5_lab.ipynb

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        "%reload_ext autoreload\n",
        "%autoreload 2\n",
        "%matplotlib inline\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "from pathlib import Path\n",
        "from sklearn import metrics\n",
        "import random\n",
        "\n",
        "import torch\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "import torch.optim as optim\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "from torchvision import datasets, models\n",
        "\n",
        "from datetime import datetime\n",
        "import cv2"
      ],
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      "outputs": []
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          "text": [
            "Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to cifardata/cifar-10-python.tar.gz\n"
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            "Extracting cifardata/cifar-10-python.tar.gz to cifardata\n",
            "Files already downloaded and verified\n"
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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ov2mANm47I7j"
      },
      "source": [
        "x = np.array(x)\n",
        "pad = 4\n",
        "x = cv2.copyMakeBorder(x, pad, pad, pad, pad, cv2.BORDER_REFLECT)"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        },
        "id": "mIurrX5R7Xwx",
        "outputId": "1519c8f8-7bbf-49fc-e16f-d0de2ebc2e75"
      },
      "source": [
        "plt.imshow(x)\n",
        "x.shape"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(40, 40, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 7
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}