EVAP2S5_PyTorch.ipynb

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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/kaustubhharapanahalli/EVA-Assignment-Submission-Repo/blob/master/EVAP2S5_PyTorch.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aO-7t1Y7-hV4",
        "colab_type": "text"
      },
      "source": [
        "# Import Libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8kH16rnZ7wt_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from __future__ import print_function\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "import torch.optim as optim\n",
        "from torchvision import datasets, transforms"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ky3f_Odl-7um",
        "colab_type": "text"
      },
      "source": [
        "## Data Transformations\n",
        "\n",
        "We first start with defining our data transformations. We need to think what our data is and how can we augment it to correct represent images which it might not see otherwise. \n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YtssFUKb-jqx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Train Phase transformations\n",
        "train_transforms = transforms.Compose([\n",
        "                                    #    transforms.Resize((28, 28)),\n",
        "                                       transforms.ColorJitter(brightness=0.10, contrast=0.1, saturation=0.10, hue=0.1),\n",
        "                                       transforms.RandomRotation(25, fill=(0,)),\n",
        "                                       transforms.ToTensor(),\n",
        "                                       transforms.Normalize((0.1307,), (0.3081,)) # The mean and std have to be sequences (e.g., tuples), therefore you should add a comma after the values. \n",
        "                                       # Note the difference between (0.1307) and (0.1307,)\n",
        "                                       ])\n",
        "\n",
        "# Test Phase transformations\n",
        "test_transforms = transforms.Compose([\n",
        "                                    #    transforms.Resize((28, 28)),\n",
        "                                       transforms.ColorJitter(brightness=0.10, contrast=0.1, saturation=0.10, hue=0.1),\n",
        "                                       transforms.RandomRotation(25, fill=(0,)),\n",
        "                                       transforms.ToTensor(),\n",
        "                                       transforms.Normalize((0.1307,), (0.3081,))\n",
        "                                       ])\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oQciFYo2B1mO",
        "colab_type": "text"
      },
      "source": [
        "# Dataset and Creating Train/Test Split"
      ]
    },
    {
      "cell_type": "code",
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        "id": "_4A84rlfDA23",
        "colab_type": "code",
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          ]
        },
        "outputId": "5e1fc3c3-c705-44f7-f371-492cb208edc1"
      },
      "source": [
        "train = datasets.MNIST('./data', train=True, download=True, transform=train_transforms)\n",
        "test = datasets.MNIST('./data', train=False, download=True, transform=test_transforms)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to ./data/MNIST/raw/train-images-idx3-ubyte.gz\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
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        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Extracting ./data/MNIST/raw/train-images-idx3-ubyte.gz to ./data/MNIST/raw\n",
            "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz to ./data/MNIST/raw/train-labels-idx1-ubyte.gz\n"
          ],
          "name": "stdout"
        },
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        },
        {
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          "text": [
            "\n",
            "Extracting ./data/MNIST/raw/train-labels-idx1-ubyte.gz to ./data/MNIST/raw\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz to ./data/MNIST/raw/t10k-images-idx3-ubyte.gz\n"
          ],
          "name": "stdout"
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          },
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        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Extracting ./data/MNIST/raw/t10k-images-idx3-ubyte.gz to ./data/MNIST/raw\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz to ./data/MNIST/raw/t10k-labels-idx1-ubyte.gz\n"
          ],
          "name": "stdout"
        },
        {
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        {
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          "text": [
            "\n",
            "Extracting ./data/MNIST/raw/t10k-labels-idx1-ubyte.gz to ./data/MNIST/raw\n",
            "Processing...\n",
            "Done!\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qgldp_3-Dn0c",
        "colab_type": "text"
      },
      "source": [
        "# Dataloader Arguments & Test/Train Dataloaders\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C8OLDR79DrHG",
        "colab_type": "code",
        "outputId": "3a37b0a2-cbdb-42ba-f8cf-05dd5650e1dc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "SEED = 1\n",
        "\n",
        "# CUDA?\n",
        "cuda = torch.cuda.is_available()\n",
        "print(\"CUDA Available?\", cuda)\n",
        "\n",
        "# For reproducibility\n",
        "torch.manual_seed(SEED)\n",
        "\n",
        "if cuda:\n",
        "    torch.cuda.manual_seed(SEED)\n",
        "\n",
        "# dataloader arguments - something you'll fetch these from cmdprmt\n",
        "dataloader_args = dict(shuffle=True, batch_size=128, num_workers=4, pin_memory=True) if cuda else dict(shuffle=True, batch_size=64)\n",
        "\n",
        "# train dataloader\n",
        "train_loader = torch.utils.data.DataLoader(train, **dataloader_args)\n",
        "\n",
        "# test dataloader\n",
        "test_loader = torch.utils.data.DataLoader(test, **dataloader_args)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "CUDA Available? True\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-TFjoFekE_va",
        "colab_type": "text"
      },
      "source": [
        "# Data Statistics\n",
        "\n",
        "It is important to know your data very well. Let's check some of the statistics around our data and how it actually looks like"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hWZPPo3yEHDW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# # We'd need to convert it into Numpy! Remember above we have converted it into tensors already\n",
        "# train_data = train.train_data\n",
        "# train_data = train.transform(train_data)\n",
        "\n",
        "# print('[Train]')\n",
        "# print(' - Numpy Shape:', train.train_data.cpu().numpy().shape)\n",
        "# print(' - Tensor Shape:', train.train_data.size())\n",
        "# print(' - min:', torch.min(train_data))\n",
        "# print(' - max:', torch.max(train_data))\n",
        "# print(' - mean:', torch.mean(train_data))\n",
        "# print(' - std:', torch.std(train_data))\n",
        "# print(' - var:', torch.var(train_data))\n",
        "\n",
        "# dataiter = iter(train_loader)\n",
        "# images, labels = dataiter.next()\n",
        "\n",
        "# print(images.shape)\n",
        "# print(labels.shape)\n",
        "\n",
        "# # Let's visualize some of the images\n",
        "# %matplotlib inline\n",
        "# import matplotlib.pyplot as plt\n",
        "\n",
        "# plt.imshow(images[0].numpy().squeeze(), cmap='gray_r')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7l9lNaWYKuik",
        "colab_type": "text"
      },
      "source": [
        "## MORE\n",
        "\n",
        "It is important that we view as many images as possible. This is required to get some idea on image augmentation later on"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hXXAg8hbK16u",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# figure = plt.figure()\n",
        "# num_of_images = 60\n",
        "# for index in range(1, num_of_images + 1):\n",
        "#     plt.subplot(6, 10, index)\n",
        "#     plt.axis('off')\n",
        "#     plt.imshow(images[index].numpy().squeeze(), cmap='gray_r')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ubQL3H6RJL3h",
        "colab_type": "text"
      },
      "source": [
        "# The model\n",
        "Let's start with the model we first saw"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7FXQlB9kH1ov",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class Net(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(Net, self).__init__()\n",
        "        # Input Block\n",
        "        self.convblock1 = nn.Sequential(\n",
        "            nn.Conv2d(in_channels=1, out_channels=8, kernel_size=(3, 3), padding=0, bias=False),\n",
        "            nn.BatchNorm2d(8),\n",
        "            nn.Dropout(0.1),\n",
        "            nn.ReLU()\n",
        "        ) # output_size = 26\n",
        "\n",
        "        # CONVOLUTION BLOCK 1\n",
        "        self.convblock2 = nn.Sequential(\n",
        "            nn.Conv2d(in_channels=8, out_channels=16, kernel_size=(3, 3), padding=0, bias=False),\n",
        "            nn.BatchNorm2d(16),\n",
        "            nn.Dropout(0.1),\n",
        "            nn.ReLU()\n",
        "        ) # output_size = 24\n",
        "        self.convblock3 = nn.Sequential(\n",
        "            nn.Conv2d(in_channels=16, out_channels=32, kernel_size=(3, 3), padding=0, bias=False),\n",
        "            nn.BatchNorm2d(32),\n",
        "            nn.Dropout(0.1),\n",
        "            nn.ReLU()\n",
        "        ) # output_size = 22\n",
        "\n",
        "        # TRANSITION BLOCK 1\n",
        "        self.pool1 = nn.MaxPool2d(2, 2) # output_size = 11\n",
        "        self.convblock4 = nn.Sequential(\n",
        "            nn.Conv2d(in_channels=32, out_channels=8, kernel_size=(1, 1), padding=0, bias=False),\n",
        "            nn.BatchNorm2d(8),\n",
        "            nn.Dropout(0.1),\n",
        "            nn.ReLU()\n",
        "        ) # output_size = 11\n",
        "\n",
        "        # CONVOLUTION BLOCK 2\n",
        "        self.convblock5 = nn.Sequential(\n",
        "            nn.Conv2d(in_channels=8, out_channels=16, kernel_size=(3, 3), padding=0, bias=False),\n",
        "            nn.BatchNorm2d(16),\n",
        "            nn.Dropout(0.1),\n",
        "            nn.ReLU()\n",
        "        ) # output_size = 9\n",
        "        self.convblock6 = nn.Sequential(\n",
        "            nn.Conv2d(in_channels=16, out_channels=16, kernel_size=(3, 3), padding=0, bias=False),\n",
        "            nn.BatchNorm2d(16),\n",
        "            nn.Dropout(0.1),\n",
        "            nn.ReLU()\n",
        "        ) # output_size = 7\n",
        "\n",
        "        # OUTPUT BLOCK\n",
        "        self.convblock7 = nn.Sequential(\n",
        "            nn.Conv2d(in_channels=16, out_channels=10, kernel_size=(1, 1), padding=0, bias=False),\n",
        "            nn.ReLU()\n",
        "        ) # output_size = 7\n",
        "        self.convblock8 = nn.Sequential(\n",
        "            nn.Conv2d(in_channels=10, out_channels=10, kernel_size=(7, 7), padding=0, bias=False),\n",
        "            # nn.ReLU() NEVER!\n",
        "        ) # output_size = 1\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = self.convblock1(x)\n",
        "        x = self.convblock2(x)\n",
        "        x = self.convblock3(x)\n",
        "        x = self.pool1(x)\n",
        "        x = self.convblock4(x)\n",
        "        x = self.convblock5(x)\n",
        "        x = self.convblock6(x)\n",
        "        x = self.convblock7(x)\n",
        "        x = self.convblock8(x)\n",
        "        x = x.view(-1, 10)\n",
        "        return F.log_softmax(x, dim=-1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M3-vp8X9LCWo",
        "colab_type": "text"
      },
      "source": [
        "# Model Params\n",
        "Can't emphasize on how important viewing Model Summary is. \n",
        "Unfortunately, there is no in-built model visualizer, so we have to take external help"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5skB97zIJQQe",
        "colab_type": "code",
        "outputId": "e246dd54-c4ce-4641-d8f4-fde8e6186053",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 748
        }
      },
      "source": [
        "!pip install torchsummary\n",
        "from torchsummary import summary\n",
        "use_cuda = torch.cuda.is_available()\n",
        "device = torch.device(\"cuda\" if use_cuda else \"cpu\")\n",
        "print(device)\n",
        "model = Net().to(device)\n",
        "summary(model, input_size=(1, 28, 28))"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: torchsummary in /usr/local/lib/python3.6/dist-packages (1.5.1)\n",
            "cuda\n",
            "----------------------------------------------------------------\n",
            "        Layer (type)               Output Shape         Param #\n",
            "================================================================\n",
            "            Conv2d-1            [-1, 8, 26, 26]              72\n",
            "       BatchNorm2d-2            [-1, 8, 26, 26]              16\n",
            "           Dropout-3            [-1, 8, 26, 26]               0\n",
            "              ReLU-4            [-1, 8, 26, 26]               0\n",
            "            Conv2d-5           [-1, 16, 24, 24]           1,152\n",
            "       BatchNorm2d-6           [-1, 16, 24, 24]              32\n",
            "           Dropout-7           [-1, 16, 24, 24]               0\n",
            "              ReLU-8           [-1, 16, 24, 24]               0\n",
            "            Conv2d-9           [-1, 32, 22, 22]           4,608\n",
            "      BatchNorm2d-10           [-1, 32, 22, 22]              64\n",
            "          Dropout-11           [-1, 32, 22, 22]               0\n",
            "             ReLU-12           [-1, 32, 22, 22]               0\n",
            "        MaxPool2d-13           [-1, 32, 11, 11]               0\n",
            "           Conv2d-14            [-1, 8, 11, 11]             256\n",
            "      BatchNorm2d-15            [-1, 8, 11, 11]              16\n",
            "          Dropout-16            [-1, 8, 11, 11]               0\n",
            "             ReLU-17            [-1, 8, 11, 11]               0\n",
            "           Conv2d-18             [-1, 16, 9, 9]           1,152\n",
            "      BatchNorm2d-19             [-1, 16, 9, 9]              32\n",
            "          Dropout-20             [-1, 16, 9, 9]               0\n",
            "             ReLU-21             [-1, 16, 9, 9]               0\n",
            "           Conv2d-22             [-1, 16, 7, 7]           2,304\n",
            "      BatchNorm2d-23             [-1, 16, 7, 7]              32\n",
            "          Dropout-24             [-1, 16, 7, 7]               0\n",
            "             ReLU-25             [-1, 16, 7, 7]               0\n",
            "           Conv2d-26             [-1, 10, 7, 7]             160\n",
            "             ReLU-27             [-1, 10, 7, 7]               0\n",
            "           Conv2d-28             [-1, 10, 1, 1]           4,900\n",
            "================================================================\n",
            "Total params: 14,796\n",
            "Trainable params: 14,796\n",
            "Non-trainable params: 0\n",
            "----------------------------------------------------------------\n",
            "Input size (MB): 0.00\n",
            "Forward/backward pass size (MB): 1.05\n",
            "Params size (MB): 0.06\n",
            "Estimated Total Size (MB): 1.11\n",
            "----------------------------------------------------------------\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1__x_SbrL7z3",
        "colab_type": "text"
      },
      "source": [
        "# Training and Testing\n",
        "\n",
        "Looking at logs can be boring, so we'll introduce **tqdm** progressbar to get cooler logs. \n",
        "\n",
        "Let's write train and test functions"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fbkF2nN_LYIb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from tqdm import tqdm\n",
        "\n",
        "train_losses = []\n",
        "test_losses = []\n",
        "train_acc = []\n",
        "test_acc = []\n",
        "\n",
        "def train(model, device, train_loader, optimizer, epoch):\n",
        "  model.train()\n",
        "  pbar = tqdm(train_loader)\n",
        "  correct = 0\n",
        "  processed = 0\n",
        "  for batch_idx, (data, target) in enumerate(pbar):\n",
        "    # get samples\n",
        "    data, target = data.to(device), target.to(device)\n",
        "\n",
        "    # Init\n",
        "    optimizer.zero_grad()\n",
        "    # In PyTorch, we need to set the gradients to zero before starting to do backpropragation because PyTorch accumulates the gradients on subsequent backward passes. \n",
        "    # Because of this, when you start your training loop, ideally you should zero out the gradients so that you do the parameter update correctly.\n",
        "\n",
        "    # Predict\n",
        "    y_pred = model(data)\n",
        "\n",
        "    # Calculate loss\n",
        "    loss = F.nll_loss(y_pred, target)\n",
        "    train_losses.append(loss)\n",
        "\n",
        "    # Backpropagation\n",
        "    loss.backward()\n",
        "    optimizer.step()\n",
        "\n",
        "    # Update pbar-tqdm\n",
        "    \n",
        "    pred = y_pred.argmax(dim=1, keepdim=True)  # get the index of the max log-probability\n",
        "    correct += pred.eq(target.view_as(pred)).sum().item()\n",
        "    processed += len(data)\n",
        "\n",
        "    pbar.set_description(desc= f'Loss={loss.item()} Batch_id={batch_idx} Accuracy={100*correct/processed:0.2f}')\n",
        "    train_acc.append(100*correct/processed)\n",
        "\n",
        "def test(model, device, test_loader):\n",
        "    model.eval()\n",
        "    test_loss = 0\n",
        "    correct = 0\n",
        "    with torch.no_grad():\n",
        "        for data, target in test_loader:\n",
        "            data, target = data.to(device), target.to(device)\n",
        "            output = model(data)\n",
        "            test_loss += F.nll_loss(output, target, reduction='sum').item()  # sum up batch loss\n",
        "            pred = output.argmax(dim=1, keepdim=True)  # get the index of the max log-probability\n",
        "            correct += pred.eq(target.view_as(pred)).sum().item()\n",
        "\n",
        "    test_loss /= len(test_loader.dataset)\n",
        "    test_losses.append(test_loss)\n",
        "\n",
        "    print('\\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.2f}%)\\n'.format(\n",
        "        test_loss, correct, len(test_loader.dataset),\n",
        "        100. * correct / len(test_loader.dataset)))\n",
        "    \n",
        "    test_acc.append(100. * correct / len(test_loader.dataset))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "drokW8wWODKq",
        "colab_type": "text"
      },
      "source": [
        "# Let's Train and test our model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xMCFxeAKOB53",
        "colab_type": "code",
        "outputId": "c71fe7c3-f6c4-4829-dd08-f7dfa17d68a1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "model =  Net().to(device)\n",
        "optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.9)\n",
        "scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=6, gamma=0.1)\n",
        "EPOCHS = 15\n",
        "for epoch in range(EPOCHS):\n",
        "    print(\"EPOCH:\", epoch)\n",
        "    train(model, device, train_loader, optimizer, epoch)\n",
        "    test(model, device, test_loader)\n",
        "    scheduler.step()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\r  0%|          | 0/469 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "EPOCH: 0\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Loss=0.11683563143014908 Batch_id=468 Accuracy=85.73: 100%|██████████| 469/469 [00:20<00:00, 23.01it/s]\n",
            "  0%|          | 0/469 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test set: Average loss: 0.1236, Accuracy: 9602/10000 (96.02%)\n",
            "\n",
            "EPOCH: 1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Loss=0.2384762167930603 Batch_id=468 Accuracy=95.80: 100%|██████████| 469/469 [00:19<00:00, 23.50it/s]\n",
            "  0%|          | 0/469 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test set: Average loss: 0.0869, Accuracy: 9725/10000 (97.25%)\n",
            "\n",
            "EPOCH: 2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Loss=0.23561756312847137 Batch_id=468 Accuracy=96.61: 100%|██████████| 469/469 [00:19<00:00, 30.72it/s]\n",
            "  0%|          | 0/469 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test set: Average loss: 0.0691, Accuracy: 9793/10000 (97.93%)\n",
            "\n",
            "EPOCH: 3\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Loss=0.13036887347698212 Batch_id=468 Accuracy=97.07: 100%|██████████| 469/469 [00:20<00:00, 23.08it/s]\n",
            "  0%|          | 0/469 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test set: Average loss: 0.0573, Accuracy: 9826/10000 (98.26%)\n",
            "\n",
            "EPOCH: 4\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Loss=0.0545179508626461 Batch_id=468 Accuracy=97.30: 100%|██████████| 469/469 [00:20<00:00, 22.98it/s]\n",
            "  0%|          | 0/469 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test set: Average loss: 0.0702, Accuracy: 9765/10000 (97.65%)\n",
            "\n",
            "EPOCH: 5\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Loss=0.08856606483459473 Batch_id=468 Accuracy=97.37: 100%|██████████| 469/469 [00:20<00:00, 22.89it/s]\n",
            "  0%|          | 0/469 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test set: Average loss: 0.0564, Accuracy: 9821/10000 (98.21%)\n",
            "\n",
            "EPOCH: 6\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Loss=0.09887372702360153 Batch_id=468 Accuracy=97.77: 100%|██████████| 469/469 [00:20<00:00, 23.09it/s]\n",
            "  0%|          | 0/469 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test set: Average loss: 0.0481, Accuracy: 9833/10000 (98.33%)\n",
            "\n",
            "EPOCH: 7\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Loss=0.04202224314212799 Batch_id=468 Accuracy=97.82: 100%|██████████| 469/469 [00:20<00:00, 23.14it/s]\n",
            "  0%|          | 0/469 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test set: Average loss: 0.0476, Accuracy: 9838/10000 (98.38%)\n",
            "\n",
            "EPOCH: 8\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Loss=0.035700514912605286 Batch_id=468 Accuracy=97.85: 100%|██████████| 469/469 [00:20<00:00, 23.38it/s]\n",
            "  0%|          | 0/469 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test set: Average loss: 0.0481, Accuracy: 9843/10000 (98.43%)\n",
            "\n",
            "EPOCH: 9\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Loss=0.059396374970674515 Batch_id=468 Accuracy=97.86: 100%|██████████| 469/469 [00:19<00:00, 23.49it/s]\n",
            "  0%|          | 0/469 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test set: Average loss: 0.0473, Accuracy: 9843/10000 (98.43%)\n",
            "\n",
            "EPOCH: 10\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Loss=0.08252434432506561 Batch_id=468 Accuracy=97.91: 100%|██████████| 469/469 [00:19<00:00, 23.48it/s]\n",
            "  0%|          | 0/469 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test set: Average loss: 0.0458, Accuracy: 9855/10000 (98.55%)\n",
            "\n",
            "EPOCH: 11\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Loss=0.03179042413830757 Batch_id=468 Accuracy=97.95: 100%|██████████| 469/469 [00:20<00:00, 23.36it/s]\n",
            "  0%|          | 0/469 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test set: Average loss: 0.0454, Accuracy: 9859/10000 (98.59%)\n",
            "\n",
            "EPOCH: 12\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Loss=0.098569355905056 Batch_id=468 Accuracy=97.97: 100%|██████████| 469/469 [00:20<00:00, 23.41it/s]\n",
            "  0%|          | 0/469 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test set: Average loss: 0.0473, Accuracy: 9842/10000 (98.42%)\n",
            "\n",
            "EPOCH: 13\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Loss=0.062221791595220566 Batch_id=468 Accuracy=97.97: 100%|██████████| 469/469 [00:20<00:00, 23.40it/s]\n",
            "  0%|          | 0/469 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test set: Average loss: 0.0468, Accuracy: 9849/10000 (98.49%)\n",
            "\n",
            "EPOCH: 14\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Loss=0.03684644773602486 Batch_id=468 Accuracy=97.96: 100%|██████████| 469/469 [00:20<00:00, 23.33it/s]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test set: Average loss: 0.0462, Accuracy: 9852/10000 (98.52%)\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "87RaqGSEOWDe",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# fig, axs = plt.subplots(2,2,figsize=(15,10))\n",
        "# axs[0, 0].plot(train_losses)\n",
        "# axs[0, 0].set_title(\"Training Loss\")\n",
        "# axs[1, 0].plot(train_acc)\n",
        "# axs[1, 0].set_title(\"Training Accuracy\")\n",
        "# axs[0, 1].plot(test_losses)\n",
        "# axs[0, 1].set_title(\"Test Loss\")\n",
        "# axs[1, 1].plot(test_acc)\n",
        "# axs[1, 1].set_title(\"Test Accuracy\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KYZsr1NLR2AF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}