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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/ram-kumar-m/EVA_4/blob/master/S5/EVA4S5_4.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": "UpB7lwAJ4iQ8",
        "colab_type": "text"
      },
      "source": [
        "Target:\n",
        "\n",
        "\n",
        "1. Add Dropout\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "Results: \n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "Parameters: 9.18k\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "Best Train Accuracy: 99.75 at 15th epoch\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "Best Test Accuracy: 99.16 at 11th epoch\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "Analysis:\n",
        "\n",
        "\n",
        "1.   The model is underfitting a bit.\n",
        "2.   Need to add GAP\n"
      ]
    },
    {
      "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.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.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"
      ]
    },
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      "metadata": {
        "id": "_4A84rlfDA23",
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            "b244701f981846eebc12c00f72baff56"
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      },
      "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": 0,
      "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"
        },
        {
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        {
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          "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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        },
        {
          "output_type": "stream",
          "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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          "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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          "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": "c48c1b88-495a-4292-bda3-e76c037e44d3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "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=8, 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": 0,
      "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",
        "outputId": "6b81f931-57f6-4c27-e30b-a79b41538afb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 495
        }
      },
      "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.numpy())\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')\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/torchvision/datasets/mnist.py:55: UserWarning: train_data has been renamed data\n",
            "  warnings.warn(\"train_data has been renamed data\")\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[Train]\n",
            " - Numpy Shape: (60000, 28, 28)\n",
            " - Tensor Shape: torch.Size([60000, 28, 28])\n",
            " - min: tensor(-0.4242)\n",
            " - max: tensor(2.8215)\n",
            " - mean: tensor(0.0009)\n",
            " - std: tensor(1.0000)\n",
            " - var: tensor(1.0001)\n",
            "torch.Size([128, 1, 28, 28])\n",
            "torch.Size([128])\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f92e014ca20>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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YgSAIOxAEYQeCIOxAEIQdCOL/AYXSPzsanQusAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "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",
        "outputId": "6faa18dc-65c8-416f-9eeb-536564fef0c0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 246
        }
      },
      "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": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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3N5dGjhyp0IpEllavXs1VMm8d+fn5Sob7/fffs+vT09PZazk+Pt5gOuTRZLrj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\ngg==\n",
            "text/plain": [
              "<Figure size 432x288 with 60 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "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": [
        "dropout_value = 0.1\n",
        "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=4, kernel_size=(3, 3), padding=0, bias=False),\n",
        "            nn.BatchNorm2d(4),\n",
        "            nn.ReLU(),\n",
        "            nn.Dropout(dropout_value)\n",
        "        ) # output_size = 26, RF = 3\n",
        "\n",
        "        # CONVOLUTION BLOCK 1\n",
        "        self.convblock2 = nn.Sequential(\n",
        "            nn.Conv2d(in_channels=4, out_channels=4, kernel_size=(3, 3), padding=0, bias=False),\n",
        "            nn.BatchNorm2d(4),\n",
        "            nn.ReLU(),\n",
        "            nn.Dropout(dropout_value)\n",
        "        ) # output_size = 24, RF = 5\n",
        "        self.convblock3 = nn.Sequential(\n",
        "            nn.Conv2d(in_channels=4, out_channels=8, kernel_size=(3, 3), padding=0, bias=False),\n",
        "            nn.BatchNorm2d(8),\n",
        "            nn.ReLU(),\n",
        "            nn.Dropout(dropout_value)\n",
        "\n",
        "        ) # output_size = 22, RF = 7\n",
        "\n",
        "        # TRANSITION BLOCK 1\n",
        "        self.pool1 = nn.MaxPool2d(2, 2) # output_size = 11, RF = 8\n",
        "        self.convblock4 = nn.Sequential(\n",
        "            nn.Conv2d(in_channels=8, out_channels=8, kernel_size=(1, 1), padding=0, bias=False),\n",
        "            nn.BatchNorm2d(8),\n",
        "            nn.ReLU(),\n",
        "            nn.Dropout(dropout_value)\n",
        "\n",
        "        ) # output_size = 11, RF = 8\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.ReLU(),\n",
        "            nn.Dropout(dropout_value)\n",
        "\n",
        "        ) # output_size = 9, RF = 12\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.ReLU(),\n",
        "            nn.Dropout(dropout_value)\n",
        "        ) # output_size = 7, RF = 16\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.BatchNorm2d(10),\n",
        "            nn.ReLU(),\n",
        "            nn.Dropout(dropout_value)\n",
        "\n",
        "        ) # output_size = 7, RF = 16\n",
        "        self.convblock8 = nn.Sequential(\n",
        "            nn.Conv2d(in_channels=10, out_channels=10, kernel_size=(7, 7), padding=0, bias=False),\n",
        "        ) # output_size = 1, RF = 28\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": "5c0c4359-27b0-460f-ef93-053af0a6d326",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 814
        }
      },
      "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": 0,
      "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, 4, 26, 26]              36\n",
            "       BatchNorm2d-2            [-1, 4, 26, 26]               8\n",
            "              ReLU-3            [-1, 4, 26, 26]               0\n",
            "           Dropout-4            [-1, 4, 26, 26]               0\n",
            "            Conv2d-5            [-1, 4, 24, 24]             144\n",
            "       BatchNorm2d-6            [-1, 4, 24, 24]               8\n",
            "              ReLU-7            [-1, 4, 24, 24]               0\n",
            "           Dropout-8            [-1, 4, 24, 24]               0\n",
            "            Conv2d-9            [-1, 8, 22, 22]             288\n",
            "      BatchNorm2d-10            [-1, 8, 22, 22]              16\n",
            "             ReLU-11            [-1, 8, 22, 22]               0\n",
            "          Dropout-12            [-1, 8, 22, 22]               0\n",
            "        MaxPool2d-13            [-1, 8, 11, 11]               0\n",
            "           Conv2d-14            [-1, 8, 11, 11]              64\n",
            "      BatchNorm2d-15            [-1, 8, 11, 11]              16\n",
            "             ReLU-16            [-1, 8, 11, 11]               0\n",
            "          Dropout-17            [-1, 8, 11, 11]               0\n",
            "           Conv2d-18             [-1, 16, 9, 9]           1,152\n",
            "      BatchNorm2d-19             [-1, 16, 9, 9]              32\n",
            "             ReLU-20             [-1, 16, 9, 9]               0\n",
            "          Dropout-21             [-1, 16, 9, 9]               0\n",
            "           Conv2d-22             [-1, 16, 7, 7]           2,304\n",
            "      BatchNorm2d-23             [-1, 16, 7, 7]              32\n",
            "             ReLU-24             [-1, 16, 7, 7]               0\n",
            "          Dropout-25             [-1, 16, 7, 7]               0\n",
            "           Conv2d-26             [-1, 10, 7, 7]             160\n",
            "      BatchNorm2d-27             [-1, 10, 7, 7]              20\n",
            "             ReLU-28             [-1, 10, 7, 7]               0\n",
            "          Dropout-29             [-1, 10, 7, 7]               0\n",
            "           Conv2d-30             [-1, 10, 1, 1]           4,900\n",
            "================================================================\n",
            "Total params: 9,180\n",
            "Trainable params: 9,180\n",
            "Non-trainable params: 0\n",
            "----------------------------------------------------------------\n",
            "Input size (MB): 0.00\n",
            "Forward/backward pass size (MB): 0.39\n",
            "Params size (MB): 0.04\n",
            "Estimated Total Size (MB): 0.42\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": "148a73e6-ac2d-4d07-ed7c-a1995772bc23",
        "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",
        "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)"
      ],
      "execution_count": 0,
      "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=0.0671854093670845 Batch_id=468 Accuracy=90.26: 100%|██████████| 469/469 [00:11<00:00, 40.95it/s]\n",
            "  0%|          | 0/469 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test set: Average loss: 0.0731, Accuracy: 9776/10000 (97.76%)\n",
            "\n",
            "EPOCH: 1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Loss=0.1275346279144287 Batch_id=468 Accuracy=97.17: 100%|██████████| 469/469 [00:11<00:00, 55.93it/s]\n",
            "  0%|          | 0/469 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test set: Average loss: 0.0446, Accuracy: 9855/10000 (98.55%)\n",
            "\n",
            "EPOCH: 2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Loss=0.07476679235696793 Batch_id=468 Accuracy=97.67: 100%|██████████| 469/469 [00:11<00:00, 41.36it/s]\n",
            "  0%|          | 0/469 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test set: Average loss: 0.0380, Accuracy: 9873/10000 (98.73%)\n",
            "\n",
            "EPOCH: 3\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Loss=0.06958319991827011 Batch_id=468 Accuracy=98.04: 100%|██████████| 469/469 [00:11<00:00, 42.05it/s]\n",
            "  0%|          | 0/469 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test set: Average loss: 0.0376, Accuracy: 9872/10000 (98.72%)\n",
            "\n",
            "EPOCH: 4\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Loss=0.03172748163342476 Batch_id=468 Accuracy=98.12: 100%|██████████| 469/469 [00:11<00:00, 40.68it/s]\n",
            "  0%|          | 0/469 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test set: Average loss: 0.0328, Accuracy: 9892/10000 (98.92%)\n",
            "\n",
            "EPOCH: 5\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Loss=0.012614801526069641 Batch_id=468 Accuracy=98.28: 100%|██████████| 469/469 [00:11<00:00, 41.49it/s]\n",
            "  0%|          | 0/469 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test set: Average loss: 0.0328, Accuracy: 9884/10000 (98.84%)\n",
            "\n",
            "EPOCH: 6\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Loss=0.09481721371412277 Batch_id=468 Accuracy=98.41: 100%|██████████| 469/469 [00:11<00:00, 42.10it/s]\n",
            "  0%|          | 0/469 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test set: Average loss: 0.0295, Accuracy: 9896/10000 (98.96%)\n",
            "\n",
            "EPOCH: 7\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Loss=0.020724406465888023 Batch_id=468 Accuracy=98.51: 100%|██████████| 469/469 [00:11<00:00, 41.19it/s]\n",
            "  0%|          | 0/469 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test set: Average loss: 0.0306, Accuracy: 9890/10000 (98.90%)\n",
            "\n",
            "EPOCH: 8\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Loss=0.05656299367547035 Batch_id=468 Accuracy=98.53: 100%|██████████| 469/469 [00:11<00:00, 41.43it/s]\n",
            "  0%|          | 0/469 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test set: Average loss: 0.0280, Accuracy: 9897/10000 (98.97%)\n",
            "\n",
            "EPOCH: 9\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Loss=0.04164441302418709 Batch_id=468 Accuracy=98.61: 100%|██████████| 469/469 [00:11<00:00, 40.64it/s]\n",
            "  0%|          | 0/469 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test set: Average loss: 0.0266, Accuracy: 9905/10000 (99.05%)\n",
            "\n",
            "EPOCH: 10\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Loss=0.07741354405879974 Batch_id=468 Accuracy=98.56: 100%|██████████| 469/469 [00:11<00:00, 40.64it/s]\n",
            "  0%|          | 0/469 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test set: Average loss: 0.0281, Accuracy: 9898/10000 (98.98%)\n",
            "\n",
            "EPOCH: 11\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Loss=0.007275452371686697 Batch_id=468 Accuracy=98.63: 100%|██████████| 469/469 [00:11<00:00, 41.95it/s]\n",
            "  0%|          | 0/469 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test set: Average loss: 0.0229, Accuracy: 9916/10000 (99.16%)\n",
            "\n",
            "EPOCH: 12\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Loss=0.17290759086608887 Batch_id=468 Accuracy=98.69: 100%|██████████| 469/469 [00:11<00:00, 42.04it/s]\n",
            "  0%|          | 0/469 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test set: Average loss: 0.0240, Accuracy: 9913/10000 (99.13%)\n",
            "\n",
            "EPOCH: 13\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Loss=0.019351720809936523 Batch_id=468 Accuracy=98.72: 100%|██████████| 469/469 [00:11<00:00, 40.52it/s]\n",
            "  0%|          | 0/469 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test set: Average loss: 0.0242, Accuracy: 9912/10000 (99.12%)\n",
            "\n",
            "EPOCH: 14\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Loss=0.008858054876327515 Batch_id=468 Accuracy=98.75: 100%|██████████| 469/469 [00:11<00:00, 42.12it/s]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test set: Average loss: 0.0241, Accuracy: 9908/10000 (99.08%)\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bThPVxrz92wW",
        "colab_type": "code",
        "outputId": "a796684a-6247-4eee-957a-caedd320d5dc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        }
      },
      "source": [
        "!nvidia-smi"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Sat Feb 22 15:21:08 2020       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 440.48.02    Driver Version: 418.67       CUDA Version: 10.1     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla P4            Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   45C    P0    23W /  75W |    595MiB /  7611MiB |      0%      Default |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                       GPU Memory |\n",
            "|  GPU       PID   Type   Process name                             Usage      |\n",
            "|=============================================================================|\n",
            "+-----------------------------------------------------------------------------+\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "87RaqGSEOWDe",
        "colab_type": "code",
        "outputId": "e0142fbf-4cb9-44da-ad29-def7c023b1e5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 625
        }
      },
      "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": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Test Accuracy')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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P0PBIERGRMA3o2ZnUzolkaV6biEjc8kzSpuGRIiIi4TOzQDES9bSJiMQtzyRtGh4pIiLS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\nFm2SJEmS1MEs2iRJkiSpg/1/yKD9+xqoIwMAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1080x720 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}