",
            "  Predicted class: ekara, with probability: 100.00%\n",
            "  Predicted class: tabla, with probability: 100.00%\n",
            "Image 406:\n",
            "  Predicted class: cycle, with probability: 100.00%\n",
            "Image 407:\n",
            "  Predicted class: boat, with probability: 100.00%\n",
            "Image 408:\n",
            "  Predicted class: flutes, with probability: 100.00%\n",
            "Image 409:\n",
            "  Predicted class: boat, with probability: 100.00%\n",
            "Image 410:\n",
            "  Predicted class: tabla, with probability: 100.00%\n",
            "Image 411:\n",
            "  Predicted class: motorcycle, with probability: 100.00%\n",
            "Image 412:\n",
            "  Predicted class: truck, with probability: 99.73%\n",
            "Image 413:\n",
            "  Predicted class: bus, with probability: 99.99%\n",
            "Image 414:\n",
            "  Predicted class: ekara, with probability: 100.00%\n",
            "Image 415:\n",
            "  Predicted class: motorcycle, with probability: 100.00%\n",
            "Image 416:\n",
            "  Predicted class: star, with probability: 100.00%\n",
            "Image 417:\n",
            "  Predicted class: flutes, with probability: 100.00%\n",
            "Image 418:\n",
            "  Predicted class: flutes, with probability: 100.00%\n",
            "  Predicted class: tabla, with probability: 100.00%\n",
            "Image 419:\n",
            "  Predicted class: boat, with probability: 100.00%\n",
            "Image 420:\n",
            "  Predicted class: tabla, with probability: 99.91%\n",
            "Image 421:\n",
            "  Predicted class: cycle, with probability: 99.52%\n",
            "Image 422:\n",
            "  Predicted class: boat, with probability: 100.00%\n",
            "Image 423:\n",
            "  Predicted class: star, with probability: 100.00%\n",
            "Image 424:\n",
            "  Predicted class: boat, with probability: 97.65%\n",
            "Image 425:\n",
            "  Predicted class: truck, with probability: 99.14%\n",
            "Image 426:\n",
            "  Predicted class: boat, with probability: 98.22%\n",
            "Image 427:\n",
            "  Predicted class: boat, with probability: 100.00%\n",
            "Image 428:\n",
            "  Predicted class: bus, with probability: 99.75%\n",
            "Image 429:\n",
            "  Predicted class: flutes, with probability: 100.00%\n",
            "  Predicted class: tabla, with probability: 100.00%\n",
            "Image 430:\n",
            "  Predicted class: boat, with probability: 100.00%\n",
            "Image 431:\n",
            "  Predicted class: cycle, with probability: 100.00%\n",
            "Image 432:\n",
            "  Predicted class: tabla, with probability: 100.00%\n",
            "Image 433:\n",
            "  Predicted class: boat, with probability: 100.00%\n",
            "Image 434:\n",
            "  Predicted class: cycle, with probability: 100.00%\n",
            "Image 435:\n",
            "  Predicted class: truck, with probability: 99.99%\n",
            "Image 436:\n",
            "  Predicted class: flutes, with probability: 100.00%\n",
            "  Predicted class: tabla, with probability: 100.00%\n",
            "Image 437:\n",
            "  Predicted class: motorcycle, with probability: 100.00%\n",
            "Image 438:\n",
            "  Predicted class: flutes, with probability: 100.00%\n",
            "Image 439:\n",
            "  Predicted class: bus, with probability: 99.91%\n",
            "Image 440:\n",
            "  Predicted class: ekara, with probability: 100.00%\n",
            "  Predicted class: flutes, with probability: 100.00%\n",
            "  Predicted class: tabla, with probability: 100.00%\n",
            "  Predicted class: harmonium, with probability: 99.98%\n",
            "Image 441:\n",
            "  Predicted class: motorcycle, with probability: 99.88%\n",
            "Image 442:\n",
            "  Predicted class: motorcycle, with probability: 99.67%\n",
            "  Predicted class: boat, with probability: 94.30%\n",
            "Image 443:\n",
            "  Predicted class: cycle, with probability: 100.00%\n",
            "Image 444:\n",
            "  Predicted class: boat, with probability: 100.00%\n",
            "Image 445:\n",
            "  Predicted class: star, with probability: 100.00%\n",
            "  Predicted class: tabla, with probability: 100.00%\n",
            "  Predicted class: harmonium, with probability: 99.99%\n",
            "Image 446:\n",
            "  Predicted class: motorcycle, with probability: 100.00%\n",
            "Image 447:\n",
            "  Predicted class: bus, with probability: 99.94%\n",
            "Image 448:\n",
            "  Predicted class: boat, with probability: 100.00%\n",
            "Image 449:\n",
            "  Predicted class: cycle, with probability: 100.00%\n",
            "Image 450:\n",
            "  Predicted class: boat, with probability: 97.82%\n",
            "Image 451:\n",
            "  Predicted class: tabla, with probability: 98.25%\n",
            "Image 452:\n",
            "  Predicted class: boat, with probability: 69.03%\n",
            "Image 453:\n",
            "  Predicted class: boat, with probability: 100.00%\n",
            "Image 454:\n",
            "  Predicted class: ekara, with probability: 100.00%\n",
            "  Predicted class: flutes, with probability: 100.00%\n",
            "  Predicted class: tabla, with probability: 100.00%\n",
            "  Predicted class: harmonium, with probability: 100.00%\n",
            "Image 455:\n",
            "  Predicted class: cycle, with probability: 100.00%\n",
            "Image 456:\n",
            "  Predicted class: cycle, with probability: 99.73%\n",
            "Image 457:\n",
            "  Predicted class: star, with probability: 99.99%\n",
            "  Predicted class: tabla, with probability: 100.00%\n",
            "  Predicted class: harmonium, with probability: 100.00%\n",
            "Image 458:\n",
            "  Predicted class: boat, with probability: 100.00%\n",
            "Image 459:\n",
            "  Predicted class: boat, with probability: 86.43%\n",
            "Image 460:\n",
            "  Predicted class: star, with probability: 100.00%\n",
            "  Predicted class: tabla, with probability: 100.00%\n",
            "Image 461:\n",
            "  Predicted class: truck, with probability: 99.69%\n",
            "Image 462:\n",
            "  Predicted class: flutes, with probability: 100.00%\n",
            "  Predicted class: tabla, with probability: 100.00%\n",
            "Image 463:\n",
            "  Predicted class: cycle, with probability: 85.96%\n",
            "Image 464:\n",
            "  Predicted class: truck, with probability: 85.55%\n",
            "Image 465:\n",
            "  Predicted class: motorcycle, with probability: 93.83%\n",
            "  Predicted class: truck, with probability: 73.60%\n",
            "  Predicted class: boat, with probability: 76.03%\n",
            "Image 466:\n",
            "  Predicted class: motorcycle, with probability: 92.71%\n",
            "  Predicted class: truck, with probability: 66.13%\n",
            "  Predicted class: boat, with probability: 70.42%\n",
            "Image 467:\n",
            "  Predicted class: motorcycle, with probability: 99.80%\n",
            "Image 468:\n",
            "  Predicted class: truck, with probability: 54.99%\n",
            "  Predicted class: bus, with probability: 81.30%\n",
            "Image 469:\n",
            "  Predicted class: motorcycle, with probability: 100.00%\n",
            "Image 470:\n",
            "  Predicted class: motorcycle, with probability: 100.00%\n",
            "Image 471:\n",
            "  Predicted class: truck, with probability: 58.47%\n",
            "  Predicted class: bus, with probability: 63.35%\n",
            "Image 472:\n",
            "  Predicted class: flutes, with probability: 100.00%\n",
            "  Predicted class: tabla, with probability: 100.00%\n",
            "Image 473:\n",
            "  Predicted class: boat, with probability: 100.00%\n",
            "Image 474:\n",
            "  Predicted class: motorcycle, with probability: 99.66%\n",
            "Image 475:\n",
            "  Predicted class: star, with probability: 100.00%\n",
            "  Predicted class: tabla, with probability: 100.00%\n",
            "  Predicted class: harmonium, with probability: 100.00%\n",
            "Image 476:\n",
            "  Predicted class: motorcycle, with probability: 99.92%\n",
            "Image 477:\n",
            "  Predicted class: boat, with probability: 100.00%\n",
            "Image 478:\n",
            "  Predicted class: ekara, with probability: 100.00%\n",
            "Image 479:\n",
            "  Predicted class: cycle, with probability: 100.00%\n",
            "Image 480:\n",
            "  Predicted class: boat, with probability: 100.00%\n",
            "Image 481:\n",
            "  Predicted class: cycle, with probability: 100.00%\n",
            "Image 482:\n",
            "  Predicted class: truck, with probability: 100.00%\n",
            "Image 483:\n",
            "  Predicted class: harmonium, with probability: 100.00%\n",
            "Image 484:\n",
            "  Predicted class: cycle, with probability: 99.99%\n",
            "Image 485:\n",
            "  Predicted class: motorcycle, with probability: 99.91%\n",
            "Image 486:\n",
            "  Predicted class: cycle, with probability: 100.00%\n",
            "Image 487:\n",
            "  Predicted class: boat, with probability: 98.42%\n",
            "Image 488:\n",
            "  Predicted class: flutes, with probability: 100.00%\n",
            "Image 489:\n",
            "  Predicted class: motorcycle, with probability: 100.00%\n",
            "Image 490:\n",
            "  Predicted class: ekara, with probability: 99.98%\n",
            "Image 491:\n",
            "  Predicted class: boat, with probability: 100.00%\n",
            "Image 492:\n",
            "  Predicted class: cycle, with probability: 100.00%\n",
            "Image 493:\n",
            "  Predicted class: motorcycle, with probability: 98.53%\n",
            "  Predicted class: boat, with probability: 88.96%\n",
            "Image 494:\n",
            "  Predicted class: tabla, with probability: 100.00%\n",
            "  Predicted class: harmonium, with probability: 100.00%\n",
            "Image 495:\n",
            "  Predicted class: boat, with probability: 100.00%\n",
            "Image 496:\n",
            "  Predicted class: cycle, with probability: 100.00%\n",
            "Image 497:\n",
            "  Predicted class: ekara, with probability: 100.00%\n",
            "  Predicted class: tabla, with probability: 100.00%\n",
            "Image 498:\n",
            "  Predicted class: boat, with probability: 100.00%\n",
            "Image 499:\n",
            "  Predicted class: cycle, with probability: 100.00%\n",
            "Image 500:\n",
            "  Predicted class: boat, with probability: 100.00%\n",
            "Image 501:\n",
            "  Predicted class: cycle, with probability: 100.00%\n",
            "Image 502:\n",
            "  Predicted class: motorcycle, with probability: 99.88%\n",
            "Image 503:\n",
            "  Predicted class: truck, with probability: 99.90%\n",
            "Image 504:\n",
            "  Predicted class: cycle, with probability: 100.00%\n",
            "Image 505:\n",
            "  Predicted class: motorcycle, with probability: 99.49%\n",
            "  Predicted class: truck, with probability: 64.00%\n",
            "  Predicted class: boat, with probability: 86.22%\n",
            "Image 506:\n",
            "  Predicted class: flutes, with probability: 99.99%\n",
            "  Predicted class: tabla, with probability: 100.00%\n",
            "Image 507:\n",
            "  Predicted class: boat, with probability: 99.29%\n",
            "Image 508:\n",
            "  Predicted class: boat, with probability: 94.58%\n",
            "Image 509:\n",
            "  Predicted class: truck, with probability: 100.00%\n",
            "Image 510:\n",
            "  Predicted class: truck, with probability: 99.85%\n",
            "Image 511:\n",
            "  Predicted class: tabla, with probability: 100.00%\n",
            "Image 512:\n",
            "  Predicted class: boat, with probability: 99.70%\n",
            "Image 513:\n",
            "  Predicted class: motorcycle, with probability: 100.00%\n",
            "Image 514:\n",
            "  Predicted class: flutes, with probability: 100.00%\n",
            "Image 515:\n",
            "  Predicted class: bus, with probability: 98.52%\n",
            "Image 516:\n",
            "  Predicted class: cycle, with probability: 100.00%\n",
            "Image 517:\n",
            "  Predicted class: truck, with probability: 100.00%\n",
            "Image 518:\n",
            "  Predicted class: boat, with probability: 100.00%\n",
            "Image 519:\n",
            "  Predicted class: ekara, with probability: 100.00%\n",
            "  Predicted class: flutes, with probability: 100.00%\n",
            "  Predicted class: tabla, with probability: 100.00%\n",
            "  Predicted class: harmonium, with probability: 100.00%\n",
            "Image 520:\n",
            "  Predicted class: motorcycle, with probability: 99.11%\n",
            "  Predicted class: boat, with probability: 91.37%\n",
            "Image 521:\n",
            "  Predicted class: motorcycle, with probability: 99.72%\n",
            "Image 522:\n",
            "  Predicted class: truck, with probability: 100.00%\n",
            "Image 523:\n",
            "  Predicted class: cycle, with probability: 100.00%\n",
            "Image 524:\n",
            "  Predicted class: boat, with probability: 99.86%\n",
            "Image 525:\n",
            "  Predicted class: cycle, with probability: 99.87%\n",
            "Image 526:\n",
            "  Predicted class: motorcycle, with probability: 99.24%\n",
            "  Predicted class: truck, with probability: 62.55%\n",
            "  Predicted class: boat, with probability: 70.06%\n",
            "Image 527:\n",
            "  Predicted class: tabla, with probability: 99.95%\n",
            "  Predicted class: harmonium, with probability: 99.20%\n",
            "Image 528:\n",
            "  Predicted class: boat, with probability: 100.00%\n",
            "Image 529:\n",
            "  Predicted class: motorcycle, with probability: 70.23%\n",
            "Image 530:\n",
            "  Predicted class: bus, with probability: 100.00%\n",
            "Image 531:\n",
            "  Predicted class: tabla, with probability: 99.99%\n",
            "Image 532:\n",
            "  Predicted class: truck, with probability: 99.78%\n",
            "Image 533:\n",
            "  Predicted class: boat, with probability: 100.00%\n",
            "Image 534:\n",
            "  Predicted class: star, with probability: 99.89%\n",
            "  Predicted class: tabla, with probability: 92.89%\n",
            "Image 535:\n",
            "  Predicted class: cycle, with probability: 100.00%\n",
            "Image 536:\n",
            "  Predicted class: star, with probability: 100.00%\n",
            "Image 537:\n",
            "Image 538:\n",
            "  Predicted class: harmonium, with probability: 100.00%\n",
            "Image 539:\n",
            "  Predicted class: boat, with probability: 100.00%\n",
            "Image 540:\n",
            "  Predicted class: cycle, with probability: 100.00%\n",
            "Image 541:\n",
            "  Predicted class: boat, with probability: 100.00%\n",
            "Image 542:\n",
            "  Predicted class: harmonium, with probability: 100.00%\n",
            "Image 543:\n",
            "  Predicted class: cycle, with probability: 100.00%\n",
            "Image 544:\n",
            "  Predicted class: bus, with probability: 100.00%\n",
            "Image 545:\n",
            "  Predicted class: bus, with probability: 88.91%\n",
            "Image 546:\n",
            "  Predicted class: motorcycle, with probability: 100.00%\n",
            "Image 547:\n",
            "  Predicted class: motorcycle, with probability: 99.94%\n",
            "Image 548:\n",
            "Image 549:\n",
            "  Predicted class: truck, with probability: 74.10%\n",
            "  Predicted class: bus, with probability: 63.40%\n",
            "Image 550:\n",
            "  Predicted class: tabla, with probability: 97.89%\n",
            "Image 551:\n",
            "  Predicted class: cycle, with probability: 100.00%\n",
            "Image 552:\n",
            "  Predicted class: truck, with probability: 100.00%\n",
            "Image 553:\n",
            "  Predicted class: truck, with probability: 99.99%\n",
            "Image 554:\n",
            "  Predicted class: boat, with probability: 99.95%\n",
            "Image 555:\n",
            "  Predicted class: flutes, with probability: 100.00%\n",
            "Image 556:\n",
            "  Predicted class: cycle, with probability: 100.00%\n",
            "Image 557:\n",
            "  Predicted class: boat, with probability: 100.00%\n",
            "Image 558:\n",
            "  Predicted class: truck, with probability: 99.29%\n",
            "Image 559:\n",
            "  Predicted class: boat, with probability: 99.98%\n",
            "Image 560:\n",
            "  Predicted class: flutes, with probability: 100.00%\n",
            "  Predicted class: tabla, with probability: 100.00%\n",
            "Image 561:\n",
            "  Predicted class: boat, with probability: 100.00%\n",
            "Image 562:\n",
            "  Predicted class: motorcycle, with probability: 100.00%\n",
            "Image 563:\n",
            "  Predicted class: tabla, with probability: 100.00%\n",
            "Image 564:\n",
            "  Predicted class: cycle, with probability: 100.00%\n",
            "Image 565:\n",
            "  Predicted class: star, with probability: 100.00%\n",
            "Image 566:\n",
            "  Predicted class: flutes, with probability: 100.00%\n",
            "  Predicted class: tabla, with probability: 100.00%\n",
            "  Predicted class: harmonium, with probability: 100.00%\n",
            "Image 567:\n",
            "  Predicted class: bus, with probability: 96.45%\n",
            "Image 568:\n",
            "  Predicted class: motorcycle, with probability: 100.00%\n",
            "Image 569:\n",
            "  Predicted class: motorcycle, with probability: 99.96%\n",
            "Image 570:\n",
            "  Predicted class: cycle, with probability: 100.00%\n",
            "Image 571:\n",
            "  Predicted class: motorcycle, with probability: 99.94%\n",
            "Image 572:\n",
            "  Predicted class: flutes, with probability: 100.00%\n",
            "  Predicted class: tabla, with probability: 100.00%\n",
            "  Predicted class: harmonium, with probability: 100.00%\n",
            "Image 573:\n",
            "  Predicted class: boat, with probability: 99.99%\n",
            "Image 574:\n",
            "  Predicted class: truck, with probability: 100.00%\n",
            "Image 575:\n",
            "  Predicted class: boat, with probability: 100.00%\n",
            "Image 576:\n",
            "  Predicted class: tabla, with probability: 100.00%\n",
            "Image 577:\n",
            "  Predicted class: truck, with probability: 100.00%\n",
            "Image 578:\n",
            "  Predicted class: tabla, with probability: 99.98%\n",
            "Image 579:\n",
            "  Predicted class: boat, with probability: 100.00%\n",
            "Image 580:\n",
            "  Predicted class: tabla, with probability: 99.97%\n",
            "Image 581:\n",
            "  Predicted class: tabla, with probability: 100.00%\n",
            "Image 582:\n",
            "  Predicted class: motorcycle, with probability: 98.61%\n",
            "Image 583:\n",
            "  Predicted class: motorcycle, with probability: 100.00%\n",
            "Image 584:\n",
            "  Predicted class: boat, with probability: 99.96%\n",
            "Image 585:\n",
            "  Predicted class: truck, with probability: 97.41%\n",
            "Image 586:\n",
            "  Predicted class: cycle, with probability: 99.97%\n",
            "Image 587:\n",
            "  Predicted class: motorcycle, with probability: 99.98%\n",
            "Image 588:\n",
            "  Predicted class: star, with probability: 66.05%\n",
            "Image 589:\n",
            "  Predicted class: ekara, with probability: 100.00%\n",
            "  Predicted class: flutes, with probability: 99.98%\n",
            "  Predicted class: tabla, with probability: 100.00%\n",
            "  Predicted class: harmonium, with probability: 100.00%\n",
            "Image 590:\n",
            "  Predicted class: motorcycle, with probability: 98.21%\n",
            "Image 591:\n",
            "  Predicted class: boat, with probability: 100.00%\n",
            "Image 592:\n",
            "  Predicted class: boat, with probability: 100.00%\n",
            "Image 593:\n",
            "  Predicted class: boat, with probability: 100.00%\n",
            "Image 594:\n",
            "  Predicted class: boat, with probability: 100.00%\n",
            "Image 595:\n",
            "  Predicted class: motorcycle, with probability: 100.00%\n",
            "Image 596:\n",
            "  Predicted class: truck, with probability: 72.90%\n",
            "  Predicted class: boat, with probability: 53.70%\n",
            "Image 597:\n",
            "  Predicted class: bus, with probability: 99.32%\n",
            "Image 598:\n",
            "  Predicted class: cycle, with probability: 100.00%\n",
            "Image 599:\n",
            "  Predicted class: star, with probability: 99.56%\n",
            "Image 600:\n",
            "  Predicted class: boat, with probability: 100.00%\n",
            "Image 601:\n",
            "  Predicted class: boat, with probability: 100.00%\n",
            "Image 602:\n",
            "  Predicted class: bus, with probability: 100.00%\n",
            "Image 603:\n",
            "  Predicted class: tabla, with probability: 100.00%\n",
            "  Predicted class: harmonium, with probability: 100.00%\n",
            "Image 604:\n",
            "  Predicted class: cycle, with probability: 100.00%\n",
            "Image 605:\n",
            "  Predicted class: cycle, with probability: 99.26%\n",
            "Image 606:\n",
            "  Predicted class: truck, with probability: 99.93%\n",
            "Image 607:\n",
            "  Predicted class: truck, with probability: 99.83%\n",
            "Image 608:\n",
            "  Predicted class: star, with probability: 95.46%\n",
            "  Predicted class: tabla, with probability: 68.34%\n",
            "Image 609:\n",
            "  Predicted class: boat, with probability: 100.00%\n",
            "Image 610:\n",
            "  Predicted class: boat, with probability: 100.00%\n",
            "Image 611:\n",
            "  Predicted class: cycle, with probability: 88.09%\n",
            "Image 612:\n",
            "  Predicted class: truck, with probability: 99.66%\n",
            "Image 613:\n",
            "  Predicted class: motorcycle, with probability: 63.55%\n",
            "  Predicted class: cycle, with probability: 86.94%\n",
            "Image 614:\n",
            "  Predicted class: motorcycle, with probability: 99.99%\n",
            "Image 615:\n",
            "  Predicted class: flutes, with probability: 99.98%\n",
            "Image 616:\n",
            "  Predicted class: truck, with probability: 100.00%\n",
            "Image 617:\n",
            "  Predicted class: tabla, with probability: 100.00%\n",
            "Image 618:\n",
            "  Predicted class: boat, with probability: 100.00%\n",
            "Image 619:\n",
            "  Predicted class: boat, with probability: 100.00%\n",
            "Image 620:\n",
            "  Predicted class: tabla, with probability: 100.00%\n",
            "Image 621:\n",
            "  Predicted class: cycle, with probability: 100.00%\n",
            "Image 622:\n",
            "  Predicted class: cycle, with probability: 99.98%\n",
            "Image 623:\n",
            "  Predicted class: boat, with probability: 100.00%\n",
            "Image 624:\n",
            "  Predicted class: truck, with probability: 99.77%\n",
            "Image 625:\n",
            "  Predicted class: cycle, with probability: 100.00%\n",
            "Image 626:\n",
            "  Predicted class: cycle, with probability: 100.00%\n",
            "Image 627:\n",
            "  Predicted class: boat, with probability: 100.00%\n",
            "Image 628:\n",
            "  Predicted class: boat, with probability: 69.23%\n",
            "Image 629:\n",
            "  Predicted class: motorcycle, with probability: 100.00%\n",
            "Image 630:\n",
            "  Predicted class: cycle, with probability: 100.00%\n",
            "Image 631:\n",
            "  Predicted class: boat, with probability: 88.28%\n",
            "Image 632:\n",
            "  Predicted class: tabla, with probability: 100.00%\n",
            "Image 633:\n",
            "  Predicted class: truck, with probability: 82.89%\n",
            "Image 634:\n",
            "  Predicted class: flutes, with probability: 100.00%\n",
            "Image 635:\n",
            "  Predicted class: tabla, with probability: 100.00%\n",
            "Image 636:\n",
            "  Predicted class: truck, with probability: 100.00%\n",
            "Image 637:\n",
            "  Predicted class: truck, with probability: 99.95%\n",
            "Image 638:\n",
            "  Predicted class: star, with probability: 100.00%\n",
            "  Predicted class: tabla, with probability: 100.00%\n",
            "  Predicted class: harmonium, with probability: 100.00%\n",
            "Image 639:\n",
            "  Predicted class: star, with probability: 100.00%\n",
            "  Predicted class: tabla, with probability: 100.00%\n",
            "Image 640:\n",
            "  Predicted class: truck, with probability: 100.00%\n",
            "Image 641:\n",
            "  Predicted class: truck, with probability: 97.17%\n",
            "Image 642:\n",
            "  Predicted class: motorcycle, with probability: 100.00%\n",
            "Image 643:\n",
            "  Predicted class: motorcycle, with probability: 83.00%\n",
            "Image 644:\n",
            "  Predicted class: cycle, with probability: 100.00%\n",
            "Image 645:\n",
            "  Predicted class: truck, with probability: 99.95%\n",
            "Image 646:\n",
            "  Predicted class: ekara, with probability: 100.00%\n",
            "  Predicted class: tabla, with probability: 99.97%\n",
            "Image 647:\n",
            "  Predicted class: boat, with probability: 100.00%\n",
            "Image 648:\n",
            "  Predicted class: motorcycle, with probability: 99.23%\n",
            "  Predicted class: truck, with probability: 63.44%\n",
            "  Predicted class: boat, with probability: 68.69%\n",
            "Image 649:\n",
            "  Predicted class: ekara, with probability: 100.00%\n",
            "Image 650:\n",
            "  Predicted class: cycle, with probability: 97.11%\n",
            "Image 651:\n",
            "  Predicted class: star, with probability: 100.00%\n",
            "  Predicted class: tabla, with probability: 100.00%\n",
            "Image 652:\n",
            "  Predicted class: boat, with probability: 100.00%\n",
            "Image 653:\n",
            "  Predicted class: tabla, with probability: 100.00%\n",
            "Image 654:\n",
            "  Predicted class: boat, with probability: 100.00%\n",
            "Image 655:\n",
            "  Predicted class: motorcycle, with probability: 99.00%\n",
            "Image 656:\n",
            "  Predicted class: motorcycle, with probability: 100.00%\n",
            "Image 657:\n",
            "  Predicted class: truck, with probability: 72.02%\n",
            "  Predicted class: bus, with probability: 59.55%\n",
            "Image 658:\n",
            "  Predicted class: boat, with probability: 100.00%\n",
            "Image 659:\n",
            "  Predicted class: cycle, with probability: 100.00%\n",
            "Image 660:\n",
            "  Predicted class: bus, with probability: 99.88%\n",
            "Image 661:\n",
            "  Predicted class: boat, with probability: 100.00%\n",
            "Image 662:\n",
            "  Predicted class: cycle, with probability: 100.00%\n",
            "Image 663:\n",
            "  Predicted class: truck, with probability: 99.56%\n",
            "Image 664:\n",
            "  Predicted class: truck, with probability: 99.81%\n",
            "Image 665:\n",
            "  Predicted class: boat, with probability: 100.00%\n",
            "Image 666:\n",
            "  Predicted class: motorcycle, with probability: 100.00%\n",
            "Image 667:\n",
            "  Predicted class: harmonium, with probability: 64.56%\n",
            "Image 668:\n",
            "  Predicted class: tabla, with probability: 100.00%\n",
            "Image 669:\n",
            "  Predicted class: motorcycle, with probability: 96.76%\n",
            "  Predicted class: boat, with probability: 80.72%\n",
            "Image 670:\n",
            "  Predicted class: cycle, with probability: 100.00%\n",
            "Image 671:\n",
            "  Predicted class: flutes, with probability: 100.00%\n",
            "  Predicted class: tabla, with probability: 100.00%\n",
            "  Predicted class: harmonium, with probability: 100.00%\n",
            "Image 672:\n",
            "  Predicted class: boat, with probability: 99.95%\n",
            "Image 673:\n",
            "  Predicted class: bus, with probability: 100.00%\n",
            "Image 674:\n",
            "  Predicted class: cycle, with probability: 100.00%\n",
            "Image 675:\n",
            "  Predicted class: motorcycle, with probability: 63.39%\n",
            "  Predicted class: truck, with probability: 92.07%\n",
            "Image 676:\n",
            "  Predicted class: cycle, with probability: 97.76%\n",
            "Image 677:\n",
            "  Predicted class: motorcycle, with probability: 99.72%\n",
            "Image 678:\n",
            "  Predicted class: tabla, with probability: 98.61%\n",
            "Image 679:\n",
            "  Predicted class: boat, with probability: 100.00%\n",
            "Image 680:\n",
            "  Predicted class: tabla, with probability: 100.00%\n",
            "Image 681:\n",
            "  Predicted class: cycle, with probability: 100.00%\n",
            "Image 682:\n",
            "  Predicted class: flutes, with probability: 100.00%\n",
            "  Predicted class: tabla, with probability: 100.00%\n",
            "Image 683:\n",
            "  Predicted class: ekara, with probability: 100.00%\n",
            "  Predicted class: flutes, with probability: 100.00%\n",
            "  Predicted class: tabla, with probability: 100.00%\n",
            "  Predicted class: harmonium, with probability: 99.98%\n",
            "Image 684:\n",
            "  Predicted class: tabla, with probability: 100.00%\n",
            "Image 685:\n",
            "  Predicted class: tabla, with probability: 100.00%\n",
            "Image 686:\n",
            "  Predicted class: boat, with probability: 100.00%\n",
            "Image 687:\n",
            "  Predicted class: truck, with probability: 95.38%\n",
            "Image 688:\n",
            "  Predicted class: flutes, with probability: 99.97%\n",
            "  Predicted class: tabla, with probability: 99.98%\n",
            "Image 689:\n",
            "  Predicted class: boat, with probability: 99.83%\n",
            "Image 690:\n",
            "  Predicted class: boat, with probability: 100.00%\n",
            "Image 691:\n",
            "  Predicted class: flutes, with probability: 100.00%\n",
            "Image 692:\n",
            "  Predicted class: cycle, with probability: 100.00%\n",
            "Image 693:\n",
            "  Predicted class: harmonium, with probability: 100.00%\n",
            "Image 694:\n",
            "  Predicted class: tabla, with probability: 100.00%\n",
            "Image 695:\n",
            "  Predicted class: star, with probability: 100.00%\n",
            "Image 696:\n",
            "  Predicted class: cycle, with probability: 100.00%\n",
            "Image 697:\n",
            "  Predicted class: truck, with probability: 100.00%\n",
            "Image 698:\n",
            "  Predicted class: boat, with probability: 100.00%\n",
            "Image 699:\n",
            "  Predicted class: motorcycle, with probability: 100.00%\n",
            "Image 700:\n",
            "  Predicted class: motorcycle, with probability: 100.00%\n"
          ]
        }
      ],
      "source": [
        "# Assuming the test_dataloader, model, and device are already set up\n",
        "predicted_classes, predicted_probabilities = predict_with_dataloader(test_loader, vgg16, device, classes)\n",
        "\n",
        "# Print the predictions for checking\n",
        "for i, (clss, probs) in enumerate(zip(predicted_classes, predicted_probabilities), start=1):\n",
        "    print(f\"Image {i}:\")\n",
        "    for cls, prob in zip(clss, probs):\n",
        "        print(f\"  Predicted class: {cls}, with probability: {prob:.2f}%\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "pU9JYhQRxAKr"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Plot training dan validation loss\n",
        "plt.figure(figsize=(10, 5))\n",
        "plt.plot(train_accs, label = 'training_accuracy')\n",
        "plt.plot(val_accs, label='Validation accuracy')\n",
        "plt.title('Training and Validation accuracy per Epoch')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {},
      "outputs": [],
      "source": [
        "import torch\n",
        "from sklearn.metrics import hamming_loss\n",
        "\n",
        "def calculate_hamming_loss(model, dataloader, device):\n",
        "    model.eval()\n",
        "    all_preds = []\n",
        "    all_labels = []\n",
        "\n",
        "    with torch.no_grad():\n",
        "        for inputs, labels in dataloader:\n",
        "            inputs = inputs.to(device)\n",
        "            outputs = model(inputs)\n",
        "            predictions = torch.sigmoid(outputs) >= 0.7  \n",
        "            predictions = predictions.to('cpu').numpy()\n",
        "            labels = labels.to('cpu').numpy()\n",
        "            \n",
        "            all_preds.extend(predictions)\n",
        "            all_labels.extend(labels)\n",
        "\n",
        "    return hamming_loss(all_labels, all_preds)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Computed Hamming Loss: 0.014285714285714285\n"
          ]
        }
      ],
      "source": [
        "# calculate hamming loss\n",
        "loss = calculate_hamming_loss(vgg16, test_loader, device)\n",
        "print(\"Computed Hamming Loss:\", loss)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {},
      "outputs": [],
      "source": [
        "import torch\n",
        "\n",
        "def eval_with_dataloader(test_dataloader, model, device):\n",
        "    model.eval()\n",
        "    all_preds = []\n",
        "    all_targets = []\n",
        "\n",
        "    with torch.no_grad():\n",
        "        for images, labels in test_dataloader:  # Now labels are also considered\n",
        "            images = images.to(device)\n",
        "            outputs = model(images)\n",
        "            predictions = torch.sigmoid(outputs) > 0.5  # Get binary predictions\n",
        "            all_preds.append(predictions.float())\n",
        "            all_targets.append(labels.to(device))  # Ensure labels are on the same device\n",
        "\n",
        "    all_preds = torch.cat(all_preds)\n",
        "    all_targets = torch.cat(all_targets)\n",
        "    return all_preds, all_targets\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "tensor([[[588,   6],\n",
            "         [  0, 106]],\n",
            "\n",
            "        [[571,  28],\n",
            "         [  1, 100]],\n",
            "\n",
            "        [[534,   9],\n",
            "         [  9, 148]],\n",
            "\n",
            "        [[645,   2],\n",
            "         [  8,  45]],\n",
            "\n",
            "        [[572,   5],\n",
            "         [  3, 120]],\n",
            "\n",
            "        [[663,   2],\n",
            "         [  1,  34]],\n",
            "\n",
            "        [[666,   0],\n",
            "         [  3,  31]],\n",
            "\n",
            "        [[645,   1],\n",
            "         [  1,  53]],\n",
            "\n",
            "        [[572,   3],\n",
            "         [  2, 123]],\n",
            "\n",
            "        [[639,   1],\n",
            "         [  2,  58]]], device='cuda:0')\n"
          ]
        }
      ],
      "source": [
        "from torchmetrics.classification import MultilabelConfusionMatrix\n",
        "\n",
        "preds, targets = eval_with_dataloader(test_loader, vgg16, device)\n",
        "# Initialize the metric for the number of labels\n",
        "num_labels = targets.shape[1]  \n",
        "metric = MultilabelConfusionMatrix(num_labels=num_labels).to(device)\n",
        "\n",
        "# Compute the confusion matrix\n",
        "confusion_matrix = metric(preds,targets)\n",
        "print(confusion_matrix)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {},
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "def plot_confusion_matrix(cm, class_name):\n",
        "    fig, ax = plt.subplots()\n",
        "    im = ax.imshow(cm, interpolation='nearest', cmap=plt.cm.Blues)\n",
        "    ax.figure.colorbar(im, ax=ax)\n",
        "    ax.set(xticks=np.arange(cm.shape[1]),\n",
        "           yticks=np.arange(cm.shape[0]),\n",
        "           xticklabels=['False', 'True'], yticklabels=['False', 'True'],\n",
        "           title=f'Confusion Matrix for {class_name}',\n",
        "           ylabel='True label',\n",
        "           xlabel='Predicted label')\n",
        "\n",
        "    # Loop over data dimensions and create text annotations.\n",
        "    thresh = cm.max() / 2.\n",
        "    for i in range(cm.shape[0]):\n",
        "        for j in range(cm.shape[1]):\n",
        "            ax.text(j, i, format(cm[i, j], 'd'),\n",
        "                    ha=\"center\", va=\"center\",\n",
        "                    color=\"white\" if cm[i, j] > thresh else \"black\")\n",
        "    fig.tight_layout()\n",
        "    return ax"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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GjRvnXPY8b948tW7dWmPGjNGUKVPc6k+6PAF1zpw56t+/v7Zt26Zq1app2bJl2rhxo1577bV8T87OS9OmTbV69WpNnTpVYWFhioyMVLNmzdS5c2ctXLhQdrtdUVFRSkxM1OrVq1WhQoU8+8nMzNQdd9yhXr16ad++fZo5c6ZuvfVW3XPPPVe9d+fOnfXRRx+pW7du6tSpkw4fPqzZs2crKipKaWlp1/xMgOl4dQ0R8D+//PKL8fDDDxvVqlUzfH19jaCgIKNFixbGjBkzjPT0dGe7rKwsY8KECUZkZKRRpkwZIzw83HjmmWdc2hhG3stODSP3ks2rLTM2DMP46quvjPr16xu+vr5GnTp1jPfeey/XMuOvv/7a6NKlixEWFmb4+voaYWFhRp8+fYxffvkl1z2uXIq7evVqo0WLFoa/v79hs9mMu+++29i9e7dLm5z7XbmMOWeZ6uHDh6/6PTUM12XGV3O1ZcZPPfWUUblyZcPf399o0aKFkZiYmOfy4E8//dSIiooySpcu7fKcrVq1Mm644YY87/nnflJTU42IiAijSZMmRlZWlku7uLg4w8fHx0hMTPzLZ7jan/fJkyeNAQMGGNddd53h6+trNGjQINefw1/9DFzN3r17jdtuu83w9/c3JDmXHP/xxx/O+wUGBhrt27c39u7da0RERLgsS87581u3bp3xyCOPGOXKlTMCAwONvn37uiw9N4zcP7MOh8OYPHmyERERYVitVuPGG280VqxYcdWl8UBxZTEMN2bZAQAAFAHmoAAAANMhQQEAAKZDggIAAEyHBAUAAJgOCQoAADAdEhQAAGA6vKgtHxwOh44fP66goCCPvtobAGB+hmHo3LlzCgsLy/Xhk4UlPT1dmZmZHunL19dXfn5+HumrKJGg5MPx48cVHh7u7TAAAF507NgxValSpdDvk56eLv+gCtKlCx7pLzQ0VIcPHy52SQoJSj7kvBLbNypGllL5/+h44J8iae0r3g4B8JpzqamqGRleoI9HcEdmZqZ06YKsUTFSQX/nZGcqeXeCMjMzSVD+iXKGdSylfElQUCLZbDZvhwB4XZEP8Zf2K/DvHMNSfKeakqAAAGBGFkkFTYqK8bTJ4ptaAQCAfywqKAAAmJHF5/JW0D6KKRIUAADMyGLxwBBP8R3jKb6pFQAA+MeiggIAgBkxxAMAAEyHIR4AAABzoYICAIApeWCIpxjXIUhQAAAwI4Z4AAAAzIUKCgAAZsQqHgAAYDoM8QAAAJgLFRQAAMyIIR4AAGA6DPEAAACYCxUUAADMiCEeAABgOhaLBxIUhngAAAA8hgoKAABm5GO5vBW0j2KKBAUAADMq4XNQim/kAADgH4sKCgAAZlTC34NCggIAgBkxxAMAAGAuVFAAADAjhngAAIDpMMQDAABgLlRQAAAwI4Z4AACA6TDEAwAAYC5UUAAAMCOGeAAAgPl4YIinGA+UFN/IAQDAPxYVFAAAzIghHgAAYDoWiwdW8RTfBIUhHgAAYDpUUAAAMKMS/h4UEhQAAMyohM9BKb6pFQAA+MciQQEAwIxyhngKurlh/PjxslgsLlvdunWd59PT0zVkyBBVqFBBgYGB6tGjh06ePOnSR1JSkjp16qSyZcuqUqVKGjlypC5duuT24zPEAwCAGXlpiOeGG27Q6tWrnfulS///VCEuLk4rV67UBx98ILvdrtjYWHXv3l0bN26UJGVnZ6tTp04KDQ3Vpk2bdOLECfXr109lypTR5MmT3YqDBAUAADiVLl1aoaGhuY6npKRo7ty5Wrx4sW6//XZJ0rx581SvXj199913at68ub766ivt3r1bq1evVkhIiBo3bqxJkyZp1KhRGj9+vHx9ffMdB0M8AACYkReGeCRp//79CgsLU/Xq1dW3b18lJSVJkrZt26asrCy1bdvW2bZu3bqqWrWqEhMTJUmJiYlq0KCBQkJCnG3at2+v1NRU7dq1y604qKAAAGBGHhziSU1NdTlstVpltVpzNW/WrJnmz5+vOnXq6MSJE5owYYJatmypnTt3Kjk5Wb6+vgoODna5JiQkRMnJyZKk5ORkl+Qk53zOOXeQoAAA8A8XHh7usj9u3DiNHz8+V7uOHTs6v27YsKGaNWumiIgILV26VP7+/oUdpgsSFAAATChnFU0BO5EkHTt2TDabzXk4r+pJXoKDg1W7dm0dOHBAd955pzIzM3X27FmXKsrJkyedc1ZCQ0P1/fffu/SRs8onr3ktf4U5KAAAmNCVy32vdZMkm83msuU3QUlLS9PBgwdVuXJlNW3aVGXKlNHXX3/tPL9v3z4lJSUpOjpakhQdHa0dO3bo1KlTzjarVq2SzWZTVFSUW89PBQUAAEiSRowYobvvvlsRERE6fvy4xo0bp1KlSqlPnz6y2+0aOHCghg8frvLly8tms+mJJ55QdHS0mjdvLklq166doqKi9OCDD2rKlClKTk7Wc889pyFDhuQ7KcpBggIAgBlZ/rcVtA83/Prrr+rTp49Onz6tihUr6tZbb9V3332nihUrSpKmTZsmHx8f9ejRQxkZGWrfvr1mzpzpvL5UqVJasWKFBg8erOjoaAUEBCgmJkYTJ050P3TDMAy3ryphUlNTZbfbZW3wsCyl8r+GG/in+GPLG94OAfCa1NRUhVSwKyUlxWUeR2Hez263q2zXmbKUKdjEVCProi588niRxe5JzEEBAACmwxAPAAAm5MlVPMURCQoAACZU0hMUhngAAIDpUEEBAMCESnoFhQQFAAAz8sIyYzNhiAcAAJgOFRQAAEyIIR4AAGA6Fos8kKB4JhZvYIgHAACYDhUUAABMyCIPDPEU4xIKCQoAACZU0uegMMQDAABMhwoKAABmVMLfg0KCAgCAGXlgiMdgiAcAAMBzqKAAAGBCnpgkW/BVQN5DggIAgAmV9ASFIR4AAGA6VFAAADAjVvEAAACzYYgHAADAZKigAABgQiW9gkKCAgCACZX0BIUhHgAAYDpUUAAAMKGSXkEhQQEAwIxK+DJjhngAAIDpUEEBAMCEGOIBAACmU9ITFIZ4AACA6VBBAQDAhEp6BYUEBQAAM2IVDwAAgLlQQQEAwIQY4gG84NlH79Jzj93lcmzf4WQ17v68qlYur32fTczzur4j5+qj1T+qQe3rNWLAnbqlcQ1VCA7Q0eNn9M6yDXrzP2uLIHqg8L01e5benjNLR48ekSTVi7pB/35urNp36OjdwFBkSFCKofnz52vYsGE6e/ast0NBAew6cFydHpvh3L+U7ZAk/XryD1Vr+4xL24d6tFBcv7b6cuMuSdKN9cL1+5lzGvBcgn5N/kPNG1XXm8/1UbbDodlL1hfdQwCF5PoqVTRp8ouqWbOWDMPQewsTdG/3Lvpuy4+KuuEGb4cHFDqvJij9+/dXQkJCruP79+9XzZo1vRARitKlbIdOnj6X67jDYeQ6fk+bRvpw1Q86fzFTkrTg0+9czh/57bSaNYxUl9sbkaDgH6FT57td9idMekFvz5ml7zd/R4JSQljkgQpKMZ4l6/UKSocOHTRv3jyXYxUrVvRSNChKNatW1KGvXlB6RpY2/3xYY2f8V8eS/8jV7sZ64WpcN1xxLy79y/7sgX76I/VCYYULeE12drY+XPaBzp8/r2bNo70dDopISR/i8foqHqvVqtDQUJft9ddfV4MGDRQQEKDw8HA9/vjjSktLu2ofP/30k9q0aaOgoCDZbDY1bdpUW7dudZ7fsGGDWrZsKX9/f4WHh2vo0KE6f/58UTwermLLziN6ZOx7umfImxo6eYmqXV9Bq9+NU2BZa662MV2jtefQCX330+Gr9te8UaR6tmuquR9uLMywgSK1c8cOXRccKHuAVUOHPKYlyz5Wvagob4cFFAmvJyh58fHx0fTp07Vr1y4lJCRozZo1evrpp6/avm/fvqpSpYq2bNmibdu2afTo0SpTpowk6eDBg+rQoYN69Oihn3/+WUuWLNGGDRsUGxt71f4yMjKUmprqssGzvtq4Wx+t/lE79x/X6sQ96ho7S/ZAf/Vo18SlnZ+1jO7reJMSPkm8al9RNSpr6bRH9MJbn+nr7/YWduhAkaldp442b92u9Rs36+FHB+vhh2K0Z/dub4eFomLx0FZMeX2IZ8WKFQoMDHTud+zYUR988IFzv1q1anr++ef12GOPaebMmXn2kZSUpJEjR6pu3bqSpFq1ajnPxcfHq2/fvho2bJjz3PTp09WqVSvNmjVLfn5+ufqLj4/XhAkTPPF4yKeUtIs6kHRKNcJdh/e6tW2ssn6+WrTi+zyvq1s9VJ/NeULvfrhJL73zZVGEChQZX19f1fjffLwmTZtq29YtenPG63pj1hwvR4aiUNKHeLyeoLRp00azZs1y7gcEBGj16tWKj4/X3r17lZqaqkuXLik9PV0XLlxQ2bJlc/UxfPhwDRo0SAsXLlTbtm117733qkaNGpIuD//8/PPPWrRokbO9YRhyOBw6fPiw6tWrl6u/Z555RsOHD3fup6amKjw83JOPjSsE+Psqssp1Sl7pmoj073qLVq7bof/7I/cQX73qofr8raFatHyzxr+5vKhCBbzG4XAoIyPD22EARcLrQzwBAQGqWbOmc8vIyFDnzp3VsGFDffjhh9q2bZvefPNNSVJmZmaefYwfP167du1Sp06dtGbNGkVFRenjjz+WJKWlpenRRx/V9u3bndtPP/2k/fv3O5OYK1mtVtlsNpcNnhUf1023Nq2pqpXLq3mjSC2Z+oiyHQ4t/WKbs0318Ot0a5MamvfxplzXR9WorC/eflJfJ+7V9PfWKKRCkEIqBOm6coG52gLF0Zhnn9GGb9fr6JEj2rljh8Y8+4zWr1ur3vf39XZoKCI5FZSCbsWV1ysoV9q2bZscDodeffVV+fhczp+WLv3r1RuSVLt2bdWuXVtxcXHq06eP5s2bp27duqlJkybavXs3y5ZN5vqQYC2IH6Dy9rL6vz/StGn7IbXq96pLpSSmS7R+O3lWqxNzzyvp1vZGVSofpPs7/0v3d/6X8/jR46dVt9O4InkGoDD9fuqUBg7op+QTJ2S321W/QUMt/+xL3dH2Tm+HhiJisVzeCtpHcWW6BKVmzZrKysrSjBkzdPfdd2vjxo2aPXv2VdtfvHhRI0eOVM+ePRUZGalff/1VW7ZsUY8ePSRJo0aNUvPmzRUbG6tBgwYpICBAu3fv1qpVq/TGG28U1WPhCv1Gz/vbNuPeWK5xb+Q9dPPCnM/0wpzPPB0WYBqz357r7RAAr/L6EM+VGjVqpKlTp+qll15S/fr1tWjRIsXHx1+1falSpXT69Gn169dPtWvXVq9evdSxY0fnJNeGDRtq3bp1+uWXX9SyZUvdeOONGjt2rMLCworqkQAAcNvlCkpBh3i8/RTXzmIYhuHtIMwuNTVVdrtd1gYPy1LK19vhAEXujy1UG1FypaamKqSCXSkpKUUyJzHnd071octUyhpQoL6yM87r0PSeRRa7J5muggIAAGC6OSgAAID3oJCgAABgQiV9FQ9DPAAAwHSooAAAYEI+Phb5+BSsBGIU8HpvIkEBAMCEGOIBAAAwGSooAACYEKt4AACA6TDEAwAAkIcXX3xRFotFw4YNcx5LT0/XkCFDVKFCBQUGBqpHjx46efKky3VJSUnq1KmTypYtq0qVKmnkyJG6dOmSW/cmQQEAwIQK/jk8BRsi2rJli+bMmaOGDRu6HI+Li9Py5cv1wQcfaN26dTp+/Li6d+/uPJ+dna1OnTopMzNTmzZtUkJCgubPn6+xY8e6dX8SFAAATMibCUpaWpr69u2rt99+W+XKlXMeT0lJ0dy5czV16lTdfvvtatq0qebNm6dNmzbpu+++kyR99dVX2r17t9577z01btxYHTt21KRJk/Tmm28qMzMz3zGQoAAA8A+XmprqsmVkZPxl+yFDhqhTp05q27aty/Ft27YpKyvL5XjdunVVtWpVJSYmSpISExPVoEEDhYSEONu0b99eqamp2rVrV75jJkEBAMCEcibJFnSTpPDwcNntducWHx9/1fu+//77+uGHH/Jsk5ycLF9fXwUHB7scDwkJUXJysrPNn5OTnPM55/KLVTwAAJiQRR5YZqzL1x87dkw2m8153Gq15tn+2LFjevLJJ7Vq1Sr5+fkV6N4FRQUFAIB/OJvN5rJdLUHZtm2bTp06pSZNmqh06dIqXbq01q1bp+nTp6t06dIKCQlRZmamzp4963LdyZMnFRoaKkkKDQ3NtaonZz+nTX6QoAAAYEKeHOLJrzvuuEM7duzQ9u3bndtNN92kvn37Or8uU6aMvv76a+c1+/btU1JSkqKjoyVJ0dHR2rFjh06dOuVss2rVKtlsNkVFReU7FoZ4AAAwIW+8STYoKEj169d3ORYQEKAKFSo4jw8cOFDDhw9X+fLlZbPZ9MQTTyg6OlrNmzeXJLVr105RUVF68MEHNWXKFCUnJ+u5557TkCFDrlq5yQsJCgAAyLdp06bJx8dHPXr0UEZGhtq3b6+ZM2c6z5cqVUorVqzQ4MGDFR0drYCAAMXExGjixIlu3YcEBQAAEzLLq+7Xrl3rsu/n56c333xTb7755lWviYiI0GeffVag+5KgAABgQiX9wwKZJAsAAEyHCgoAACZkliEebyFBAQDAhBjiAQAAMBkqKAAAmJEHhnhUfAsoJCgAAJgRQzwAAAAmQwUFAAATYhUPAAAwHYZ4AAAATIYKCgAAJsQQDwAAMB2GeAAAAEyGCgoAACZU0isoJCgAAJhQSZ+DwhAPAAAwHSooAACYEEM8AADAdBjiAQAAMBkqKAAAmBBDPAAAwHQs8sAQj0ci8Q6GeAAAgOlQQQEAwIR8LBb5FLCEUtDrvYkEBQAAE2IVDwAAgMlQQQEAwIRYxQMAAEzHx3J5K2gfxRVDPAAAwHSooAAAYEYWDwzRFOMKCgkKAAAmxCoeAAAAk6GCAgCACVn+919B+yiuSFAAADAhVvEAAACYDBUUAABMiBe1AQAA0ynpq3jylaD897//zXeH99xzzzUHAwAAIOUzQenatWu+OrNYLMrOzi5IPAAAQJKPxSKfApZACnq9N+UrQXE4HIUdBwAA+JOSPsRToFU86enpnooDAADAye0EJTs7W5MmTdL111+vwMBAHTp0SJI0ZswYzZ071+MBAgBQEuWs4inoVly5naC88MILmj9/vqZMmSJfX1/n8fr16+udd97xaHAAAKBkcjtBWbBggd566y317dtXpUqVch5v1KiR9u7d69HgAAAoqXLmoBR0K67cfg/Kb7/9ppo1a+Y67nA4lJWV5ZGgAAAo6Ur6Kh63KyhRUVH69ttvcx1ftmyZbrzxRo8EBQAASja3Kyhjx45VTEyMfvvtNzkcDn300Ufat2+fFixYoBUrVhRGjAAAlDiW/20F7aO4cruC0qVLFy1fvlyrV69WQECAxo4dqz179mj58uW68847CyNGAABKnJK+iueaPounZcuWWrVqladjAQAAkFSADwvcunWr9uzZI+nyvJSmTZt6LCgAAEo6H8vlraB9FFduJyi//vqr+vTpo40bNyo4OFiSdPbsWd1yyy16//33VaVKFU/HCABAieOJIZriPMTj9hyUQYMGKSsrS3v27NGZM2d05swZ7dmzRw6HQ4MGDSqMGAEAQAnjdgVl3bp12rRpk+rUqeM8VqdOHc2YMUMtW7b0aHAAAJRkxbgAUmBuJyjh4eF5vpAtOztbYWFhHgkKAICSjiEeN7388st64okntHXrVuexrVu36sknn9Qrr7zi0eAAAEDJlK8KSrly5VyysPPnz6tZs2YqXfry5ZcuXVLp0qX10EMPqWvXroUSKAAAJQmrePLhtddeK+QwAADAn3ljiGfWrFmaNWuWjhw5Ikm64YYbNHbsWHXs2FGSlJ6erqeeekrvv/++MjIy1L59e82cOVMhISHOPpKSkjR48GB98803CgwMVExMjOLj451FjfzKV+uYmBi3OgUAAMVPlSpV9OKLL6pWrVoyDEMJCQnq0qWLfvzxR91www2Ki4vTypUr9cEHH8hutys2Nlbdu3fXxo0bJV2ej9qpUyeFhoZq06ZNOnHihPr166cyZcpo8uTJbsViMQzDuNYHSU9PV2Zmpssxm812rd2ZVmpqqux2u6wNHpallK+3wwGK3B9b3vB2CIDXpKamKqSCXSkpKUXyOy7nd07fuZvkWzawQH1lXkjTooG3FCj28uXL6+WXX1bPnj1VsWJFLV68WD179pQk7d27V/Xq1VNiYqKaN2+uzz//XJ07d9bx48edVZXZs2dr1KhR+v333+Xrm//foW5Pkj1//rxiY2NVqVIlBQQEqFy5ci4bAAAoOB+LxSPbtcrOztb777+v8+fPKzo6Wtu2bVNWVpbatm3rbFO3bl1VrVpViYmJkqTExEQ1aNDAZcinffv2Sk1N1a5du9x7fncDfvrpp7VmzRrNmjVLVqtV77zzjiZMmKCwsDAtWLDA3e4AAEAhS01NddkyMjKu2nbHjh0KDAyU1WrVY489po8//lhRUVFKTk6Wr6+v8y3yOUJCQpScnCxJSk5OdklOcs7nnHOH2wnK8uXLNXPmTPXo0UOlS5dWy5Yt9dxzz2ny5MlatGiRu90BAIA8WCye2aTL7zCz2+3OLT4+/qr3rVOnjrZv367Nmzdr8ODBiomJ0e7du4voqf8/t1/UdubMGVWvXl3S5fkmZ86ckSTdeuutGjx4sGejAwCghPLkKp5jx465zEGxWq1XvcbX11c1a9aUJDVt2lRbtmzR66+/rvvuu0+ZmZk6e/asSxXl5MmTCg0NlSSFhobq+++/d+nv5MmTznPucLuCUr16dR0+fFjS5bGnpUuXSrpcWbmy7AMAALzPZrO5bH+VoFzJ4XAoIyNDTZs2VZkyZfT11187z+3bt09JSUmKjo6WJEVHR2vHjh06deqUs82qVatks9kUFRXlVsxuV1AGDBign376Sa1atdLo0aN1991364033lBWVpamTp3qbncAACAPfx6iKUgf7njmmWfUsWNHVa1aVefOndPixYu1du1affnll7Lb7Ro4cKCGDx+u8uXLy2az6YknnlB0dLSaN28uSWrXrp2ioqL04IMPasqUKUpOTtZzzz2nIUOGuJUUSdeQoMTFxTm/btu2rfbu3att27apZs2aatiwobvdAQCAPBR0FU5OH+44deqU+vXrpxMnTshut6thw4b68ssvdeedd0qSpk2bJh8fH/Xo0cPlRW05SpUqpRUrVmjw4MGKjo5WQECAYmJiNHHiRLdjL9B7UEoK3oOCko73oKAk89Z7UB5asNkj70F5t1+zIovdk/JVQZk+fXq+Oxw6dOg1BwMAAC7zxhCPmeQrQZk2bVq+OrNYLCQoAAB4gDc+i8dM8pWg5KzaKemS1r5S7EpkgCfs/jXV2yEAXpN2jp9/b3B7kiwAACh8PrqGd4Hk0UdxRYICAIAJlfQhnuKcXAEAgH8oKigAAJiQxSL5sIoHAACYiY8HEpSCXu9N1zTE8+233+qBBx5QdHS0fvvtN0nSwoULtWHDBo8GBwAASia3E5QPP/xQ7du3l7+/v3788UdlZGRIklJSUjR58mSPBwgAQEmUM0m2oFtx5XaC8vzzz2v27Nl6++23VaZMGefxFi1a6IcffvBocAAAlFQ5QzwF3YortxOUffv26bbbbst13G636+zZs56ICQAAlHBuJyihoaE6cOBAruMbNmxQ9erVPRIUAAAlXc5n8RR0K67cTlAefvhhPfnkk9q8ebMsFouOHz+uRYsWacSIERo8eHBhxAgAQInjY7F4ZCuu3F5mPHr0aDkcDt1xxx26cOGCbrvtNlmtVo0YMUJPPPFEYcQIAABKGLcTFIvFomeffVYjR47UgQMHlJaWpqioKAUGBhZGfAAAlEh8Fs818vX1VVRUlCdjAQAA/+OJOSTFeITH/QSlTZs2f7mues2aNQUKCAAAwO0EpXHjxi77WVlZ2r59u3bu3KmYmBhPxQUAQInmo4JPcvVR8S2huJ2gTJs2Lc/j48ePV1paWoEDAgAADPF4bP7MAw88oHfffddT3QEAgBLMY59mnJiYKD8/P091BwBAiVbSP83Y7QSle/fuLvuGYejEiRPaunWrxowZ47HAAAAoySwWFXgOSnEe4nE7QbHb7S77Pj4+qlOnjiZOnKh27dp5LDAAAFByuZWgZGdna8CAAWrQoIHKlStXWDEBAFDiMUnWDaVKlVK7du341GIAAApZzhyUgm7FldureOrXr69Dhw4VRiwAAACSriFBef755zVixAitWLFCJ06cUGpqqssGAAAKzuKh/4qrfM9BmThxop566indddddkqR77rnH5ZX3hmHIYrEoOzvb81ECAFDCsMw4nyZMmKDHHntM33zzTWHGAwAAkP8ExTAMSVKrVq0KLRgAAHAZFRQ3/NWnGAMAAM+xWCwF/r1bnH9vu5Wg1K5d+28f9syZMwUKCAAAwK0EZcKECbneJAsAADyPIR439O7dW5UqVSqsWAAAwP/wJtl8Ks7jWAAAoHhxexUPAAAofD4WS4E/zbig13tTvhMUh8NRmHEAAIA/KelzUNx+1T0AAEBhc2uSLAAAKCIemCRbjD+KhwQFAAAz8pFFPgXMMAp6vTcxxAMAAEyHCgoAACZU0t+DQoICAIAJsYoHAADAZKigAABgQryoDQAAmE5Jn4PCEA8AADAdKigAAJiQjzwwxFOM34NCggIAgAkxxAMAAGAyVFAAADAhHxW8ilCcqxAkKAAAmJDFYpGlgGM0Bb3em4pzcgUAAP6hqKAAAGBClv9tBe2juCJBAQDAhEr6m2QZ4gEAAKZDggIAgElZCri5Kz4+XjfffLOCgoJUqVIlde3aVfv27XNpk56eriFDhqhChQoKDAxUjx49dPLkSZc2SUlJ6tSpk8qWLatKlSpp5MiRunTpkluxkKAAAGBCOS9qK+jmjnXr1mnIkCH67rvvtGrVKmVlZaldu3Y6f/68s01cXJyWL1+uDz74QOvWrdPx48fVvXt35/ns7Gx16tRJmZmZ2rRpkxISEjR//nyNHTvWvec3DMNwL/ySJzU1VXa7XSdPp8hms3k7HKDI7f411dshAF6Tdi5VrRqGKyWlaH4H5PzOeXvdbpUNDCpQXxfSzunhVlHXHPvvv/+uSpUqad26dbrtttuUkpKiihUravHixerZs6ckae/evapXr54SExPVvHlzff755+rcubOOHz+ukJAQSdLs2bM1atQo/f777/L19c3XvamgAABgQjnvQSnoJl1Oev68ZWRk5CuGlJQUSVL58uUlSdu2bVNWVpbatm3rbFO3bl1VrVpViYmJkqTExEQ1aNDAmZxIUvv27ZWamqpdu3bl+/lJUAAAMCEfD22SFB4eLrvd7tzi4+P/9v4Oh0PDhg1TixYtVL9+fUlScnKyfH19FRwc7NI2JCREycnJzjZ/Tk5yzuecyy+WGQMA8A937NgxlyEeq9X6t9cMGTJEO3fu1IYNGwoztKsiQQEAwIQ8+ap7m83m1hyU2NhYrVixQuvXr1eVKlWcx0NDQ5WZmamzZ8+6VFFOnjyp0NBQZ5vvv//epb+cVT45bfKDIR4AAEyooEuMr2WpsWEYio2N1ccff6w1a9YoMjLS5XzTpk1VpkwZff31185j+/btU1JSkqKjoyVJ0dHR2rFjh06dOuVss2rVKtlsNkVFReU7FiooAABA0uVhncWLF+vTTz9VUFCQc86I3W6Xv7+/7Ha7Bg4cqOHDh6t8+fKy2Wx64oknFB0drebNm0uS2rVrp6ioKD344IOaMmWKkpOT9dxzz2nIkCH5GlrKQYICAIAJeePTjGfNmiVJat26tcvxefPmqX///pKkadOmycfHRz169FBGRobat2+vmTNnOtuWKlVKK1as0ODBgxUdHa2AgADFxMRo4sSJbsVCggIAgAn9eRVOQfpwR35ejebn56c333xTb7755lXbRERE6LPPPnPz7q6YgwIAAEyHCgoAACbkjSEeMyFBAQDAhK71A/+u7KO4YogHAACYDhUUAABM6Fo+jTivPoorEhQAAEzIRxb5FHCQpqDXexNDPAAAwHSooAAAYEIM8QAAANOx/O+/gvZRXDHEAwAATIcKCgAAJsQQDwAAMB2LB1bxMMQDAADgQVRQAAAwIYZ4AACA6ZT0BIUhHgAAYDpUUAAAMKGS/h4UEhQAAEzIx3J5K2gfxRVDPAAAwHSooAAAYEIM8QAAANNhFQ8AAIDJUEEBAMCELCr4EE0xLqCQoAAAYEas4gEAADAZEhSY1ssvxatF85tVsVyQqoZV0r09uuqXffu8HRbgET9s3qhhA+9T+2Z11DTSrm++WuE8l5WVpekvjlWvDtFqEVVZ7ZvV0djhj+r3kydc+ogb1Ft3tbhB0XUqqd2/amtM3CO52qD4snjov+LKVAmKxWL5y238+PHeDhFF6Nv16/TY4CFat+E7rfh8lS5lZanzXe10/vx5b4cGFNjFixdUu159jZr4Sq5z6RcvaO/OnzQodqQWLV+vV2a/pyOH9ivu4d4u7W6KbqmX3pivj77eqpdnLdSvSYf19OP9iuoRUMhyVvEUdCuuTDUH5cSJ/5/5L1myRGPHjtW+P/2LOTAw0Pm1YRjKzs5W6dKmegR40H9XfuGy/9bc+aoaVkk//rBNt7a8zUtRAZ7RovWdatH6zjzPBdnsmvnepy7HRk14Wf263q4Tvx1T5evDJUl9Bw5xnq9cpar6Pxanpx69X1lZWSpTpkzhBQ8UAVNVUEJDQ52b3W6XxWJx7u/du1dBQUH6/PPP1bRpU1mtVm3YsEH9+/dX165dXfoZNmyYWrdu7dx3OByKj49XZGSk/P391ahRIy1btqxoHw4FlpqSIkkqV668lyMBil7auVRZLBYF2ex5nk85e0aff7pUDZs2Izn5h7B4aCuuil35YfTo0XrllVdUvXp1lStXLl/XxMfH67333tPs2bNVq1YtrV+/Xg888IAqVqyoVq1aFXLE8ASHw6GRTw1T9C0tdEP9+t4OByhSGRnpmv7SOLW/p6cCg2wu56a/OFZLFryt9IsX1ODGm/Xa3KVeihKe5iOLfAo4RuNTjFOUYpegTJw4UXfemXdZNC8ZGRmaPHmyVq9erejoaElS9erVtWHDBs2ZMyfPBCUjI0MZGRnO/dTU1IIHjgIZ9sQQ7dq1U1+v3eDtUIAilZWVpdFD+sswDD0zaWqu8w8+8qS69OqnE78l6a3pL2nsU4/q9blLZSnOkw8AFcME5aabbnKr/YEDB3ThwoVcSU1mZqZuvPHGPK+Jj4/XhAkTrjlGeNawobH67LMVWr1mvapUqeLtcIAik5WVpdGx/XXit2OavXh5ruqJJJUrX0HlyldQRPWaiqxZR3fdEqUdP25Rwyb/8kLE8CRPDNEU5zS12CUoAQEBLvs+Pj4yDMPlWFZWlvPrtLQ0SdLKlSt1/fXXu7SzWq153uOZZ57R8OHDnfupqakKDw8vUNxwn2EYinvyCf3304/11eq1qhYZ6e2QgCKTk5wcO3JQcxavUHA+5l45HA5JUmZmxt+0RLFQwjOUYpegXKlixYrauXOny7Ht27c7J4lFRUXJarUqKSkp3/NNrFbrVZMXFJ1hTwzRkvcX64OPPlVgUJCSk5MlSXa7Xf7+/l6ODiiYC+fTdOzoIef+8WNHtW/3z7LZy+m6SqEa9Xg/7d31k157Z4myHdn6v99PSpLs9nIq4+urHT9u1e6ff1Djm5vLZgvWsaTDmj31BVWJiFTDG6meoPgr9gnK7bffrpdfflkLFixQdHS03nvvPe3cudM5fBMUFKQRI0YoLi5ODodDt956q1JSUrRx40bZbDbFxMR4+QlwNW/NmSVJandHa9fj78zTgzH9iz4gwIN27/hRj/bp7Nyf+vy/JUmde9yvR4eN1rrVn0mS+nS61eW6Of9ZoZuat5Sfv7/WfPlfzXltsi5euKDrKoUoulVbvRg7X778A+sfwRMvWivOL2or9glK+/btNWbMGD399NNKT0/XQw89pH79+mnHjh3ONpMmTVLFihUVHx+vQ4cOKTg4WE2aNNG///1vL0aOv3Mxy/j7RkAxdVPzltp2OOWq5//qnCTVqnuD5ixe8ZdtUMx54kVrxTc/kcW4cgIHcklNTZXdbtfJ0ymy2XJPUgP+6Xb/yko2lFxp51LVqmG4UlKK5ndAzu+cr7cn5Tkx2h1p51J1R+OqRRa7JxX7CgoAAP9EJXyOLAkKAACmVMIzFFO96h4AAECiggIAgCmxigcAAJiOxQOreIrzJx4wxAMAAEyHCgoAACZUwufIkqAAAGBKJTxDYYgHAACYDhUUAABMiFU8AADAdFjFAwAAYDJUUAAAMKESPkeWBAUAAFMq4RkKQzwAAMB0qKAAAGBCrOIBAACmwyoeAAAAk6GCAgCACZXwObJUUAAAMCWLhzY3rV+/XnfffbfCwsJksVj0ySefuJw3DENjx45V5cqV5e/vr7Zt22r//v0ubc6cOaO+ffvKZrMpODhYAwcOVFpamltxkKAAAACn8+fPq1GjRnrzzTfzPD9lyhRNnz5ds2fP1ubNmxUQEKD27dsrPT3d2aZv377atWuXVq1apRUrVmj9+vV65JFH3IqDIR4AAEzIW6t4OnbsqI4dO+Z5zjAMvfbaa3ruuefUpUsXSdKCBQsUEhKiTz75RL1799aePXv0xRdfaMuWLbrpppskSTNmzNBdd92lV155RWFhYfmKgwoKAAAmlLOKp6CbJx0+fFjJyclq27at85jdblezZs2UmJgoSUpMTFRwcLAzOZGktm3bysfHR5s3b873vaigAADwD5eamuqyb7VaZbVa3e4nOTlZkhQSEuJyPCQkxHkuOTlZlSpVcjlfunRplS9f3tkmP6igAABgQp6cIxseHi673e7c4uPji/JRrgkVFAAAzMiD64yPHTsmm83mPHwt1RNJCg0NlSSdPHlSlStXdh4/efKkGjdu7Gxz6tQpl+suXbqkM2fOOK/PDyooAAD8w9lsNpftWhOUyMhIhYaG6uuvv3YeS01N1ebNmxUdHS1Jio6O1tmzZ7Vt2zZnmzVr1sjhcKhZs2b5vhcVFAAATMhbq3jS0tJ04MAB5/7hw4e1fft2lS9fXlWrVtWwYcP0/PPPq1atWoqMjNSYMWMUFhamrl27SpLq1aunDh066OGHH9bs2bOVlZWl2NhY9e7dO98reCQSFAAATMlbn8WzdetWtWnTxrk/fPhwSVJMTIzmz5+vp59+WufPn9cjjzyis2fP6tZbb9UXX3whPz8/5zWLFi1SbGys7rjjDvn4+KhHjx6aPn26e7EbhmG4H37JkpqaKrvdrpOnU1zG8ICSYvevqX/fCPiHSjuXqlYNw5WSUjS/A3J+52z95YQCgwp2v7RzqbqpduUii92TqKAAAGBCJf2zeEhQAAAwoxKeobCKBwAAmA4VFAAATMhbq3jMggQFAAAz8sRn6RTf/IQhHgAAYD5UUAAAMKESPkeWBAUAAFMq4RkKQzwAAMB0qKAAAGBCrOIBAACm463P4jELhngAAIDpUEEBAMCESvgcWRIUAABMqYRnKAzxAAAA06GCAgCACbGKBwAAmI5FHljF45FIvIMhHgAAYDpUUAAAMKESPkeWBAUAADPiRW0AAAAmQwUFAABTKtmDPCQoAACYEEM8AAAAJkMFBQAAEyrZAzwkKAAAmBJDPAAAACZDBQUAABPis3gAAID5lPBJKAzxAAAA06GCAgCACZXwAgoJCgAAZsQqHgAAAJOhggIAgAmxigcAAJhPCZ+EwhAPAAAwHSooAACYUAkvoJCgAABgRqziAQAAMBkqKAAAmFLBV/EU50EeEhQAAEyIIR4AAACTIUEBAACmwxAPAAAmxBAPAACAyVBBAQDAhPgsHgAAYDoM8QAAAJgMFRQAAEyIz+IBAADmU8IzFIZ4AACA6VBBAQDAhFjFAwAATIdVPAAAACZDBQUAABMq4XNkSVAAADClEp6hMMQDAABMhwoKAAAmxCoeAABgOiV9FQ8JSj4YhiFJOpea6uVIAO9IO8fPPkqu82nnJP3/3wVFJdUDv3M80Ye3kKDkw7lzl384a0aGezkSAIC3nDt3Tna7vdDv4+vrq9DQUNXy0O+c0NBQ+fr6eqSvomQxijolLIYcDoeOHz+uoKAgWYpzvayYSk1NVXh4uI4dOyabzebtcIAixc+/9xmGoXPnziksLEw+PkWztiQ9PV2ZmZke6cvX11d+fn4e6asoUUHJBx8fH1WpUsXbYZR4NpuNv6BRYvHz711FUTn5Mz8/v2KZVHgSy4wBAIDpkKAAAADTIUGB6VmtVo0bN05Wq9XboQBFjp9/lFRMkgUAAKZDBQUAAJgOCQoAADAdEhSY2vz58xUcHOztMAAARYwEBUWif//+slgsubYDBw54OzSgyOT1/8Cft/Hjx3s7RMA0eFEbikyHDh00b948l2MVK1b0UjRA0Ttx4oTz6yVLlmjs2LHat2+f81hgYKDza8MwlJ2drdKl+WsaJRMVFBQZq9Wq0NBQl+31119XgwYNFBAQoPDwcD3++ONKS0u7ah8//fST2rRpo6CgINlsNjVt2lRbt251nt+wYYNatmwpf39/hYeHa+jQoTp//nxRPB7wt/78s2+322WxWJz7e/fuVVBQkD7//HM1bdpUVqtVGzZsUP/+/dW1a1eXfoYNG6bWrVs79x0Oh+Lj4xUZGSl/f381atRIy5YtK9qHAzyMBAVe5ePjo+nTp2vXrl1KSEjQmjVr9PTTT1+1fd++fVWlShVt2bJF27Zt0+jRo1WmTBlJ0sGDB9WhQwf16NFDP//8s5YsWaINGzYoNja2qB4HKLDRo0frxRdf1J49e9SwYcN8XRMfH68FCxZo9uzZ2rVrl+Li4vTAAw9o3bp1hRwtUHioHaLIrFixwqWE3bFjR33wwQfO/WrVqun555/XY489ppkzZ+bZR1JSkkaOHKm6detKkmrVquU8Fx8fr759+2rYsGHOc9OnT1erVq00a9asEv+5FigeJk6cqDvvvDPf7TMyMjR58mStXr1a0dHRkqTq1atrw4YNmjNnjlq1alVYoQKFigQFRaZNmzaaNWuWcz8gIECrV69WfHy89u7dq9TUVF26dEnp6em6cOGCypYtm6uP4cOHa9CgQVq4cKHatm2re++9VzVq1JB0efjn559/1qJFi5ztDcOQw+HQ4cOHVa9evcJ/SKCAbrrpJrfaHzhwQBcuXMiV1GRmZurGG2/0ZGhAkSJBQZEJCAhQzZo1nftHjhxR586dNXjwYL3wwgsqX768NmzYoIEDByozMzPPBGX8+PG6//77tXLlSn3++ecaN26c3n//fXXr1k1paWl69NFHNXTo0FzXVa1atVCfDfCUgIAAl30fHx9d+cLvrKws59c5c7ZWrlyp66+/3qUdr8dHcUaCAq/Ztm2bHA6HXn31Vfn4XJ4OtXTp0r+9rnbt2qpdu7bi4uLUp08fzZs3T926dVOTJk20e/dulyQIKO4qVqyonTt3uhzbvn27c+5VVFSUrFarkpKSGM7BPwqTZOE1NWvWVFZWlmbMmKFDhw5p4cKFmj179lXbX7x4UbGxsVq7dq2OHj2qjRs3asuWLc6hm1GjRmnTpk2KjY3V9u3btX//fn366adMkkWxdvvtt2vr1q1asGCB9u/fr3HjxrkkLEFBQRoxYoTi4uKUkJCggwcP6ocfftCMGTOUkJDgxciBgiFBgdc0atRIU6dO1UsvvaT69etr0aJFio+Pv2r7UqVK6fTp0+rXr59q166tXr16qWPHjpowYYIkqWHDhlq3bp1++eUXtWzZUjfeeKPGjh2rsLCwonokwOPat2+vMWPG6Omnn9bNN9+sc+fOqV+/fi5tJk2apDFjxig+Pl716tVThw4dtHLlSkVGRnopaqDg+DRjAABgOlRQAACA6ZCgAAAA0yFBAQAApkOCAgAATIcEBQAAmA4JCgAAMB0SFAAAYDokKAAAwHRIUIB/iP79+6tr167O/datW2vYsGFFHsfatWtlsVh09uzZq7axWCz65JNP8t3n+PHj1bhx4wLFdeTIEVksFm3fvr1A/QAoGiQoQCHq37+/LBaLLBaLfH19VbNmTU2cOFGXLl0q9Ht/9NFHmjRpUr7a5iepAICixKcZA4WsQ4cOmjdvnjIyMvTZZ59pyJAhKlOmjJ555plcbTMzM+Xr6+uR+5YvX94j/QCAN1BBAQqZ1WpVaGioIiIiNHjwYLVt21b//e9/Jf3/YZkXXnhBYWFhqlOnjiTp2LFj6tWrl4KDg1W+fHl16dJFR44ccfaZnZ2t4cOHKzg4WBUqVNDTTz+tKz9W68ohnoyMDI0aNUrh4eGyWq2qWbOm5s6dqyNHjqhNmzaSpHLlyslisah///6SJIfDofj4eEVGRsrf31+NGjXSsmXLXO7z2WefqXbt2vL391ebNm1c4syvUaNGqXbt2ipbtqyqV6+uMWPGKCsrK1e7OXPmKDw8XGXLllWvXr2UkpLicv6dd95RvXr15Ofnp7p162rmzJluxwLAHEhQgCLm7++vzMxM5/7XX3+tffv2adWqVVqxYoWysrLUvn17BQUF6dtvv9XGjRsVGBioDh06OK979dVXNX/+fL377rvasGGDzpw5o48//vgv79uvXz/95z//0fTp07Vnzx7NmTNHgYGBCg8P14cffihJ2rdvn06cOKHXX39dkhQfH68FCxZo9uzZ2rVrl+Li4vTAAw9o3bp1ki4nUt27d9fdd9+t7du3a9CgQRo9erTb35OgoCDNnz9fu3fv1uuvv663335b06ZNc2lz4MABLV26VMuXL9cXX3yhH3/8UY8//rjz/KJFizR27Fi98MIL2rNnjyZPnqwxY8YoISHB7XgAmIABoNDExMQYXbp0MQzDMBwOh7Fq1SrDarUaI0aMcJ4PCQkxMjIynNcsXLjQqFOnjuFwOJzHMjIyDH9/f+PLL780DMMwKleubEyZMsV5Pisry6hSpYrzXoZhGK1atTKefPJJwzAMY9++fYYkY9WqVXnG+c033xiSjD/++MN5LD093ShbtqyxadMml7YDBw40+vTpYxiGYTzzzDNGVFSUy/lRo0bl6utKkoyPP/74qudffvllo2nTps79cePGGaVKlTJ+/fVX57HPP//c8PHxMU6cOGEYhmHUqFHDWLx4sUs/kyZNMqKjow3DMIzDhw8bkowff/zxqvcFYB7MQQEK2YoVKxQYGKisrCw5HA7df//9Gj9+vPN8gwYNXOad/PTTTzpw4ICCgoJc+klPT9fBgweVkpKiEydOqFmzZs5zpUuX1k033ZRrmCfH9u3bVapUKbVq1SrfcR84cEAXLlzQnXfe6XI8MzNTN954oyRpz549LnFIUnR0dL7vkWPJkiWaPn26Dh48qLS0NF26dEk2m82lTdWqVXX99de73MfhcGjfvn0KCgrSwYMHNXDgQD388MPONpcuXZLdbnc7HgDeR4ICFLI2bdpo1qxZ8vX1VVhYmEqXdv3fLiAgwGU/LS1NTZs21aJFi3L1VbFixWuKwd/f3+1r0tLSJEkrV650SQyky/NqPCUxMVF9+/bVhAkT1L59e9ntdr3//vt69dVX3Y717bffzpUwlSpVymOxAig6JChAIQsICFDNmjXz3b5JkyZasmSJKlWqlKuKkKNy5cravHmzbrvtNkmXKwXbtm1TkyZN8mzfoEEDORwOrVu3Tm3bts11PqeCk52d7TwWFRUlq9WqpKSkq1Ze6tWr55zwm+O77777+4f8k02bNikiIkLPPvus89jRo0dztUtKStLx48cVFhbmvI+Pj4/q1KmjkJAQhYWF6dChQ+rbt69b9wdgTkySBUymb9++uu6669SlSxd9++23Onz4sNauXauhQ4fq119/lSQ9+eSTevHFF/XJJ59o7969evzxx//yHSbVqlVTTEyMHnroIX3yySfOPpcuXSpJioiIkMVi0YoVK/T7778rLS1NQUFBGjFihOLi4pSQkKCDBw/qhx9+0IwZM5wTTx977DHt379fI0eO1L59+7R48WLNnz/freetVauWkpKS9P777+vgwYOaPn16nhN+/fz8FBMTo59++knffvuthg4dql69eik0NFSSNGHCBMXHx2v69On65ZdftGPHDs2bN09Tp051Kx4A5kCCAphM2bJltX79elWtWlXdu3dXvXr1NHDgQKWnpzsrKk899ZQefPBBxcTEKDo6WkFBQerWrdtf9jtr1iz17NlTjz/+uOrWrauHH35Y58+flyRdf/31mjBhgkaPHq2QkBDFxsZKkiZNmqQxY8YoPj5e9erVU4cOHbRy5UpFRkZKujwv5MMPP9Qnn3yiRo0aafbs2Zo8ebJbz3vPPfcoLi5OsbGxaty4sTZt2qQxY8bkalezZk11795dd911l9q1a6eGDRu6LCMeNGiQ3nnnHc2bN08NGjRQq1atNH/+fGesAIoXi3G1WXUAAABeQgUFAACYDgkKAAAwHRIUAABgOiQoAADAdEhQAACA6ZCgAAAA0yFBAQAApkOCAgAATIcEBQAAmA4JCgAAMB0SFAAAYDokKAAAwHT+H+jihpglelCGAAAAAElFTkSuQmCC",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Assuming 'confusion_matrix' is the result from the MultilabelConfusionMatrix metric\n",
        "num_labels = confusion_matrix.shape[0]\n",
        "labels = ['motorcycle', 'truck', 'boat', 'bus', 'cycle', 'sitar', 'ektara',\n",
        "       'flutes', 'tabla', 'harmonium'] \n",
        "\n",
        "# Plot each confusion matrix\n",
        "for i in range(num_labels):\n",
        "    cm = confusion_matrix[i].cpu().numpy()  # Assuming the tensor is on a GPU, move it to CPU\n",
        "    plot_confusion_matrix(cm, labels[i])\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {},
      "outputs": [],
      "source": [
        "from torchmetrics import F1Score\n",
        "import torch\n",
        "\n",
        "# Initialize the metric\n",
        "f1_score = F1Score(task=\"multiclass\", num_classes=10).to(device)\n",
        "\n",
        "# Assuming 'preds' and 'targets' are your model's predictions and true labels, respectively\n",
        "# Ensure these are torch tensors; convert them if they are not\n",
        "f1_scores = f1_score(preds, targets)\n",
        "\n",
        "# Convert to numpy for easier handling in plotting\n",
        "f1_scores = f1_scores.detach().cpu().numpy() "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array(0.9875714, dtype=float32)"
            ]
          },
          "execution_count": 35,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "f1_scores"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "colors = ['#a6cee3', '#b2df8a', '#cab2d6']\n",
        "\n",
        "plt.figure(figsize=(10, 5))\n",
        "plt.bar(labels, f1_scores, color=colors)\n",
        "plt.xlabel('Labels')\n",
        "plt.ylabel('F1 Score')\n",
        "plt.title('F1 Score for Each Label')\n",
        "plt.ylim([0, 1])  # F1 scores range from 0 to 1\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "gpuType": "T4",
      "machine_shape": "hm",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.11.7"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}