cv_cnn.ipynb

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        "id": "bVqXzMbOOtZM"
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      "source": [
        "import torch\n",
        "from torch import nn\n",
        "\n",
        "import torchvision\n",
        "from torchvision import datasets\n",
        "from torchvision import transforms\n",
        "from torchvision.transforms import ToTensor\n",
        "import matplotlib.pyplot as plt\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_data = datasets.FashionMNIST(\n",
        "    root = 'data',\n",
        "    train = True,\n",
        "    download = True,\n",
        "    transform = ToTensor(),\n",
        "    target_transform = None\n",
        ")\n",
        "\n",
        "test_data = datasets.FashionMNIST(\n",
        "    root = 'data',\n",
        "    train = False,\n",
        "    download = True,\n",
        "    transform = ToTensor(),\n",
        "    target_transform = None\n",
        ")"
      ],
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        "id": "XmC_6qz6UgzT",
        "outputId": "0a709cdc-e73d-4177-cd1e-a46184d0edb8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz to data/FashionMNIST/raw/train-images-idx3-ubyte.gz\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 26421880/26421880 [00:08<00:00, 2988727.82it/s] \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting data/FashionMNIST/raw/train-images-idx3-ubyte.gz to data/FashionMNIST/raw\n",
            "\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz to data/FashionMNIST/raw/train-labels-idx1-ubyte.gz\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 29515/29515 [00:00<00:00, 274126.29it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting data/FashionMNIST/raw/train-labels-idx1-ubyte.gz to data/FashionMNIST/raw\n",
            "\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz to data/FashionMNIST/raw/t10k-images-idx3-ubyte.gz\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 4422102/4422102 [00:00<00:00, 5036621.33it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting data/FashionMNIST/raw/t10k-images-idx3-ubyte.gz to data/FashionMNIST/raw\n",
            "\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz to data/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 5148/5148 [00:00<00:00, 12466672.63it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting data/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz to data/FashionMNIST/raw\n",
            "\n"
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            "\n"
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    {
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      "source": [
        "len(train_data), len(test_data)"
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        "id": "wj5a5MDdcm9q",
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        {
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              "(60000, 10000)"
            ]
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          "metadata": {},
          "execution_count": 3
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    {
      "cell_type": "code",
      "source": [
        "class_names = train_data.classes\n",
        "class_names"
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        },
        "id": "D_TiBiqFczlo",
        "outputId": "aaeda9c9-2c26-4fba-c009-b6c3aadc1840"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['T-shirt/top',\n",
              " 'Trouser',\n",
              " 'Pullover',\n",
              " 'Dress',\n",
              " 'Coat',\n",
              " 'Sandal',\n",
              " 'Shirt',\n",
              " 'Sneaker',\n",
              " 'Bag',\n",
              " 'Ankle boot']"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class_to_idx = train_data.class_to_idx\n",
        "class_to_idx"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_mofdrMtTfgC",
        "outputId": "d2b8a32c-977e-4b31-e4fe-74a1bea2b006"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'T-shirt/top': 0,\n",
              " 'Trouser': 1,\n",
              " 'Pullover': 2,\n",
              " 'Dress': 3,\n",
              " 'Coat': 4,\n",
              " 'Sandal': 5,\n",
              " 'Shirt': 6,\n",
              " 'Sneaker': 7,\n",
              " 'Bag': 8,\n",
              " 'Ankle boot': 9}"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
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      "source": [
        "image, label = train_data[0]\n",
        "image, label"
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              "           0.0000, 0.0000, 0.0000, 0.0039, 0.0000, 0.2706, 0.8118, 0.8745,\n",
              "           0.8549, 0.8471, 0.8471, 0.6392, 0.4980, 0.4745, 0.4784, 0.5725,\n",
              "           0.5529, 0.3451, 0.6745, 0.2588],\n",
              "          [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
              "           0.0000, 0.0039, 0.0039, 0.0039, 0.0000, 0.7843, 0.9098, 0.9098,\n",
              "           0.9137, 0.8980, 0.8745, 0.8745, 0.8431, 0.8353, 0.6431, 0.4980,\n",
              "           0.4824, 0.7686, 0.8980, 0.0000],\n",
              "          [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
              "           0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.7176, 0.8824, 0.8471,\n",
              "           0.8745, 0.8941, 0.9216, 0.8902, 0.8784, 0.8706, 0.8784, 0.8667,\n",
              "           0.8745, 0.9608, 0.6784, 0.0000],\n",
              "          [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
              "           0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.7569, 0.8941, 0.8549,\n",
              "           0.8353, 0.7765, 0.7059, 0.8314, 0.8235, 0.8275, 0.8353, 0.8745,\n",
              "           0.8627, 0.9529, 0.7922, 0.0000],\n",
              "          [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
              "           0.0000, 0.0039, 0.0118, 0.0000, 0.0471, 0.8588, 0.8627, 0.8314,\n",
              "           0.8549, 0.7529, 0.6627, 0.8902, 0.8157, 0.8549, 0.8784, 0.8314,\n",
              "           0.8863, 0.7725, 0.8196, 0.2039],\n",
              "          [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
              "           0.0000, 0.0000, 0.0235, 0.0000, 0.3882, 0.9569, 0.8706, 0.8627,\n",
              "           0.8549, 0.7961, 0.7765, 0.8667, 0.8431, 0.8353, 0.8706, 0.8627,\n",
              "           0.9608, 0.4667, 0.6549, 0.2196],\n",
              "          [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
              "           0.0000, 0.0157, 0.0000, 0.0000, 0.2157, 0.9255, 0.8941, 0.9020,\n",
              "           0.8941, 0.9412, 0.9098, 0.8353, 0.8549, 0.8745, 0.9176, 0.8510,\n",
              "           0.8510, 0.8196, 0.3608, 0.0000],\n",
              "          [0.0000, 0.0000, 0.0039, 0.0157, 0.0235, 0.0275, 0.0078, 0.0000,\n",
              "           0.0000, 0.0000, 0.0000, 0.0000, 0.9294, 0.8863, 0.8510, 0.8745,\n",
              "           0.8706, 0.8588, 0.8706, 0.8667, 0.8471, 0.8745, 0.8980, 0.8431,\n",
              "           0.8549, 1.0000, 0.3020, 0.0000],\n",
              "          [0.0000, 0.0118, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
              "           0.0000, 0.2431, 0.5686, 0.8000, 0.8941, 0.8118, 0.8353, 0.8667,\n",
              "           0.8549, 0.8157, 0.8275, 0.8549, 0.8784, 0.8745, 0.8588, 0.8431,\n",
              "           0.8784, 0.9569, 0.6235, 0.0000],\n",
              "          [0.0000, 0.0000, 0.0000, 0.0000, 0.0706, 0.1725, 0.3216, 0.4196,\n",
              "           0.7412, 0.8941, 0.8627, 0.8706, 0.8510, 0.8863, 0.7843, 0.8039,\n",
              "           0.8275, 0.9020, 0.8784, 0.9176, 0.6902, 0.7373, 0.9804, 0.9725,\n",
              "           0.9137, 0.9333, 0.8431, 0.0000],\n",
              "          [0.0000, 0.2235, 0.7333, 0.8157, 0.8784, 0.8667, 0.8784, 0.8157,\n",
              "           0.8000, 0.8392, 0.8157, 0.8196, 0.7843, 0.6235, 0.9608, 0.7569,\n",
              "           0.8078, 0.8745, 1.0000, 1.0000, 0.8667, 0.9176, 0.8667, 0.8275,\n",
              "           0.8627, 0.9098, 0.9647, 0.0000],\n",
              "          [0.0118, 0.7922, 0.8941, 0.8784, 0.8667, 0.8275, 0.8275, 0.8392,\n",
              "           0.8039, 0.8039, 0.8039, 0.8627, 0.9412, 0.3137, 0.5882, 1.0000,\n",
              "           0.8980, 0.8667, 0.7373, 0.6039, 0.7490, 0.8235, 0.8000, 0.8196,\n",
              "           0.8706, 0.8941, 0.8824, 0.0000],\n",
              "          [0.3843, 0.9137, 0.7765, 0.8235, 0.8706, 0.8980, 0.8980, 0.9176,\n",
              "           0.9765, 0.8627, 0.7608, 0.8431, 0.8510, 0.9451, 0.2549, 0.2863,\n",
              "           0.4157, 0.4588, 0.6588, 0.8588, 0.8667, 0.8431, 0.8510, 0.8745,\n",
              "           0.8745, 0.8784, 0.8980, 0.1137],\n",
              "          [0.2941, 0.8000, 0.8314, 0.8000, 0.7569, 0.8039, 0.8275, 0.8824,\n",
              "           0.8471, 0.7255, 0.7725, 0.8078, 0.7765, 0.8353, 0.9412, 0.7647,\n",
              "           0.8902, 0.9608, 0.9373, 0.8745, 0.8549, 0.8314, 0.8196, 0.8706,\n",
              "           0.8627, 0.8667, 0.9020, 0.2627],\n",
              "          [0.1882, 0.7961, 0.7176, 0.7608, 0.8353, 0.7725, 0.7255, 0.7451,\n",
              "           0.7608, 0.7529, 0.7922, 0.8392, 0.8588, 0.8667, 0.8627, 0.9255,\n",
              "           0.8824, 0.8471, 0.7804, 0.8078, 0.7294, 0.7098, 0.6941, 0.6745,\n",
              "           0.7098, 0.8039, 0.8078, 0.4510],\n",
              "          [0.0000, 0.4784, 0.8588, 0.7569, 0.7020, 0.6706, 0.7176, 0.7686,\n",
              "           0.8000, 0.8235, 0.8353, 0.8118, 0.8275, 0.8235, 0.7843, 0.7686,\n",
              "           0.7608, 0.7490, 0.7647, 0.7490, 0.7765, 0.7529, 0.6902, 0.6118,\n",
              "           0.6549, 0.6941, 0.8235, 0.3608],\n",
              "          [0.0000, 0.0000, 0.2902, 0.7412, 0.8314, 0.7490, 0.6863, 0.6745,\n",
              "           0.6863, 0.7098, 0.7255, 0.7373, 0.7412, 0.7373, 0.7569, 0.7765,\n",
              "           0.8000, 0.8196, 0.8235, 0.8235, 0.8275, 0.7373, 0.7373, 0.7608,\n",
              "           0.7529, 0.8471, 0.6667, 0.0000],\n",
              "          [0.0078, 0.0000, 0.0000, 0.0000, 0.2588, 0.7843, 0.8706, 0.9294,\n",
              "           0.9373, 0.9490, 0.9647, 0.9529, 0.9569, 0.8667, 0.8627, 0.7569,\n",
              "           0.7490, 0.7020, 0.7137, 0.7137, 0.7098, 0.6902, 0.6510, 0.6588,\n",
              "           0.3882, 0.2275, 0.0000, 0.0000],\n",
              "          [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.1569,\n",
              "           0.2392, 0.1725, 0.2824, 0.1608, 0.1373, 0.0000, 0.0000, 0.0000,\n",
              "           0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
              "           0.0000, 0.0000, 0.0000, 0.0000],\n",
              "          [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
              "           0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
              "           0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
              "           0.0000, 0.0000, 0.0000, 0.0000],\n",
              "          [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
              "           0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
              "           0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
              "           0.0000, 0.0000, 0.0000, 0.0000]]]),\n",
              " 9)"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "image.shape, label"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Hf0gtSwYdj54",
        "outputId": "4e5ae956-6ea5-4c6a-cf70-bd4c31b1d70c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(torch.Size([1, 28, 28]), 9)"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.imshow(image.squeeze())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        },
        "id": "40xWQbRZeM3u",
        "outputId": "55d7e5ec-6684-4ae7-9ba9-4859c8164f86"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7a6cbd9f39a0>"
            ]
          },
          "metadata": {},
          "execution_count": 8
        },
        {
          "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": [
        "rows, cols = 3,3\n",
        "for i in range(1, rows*cols+1):\n",
        "    random_idx = torch.randint(0, len(train_data), size = [1]).item()\n",
        "    image, label = train_data[random_idx]\n",
        "    plt.subplot(rows, cols, i)\n",
        "    plt.imshow(image.squeeze(), cmap = 'gray')\n",
        "    plt.title(class_names[label])\n",
        "    plt.axis(False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        },
        "id": "q5Z0qxdWfeDq",
        "outputId": "55e4f167-3bcf-4ac1-d407-20707a23a14b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 9 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from torch.utils.data import DataLoader\n",
        "BATCH_SIZE = 32\n",
        "\n",
        "train_dataloader = DataLoader(train_data,\n",
        "                              BATCH_SIZE,\n",
        "                              shuffle = True)\n",
        "\n",
        "test_dataloader = DataLoader(test_data,\n",
        "                             BATCH_SIZE,\n",
        "                             shuffle = False)\n",
        "len(train_dataloader), len(test_dataloader)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gSuPaoHImN6M",
        "outputId": "276784f5-0315-469d-8d90-1920689e9a1e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1875, 313)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_features_batch, train_labels_batch = next(iter(train_dataloader))\n",
        "train_features_batch.shape, train_labels_batch.shape\n"
      ],
      "metadata": {
        "id": "VxKGjWxc4bXm",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3a581748-3331-4506-b0a4-2b1f786ff53b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(torch.Size([32, 1, 28, 28]), torch.Size([32]))"
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "random_idx = torch.randint(0, len(train_features_batch), size = [1]).item()\n",
        "img, label = train_features_batch[random_idx], train_labels_batch[random_idx]\n",
        "plt.imshow(img.squeeze(), cmap = 'gray')\n",
        "plt.title(class_names[label])\n",
        "plt.axis(False)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 446
        },
        "id": "gYaz5bmz53D6",
        "outputId": "5ae14799-ca81-4774-daa2-f9d5514ea8d8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(-0.5, 27.5, 27.5, -0.5)"
            ]
          },
          "metadata": {},
          "execution_count": 53
        },
        {
          "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": [
        "# Create a flatten layer\n",
        "flatten_model = nn.Flatten() # all nn modules function as a model (can do a forward pass)\n",
        "\n",
        "# Get a single sample\n",
        "x = train_features_batch[0]\n",
        "\n",
        "# Flatten the sample\n",
        "output = flatten_model(x) # perform forward pass\n",
        "\n",
        "# Print out what happened\n",
        "print(f\"Shape before flattening: {x.shape} -> [color_channels, height, width]\")\n",
        "print(f\"Shape after flattening: {output.shape} -> [color_channels, height*width]\")\n",
        "\n",
        "# Try uncommenting below and see what happens\n",
        "# print(x)\n",
        "# print(output)"
      ],
      "metadata": {
        "id": "j_C-j8l36QhB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5f41cf7c-fbf4-4653-847f-ab34daddd610"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape before flattening: torch.Size([1, 28, 28]) -> [color_channels, height, width]\n",
            "Shape after flattening: torch.Size([1, 784]) -> [color_channels, height*width]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class FashionModel(nn.Module):\n",
        "  def __init__(self,\n",
        "               input_shape: int,\n",
        "               hidden_units: int,\n",
        "               output_shape: int):\n",
        "    super().__init__()\n",
        "    self.layer_stack = nn.Sequential(\n",
        "        nn.Flatten(),\n",
        "        nn.Linear(in_features = input_shape, out_features = hidden_units),\n",
        "        nn.Linear(in_features = hidden_units, out_features = output_shape)\n",
        "    )\n",
        "  def forward(self, x):\n",
        "    return self.layer_stack(x)"
      ],
      "metadata": {
        "id": "vDbSIqaD7Zvt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_0 = FashionModel(input_shape = 28*28,\n",
        "                      hidden_units = 10,\n",
        "                      output_shape = len(class_names))"
      ],
      "metadata": {
        "id": "8CBxjq0o8a32"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_0.state_dict()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v2yh6gKV8h6o",
        "outputId": "132d2f3a-5439-4a1d-f40d-8a1eeff3fd71"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "OrderedDict([('layer_stack.1.weight',\n",
              "              tensor([[ 0.0037,  0.0260, -0.0144,  ...,  0.0315, -0.0059,  0.0213],\n",
              "                      [-0.0330,  0.0270,  0.0021,  ..., -0.0250,  0.0071,  0.0197],\n",
              "                      [ 0.0081,  0.0258,  0.0261,  ...,  0.0304,  0.0260,  0.0054],\n",
              "                      ...,\n",
              "                      [-0.0262,  0.0314,  0.0262,  ..., -0.0227,  0.0254,  0.0080],\n",
              "                      [ 0.0077, -0.0022,  0.0263,  ..., -0.0164,  0.0155, -0.0100],\n",
              "                      [ 0.0255,  0.0205, -0.0186,  ..., -0.0336, -0.0231, -0.0242]])),\n",
              "             ('layer_stack.1.bias',\n",
              "              tensor([ 0.0320,  0.0085, -0.0144, -0.0193, -0.0020,  0.0332,  0.0158, -0.0182,\n",
              "                      -0.0296,  0.0126])),\n",
              "             ('layer_stack.2.weight',\n",
              "              tensor([[ 0.0981, -0.0110,  0.2573, -0.0742,  0.3012, -0.2304,  0.1849,  0.2557,\n",
              "                       -0.1066,  0.2393],\n",
              "                      [ 0.0285, -0.2012,  0.0507, -0.1145, -0.1108, -0.3018,  0.2403, -0.0151,\n",
              "                        0.2489,  0.1081],\n",
              "                      [ 0.0666,  0.2460, -0.0186,  0.1794,  0.2803, -0.1261, -0.0823,  0.1960,\n",
              "                        0.1328, -0.2510],\n",
              "                      [-0.0280,  0.2867,  0.2621, -0.2198, -0.1428,  0.3070,  0.1274, -0.2554,\n",
              "                        0.2132,  0.1979],\n",
              "                      [-0.0102,  0.0929, -0.3050, -0.0800, -0.2928, -0.1803, -0.1233, -0.2576,\n",
              "                       -0.2572, -0.1061],\n",
              "                      [-0.0575, -0.0979, -0.2153, -0.0344, -0.1814,  0.2223, -0.1870,  0.0260,\n",
              "                       -0.0065, -0.2737],\n",
              "                      [ 0.0905, -0.1274, -0.0999,  0.0959,  0.0488,  0.2083, -0.1580, -0.0907,\n",
              "                       -0.0843,  0.2215],\n",
              "                      [ 0.2081,  0.1197,  0.1439,  0.1801, -0.0807,  0.0968, -0.2076,  0.2180,\n",
              "                        0.1949,  0.2102],\n",
              "                      [ 0.0401,  0.2509, -0.0372, -0.1849,  0.0218, -0.0260, -0.2860, -0.2119,\n",
              "                       -0.1894, -0.1966],\n",
              "                      [ 0.0246, -0.1914,  0.1417,  0.1250, -0.2734, -0.1004,  0.2351,  0.1533,\n",
              "                       -0.1799,  0.0939]])),\n",
              "             ('layer_stack.2.bias',\n",
              "              tensor([ 0.0252,  0.0364, -0.1513, -0.2544,  0.0103, -0.1290, -0.0512,  0.2583,\n",
              "                      -0.1388, -0.0469]))])"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "loss_fn = nn.CrossEntropyLoss()\n",
        "optimizer = torch.optim.SGD(params = model_0.parameters(),\n",
        "                            lr = 0.01)\n",
        "\n",
        "def acc(y_true, y_pred):\n",
        "  correct = torch.eq(y_true, y_pred).sum().item()\n",
        "  acc = (correct/len(y_pred))*100\n",
        "  return acc\n"
      ],
      "metadata": {
        "id": "r9fJDWOg8kvg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from tqdm.auto import tqdm\n",
        "epochs = 5\n",
        "for epoch in tqdm(range(epochs)):\n",
        "  train_loss = 0\n",
        "  for batch, (X, y) in enumerate(train_dataloader):\n",
        "    model_0.train()\n",
        "\n",
        "    y_pred = model_0(X)\n",
        "\n",
        "    loss = loss_fn(y_pred, y)\n",
        "    train_loss += loss.item()\n",
        "\n",
        "    optimizer.zero_grad()\n",
        "    loss.backward()\n",
        "    optimizer.step()\n",
        "\n",
        "    if batch % 400 == 0:\n",
        "      print(f'Looked at {batch * len(X)}/{len(train_dataloader.dataset)} samples.')\n",
        "\n",
        "  train_loss /= len(train_dataloader)\n",
        "\n",
        "  ###testing\n",
        "  test_loss, test_acc = 0, 0\n",
        "  model_0.eval()\n",
        "  with torch.inference_mode():\n",
        "    for X_test, y_test in test_dataloader:\n",
        "      test_pred = model_0(X_test)\n",
        "      test_loss += loss_fn(test_pred, y_test)\n",
        "      test_acc += acc(y_test, test_pred.argmax(dim = 1))\n",
        "\n",
        "    test_loss /= len(test_dataloader)\n",
        "    test_acc /= len(test_dataloader)\n",
        "\n",
        "  print(f'train_loss: {train_loss} | test_loss: {test_loss} | test_acc: {test_acc}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 593,
          "referenced_widgets": [
            "fabbfcc41f7343249691b5994dbe984c",
            "fcc150a0893842ac807afce488c4d0f5",
            "99f4c4416365483b913ef6aa417902ee",
            "3f7e4c48440747eea957805d5313ab8e",
            "1f55d278b7b6430ba81450ba75aae23a",
            "c4141a2f249840d194baa812c21fb4f3",
            "158454932ef847bbbedbfe8bc5ca8f7b",
            "880e4c3caa3e48d7857172ee5dcdc51f",
            "f0035ffa398b4b128cf8f4376a5f9d1d",
            "1f1e5856a03c4176a23be90bbc141795",
            "5f11177dfa1d46c6b0b274d1974d4f09"
          ]
        },
        "id": "jOBzRlM59Cjb",
        "outputId": "5d82838a-414f-4b66-c2c5-546d06620ac1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/5 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "fabbfcc41f7343249691b5994dbe984c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looked at 0/60000 samples.\n",
            "Looked at 12800/60000 samples.\n",
            "Looked at 25600/60000 samples.\n",
            "Looked at 38400/60000 samples.\n",
            "Looked at 51200/60000 samples.\n",
            "train_loss: 0.8690609808921814 | test_loss: 0.6342567801475525 | test_acc: 77.7655750798722\n",
            "Looked at 0/60000 samples.\n",
            "Looked at 12800/60000 samples.\n",
            "Looked at 25600/60000 samples.\n",
            "Looked at 38400/60000 samples.\n",
            "Looked at 51200/60000 samples.\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-59-e0c6ae506572>\u001b[0m in \u001b[0;36m<cell line: 3>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     24\u001b[0m   \u001b[0mmodel_0\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0meval\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     25\u001b[0m   \u001b[0;32mwith\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minference_mode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 26\u001b[0;31m     \u001b[0;32mfor\u001b[0m \u001b[0mX_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_test\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtest_dataloader\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     27\u001b[0m       \u001b[0mtest_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel_0\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     28\u001b[0m       \u001b[0mtest_loss\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mloss_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m__next__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    629\u001b[0m                 \u001b[0;31m# TODO(https://github.com/pytorch/pytorch/issues/76750)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    630\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# type: ignore[call-arg]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 631\u001b[0;31m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_next_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    632\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_num_yielded\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    633\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dataset_kind\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0m_DatasetKind\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mIterable\u001b[0m \u001b[0;32mand\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m_next_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    673\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_next_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    674\u001b[0m         \u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_next_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# may raise StopIteration\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 675\u001b[0;31m         \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dataset_fetcher\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfetch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# may raise StopIteration\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    676\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_pin_memory\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    677\u001b[0m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_utils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpin_memory\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpin_memory\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_pin_memory_device\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/utils/data/_utils/fetch.py\u001b[0m in \u001b[0;36mfetch\u001b[0;34m(self, possibly_batched_index)\u001b[0m\n\u001b[1;32m     49\u001b[0m                 \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getitems__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpossibly_batched_index\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     50\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 51\u001b[0;31m                 \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0midx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mpossibly_batched_index\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     52\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     53\u001b[0m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mpossibly_batched_index\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/utils/data/_utils/fetch.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m     49\u001b[0m                 \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getitems__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpossibly_batched_index\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     50\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 51\u001b[0;31m                 \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0midx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mpossibly_batched_index\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     52\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     53\u001b[0m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mpossibly_batched_index\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torchvision/datasets/mnist.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, index)\u001b[0m\n\u001b[1;32m    141\u001b[0m         \u001b[0;31m# doing this so that it is consistent with all other datasets\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    142\u001b[0m         \u001b[0;31m# to return a PIL Image\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 143\u001b[0;31m         \u001b[0mimg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mImage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfromarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"L\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    144\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    145\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/PIL/Image.py\u001b[0m in \u001b[0;36mfromarray\u001b[0;34m(obj, mode)\u001b[0m\n\u001b[1;32m   3101\u001b[0m             \u001b[0mobj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtostring\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3102\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3103\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mfrombuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msize\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"raw\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrawmode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3104\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3105\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/PIL/Image.py\u001b[0m in \u001b[0;36mfrombuffer\u001b[0;34m(mode, size, data, decoder_name, *args)\u001b[0m\n\u001b[1;32m   3016\u001b[0m             \u001b[0margs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3017\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32min\u001b[0m \u001b[0m_MAPMODES\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3018\u001b[0;31m             \u001b[0mim\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnew\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3019\u001b[0m             \u001b[0mim\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mim\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_new\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap_buffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msize\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdecoder_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3020\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mmode\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"P\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/PIL/Image.py\u001b[0m in \u001b[0;36mnew\u001b[0;34m(mode, size, color)\u001b[0m\n\u001b[1;32m   2930\u001b[0m         \u001b[0mim\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpalette\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mImagePalette\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mImagePalette\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2931\u001b[0m         \u001b[0mcolor\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mim\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpalette\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetcolor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2932\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mim\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_new\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfill\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msize\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2933\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2934\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
        "device"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "VZ67fLAZYzuV",
        "outputId": "4485aa42-ee49-4677-c5d6-d39c68439794"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'cpu'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class FashionMNISTModelV2(nn.Module):\n",
        "    \"\"\"\n",
        "    Model architecture copying TinyVGG from:\n",
        "    https://poloclub.github.io/cnn-explainer/\n",
        "    \"\"\"\n",
        "    def __init__(self, input_shape: int, hidden_units: int, output_shape: int):\n",
        "        super().__init__()\n",
        "        self.block_1 = nn.Sequential(\n",
        "            nn.Conv2d(in_channels=input_shape,\n",
        "                      out_channels=hidden_units,\n",
        "                      kernel_size=3, # how big is the square that's going over the image?\n",
        "                      stride=1, # default\n",
        "                      padding=1),# options = \"valid\" (no padding) or \"same\" (output has same shape as input) or int for specific number\n",
        "            nn.ReLU(),\n",
        "            nn.Conv2d(in_channels=hidden_units,\n",
        "                      out_channels=hidden_units,\n",
        "                      kernel_size=3,\n",
        "                      stride=1,\n",
        "                      padding=1),\n",
        "            nn.ReLU(),\n",
        "            nn.MaxPool2d(kernel_size=2,\n",
        "                         stride=2) # default stride value is same as kernel_size\n",
        "        )\n",
        "        self.block_2 = nn.Sequential(\n",
        "            nn.Conv2d(hidden_units, hidden_units, 3, padding=1),\n",
        "            nn.ReLU(),\n",
        "            nn.Conv2d(hidden_units, hidden_units, 3, padding=1),\n",
        "            nn.ReLU(),\n",
        "            nn.MaxPool2d(2)\n",
        "        )\n",
        "        self.classifier = nn.Sequential(\n",
        "            nn.Flatten(),\n",
        "            # Where did this in_features shape come from?\n",
        "            # It's because each layer of our network compresses and changes the shape of our inputs data.\n",
        "            nn.Linear(in_features=hidden_units*7*7,\n",
        "                      out_features=output_shape)\n",
        "        )\n",
        "\n",
        "    def forward(self, x: torch.Tensor):\n",
        "        x = self.block_1(x)\n",
        "        # print(x.shape)\n",
        "        x = self.block_2(x)\n",
        "        # print(x.shape)\n",
        "        x = self.classifier(x)\n",
        "        # print(x.shape)\n",
        "        return x\n",
        "\n",
        "torch.manual_seed(42)\n",
        "model_2 = FashionMNISTModelV2(input_shape=1,\n",
        "    hidden_units=10,\n",
        "    output_shape=len(class_names)).to(device)\n",
        "model_2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jLE-SM_vYx7m",
        "outputId": "881a4f63-4031-4455-d5f0-a269ee3137a3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "FashionMNISTModelV2(\n",
              "  (block_1): Sequential(\n",
              "    (0): Conv2d(1, 10, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (1): ReLU()\n",
              "    (2): Conv2d(10, 10, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (3): ReLU()\n",
              "    (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "  )\n",
              "  (block_2): Sequential(\n",
              "    (0): Conv2d(10, 10, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (1): ReLU()\n",
              "    (2): Conv2d(10, 10, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (3): ReLU()\n",
              "    (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "  )\n",
              "  (classifier): Sequential(\n",
              "    (0): Flatten(start_dim=1, end_dim=-1)\n",
              "    (1): Linear(in_features=490, out_features=10, bias=True)\n",
              "  )\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class FashionModelV1(nn.Module):\n",
        "  def __init__(self,\n",
        "               input_shape: int,\n",
        "               hidden_units: int,\n",
        "               output_shape: int):\n",
        "    super().__init__()\n",
        "    self.conv_block_1 = nn.Sequential(nn.Conv2d(in_channels = input_shape,\n",
        "                                                out_channels = hidden_units,\n",
        "                                                kernel_size = 3,\n",
        "                                                stride = 1,\n",
        "                                                padding = 1),\n",
        "                                      nn.ReLU(),\n",
        "                                      nn.Conv2d(in_channels = hidden_units,\n",
        "                                                out_channels = hidden_units,\n",
        "                                                padding = 1,\n",
        "                                                stride = 1,\n",
        "                                                kernel_size = 3),\n",
        "                                      nn.ReLU(),\n",
        "                                      nn.MaxPool2d(kernel_size = 2),\n",
        "                                      )\n",
        "    self.conv_block_2 = nn.Sequential(nn.Conv2d(in_channels = hidden_units,\n",
        "                                                out_channels = hidden_units,\n",
        "                                                kernel_size = 3,\n",
        "                                                stride = 1,\n",
        "                                                padding = 1),\n",
        "                                      nn.ReLU(),\n",
        "                                      nn.Conv2d(in_channels = hidden_units,\n",
        "                                                out_channels = hidden_units,\n",
        "                                                padding = 1,\n",
        "                                                stride = 1,\n",
        "                                                kernel_size = 3),\n",
        "                                      nn.ReLU(),\n",
        "                                      nn.MaxPool2d(kernel_size = 2))\n",
        "    self.classifier = nn.Sequential(nn.Flatten(),\n",
        "                                    nn.Linear(in_features = hidden_units*7*7,\n",
        "                                              out_features = len(class_names)))\n",
        "\n",
        "  def forward(self,x):\n",
        "    x = self.conv_block_1(x)\n",
        "    print(x.shape)\n",
        "    x = self.conv_block_2(x)\n",
        "    print(x.shape)\n",
        "    x = self.classifier(x)\n",
        "    print(x.shape)"
      ],
      "metadata": {
        "id": "1p6Kw88eFXtI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_1 = FashionModelV1(input_shape = 1,\n",
        "                      hidden_units = 10,\n",
        "                      output_shape = len(class_names))"
      ],
      "metadata": {
        "id": "g6JZwhgLYiqw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DEWlKVgFYlhu",
        "outputId": "aa98a8a7-ef75-44a3-9c70-45851e2ebd3a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "FashionModelV1(\n",
              "  (conv_block_1): Sequential(\n",
              "    (0): Conv2d(1, 10, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (1): ReLU()\n",
              "    (2): Conv2d(10, 10, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (3): ReLU()\n",
              "    (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "  )\n",
              "  (conv_block_2): Sequential(\n",
              "    (0): Conv2d(10, 10, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (1): ReLU()\n",
              "    (2): Conv2d(10, 10, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (3): ReLU()\n",
              "    (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "  )\n",
              "  (classifier): Sequential(\n",
              "    (0): Flatten(start_dim=1, end_dim=-1)\n",
              "    (1): Linear(in_features=490, out_features=10, bias=True)\n",
              "  )\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rand_image_tensor = torch.randn(size=(1,28,28))\n",
        "rand_image_tensor.shape"
      ],
      "metadata": {
        "id": "M9I7PgXwYm3w",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "15fc2a35-f0ed-474e-c7d1-d63031e422bc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([1, 28, 28])"
            ]
          },
          "metadata": {},
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_1(rand_image_tensor)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 329
        },
        "id": "m0d8Z-OLV9o5",
        "outputId": "cfe884f2-5515-4085-c7f0-29e2b5287ae3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch.Size([10, 14, 14])\n",
            "torch.Size([10, 7, 7])\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "RuntimeError",
          "evalue": "mat1 and mat2 shapes cannot be multiplied (10x49 and 490x10)",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-77-c50101d65609>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmodel_1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrand_image_tensor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1530\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compiled_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# type: ignore[misc]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1531\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1532\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1533\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1534\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1539\u001b[0m                 \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1540\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1541\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1542\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1543\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-60-d5367ae1013b>\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m     41\u001b[0m     \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconv_block_2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     42\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 43\u001b[0;31m     \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclassifier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     44\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1530\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compiled_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# type: ignore[misc]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1531\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1532\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1533\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1534\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1539\u001b[0m                 \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1540\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1541\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1542\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1543\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/container.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m    215\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    216\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mmodule\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 217\u001b[0;31m             \u001b[0minput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodule\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    218\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    219\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1530\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compiled_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# type: ignore[misc]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1531\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1532\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1533\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1534\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1539\u001b[0m                 \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1540\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1541\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1542\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1543\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/linear.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m    114\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    115\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 116\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mF\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlinear\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbias\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    117\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    118\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mextra_repr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mRuntimeError\u001b[0m: mat1 and mat2 shapes cannot be multiplied (10x49 and 490x10)"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "7UAgvptmV_9T"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}