05 - Coding Drill Down/EVA4S5 - Step 3.ipynb

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      "cell_type": "markdown",
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      },
      "source": [
        "# Step 3\n",
        "\n",
        "### Target\n",
        "- Apply Batch Normalization to increase model accuracy\n",
        "\n",
        "### Result\n",
        "- Parameters: 9,142\n",
        "- Best Training Accuracy: 99.38\n",
        "- Best Test Accuracy: 99.35\n",
        "\n",
        "### Analysis\n",
        "- Good Model, very close to the target accuracy\n",
        "- The model is not very robust as the test accuracy keeps on fluctuating\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Rg1ZVZ8xHEXI",
        "colab_type": "text"
      },
      "source": [
        "# Installing Packages\n",
        "The following packages need to be installed before running the code below\n",
        "- torchsummary"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zMOoKk0OHEqe",
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "!pip install torchsummary"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: torchsummary in /usr/local/lib/python3.6/dist-packages (1.5.1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aO-7t1Y7-hV4",
        "colab_type": "text"
      },
      "source": [
        "# Imports\n",
        "Importing necessary packages"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8kH16rnZ7wt_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from __future__ import print_function\n",
        "\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "import torch.optim as optim\n",
        "\n",
        "from tqdm import tqdm\n",
        "from torchsummary import summary\n",
        "from torchvision import datasets, transforms"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OfOSzHr1GW2c",
        "colab_type": "text"
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      "source": [
        "# Dataset Preparation and Loading\n",
        "The following steps are performed for preparing the dataset for the model.\n",
        "- Downloading the MNIST dataset\n",
        "- Visualizing the dataset and the data statistics\n",
        "- Defining data transformations\n",
        "- Splitting the dataset into train and test set\n",
        "- Creating data loader for train and test set"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ffnfhydPLQk5",
        "colab_type": "text"
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      "source": [
        "## Data Visualization and Statistics\n",
        "Let's see how our data looks like and what are the mean and standard deviation values of the dataset. This information will help us decide the transformations that can be used on the dataset."
      ]
    },
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      "cell_type": "code",
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      "source": [
        "# Download data\n",
        "sample_data = datasets.MNIST('./data', train=True, download=True).data\n",
        "\n",
        "# Setti32ng the values in the data to be within the range [0, 1]\n",
        "sample_data = sample_data.numpy() / 255\n",
        "\n",
        "# Display some data statistics\n",
        "print('Data Statistics:')\n",
        "print(' - Data Shape:', sample_data.shape)\n",
        "print(' - min:', np.min(sample_data))\n",
        "print(' - max:', np.max(sample_data))\n",
        "print(' - mean: %.4f' % np.mean(sample_data))\n",
        "print(' - std: %.4f' % np.std(sample_data))\n",
        "print(' - var: %.4f\\n' % np.var(sample_data))\n",
        "\n",
        "# Visualize a sample from the data\n",
        "plt.imshow(sample_data[0], cmap='gray_r')"
      ],
      "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"
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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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          "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"
          ],
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            "\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"
          ],
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          "text": [
            "\n",
            "Extracting ./data/MNIST/raw/t10k-labels-idx1-ubyte.gz to ./data/MNIST/raw\n",
            "Processing...\n",
            "Done!\n",
            "Data Statistics:\n",
            " - Data Shape: (60000, 28, 28)\n",
            " - min: 0.0\n",
            " - max: 1.0\n",
            " - mean: 0.1307\n",
            " - std: 0.3081\n",
            " - var: 0.0949\n",
            "\n"
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              "<matplotlib.image.AxesImage at 0x7f3b4139d400>"
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          "data": {
            "image/png": 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GSpqt9luKepukRdntRZJeL3CWH2iXZbyrLTOugh+7wpc/d/eWf0i6W32vyH8maVURM1SZ\n65eS/jf7OFD0bJI2qe9p3Xfqe23jYUn/JmmnpEOS3pI0po1m+29JH0rqUV+xOgua7Rb1PUXvkbQv\n+7i76McuMVdLHjd+XRYIghfogCAoOxAEZQeCoOxAEJQdCIKyA0FQdiCI/wfvpjt5Q0mdXQAAAABJ\nRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7tClldqIR0zA",
        "colab_type": "text"
      },
      "source": [
        "Let's view some more images. This will help in getting any ideas for data augmentation later on"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k7TxQifTMYLC",
        "colab_type": "code",
        "outputId": "5dd65fd0-4913-447b-dd0b-f0e9e6e98642",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 245
        }
      },
      "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(sample_data[index], cmap='gray_r')"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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FXrhwwVB/ZZM8jGXSpEnisTFi9GZ1HqYF3c2bN1MQBHp5eVWb4+LFi8XhwTX5lNJgraCr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\nOejKyMjI2BA56MrIyMjYkP8DvQJsBCXRP4sAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 60 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "grHT765783fO",
        "colab_type": "text"
      },
      "source": [
        "## Data Transformations\n",
        "\n",
        "The following transformations will be used\n",
        "- ToTensor\n",
        "- Normalize"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BSpchiU79LnW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Train phase transformations\n",
        "train_transforms = transforms.Compose([\n",
        "    # convert the data to torch.FloatTensor with values within the range [0.0 ,1.0]\n",
        "    transforms.ToTensor(),\n",
        "\n",
        "    # normalize the data with mean and standard deviation\n",
        "    # these values were obtained from the data statistics above\n",
        "    transforms.Normalize((0.1307,), (0.3081,))\n",
        "])\n",
        "\n",
        "# Test phase transformations\n",
        "test_transforms = transforms.Compose([\n",
        "    transforms.ToTensor(),\n",
        "    transforms.Normalize((0.1307,), (0.3081,))\n",
        "])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1g-Wd56S-3tg",
        "colab_type": "text"
      },
      "source": [
        "## Train Data and Test Data Split\n",
        "The data is downloaded and split into two sets: train and test"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NHJkLW3H_DZb",
        "colab_type": "code",
        "colab": {}
      },
      "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": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qFcWRAeY_MsB",
        "colab_type": "text"
      },
      "source": [
        "## Train and Test Dataloaders\n",
        "This is the final step in data preparation. It sets the dataloader arguments and then creates the dataloader"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uT2fytsOGNV2",
        "colab_type": "code",
        "outputId": "7255ecb0-224c-4cd8-97ef-b5f5db2b3948",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "SEED = 1\n",
        "\n",
        "cuda = torch.cuda.is_available()\n",
        "print('CUDA Available?', cuda)\n",
        "\n",
        "# For reproducibility of results\n",
        "torch.manual_seed(SEED)\n",
        "if cuda:\n",
        "    torch.cuda.manual_seed(SEED)\n",
        "\n",
        "# dataloader arguments\n",
        "dataloader_args = dict(shuffle=True, batch_size=64, num_workers=4, pin_memory=True) if cuda else dict(shuffle=True, batch_size=32)\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": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "CUDA Available? True\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HSeDIqBq-6pC",
        "colab_type": "text"
      },
      "source": [
        "# Model Architecture\n",
        "Designing the model structure"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f-fLk9QHJGFt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class Net(nn.Module):\n",
        "    def __init__(self):\n",
        "        \"\"\" This function instantiates all the model layers \"\"\"\n",
        "        super(Net, self).__init__()\n",
        "\n",
        "        self.convblock1 = nn.Sequential(\n",
        "            nn.Conv2d(in_channels=1, out_channels=8, kernel_size=3),\n",
        "            nn.ReLU(),\n",
        "            nn.BatchNorm2d(8)\n",
        "        )  # Input: 28x28x1 | Output: 26x26x8 | RF: 3x3\n",
        "\n",
        "        self.convblock2 = nn.Sequential(\n",
        "            nn.Conv2d(in_channels=8, out_channels=8, kernel_size=3),\n",
        "            nn.ReLU(),\n",
        "            nn.BatchNorm2d(8)\n",
        "        )  # Input: 26x26x8 | Output: 24x24x8 | RF: 5x5\n",
        "\n",
        "        self.convblock3 = nn.Sequential(\n",
        "            nn.Conv2d(in_channels=8, out_channels=16, kernel_size=3),\n",
        "            nn.ReLU(),\n",
        "            nn.BatchNorm2d(16)\n",
        "        )  # Input: 24x24x8 | Output: 22x22x16 | RF: 7x7\n",
        "\n",
        "        self.convblock4 = nn.Sequential(\n",
        "            nn.Conv2d(in_channels=16, out_channels=16, kernel_size=3),\n",
        "            nn.ReLU(),\n",
        "            nn.BatchNorm2d(16)\n",
        "        )  # Input: 22x22x16 | Output: 20x20x16 | RF: 9x9\n",
        "\n",
        "        self.pool = nn.MaxPool2d(2, 2)  # Input: 20x20x16 | Output: 10x10x16 | RF: 10x10\n",
        "\n",
        "        self.convblock5 = nn.Sequential(\n",
        "            nn.Conv2d(in_channels=16, out_channels=16, kernel_size=3),\n",
        "            nn.ReLU(),\n",
        "            nn.BatchNorm2d(16)\n",
        "        )  # Input: 10x10x16 | Output: 8x8x16 | RF: 14x14\n",
        "\n",
        "        self.convblock6 = nn.Sequential(\n",
        "            nn.Conv2d(in_channels=16, out_channels=16, kernel_size=3),\n",
        "            nn.ReLU(),\n",
        "            nn.BatchNorm2d(16)\n",
        "        )  # Input: 8x8x16 | Output: 6x6x16 | RF: 18x18\n",
        "\n",
        "        self.convblock7 = nn.Sequential(\n",
        "            nn.Conv2d(in_channels=16, out_channels=10, kernel_size=1),\n",
        "            nn.ReLU(),\n",
        "            nn.BatchNorm2d(10)\n",
        "        )  # Input: 6x6x16 | Output: 6x6x10 | RF: 18x18\n",
        "\n",
        "        self.gap = nn.Sequential(\n",
        "            nn.AdaptiveAvgPool2d(1)\n",
        "        )  # Input: 6x6x10 | Output: 1x1x10 | RF: 28x28\n",
        "    \n",
        "    def forward(self, x):\n",
        "        \"\"\" This function defines the network structure \"\"\"\n",
        "        x = self.convblock1(x)\n",
        "        x = self.convblock2(x)\n",
        "        x = self.convblock3(x)\n",
        "        x = self.convblock4(x)\n",
        "        x = self.pool(x)\n",
        "        x = self.convblock5(x)\n",
        "        x = self.convblock6(x)\n",
        "        x = self.convblock7(x)\n",
        "        x = self.gap(x)\n",
        "        x = x.view(-1, 10)\n",
        "        return F.log_softmax(x, dim=-1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-yrNmc72D5yE",
        "colab_type": "text"
      },
      "source": [
        "## Model Parameters\n",
        "Let's see the model summary"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FJGvqMnvFvLb",
        "colab_type": "code",
        "outputId": "59121068-cbd0-47f3-d504-9afa9a0521c9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 674
        }
      },
      "source": [
        "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": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "cuda\n",
            "----------------------------------------------------------------\n",
            "        Layer (type)               Output Shape         Param #\n",
            "================================================================\n",
            "            Conv2d-1            [-1, 8, 26, 26]              80\n",
            "              ReLU-2            [-1, 8, 26, 26]               0\n",
            "       BatchNorm2d-3            [-1, 8, 26, 26]              16\n",
            "            Conv2d-4            [-1, 8, 24, 24]             584\n",
            "              ReLU-5            [-1, 8, 24, 24]               0\n",
            "       BatchNorm2d-6            [-1, 8, 24, 24]              16\n",
            "            Conv2d-7           [-1, 16, 22, 22]           1,168\n",
            "              ReLU-8           [-1, 16, 22, 22]               0\n",
            "       BatchNorm2d-9           [-1, 16, 22, 22]              32\n",
            "           Conv2d-10           [-1, 16, 20, 20]           2,320\n",
            "             ReLU-11           [-1, 16, 20, 20]               0\n",
            "      BatchNorm2d-12           [-1, 16, 20, 20]              32\n",
            "        MaxPool2d-13           [-1, 16, 10, 10]               0\n",
            "           Conv2d-14             [-1, 16, 8, 8]           2,320\n",
            "             ReLU-15             [-1, 16, 8, 8]               0\n",
            "      BatchNorm2d-16             [-1, 16, 8, 8]              32\n",
            "           Conv2d-17             [-1, 16, 6, 6]           2,320\n",
            "             ReLU-18             [-1, 16, 6, 6]               0\n",
            "      BatchNorm2d-19             [-1, 16, 6, 6]              32\n",
            "           Conv2d-20             [-1, 10, 6, 6]             170\n",
            "             ReLU-21             [-1, 10, 6, 6]               0\n",
            "      BatchNorm2d-22             [-1, 10, 6, 6]              20\n",
            "AdaptiveAvgPool2d-23             [-1, 10, 1, 1]               0\n",
            "================================================================\n",
            "Total params: 9,142\n",
            "Trainable params: 9,142\n",
            "Non-trainable params: 0\n",
            "----------------------------------------------------------------\n",
            "Input size (MB): 0.00\n",
            "Forward/backward pass size (MB): 0.61\n",
            "Params size (MB): 0.03\n",
            "Estimated Total Size (MB): 0.65\n",
            "----------------------------------------------------------------\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HcyKrcQEGcdp",
        "colab_type": "text"
      },
      "source": [
        "# Model Training and Evaluation\n",
        "Training the model and testing it on the test dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1N1CK2-eF1Y7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_losses, train_accuracies = [], []\n",
        "test_losses, test_accuracies = [], []\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",
        "        # Set gradients to zero before starting backpropagation\n",
        "        optimizer.zero_grad()\n",
        "\n",
        "        # Predict output\n",
        "        y_pred = model(data)\n",
        "\n",
        "        # Calculate loss\n",
        "        loss = F.nll_loss(y_pred, target)\n",
        "        train_losses.append(loss)\n",
        "\n",
        "        # Perform backpropagation\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "\n",
        "        # Update Progress Bar\n",
        "        pred = y_pred.argmax(dim=1, keepdim=True)\n",
        "        correct += pred.eq(target.view_as(pred)).sum().item()\n",
        "        processed += len(data)\n",
        "        train_accuracies.append(100 * correct / processed)\n",
        "        pbar.set_description(desc=f'Loss={loss.item():0.2f} Batch_ID={batch_idx} Accuracy={train_accuracies[-1]:.2f}')\n",
        "\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",
        "    test_accuracies.append(100. * correct / len(test_loader.dataset))\n",
        "\n",
        "    print(f'\\nTest set: Average loss: {test_loss:.4f}, Accuracy: {correct}/{len(test_loader.dataset)} ({test_accuracies[-1]:.2f}%)\\n')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vddk65TZGeZx",
        "colab_type": "code",
        "outputId": "5a7e6ea2-32d6-458f-c946-6580dfd5abc4",
        "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",
        "\n",
        "for epoch in range(1, epochs + 1):\n",
        "    print(f'Epoch {epoch}:')\n",
        "    train(model, device, train_loader, optimizer, epoch)\n",
        "    test(model, device, test_loader)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\r  0%|          | 0/938 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 1:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Loss=0.24 Batch_ID=937 Accuracy=94.03: 100%|██████████| 938/938 [00:16<00:00, 57.87it/s]\n",
            "  0%|          | 0/938 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test set: Average loss: 0.0792, Accuracy: 9845/10000 (98.45%)\n",
            "\n",
            "Epoch 2:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Loss=0.09 Batch_ID=937 Accuracy=97.98: 100%|██████████| 938/938 [00:16<00:00, 56.21it/s]\n",
            "  0%|          | 0/938 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test set: Average loss: 0.0548, Accuracy: 9876/10000 (98.76%)\n",
            "\n",
            "Epoch 3:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Loss=0.02 Batch_ID=937 Accuracy=98.41: 100%|██████████| 938/938 [00:18<00:00, 51.76it/s]\n",
            "  0%|          | 0/938 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test set: Average loss: 0.0507, Accuracy: 9877/10000 (98.77%)\n",
            "\n",
            "Epoch 4:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Loss=0.02 Batch_ID=937 Accuracy=98.66: 100%|██████████| 938/938 [00:17<00:00, 54.83it/s]\n",
            "  0%|          | 0/938 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test set: Average loss: 0.0332, Accuracy: 9918/10000 (99.18%)\n",
            "\n",
            "Epoch 5:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Loss=0.06 Batch_ID=937 Accuracy=98.78: 100%|██████████| 938/938 [00:16<00:00, 64.85it/s]\n",
            "  0%|          | 0/938 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test set: Average loss: 0.0338, Accuracy: 9905/10000 (99.05%)\n",
            "\n",
            "Epoch 6:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Loss=0.01 Batch_ID=937 Accuracy=98.85: 100%|██████████| 938/938 [00:17<00:00, 52.60it/s]\n",
            "  0%|          | 0/938 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test set: Average loss: 0.0339, Accuracy: 9910/10000 (99.10%)\n",
            "\n",
            "Epoch 7:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Loss=0.04 Batch_ID=937 Accuracy=99.03: 100%|██████████| 938/938 [00:16<00:00, 56.58it/s]\n",
            "  0%|          | 0/938 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test set: Average loss: 0.0311, Accuracy: 9913/10000 (99.13%)\n",
            "\n",
            "Epoch 8:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Loss=0.11 Batch_ID=937 Accuracy=99.06: 100%|██████████| 938/938 [00:16<00:00, 56.78it/s]\n",
            "  0%|          | 0/938 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test set: Average loss: 0.0249, Accuracy: 9930/10000 (99.30%)\n",
            "\n",
            "Epoch 9:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Loss=0.02 Batch_ID=937 Accuracy=99.05: 100%|██████████| 938/938 [00:16<00:00, 55.28it/s]\n",
            "  0%|          | 0/938 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test set: Average loss: 0.0247, Accuracy: 9933/10000 (99.33%)\n",
            "\n",
            "Epoch 10:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Loss=0.04 Batch_ID=937 Accuracy=99.14: 100%|██████████| 938/938 [00:16<00:00, 57.88it/s]\n",
            "  0%|          | 0/938 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test set: Average loss: 0.0249, Accuracy: 9927/10000 (99.27%)\n",
            "\n",
            "Epoch 11:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Loss=0.01 Batch_ID=937 Accuracy=99.20: 100%|██████████| 938/938 [00:15<00:00, 59.74it/s]\n",
            "  0%|          | 0/938 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test set: Average loss: 0.0228, Accuracy: 9935/10000 (99.35%)\n",
            "\n",
            "Epoch 12:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Loss=0.01 Batch_ID=937 Accuracy=99.24: 100%|██████████| 938/938 [00:15<00:00, 59.60it/s]\n",
            "  0%|          | 0/938 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test set: Average loss: 0.0281, Accuracy: 9921/10000 (99.21%)\n",
            "\n",
            "Epoch 13:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Loss=0.01 Batch_ID=937 Accuracy=99.30: 100%|██████████| 938/938 [00:16<00:00, 58.20it/s]\n",
            "  0%|          | 0/938 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test set: Average loss: 0.0248, Accuracy: 9927/10000 (99.27%)\n",
            "\n",
            "Epoch 14:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Loss=0.01 Batch_ID=937 Accuracy=99.28: 100%|██████████| 938/938 [00:16<00:00, 58.30it/s]\n",
            "  0%|          | 0/938 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test set: Average loss: 0.0249, Accuracy: 9935/10000 (99.35%)\n",
            "\n",
            "Epoch 15:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Loss=0.00 Batch_ID=937 Accuracy=99.38: 100%|██████████| 938/938 [00:15<00:00, 61.05it/s]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test set: Average loss: 0.0226, Accuracy: 9926/10000 (99.26%)\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2o99Jq1XTdo-",
        "colab_type": "text"
      },
      "source": [
        "## Result Analysis\n",
        "Plotting various metrics obtained during training and testing and analyzing them for futher improvement"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0X1Y72EbGgpr",
        "colab_type": "code",
        "outputId": "8d52f94b-92fe-4bf1-ac29-907c6b43613e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 624
        }
      },
      "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_accuracies)\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_accuracies)\n",
        "axs[1, 1].set_title(\"Test Accuracy\")"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Test Accuracy')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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DOr4cNib/q2Z2p5nd6bWZDGQBG4DngLsiV27NtBCJiIg0F2Z2NlDonFtZW5tITiFoiMp5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ipvOkv+vxP4qYh4DzAXWATcDSyJiO6it+1i4MXph3l2NXvaJElqms3bB1i/ZhnnL5hTdiiSVHnn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\n",
            "text/plain": [
              "<Figure size 1080x720 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-NXcw_YvVk0J",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}