ViT_MNIST.ipynb

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            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
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            "_view_count": null,
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            "_model_name": "LayoutModel",
            "justify_items": null,
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          }
        }
      }
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/cserajdeep/ViT-MNIST-DIGIT-CLASSIFICATION/blob/main/ViT_MNIST.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CuafEaetB0-S"
      },
      "source": [
        "## https://towardsdatascience.com/a-demonstration-of-using-vision-transformers-in-pytorch-mnist-handwritten-digit-recognition-407eafbc15b0"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
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      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 633,
          "referenced_widgets": [
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            "9308969e9f00492b9440b9b2623d5859",
            "325e38fb9dc545c384eee1b5c91f80b9"
          ]
        },
        "id": "k24kAG9lB6TD",
        "outputId": "e9ad481e-570f-431f-d1d4-52b1c24a37c4"
      },
      "source": [
        "import torch\n",
        "import torchvision\n",
        "\n",
        "torch.manual_seed(42)\n",
        "\n",
        "DOWNLOAD_PATH = '/data/mnist'\n",
        "BATCH_SIZE_TRAIN = 100\n",
        "BATCH_SIZE_TEST = 1000\n",
        "\n",
        "transform_mnist = torchvision.transforms.Compose([torchvision.transforms.ToTensor(),\n",
        "                               torchvision.transforms.Normalize((0.1307,), (0.3081,))])\n",
        "\n",
        "train_set = torchvision.datasets.MNIST(DOWNLOAD_PATH, train=True, download=True,\n",
        "                                       transform=transform_mnist)\n",
        "train_loader = torch.utils.data.DataLoader(train_set, batch_size=BATCH_SIZE_TRAIN, shuffle=True)\n",
        "\n",
        "test_set = torchvision.datasets.MNIST(DOWNLOAD_PATH, train=False, download=True,\n",
        "                                      transform=transform_mnist)\n",
        "test_loader = torch.utils.data.DataLoader(test_set, batch_size=BATCH_SIZE_TEST, shuffle=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "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 /data/mnist/MNIST/raw/train-images-idx3-ubyte.gz\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ee93c6c26ebd42c89072e43e4fb14aa8",
              "version_minor": 0,
              "version_major": 2
            },
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              "  0%|          | 0/9912422 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "text": [
            "Extracting /data/mnist/MNIST/raw/train-images-idx3-ubyte.gz to /data/mnist/MNIST/raw\n",
            "\n",
            "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz\n",
            "Failed to download (trying next):\n",
            "HTTP Error 503: Service Unavailable\n",
            "\n",
            "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-labels-idx1-ubyte.gz\n",
            "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-labels-idx1-ubyte.gz to /data/mnist/MNIST/raw/train-labels-idx1-ubyte.gz\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e56ce0310edb45e9a27baf0bb614047c",
              "version_minor": 0,
              "version_major": 2
            },
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            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "text": [
            "Extracting /data/mnist/MNIST/raw/train-labels-idx1-ubyte.gz to /data/mnist/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 /data/mnist/MNIST/raw/t10k-images-idx3-ubyte.gz\n",
            "Failed to download (trying next):\n",
            "HTTP Error 503: Service Unavailable\n",
            "\n",
            "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-images-idx3-ubyte.gz\n",
            "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-images-idx3-ubyte.gz to /data/mnist/MNIST/raw/t10k-images-idx3-ubyte.gz\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "bd335e38dde7464cb430f545dbe2db19",
              "version_minor": 0,
              "version_major": 2
            },
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              "  0%|          | 0/1648877 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "text": [
            "Extracting /data/mnist/MNIST/raw/t10k-images-idx3-ubyte.gz to /data/mnist/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 /data/mnist/MNIST/raw/t10k-labels-idx1-ubyte.gz\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "0c64f476e48547c897c95e0078bbd1ae",
              "version_minor": 0,
              "version_major": 2
            },
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              "  0%|          | 0/4542 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "text": [
            "Extracting /data/mnist/MNIST/raw/t10k-labels-idx1-ubyte.gz to /data/mnist/MNIST/raw\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/torchvision/datasets/mnist.py:498: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at  /pytorch/torch/csrc/utils/tensor_numpy.cpp:180.)\n",
            "  return torch.from_numpy(parsed.astype(m[2], copy=False)).view(*s)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XRn8xvvvB6YF"
      },
      "source": [
        "def train_epoch(model, optimizer, data_loader, loss_history):\n",
        "    total_samples = len(data_loader.dataset)\n",
        "    model.train()\n",
        "\n",
        "    for i, (data, target) in enumerate(data_loader):\n",
        "        optimizer.zero_grad()\n",
        "        output = F.log_softmax(model(data), dim=1)\n",
        "        loss = F.nll_loss(output, target)\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "\n",
        "        if i % 100 == 0:\n",
        "            print('[' +  '{:5}'.format(i * len(data)) + '/' + '{:5}'.format(total_samples) +\n",
        "                  ' (' + '{:3.0f}'.format(100 * i / len(data_loader)) + '%)]  Loss: ' +\n",
        "                  '{:6.4f}'.format(loss.item()))\n",
        "            loss_history.append(loss.item())"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QyTo9JF4B6cd"
      },
      "source": [
        "def evaluate(model, data_loader, loss_history):\n",
        "    model.eval()\n",
        "    \n",
        "    total_samples = len(data_loader.dataset)\n",
        "    correct_samples = 0\n",
        "    total_loss = 0\n",
        "\n",
        "    with torch.no_grad():\n",
        "        for data, target in data_loader:\n",
        "            output = F.log_softmax(model(data), dim=1)\n",
        "            loss = F.nll_loss(output, target, reduction='sum')\n",
        "            _, pred = torch.max(output, dim=1)\n",
        "            \n",
        "            total_loss += loss.item()\n",
        "            correct_samples += pred.eq(target).sum()\n",
        "\n",
        "    avg_loss = total_loss / total_samples\n",
        "    loss_history.append(avg_loss)\n",
        "    print('\\nAverage test loss: ' + '{:.4f}'.format(avg_loss) +\n",
        "          '  Accuracy:' + '{:5}'.format(correct_samples) + '/' +\n",
        "          '{:5}'.format(total_samples) + ' (' +\n",
        "          '{:4.2f}'.format(100.0 * correct_samples / total_samples) + '%)\\n')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z0tHaelMJVSB",
        "outputId": "d030a8c2-9fa1-4429-e999-247b9f83173f"
      },
      "source": [
        "!pip install einops"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting einops\n",
            "  Downloading einops-0.3.0-py2.py3-none-any.whl (25 kB)\n",
            "Installing collected packages: einops\n",
            "Successfully installed einops-0.3.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lo96-mnXDHwK"
      },
      "source": [
        "import torch\n",
        "import torch.nn.functional as F\n",
        "\n",
        "from torch import nn\n",
        "from einops import rearrange\n",
        "import time\n",
        "\n",
        "class Residual(nn.Module):\n",
        "    def __init__(self, fn):\n",
        "        super().__init__()\n",
        "        self.fn = fn\n",
        "\n",
        "    def forward(self, x, **kwargs):\n",
        "        return self.fn(x, **kwargs) + x\n",
        "\n",
        "class PreNorm(nn.Module):\n",
        "    def __init__(self, dim, fn):\n",
        "        super().__init__()\n",
        "        self.norm = nn.LayerNorm(dim)\n",
        "        self.fn = fn\n",
        "\n",
        "    def forward(self, x, **kwargs):\n",
        "        return self.fn(self.norm(x), **kwargs)\n",
        "\n",
        "class FeedForward(nn.Module):\n",
        "    def __init__(self, dim, hidden_dim):\n",
        "        super().__init__()\n",
        "        self.net = nn.Sequential(\n",
        "            nn.Linear(dim, hidden_dim),\n",
        "            nn.GELU(),\n",
        "            nn.Linear(hidden_dim, dim)\n",
        "        )\n",
        "\n",
        "    def forward(self, x):\n",
        "        return self.net(x)\n",
        "\n",
        "class Attention(nn.Module):\n",
        "    def __init__(self, dim, heads=8):\n",
        "        super().__init__()\n",
        "        self.heads = heads\n",
        "        self.scale = dim ** -0.5\n",
        "\n",
        "        self.to_qkv = nn.Linear(dim, dim * 3, bias=False)\n",
        "        self.to_out = nn.Linear(dim, dim)\n",
        "\n",
        "    def forward(self, x, mask = None):\n",
        "        b, n, _, h = *x.shape, self.heads\n",
        "        qkv = self.to_qkv(x)\n",
        "        q, k, v = rearrange(qkv, 'b n (qkv h d) -> qkv b h n d', qkv=3, h=h)\n",
        "\n",
        "        dots = torch.einsum('bhid,bhjd->bhij', q, k) * self.scale\n",
        "\n",
        "        if mask is not None:\n",
        "            mask = F.pad(mask.flatten(1), (1, 0), value = True)\n",
        "            assert mask.shape[-1] == dots.shape[-1], 'mask has incorrect dimensions'\n",
        "            mask = mask[:, None, :] * mask[:, :, None]\n",
        "            dots.masked_fill_(~mask, float('-inf'))\n",
        "            del mask\n",
        "\n",
        "        attn = dots.softmax(dim=-1)\n",
        "\n",
        "        out = torch.einsum('bhij,bhjd->bhid', attn, v)\n",
        "        out = rearrange(out, 'b h n d -> b n (h d)')\n",
        "        out =  self.to_out(out)\n",
        "        return out\n",
        "\n",
        "class Transformer(nn.Module):\n",
        "    def __init__(self, dim, depth, heads, mlp_dim):\n",
        "        super().__init__()\n",
        "        self.layers = nn.ModuleList([])\n",
        "        for _ in range(depth):\n",
        "            self.layers.append(nn.ModuleList([\n",
        "                Residual(PreNorm(dim, Attention(dim, heads = heads))),\n",
        "                Residual(PreNorm(dim, FeedForward(dim, mlp_dim)))\n",
        "            ]))\n",
        "\n",
        "    def forward(self, x, mask=None):\n",
        "        for attn, ff in self.layers:\n",
        "            x = attn(x, mask=mask)\n",
        "            x = ff(x)\n",
        "        return x\n",
        "\n",
        "class ViT(nn.Module):\n",
        "    def __init__(self, *, image_size, patch_size, num_classes, dim, depth, heads, mlp_dim, channels=3):\n",
        "        super().__init__()\n",
        "        assert image_size % patch_size == 0, 'image dimensions must be divisible by the patch size'\n",
        "        num_patches = (image_size // patch_size) ** 2\n",
        "        patch_dim = channels * patch_size ** 2\n",
        "\n",
        "        self.patch_size = patch_size\n",
        "\n",
        "        self.pos_embedding = nn.Parameter(torch.randn(1, num_patches + 1, dim))\n",
        "        self.patch_to_embedding = nn.Linear(patch_dim, dim)\n",
        "        self.cls_token = nn.Parameter(torch.randn(1, 1, dim))\n",
        "        self.transformer = Transformer(dim, depth, heads, mlp_dim)\n",
        "\n",
        "        self.to_cls_token = nn.Identity()\n",
        "\n",
        "        self.mlp_head = nn.Sequential(\n",
        "            nn.Linear(dim, mlp_dim),\n",
        "            nn.GELU(),\n",
        "            nn.Linear(mlp_dim, num_classes)\n",
        "        )\n",
        "\n",
        "    def forward(self, img, mask=None):\n",
        "        p = self.patch_size\n",
        "\n",
        "        x = rearrange(img, 'b c (h p1) (w p2) -> b (h w) (p1 p2 c)', p1 = p, p2 = p)\n",
        "        x = self.patch_to_embedding(x)\n",
        "\n",
        "        cls_tokens = self.cls_token.expand(img.shape[0], -1, -1)\n",
        "        x = torch.cat((cls_tokens, x), dim=1)\n",
        "        x += self.pos_embedding\n",
        "        x = self.transformer(x, mask)\n",
        "\n",
        "        x = self.to_cls_token(x[:, 0])\n",
        "        return self.mlp_head(x)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cYH7rxM0CVtz",
        "outputId": "2a8cb60c-add2-4084-a2ee-3c1b78210010"
      },
      "source": [
        "N_EPOCHS = 25\n",
        "\n",
        "start_time = time.time()\n",
        "model = ViT(\n",
        "    image_size=28, \n",
        "    patch_size=7, \n",
        "    num_classes=10, \n",
        "    channels=1, \n",
        "    dim=64, \n",
        "    depth=6, \n",
        "    heads=8, \n",
        "    mlp_dim=128,\n",
        "    dropout = 0.1,\n",
        "    emb_dropout = 0.1)\n",
        "optimizer = optim.Adam(model.parameters(), lr=0.003)\n",
        "\n",
        "train_loss_history, test_loss_history = [], []\n",
        "for epoch in range(1, N_EPOCHS + 1):\n",
        "    print('Epoch:', epoch)\n",
        "    train_epoch(model, optimizer, train_loader, train_loss_history)\n",
        "    evaluate(model, test_loader, test_loss_history)\n",
        "\n",
        "print('Execution time:', '{:5.2f}'.format(time.time() - start_time), 'seconds')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch: 1\n",
            "[    0/60000 (  0%)]  Loss: 2.3330\n",
            "[10000/60000 ( 17%)]  Loss: 0.3244\n",
            "[20000/60000 ( 33%)]  Loss: 0.3293\n",
            "[30000/60000 ( 50%)]  Loss: 0.1572\n",
            "[40000/60000 ( 67%)]  Loss: 0.1296\n",
            "[50000/60000 ( 83%)]  Loss: 0.1162\n",
            "\n",
            "Average test loss: 0.1538  Accuracy: 9516/10000 (95.16%)\n",
            "\n",
            "Epoch: 2\n",
            "[    0/60000 (  0%)]  Loss: 0.1916\n",
            "[10000/60000 ( 17%)]  Loss: 0.1409\n",
            "[20000/60000 ( 33%)]  Loss: 0.0399\n",
            "[30000/60000 ( 50%)]  Loss: 0.1252\n",
            "[40000/60000 ( 67%)]  Loss: 0.1743\n",
            "[50000/60000 ( 83%)]  Loss: 0.0826\n",
            "\n",
            "Average test loss: 0.1502  Accuracy: 9551/10000 (95.51%)\n",
            "\n",
            "Epoch: 3\n",
            "[    0/60000 (  0%)]  Loss: 0.1901\n",
            "[10000/60000 ( 17%)]  Loss: 0.0654\n",
            "[20000/60000 ( 33%)]  Loss: 0.1272\n",
            "[30000/60000 ( 50%)]  Loss: 0.0564\n",
            "[40000/60000 ( 67%)]  Loss: 0.0696\n",
            "[50000/60000 ( 83%)]  Loss: 0.0540\n",
            "\n",
            "Average test loss: 0.1119  Accuracy: 9682/10000 (96.82%)\n",
            "\n",
            "Epoch: 4\n",
            "[    0/60000 (  0%)]  Loss: 0.0449\n",
            "[10000/60000 ( 17%)]  Loss: 0.0482\n",
            "[20000/60000 ( 33%)]  Loss: 0.0806\n",
            "[30000/60000 ( 50%)]  Loss: 0.0687\n",
            "[40000/60000 ( 67%)]  Loss: 0.1400\n",
            "[50000/60000 ( 83%)]  Loss: 0.0183\n",
            "\n",
            "Average test loss: 0.0916  Accuracy: 9734/10000 (97.34%)\n",
            "\n",
            "Epoch: 5\n",
            "[    0/60000 (  0%)]  Loss: 0.0523\n",
            "[10000/60000 ( 17%)]  Loss: 0.0301\n",
            "[20000/60000 ( 33%)]  Loss: 0.0639\n",
            "[30000/60000 ( 50%)]  Loss: 0.0450\n",
            "[40000/60000 ( 67%)]  Loss: 0.0577\n",
            "[50000/60000 ( 83%)]  Loss: 0.0837\n",
            "\n",
            "Average test loss: 0.0872  Accuracy: 9758/10000 (97.58%)\n",
            "\n",
            "Epoch: 6\n",
            "[    0/60000 (  0%)]  Loss: 0.0323\n",
            "[10000/60000 ( 17%)]  Loss: 0.1211\n",
            "[20000/60000 ( 33%)]  Loss: 0.1351\n",
            "[30000/60000 ( 50%)]  Loss: 0.1601\n",
            "[40000/60000 ( 67%)]  Loss: 0.0812\n",
            "[50000/60000 ( 83%)]  Loss: 0.2638\n",
            "\n",
            "Average test loss: 0.0870  Accuracy: 9737/10000 (97.37%)\n",
            "\n",
            "Epoch: 7\n",
            "[    0/60000 (  0%)]  Loss: 0.0542\n",
            "[10000/60000 ( 17%)]  Loss: 0.0049\n",
            "[20000/60000 ( 33%)]  Loss: 0.0179\n",
            "[30000/60000 ( 50%)]  Loss: 0.0486\n",
            "[40000/60000 ( 67%)]  Loss: 0.0588\n",
            "[50000/60000 ( 83%)]  Loss: 0.0577\n",
            "\n",
            "Average test loss: 0.0854  Accuracy: 9767/10000 (97.67%)\n",
            "\n",
            "Epoch: 8\n",
            "[    0/60000 (  0%)]  Loss: 0.0400\n",
            "[10000/60000 ( 17%)]  Loss: 0.0643\n",
            "[20000/60000 ( 33%)]  Loss: 0.0259\n",
            "[30000/60000 ( 50%)]  Loss: 0.1230\n",
            "[40000/60000 ( 67%)]  Loss: 0.0173\n",
            "[50000/60000 ( 83%)]  Loss: 0.0383\n",
            "\n",
            "Average test loss: 0.0804  Accuracy: 9789/10000 (97.89%)\n",
            "\n",
            "Epoch: 9\n",
            "[    0/60000 (  0%)]  Loss: 0.0073\n",
            "[10000/60000 ( 17%)]  Loss: 0.0288\n",
            "[20000/60000 ( 33%)]  Loss: 0.0536\n",
            "[30000/60000 ( 50%)]  Loss: 0.0482\n",
            "[40000/60000 ( 67%)]  Loss: 0.0913\n",
            "[50000/60000 ( 83%)]  Loss: 0.0071\n",
            "\n",
            "Average test loss: 0.1003  Accuracy: 9707/10000 (97.07%)\n",
            "\n",
            "Epoch: 10\n",
            "[    0/60000 (  0%)]  Loss: 0.0142\n",
            "[10000/60000 ( 17%)]  Loss: 0.0509\n",
            "[20000/60000 ( 33%)]  Loss: 0.0161\n",
            "[30000/60000 ( 50%)]  Loss: 0.0313\n",
            "[40000/60000 ( 67%)]  Loss: 0.0924\n",
            "[50000/60000 ( 83%)]  Loss: 0.0117\n",
            "\n",
            "Average test loss: 0.0829  Accuracy: 9763/10000 (97.63%)\n",
            "\n",
            "Epoch: 11\n",
            "[    0/60000 (  0%)]  Loss: 0.0140\n",
            "[10000/60000 ( 17%)]  Loss: 0.0772\n",
            "[20000/60000 ( 33%)]  Loss: 0.0090\n",
            "[30000/60000 ( 50%)]  Loss: 0.0303\n",
            "[40000/60000 ( 67%)]  Loss: 0.0297\n",
            "[50000/60000 ( 83%)]  Loss: 0.0839\n",
            "\n",
            "Average test loss: 0.0778  Accuracy: 9816/10000 (98.16%)\n",
            "\n",
            "Epoch: 12\n",
            "[    0/60000 (  0%)]  Loss: 0.0028\n",
            "[10000/60000 ( 17%)]  Loss: 0.0189\n",
            "[20000/60000 ( 33%)]  Loss: 0.1025\n",
            "[30000/60000 ( 50%)]  Loss: 0.0134\n",
            "[40000/60000 ( 67%)]  Loss: 0.0571\n",
            "[50000/60000 ( 83%)]  Loss: 0.0722\n",
            "\n",
            "Average test loss: 0.0911  Accuracy: 9749/10000 (97.49%)\n",
            "\n",
            "Epoch: 13\n",
            "[    0/60000 (  0%)]  Loss: 0.0312\n",
            "[10000/60000 ( 17%)]  Loss: 0.0438\n",
            "[20000/60000 ( 33%)]  Loss: 0.0260\n",
            "[30000/60000 ( 50%)]  Loss: 0.0260\n",
            "[40000/60000 ( 67%)]  Loss: 0.0331\n",
            "[50000/60000 ( 83%)]  Loss: 0.0600\n",
            "\n",
            "Average test loss: 0.0862  Accuracy: 9781/10000 (97.81%)\n",
            "\n",
            "Epoch: 14\n",
            "[    0/60000 (  0%)]  Loss: 0.0462\n",
            "[10000/60000 ( 17%)]  Loss: 0.0598\n",
            "[20000/60000 ( 33%)]  Loss: 0.0155\n",
            "[30000/60000 ( 50%)]  Loss: 0.0369\n",
            "[40000/60000 ( 67%)]  Loss: 0.0256\n",
            "[50000/60000 ( 83%)]  Loss: 0.0685\n",
            "\n",
            "Average test loss: 0.0806  Accuracy: 9801/10000 (98.01%)\n",
            "\n",
            "Epoch: 15\n",
            "[    0/60000 (  0%)]  Loss: 0.0466\n",
            "[10000/60000 ( 17%)]  Loss: 0.0114\n",
            "[20000/60000 ( 33%)]  Loss: 0.0042\n",
            "[30000/60000 ( 50%)]  Loss: 0.0167\n",
            "[40000/60000 ( 67%)]  Loss: 0.0323\n",
            "[50000/60000 ( 83%)]  Loss: 0.0291\n",
            "\n",
            "Average test loss: 0.0875  Accuracy: 9800/10000 (98.00%)\n",
            "\n",
            "Epoch: 16\n",
            "[    0/60000 (  0%)]  Loss: 0.0031\n",
            "[10000/60000 ( 17%)]  Loss: 0.0503\n",
            "[20000/60000 ( 33%)]  Loss: 0.0055\n",
            "[30000/60000 ( 50%)]  Loss: 0.0455\n",
            "[40000/60000 ( 67%)]  Loss: 0.0507\n",
            "[50000/60000 ( 83%)]  Loss: 0.0097\n",
            "\n",
            "Average test loss: 0.0593  Accuracy: 9858/10000 (98.58%)\n",
            "\n",
            "Epoch: 17\n",
            "[    0/60000 (  0%)]  Loss: 0.0381\n",
            "[10000/60000 ( 17%)]  Loss: 0.0407\n",
            "[20000/60000 ( 33%)]  Loss: 0.0790\n",
            "[30000/60000 ( 50%)]  Loss: 0.0436\n",
            "[40000/60000 ( 67%)]  Loss: 0.0026\n",
            "[50000/60000 ( 83%)]  Loss: 0.0991\n",
            "\n",
            "Average test loss: 0.0764  Accuracy: 9823/10000 (98.23%)\n",
            "\n",
            "Epoch: 18\n",
            "[    0/60000 (  0%)]  Loss: 0.0256\n",
            "[10000/60000 ( 17%)]  Loss: 0.1213\n",
            "[20000/60000 ( 33%)]  Loss: 0.1019\n",
            "[30000/60000 ( 50%)]  Loss: 0.0073\n",
            "[40000/60000 ( 67%)]  Loss: 0.0013\n",
            "[50000/60000 ( 83%)]  Loss: 0.0218\n",
            "\n",
            "Average test loss: 0.0658  Accuracy: 9824/10000 (98.24%)\n",
            "\n",
            "Epoch: 19\n",
            "[    0/60000 (  0%)]  Loss: 0.0378\n",
            "[10000/60000 ( 17%)]  Loss: 0.0283\n",
            "[20000/60000 ( 33%)]  Loss: 0.0171\n",
            "[30000/60000 ( 50%)]  Loss: 0.0333\n",
            "[40000/60000 ( 67%)]  Loss: 0.0030\n",
            "[50000/60000 ( 83%)]  Loss: 0.0265\n",
            "\n",
            "Average test loss: 0.0721  Accuracy: 9845/10000 (98.45%)\n",
            "\n",
            "Epoch: 20\n",
            "[    0/60000 (  0%)]  Loss: 0.0206\n",
            "[10000/60000 ( 17%)]  Loss: 0.0011\n",
            "[20000/60000 ( 33%)]  Loss: 0.0323\n",
            "[30000/60000 ( 50%)]  Loss: 0.0140\n",
            "[40000/60000 ( 67%)]  Loss: 0.0418\n",
            "[50000/60000 ( 83%)]  Loss: 0.0240\n",
            "\n",
            "Average test loss: 0.0892  Accuracy: 9777/10000 (97.77%)\n",
            "\n",
            "Epoch: 21\n",
            "[    0/60000 (  0%)]  Loss: 0.0548\n",
            "[10000/60000 ( 17%)]  Loss: 0.0331\n",
            "[20000/60000 ( 33%)]  Loss: 0.0091\n",
            "[30000/60000 ( 50%)]  Loss: 0.0033\n",
            "[40000/60000 ( 67%)]  Loss: 0.0051\n",
            "[50000/60000 ( 83%)]  Loss: 0.0816\n",
            "\n",
            "Average test loss: 0.0729  Accuracy: 9806/10000 (98.06%)\n",
            "\n",
            "Epoch: 22\n",
            "[    0/60000 (  0%)]  Loss: 0.0181\n",
            "[10000/60000 ( 17%)]  Loss: 0.0772\n",
            "[20000/60000 ( 33%)]  Loss: 0.1207\n",
            "[30000/60000 ( 50%)]  Loss: 0.0195\n",
            "[40000/60000 ( 67%)]  Loss: 0.0172\n",
            "[50000/60000 ( 83%)]  Loss: 0.0112\n",
            "\n",
            "Average test loss: 0.0737  Accuracy: 9826/10000 (98.26%)\n",
            "\n",
            "Epoch: 23\n",
            "[    0/60000 (  0%)]  Loss: 0.0133\n",
            "[10000/60000 ( 17%)]  Loss: 0.0070\n",
            "[20000/60000 ( 33%)]  Loss: 0.0008\n",
            "[30000/60000 ( 50%)]  Loss: 0.0060\n",
            "[40000/60000 ( 67%)]  Loss: 0.0005\n",
            "[50000/60000 ( 83%)]  Loss: 0.0196\n",
            "\n",
            "Average test loss: 0.0953  Accuracy: 9791/10000 (97.91%)\n",
            "\n",
            "Epoch: 24\n",
            "[    0/60000 (  0%)]  Loss: 0.0241\n",
            "[10000/60000 ( 17%)]  Loss: 0.0054\n",
            "[20000/60000 ( 33%)]  Loss: 0.0159\n",
            "[30000/60000 ( 50%)]  Loss: 0.0077\n",
            "[40000/60000 ( 67%)]  Loss: 0.0053\n",
            "[50000/60000 ( 83%)]  Loss: 0.1442\n",
            "\n",
            "Average test loss: 0.0958  Accuracy: 9793/10000 (97.93%)\n",
            "\n",
            "Epoch: 25\n",
            "[    0/60000 (  0%)]  Loss: 0.0143\n",
            "[10000/60000 ( 17%)]  Loss: 0.0377\n",
            "[20000/60000 ( 33%)]  Loss: 0.0299\n",
            "[30000/60000 ( 50%)]  Loss: 0.0067\n",
            "[40000/60000 ( 67%)]  Loss: 0.0000\n",
            "[50000/60000 ( 83%)]  Loss: 0.0310\n",
            "\n",
            "Average test loss: 0.0797  Accuracy: 9836/10000 (98.36%)\n",
            "\n",
            "Execution time: 2504.85 seconds\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TZJIPI5sCV25",
        "outputId": "abfe5379-bdfb-4e25-a1f4-bd3838161955"
      },
      "source": [
        "examples = enumerate(test_loader)\n",
        "batch_idx, (example_data, example_targets) = next(examples)\n",
        "example_data.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([1000, 1, 28, 28])"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V3y33HsMYP5T"
      },
      "source": [
        " torch.save(model.state_dict(), 'model-vit-mnist.pth')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        },
        "id": "RXVfss7bCV7l",
        "outputId": "cb82968b-02d3-412f-db23-e5dbfe642ac6"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "with torch.no_grad():\n",
        "  output = model(example_data)\n",
        "\n",
        "fig = plt.figure()\n",
        "for i in range(6):\n",
        "  plt.subplot(2,3,i+1)\n",
        "  plt.tight_layout()\n",
        "  plt.imshow(example_data[i][0], cmap='gray', interpolation='none')\n",
        "  plt.title(\"Prediction: {}\".format(\n",
        "    output.data.max(1, keepdim=True)[1][i].item()))\n",
        "  plt.xticks([])\n",
        "  plt.yticks([])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 6 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "0lCv_oL5YsbL",
        "outputId": "6eddd4ec-8e4f-4f36-ed33-dd45a5909429"
      },
      "source": [
        "def predict_image(img, model):\n",
        "    xb = img.unsqueeze(0)\n",
        "    yb =  model(xb)\n",
        "    _, preds  = torch.max(yb, dim=1)\n",
        "    return preds[0].item()\n",
        "\n",
        "k = 27\n",
        "img = example_data[k]\n",
        "plt.imshow(img[0], cmap='gray')\n",
        "print('Label:', example_targets[k].item(), ', Predicted:', predict_image(img, model))  "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Label: 7 , Predicted: 7\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RqPhOe1qYykN",
        "outputId": "5f2ab6a6-b030-4c02-dd93-e1187d0e089a"
      },
      "source": [
        "example_targets"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([4, 0, 0, 9, 3, 0, 5, 4, 3, 5, 8, 3, 5, 4, 2, 1, 5, 4, 3, 0, 8, 5, 3, 1,\n",
              "        9, 8, 6, 7, 9, 3, 5, 9, 7, 9, 5, 2, 0, 0, 4, 7, 4, 1, 1, 8, 2, 4, 8, 7,\n",
              "        1, 3, 2, 0, 6, 3, 9, 6, 5, 5, 6, 2, 7, 8, 4, 9, 1, 3, 3, 9, 3, 4, 5, 4,\n",
              "        3, 9, 1, 6, 2, 8, 9, 1, 0, 3, 2, 3, 9, 7, 7, 8, 9, 3, 0, 8, 2, 1, 0, 5,\n",
              "        5, 4, 8, 7, 8, 2, 6, 4, 4, 4, 2, 7, 2, 2, 9, 7, 5, 3, 1, 8, 4, 3, 1, 3,\n",
              "        8, 8, 0, 2, 6, 1, 5, 5, 9, 0, 0, 3, 8, 3, 7, 1, 5, 7, 1, 0, 6, 6, 2, 6,\n",
              "        9, 7, 7, 3, 1, 1, 1, 6, 4, 7, 7, 2, 6, 7, 8, 1, 0, 1, 5, 3, 1, 7, 6, 3,\n",
              "        2, 4, 1, 6, 5, 4, 8, 6, 5, 3, 9, 4, 0, 9, 2, 9, 5, 2, 0, 0, 5, 5, 9, 5,\n",
              "        7, 5, 0, 1, 7, 6, 1, 7, 1, 2, 2, 6, 5, 5, 4, 5, 4, 9, 9, 2, 5, 3, 1, 0,\n",
              "        8, 2, 2, 1, 8, 2, 8, 0, 8, 9, 5, 3, 0, 2, 2, 4, 8, 4, 1, 6, 8, 9, 7, 4,\n",
              "        7, 6, 1, 4, 1, 1, 3, 6, 3, 5, 0, 1, 8, 3, 7, 0, 1, 7, 5, 9, 1, 0, 2, 4,\n",
              "        4, 8, 2, 9, 3, 0, 1, 4, 8, 8, 4, 9, 3, 8, 7, 4, 7, 2, 0, 3, 7, 7, 1, 0,\n",
              "        1, 4, 0, 2, 7, 9, 9, 7, 1, 8, 9, 9, 2, 0, 2, 0, 3, 9, 4, 8, 5, 3, 6, 7,\n",
              "        1, 0, 4, 9, 3, 5, 2, 6, 5, 7, 0, 5, 5, 4, 8, 0, 4, 0, 2, 7, 6, 5, 6, 4,\n",
              "        2, 7, 1, 6, 9, 7, 9, 5, 9, 9, 2, 2, 8, 3, 5, 0, 3, 0, 1, 1, 7, 4, 6, 6,\n",
              "        8, 2, 2, 6, 2, 5, 6, 1, 4, 6, 6, 5, 7, 4, 1, 5, 1, 7, 2, 5, 5, 9, 4, 3,\n",
              "        0, 7, 3, 9, 2, 3, 2, 3, 6, 2, 0, 4, 9, 2, 4, 1, 2, 1, 7, 2, 4, 0, 2, 2,\n",
              "        7, 2, 4, 4, 4, 9, 6, 6, 2, 2, 3, 6, 7, 0, 3, 9, 7, 9, 3, 7, 6, 6, 2, 0,\n",
              "        1, 3, 8, 0, 3, 3, 0, 8, 6, 5, 6, 5, 3, 5, 8, 0, 7, 8, 0, 9, 7, 4, 6, 5,\n",
              "        5, 2, 3, 0, 5, 5, 6, 5, 2, 6, 8, 1, 9, 1, 8, 5, 6, 8, 9, 8, 1, 5, 7, 6,\n",
              "        6, 2, 5, 4, 1, 7, 5, 1, 2, 4, 5, 0, 7, 7, 3, 3, 7, 3, 1, 0, 4, 0, 7, 2,\n",
              "        6, 1, 4, 1, 6, 9, 4, 0, 1, 8, 9, 0, 4, 7, 8, 3, 6, 0, 7, 5, 2, 6, 0, 1,\n",
              "        9, 2, 6, 8, 6, 6, 5, 5, 7, 5, 4, 0, 3, 6, 8, 0, 4, 6, 9, 4, 2, 9, 1, 3,\n",
              "        2, 0, 8, 7, 2, 0, 5, 8, 4, 9, 4, 8, 4, 3, 8, 3, 7, 1, 9, 9, 7, 4, 1, 6,\n",
              "        2, 3, 4, 7, 9, 4, 1, 4, 6, 8, 3, 1, 2, 4, 4, 0, 7, 5, 5, 9, 5, 4, 4, 0,\n",
              "        2, 1, 2, 3, 6, 9, 4, 7, 1, 0, 5, 2, 6, 0, 2, 4, 1, 2, 8, 3, 2, 5, 1, 6,\n",
              "        9, 3, 6, 2, 4, 9, 6, 6, 5, 7, 6, 5, 1, 1, 1, 9, 8, 7, 4, 6, 4, 9, 5, 1,\n",
              "        8, 7, 5, 7, 7, 7, 8, 1, 1, 0, 0, 3, 5, 7, 2, 8, 8, 8, 2, 9, 0, 9, 1, 3,\n",
              "        9, 2, 3, 0, 9, 8, 2, 7, 3, 2, 8, 9, 2, 9, 7, 8, 2, 9, 1, 3, 0, 5, 1, 3,\n",
              "        7, 6, 3, 6, 0, 0, 8, 3, 3, 0, 8, 7, 7, 3, 4, 7, 2, 4, 3, 4, 3, 9, 0, 4,\n",
              "        9, 1, 4, 5, 7, 0, 2, 3, 8, 1, 8, 3, 3, 8, 3, 8, 8, 4, 6, 1, 3, 7, 6, 1,\n",
              "        0, 9, 4, 3, 2, 3, 0, 4, 8, 0, 9, 1, 9, 1, 4, 6, 6, 9, 6, 7, 5, 0, 9, 6,\n",
              "        9, 4, 6, 7, 6, 3, 0, 1, 7, 4, 2, 4, 2, 6, 1, 5, 0, 6, 7, 6, 2, 2, 2, 4,\n",
              "        4, 9, 0, 7, 6, 6, 4, 8, 4, 6, 3, 2, 0, 0, 5, 4, 5, 2, 7, 5, 1, 7, 6, 7,\n",
              "        3, 1, 5, 5, 6, 6, 8, 1, 8, 9, 6, 2, 7, 0, 6, 5, 4, 6, 0, 4, 1, 2, 6, 1,\n",
              "        9, 6, 7, 0, 7, 1, 6, 9, 9, 4, 2, 2, 1, 4, 8, 5, 1, 4, 6, 0, 2, 8, 7, 6,\n",
              "        9, 8, 8, 7, 8, 0, 7, 2, 1, 3, 6, 1, 8, 0, 6, 1, 0, 7, 4, 5, 7, 8, 4, 7,\n",
              "        6, 6, 2, 8, 9, 6, 6, 4, 9, 5, 7, 3, 1, 6, 0, 8, 8, 2, 1, 3, 3, 8, 8, 1,\n",
              "        2, 3, 5, 8, 8, 4, 9, 5, 6, 1, 6, 1, 4, 1, 0, 5, 4, 9, 3, 1, 2, 6, 5, 9,\n",
              "        6, 6, 2, 8, 0, 5, 6, 9, 8, 0, 4, 5, 8, 9, 4, 9, 8, 6, 1, 1, 7, 2, 1, 2,\n",
              "        0, 3, 0, 8, 3, 3, 2, 9, 2, 3, 9, 0, 9, 2, 9, 2, 0, 7, 8, 2, 9, 1, 2, 1,\n",
              "        8, 8, 1, 7, 0, 3, 5, 4, 0, 0, 4, 5, 9, 1, 4, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    }
  ]
}