CNN_model.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "VRS8z-i3tz7O"
      },
      "outputs": [],
      "source": [
        "import random\n",
        "import torch\n",
        "import copy\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "from torchvision import datasets, transforms\n",
        "from torch.utils.data import random_split\n",
        "import torch.optim as optim\n",
        "import numpy as np\n",
        "from sklearn.metrics import confusion_matrix\n",
        "import pandas as pd\n",
        "import requests\n",
        "from PIL import Image\n",
        "from io import BytesIO\n",
        "import torch.nn.functional as F"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(torch.__version__)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gYqoOJKMV0Sk",
        "outputId": "d25dd733-4110-41f2-f130-92825878eb6f"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2.0.1+cu118\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_path = \"/content/datasets\"\n",
        "num_batch = 64"
      ],
      "metadata": {
        "id": "X5ABKUmSxPN5"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#define the mean and standrad deviation values for normalization\n",
        "#use to transform datasets in\n",
        "# number system range 0-1 so that torch library can understand\n",
        "T = transforms.Compose([\n",
        "       transforms.ToTensor()\n",
        "    ])"
      ],
      "metadata": {
        "id": "MQjCO5auu6pw"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#The MNIST dataset consists of handwritten digit images\n",
        "train_dataset = datasets.MNIST(root=data_path,\n",
        "                               train=True,\n",
        "                               download=True,\n",
        "                               transform=T)\n",
        "\n",
        "val_dataset = datasets.MNIST(root=data_path,\n",
        "                              download=False,\n",
        "                              transform=T)\n",
        "#train data loder\n",
        "train_dl = torch.utils.data.DataLoader(train_dataset, batch_size=num_batch)\n",
        "val_dl = torch.utils.data.DataLoader(val_dataset, batch_size=num_batch)\n"
      ],
      "metadata": {
        "id": "7IqSU06tuaFw",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ef35ea33-df46-4301-fe7f-a9f8d5d4cc0e"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\n",
            "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to /content/datasets/MNIST/raw/train-images-idx3-ubyte.gz\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 9912422/9912422 [00:00<00:00, 90585601.64it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting /content/datasets/MNIST/raw/train-images-idx3-ubyte.gz to /content/datasets/MNIST/raw\n",
            "\n",
            "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz\n",
            "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz to /content/datasets/MNIST/raw/train-labels-idx1-ubyte.gz\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 28881/28881 [00:00<00:00, 111031800.02it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting /content/datasets/MNIST/raw/train-labels-idx1-ubyte.gz to /content/datasets/MNIST/raw\n",
            "\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz to /content/datasets/MNIST/raw/t10k-images-idx3-ubyte.gz\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 1648877/1648877 [00:00<00:00, 25012355.91it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting /content/datasets/MNIST/raw/t10k-images-idx3-ubyte.gz to /content/datasets/MNIST/raw\n",
            "\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz to /content/datasets/MNIST/raw/t10k-labels-idx1-ubyte.gz\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 4542/4542 [00:00<00:00, 20888737.68it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting /content/datasets/MNIST/raw/t10k-labels-idx1-ubyte.gz to /content/datasets/MNIST/raw\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Look into input data**"
      ],
      "metadata": {
        "id": "D77raIXqqi7a"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import random\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Display a few sample images from the MNIST dataset- define number\n",
        "num_samples = 5\n",
        "\n",
        "# Get random indices for the images in the dataset\n",
        "random_indices = random.sample(range(len(train_dataset)), num_samples)\n",
        "\n",
        "#iterate labels with images and display it\n",
        "for i , idx in enumerate(random_indices):\n",
        "  image, label = train_dataset[idx]\n",
        "  image = image.squeeze()  # Remove the single-dimensional channel dimension if present\n",
        "\n",
        "  #plot images\n",
        "  plt.subplot(1, num_samples, i + 1)\n",
        "  plt.imshow(image, cmap='gray')\n",
        "  plt.title(f\"label : {label}\")\n",
        "  plt.axis('off')\n",
        "\n",
        "# Show the plot\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "qz2JNjoauaL2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 144
        },
        "outputId": "c5a539e1-b1b1-47de-ff14-56bdf914a6a0"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 5 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(train_dataset)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jdeq4SFqsdWT",
        "outputId": "abb1cde6-9eed-4e49-9dda-00425deab3ae"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "60000"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('total images :', train_dataset.data.shape[0])\n",
        "print('size: ', train_dataset.data.shape[1:])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Hg7hdkaClAyA",
        "outputId": "a69e2818-4069-4c85-cc35-0797ec316889"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "total images : 60000\n",
            "size:  torch.Size([28, 28])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Prepare input data**"
      ],
      "metadata": {
        "id": "MmdpZtYQq-FL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def create_lenet():\n",
        "    model = nn.Sequential(\n",
        "        nn.Conv2d(1, 6, 5, padding=2),\n",
        "        nn.ReLU(),\n",
        "        nn.AvgPool2d(2, stride=2),\n",
        "\n",
        "        nn.Conv2d(6, 16, 5, padding=0),\n",
        "        nn.ReLU(),\n",
        "        nn.AvgPool2d(2, stride=2),\n",
        "\n",
        "        nn.Conv2d(16, 32, 3, padding=0),\n",
        "        nn.ReLU(),\n",
        "        nn.AvgPool2d(2, stride=2),\n",
        "\n",
        "        nn.Flatten(),\n",
        "        nn.Linear(32 * 1 * 1, 120),\n",
        "        nn.ReLU(),\n",
        "        nn.Linear(120, 84),\n",
        "        nn.ReLU(),\n",
        "        nn.Linear(84, 10)\n",
        "    )\n",
        "    return model\n"
      ],
      "metadata": {
        "id": "7icigyGclA-j"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def validate(model, data):\n",
        "  total = 0\n",
        "  correct = 0\n",
        "  for i, (images, labels) in enumerate(data):\n",
        "    images = images.cuda()\n",
        "    x = model(images)\n",
        "    value, pred = torch.max(x, 1)\n",
        "\n",
        "    pred = pred.data.cpu()\n",
        "    total += x.size(0)\n",
        "    correct += torch.sum(pred == labels)\n",
        "  return correct*100./total"
      ],
      "metadata": {
        "id": "6wfPS5XclBCW"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def train(num_epochs=3, lr=1e-3, device='cpu'):\n",
        "    accuracies = []\n",
        "    cnn = create_lenet().to(device)\n",
        "    cec = nn.CrossEntropyLoss()\n",
        "    optimizer = optim.Adam(cnn.parameters(), lr=lr)\n",
        "    max_accuracy = 0\n",
        "\n",
        "    for epoch in range(num_epochs):\n",
        "        for i, (images, labels) in enumerate(train_dl):\n",
        "            print(\"Type of labels:\", type(labels))\n",
        "            print(\"Value of labels:\", labels)\n",
        "            images = images.to(device)\n",
        "            labels = labels.to(device)\n",
        "            optimizer.zero_grad()\n",
        "            pred = cnn(images)\n",
        "            loss = cec(pred, labels)\n",
        "            loss.backward()\n",
        "            optimizer.step()\n",
        "\n",
        "        accuracy = float(validate(cnn, val_dl))\n",
        "        accuracies.append(accuracy)\n",
        "        if accuracy > max_accuracy:\n",
        "            best_model = copy.deepcopy(cnn)\n",
        "            max_accuracy = accuracy\n",
        "            print(\"Saving best model with accuracy:\", accuracy)\n",
        "        print(\"Epoch:\", epoch + 1, \"Accuracy:\", accuracy, \"%\")\n",
        "\n",
        "    plt.plot(accuracies)\n",
        "    return best_model\n"
      ],
      "metadata": {
        "id": "t3N2dIDs9C6B"
      },
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "if torch.cuda.is_available():\n",
        "  device = torch.device(\"cuda:0\")\n",
        "else:\n",
        "  device = torch.device(\"cpu\")"
      ],
      "metadata": {
        "id": "kV9H6bbnlBJ1"
      },
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "device"
      ],
      "metadata": {
        "id": "1bRuGHeqlBMa",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "19a56d34-4f19-4d04-e545-b562f2340840"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "device(type='cuda', index=0)"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lenet = train(40, device=device)"
      ],
      "metadata": {
        "id": "mhwxEuMnlBPC",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "1826b528-d687-4eb2-85be-a894490e98c0"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
            "Type of labels: <class 'torch.Tensor'>\n",
            "Value of labels: tensor([8, 9, 4, 7, 3, 1, 7, 1, 6, 9, 0, 0, 2, 7, 3, 3, 7, 4, 0, 8, 2, 6, 1, 3,\n",
            "        5, 8, 2, 0, 8, 6, 4, 2, 2, 1, 1, 0, 3, 1, 9, 0, 3, 6, 8, 2, 0, 3, 8, 8,\n",
            "        4, 9, 7, 0, 4, 7, 7, 2, 2, 3, 0, 4, 6, 5, 9, 5])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        6, 3, 8, 3, 7, 4, 1, 7, 0, 0, 1, 1, 4, 9, 2, 1])\n",
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            "        7, 2, 7, 9, 9, 1, 3, 3, 2, 1, 7, 4, 1, 8, 3, 6])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        5, 6, 5, 7, 7, 8, 1, 9, 9, 0, 7, 1, 4, 2, 9, 3])\n",
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            "        9, 8, 2, 4, 2, 6, 3, 5, 1, 0, 4, 1, 3, 2, 0, 3])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        2, 9, 0, 7, 0, 5, 7, 2, 3, 9, 8, 0, 3, 1, 3, 5])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        9, 1, 8, 6, 3, 9, 2, 3, 3, 2, 0, 9, 6, 1, 5, 6])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        2, 7, 2, 1, 1, 2, 5, 5, 6, 8, 8, 1, 9, 9, 2, 3])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        2, 5, 7, 6, 3, 7, 7, 8, 7, 9, 6, 0, 8, 1, 7, 2])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        6, 2, 5, 0, 1, 0, 1, 8, 3, 2, 2, 5, 4, 9, 5, 8])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        8, 6, 5, 4, 5, 3, 5, 8, 9, 2, 2, 8, 4, 3, 6, 8])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        5, 6, 1, 4, 6, 3, 5, 2, 2, 1, 1, 8, 8, 6, 6, 1])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        2, 1, 4, 2, 4, 3, 5, 4, 6, 5, 2, 6, 3, 7, 6, 8])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        5, 6, 8, 5, 4, 0, 3, 4, 1, 1, 0, 7, 1, 4, 7, 3])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        2, 1, 3, 7, 1, 9, 5, 6, 2, 2, 1, 7, 5, 3, 7, 3])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        8, 9, 2, 0, 8, 1, 9, 2, 4, 3, 0, 4, 1, 5, 5, 6])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        9, 3, 7, 9, 6, 7, 8, 2, 9, 1, 7, 2, 8, 8, 3, 9])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        3, 8, 4, 6, 7, 5, 8, 9, 0, 0, 3, 0, 2, 0, 9, 3])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        9, 6, 5, 2, 3, 2, 9, 0, 8, 9, 9, 4, 9, 0, 0, 1])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        7, 9, 6, 9, 9, 9, 4, 8, 4, 1, 2, 4, 2, 1, 0, 2])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        8, 7, 3, 5, 5, 2, 7, 1, 5, 4, 9, 4, 7, 3, 1, 0])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        0, 2, 1, 3, 7, 4, 9, 5, 3, 6, 6, 7, 1, 8, 1, 9])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        8, 2, 2, 4, 0, 1, 0, 4, 3, 1, 3, 4, 4, 9, 8, 6])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        6, 6, 3, 1, 9, 5, 4, 8, 0, 4, 6, 2, 9, 3, 3, 9])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        9, 2, 9, 9, 5, 4, 5, 9, 3, 7, 6, 0, 2, 0, 2, 2])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        4, 7, 4, 8, 3, 9, 7, 0, 7, 1, 0, 2, 5, 3, 3, 4])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "Type of labels: <class 'torch.Tensor'>\n",
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            "        9, 4, 4, 7, 3, 8, 0, 0, 9, 7, 6, 0, 8, 6, 0, 9])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "Type of labels: <class 'torch.Tensor'>\n",
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            "Type of labels: <class 'torch.Tensor'>\n",
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            "Type of labels: <class 'torch.Tensor'>\n",
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            "Type of labels: <class 'torch.Tensor'>\n",
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            "Type of labels: <class 'torch.Tensor'>\n",
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            "        7, 2, 0, 3, 9, 7, 3, 2, 4, 5, 8, 1, 5, 7, 7, 2])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        6, 1, 8, 6, 3, 8, 8, 5, 0, 6, 7, 0, 8, 8, 5, 9])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        8, 2, 7, 3, 4, 4, 9, 5, 9, 6, 9, 7, 7, 8, 1, 9])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        9, 5, 4, 0, 9, 1, 4, 3, 1, 6, 9, 4, 9, 8, 3, 9])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "Type of labels: <class 'torch.Tensor'>\n",
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            "Type of labels: <class 'torch.Tensor'>\n",
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            "        7, 2, 8, 3, 0, 4, 6, 4, 4, 5, 1, 6, 9, 6, 3, 3])\n",
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            "        5, 3, 9, 7, 0, 8, 3, 4, 1, 0, 2, 2, 1, 4, 3, 0])\n",
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            "        5, 9, 8, 5, 4, 8, 3, 9, 2, 6, 8, 5, 8, 2, 5, 6])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        4, 7, 8, 0, 3, 1, 0, 2, 9, 3, 3, 5, 8, 6, 4, 7])\n",
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            "        9, 2, 2, 6, 4, 3, 1, 0, 9, 7, 0, 2, 1, 6, 0, 1])\n",
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            "        4, 8, 2, 0, 1, 3, 4, 5, 3, 3, 3, 6, 6, 0, 0, 1])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        7, 3, 4, 6, 5, 5, 3, 9, 4, 1, 4, 3, 2, 3, 0, 0])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        0, 8, 5, 5, 9, 1, 4, 3, 0, 7, 1, 4, 7, 9, 0, 8])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        2, 1, 8, 2, 0, 3, 5, 4, 9, 5, 4, 6, 8, 7, 0, 8])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        6, 3, 7, 0, 8, 9, 5, 9, 0, 6, 1, 1, 2, 4, 0, 5])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        1, 5, 0, 1, 6, 1, 4, 7, 8, 6, 5, 6, 3, 6, 3, 8])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        6, 8, 7, 0, 5, 3, 9, 0, 9, 8, 9, 3, 9, 9, 5, 0])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        5, 1, 8, 7, 6, 8, 6, 5, 0, 3, 0, 7, 8, 0, 1, 0])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        7, 3, 3, 4, 1, 3, 7, 9, 1, 6, 0, 0, 0, 6, 2, 9])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "Type of labels: <class 'torch.Tensor'>\n",
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            "        4, 0, 9, 8, 0, 1, 6, 7, 0, 7, 4, 1, 8, 3, 5, 2])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        2, 4, 2, 6, 0, 2, 2, 5, 5, 5, 1, 1, 5, 3, 2, 3])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        1, 3, 5, 4, 9, 5, 7, 6, 0, 7, 4, 8, 1, 0, 9, 1])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        9, 5, 7, 2, 2, 7, 6, 2, 3, 3, 2, 6, 2, 2, 7, 1])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        3, 6, 6, 0, 1, 9, 0, 2, 2, 6, 7, 4, 7, 3, 4, 9])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        1, 3, 6, 8, 0, 7, 7, 6, 8, 9, 0, 3, 8, 3, 7, 7])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        2, 8, 6, 9, 7, 0, 9, 1, 6, 2, 8, 3, 6, 4, 9, 5])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        6, 4, 7, 9, 4, 6, 5, 2, 6, 9, 8, 8, 8, 5, 9, 3])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        7, 1, 9, 7, 9, 2, 2, 8, 1, 7, 7, 0, 0, 1, 8, 9])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        3, 6, 2, 0, 1, 1, 6, 2, 5, 3, 0, 4, 3, 5, 8, 6])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        5, 0, 0, 6, 1, 0, 9, 7, 7, 3, 1, 8, 3, 7, 5, 1])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "Type of labels: <class 'torch.Tensor'>\n",
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            "Type of labels: <class 'torch.Tensor'>\n",
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            "Type of labels: <class 'torch.Tensor'>\n",
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            "Type of labels: <class 'torch.Tensor'>\n",
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            "        3, 4, 2, 5, 2, 5, 1, 3, 3, 7, 1, 3, 0, 1, 9, 8])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "Type of labels: <class 'torch.Tensor'>\n",
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            "Type of labels: <class 'torch.Tensor'>\n",
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            "        1, 0, 3, 8, 0, 4, 4, 1, 3, 8, 2, 8, 1, 1, 4, 3])\n",
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            "        1, 6, 8, 5, 9, 2, 7, 2, 0, 2, 4, 5, 8, 6, 4, 7])\n",
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            "        7, 2, 7, 3, 9, 3, 7, 0, 6, 9, 4, 5, 2, 4, 9, 3])\n",
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            "        7, 0, 8, 6, 2, 8, 1, 5, 9, 5, 6, 0, 2, 5, 6, 3])\n",
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            "        4, 2, 4, 3, 8, 4, 1, 5, 0, 6, 0, 7, 3, 8, 4, 9])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        7, 0, 7, 9, 0, 5, 6, 4, 2, 3, 0, 0, 5, 8, 1, 4])\n",
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            "Type of labels: <class 'torch.Tensor'>\n",
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            "Type of labels: <class 'torch.Tensor'>\n",
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            "Type of labels: <class 'torch.Tensor'>\n",
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            "        1, 2, 0, 9, 7, 3, 5, 2, 8, 1, 7, 4, 4, 5, 9, 5])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "Type of labels: <class 'torch.Tensor'>\n",
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            "        1, 1, 7, 6, 4, 1, 3, 9, 2, 0, 7, 1, 7, 2, 5, 3])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
            "Value of labels: tensor([8, 4, 9, 5, 8, 6, 3, 7, 2, 8, 6, 0, 7, 1, 1, 2, 2, 3, 0, 4, 9, 5, 9, 6,\n",
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            "        2, 1, 9, 0, 0, 4, 0, 5, 5, 6, 7, 6, 2, 3, 8, 4])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
            "Value of labels: tensor([3, 4, 6, 1, 1, 0, 9, 6, 1, 4, 8, 9, 2, 7, 0, 2, 3, 3, 4, 3, 2, 9, 8, 2,\n",
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            "        9, 3, 2, 7, 8, 4, 0, 2, 2, 4, 7, 8, 0, 7, 0, 6])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
            "Value of labels: tensor([9, 3, 2, 6, 0, 5, 7, 5, 1, 0, 8, 1, 6, 7, 2, 9, 7, 9, 5, 8, 9, 6, 5, 2,\n",
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            "        0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
            "Value of labels: tensor([6, 7, 8, 9, 0, 1, 2, 3, 4, 7, 8, 9, 7, 1, 5, 6, 2, 5, 4, 8, 1, 7, 1, 8,\n",
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            "        6, 4, 6, 3, 7, 0, 1, 4, 8, 8, 1, 2, 9, 3, 1, 8])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
            "Value of labels: tensor([4, 2, 9, 9, 2, 9, 6, 0, 5, 3, 3, 6, 3, 1, 2, 5, 0, 3, 7, 6, 1, 5, 5, 9,\n",
            "        9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2,\n",
            "        3, 4, 5, 6, 7, 8, 9, 6, 8, 2, 3, 7, 4, 6, 8, 6])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
            "Value of labels: tensor([6, 8, 0, 1, 7, 1, 7, 3, 5, 0, 2, 2, 0, 2, 5, 4, 1, 5, 1, 2, 3, 4, 8, 3,\n",
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            "        6, 0, 6, 1, 0, 5, 8, 5, 0, 1, 0, 9, 4, 5, 9, 7])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
            "Value of labels: tensor([1, 9, 6, 8, 9, 2, 8, 5, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5,\n",
            "        6, 7, 8, 9, 0, 1, 2, 3, 4, 9, 0, 5, 6, 8, 3, 9, 0, 2, 3, 3, 3, 9, 5, 6,\n",
            "        4, 4, 2, 5, 4, 3, 9, 6, 8, 7, 9, 1, 5, 2, 6, 3])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
            "Value of labels: tensor([4, 4, 1, 6, 4, 9, 6, 5, 1, 2, 6, 6, 7, 8, 8, 1, 8, 3, 7, 1, 2, 7, 0, 7,\n",
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            "        7, 1, 4, 3, 9, 7, 1, 2, 6, 0, 7, 3, 0, 1, 2, 3])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
            "Value of labels: tensor([4, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,\n",
            "        7, 0, 6, 0, 7, 3, 7, 3, 7, 9, 7, 4, 0, 2, 6, 5, 6, 5, 2, 9, 5, 3, 9, 8,\n",
            "        3, 4, 0, 5, 4, 6, 3, 4, 8, 0, 6, 4, 8, 1, 3, 4])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
            "Value of labels: tensor([0, 1, 9, 2, 3, 7, 9, 1, 2, 5, 1, 7, 2, 8, 7, 0, 1, 9, 6, 4, 1, 2, 3, 7,\n",
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            "        9, 0, 1, 2, 3, 4, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
            "Value of labels: tensor([7, 8, 6, 4, 2, 6, 4, 7, 5, 5, 4, 7, 8, 9, 2, 9, 3, 9, 3, 8, 2, 0, 5, 0,\n",
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            "        1, 1, 8, 1, 7, 1, 3, 8, 9, 7, 6, 7, 4, 1, 6, 7])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
            "Value of labels: tensor([5, 1, 7, 1, 9, 8, 0, 6, 9, 4, 9, 9, 3, 7, 1, 9, 2, 2, 5, 3, 7, 8, 2, 3,\n",
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            "        8, 1, 0, 5, 5, 1, 9, 0, 4, 1, 9, 3, 8, 4, 7, 7])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
            "Value of labels: tensor([8, 5, 0, 6, 5, 5, 3, 3, 3, 9, 8, 1, 4, 0, 6, 1, 0, 0, 6, 2, 1, 1, 3, 2,\n",
            "        8, 8, 7, 8, 4, 6, 0, 2, 0, 3, 6, 8, 7, 1, 5, 9, 9, 3, 2, 4, 9, 4, 6, 5,\n",
            "        3, 2, 5, 5, 9, 4, 1, 6, 5, 0, 1, 2, 3, 4, 5, 6])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
            "Value of labels: tensor([7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 6,\n",
            "        4, 2, 6, 4, 7, 5, 5, 4, 7, 8, 9, 2, 9, 3, 9, 3, 8, 2, 0, 9, 8, 0, 5, 6,\n",
            "        0, 1, 0, 4, 2, 6, 5, 5, 5, 4, 3, 4, 1, 5, 3, 0])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
            "Value of labels: tensor([8, 3, 0, 6, 2, 7, 1, 1, 8, 1, 7, 1, 3, 8, 5, 4, 2, 0, 9, 7, 6, 7, 4, 1,\n",
            "        6, 8, 4, 7, 5, 1, 2, 6, 7, 1, 9, 8, 0, 6, 9, 4, 9, 9, 6, 2, 3, 7, 1, 9,\n",
            "        2, 2, 5, 3, 7, 8, 0, 1, 2, 3, 4, 5, 6, 7, 8, 0])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
            "Value of labels: tensor([1, 2, 3, 4, 5, 6, 7, 8, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 2, 1, 2, 1, 3, 9,\n",
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            "        4, 8, 4, 1, 8, 6, 6, 4, 6, 3, 5, 7, 2, 5, 9, 2])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
            "Value of labels: tensor([6, 2, 1, 2, 0, 8, 3, 8, 3, 0, 8, 7, 4, 9, 5, 0, 9, 7, 4, 9, 1, 6, 2, 7,\n",
            "        6, 1, 8, 6, 1, 0, 3, 6, 2, 1, 4, 0, 1, 2, 3, 4, 7, 8, 9, 0, 1, 2, 3, 4,\n",
            "        7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 7, 4, 3, 1])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
            "Value of labels: tensor([8, 6, 1, 9, 2, 4, 0, 9, 9, 3, 7, 7, 9, 1, 8, 4, 7, 5, 8, 5, 3, 2, 2, 0,\n",
            "        5, 8, 6, 0, 3, 8, 1, 0, 3, 0, 4, 7, 4, 9, 2, 9, 1, 2, 1, 7, 1, 6, 7, 3,\n",
            "        3, 6, 2, 8, 7, 6, 4, 9, 8, 8, 2, 9, 6, 5, 5, 9])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
            "Value of labels: tensor([5, 3, 7, 4, 7, 6, 0, 0, 4, 6, 6, 9, 0, 1, 1, 3, 2, 1, 0, 0, 1, 2, 3, 4,\n",
            "        5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8,\n",
            "        9, 4, 2, 0, 2, 1, 5, 9, 6, 5, 9, 3, 5, 0, 7, 1])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
            "Value of labels: tensor([1, 5, 3, 0, 7, 9, 6, 1, 6, 7, 4, 9, 5, 7, 9, 8, 5, 6, 2, 4, 8, 9, 2, 2,\n",
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            "        1, 0, 7, 6, 2, 2, 7, 6, 0, 6, 9, 4, 8, 4, 3, 3])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
            "Value of labels: tensor([7, 8, 1, 0, 4, 5, 8, 3, 0, 6, 8, 3, 7, 8, 5, 7, 8, 2, 3, 4, 6, 7, 8, 9,\n",
            "        0, 1, 2, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 8, 7, 5, 7, 3, 3, 8, 6, 5,\n",
            "        4, 0, 9, 3, 2, 2, 3, 0, 6, 4, 3, 7, 6, 9, 2, 2])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
            "Value of labels: tensor([3, 5, 5, 0, 7, 2, 3, 4, 0, 0, 5, 1, 8, 9, 0, 8, 9, 6, 5, 6, 7, 5, 3, 5,\n",
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            "        4, 5, 6, 7, 8, 9, 1, 3, 5, 9, 4, 1, 7, 7, 2, 1])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
            "Value of labels: tensor([4, 8, 3, 8, 2, 9, 3, 8, 5, 4, 8, 8, 9, 7, 5, 4, 3, 9, 2, 5, 7, 4, 1, 2,\n",
            "        3, 6, 0, 1, 0, 0, 2, 8, 7, 2, 5, 1, 1, 8, 5, 6, 4, 0, 4, 7, 3, 6, 8, 0,\n",
            "        3, 7, 6, 9, 2, 6, 5, 8, 6, 9, 0, 4, 0, 6, 1, 9])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
            "Value of labels: tensor([2, 0, 9, 5, 1, 3, 7, 6, 9, 4, 8, 3, 7, 7, 6, 3, 0, 5, 6, 2, 2, 0, 1, 2,\n",
            "        3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6,\n",
            "        7, 8, 9, 5, 6, 6, 0, 8, 3, 7, 9, 4, 7, 1, 9, 1])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
            "Value of labels: tensor([7, 1, 4, 1, 7, 5, 7, 1, 3, 3, 1, 6, 9, 7, 4, 3, 0, 2, 5, 2, 6, 0, 8, 4,\n",
            "        8, 1, 5, 0, 6, 6, 3, 4, 7, 5, 7, 2, 2, 0, 0, 1, 7, 7, 9, 5, 9, 8, 9, 6,\n",
            "        8, 3, 6, 1, 2, 9, 5, 2, 5, 2, 6, 2, 4, 8, 4, 6])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
            "Value of labels: tensor([5, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2,\n",
            "        4, 5, 6, 7, 8, 0, 4, 0, 1, 7, 5, 1, 4, 2, 8, 4, 3, 1, 7, 8, 2, 4, 3, 3,\n",
            "        6, 9, 9, 5, 8, 6, 7, 0, 6, 2, 6, 3, 9, 1, 7, 4])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
            "Value of labels: tensor([8, 8, 9, 0, 3, 0, 5, 2, 9, 4, 1, 0, 3, 7, 5, 8, 7, 7, 8, 2, 9, 5, 5, 1,\n",
            "        2, 6, 4, 2, 5, 2, 3, 6, 0, 0, 7, 5, 2, 8, 1, 6, 1, 0, 4, 3, 1, 6, 1, 9,\n",
            "        0, 1, 2, 3, 4, 5, 6, 7, 0, 1, 2, 3, 4, 6, 7, 8])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
            "Value of labels: tensor([0, 1, 2, 3, 4, 7, 8, 9, 2, 2, 4, 0, 7, 3, 5, 4, 1, 8, 0, 5, 2, 7, 2, 3,\n",
            "        6, 2, 1, 7, 7, 9, 2, 4, 6, 7, 7, 5, 9, 9, 1, 8, 6, 4, 6, 0, 9, 2, 6, 4,\n",
            "        3, 9, 0, 8, 8, 9, 4, 7, 1, 3, 6, 9, 4, 1, 7, 6])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
            "Value of labels: tensor([9, 3, 3, 7, 4, 1, 9, 1, 0, 6, 0, 1, 2, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4,\n",
            "        5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 3, 2, 9, 3, 2, 1, 4, 5, 5,\n",
            "        2, 3, 2, 1, 3, 9, 7, 2, 1, 2, 8, 9, 1, 8, 8, 7])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
            "Value of labels: tensor([0, 6, 7, 7, 8, 7, 5, 0, 6, 1, 5, 7, 4, 6, 1, 2, 7, 9, 9, 0, 3, 8, 4, 4,\n",
            "        1, 8, 6, 5, 0, 0, 3, 7, 1, 6, 4, 2, 6, 6, 0, 4, 5, 4, 1, 3, 8, 6, 3, 9,\n",
            "        9, 5, 9, 3, 6, 4, 7, 6, 2, 2, 0, 9, 4, 0, 1, 2])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
            "Value of labels: tensor([3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,\n",
            "        6, 0, 3, 4, 1, 4, 0, 7, 8, 7, 7, 9, 0, 4, 9, 4, 0, 5, 8, 5, 9, 8, 8, 4,\n",
            "        0, 7, 1, 3, 5, 3, 1, 6, 5, 3, 8, 7, 3, 1, 6, 8])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
            "Value of labels: tensor([5, 9, 2, 2, 0, 9, 2, 4, 6, 7, 3, 1, 3, 6, 6, 2, 1, 2, 6, 0, 7, 8, 9, 2,\n",
            "        9, 5, 1, 8, 3, 5, 6, 8])\n",
            "Epoch: 39 Accuracy: 99.18333435058594 %\n",
            "Type of labels: <class 'torch.Tensor'>\n",
            "Value of labels: tensor([5, 0, 4, 1, 9, 2, 1, 3, 1, 4, 3, 5, 3, 6, 1, 7, 2, 8, 6, 9, 4, 0, 9, 1,\n",
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            "        9, 3, 3, 0, 7, 4, 9, 8, 0, 9, 4, 1, 4, 4, 6, 0])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
            "Value of labels: tensor([4, 5, 6, 1, 0, 0, 1, 7, 1, 6, 3, 0, 2, 1, 1, 7, 9, 0, 2, 6, 7, 8, 3, 9,\n",
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            "        1, 1, 0, 4, 9, 2, 0, 0, 2, 0, 2, 7, 1, 8, 6, 4])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
            "Value of labels: tensor([1, 6, 3, 4, 5, 9, 1, 3, 3, 8, 5, 4, 7, 7, 4, 2, 8, 5, 8, 6, 7, 3, 4, 6,\n",
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            "        9, 1, 2, 3, 2, 3, 5, 9, 1, 7, 6, 2, 8, 2, 2, 5])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
            "Value of labels: tensor([0, 7, 4, 9, 7, 8, 3, 2, 1, 1, 8, 3, 6, 1, 0, 3, 1, 0, 0, 1, 7, 2, 7, 3,\n",
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            "        8, 6, 3, 7, 5, 8, 0, 9, 1, 0, 3, 1, 2, 2, 3, 3])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
            "Value of labels: tensor([6, 4, 7, 5, 0, 6, 2, 7, 9, 8, 5, 9, 2, 1, 1, 4, 4, 5, 6, 4, 1, 2, 5, 3,\n",
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            "        9, 7, 5, 7, 2, 1, 1, 6, 8, 9, 4, 1, 5, 2, 2, 9])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
            "Value of labels: tensor([0, 3, 9, 6, 7, 2, 0, 3, 5, 4, 3, 6, 5, 8, 9, 5, 4, 7, 4, 2, 7, 3, 4, 8,\n",
            "        9, 1, 9, 2, 8, 7, 9, 1, 8, 7, 4, 1, 3, 1, 1, 0, 2, 3, 9, 4, 9, 2, 1, 6,\n",
            "        8, 4, 7, 7, 4, 4, 9, 2, 5, 7, 2, 4, 4, 2, 1, 9])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
            "Value of labels: tensor([7, 2, 8, 7, 6, 9, 2, 2, 3, 8, 1, 6, 5, 1, 1, 0, 2, 6, 4, 5, 8, 3, 1, 5,\n",
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            "        2, 3, 9, 0, 7, 5, 4, 8, 0, 9, 4, 1, 2, 8, 7, 1])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
            "Value of labels: tensor([2, 6, 1, 0, 3, 0, 1, 1, 8, 2, 0, 3, 9, 4, 0, 5, 0, 6, 1, 7, 7, 8, 1, 9,\n",
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            "        5, 7, 6, 8, 3, 9, 5, 8, 5, 7, 6, 1, 1, 3, 1, 7])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
            "Value of labels: tensor([5, 5, 5, 2, 5, 8, 7, 0, 9, 7, 7, 5, 0, 9, 0, 0, 8, 9, 2, 4, 8, 1, 6, 1,\n",
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            "        7, 3, 7, 2, 4, 6, 9, 7, 2, 4, 2, 8, 1, 1, 3, 8])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
            "Value of labels: tensor([4, 0, 6, 5, 9, 3, 0, 9, 2, 4, 7, 1, 2, 9, 4, 2, 6, 1, 8, 9, 0, 6, 6, 7,\n",
            "        9, 9, 8, 0, 1, 4, 4, 6, 7, 1, 5, 7, 0, 3, 5, 8, 4, 7, 1, 2, 5, 9, 5, 6,\n",
            "        7, 5, 9, 8, 8, 3, 6, 9, 7, 0, 7, 5, 7, 1, 1, 0])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
            "Value of labels: tensor([7, 9, 2, 3, 7, 3, 2, 4, 1, 6, 2, 7, 5, 5, 7, 4, 0, 2, 6, 3, 6, 4, 0, 4,\n",
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            "        2, 0, 5, 1, 4, 2, 8, 3, 2, 4, 1, 5, 4, 6, 0, 7])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
            "Value of labels: tensor([9, 8, 4, 9, 8, 0, 1, 1, 0, 2, 2, 3, 2, 4, 4, 5, 8, 6, 5, 7, 7, 8, 8, 9,\n",
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            "        3, 7, 4, 9, 8, 5, 8, 6, 3, 8, 6, 9, 9, 1, 8, 3])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
            "Value of labels: tensor([5, 8, 6, 5, 9, 7, 2, 5, 0, 8, 5, 1, 1, 0, 9, 1, 8, 6, 7, 0, 9, 3, 0, 8,\n",
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            "        6, 8, 0, 7, 7, 1, 9, 8, 7, 0, 9, 9, 4, 6, 2, 8])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
            "Value of labels: tensor([5, 1, 4, 1, 5, 5, 1, 7, 3, 6, 4, 3, 2, 5, 6, 4, 4, 0, 4, 4, 6, 7, 2, 4,\n",
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            "        7, 6, 4, 7, 7, 8, 5, 9, 7, 0, 3, 1, 6, 2, 4, 3])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
            "Value of labels: tensor([4, 4, 7, 5, 9, 6, 9, 0, 7, 1, 4, 2, 7, 3, 6, 7, 5, 8, 4, 5, 5, 2, 7, 1,\n",
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            "        6, 0, 1, 1, 1, 4, 8, 8, 6, 8, 1, 1, 0, 2, 1, 9])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        2, 5, 6, 9, 7, 8, 3, 0, 7, 9, 9, 0, 2, 1, 6, 2])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        2, 7, 0, 8, 8, 9, 2, 6, 4, 1, 6, 9, 9, 9, 3, 9])\n",
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            "        2, 0, 4, 8, 8, 1, 6, 8, 1, 2, 3, 6, 3, 2, 9, 0])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        2, 9, 6, 7, 2, 8, 6, 8, 0, 7, 0, 9, 0, 6, 9, 3])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        8, 7, 4, 8, 3, 9, 2, 0, 7, 3, 8, 4, 0, 7, 1, 8])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        4, 6, 7, 3, 0, 2, 3, 0, 0, 9, 4, 0, 8, 6, 9, 1])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        3, 2, 6, 3, 6, 1, 4, 1, 6, 4, 4, 5, 1, 5, 2, 0])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "Type of labels: <class 'torch.Tensor'>\n",
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            "Type of labels: <class 'torch.Tensor'>\n",
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            "        7, 4, 9, 3, 5, 0, 8, 2, 5, 5, 1, 2, 7, 6, 9, 0])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "Type of labels: <class 'torch.Tensor'>\n",
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            "Type of labels: <class 'torch.Tensor'>\n",
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            "Type of labels: <class 'torch.Tensor'>\n",
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            "Type of labels: <class 'torch.Tensor'>\n",
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            "        9, 1, 2, 2, 9, 3, 0, 4, 1, 5, 6, 6, 1, 7, 0, 8])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        1, 7, 1, 1, 6, 0, 8, 9, 7, 0, 2, 5, 3, 9, 6, 7])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "Type of labels: <class 'torch.Tensor'>\n",
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            "Type of labels: <class 'torch.Tensor'>\n",
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            "Type of labels: <class 'torch.Tensor'>\n",
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            "Type of labels: <class 'torch.Tensor'>\n",
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            "Type of labels: <class 'torch.Tensor'>\n",
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            "Type of labels: <class 'torch.Tensor'>\n",
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            "Type of labels: <class 'torch.Tensor'>\n",
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            "Type of labels: <class 'torch.Tensor'>\n",
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            "        3, 7, 1, 1, 7, 6, 1, 7, 2, 3, 0, 3, 3, 6, 8, 5])\n",
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            "        9, 9, 8, 0, 2, 1, 3, 1, 4, 3, 0, 2, 4, 1, 8, 0])\n",
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            "Type of labels: <class 'torch.Tensor'>\n",
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            "        7, 3, 1, 3, 2, 6, 9, 4, 4, 9, 2, 0, 6, 9, 1, 5])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        7, 3, 5, 6, 6, 9, 9, 0, 0, 1, 7, 7, 4, 5, 6, 4])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        6, 4, 5, 5, 2, 6, 0, 7, 9, 8, 6, 9, 4, 0, 3, 1])\n",
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            "        1, 2, 6, 6, 1, 5, 8, 3, 3, 0, 8, 7, 9, 2, 3, 7])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "Type of labels: <class 'torch.Tensor'>\n",
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            "Type of labels: <class 'torch.Tensor'>\n",
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            "Type of labels: <class 'torch.Tensor'>\n",
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            "Type of labels: <class 'torch.Tensor'>\n",
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            "Type of labels: <class 'torch.Tensor'>\n",
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            "        8, 0, 2, 1, 5, 2, 0, 3, 9, 4, 9, 5, 4, 6, 5, 7])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "Type of labels: <class 'torch.Tensor'>\n",
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            "Type of labels: <class 'torch.Tensor'>\n",
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            "Type of labels: <class 'torch.Tensor'>\n",
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            "        7, 3, 9, 7, 9, 8, 6, 0, 0, 1, 4, 2, 0, 3, 6, 4])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        1, 1, 7, 7, 6, 2, 0, 4, 0, 1, 3, 4, 7, 1, 0, 8])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        5, 6, 3, 9, 8, 1, 3, 9, 0, 0, 2, 6, 5, 7, 7, 1])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        4, 7, 1, 4, 7, 9, 6, 9, 6, 2, 2, 8, 2, 1, 1, 5])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        5, 7, 8, 8, 5, 9, 6, 0, 5, 1, 0, 2, 2, 3, 5, 4])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        2, 3, 0, 8, 4, 1, 8, 2, 8, 9, 7, 6, 6, 7, 3, 3])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        7, 1, 5, 3, 0, 6, 8, 7, 7, 3, 9, 4, 8, 9, 1, 6])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        7, 0, 0, 1, 1, 2, 5, 3, 4, 4, 4, 5, 7, 6, 0, 7])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        1, 7, 3, 1, 3, 8, 5, 3, 1, 8, 6, 7, 5, 1, 8, 7])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        8, 6, 4, 3, 6, 7, 4, 0, 8, 1, 5, 4, 9, 8, 5, 1])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        7, 2, 0, 5, 8, 0, 3, 3, 3, 7, 6, 6, 1, 1, 3, 5])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        6, 9, 1, 0, 3, 1, 4, 2, 9, 3, 9, 4, 4, 5, 5, 6])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        8, 4, 3, 2, 9, 5, 7, 8, 6, 5, 0, 9, 1, 8, 2, 8])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        8, 3, 2, 1, 5, 3, 1, 6, 7, 2, 5, 1, 7, 2, 9, 6])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        8, 9, 2, 3, 7, 1, 0, 1, 2, 0, 8, 3, 3, 4, 6, 5])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        9, 0, 4, 5, 8, 6, 6, 7, 8, 3, 8, 9, 8, 9, 6, 3])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        8, 9, 7, 8, 9, 1, 4, 1, 0, 6, 6, 6, 3, 8, 6, 2])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        1, 3, 1, 4, 2, 7, 1, 8, 2, 9, 3, 8, 5, 6, 2, 5])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        6, 0, 3, 6, 1, 8, 6, 4, 5, 7, 4, 9, 4, 8, 1, 6])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        1, 5, 4, 6, 4, 3, 2, 6, 2, 9, 3, 6, 8, 3, 7, 2])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        7, 2, 5, 3, 7, 4, 9, 7, 7, 8, 3, 9, 3, 0, 6, 1])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "Type of labels: <class 'torch.Tensor'>\n",
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            "Type of labels: <class 'torch.Tensor'>\n",
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            "Type of labels: <class 'torch.Tensor'>\n",
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            "Type of labels: <class 'torch.Tensor'>\n",
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            "Type of labels: <class 'torch.Tensor'>\n",
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            "Type of labels: <class 'torch.Tensor'>\n",
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            "        6, 5, 8, 3, 5, 9, 6, 3, 9, 7, 7, 1, 2, 8, 4, 3])\n",
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            "        7, 0, 9, 3, 8, 1, 3, 0, 9, 4, 9, 9, 2, 5, 0, 4])\n",
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            "        6, 0, 5, 1, 3, 2, 4, 3, 7, 4, 1, 5, 5, 6, 4, 7])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        9, 5, 0, 4, 9, 3, 1, 9, 2, 2, 4, 5, 1, 7, 4, 4])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        4, 7, 3, 3, 4, 6, 1, 8, 1, 0, 3, 3, 3, 7, 3, 4])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        3, 8, 3, 3, 1, 7, 6, 7, 0, 6, 2, 3, 4, 0, 3, 5])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
            "Value of labels: tensor([7, 6, 7, 2, 5, 2, 7, 0, 3, 1, 2, 2, 4, 3, 5, 4, 3, 5, 4, 6, 2, 7, 5, 8,\n",
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            "        4, 1, 1, 2, 6, 3, 2, 4, 9, 5, 6, 6, 2, 7, 9, 8])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        6, 7, 5, 7, 2, 2, 7, 1, 8, 1, 4, 2, 9, 8, 6, 0])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        1, 5, 5, 9, 0, 4, 2, 2, 2, 8, 0, 2, 3, 5, 3, 1])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        9, 6, 6, 7, 6, 8, 4, 9, 5, 0, 8, 1, 5, 2, 0, 3])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        8, 9, 3, 6, 8, 1, 7, 4, 1, 5, 1, 4, 9, 6, 9, 2])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        6, 6, 2, 6, 9, 8, 9, 2, 7, 5, 4, 2, 5, 2, 1, 7])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        6, 6, 9, 3, 1, 0, 7, 1, 4, 2, 9, 3, 3, 4, 1, 5])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        1, 7, 9, 7, 2, 5, 4, 7, 6, 9, 3, 9, 2, 4, 8, 7])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        9, 7, 3, 2, 4, 5, 1, 0, 4, 8, 1, 3, 7, 0, 4, 8])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        0, 2, 1, 3, 1, 4, 0, 5, 9, 6, 5, 7, 4, 8, 6, 9])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        5, 1, 0, 0, 3, 6, 0, 7, 2, 2, 4, 3, 6, 3, 6, 9])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        4, 2, 3, 4, 9, 8, 4, 6, 1, 0, 2, 5, 9, 7, 9, 5])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        0, 1, 6, 3, 7, 8, 4, 4, 6, 9, 9, 4, 1, 5, 5, 1])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        1, 0, 9, 1, 2, 2, 9, 3, 6, 4, 3, 5, 6, 6, 0, 7])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
            "Value of labels: tensor([0, 8, 1, 9, 1, 0, 5, 8, 5, 7, 2, 8, 8, 9, 1, 9, 1, 0, 2, 1, 0, 4, 8, 5,\n",
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            "        4, 9, 6, 3, 3, 8, 8, 9, 4, 6, 4, 7, 1, 5, 4, 6])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        3, 2, 2, 9, 2, 3, 1, 4, 0, 4, 1, 8, 1, 1, 2, 2])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        0, 2, 9, 8, 8, 0, 7, 0, 4, 1, 6, 2, 9, 3, 9, 4])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        4, 7, 0, 7, 3, 3, 5, 9, 1, 8, 2, 8, 7, 3, 9, 1])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        2, 9, 3, 1, 7, 3, 2, 6, 9, 3, 5, 3, 6, 2, 7, 2])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        1, 3, 8, 7, 6, 9, 4, 3, 8, 0, 1, 1, 7, 0, 3, 1])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        3, 7, 6, 8, 1, 9, 8, 0, 7, 1, 6, 2, 6, 3, 6, 4])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        8, 4, 2, 4, 3, 1, 8, 6, 6, 1, 5, 3, 9, 6, 8, 9])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        2, 1, 4, 6, 6, 1, 4, 7, 9, 2, 3, 3, 4, 6, 4, 6])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        0, 8, 4, 9, 7, 0, 9, 1, 5, 2, 5, 3, 9, 4, 5, 7])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "Type of labels: <class 'torch.Tensor'>\n",
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            "        4, 1, 7, 6, 5, 8, 4, 5, 3, 9, 8, 2, 5, 2, 7, 0])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        0, 8, 3, 3, 9, 5, 5, 6, 5, 8, 9, 0, 1, 0, 6, 9])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        5, 0, 2, 1, 3, 2, 7, 3, 2, 4, 7, 7, 8, 8, 5, 9])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        0, 1, 5, 2, 3, 5, 1, 9, 9, 8, 3, 9, 9, 5, 5, 4])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        5, 1, 6, 5, 6, 5, 0, 5, 5, 3, 8, 1, 7, 4, 2, 8])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "Type of labels: <class 'torch.Tensor'>\n",
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            "        8, 7, 2, 8, 4, 9, 9, 2, 5, 9, 6, 3, 2, 9, 9, 3])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        8, 4, 4, 1, 2, 5, 2, 3, 4, 0, 1, 8, 2, 3, 1, 6])\n",
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            "Type of labels: <class 'torch.Tensor'>\n",
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            "        8, 8, 6, 9, 4, 0, 9, 1, 3, 2, 5, 3, 5, 4, 7, 5])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        6, 9, 1, 3, 0, 8, 5, 8, 8, 9, 7, 7, 4, 5, 5, 9])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "Type of labels: <class 'torch.Tensor'>\n",
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            "        4, 2, 7, 3, 1, 4, 6, 5, 0, 6, 3, 7, 0, 0, 3, 1])\n",
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            "        9, 9, 8, 9, 7, 3, 7, 7, 5, 7, 4, 9, 9, 1, 0, 4])\n",
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            "        3, 7, 8, 1, 1, 7, 9, 1, 7, 6, 2, 7, 7, 3, 5, 3])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        1, 1, 1, 0, 1, 0, 5, 1, 2, 2, 1, 3, 0, 4, 6, 7])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        1, 0, 2, 8, 7, 0, 9, 2, 0, 3, 0, 7, 7, 9, 7, 4])\n",
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            "        5, 2, 8, 3, 5, 4, 9, 5, 2, 6, 2, 9, 1, 9, 9, 6])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        4, 4, 4, 7, 3, 0, 7, 1, 4, 6, 2, 3, 1, 7, 3, 9])\n",
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            "        5, 9, 5, 4, 7, 2, 8, 4, 3, 1, 6, 5, 8, 6, 9, 1])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        0, 1, 1, 1, 4, 2, 4, 8, 0, 7, 3, 6, 4, 5, 0, 3])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        5, 4, 7, 6, 6, 6, 4, 9, 9, 4, 1, 2, 9, 8, 1, 2])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "Type of labels: <class 'torch.Tensor'>\n",
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            "Type of labels: <class 'torch.Tensor'>\n",
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            "Type of labels: <class 'torch.Tensor'>\n",
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            "Type of labels: <class 'torch.Tensor'>\n",
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            "Type of labels: <class 'torch.Tensor'>\n",
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            "        9, 5, 4, 6, 2, 7, 4, 8, 4, 9, 3, 0, 6, 1, 4, 2])\n",
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            "        0, 5, 2, 9, 3, 5, 6, 3, 7, 7, 0, 4, 6, 3, 1, 6])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        5, 3, 3, 4, 5, 5, 9, 6, 9, 7, 0, 8, 6, 9, 2, 0])\n",
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            "        7, 1, 8, 4, 0, 0, 1, 5, 8, 5, 8, 4, 9, 0, 6, 7])\n",
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            "Type of labels: <class 'torch.Tensor'>\n",
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            "Type of labels: <class 'torch.Tensor'>\n",
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            "Type of labels: <class 'torch.Tensor'>\n",
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            "Type of labels: <class 'torch.Tensor'>\n",
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            "Type of labels: <class 'torch.Tensor'>\n",
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            "        3, 0, 8, 9, 3, 6, 4, 9, 4, 2, 2, 2, 6, 1, 4, 7])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "Type of labels: <class 'torch.Tensor'>\n",
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            "        0, 6, 5, 4, 8, 2, 4, 5, 7, 2, 4, 3, 7, 6, 3, 6])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        3, 7, 4, 8, 9, 9, 2, 0, 6, 1, 4, 2, 0, 3, 6, 4])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        6, 8, 2, 9, 1, 1, 3, 4, 0, 0, 9, 5, 3, 1, 8, 5])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        0, 1, 5, 0, 2, 0, 2, 7, 8, 8, 4, 4, 7, 9, 6, 3])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        7, 2, 8, 5, 9, 7, 8, 9, 3, 2, 2, 1, 9, 3, 6, 0])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        1, 5, 1, 6, 1, 7, 4, 8, 0, 9, 8, 8, 5, 9, 9, 5])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        8, 2, 9, 3, 1, 2, 2, 3, 1, 5, 1, 5, 7, 7, 4, 9])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        4, 9, 7, 0, 4, 7, 7, 2, 2, 3, 0, 4, 6, 5, 9, 5])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        6, 9, 1, 0, 0, 1, 5, 2, 3, 3, 7, 4, 0, 5, 1, 6])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        0, 0, 7, 7, 6, 8, 0, 3, 1, 0, 9, 0, 8, 5, 3, 5])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        6, 3, 8, 3, 7, 4, 1, 7, 0, 0, 1, 1, 4, 9, 2, 1])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        7, 2, 7, 9, 9, 1, 3, 3, 2, 1, 7, 4, 1, 8, 3, 6])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        5, 6, 5, 7, 7, 8, 1, 9, 9, 0, 7, 1, 4, 2, 9, 3])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        4, 2, 2, 9, 7, 6, 3, 8, 1, 7, 7, 9, 9, 1, 7, 5])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        6, 1, 2, 0, 6, 0, 2, 5, 1, 2, 3, 8, 3, 4, 3, 3])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        5, 6, 8, 7, 9, 8, 0, 9, 2, 0, 9, 1, 3, 2, 7, 3])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        5, 8, 0, 3, 1, 1, 1, 5, 1, 8, 8, 2, 7, 7, 4, 4])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        3, 9, 6, 9, 5, 6, 8, 5, 0, 5, 9, 3, 9, 3, 4, 8])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
            "Value of labels: tensor([7, 1, 1, 6, 9, 5, 7, 6, 5, 8, 0, 1, 0, 9, 2, 7, 7, 6, 9, 8, 2, 3, 1, 7,\n",
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            "        2, 0, 3, 1, 0, 0, 7, 1, 5, 0, 1, 4, 6, 0, 3, 1])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        1, 2, 8, 3, 1, 4, 2, 5, 0, 6, 9, 7, 3, 8, 6, 0])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        1, 8, 4, 3, 2, 2, 4, 2, 6, 8, 1, 4, 2, 8, 0, 5])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        9, 7, 3, 1, 0, 8, 9, 3, 5, 8, 6, 1, 4, 9, 4, 5])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        5, 1, 2, 4, 1, 0, 8, 4, 2, 9, 2, 7, 9, 3, 6, 2])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        3, 0, 0, 1, 5, 2, 6, 3, 5, 4, 1, 5, 7, 6, 1, 7])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        8, 6, 5, 8, 2, 1, 4, 2, 5, 1, 1, 0, 3, 6, 6, 5])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        2, 0, 1, 3, 6, 1, 9, 8, 6, 6, 9, 7, 2, 5, 5, 9])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        7, 9, 2, 0, 1, 1, 9, 2, 7, 3, 0, 7, 2, 0, 4, 3])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        1, 8, 8, 9, 8, 2, 2, 2, 6, 0, 5, 9, 3, 7, 0, 3])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "Type of labels: <class 'torch.Tensor'>\n",
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            "        7, 5, 5, 5, 1, 1, 1, 7, 4, 8, 7, 4, 7, 5, 0, 7])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        7, 1, 6, 5, 0, 9, 3, 1, 8, 2, 9, 2, 1, 7, 8, 9])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        4, 1, 7, 2, 8, 3, 6, 4, 7, 5, 1, 6, 7, 7, 2, 8])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        8, 0, 4, 1, 4, 7, 8, 5, 3, 2, 4, 1, 4, 7, 6, 3])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        3, 1, 6, 7, 4, 3, 6, 9, 1, 6, 5, 9, 7, 0, 6, 9])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        9, 2, 6, 3, 7, 4, 4, 5, 6, 6, 3, 7, 1, 8, 7, 9])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        5, 9, 4, 0, 1, 1, 7, 2, 6, 4, 1, 4, 2, 3, 9, 7])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        3, 3, 5, 1, 7, 8, 6, 6, 6, 4, 3, 9, 5, 4, 9, 9])\n",
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            "        4, 4, 8, 9, 7, 2, 0, 1, 1, 3, 4, 3, 1, 0, 5, 5])\n",
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            "        6, 3, 6, 7, 7, 9, 6, 4, 2, 7, 0, 1, 6, 9, 6, 1])\n",
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            "        1, 9, 7, 8, 7, 9, 0, 6, 7, 8, 9, 8, 4, 2, 4, 3])\n",
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            "        7, 2, 7, 3, 9, 4, 0, 5, 9, 6, 0, 7, 8, 8, 2, 9])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        7, 8, 7, 3, 6, 4, 1, 3, 1, 4, 9, 7, 5, 2, 7, 5])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        5, 6, 9, 0, 4, 1, 1, 0, 0, 6, 6, 2, 9, 3, 1, 8])\n",
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            "        7, 3, 1, 4, 7, 7, 9, 9, 0, 4, 5, 5, 5, 9, 6, 0])\n",
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            "        8, 8, 9, 7, 1, 2, 4, 9, 0, 6, 2, 5, 7, 8, 4, 7])\n",
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            "        5, 0, 7, 3, 2, 9, 6, 6, 5, 9, 7, 1, 3, 5, 6, 2])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        7, 4, 0, 1, 8, 4, 6, 3, 9, 9, 1, 7, 8, 1, 9, 0])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        7, 2, 8, 3, 0, 4, 6, 4, 4, 5, 1, 6, 9, 6, 3, 3])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        5, 3, 9, 7, 0, 8, 3, 4, 1, 0, 2, 2, 1, 4, 3, 0])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        5, 9, 8, 5, 4, 8, 3, 9, 2, 6, 8, 5, 8, 2, 5, 6])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        4, 7, 8, 0, 3, 1, 0, 2, 9, 3, 3, 5, 8, 6, 4, 7])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        9, 2, 2, 6, 4, 3, 1, 0, 9, 7, 0, 2, 1, 6, 0, 1])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        9, 9, 9, 6, 3, 0, 3, 9, 3, 5, 6, 3, 7, 1, 7, 3])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        4, 2, 3, 6, 7, 7, 3, 8, 3, 9, 3, 0, 4, 1, 1, 2])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        0, 9, 4, 5, 1, 2, 6, 1, 8, 7, 7, 5, 1, 6, 9, 6])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        1, 4, 8, 2, 7, 9, 2, 9, 7, 7, 2, 1, 1, 3, 8, 9])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "Type of labels: <class 'torch.Tensor'>\n",
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            "Type of labels: <class 'torch.Tensor'>\n",
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            "Type of labels: <class 'torch.Tensor'>\n",
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            "        1, 5, 7, 7, 0, 2, 1, 8, 1, 6, 5, 3, 6, 3, 6, 8])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        5, 4, 9, 0, 6, 6, 0, 1, 8, 2, 2, 6, 3, 9, 6, 2])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        5, 7, 7, 4, 4, 9, 6, 8, 7, 8, 8, 9, 3, 0, 0, 9])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        6, 1, 9, 2, 2, 3, 2, 4, 4, 5, 9, 6, 4, 7, 1, 8])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "Type of labels: <class 'torch.Tensor'>\n",
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            "        7, 2, 3, 2, 7, 0, 3, 9, 1, 2, 1, 4, 6, 6, 4, 5])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "Type of labels: <class 'torch.Tensor'>\n",
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            "Type of labels: <class 'torch.Tensor'>\n",
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            "Type of labels: <class 'torch.Tensor'>\n",
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            "Type of labels: <class 'torch.Tensor'>\n",
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            "Type of labels: <class 'torch.Tensor'>\n",
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            "Type of labels: <class 'torch.Tensor'>\n",
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            "Type of labels: <class 'torch.Tensor'>\n",
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            "Type of labels: <class 'torch.Tensor'>\n",
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            "Type of labels: <class 'torch.Tensor'>\n",
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            "Type of labels: <class 'torch.Tensor'>\n",
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            "Type of labels: <class 'torch.Tensor'>\n",
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            "        9, 7, 1, 5, 7, 8, 4, 3, 8, 4, 4, 7, 4, 6, 1, 6])\n",
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            "        1, 5, 6, 0, 8, 4, 5, 8, 4, 7, 2, 4, 6, 9, 8, 2])\n",
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            "        7, 6, 0, 3, 4, 6, 0, 0, 5, 1, 4, 2, 1, 3, 2, 4])\n",
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            "        4, 2, 3, 3, 3, 7, 2, 8, 9, 6, 2, 6, 4, 8, 1, 0])\n",
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            "        7, 2, 6, 3, 5, 0, 9, 2, 8, 9, 3, 4, 2, 9, 9, 7])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        9, 2, 7, 3, 7, 4, 0, 5, 3, 6, 2, 7, 9, 8, 5, 9])\n",
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            "        2, 9, 5, 3, 5, 7, 7, 1, 3, 8, 9, 3, 1, 8, 0, 5])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        0, 7, 0, 9, 0, 7, 8, 0, 7, 1, 0, 2, 1, 3, 4, 7])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        1, 0, 3, 8, 0, 4, 4, 1, 3, 8, 2, 8, 1, 1, 4, 3])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "Type of labels: <class 'torch.Tensor'>\n",
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            "Type of labels: <class 'torch.Tensor'>\n",
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            "        8, 1, 1, 2, 1, 3, 5, 4, 1, 7, 8, 8, 7, 9, 9, 0])\n",
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            "Type of labels: <class 'torch.Tensor'>\n",
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            "Type of labels: <class 'torch.Tensor'>\n",
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            "Type of labels: <class 'torch.Tensor'>\n",
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            "Type of labels: <class 'torch.Tensor'>\n",
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            "Type of labels: <class 'torch.Tensor'>\n",
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            "Type of labels: <class 'torch.Tensor'>\n",
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            "Type of labels: <class 'torch.Tensor'>\n",
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            "Type of labels: <class 'torch.Tensor'>\n",
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            "Type of labels: <class 'torch.Tensor'>\n",
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            "        2, 1, 3, 3, 4, 0, 7, 3, 5, 6, 4, 9, 8, 3, 7, 7])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
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            "        3, 4, 0, 6, 2, 4, 9, 6, 2, 5, 3, 0, 8, 2, 3, 1])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
            "Value of labels: tensor([7, 0, 8, 1, 2, 2, 2, 1, 3, 7, 5, 8, 3, 1, 1, 2, 8, 8, 8, 2, 2, 7, 6, 9,\n",
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            "        8, 2, 0, 3, 7, 7, 2, 8, 6, 9, 2, 0, 3, 1, 5, 7])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
            "Value of labels: tensor([8, 8, 8, 9, 8, 2, 9, 5, 7, 1, 9, 6, 7, 4, 8, 3, 3, 9, 0, 9, 1, 0, 0, 4,\n",
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            "        4, 4, 1, 1, 2, 3, 7, 6, 0, 4, 1, 7, 4, 7, 2, 0])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
            "Value of labels: tensor([9, 9, 0, 8, 9, 4, 3, 3, 8, 8, 1, 3, 1, 8, 6, 5, 9, 8, 0, 4, 7, 1, 3, 7,\n",
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            "        9, 6, 1, 4, 1, 3, 9, 6, 0, 1, 9, 5, 3, 5, 2, 6])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
            "Value of labels: tensor([7, 5, 9, 0, 9, 1, 0, 2, 8, 3, 6, 4, 3, 5, 3, 6, 2, 7, 0, 0, 5, 1, 5, 2,\n",
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            "        4, 4, 6, 0, 4, 4, 6, 0, 5, 1, 6, 7, 5, 5, 9, 1])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
            "Value of labels: tensor([3, 4, 7, 2, 6, 4, 4, 3, 3, 1, 7, 7, 3, 8, 8, 2, 3, 4, 9, 4, 2, 3, 2, 3,\n",
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            "        2, 7, 1, 0, 2, 3, 2, 2, 0, 9, 3, 4, 0, 0, 8, 3])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
            "Value of labels: tensor([3, 7, 8, 7, 0, 7, 5, 7, 2, 1, 2, 1, 3, 2, 4, 6, 2, 4, 4, 2, 4, 3, 5, 6,\n",
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            "        1, 1, 7, 6, 4, 1, 3, 9, 2, 0, 7, 1, 7, 2, 5, 3])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
            "Value of labels: tensor([8, 4, 9, 5, 8, 6, 3, 7, 2, 8, 6, 0, 7, 1, 1, 2, 2, 3, 0, 4, 9, 5, 9, 6,\n",
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            "        2, 1, 9, 0, 0, 4, 0, 5, 5, 6, 7, 6, 2, 3, 8, 4])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
            "Value of labels: tensor([3, 4, 6, 1, 1, 0, 9, 6, 1, 4, 8, 9, 2, 7, 0, 2, 3, 3, 4, 3, 2, 9, 8, 2,\n",
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            "        9, 3, 2, 7, 8, 4, 0, 2, 2, 4, 7, 8, 0, 7, 0, 6])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
            "Value of labels: tensor([9, 3, 2, 6, 0, 5, 7, 5, 1, 0, 8, 1, 6, 7, 2, 9, 7, 9, 5, 8, 9, 6, 5, 2,\n",
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            "        0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
            "Value of labels: tensor([6, 7, 8, 9, 0, 1, 2, 3, 4, 7, 8, 9, 7, 1, 5, 6, 2, 5, 4, 8, 1, 7, 1, 8,\n",
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            "        6, 4, 6, 3, 7, 0, 1, 4, 8, 8, 1, 2, 9, 3, 1, 8])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
            "Value of labels: tensor([4, 2, 9, 9, 2, 9, 6, 0, 5, 3, 3, 6, 3, 1, 2, 5, 0, 3, 7, 6, 1, 5, 5, 9,\n",
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            "        3, 4, 5, 6, 7, 8, 9, 6, 8, 2, 3, 7, 4, 6, 8, 6])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
            "Value of labels: tensor([6, 8, 0, 1, 7, 1, 7, 3, 5, 0, 2, 2, 0, 2, 5, 4, 1, 5, 1, 2, 3, 4, 8, 3,\n",
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            "        6, 0, 6, 1, 0, 5, 8, 5, 0, 1, 0, 9, 4, 5, 9, 7])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
            "Value of labels: tensor([1, 9, 6, 8, 9, 2, 8, 5, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5,\n",
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            "        4, 4, 2, 5, 4, 3, 9, 6, 8, 7, 9, 1, 5, 2, 6, 3])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
            "Value of labels: tensor([4, 4, 1, 6, 4, 9, 6, 5, 1, 2, 6, 6, 7, 8, 8, 1, 8, 3, 7, 1, 2, 7, 0, 7,\n",
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            "        7, 1, 4, 3, 9, 7, 1, 2, 6, 0, 7, 3, 0, 1, 2, 3])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
            "Value of labels: tensor([4, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,\n",
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            "        3, 4, 0, 5, 4, 6, 3, 4, 8, 0, 6, 4, 8, 1, 3, 4])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
            "Value of labels: tensor([0, 1, 9, 2, 3, 7, 9, 1, 2, 5, 1, 7, 2, 8, 7, 0, 1, 9, 6, 4, 1, 2, 3, 7,\n",
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            "        9, 0, 1, 2, 3, 4, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
            "Value of labels: tensor([7, 8, 6, 4, 2, 6, 4, 7, 5, 5, 4, 7, 8, 9, 2, 9, 3, 9, 3, 8, 2, 0, 5, 0,\n",
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            "        1, 1, 8, 1, 7, 1, 3, 8, 9, 7, 6, 7, 4, 1, 6, 7])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
            "Value of labels: tensor([5, 1, 7, 1, 9, 8, 0, 6, 9, 4, 9, 9, 3, 7, 1, 9, 2, 2, 5, 3, 7, 8, 2, 3,\n",
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            "        8, 1, 0, 5, 5, 1, 9, 0, 4, 1, 9, 3, 8, 4, 7, 7])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
            "Value of labels: tensor([8, 5, 0, 6, 5, 5, 3, 3, 3, 9, 8, 1, 4, 0, 6, 1, 0, 0, 6, 2, 1, 1, 3, 2,\n",
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            "        3, 2, 5, 5, 9, 4, 1, 6, 5, 0, 1, 2, 3, 4, 5, 6])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
            "Value of labels: tensor([7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 6,\n",
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            "        0, 1, 0, 4, 2, 6, 5, 5, 5, 4, 3, 4, 1, 5, 3, 0])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
            "Value of labels: tensor([8, 3, 0, 6, 2, 7, 1, 1, 8, 1, 7, 1, 3, 8, 5, 4, 2, 0, 9, 7, 6, 7, 4, 1,\n",
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            "        2, 2, 5, 3, 7, 8, 0, 1, 2, 3, 4, 5, 6, 7, 8, 0])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
            "Value of labels: tensor([1, 2, 3, 4, 5, 6, 7, 8, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 2, 1, 2, 1, 3, 9,\n",
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            "        4, 8, 4, 1, 8, 6, 6, 4, 6, 3, 5, 7, 2, 5, 9, 2])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
            "Value of labels: tensor([6, 2, 1, 2, 0, 8, 3, 8, 3, 0, 8, 7, 4, 9, 5, 0, 9, 7, 4, 9, 1, 6, 2, 7,\n",
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            "        7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 7, 4, 3, 1])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
            "Value of labels: tensor([8, 6, 1, 9, 2, 4, 0, 9, 9, 3, 7, 7, 9, 1, 8, 4, 7, 5, 8, 5, 3, 2, 2, 0,\n",
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            "        3, 6, 2, 8, 7, 6, 4, 9, 8, 8, 2, 9, 6, 5, 5, 9])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
            "Value of labels: tensor([5, 3, 7, 4, 7, 6, 0, 0, 4, 6, 6, 9, 0, 1, 1, 3, 2, 1, 0, 0, 1, 2, 3, 4,\n",
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            "        9, 4, 2, 0, 2, 1, 5, 9, 6, 5, 9, 3, 5, 0, 7, 1])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
            "Value of labels: tensor([1, 5, 3, 0, 7, 9, 6, 1, 6, 7, 4, 9, 5, 7, 9, 8, 5, 6, 2, 4, 8, 9, 2, 2,\n",
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            "        1, 0, 7, 6, 2, 2, 7, 6, 0, 6, 9, 4, 8, 4, 3, 3])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
            "Value of labels: tensor([7, 8, 1, 0, 4, 5, 8, 3, 0, 6, 8, 3, 7, 8, 5, 7, 8, 2, 3, 4, 6, 7, 8, 9,\n",
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            "        4, 0, 9, 3, 2, 2, 3, 0, 6, 4, 3, 7, 6, 9, 2, 2])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
            "Value of labels: tensor([3, 5, 5, 0, 7, 2, 3, 4, 0, 0, 5, 1, 8, 9, 0, 8, 9, 6, 5, 6, 7, 5, 3, 5,\n",
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            "        4, 5, 6, 7, 8, 9, 1, 3, 5, 9, 4, 1, 7, 7, 2, 1])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
            "Value of labels: tensor([4, 8, 3, 8, 2, 9, 3, 8, 5, 4, 8, 8, 9, 7, 5, 4, 3, 9, 2, 5, 7, 4, 1, 2,\n",
            "        3, 6, 0, 1, 0, 0, 2, 8, 7, 2, 5, 1, 1, 8, 5, 6, 4, 0, 4, 7, 3, 6, 8, 0,\n",
            "        3, 7, 6, 9, 2, 6, 5, 8, 6, 9, 0, 4, 0, 6, 1, 9])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
            "Value of labels: tensor([2, 0, 9, 5, 1, 3, 7, 6, 9, 4, 8, 3, 7, 7, 6, 3, 0, 5, 6, 2, 2, 0, 1, 2,\n",
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            "        7, 8, 9, 5, 6, 6, 0, 8, 3, 7, 9, 4, 7, 1, 9, 1])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
            "Value of labels: tensor([7, 1, 4, 1, 7, 5, 7, 1, 3, 3, 1, 6, 9, 7, 4, 3, 0, 2, 5, 2, 6, 0, 8, 4,\n",
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            "        8, 3, 6, 1, 2, 9, 5, 2, 5, 2, 6, 2, 4, 8, 4, 6])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
            "Value of labels: tensor([5, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2,\n",
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            "        6, 9, 9, 5, 8, 6, 7, 0, 6, 2, 6, 3, 9, 1, 7, 4])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
            "Value of labels: tensor([8, 8, 9, 0, 3, 0, 5, 2, 9, 4, 1, 0, 3, 7, 5, 8, 7, 7, 8, 2, 9, 5, 5, 1,\n",
            "        2, 6, 4, 2, 5, 2, 3, 6, 0, 0, 7, 5, 2, 8, 1, 6, 1, 0, 4, 3, 1, 6, 1, 9,\n",
            "        0, 1, 2, 3, 4, 5, 6, 7, 0, 1, 2, 3, 4, 6, 7, 8])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
            "Value of labels: tensor([0, 1, 2, 3, 4, 7, 8, 9, 2, 2, 4, 0, 7, 3, 5, 4, 1, 8, 0, 5, 2, 7, 2, 3,\n",
            "        6, 2, 1, 7, 7, 9, 2, 4, 6, 7, 7, 5, 9, 9, 1, 8, 6, 4, 6, 0, 9, 2, 6, 4,\n",
            "        3, 9, 0, 8, 8, 9, 4, 7, 1, 3, 6, 9, 4, 1, 7, 6])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
            "Value of labels: tensor([9, 3, 3, 7, 4, 1, 9, 1, 0, 6, 0, 1, 2, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4,\n",
            "        5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 3, 2, 9, 3, 2, 1, 4, 5, 5,\n",
            "        2, 3, 2, 1, 3, 9, 7, 2, 1, 2, 8, 9, 1, 8, 8, 7])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
            "Value of labels: tensor([0, 6, 7, 7, 8, 7, 5, 0, 6, 1, 5, 7, 4, 6, 1, 2, 7, 9, 9, 0, 3, 8, 4, 4,\n",
            "        1, 8, 6, 5, 0, 0, 3, 7, 1, 6, 4, 2, 6, 6, 0, 4, 5, 4, 1, 3, 8, 6, 3, 9,\n",
            "        9, 5, 9, 3, 6, 4, 7, 6, 2, 2, 0, 9, 4, 0, 1, 2])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
            "Value of labels: tensor([3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,\n",
            "        6, 0, 3, 4, 1, 4, 0, 7, 8, 7, 7, 9, 0, 4, 9, 4, 0, 5, 8, 5, 9, 8, 8, 4,\n",
            "        0, 7, 1, 3, 5, 3, 1, 6, 5, 3, 8, 7, 3, 1, 6, 8])\n",
            "Type of labels: <class 'torch.Tensor'>\n",
            "Value of labels: tensor([5, 9, 2, 2, 0, 9, 2, 4, 6, 7, 3, 1, 3, 6, 6, 2, 1, 2, 6, 0, 7, 8, 9, 2,\n",
            "        9, 5, 1, 8, 3, 5, 6, 8])\n",
            "Epoch: 40 Accuracy: 99.2300033569336 %\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def predict_dl(model, data):\n",
        "  y_pred = []\n",
        "  y_true = []\n",
        "  for i, (images, labels) in enumerate(data):\n",
        "    images = images.cuda()\n",
        "    x = model(images)\n",
        "    value, pred = torch.max(x, 1)\n",
        "    pred = pred.data.cpu()\n",
        "    y_pred.extend(list(pred.numpy()))\n",
        "    y_true.extend(list(labels.numpy()))\n",
        "  return np.array(y_pred), np.array(y_true)"
      ],
      "metadata": {
        "id": "Fz8-JzV0lBR1"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred, y_true = predict_dl(lenet, val_dl)"
      ],
      "metadata": {
        "id": "ozNdz9OqF_DL"
      },
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pd.DataFrame(confusion_matrix(y_true, y_pred, labels= np.arange(0, 10)))"
      ],
      "metadata": {
        "id": "L_peOO5XF_GP",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "outputId": "12708cbf-529e-4aee-d10f-c07ba79a0bfd"
      },
      "execution_count": 49,
      "outputs": [
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            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#define inference function"
      ],
      "metadata": {
        "id": "obatik_3B19a"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def inference(path, model, device):\n",
        "    r = requests.get(path)\n",
        "    with BytesIO(r.content) as f:\n",
        "        img = Image.open(f).convert(mode=\"L\")\n",
        "        img = img.resize((28, 28))\n",
        "        x = (255 - np.expand_dims(np.array(img), -1)) / 255.0\n",
        "    with torch.no_grad():\n",
        "        pred = model(torch.unsqueeze(T(x), axis=0).float().to(device))\n",
        "        return F.softmax(pred, dim=-1).cpu().numpy()"
      ],
      "metadata": {
        "id": "SXZ7jUThk3In"
      },
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import requests\n",
        "from PIL import Image\n",
        "import numpy as np\n",
        "\n",
        "url = \"https://previews.123rf.com/images/aroas/aroas1704/aroas170400068/79321959-handwritten-sketch-black-number-8-on-white-background.jpg\"\n",
        "response = requests.get(url)\n",
        "\n",
        "with Image.open(BytesIO(response.content)) as img:\n",
        "    img = img.convert(\"L\")  # Convert to grayscale\n",
        "    img = img.resize((28, 28))  # Resize to 28x28 pixels\n",
        "    x = (255 - np.expand_dims(np.array(img), -1)) / 255.0\n"
      ],
      "metadata": {
        "id": "iuxhfgXogTev"
      },
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.imshow(x.squeeze(-1), cmap = \"gray\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "yhmh9x2Agj2T",
        "outputId": "9cb668fb-00e9-4446-c100-ba99a53ea0f0"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7ff25c353010>"
            ]
          },
          "metadata": {},
          "execution_count": 52
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pred = inference(url, lenet, device=device)\n",
        "pred_idx = np.argmax(pred)\n",
        "print(f\"predicted: {pred_idx}, prob: {pred[0][pred_idx]*100} %\")"
      ],
      "metadata": {
        "id": "NkYkqKRoF_Pa",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7409713a-cba7-4e92-a3b4-7f2346e4338b"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "predicted: 8, prob: 99.9998927116394 %\n"
          ]
        }
      ]
    }
  ]
}