05b_cifar10_resnet.ipynb

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        "<a href=\"https://colab.research.google.com/github/SubhashKrYadav/RoadToLearnDeepLearning/blob/main/05b_cifar10_resnet.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "cell_type": "markdown",
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        "id": "3rArSzAI3KUV"
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      "source": [
        "# Classifying CIFAR10 images using ResNets, Regularization and Data Augmentation in PyTorch\n",
        "\n",
        "_A.K.A. Training an image classifier from scratch to over 90% accuracy in less than 5 minutes on a single GPU_\n",
        "\n",
        "### Part 6 of \"Deep Learning with Pytorch: Zero to GANs\"\n",
        "\n",
        "This tutorial series is a hands-on beginner-friendly introduction to deep learning using [PyTorch](https://pytorch.org), an open-source neural networks library. These tutorials take a practical and coding-focused approach. The best way to learn the material is to execute the code and experiment with it yourself. Check out the full series here:\n",
        "\n",
        "1. [PyTorch Basics: Tensors & Gradients](https://jovian.ai/aakashns/01-pytorch-basics)\n",
        "2. [Gradient Descent & Linear Regression](https://jovian.ai/aakashns/02-linear-regression)\n",
        "3. [Working with Images & Logistic Regression](https://jovian.ai/aakashns/03-logistic-regression)\n",
        "4. [Training Deep Neural Networks on a GPU](https://jovian.ai/aakashns/04-feedforward-nn)\n",
        "5. [Image Classification using Convolutional Neural Networks](https://jovian.ai/aakashns/05-cifar10-cnn)\n",
        "6. [Data Augmentation, Regularization and ResNets](https://jovian.ai/aakashns/05b-cifar10-resnet)\n",
        "7. [Generating Images using Generative Adversarial Networks](https://jovian.ai/aakashns/06b-anime-dcgan/)\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t4eFwnWk5RoJ"
      },
      "source": [
        "In this tutorial, we'll use the following techniques to train a state-of-the-art model in less than 5 minutes to achieve over 90% accuracy in  classifying images from the CIFAR10 dataset:\n",
        "\n",
        "- Data normalization\n",
        "- Data augmentation\n",
        "- Residual connections\n",
        "- Batch normalization\n",
        "- Learning rate scheduling\n",
        "- Weight Decay\n",
        "- Gradient clipping\n",
        "- Adam optimizer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "ZSdBL2673KUX"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "import torch\n",
        "import torchvision\n",
        "import tarfile\n",
        "import torch.nn as nn\n",
        "import numpy as np\n",
        "import torch.nn.functional as F\n",
        "from torchvision.datasets.utils import download_url\n",
        "from torchvision.datasets import ImageFolder\n",
        "from torch.utils.data import DataLoader\n",
        "import torchvision.transforms as tt\n",
        "from torch.utils.data import random_split\n",
        "from torchvision.utils import make_grid\n",
        "import matplotlib\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "\n",
        "matplotlib.rcParams['figure.facecolor'] = '#ffffff'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "rt4FEe853KUX"
      },
      "outputs": [],
      "source": [
        "project_name='05b-cifar10-resnet'"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hKeo3ZT43KUX"
      },
      "source": [
        "## Preparing the CIFAR10 Dataset\n",
        "\n",
        "This notebook is an extension to the tutorial [Image Classification using CNNs in PyTorch](https://jovian.ai/aakashns/05-cifar10-cnn), where we trained a deep convolutional neural network to classify images from the CIFAR10 dataset with around 75% accuracy. Here are some images from the dataset:\n",
        "\n",
        "![cifar10](https://miro.medium.com/max/709/1*LyV7_xga4jUHdx4_jHk1PQ.png)\n",
        "\n",
        "\n",
        "Let's begin by downloading the dataset and creating PyTorch datasets to load the data, just as we did in the previous tutorial."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rbNcpumM3KUX",
        "outputId": "a8d5b469-a577-424a-f97b-e9afb18106a2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading https://s3.amazonaws.com/fast-ai-imageclas/cifar10.tgz to ./cifar10.tgz\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 135107811/135107811 [00:03<00:00, 36487064.91it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['test', 'train']\n",
            "['ship', 'airplane', 'horse', 'frog', 'cat', 'bird', 'automobile', 'dog', 'deer', 'truck']\n"
          ]
        }
      ],
      "source": [
        "from torchvision.datasets.utils import download_url\n",
        "\n",
        "\n",
        "# Dowload the dataset\n",
        "dataset_url = \"https://s3.amazonaws.com/fast-ai-imageclas/cifar10.tgz\"\n",
        "download_url(dataset_url, '.')\n",
        "\n",
        "# Extract from archive\n",
        "with tarfile.open('./cifar10.tgz', 'r:gz') as tar:\n",
        "    tar.extractall(path='./data')\n",
        "\n",
        "# Look into the data directory\n",
        "data_dir = './data/cifar10'\n",
        "print(os.listdir(data_dir))\n",
        "classes = os.listdir(data_dir + \"/train\")\n",
        "print(classes)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q0e9CopR7yJU"
      },
      "source": [
        "We can create training and validation datasets using the `ImageFolder` class from `torchvision`. In addition to the `ToTensor` transform, we'll also apply some other transforms to the images. There are a few important changes we'll make while creating PyTorch datasets for training and validation:\n",
        "\n",
        "1. **Use test set for validation**: Instead of setting aside a fraction (e.g. 10%) of the data from the training set for validation, we'll simply use the test set as our validation set. This just gives a little more data to train with. In general, once you have picked the best model architecture & hypeparameters using a fixed validation set, it is a good idea to retrain the same model on the entire dataset just to give it a small final boost in performance.\n",
        "2. **Channel-wise data normalization**: We will normalize the image tensors by subtracting the mean and dividing by the standard deviation across each channel. As a result, the mean of the data across each channel is 0, and standard deviation is 1. Normalizing the data prevents the values from any one channel from disproportionately affecting the losses and gradients while training, simply by having a higher or wider range of values that others.\n",
        "\n",
        "<img src=\"https://i.imgur.com/LYxXBVg.png\" width=\"360\">\n",
        "\n",
        "\n",
        "3. **Randomized data augmentations**: We will apply randomly chosen transformations while loading images from the training dataset. Specifically, we will pad each image by 4 pixels, and then take a random crop of size 32 x 32 pixels, and then flip the image horizontally with a 50% probability. Since the transformation will be applied randomly and dynamically each time a particular image is loaded, the model sees slightly different images in each epoch of training, which allows it generalize better.\n",
        "\n",
        "![data-augmentation](https://imgaug.readthedocs.io/en/latest/_images/cropandpad_percent.jpg)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "TVz0gcdX3KUZ"
      },
      "outputs": [],
      "source": [
        "# Data transforms (normalization & data augmentation)\n",
        "stats = ((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010))\n",
        "train_tfms = tt.Compose([tt.RandomCrop(32, padding=4, padding_mode='reflect'),\n",
        "                         tt.RandomHorizontalFlip(),\n",
        "                         # tt.RandomRotate\n",
        "                         # tt.RandomResizedCrop(256, scale=(0.5,0.9), ratio=(1, 1)),\n",
        "                         # tt.ColorJitter(brightness=0.1, contrast=0.1, saturation=0.1, hue=0.1),\n",
        "                         tt.ToTensor(),\n",
        "                         tt.Normalize(*stats,inplace=True)])\n",
        "valid_tfms = tt.Compose([tt.ToTensor(), tt.Normalize(*stats)])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "Rnb8LqXT3KUZ"
      },
      "outputs": [],
      "source": [
        "# PyTorch datasets\n",
        "train_ds = ImageFolder(data_dir+'/train', train_tfms)\n",
        "valid_ds = ImageFolder(data_dir+'/test', valid_tfms)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RPWrNg383KUa"
      },
      "source": [
        "Next, we can create data loaders for retrieving images in batches. We'll use a relatively large batch size of 500 to utlize a larger portion of the GPU RAM. You can try reducing the batch size & restarting the kernel if you face an \"out of memory\" error."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "k8fLEcj73KUa"
      },
      "outputs": [],
      "source": [
        "batch_size = 400"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "kPY8yg543KUa",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9bd41d32-0af9-4b89-85f5-7e26f29ee047"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py:557: UserWarning: This DataLoader will create 3 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  warnings.warn(_create_warning_msg(\n"
          ]
        }
      ],
      "source": [
        "# PyTorch data loaders\n",
        "train_dl = DataLoader(train_ds, batch_size, shuffle=True, num_workers=3, pin_memory=True)\n",
        "valid_dl = DataLoader(valid_ds, batch_size*2, num_workers=3, pin_memory=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IyTuz4OV3KUa"
      },
      "source": [
        "Let's take a look at some sample images from the training dataloader. To display the images, we'll need to _denormalize_ the pixels values to bring them back into the range `(0,1)`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "LaBjAspF3KUb"
      },
      "outputs": [],
      "source": [
        "def denormalize(images, means, stds):\n",
        "    means = torch.tensor(means).reshape(1, 3, 1, 1)\n",
        "    stds = torch.tensor(stds).reshape(1, 3, 1, 1)\n",
        "    return images * stds + means\n",
        "\n",
        "def show_batch(dl):\n",
        "    for images, labels in dl:\n",
        "        fig, ax = plt.subplots(figsize=(12, 12))\n",
        "        ax.set_xticks([]); ax.set_yticks([])\n",
        "        denorm_images = denormalize(images, *stats)\n",
        "        ax.imshow(make_grid(denorm_images[:64], nrow=8).permute(1, 2, 0).clamp(0,1))\n",
        "        break"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 961
        },
        "id": "3gTy0EMt3KUb",
        "outputId": "a650ef0b-764e-4e9c-c369-ef05438bdd87"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x1200 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "show_batch(train_dl)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kxOIPu8l3KUc"
      },
      "source": [
        "The colors seem out of place because of the normalization. Note that normalization is also applied during inference. If you look closely, you can see the cropping and reflection padding in some of the images. Horizontal flip is a bit difficult to detect from visual inspection.\n",
        "\n",
        "## Using a GPU\n",
        "\n",
        "To seamlessly use a GPU, if one is available, we define a couple of helper functions (`get_default_device` & `to_device`) and a helper class `DeviceDataLoader` to move our model & data to the GPU as required. These are described in more detail in a [previous tutorial](https://jovian.ml/aakashns/04-feedforward-nn#C21)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "x1Cc0r9p3KUc"
      },
      "outputs": [],
      "source": [
        "def get_default_device():\n",
        "    \"\"\"Pick GPU if available, else CPU\"\"\"\n",
        "    if torch.cuda.is_available():\n",
        "        return torch.device('cuda')\n",
        "    else:\n",
        "        return torch.device('cpu')\n",
        "\n",
        "def to_device(data, device):\n",
        "    \"\"\"Move tensor(s) to chosen device\"\"\"\n",
        "    if isinstance(data, (list,tuple)):\n",
        "        return [to_device(x, device) for x in data]\n",
        "    return data.to(device, non_blocking=True)\n",
        "\n",
        "class DeviceDataLoader():\n",
        "    \"\"\"Wrap a dataloader to move data to a device\"\"\"\n",
        "    def __init__(self, dl, device):\n",
        "        self.dl = dl\n",
        "        self.device = device\n",
        "\n",
        "    def __iter__(self):\n",
        "        \"\"\"Yield a batch of data after moving it to device\"\"\"\n",
        "        for b in self.dl:\n",
        "            yield to_device(b, self.device)\n",
        "\n",
        "    def __len__(self):\n",
        "        \"\"\"Number of batches\"\"\"\n",
        "        return len(self.dl)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BMdyULWT3KUc"
      },
      "source": [
        "Based on where you're running this notebook, your default device could be a CPU (`torch.device('cpu')`) or a GPU (`torch.device('cuda')`)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Muq1t4gW3KUd",
        "outputId": "ee57fc1d-adca-4760-e0ce-bf7da90431ad"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "device(type='cuda')"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "device = get_default_device()\n",
        "device"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZLzf7Mg43KUd"
      },
      "source": [
        "We can now wrap our training and validation data loaders using `DeviceDataLoader` for automatically transferring batches of data to the GPU (if available)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "PEPfNNGc3KUd"
      },
      "outputs": [],
      "source": [
        "train_dl = DeviceDataLoader(train_dl, device)\n",
        "valid_dl = DeviceDataLoader(valid_dl, device)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "af76KiJg3KUd"
      },
      "source": [
        "## Model with Residual Blocks and Batch Normalization\n",
        "\n",
        "One of the key changes to our CNN model this time is the addition of the resudial block, which adds the original input back to the output feature map obtained by passing the input through one or more convolutional layers.\n",
        "\n",
        "![](https://miro.medium.com/max/1140/1*D0F3UitQ2l5Q0Ak-tjEdJg.png)\n",
        "\n",
        "Here is a very simple Residual block:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "2RFGY2Y63KUe"
      },
      "outputs": [],
      "source": [
        "class SimpleResidualBlock(nn.Module):\n",
        "    def __init__(self):\n",
        "        super().__init__()\n",
        "        self.conv1 = nn.Conv2d(in_channels=3, out_channels=3, kernel_size=3, stride=1, padding=1)\n",
        "        self.relu1 = nn.ReLU()\n",
        "        self.conv2 = nn.Conv2d(in_channels=3, out_channels=3, kernel_size=3, stride=1, padding=1)\n",
        "        self.relu2 = nn.ReLU()\n",
        "\n",
        "    def forward(self, x):\n",
        "        out = self.conv1(x)\n",
        "        out = self.relu1(out)\n",
        "        out = self.conv2(out)\n",
        "        out = out + x\n",
        "        return self.relu2(out)# ReLU can be applied before or after adding the input"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TTDuMm1r3KUe",
        "outputId": "7e10746b-8a7e-4d65-8b0b-0ebd4cb6fedb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py:557: UserWarning: This DataLoader will create 3 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  warnings.warn(_create_warning_msg(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch.Size([400, 3, 32, 32])\n"
          ]
        }
      ],
      "source": [
        "simple_resnet = to_device(SimpleResidualBlock(), device)\n",
        "\n",
        "for images, labels in train_dl:\n",
        "    out = simple_resnet(images)\n",
        "    print(out.shape)\n",
        "    break\n",
        "\n",
        "del simple_resnet, images, labels\n",
        "torch.cuda.empty_cache()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cuJVqc3Z3KUe"
      },
      "source": [
        "This seeming small change produces a drastic improvement in the performance of the model. Also, after each convolutional layer, we'll add a batch normalization layer, which normalizes the outputs of the previous layer.\n",
        "\n",
        "Go through the following blog posts to learn more:\n",
        "\n",
        "* Why and how residual blocks work: https://towardsdatascience.com/residual-blocks-building-blocks-of-resnet-fd90ca15d6ec\n",
        "* Batch normalization and dropout explained: https://towardsdatascience.com/batch-normalization-and-dropout-in-neural-networks-explained-with-pytorch-47d7a8459bcd\n",
        "\n",
        "We will use the ResNet9 architecture, as described in [this blog series](https://www.myrtle.ai/2018/09/24/how_to_train_your_resnet/) :\n",
        "\n",
        "![resnet-9](https://github.com/lambdal/cifar10-fast/raw/master/net.svg?sanitize=true)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "dQI-Dbil3KUf"
      },
      "outputs": [],
      "source": [
        "def accuracy(outputs, labels):\n",
        "    _, preds = torch.max(outputs, dim=1)\n",
        "    return torch.tensor(torch.sum(preds == labels).item() / len(preds))\n",
        "\n",
        "class ImageClassificationBase(nn.Module):\n",
        "    def training_step(self, batch):\n",
        "        images, labels = batch\n",
        "        out = self(images)                  # Generate predictions\n",
        "        loss = F.cross_entropy(out, labels) # Calculate loss\n",
        "        return loss\n",
        "\n",
        "    def validation_step(self, batch):\n",
        "        images, labels = batch\n",
        "        out = self(images)                    # Generate predictions\n",
        "        loss = F.cross_entropy(out, labels)   # Calculate loss\n",
        "        acc = accuracy(out, labels)           # Calculate accuracy\n",
        "        return {'val_loss': loss.detach(), 'val_acc': acc}\n",
        "\n",
        "    def validation_epoch_end(self, outputs):\n",
        "        batch_losses = [x['val_loss'] for x in outputs]\n",
        "        epoch_loss = torch.stack(batch_losses).mean()   # Combine losses\n",
        "        batch_accs = [x['val_acc'] for x in outputs]\n",
        "        epoch_acc = torch.stack(batch_accs).mean()      # Combine accuracies\n",
        "        return {'val_loss': epoch_loss.item(), 'val_acc': epoch_acc.item()}\n",
        "\n",
        "    def epoch_end(self, epoch, result):\n",
        "        print(\"Epoch [{}], last_lr: {:.5f}, train_loss: {:.4f}, val_loss: {:.4f}, val_acc: {:.4f}\".format(\n",
        "            epoch, result['lrs'][-1], result['train_loss'], result['val_loss'], result['val_acc']))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "dmXWX1aO3KUf"
      },
      "outputs": [],
      "source": [
        "def conv_block(in_channels, out_channels, pool=False):\n",
        "    layers = [nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1),\n",
        "              nn.BatchNorm2d(out_channels),\n",
        "              nn.ReLU(inplace=True)]\n",
        "    if pool: layers.append(nn.MaxPool2d(2))\n",
        "    return nn.Sequential(*layers)\n",
        "\n",
        "class ResNet9(ImageClassificationBase):\n",
        "    def __init__(self, in_channels, num_classes):\n",
        "        super().__init__()\n",
        "\n",
        "        self.conv1 = conv_block(in_channels, 64)\n",
        "        self.conv2 = conv_block(64, 128, pool=True)\n",
        "        self.res1 = nn.Sequential(conv_block(128, 128), conv_block(128, 128))\n",
        "\n",
        "        self.conv3 = conv_block(128, 256, pool=True)\n",
        "        self.conv4 = conv_block(256, 512, pool=True)\n",
        "        self.res2 = nn.Sequential(conv_block(512, 512), conv_block(512, 512))\n",
        "\n",
        "        self.classifier = nn.Sequential(nn.MaxPool2d(4),\n",
        "                                        nn.Flatten(),\n",
        "                                        nn.Dropout(0.2),\n",
        "                                        nn.Linear(512, num_classes))\n",
        "\n",
        "    def forward(self, xb):\n",
        "        out = self.conv1(xb)\n",
        "        out = self.conv2(out)\n",
        "        out = self.res1(out) + out\n",
        "        out = self.conv3(out)\n",
        "        out = self.conv4(out)\n",
        "        out = self.res2(out) + out\n",
        "        out = self.classifier(out)\n",
        "        return out"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ryss1Hhm3KUf",
        "outputId": "814c9cc3-ded3-42b0-82dd-f5f0cb99cdc8"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ResNet9(\n",
              "  (conv1): Sequential(\n",
              "    (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    (2): ReLU(inplace=True)\n",
              "  )\n",
              "  (conv2): Sequential(\n",
              "    (0): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    (2): ReLU(inplace=True)\n",
              "    (3): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "  )\n",
              "  (res1): Sequential(\n",
              "    (0): Sequential(\n",
              "      (0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "      (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (2): ReLU(inplace=True)\n",
              "    )\n",
              "    (1): Sequential(\n",
              "      (0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "      (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (2): ReLU(inplace=True)\n",
              "    )\n",
              "  )\n",
              "  (conv3): Sequential(\n",
              "    (0): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    (2): ReLU(inplace=True)\n",
              "    (3): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "  )\n",
              "  (conv4): Sequential(\n",
              "    (0): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    (2): ReLU(inplace=True)\n",
              "    (3): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "  )\n",
              "  (res2): Sequential(\n",
              "    (0): Sequential(\n",
              "      (0): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "      (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (2): ReLU(inplace=True)\n",
              "    )\n",
              "    (1): Sequential(\n",
              "      (0): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "      (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (2): ReLU(inplace=True)\n",
              "    )\n",
              "  )\n",
              "  (classifier): Sequential(\n",
              "    (0): MaxPool2d(kernel_size=4, stride=4, padding=0, dilation=1, ceil_mode=False)\n",
              "    (1): Flatten(start_dim=1, end_dim=-1)\n",
              "    (2): Dropout(p=0.2, inplace=False)\n",
              "    (3): Linear(in_features=512, out_features=10, bias=True)\n",
              "  )\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "model = to_device(ResNet9(3, 10), device)\n",
        "model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MfyLwsTh3KUf"
      },
      "source": [
        "## Training the model\n",
        "\n",
        "Before we train the model, we're going to make a bunch of small but important improvements to our `fit` function:\n",
        "\n",
        "* **Learning rate scheduling**: Instead of using a fixed learning rate, we will use a learning rate scheduler, which will change the learning rate after every batch of training. There are many strategies for varying the learning rate during training, and the one we'll use is called the **\"One Cycle Learning Rate Policy\"**, which involves starting with a low learning rate, gradually increasing it batch-by-batch to a high learning rate for about 30% of epochs, then gradually decreasing it to a very low value for the remaining epochs. Learn more: https://sgugger.github.io/the-1cycle-policy.html\n",
        "\n",
        "* **Weight decay**: We also use weight decay, which is yet another regularization technique which prevents the weights from becoming too large by adding an additional term to the loss function.Learn more: https://towardsdatascience.com/this-thing-called-weight-decay-a7cd4bcfccab\n",
        "\n",
        "* **Gradient clipping**: Apart from the layer weights and outputs, it also helpful to limit the values of gradients to a small range to prevent undesirable changes in parameters due to large gradient values. This simple yet effective technique is called gradient clipping. Learn more: https://towardsdatascience.com/what-is-gradient-clipping-b8e815cdfb48\n",
        "\n",
        "\n",
        "Let's define a `fit_one_cycle` function to incorporate these changes. We'll also record the learning rate used for each batch."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "9y_RiYdt3KUg"
      },
      "outputs": [],
      "source": [
        "@torch.no_grad()\n",
        "def evaluate(model, val_loader):\n",
        "    model.eval()\n",
        "    outputs = [model.validation_step(batch) for batch in val_loader]\n",
        "    return model.validation_epoch_end(outputs)\n",
        "\n",
        "def get_lr(optimizer):\n",
        "    for param_group in optimizer.param_groups:\n",
        "        return param_group['lr']\n",
        "\n",
        "def fit_one_cycle(epochs, max_lr, model, train_loader, val_loader,\n",
        "                  weight_decay=0, grad_clip=None, opt_func=torch.optim.SGD):\n",
        "    torch.cuda.empty_cache()\n",
        "    history = []\n",
        "\n",
        "    # Set up cutom optimizer with weight decay\n",
        "    optimizer = opt_func(model.parameters(), max_lr, weight_decay=weight_decay)\n",
        "    # Set up one-cycle learning rate scheduler\n",
        "    sched = torch.optim.lr_scheduler.OneCycleLR(optimizer, max_lr, epochs=epochs,\n",
        "                                                steps_per_epoch=len(train_loader))\n",
        "\n",
        "    for epoch in range(epochs):\n",
        "        # Training Phase\n",
        "        model.train()\n",
        "        train_losses = []\n",
        "        lrs = []\n",
        "        for batch in train_loader:\n",
        "            loss = model.training_step(batch)\n",
        "            train_losses.append(loss)\n",
        "            loss.backward()\n",
        "\n",
        "            # Gradient clipping\n",
        "            if grad_clip:\n",
        "                nn.utils.clip_grad_value_(model.parameters(), grad_clip)\n",
        "\n",
        "            optimizer.step()\n",
        "            optimizer.zero_grad()\n",
        "\n",
        "            # Record & update learning rate\n",
        "            lrs.append(get_lr(optimizer))\n",
        "            sched.step()\n",
        "\n",
        "        # Validation phase\n",
        "        result = evaluate(model, val_loader)\n",
        "        result['train_loss'] = torch.stack(train_losses).mean().item()\n",
        "        result['lrs'] = lrs\n",
        "        model.epoch_end(epoch, result)\n",
        "        history.append(result)\n",
        "    return history"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xfPP3dVf3KUg",
        "outputId": "69bd80e1-813c-45f5-aafb-8fb942e8fc11"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'val_loss': 2.305732488632202, 'val_acc': 0.09615384787321091}]"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ],
      "source": [
        "history = [evaluate(model, valid_dl)]\n",
        "history"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eOE2flHY3KUg"
      },
      "source": [
        "We're now ready to train our model. Instead of SGD (stochastic gradient descent), we'll use the Adam optimizer which uses techniques like momentum and adaptive learning rates for faster training. You can learn more about optimizers here: https://ruder.io/optimizing-gradient-descent/index.html"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "h-sJuE2G3KUi"
      },
      "outputs": [],
      "source": [
        "epochs = 8\n",
        "max_lr = 0.01\n",
        "grad_clip = 0.1\n",
        "weight_decay = 1e-4\n",
        "opt_func = torch.optim.Adam"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y387SMt03KUi",
        "outputId": "6bd1431d-0aef-4101-8707-6865fbda803d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [0], last_lr: 0.00393, train_loss: 1.5535, val_loss: 1.2339, val_acc: 0.5981\n",
            "Epoch [1], last_lr: 0.00935, train_loss: 1.1053, val_loss: 1.0152, val_acc: 0.6650\n",
            "Epoch [2], last_lr: 0.00972, train_loss: 0.8526, val_loss: 0.8795, val_acc: 0.7022\n",
            "Epoch [3], last_lr: 0.00812, train_loss: 0.6509, val_loss: 0.8457, val_acc: 0.7292\n",
            "Epoch [4], last_lr: 0.00556, train_loss: 0.5238, val_loss: 0.6874, val_acc: 0.7656\n",
            "Epoch [5], last_lr: 0.00283, train_loss: 0.4130, val_loss: 0.4457, val_acc: 0.8462\n",
            "Epoch [6], last_lr: 0.00077, train_loss: 0.3004, val_loss: 0.3307, val_acc: 0.8890\n",
            "Epoch [7], last_lr: 0.00000, train_loss: 0.2319, val_loss: 0.2890, val_acc: 0.9012\n",
            "CPU times: user 19.9 s, sys: 4.08 s, total: 24 s\n",
            "Wall time: 4min 46s\n"
          ]
        }
      ],
      "source": [
        "%%time\n",
        "history += fit_one_cycle(epochs, max_lr, model, train_dl, valid_dl,\n",
        "                             grad_clip=grad_clip,\n",
        "                             weight_decay=weight_decay,\n",
        "                             opt_func=opt_func)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "TJwhl01q3KUj"
      },
      "outputs": [],
      "source": [
        "train_time='4:46'"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pTk7xKaX3KUj"
      },
      "source": [
        "Our model trained to over **90% accuracy in under 5 minutes**! Try playing around with the data augmentations, network architecture & hyperparameters to achive the following results:\n",
        "\n",
        "1. 94% accuracy in under 10 minutes (easy)\n",
        "2. 90% accuracy in under 2.5 minutes (intermediate)\n",
        "3. 94% accuracy in under 5 minutes (hard)\n",
        "\n",
        "\n",
        "Let's plot the valdation set accuracies to study how the model improves over time."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "ZwxUEVle3KUj"
      },
      "outputs": [],
      "source": [
        "def plot_accuracies(history):\n",
        "    accuracies = [x['val_acc'] for x in history]\n",
        "    plt.plot(accuracies, '-x')\n",
        "    plt.xlabel('epoch')\n",
        "    plt.ylabel('accuracy')\n",
        "    plt.title('Accuracy vs. No. of epochs');"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "p8jtB6Hb3KUj",
        "outputId": "423dbc8c-9226-410d-f967-c879922689ae"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "plot_accuracies(history)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ytbpJx0j3KUj"
      },
      "source": [
        "We can also plot the training and validation losses to study the trend."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "jLOqvY0M3KUk"
      },
      "outputs": [],
      "source": [
        "def plot_losses(history):\n",
        "    train_losses = [x.get('train_loss') for x in history]\n",
        "    val_losses = [x['val_loss'] for x in history]\n",
        "    plt.plot(train_losses, '-bx')\n",
        "    plt.plot(val_losses, '-rx')\n",
        "    plt.xlabel('epoch')\n",
        "    plt.ylabel('loss')\n",
        "    plt.legend(['Training', 'Validation'])\n",
        "    plt.title('Loss vs. No. of epochs');"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "6tyQvu4j3KUk",
        "outputId": "4215f962-f993-4850-8a3d-4b79e21b858d"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plot_losses(history)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mpU4xTVg3KUk"
      },
      "source": [
        "It's clear from the trend that our model isn't overfitting to the training data just yet. Try removing batch normalization, data augmentation and residual layers one by one to study their effect on overfitting.\n",
        "\n",
        "Finally, let's visualize how the learning rate changed over time, batch-by-batch over all the epochs."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "id": "T_pJQ2dn3KUl"
      },
      "outputs": [],
      "source": [
        "def plot_lrs(history):\n",
        "    lrs = np.concatenate([x.get('lrs', []) for x in history])\n",
        "    plt.plot(lrs)\n",
        "    plt.xlabel('Batch no.')\n",
        "    plt.ylabel('Learning rate')\n",
        "    plt.title('Learning Rate vs. Batch no.');"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "BXoBkGE03KUl",
        "outputId": "568d08ea-2ca0-4ac7-a37d-b4a48e2db5b6"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plot_lrs(history)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "de07VEru3KUl"
      },
      "source": [
        "As expected, the learning rate starts at a low value, and gradually increases for 30% of the iterations to a maximum value of `0.01`, and then gradually decreases to a very small value."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uFwYdlWxCN0M"
      },
      "source": [
        "## Testing with individual images\n",
        "\n",
        "While we have been tracking the overall accuracy of a model so far, it's also a good idea to look at model's results on some sample images. Let's test out our model with some images from the predefined test dataset of 10000 images.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "id": "k5FLM-VrCWQ-"
      },
      "outputs": [],
      "source": [
        "def predict_image(img, model):\n",
        "    # Convert to a batch of 1\n",
        "    xb = to_device(img.unsqueeze(0), device)\n",
        "    # Get predictions from model\n",
        "    yb = model(xb)\n",
        "    # Pick index with highest probability\n",
        "    _, preds  = torch.max(yb, dim=1)\n",
        "    # Retrieve the class label\n",
        "    return train_ds.classes[preds[0].item()]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "-Asrb2CjCZzv",
        "outputId": "4f988fdf-821f-4b7f-ae9d-d873080ecb86"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Label: airplane , Predicted: airplane\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "img, label = valid_ds[0]\n",
        "plt.imshow(img.permute(1, 2, 0).clamp(0, 1))\n",
        "print('Label:', train_ds.classes[label], ', Predicted:', predict_image(img, model))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 465
        },
        "id": "u0rmk1WjCcPy",
        "outputId": "8be9b305-2b3f-4035-f53b-51271cdebb11"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Label: automobile , Predicted: automobile\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "img, label = valid_ds[1002]\n",
        "plt.imshow(img.permute(1, 2, 0))\n",
        "print('Label:', valid_ds.classes[label], ', Predicted:', predict_image(img, model))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 465
        },
        "id": "u5LAdSLZC5j9",
        "outputId": "e9c6beec-76f7-4ff8-e863-086a6cac79b1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Label: frog , Predicted: frog\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "img, label = valid_ds[6153]\n",
        "plt.imshow(img.permute(1, 2, 0))\n",
        "print('Label:', train_ds.classes[label], ', Predicted:', predict_image(img, model))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EOiOageJDJrx"
      },
      "source": [
        "Identifying where our model performs poorly can help us improve the model, by collecting more training data, increasing/decreasing the complexity of the model, and changing the hypeparameters.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EhrDZkF93KUl"
      },
      "source": [
        "## Save and Commit\n",
        "\n",
        "Let's save the weights of the model, record the hyperparameters, and commit our experiment to Jovian. As you try different ideas, make sure to record every experiment so you can look back and analyze the results."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "id": "u0j7iGi33KUl"
      },
      "outputs": [],
      "source": [
        "torch.save(model.state_dict(), 'cifar10-resnet9.pth')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "id": "wo611yz_3KUl",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d4f2f185-e235-4407-ab37-f4deba1565bc"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/68.6 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━\u001b[0m \u001b[32m61.4/68.6 kB\u001b[0m \u001b[31m3.8 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m68.6/68.6 kB\u001b[0m \u001b[31m1.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for uuid (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
          ]
        }
      ],
      "source": [
        "!pip install jovian --upgrade --quiet"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "id": "d0TJHcHd3KUl"
      },
      "outputs": [],
      "source": [
        "import jovian"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "id": "o7EK8qAd3KUm",
        "outputId": "491e06cf-613b-4bc0-f3f3-0821640752e7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[jovian] Please enter your API key ( from https://jovian.com/ ):\u001b[0m\n",
            "API KEY: ··········\n",
            "[jovian] Hyperparams logged.\u001b[0m\n"
          ]
        }
      ],
      "source": [
        "jovian.reset()\n",
        "jovian.log_hyperparams(arch='resnet9',\n",
        "                       epochs=epochs,\n",
        "                       lr=max_lr,\n",
        "                       scheduler='one-cycle',\n",
        "                       weight_decay=weight_decay,\n",
        "                       grad_clip=grad_clip,\n",
        "                       opt=opt_func.__name__)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "id": "CTXq5LjU3KUm",
        "outputId": "f4f0ab13-2b90-413c-9f87-6bc4c5ed682e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[jovian] Metrics logged.\u001b[0m\n"
          ]
        }
      ],
      "source": [
        "jovian.log_metrics(val_loss=history[-1]['val_loss'],\n",
        "                   val_acc=history[-1]['val_acc'],\n",
        "                   train_loss=history[-1]['train_loss'],\n",
        "                   time=train_time)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "id": "KviBU6Vn3KUm",
        "outputId": "c74e656e-0181-4d9d-9162-33ddb759e3b5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[jovian] Detected Colab notebook...\u001b[0m\n",
            "[jovian] jovian.commit() is no longer required on Google Colab. If you ran this notebook from Jovian, \n",
            "then just save this file in Colab using Ctrl+S/Cmd+S and it will be updated on Jovian. \n",
            "Also, you can also delete this cell, it's no longer necessary.\u001b[0m\n"
          ]
        }
      ],
      "source": [
        "jovian.commit(project=project_name, environment=None, outputs=['cifar10-resnet9.pth'])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VHrDEGvp3KUm"
      },
      "source": [
        "## Summary and Further Reading\n",
        "\n",
        "You are now ready to train state-of-the-art deep learning models from scratch. Try working on a project on your own by following these guidelines: https://jovian.ai/learn/deep-learning-with-pytorch-zero-to-gans/assignment/course-project\n",
        "\n",
        "Here's a summary of the different techniques used in this tutorial to improve our model performance and reduce the training time:\n",
        "\n",
        "\n",
        "* **Data normalization**: We normalized the image tensors by subtracting the mean and dividing by the standard deviation of pixels across each channel. Normalizing the data prevents the pixel values from any one channel from disproportionately affecting the losses and gradients. [Learn more](https://medium.com/@ml_kid/what-is-transform-and-transform-normalize-lesson-4-neural-networks-in-pytorch-ca97842336bd)\n",
        "\n",
        "* **Data augmentation**: We applied random transformations while loading images from the training dataset. Specifically, we will pad each image by 4 pixels, and then take a random crop of size 32 x 32 pixels, and then flip the image horizontally with a 50% probability. [Learn more](https://www.analyticsvidhya.com/blog/2019/12/image-augmentation-deep-learning-pytorch/)\n",
        "\n",
        "* **Residual connections**: One of the key changes to our CNN model was the addition of the resudial block, which adds the original input back to the output feature map obtained by passing the input through one or more convolutional layers. We used the ResNet9 architecture [Learn more](https://towardsdatascience.com/residual-blocks-building-blocks-of-resnet-fd90ca15d6ec).\n",
        "\n",
        "* **Batch normalization**: After each convolutional layer, we added a batch normalization layer, which normalizes the outputs of the previous layer. This is somewhat similar to data normalization, except it's applied to the outputs of a layer, and the mean and standard deviation are learned parameters. [Learn more](https://towardsdatascience.com/batch-normalization-and-dropout-in-neural-networks-explained-with-pytorch-47d7a8459bcd)\n",
        "\n",
        "* **Learning rate scheduling**: Instead of using a fixed learning rate, we will use a learning rate scheduler, which will change the learning rate after every batch of training. There are [many strategies](https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate) for varying the learning rate during training, and we used the \"One Cycle Learning Rate Policy\". [Learn more](https://sgugger.github.io/the-1cycle-policy.html)\n",
        "\n",
        "* **Weight Decay**: We added weight decay to the optimizer, yet another regularization technique which prevents the weights from becoming too large by adding an additional term to the loss function. [Learn more](https://towardsdatascience.com/this-thing-called-weight-decay-a7cd4bcfccab)\n",
        "\n",
        "* **Gradient clipping**: We also added gradient clippint, which helps limit the values of gradients to a small range to prevent undesirable changes in model parameters due to large gradient values during training.  [Learn more.](https://towardsdatascience.com/what-is-gradient-clipping-b8e815cdfb48#63e0)\n",
        "\n",
        "* **Adam optimizer**: Instead of SGD (stochastic gradient descent), we used the Adam optimizer which uses techniques like momentum and adaptive learning rates for faster training. There are many other optimizers to choose froma and experiment with. [Learn more.](https://ruder.io/optimizing-gradient-descent/index.html)\n",
        "\n",
        "\n",
        "As an exercise, you should try applying each technique independently and see how much each one affects the performance and training time. As you try different experiments, you will start to cultivate the intuition for picking the right architectures, data augmentation & regularization techniques.\n",
        "\n",
        "You are now ready to move on to the next tutorial in this series: [Generating Images using Generative Adversarial Networks](https://jovian.ai/aakashns/06b-anime-dcgan/)\n"
      ]
    }
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