05_cifar10_cnn.ipynb

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    {
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      "metadata": {
        "id": "glYHP4-7Jp--"
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      "source": [
        "# Image Classification using Convolutional Neural Networks in PyTorch\n",
        "\n",
        "### Part 5 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"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HqUkI41WKEM1"
      },
      "source": [
        "This tutorial covers the following topics: \n",
        "\n",
        "- Downloading an image dataset from web URL\n",
        "- Understanding convolution and pooling layers\n",
        "- Creating a convolutional neural network (CNN) using PyTorch\n",
        "- Training a CNN from scratch and monitoring performance\n",
        "- Underfitting, overfitting and how to overcome them"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8FTYKRrdK9qJ"
      },
      "source": [
        "### How to run the code\n",
        "\n",
        "This tutorial is an executable [Jupyter notebook](https://jupyter.org) hosted on [Jovian](https://www.jovian.ai). You can _run_ this tutorial and experiment with the code examples in a couple of ways: *using free online resources* (recommended) or *on your computer*.\n",
        "\n",
        "#### Option 1: Running using free online resources (1-click, recommended)\n",
        "\n",
        "The easiest way to start executing the code is to click the **Run** button at the top of this page and select **Run on Colab**. [Google Colab](https://colab.research.google.com) is a free online platform for running Jupyter notebooks using Google's cloud infrastructure. You can also select \"Run on Binder\" or \"Run on Kaggle\" if you face issues running the notebook on Google Colab. \n",
        "\n",
        "\n",
        "#### Option 2: Running on your computer locally\n",
        "\n",
        "To run the code on your computer locally, you'll need to set up [Python](https://www.python.org), download the notebook and install the required libraries. We recommend using the [Conda](https://docs.conda.io/projects/conda/en/latest/user-guide/install/) distribution of Python. Click the **Run** button at the top of this page, select the **Run Locally** option, and follow the instructions."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HP67kosYLAlQ"
      },
      "source": [
        "### Using a GPU for faster training\n",
        "\n",
        "You can use a [Graphics Processing Unit](https://en.wikipedia.org/wiki/Graphics_processing_unit) (GPU) to train your models faster if your execution platform is connected to a GPU manufactured by NVIDIA. Follow these instructions to use a GPU on the platform of your choice:\n",
        "\n",
        "* _Google Colab_: Use the menu option \"Runtime > Change Runtime Type\" and select \"GPU\" from the \"Hardware Accelerator\" dropdown.\n",
        "* _Kaggle_: In the \"Settings\" section of the sidebar, select \"GPU\" from the \"Accelerator\" dropdown. Use the button on the top-right to open the sidebar.\n",
        "* _Binder_: Notebooks running on Binder cannot use a GPU, as the machines powering Binder aren't connected to any GPUs.\n",
        "* _Linux_: If your laptop/desktop has an NVIDIA GPU (graphics card), make sure you have installed the [NVIDIA CUDA drivers](https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html).\n",
        "* _Windows_: If your laptop/desktop has an NVIDIA GPU (graphics card), make sure you have installed the [NVIDIA CUDA drivers](https://docs.nvidia.com/cuda/cuda-installation-guide-microsoft-windows/index.html).\n",
        "* _macOS_: macOS is not compatible with NVIDIA GPUs\n",
        "\n",
        "\n",
        "If you do not have access to a GPU or aren't sure what it is, don't worry, you can execute all the code in this tutorial just fine without a GPU."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kQxJbIfeJp-_"
      },
      "source": [
        "## Exploring the CIFAR10 Dataset\n",
        "\n",
        "In the [previous tutorial](https://jovian.ml/aakashns/04-feedforward-nn), we trained a feedfoward neural networks with a single hidden layer to classify handwritten digits from the [MNIST dataset](http://yann.lecun.com/exdb/mnist) with over 97% accuracy. For this tutorial, we'll use the CIFAR10 dataset, which consists of 60000 32x32 px colour images in 10 classes. Here are some sample images from the dataset:\n",
        "\n",
        "<img src=\"https://miro.medium.com/max/709/1*LyV7_xga4jUHdx4_jHk1PQ.png\" style=\"max-width:480px\">\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sFBq-ur9Jp-_"
      },
      "source": [
        "# Uncomment and run the appropriate command for your operating system, if required\n",
        "\n",
        "# Linux / Binder / Windows (No GPU)\n",
        "# !pip install numpy matplotlib torch==1.7.0+cpu torchvision==0.8.1+cpu torchaudio==0.7.0 -f https://download.pytorch.org/whl/torch_stable.html\n",
        "\n",
        "# Linux / Windows (GPU)\n",
        "# pip install torch==1.7.1+cu110 torchvision==0.8.2+cu110 torchaudio==0.7.2 -f https://download.pytorch.org/whl/torch_stable.html\n",
        " \n",
        "# MacOS (NO GPU)\n",
        "# !pip install numpy matplotlib torch torchvision torchaudio"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TkOhVp1FJp_A"
      },
      "source": [
        "import os\n",
        "import torch\n",
        "import torchvision\n",
        "import tarfile\n",
        "from torchvision.datasets.utils import download_url\n",
        "from torch.utils.data import random_split"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ImubC3HRJp_A"
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      "source": [
        "project_name='05-cifar10-cnn'"
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      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VkBn_XvbJp_A"
      },
      "source": [
        "We'll download the images in PNG format from [this page](https://course.fast.ai/datasets), using some helper functions from the `torchvision` and `tarfile` packages."
      ]
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      "source": [
        "# Dowload the dataset\n",
        "dataset_url = \"https://s3.amazonaws.com/fast-ai-imageclas/cifar10.tgz\"\n",
        "download_url(dataset_url, '.')"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading https://s3.amazonaws.com/fast-ai-imageclas/cifar10.tgz to ./cifar10.tgz\n"
          ],
          "name": "stdout"
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        {
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    {
      "cell_type": "code",
      "metadata": {
        "colab": {
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        },
        "id": "LF_8NbHiJp_C",
        "outputId": "a3fdec3b-d399-4876-ae91-8e5ceb5432a1"
      },
      "source": [
        "# Extract from archive\n",
        "with tarfile.open('./cifar10.tgz', 'r:gz') as tar:\n",
        "    tar.extractall(path='./data')"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n"
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    {
      "cell_type": "markdown",
      "metadata": {
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      "source": [
        "The dataset is extracted to the directory `data/cifar10`. It contains 2 folders `train` and `test`, containing the training set (50000 images) and test set (10000 images) respectively. Each of them contains 10 folders, one for each class of images. Let's verify this using `os.listdir`."
      ]
    },
    {
      "cell_type": "code",
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        "outputId": "9e05557e-e5eb-40be-c495-2e501989e93e"
      },
      "source": [
        "data_dir = './data/cifar10'\n",
        "\n",
        "print(os.listdir(data_dir))\n",
        "classes = os.listdir(data_dir + \"/train\")\n",
        "print(classes)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['train', 'test']\n",
            "['truck', 'airplane', 'cat', 'deer', 'horse', 'automobile', 'dog', 'frog', 'ship', 'bird']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9tzHkgTDJp_C"
      },
      "source": [
        "Let's look inside a couple of folders, one from the training set and another from the test set. As an exercise, you can verify that that there are an equal number of images for each class, 5000 in the training set and 1000 in the test set."
      ]
    },
    {
      "cell_type": "code",
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        "outputId": "c9aced41-7688-41c7-f379-f2c5909a1a11"
      },
      "source": [
        "airplane_files = os.listdir(data_dir + \"/train/airplane\")\n",
        "print('No. of training examples for airplanes:', len(airplane_files))\n",
        "print(airplane_files[:5])"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "No. of training examples for airplanes: 5000\n",
            "['3073.png', '3945.png', '1341.png', '2948.png', '4330.png']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rvhUgwAMJp_D",
        "outputId": "ee392c72-34e1-4e9e-bd83-b84148a96f32"
      },
      "source": [
        "ship_test_files = os.listdir(data_dir + \"/test/ship\")\n",
        "print(\"No. of test examples for ship:\", len(ship_test_files))\n",
        "print(ship_test_files[:5])"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "No. of test examples for ship: 1000\n",
            "['0725.png', '0902.png', '0852.png', '0752.png', '0707.png']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wF5kv2I3Jp_D"
      },
      "source": [
        "The above directory structure (one folder per class) is used by many computer vision datasets, and most deep learning libraries provide utilites for working with such datasets. We can use the `ImageFolder` class from `torchvision` to load the data as PyTorch tensors."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ubxtJzJjJp_E"
      },
      "source": [
        "from torchvision.datasets import ImageFolder\n",
        "from torchvision.transforms import ToTensor"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6IAxD0e9Jp_E"
      },
      "source": [
        "dataset = ImageFolder(data_dir+'/train', transform=ToTensor())"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ilbPijY3Jp_F"
      },
      "source": [
        "Let's look at a sample element from the training dataset. Each element is a tuple, containing a image tensor and a label. Since the data consists of 32x32 px color images with 3 channels (RGB), each image tensor has the shape `(3, 32, 32)`."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uFWkd84bJp_F",
        "outputId": "ab97b0e1-dd1b-4c85-9bd4-54ef51e26e80"
      },
      "source": [
        "img, label = dataset[0]\n",
        "print(img.shape, label)\n",
        "img"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "torch.Size([3, 32, 32]) 0\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[[0.7922, 0.7922, 0.8000,  ..., 0.8118, 0.8039, 0.7961],\n",
              "         [0.8078, 0.8078, 0.8118,  ..., 0.8235, 0.8157, 0.8078],\n",
              "         [0.8235, 0.8275, 0.8314,  ..., 0.8392, 0.8314, 0.8235],\n",
              "         ...,\n",
              "         [0.8549, 0.8235, 0.7608,  ..., 0.9529, 0.9569, 0.9529],\n",
              "         [0.8588, 0.8510, 0.8471,  ..., 0.9451, 0.9451, 0.9451],\n",
              "         [0.8510, 0.8471, 0.8510,  ..., 0.9373, 0.9373, 0.9412]],\n",
              "\n",
              "        [[0.8000, 0.8000, 0.8078,  ..., 0.8157, 0.8078, 0.8000],\n",
              "         [0.8157, 0.8157, 0.8196,  ..., 0.8275, 0.8196, 0.8118],\n",
              "         [0.8314, 0.8353, 0.8392,  ..., 0.8392, 0.8353, 0.8275],\n",
              "         ...,\n",
              "         [0.8510, 0.8196, 0.7608,  ..., 0.9490, 0.9490, 0.9529],\n",
              "         [0.8549, 0.8471, 0.8471,  ..., 0.9412, 0.9412, 0.9412],\n",
              "         [0.8471, 0.8431, 0.8471,  ..., 0.9333, 0.9333, 0.9333]],\n",
              "\n",
              "        [[0.7804, 0.7804, 0.7882,  ..., 0.7843, 0.7804, 0.7765],\n",
              "         [0.7961, 0.7961, 0.8000,  ..., 0.8039, 0.7961, 0.7882],\n",
              "         [0.8118, 0.8157, 0.8235,  ..., 0.8235, 0.8157, 0.8078],\n",
              "         ...,\n",
              "         [0.8706, 0.8392, 0.7765,  ..., 0.9686, 0.9686, 0.9686],\n",
              "         [0.8745, 0.8667, 0.8627,  ..., 0.9608, 0.9608, 0.9608],\n",
              "         [0.8667, 0.8627, 0.8667,  ..., 0.9529, 0.9529, 0.9529]]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KylRsYg2Jp_F"
      },
      "source": [
        "The list of classes is stored in the `.classes` property of the dataset. The numeric label for each element corresponds to index of the element's label in the list of classes."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O-XOdz6KJp_G",
        "outputId": "174a435b-c2c6-4653-cc7c-cb61fe67eb86"
      },
      "source": [
        "print(dataset.classes)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UwAIns3lJp_G"
      },
      "source": [
        "We can view the image using `matplotlib`, but we need to change the tensor dimensions to `(32,32,3)`. Let's create a helper function to display an image and its label."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JC8sRIySO187"
      },
      "source": [
        "import matplotlib\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "\n",
        "matplotlib.rcParams['figure.facecolor'] = '#ffffff'"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6TM5cZVyJp_G"
      },
      "source": [
        "\n",
        "def show_example(img, label):\n",
        "    print('Label: ', dataset.classes[label], \"(\"+str(label)+\")\")\n",
        "    plt.imshow(img.permute(1, 2, 0))"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P_Fhylr7Jp_H"
      },
      "source": [
        "Let's look at a couple of images from the dataset. As you can tell, the 32x32px images are quite difficult to identify, even for the human eye. Try changing the indices below to view different images."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "DLd1lDT0Jp_H",
        "outputId": "de432285-e42e-446b-c31b-fb658c7ab9fc"
      },
      "source": [
        "show_example(*dataset[0])"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Label:  airplane (0)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPsAAAD5CAYAAADhukOtAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAdSElEQVR4nO2dbWyU59Xn//fM2GCCwSZgMzEEy4EQAzYuNmFfqLXUJW3Z1CyYglO6cdYEd1G1ywP0hQ+VAlKVoK0i0RXpNpOyKz88Elk2egA1aRFRAs1Lk/VOgmmAZGMcTIwxDrYx2MZvM3PtB2+t0NznDHPbHie5/r9PcB1f93Xmmjnzcv3vc45jjDEghHzt8U20A4SQ5MBgJ8QSGOyEWAKDnRBLYLATYgkMdkIsITCaySdOnMD27dsRjUbx5JNPYvfu3erfZ2RkIHhfMOF1HNEgWuQ5o0FYT19LsXp1UhNLx+WBJ44XN1QV2ONzLV9TXkvVoj0L1R7W87DW1aut6OrqcrV5DvZoNIqf/OQnePXVVzFnzhwsX74c5eXlWLRokTgneF8Q/3jofyS8ls/n/gVEGo9nc7QXjmLz+fzCnLH3Q0MLCm/X1K7n4XKaH4rvsVgs8evFsUUiQ4Ib8lra/mo2zX/9mu7zYjFtLXfb4/++Wpzj+Wt8XV0d5s+fj7y8PKSmpqKyshLHjx/3ejlCyDjjOdhbWlowd+7ckf/PmTMHLS0tY+IUIWTsGdVv9rshFAohFAoBALpuuP+WIISMP54/2XNyctDc3Dzy/ytXriAnJ+cLf1dTU4NwOIxwOIyMzAyvyxFCRonnYF++fDkaGhpw6dIlDA4O4sUXX0R5eflY+kYIGUM8f40PBAI4cOAAvvOd7yAajaK6uhqLFy9W5ziOg0Ag8SWl01bpdHzYlszTeG2Ot5N67wjyoOPthFk/qU/8qN5rkmU0GhVtKSkpoi01NTXh63k9Vff5xvYU33E0dUK2SYzqN/uaNWuwZs2a0VyCEJIkeAcdIZbAYCfEEhjshFgCg50QS2CwE2IJ434H3edxAFF60+UwKRFGlt705BRv0pvsh7dsEa8SoFepT2YcpDdBalJXUiSvSCSizJSR5VL5taNJbxpack0sqtgEGS3mU+aIcp3y2hAthJCvFQx2QiyBwU6IJTDYCbEEBjshlpDU03g4jnha7CUBxeeT3fdaKkq3SWt5Ozn3khQEeCvfpPsor6XbxrgEllJNTnvMWlKLMVJ5LHGKupZnm5K4Is2LxeTHFXXc1QmexhNCGOyE2AKDnRBLYLATYgkMdkIsgcFOiCUkPRHG7x+7Om66vKYlyXhLJJHquGmJMFElAeL69XbRNi09XbSlTUkTbRJeHle8eVrjJWmLfcpzNoreSiKSGmaUbiteu75oEmBUmyck+WjJP76YBwlbtBBCvlYw2AmxBAY7IZbAYCfEEhjshFgCg50QSxiV9Jabm4v09HT4/X4EAgGEw2H17x3Hgd8vtepJXP7RpTfZD70GnTxP8jElVd7Gc+9fEG2/+28HRduj339UtK1b929FmzHu8o9Wc83vTzzjEAD8StahtJFRpU6bo2bEyfN8WkafIA8aVeaTr6fJclpnK02CjUQTl94iEffnWXu+Rq2znzp1CjNnzhztZQgh4wy/xhNiCaMKdsdx8Mgjj6C4uBihUGisfCKEjAOj+hr/1ltvIScnB5999hlWr16Nhx56CKWlpXf8TSgUGnkj6Oy8MZrlCCGjYFSf7Dk5OQCArKwsrFu3DnV1dV/4m5qaGoTDYYTDYcyYkTma5Qgho8BzsPf29qK7u3vk3ydPnsSSJUvGzDFCyNji+Wt8W1sb1q1bB2BYIvjhD3+I7373u/okxxGz3vRp7lKIXhxSK/QoyX/6NaX2Ptpj6u7uEW0f/FWW5dKnyt+Cvl32b0Tb9Iwpok1CknEAoKPjumj7rE3O2kud5J6Zt+ChBeKcSSlKpqIia2kyqyTPei0SqqJIwZosJ2XSaTKfJOVpr0XPwZ6Xl4ezZ896nU4ISTKU3gixBAY7IZbAYCfEEhjshFgCg50QS0hurzd460UmyWGaTKZJEOfPnxdtN2/eFG0rVvxL1/GpU+UCkGlpsk3r9fbBBx+KtqamFtH2jeKHXMc1KTIcfk+0/e538m3QHe1doi0tzV0C/Idd/yDOKS1dKdpMxFsxSs8ymnQ9tXBn4rIyoL1WNd89yNF37xIh5KsMg50QS2CwE2IJDHZCLIHBToglJP003gteEmG00/jmK82i7cj//F+i7dTrb7qOV2xYJ84J+OUtTp2UKtqufyYnoLz7zruibVnxooT9+OSTS6Ltg7+eE21paVNFW1fXLdfxF188Is5Z+OBC0RbMlkufmZicyDPWGKWNk1FP/r3UPdQUCPfXfuIzCCFfOxjshFgCg50QS2CwE2IJDHZCLIHBToglJF96E2QGta2OKigkfr3Sb5aKtpSALIf980t/cB3f98yvxTlz5t4n2qJKe59oTLa9/Ze/iLay1e6P7d57ZenqSvNV0ZaSMkm0pabKNmPcn2hNyjt16rRo+2HlRtGm57pIUpm3xBodb9fU6tMlvpbSRs3LMoSQrx4MdkIsgcFOiCUw2AmxBAY7IZbAYCfEEuJKb9XV1Xj55ZeRlZWFc+eGZZPOzk5s2rQJTU1NyM3NxZEjR5CZGb9powMHjvT+osgnPmGOVl9MkzOmTZsm2r73ve+Jttx57q2L/umfDolzTp16TbR1d98WbWlT7hFtDRc/Fm3PPP1fXMenpsuPue1aq2jTWmVFFOlQaoU0ODgkTvnDH9ylTQD45r/+V6Itd95c0RYzycuI84omEyuzEhoG7uKT/YknnsCJEyfuGNu3bx/KysrQ0NCAsrIy7Nu3LyE3CSHJJ26wl5aWYsaMGXeMHT9+HFVVVQCAqqoqHDt2bHy8I4SMGZ5+s7e1tSEYDAIAZs+ejba2tjF1ihAy9oz6dlnHcdTfzqFQCKHQcO3xjs6O0S5HCPGIp0/27OxstLYOH+q0trYiKytL/NuamhqEw2GEw2HcO+Neb14SQkaNp2AvLy9HbW0tAKC2thZr164dU6cIIWNP3K/xjz32GE6fPo329nbMmTMHe/fuxe7du7Fx40YcPHgQ8+bNw5EjchHBv8cxictoDoTikUJmFaC36dGUjlhMNj6Un+c6/p/+838U52RlzxBtL7xwULR13WwXbVOj6aLtrx+4Z5Wlp8tztMKdU6bKEmAkIstafX19ruMpkyaLcz65/Klo++OJP4m2rVv+g2gLBKR2Y+IU6G2XNDQJbayz7BK/XtxgP3z4sOv4a6/J+jEh5MsH76AjxBIY7IRYAoOdEEtgsBNiCQx2QixhAnq9SbKGlz5Z3iQSMfMO3ooXzp2bI87IzpZvOBoakjPAIkNyRlnXjRuibdIk9yKQgwMD4hwt6yo7O1u0aZJdvyS9KXufmTldtL3yiiy9Zc+cJdrW/rs1ruOBgNwLcDzQpGVPspyHKfxkJ8QSGOyEWAKDnRBLYLATYgkMdkIsgcFOiCVMgPQ2dujqg7fecZos5/e7b9fNm7fEOW+88bZo6++T5bBUpceaJsv133aXvPp65eKWfkWG0iQ77QmQTNn3yNl3vT29oq3l02bR9t9r5YKfhYVLXMcXLpwvzonG5Gw+XZqV0eRNXZaTrpe4D/xkJ8QSGOyEWAKDnRBLYLATYgkMdkIs4UtzGq+dVsZi7gkoPuUUM6omybhfLx5S8kRnZ5c459o1uaa+UdwYHBi8a7/uBm1/tdP97kFZadBOkQMp7m2jbt7oFOco5f/g+GTFoPXaddH21w8uuI4/+OCD8mJKyyhVyfFY185b+6fET/D5yU6IJTDYCbEEBjshlsBgJ8QSGOyEWAKDnRBLiCu9VVdX4+WXX0ZWVhbOnRtuLbRnzx688MILmDVruPbX008/jTVr3Gt9jQVG0KiiskICR2kNpUkdWl01ab3+PlkmGxrUnFTeawW5MR5+v7tEpb2rR5SN9JrAMTTovicDt+WEnJTJaaJtypSpsh/+VNH2f+redx3/9rdWiXOmZ8gtr4wq23ptG5Uc4n6yP/HEEzhx4sQXxnfs2IH6+nrU19ePa6ATQsaGuMFeWlqKGTPk5oSEkK8Gnn+zHzhwAIWFhaiursYNpbQxIeTLgadg37ZtGxobG1FfX49gMIhdu3aJfxsKhVBSUoKSkhJ0dsq3ShJCxhdPwZ6dnQ2/3w+fz4etW7eirq5O/NuamhqEw2GEw2H+HCBkAvEU7K2trSP/Pnr0KJYscS/9Qwj58hBXenvsscdw+vRptLe3Y86cOdi7dy9Onz6N+vp6OI6D3NxcPP/883e9oJY1JOH3ubup1U7T6B/oF22ffHJJtDVebHQdv3FDznrr7pazxlQJUJFxtOwqI2YIyu/rk1LkfdRaVKk16ATbgFLTzp8q193zB+SXaqoy7/U/v+k6vqy4SJyzqXK9aDNRb5Lol4G4wX748OEvjG3ZsmVcnCGEjB+8g44QS2CwE2IJDHZCLIHBToglMNgJsYSkFpw0xiitdWQd58MPP3Ydv3btmraaaGlouCjazp37IOF5/f2ylKfdNWiUNkNjnVslZQ4CgE9oazVsk2W5SEQuVCkVCVWltxT31lUA0NfbLdo0CbN/yP25OXjoH8U5qWlyFt33vvNt0TYpVd5HxxnropLuNk2W5Sc7IZbAYCfEEhjshFgCg50QS2CwE2IJDHZCLCHpvd4kCUjLDjty5Ijr+Dt/+d/inMlpcvHCnm5vMk4k4l5EUcuiSxF6ngG6DBVTZDmtKKaXvmFawUlN/pHkNUAuRqkVqezvk6U3x3dTtPkD8h5nZGa4jrdcvSrO2f+b/yra7gsGRdu/eHiZaIvFZJlS3hMt81F6Dchz+MlOiCUw2AmxBAY7IZbAYCfEEhjshFhCUk/jHcdBQDg5bW9vF+ddvOiegHLrlnyq3tcnn3RrJ+RaCoojnIKnpMjbKLVjAoA0RTHo6+2R/VBOtCVbVG3xJJqgFppTbJKLfq3jlZKs09fbK9oyMjNFm5RsNC19mjinu1te66V/Pi7alix6SLTdM2WyaJMetlprUNx7ra4hIcQKGOyEWAKDnRBLYLATYgkMdkIsgcFOiCXEld6am5vx+OOPo62tDY7joKamBtu3b0dnZyc2bdqEpqYm5Obm4siRI8hUJBAAgOMgIEhRU6dOFafNnHmv6/j1z2S5rk+pC9fTKyfdRJW6av5A4u+NmkymyXJ+n2yLKTKaVDNOk940icc77nvlKPIaYoqUp2h2Pbfk51N6bL7p08U5qZOniLZzFz4Sbc3NLaJtUf5C0SbV8tMkUammnTYn7qs3EAjg2WefxYULF/Duu+/iueeew4ULF7Bv3z6UlZWhoaEBZWVl2LdvX7xLEUImkLjBHgwGsWzZcOpeeno68vPz0dLSguPHj6OqqgoAUFVVhWPHjo2vp4SQUZHQ99KmpiacOXMGK1asQFtbG4L/P7d39uzZaGtrGxcHCSFjw13fLtvT04OKigrs378f06bdeauh4zjib9NQKIRQKAQA6OzoGIWrhJDRcFef7ENDQ6ioqMDmzZuxfv1w7+rs7Gy0trYCAFpbW5GVleU6t6amBuFwGOFwGDPudT9oI4SMP3GD3RiDLVu2ID8/Hzt37hwZLy8vR21tLQCgtrYWa9euHT8vCSGjJu7X+LfffhuHDh1CQUEBioqKAABPP/00du/ejY0bN+LgwYOYN2+eWCfuDowRM5uCSm2vJ5980nX80+ZPxTmXL18SbR9++KFo+/SyfM3PPnP/GdJ3W66dprVIkjOXgICSSTc4IMtoQ0ND7mupqW3ebKrM45Nq0CnympZFp9iGBuUMR0mWS5siy2v3TJMl5I5OuRbemfqzou3BBQ+INmkfNdnWQ6nB+MG+cuVK8YXy2muvJb4iIWRC4B10hFgCg50QS2CwE2IJDHZCLIHBToglJLXgpAEQjUjV9eR5BQUFruOFS5eIc/r7ZTmsQ7mT79PmZtF2seET1/GGBveCmADwySfucwCotxjf7pGLafZ2y8Uob9++7TquFXPUJTQ5+07pQiW2qNLW0rIAfQHZFo3K8mZkyN2PG503xDkGSjZiSqpoe+3UG6LtmytXirac+9xlZ6lY5jCJpyryk50QS2CwE2IJDHZCLIHBToglMNgJsQQGOyGWkFzpLWYwOOielSVlaw3jLqM5QmYVoOdxpaXdI9runztPtGVMn+E6Pvf++8U5ubny9bTsu2tXr4o2SV7TbLeVXmlacU6tAKdWxDIiyGFDwvMPAMrTCaNJhzHZNmWKeyHTof5Bcc61K7L8Om16hmi73HxNtJ0997Foy7kvx3XcUaQ34+Fjmp/shFgCg50QS2CwE2IJDHZCLIHBToglJPU0PhqL4pZQE0xLTuns7ExoHBgufS2htl1SbFJ5roEBuQaadgqempIi2qYoNdImTZok2jIy3E+LY8qJtaaEaLbUVDkppFvY//4+OUFJW6u7W04M6lOu2T8gKQ3y0b/mh6YKXFGSqE6ePCnaSpYudh3Pmumu/gBATGmVJcFPdkIsgcFOiCUw2AmxBAY7IZbAYCfEEhjshFhCXOmtubkZjz/+ONra2uA4DmpqarB9+3bs2bMHL7zwAmbNmgVguCXUmjVr1GuZWAz9QtLFzZtyW53Lly+7jn+kJZJck5MSNBlKqp0GyO14tDY9GprEI+0ToLdykvzXfNRsf3t+3fj7br6fR5IHNWkzPT1dtGkyn7aPPT3ustytbncJGABu3pRt169fF22TlQSrFOUlcuOGez28rFlyI1Qx+UdR5OIGeyAQwLPPPotly5ahu7sbxcXFWL16NQBgx44d+OlPfxrvEoSQLwFxgz0YDI40XUxPT0d+fj5aWlrG3TFCyNiS0G/2pqYmnDlzBitWrAAAHDhwAIWFhaiurha/ihBCvhzcdbD39PSgoqIC+/fvx7Rp07Bt2zY0Njaivr4ewWAQu3btcp0XCoVQUlKCkpISdHV1jZnjhJDEuKtgHxoaQkVFBTZv3oz169cDALKzs+H3++Hz+bB161bU1dW5zq2pqUE4HEY4HBbv2yaEjD9xg90Ygy1btiA/Px87d+4cGW9tbR3599GjR7FkidydhRAy8cQ9oHv77bdx6NAhFBQUoKioCMCwzHb48GHU19fDcRzk5ubi+eefj7tYzBgxQ0yTmuS6anIttogix0Qicm0vra6alPGkZSCpbZeUeUbRUDR50Iv0pslhWkafJpfee6+7bKRJaJMnTxZtOTnuddoAjBwgu5E5w90PLatQQ8u+SwnIWYzZWVmiLUuQN6NR+bUD6flUJL64wb5y5UpXXTeepk4I+XLBO+gIsQQGOyGWwGAnxBIY7IRYAoOdEEtIasFJB44s8yiSQURoQaS1JvIp72N+R5HDFIlKUkKMIpHEtBY+SvZaTJHetKw9R5De/Ipcp8mNmiSqyWiSj5mZmeIcrZCmVKgU0GU0Sc675x55jpbpl//QQnktpYCo9pxFBQlWLSopvEyVlxQ/2QmxBQY7IZbAYCfEEhjshFgCg50QS2CwE2IJSZXe/H4/MjLcpZfBQTlLrbfbvV9aV4fc662/X+7/FemT19IkQJ8gy2lyHRz5/VTLiNPQunxJMmBEkX40WUhbTCuz2Sv0uEtTMtu0DDvNpsmDfYJN6w8n+Q4AASVDMJKWJtq0jDh4yFRUNTZpmYRnEEK+kjDYCbEEBjshlsBgJ8QSGOyEWAKDnRBLSK705vNh6pSp7o7Mll2ZkuaeoTQ1Xc5cume63HfrYkODaOvolOU8KTsJflki8RlNlpNtmrIi9vmCopRp2VCKPKiqior4Fh1yz0gcHBwU52jymiaVabZ+wTagyHVDio9SBiYgZ0UC+utAtCkvAu1lJa6T+BRCyFcRBjshlsBgJ8QSGOyEWAKDnRBLiHsa39/fj9LSUgwMDCASiWDDhg3Yu3cvLl26hMrKSnR0dKC4uBiHDh1Sa5IBABxHvLlfqz+WJbTO0U7jZyntdubOnSvazp07L9o+vfyp63j3Tbk+WlRLdtFOW5XjczUFQrimllQhJfgAeqsp7ZpScs2g0pZLO6nXTtyl9mCaTUue0WyaYpCaKif5+HxyAs1YotU1jPvJPmnSJLz++us4e/Ys6uvrceLECbz77rv4xS9+gR07duDixYvIzMzEwYMHx9RpQsjYEjfYHcfB1KnD2vjQ0BCGhobgOA5ef/11bNiwAQBQVVWFY8eOja+nhJBRcVe/2aPRKIqKipCVlYXVq1fjgQceQEZGBgKB4V8Bc+bMQUtLy7g6SggZHXcV7H6/H/X19bhy5Qrq6urw0Ucf3fUCoVAIJSUlKCkpQWdnh2dHCSGjI6HT+IyMDKxatQrvvPMOurq6Rm4dvHLlitg/u6amBuFwGOFwGDOEXtmEkPEnbrBfv34dXV1dAIZPRF999VXk5+dj1apVeOmllwAAtbW1WLt27fh6SggZFXGlt9bWVlRVVSEajSIWi2Hjxo149NFHsWjRIlRWVuKXv/wlvvGNb2DLli1xFzPGiJKMJhlIEs+UKXKyy/1z7xdtGdPlFkT3Bd2/oQDA//3YPYGm4eOPxTmtyllGb3ePaDNKwoXjof6Ytr/JRGvZ5TVJRq1BJyXCeKx3p9kmpco2vyK9qbXmpDkJz7iLYC8sLMSZM2e+MJ6Xl4e6ujoPSxJCJgLeQUeIJTDYCbEEBjshlsBgJ8QSGOyEWIJjkqjJzJw5E7m5uQCG9ftZs2Yla2kR+kE/vk5+NDU1ob293d1oJoji4uKJWvoO6Med0I87+Tr5wa/xhFgCg50QS/Dv2bNnz0QtXlxcPFFL3wH9uBP6cSdfFz+SekBHCJk4+DWeEEuYkGA/ceIEFi5ciPnz52Pfvn0T4QIAIDc3FwUFBSgqKkJJSUnS1q2urkZWVhaWLFkyMtbZ2YnVq1djwYIFWL16NW7cuDEhfuzZswc5OTkoKipCUVER/vjHP467H83NzVi1ahUWLVqExYsX4ze/+Q2A5O+J5Eey96S/vx8PP/wwli5disWLF+Opp54CAFy6dAkrVqzA/PnzsWnTJjVL0JVRn+cnSCQSMXl5eaaxsdEMDAyYwsJCc/78+WS7YYwxZt68eeb69etJX/fPf/6zee+998zixYtHxn72s5+ZZ555xhhjzDPPPGN+/vOfT4gfTz31lPn1r3897mt/nqtXr5r33nvPGGPMrVu3zIIFC8z58+eTvieSH8nek1gsZrq7u40xxgwODpqHH37YvPPOO+YHP/iBOXz4sDHGmB//+Mfmt7/9bULXTfone11dHebPn4+8vDykpqaisrISx48fT7YbE0ppaSlmzJhxx9jx48dRVVUFIHkFPN38mAiCwSCWLVsGAEhPT0d+fj5aWlqSvieSH8lmvIq8Jj3YW1pa7qjbPpHFKh3HwSOPPILi4mKEQqEJ8eFvtLW1IRgMAgBmz56Ntra2CfPlwIEDKCwsRHV1dVJ+TnyepqYmnDlzBitWrJjQPfm8H0Dy92Q8irxafUD31ltv4f3338ef/vQnPPfcc3jjjTcm2iUAw29CXqqXjAXbtm1DY2Mj6uvrEQwGsWvXrqSt3dPTg4qKCuzfvx/Tpk27w5bMPfl7PyZiT0ZT5FUi6cGek5OD5ubmkf9rxSqT4Qsw3HFm3bp1E1p5Jzs7G62trQCGS4FJXXCS4Yff74fP58PWrVuTtidDQ0OoqKjA5s2bsX79+hFfkr0nkh8TsSeAtyKvEkkP9uXLl6OhoQGXLl3C4OAgXnzxRZSXlyfbDfT29qK7u3vk3ydPnrzjVDrZlJeXo7a2FsDEFvD8W3ABwNGjR5OyJ8YYbNmyBfn5+di5c+fIeLL3RPIj2XsybkVex/gg8a545ZVXzIIFC0xeXp751a9+NREumMbGRlNYWGgKCwvNokWLkupHZWWlmT17tgkEAiYnJ8f8/ve/N+3t7eZb3/qWmT9/vikrKzMdHR0T4sePfvQjs2TJElNQUGC+//3vm6tXr467H2+++aYBYAoKCszSpUvN0qVLzSuvvJL0PZH8SPaenD171hQVFZmCggKzePFis3fvXmPM8Gt2+fLl5oEHHjAbNmww/f39CV2Xd9ARYglWH9ARYhMMdkIsgcFOiCUw2AmxBAY7IZbAYCfEEhjshFgCg50QS/h/6Y1l3p3eVwIAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "C-bLFBZ0Jp_H",
        "outputId": "500d3f68-428d-4603-b6f7-d9cf1b2ba764"
      },
      "source": [
        "show_example(*dataset[1099])"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Label:  airplane (0)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XMpExaTwJp_I"
      },
      "source": [
        "### Save and upload your notebook\n",
        "\n",
        "Whether you're running this Jupyter notebook online or on your computer, it's essential to save your work from time to time. You can continue working on a saved notebook later or share it with friends and colleagues to let them execute your code. [Jovian](https://jovian.ai/platform-features) offers an easy way of saving and sharing your Jupyter notebooks online."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YZQLAkj-Jp_I"
      },
      "source": [
        "!pip install jovian --upgrade -q"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Lz9-PiH6Jp_I"
      },
      "source": [
        "import jovian"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 139
        },
        "id": "0UdJfJBIJp_I",
        "outputId": "52d76c2d-f7f3-4a64-85e1-7eaf1f5a8146"
      },
      "source": [
        "jovian.commit(project=project_name)"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[jovian] Detected Colab notebook...\u001b[0m\n",
            "[jovian] Please enter your API key ( from https://jovian.ai/ ):\u001b[0m\n",
            "API KEY: ··········\n",
            "[jovian] Uploading colab notebook to Jovian...\u001b[0m\n",
            "[jovian] Capturing environment..\u001b[0m\n",
            "[jovian] Committed successfully! https://jovian.ai/rahulgarg95/05-cifar10-cnn\u001b[0m\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'https://jovian.ai/rahulgarg95/05-cifar10-cnn'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AvBIchACJp_I"
      },
      "source": [
        "`jovian.commit` uploads the notebook to your Jovian account, captures the Python environment, and creates a shareable link for your notebook, as shown above. You can use this link to share your work and let anyone (including you) run your notebooks and reproduce your work."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QxuW5ucGJp_J"
      },
      "source": [
        "## Training and Validation Datasets\n",
        "\n",
        "While building real world machine learning models, it is quite common to split the dataset into 3 parts:\n",
        "\n",
        "1. **Training set** - used to train the model i.e. compute the loss and adjust the weights of the model using gradient descent.\n",
        "2. **Validation set** - used to evaluate the model while training, adjust hyperparameters (learning rate etc.) and pick the best version of the model.\n",
        "3. **Test set** - used to compare different models, or different types of modeling approaches, and report the final accuracy of the model.\n",
        "\n",
        "Since there's no predefined validation set, we can set aside a small portion (5000 images) of the training set to be used as the validation set. We'll use the `random_split` helper method from PyTorch to do this. To ensure that we always create the same validation set, we'll also set a seed for the random number generator."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MSVGdRK3Jp_J"
      },
      "source": [
        "random_seed = 42\n",
        "torch.manual_seed(random_seed);"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yy5ekvZOJp_L",
        "outputId": "8578eaa6-0e63-47b9-da24-65c7f3a47475"
      },
      "source": [
        "val_size = 5000\n",
        "train_size = len(dataset) - val_size\n",
        "\n",
        "train_ds, val_ds = random_split(dataset, [train_size, val_size])\n",
        "len(train_ds), len(val_ds)"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(45000, 5000)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2aJ9STnUJp_L"
      },
      "source": [
        "The `jovian` library also provides a simple API for recording important parameters related to the dataset, model training, results etc. for easy reference and comparison between multiple experiments. Let's record `dataset_url`, `val_pct` and `rand_seed` using `jovian.log_dataset`."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "83Z33gU7Jp_M",
        "outputId": "68f35c50-6557-4667-cf3c-85cb13b7a326"
      },
      "source": [
        "jovian.log_dataset(dataset_url=dataset_url, val_size=val_size, random_seed=random_seed)"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[jovian] Dataset logged.\u001b[0m\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s8Hu-d6mJp_M"
      },
      "source": [
        "We can now create data loaders for training and validation, to load the data in batches"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7wM8WQFfJp_M"
      },
      "source": [
        "from torch.utils.data.dataloader import DataLoader\n",
        "\n",
        "batch_size=128"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lWXX_hHGJp_M"
      },
      "source": [
        "train_dl = DataLoader(train_ds, batch_size, shuffle=True, num_workers=4, pin_memory=True)\n",
        "val_dl = DataLoader(val_ds, batch_size*2, num_workers=4, pin_memory=True)"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0YM8SZq7Jp_M"
      },
      "source": [
        "We can look at batches of images from the dataset using the `make_grid` method from `torchvision`. Each time the following code is run, we get a different bach, since the sampler shuffles the indices before creating batches."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xEYh1NZxJp_N"
      },
      "source": [
        "from torchvision.utils import make_grid\n",
        "\n",
        "def show_batch(dl):\n",
        "    for images, labels in dl:\n",
        "        fig, ax = plt.subplots(figsize=(12, 6))\n",
        "        ax.set_xticks([]); ax.set_yticks([])\n",
        "        ax.imshow(make_grid(images, nrow=16).permute(1, 2, 0))\n",
        "        break"
      ],
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 361
        },
        "id": "IMSe7bd8Jp_N",
        "outputId": "3b7f73ab-9330-4d48-a7de-0e1c8e731785"
      },
      "source": [
        "show_batch(train_dl)"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JAzaYnunJp_N"
      },
      "source": [
        "Once again, let's save and commit our work using `jovian` before proceeding further."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 105
        },
        "id": "b5O2QNSGJp_O",
        "outputId": "edbe2c27-0ae7-4f8c-8eee-98d5b4f78a26"
      },
      "source": [
        "jovian.commit(project=project_name, environment=None)"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[jovian] Detected Colab notebook...\u001b[0m\n",
            "[jovian] Uploading colab notebook to Jovian...\u001b[0m\n",
            "[jovian] Attaching records (metrics, hyperparameters, dataset etc.)\u001b[0m\n",
            "[jovian] Committed successfully! https://jovian.ai/rahulgarg95/05-cifar10-cnn\u001b[0m\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'https://jovian.ai/rahulgarg95/05-cifar10-cnn'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "isNOQSEgJp_O"
      },
      "source": [
        "After the first commit, all subsequent commits record a new version of the notebook within the same Jovian project. You can use `jovian.commit` to version Jupyter notebooks (instead of doing `File > Save As`), and keep your data science projects organized. Also check out the [**Records**](https://jovian.ml/aakashns/05-cifar10-cnn/v/2/records) tab on the project page to see how the information logged using `jovian.log_dataset` appears on the UI.\n",
        "\n",
        "<a href=\"https://jovian.ml/aakashns/05-cifar10-cnn/v/2/records\"><img src=\"https://i.imgur.com/h0zkmn9.png\" style=\"width:400px\" ></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PkRCd9f4Jp_O"
      },
      "source": [
        "## Defining the Model (Convolutional Neural Network)\n",
        "\n",
        "In our [previous tutorial](https://jovian.ml/aakashns/04-feedforward-nn), we defined a deep neural network with fully-connected layers using `nn.Linear`. For this tutorial however, we will use a convolutional neural network, using the `nn.Conv2d` class from PyTorch.\n",
        "\n",
        "> The 2D convolution is a fairly simple operation at heart: you start with a kernel, which is simply a small matrix of weights. This kernel “slides” over the 2D input data, performing an elementwise multiplication with the part of the input it is currently on, and then summing up the results into a single output pixel. - [Source](https://towardsdatascience.com/intuitively-understanding-convolutions-for-deep-learning-1f6f42faee1)\n",
        "\n",
        "<img src=\"https://miro.medium.com/max/1070/1*Zx-ZMLKab7VOCQTxdZ1OAw.gif\" style=\"max-width:400px;\">\n",
        "\n",
        "\n",
        "Let us implement a convolution operation on a 1 channel image with a 3x3 kernel."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Tc-SGGw7Jp_O"
      },
      "source": [
        "def apply_kernel(image, kernel):\n",
        "    ri, ci = image.shape       # image dimensions\n",
        "    rk, ck = kernel.shape      # kernel dimensions\n",
        "    ro, co = ri-rk+1, ci-ck+1  # output dimensions\n",
        "    output = torch.zeros([ro, co])\n",
        "    for i in range(ro): \n",
        "        for j in range(co):\n",
        "            output[i,j] = torch.sum(image[i:i+rk,j:j+ck] * kernel)\n",
        "    return output"
      ],
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2Kp_anXMJp_P",
        "outputId": "519e72a7-207f-4620-8675-3c1bac1402fb"
      },
      "source": [
        "sample_image = torch.tensor([\n",
        "    [3, 3, 2, 1, 0], \n",
        "    [0, 0, 1, 3, 1], \n",
        "    [3, 1, 2, 2, 3], \n",
        "    [2, 0, 0, 2, 2], \n",
        "    [2, 0, 0, 0, 1]\n",
        "], dtype=torch.float32)\n",
        "\n",
        "sample_kernel = torch.tensor([\n",
        "    [0, 1, 2], \n",
        "    [2, 2, 0], \n",
        "    [0, 1, 2]\n",
        "], dtype=torch.float32)\n",
        "\n",
        "apply_kernel(sample_image, sample_kernel)"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[12., 12., 17.],\n",
              "        [10., 17., 19.],\n",
              "        [ 9.,  6., 14.]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JZFVHVT7Jp_P"
      },
      "source": [
        "For multi-channel images, a different kernel is applied to each channels, and the outputs are added together pixel-wise. \n",
        "\n",
        "Checking out the following articles to gain a better understanding of convolutions:\n",
        "\n",
        "1. [Intuitively understanding Convolutions for Deep Learning](https://towardsdatascience.com/intuitively-understanding-convolutions-for-deep-learning-1f6f42faee1) by Irhum Shafkat\n",
        "2. [Convolutions in Depth](https://sgugger.github.io/convolution-in-depth.html) by Sylvian Gugger (this article implements convolutions from scratch)\n",
        "\n",
        "There are certain advantages offered by convolutional layers when working with image data:\n",
        "\n",
        "* **Fewer parameters**: A small set of parameters (the kernel) is used to calculate outputs of the entire image, so the model has much fewer parameters compared to a fully connected layer. \n",
        "* **Sparsity of connections**: In each layer, each output element only depends on a small number of input elements, which makes the forward and backward passes more efficient.\n",
        "* **Parameter sharing and spatial invariance**: The features learned by a kernel in one part of the image can be used to detect similar pattern in a different part of another image.\n",
        "\n",
        "We will also use a [max-pooling](https://computersciencewiki.org/index.php/Max-pooling_/_Pooling) layers to progressively decrease the height & width of the output tensors from each convolutional layer.\n",
        "\n",
        "<img src=\"https://computersciencewiki.org/images/8/8a/MaxpoolSample2.png\" style=\"max-width:400px;\">\n",
        "\n",
        "Before we define the entire model, let's look at how a single convolutional layer followed by a max-pooling layer operates on the data."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CKaWyJtUJp_P"
      },
      "source": [
        "import torch.nn as nn\n",
        "import torch.nn.functional as F"
      ],
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YXmQxqS0Jp_P"
      },
      "source": [
        "simple_model = nn.Sequential(\n",
        "    nn.Conv2d(3, 8, kernel_size=3, stride=1, padding=1),\n",
        "    nn.MaxPool2d(2, 2)\n",
        ")"
      ],
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UnuN34GwJp_P"
      },
      "source": [
        "Refer to [Sylvian's post](https://sgugger.github.io/convolution-in-depth.html) for an explanation of `kernel_size`, `stride` and `padding`.    "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LyxDOGH_Jp_P",
        "outputId": "733b726f-3223-40b1-c7a6-4482a81bd63b"
      },
      "source": [
        "for images, labels in train_dl:\n",
        "    print('images.shape:', images.shape)\n",
        "    out = simple_model(images)\n",
        "    print('out.shape:', out.shape)\n",
        "    break"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "images.shape: torch.Size([128, 3, 32, 32])\n",
            "out.shape: torch.Size([128, 8, 16, 16])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WSKyZ2xIJp_P"
      },
      "source": [
        "The `Conv2d` layer transforms a 3-channel image to a 16-channel *feature map*, and the `MaxPool2d` layer halves the height and width. The feature map gets smaller as we add more layers, until we are finally left with a small feature map, which can be flattened into a vector. We can then add some fully connected layers at the end to get vector of size 10 for each image.\n",
        "\n",
        "<img src=\"https://i.imgur.com/KKtPOKE.png\" style=\"max-width:540px\">\n",
        "\n",
        "Let's define the model by extending an `ImageClassificationBase` class which contains helper methods for training & validation."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WgbZ6gLOJp_Q"
      },
      "source": [
        "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 [{}], train_loss: {:.4f}, val_loss: {:.4f}, val_acc: {:.4f}\".format(\n",
        "            epoch, result['train_loss'], result['val_loss'], result['val_acc']))\n",
        "        \n",
        "def accuracy(outputs, labels):\n",
        "    _, preds = torch.max(outputs, dim=1)\n",
        "    return torch.tensor(torch.sum(preds == labels).item() / len(preds))"
      ],
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rw-AyZcjJp_Q"
      },
      "source": [
        "\n",
        "We'll use `nn.Sequential` to chain the layers and activations functions into a single network architecture."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F4xrvghlJp_Q"
      },
      "source": [
        "class Cifar10CnnModel(ImageClassificationBase):\n",
        "    def __init__(self):\n",
        "        super().__init__()\n",
        "        self.network = nn.Sequential(\n",
        "            nn.Conv2d(3, 32, kernel_size=3, padding=1),\n",
        "            nn.ReLU(),\n",
        "            nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1),\n",
        "            nn.ReLU(),\n",
        "            nn.MaxPool2d(2, 2), # output: 64 x 16 x 16\n",
        "\n",
        "            nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1),\n",
        "            nn.ReLU(),\n",
        "            nn.Conv2d(128, 128, kernel_size=3, stride=1, padding=1),\n",
        "            nn.ReLU(),\n",
        "            nn.MaxPool2d(2, 2), # output: 128 x 8 x 8\n",
        "\n",
        "            nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1),\n",
        "            nn.ReLU(),\n",
        "            nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1),\n",
        "            nn.ReLU(),\n",
        "            nn.MaxPool2d(2, 2), # output: 256 x 4 x 4\n",
        "\n",
        "            nn.Flatten(), \n",
        "            nn.Linear(256*4*4, 1024),\n",
        "            nn.ReLU(),\n",
        "            nn.Linear(1024, 512),\n",
        "            nn.ReLU(),\n",
        "            nn.Linear(512, 10))\n",
        "        \n",
        "    def forward(self, xb):\n",
        "        return self.network(xb)"
      ],
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tU5q48KeJp_Q",
        "outputId": "91e8dc2b-734a-46e7-a122-585b7a8a5f99"
      },
      "source": [
        "model = Cifar10CnnModel()\n",
        "model"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Cifar10CnnModel(\n",
              "  (network): Sequential(\n",
              "    (0): Conv2d(3, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (1): ReLU()\n",
              "    (2): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (3): ReLU()\n",
              "    (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "    (5): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (6): ReLU()\n",
              "    (7): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (8): ReLU()\n",
              "    (9): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "    (10): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (11): ReLU()\n",
              "    (12): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (13): ReLU()\n",
              "    (14): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "    (15): Flatten(start_dim=1, end_dim=-1)\n",
              "    (16): Linear(in_features=4096, out_features=1024, bias=True)\n",
              "    (17): ReLU()\n",
              "    (18): Linear(in_features=1024, out_features=512, bias=True)\n",
              "    (19): ReLU()\n",
              "    (20): Linear(in_features=512, out_features=10, bias=True)\n",
              "  )\n",
              ")"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "loJN3i6_Jp_Q"
      },
      "source": [
        "Let's verify that the model produces the expected output on a batch of training data. The 10 outputs for each image can be interpreted as probabilities for the 10 target classes (after applying softmax), and the class with the highest probability is chosen as the label predicted by the model for the input image. Check out [Part 3 (logistic regression)](https://jovian.ml/aakashns/03-logistic-regression#C50) for a more detailed discussion on interpeting the outputs, applying softmax and identifying the predicted labels."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YCm2I82pJp_Q",
        "outputId": "50fbcd43-3a63-488c-948c-c33cc112058e"
      },
      "source": [
        "for images, labels in train_dl:\n",
        "    print('images.shape:', images.shape)\n",
        "    out = model(images)\n",
        "    print('out.shape:', out.shape)\n",
        "    print('out[0]:', out[0])\n",
        "    break"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "images.shape: torch.Size([128, 3, 32, 32])\n",
            "out.shape: torch.Size([128, 10])\n",
            "out[0]: tensor([ 0.0227,  0.0050,  0.0075, -0.0167,  0.0360, -0.0371, -0.0080, -0.0307,\n",
            "        -0.0291,  0.0121], grad_fn=<SelectBackward>)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oASDWWc9Jp_R"
      },
      "source": [
        "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 the [previous tutorial](https://jovian.ml/aakashns/04-feedforward-nn#C21)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wR9oVOopJp_R"
      },
      "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)"
      ],
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5zBM9zOqJp_R"
      },
      "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",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iczDAm1SJp_R",
        "outputId": "9ff97636-2a18-4c60-8200-ddc9e1ea03fe"
      },
      "source": [
        "device = get_default_device()\n",
        "device"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "device(type='cuda')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zdlOEla3Jp_R"
      },
      "source": [
        "We can now wrap our training and validation data loaders using `DeviceDataLoader` for automatically transferring batches of data to the GPU (if available), and use `to_device` to move our model to the GPU (if available)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vx3BnG8IJp_R"
      },
      "source": [
        "train_dl = DeviceDataLoader(train_dl, device)\n",
        "val_dl = DeviceDataLoader(val_dl, device)\n",
        "to_device(model, device);"
      ],
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CICXCTtpJp_R"
      },
      "source": [
        "Once again, let's save and commit the notebook before we proceed further."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        },
        "id": "js6r9zuBJp_R",
        "outputId": "0a46ebdf-5f01-4eb5-fc71-cdb282f3ed00"
      },
      "source": [
        "jovian.commit(project=project_name)"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[jovian] Detected Colab notebook...\u001b[0m\n",
            "[jovian] Uploading colab notebook to Jovian...\u001b[0m\n",
            "[jovian] Capturing environment..\u001b[0m\n",
            "[jovian] Attaching records (metrics, hyperparameters, dataset etc.)\u001b[0m\n",
            "[jovian] Committed successfully! https://jovian.ai/rahulgarg95/05-cifar10-cnn\u001b[0m\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'https://jovian.ai/rahulgarg95/05-cifar10-cnn'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0qq2eMlEJp_S"
      },
      "source": [
        "## Training the Model\n",
        "\n",
        "We'll define two functions: `fit` and `evaluate` to train the model using gradient descent and evaluate its performance on the validation set. For a detailed walkthrough of these functions, check out the [previous tutorial](https://jovian.ai/aakashns/03-logistic-regression)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aEQkLCaYJp_S"
      },
      "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 fit(epochs, lr, model, train_loader, val_loader, opt_func=torch.optim.SGD):\n",
        "    history = []\n",
        "    optimizer = opt_func(model.parameters(), lr)\n",
        "    for epoch in range(epochs):\n",
        "        # Training Phase \n",
        "        model.train()\n",
        "        train_losses = []\n",
        "        for batch in train_loader:\n",
        "            loss = model.training_step(batch)\n",
        "            train_losses.append(loss)\n",
        "            loss.backward()\n",
        "            optimizer.step()\n",
        "            optimizer.zero_grad()\n",
        "        # Validation phase\n",
        "        result = evaluate(model, val_loader)\n",
        "        result['train_loss'] = torch.stack(train_losses).mean().item()\n",
        "        model.epoch_end(epoch, result)\n",
        "        history.append(result)\n",
        "    return history"
      ],
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mGbR0ClAJp_S"
      },
      "source": [
        "Before we begin training, let's instantiate the model once again and see how it performs on the validation set with the initial set of parameters."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FOI6pwRmJp_S"
      },
      "source": [
        "model = to_device(Cifar10CnnModel(), device)"
      ],
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rWO9y9TrJp_S",
        "outputId": "e0d93abd-5938-4ab9-b2c4-f3497cfe9096"
      },
      "source": [
        "evaluate(model, val_dl)"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'val_acc': 0.10143611580133438, 'val_loss': 2.3031394481658936}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WEuauDpwJp_S"
      },
      "source": [
        "The initial accuracy is around 10%, which is what one might expect from a randomly intialized model (since it has a 1 in 10 chance of getting a label right by guessing randomly).\n",
        "\n",
        "We'll use the following *hyperparmeters* (learning rate, no. of epochs, batch_size etc.) to train our model. As an exercise, you can try changing these to see if you have achieve a higher accuracy in a shorter time. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HdSA8RWDJp_S"
      },
      "source": [
        "num_epochs = 10\n",
        "opt_func = torch.optim.Adam\n",
        "lr = 0.001"
      ],
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-6lIc9bUJp_S"
      },
      "source": [
        "It's important to record the hyperparameters of every experiment you do, to replicate it later and compare it against other experiments. We can record them using `jovian.log_hyperparams`."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PfD4is6FJp_S",
        "outputId": "8df211b6-043a-4bbf-bd12-4b6167c04921"
      },
      "source": [
        "jovian.reset()\n",
        "jovian.log_hyperparams({\n",
        "    'num_epochs': num_epochs,\n",
        "    'opt_func': opt_func.__name__,\n",
        "    'batch_size': batch_size,\n",
        "    'lr': lr,\n",
        "})"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[jovian] Hyperparams logged.\u001b[0m\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vZK4Y2XrJp_T",
        "outputId": "59ccae20-7d60-4408-8191-af4c648cc5b6"
      },
      "source": [
        "history = fit(num_epochs, lr, model, train_dl, val_dl, opt_func)"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch [0], train_loss: 1.7821, val_loss: 1.4546, val_acc: 0.4459\n",
            "Epoch [1], train_loss: 1.2688, val_loss: 1.1522, val_acc: 0.5863\n",
            "Epoch [2], train_loss: 1.0045, val_loss: 0.9518, val_acc: 0.6582\n",
            "Epoch [3], train_loss: 0.8198, val_loss: 0.8185, val_acc: 0.7102\n",
            "Epoch [4], train_loss: 0.6833, val_loss: 0.7582, val_acc: 0.7451\n",
            "Epoch [5], train_loss: 0.5721, val_loss: 0.6938, val_acc: 0.7611\n",
            "Epoch [6], train_loss: 0.4835, val_loss: 0.7278, val_acc: 0.7599\n",
            "Epoch [7], train_loss: 0.4064, val_loss: 0.7878, val_acc: 0.7568\n",
            "Epoch [8], train_loss: 0.3291, val_loss: 0.7066, val_acc: 0.7742\n",
            "Epoch [9], train_loss: 0.2584, val_loss: 0.8169, val_acc: 0.7734\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bhybBuPkJp_T"
      },
      "source": [
        "Just as we have recorded the hyperparameters, we can also record the final metrics achieved by the model using `jovian.log_metrics` for reference, analysis and comparison."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z5Kxuq9EJp_T",
        "outputId": "47a87d39-b894-40fd-b8bb-f07a25b0caeb"
      },
      "source": [
        "jovian.log_metrics(train_loss=history[-1]['train_loss'], \n",
        "                   val_loss=history[-1]['val_loss'], \n",
        "                   val_acc=history[-1]['val_acc'])"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[jovian] Metrics logged.\u001b[0m\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "55K2kRe6Jp_T"
      },
      "source": [
        "We can also plot the valdation set accuracies to study how the model improves over time."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UP7E5_GhJp_T"
      },
      "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');"
      ],
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "KYgnxqQQJp_T",
        "outputId": "f320fa2a-c095-4531-aa4c-f64dc6895ca6"
      },
      "source": [
        "plot_accuracies(history)"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dPmvnBsaJp_T"
      },
      "source": [
        "Our model reaches an accuracy of around 75%, and by looking at the graph, it seems unlikely that the model will achieve an accuracy higher than 80% even after training for a long time. This suggests that we might need to use a more powerful model to capture the relationship between the images and the labels more accurately. This can be done by adding more convolutional layers to our model, or incrasing the no. of channels in each convolutional layer, or by using regularization techniques.\n",
        "\n",
        "We can also plot the training and validation losses to study the trend."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Oefp3XHjJp_U"
      },
      "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');"
      ],
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "KBpeN5YhJp_U",
        "outputId": "abb5aaf6-4191-4cc2-c770-ca6789db6aab"
      },
      "source": [
        "plot_losses(history)"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v0ZkttR-Jp_U"
      },
      "source": [
        "Initialy, both the training and validation losses seem to decrease over time. However, if you train the model for long enough, you will notice that the training loss continues to decrease, while the validation loss stops decreasing, and even starts to increase after a certain point! \n",
        "\n",
        "<img src=\"https://i.stack.imgur.com/1QU0m.png\" style=\"max-width:400px;\">\n",
        "\n",
        "This phenomenon is called **overfitting**, and it is the no. 1 why many machine learning models give rather terrible results on real-world data. It happens because the model, in an attempt to minimize the loss, starts to learn patters are are unique to the training data, sometimes even memorizing specific training examples. Because of this, the model does not generalize well to previously unseen data.\n",
        "\n",
        "\n",
        "Following are some common stragegies for avoiding overfitting:\n",
        "\n",
        "- Gathering and generating more training data, or adding noise to it\n",
        "- Using regularization techniques like batch normalization & dropout\n",
        "- Early stopping of model's training, when validation loss starts to increase\n",
        "\n",
        "We will cover these topics in more detail in the next tutorial in this series, and learn how we can reach an accuracy of **over 90%** by making minor but important changes to our model."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "my239tuTJp_U"
      },
      "source": [
        "Before continuing, let us save our work to the cloud using `jovian.commit`."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        },
        "id": "4bLvOFf-Jp_U",
        "outputId": "a48951e0-8e86-44dd-925b-45b8a1f8fabd"
      },
      "source": [
        "jovian.commit(project=project_name)"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[jovian] Detected Colab notebook...\u001b[0m\n",
            "[jovian] Uploading colab notebook to Jovian...\u001b[0m\n",
            "[jovian] Capturing environment..\u001b[0m\n",
            "[jovian] Attaching records (metrics, hyperparameters, dataset etc.)\u001b[0m\n",
            "[jovian] Committed successfully! https://jovian.ai/rahulgarg95/05-cifar10-cnn\u001b[0m\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'https://jovian.ai/rahulgarg95/05-cifar10-cnn'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-jcncNcgJp_U"
      },
      "source": [
        "When you try different experiments (by chaging the learning rate, batch size, optimizer etc.) and record hyperparameters and metrics with each version of your notebook, you can use the [**Compare**](https://jovian.ml/aakashns/05-cifar10-cnn/compare) view on the project page to analyze which approaches are working well and which ones aren't. You sort/filter by accuracy, loss etc., add notes for each version and even invite collaborators to contribute to your project with their own experiments.\n",
        "\n",
        "<a href=\"https://jovian.ml/aakashns/05-cifar10-cnn\"><img src=\"https://i.imgur.com/p1Z3vgN.png\"></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IqN4ozY6Jp_U"
      },
      "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. We begin by creating a test dataset using the `ImageFolder` class.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7stwSbarJp_U"
      },
      "source": [
        "test_dataset = ImageFolder(data_dir+'/test', transform=ToTensor())"
      ],
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ckEBASy7Jp_U"
      },
      "source": [
        "Let's define a helper function `predict_image`, which returns the predicted label for a single image tensor."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QidsbfBLJp_U"
      },
      "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 dataset.classes[preds[0].item()]"
      ],
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "C_fkqvIDJp_V",
        "outputId": "a0ce49bf-0c9d-47ce-8c53-91544723d21d"
      },
      "source": [
        "img, label = test_dataset[0]\n",
        "plt.imshow(img.permute(1, 2, 0))\n",
        "print('Label:', dataset.classes[label], ', Predicted:', predict_image(img, model))"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Label: airplane , Predicted: airplane\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "dNlbM9l7Jp_V",
        "outputId": "a76c5f8c-1108-44b7-f2e7-d6c27d679baa"
      },
      "source": [
        "img, label = test_dataset[1002]\n",
        "plt.imshow(img.permute(1, 2, 0))\n",
        "print('Label:', dataset.classes[label], ', Predicted:', predict_image(img, model))"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Label: automobile , Predicted: automobile\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPsAAAD5CAYAAADhukOtAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAcQklEQVR4nO2df0xW9/XH349QkSqIU/khOBg/pvymArI1DRljuMVuGMUpnUYanCymS4y6bv61YrJYkq2JTdSkjzUZswmdWaJkdbJ2tda20dKniEtrnRRBERFB1AIKItzvH0ZS6z3nkQs8j99+3q+/9HM4957nc+/hPnze95yPy7IsC4SQbz1T/B0AIcQ3MNkJMQQmOyGGwGQnxBCY7IQYApOdEEMIHI9zXV0dNm3ahOHhYfz617/Gtm3b1J+fM2cO4uLixnPKB/C1aujkfJqPy+UaTzi2jIyMTOjxtBg1m/S5tfi0uXJ6radMmdjnmdP5cPK5tdgDAgJsxy9cuIDu7m5bm+NkHx4exgsvvIB33nkHMTExyM3NRXFxMVJSUkSfuLg4eDwep6d8iKGhoQk71n20m0o6n+YzPDws2pxcTG/HvHPnju24drNpN+nUqVNFW2CgfPtIcQwODoo+mu3u3buiTSMoKMh23GnSavOhXc/bt2+LNumzzZgxQ/QJDQ21Hc/LyxN9HP/aq6+vR2JiIuLj4zF16lSUlpaitrbW6eEIIZOM42Rvb2/H/PnzR/8fExOD9vb2CQmKEDLxjOtv9kfB7XbD7XYDALq6uib7dIQQAcdP9ujoaLS1tY3+/9KlS4iOjn7o5yoqKuDxeODxeDB37lynpyOEjBPHyZ6bm4umpia0tLTgzp07ePPNN1FcXDyRsRFCJhDHX+MDAwOxa9cu/PSnP8Xw8DDKy8uRmprqOBAn0sREy0KAvsIvrRZrq9Ia2gqzLyU0DS0OacVds2lKglNVQ7NJ8WtqhxZHf3//mM8FAGfPnhVtCQkJtuOzZs0SfZwwrr/Zly5diqVLl05ULISQSYRv0BFiCEx2QgyByU6IITDZCTEEJjshhjDpb9BNBE5kKKeFExqSfKVJb1rsmmTkJA7At5WAWhxSUYgmeWnXzKlkd+vWLdtxrWhFk1+1a63ZnnzySdEWFRVlO64Vwkhzr14T0UII+VbBZCfEEJjshBgCk50QQ2CyE2IIj81qvJMiCG2lW1tR1VZ2tbZD06ZNsx13WlThtBDGSVGL01542qq1tvoszYlWPKNdFy3GgYEB0SbNoxa7dJ0BYObMmaJNQ1uNDw4Oth2f6HuAT3ZCDIHJToghMNkJMQQmOyGGwGQnxBCY7IQYgk+lN8uyRLnJSbGL5qPJWppNKpwAgL6+PttxTebTZDlN4gkJCRFtTzzxxJjP53QbJKeFH05269GOp31mTbKTrrUmXU2fPl20aTFq91VYWNiY/Zz0WNQkVj7ZCTEEJjshhsBkJ8QQmOyEGAKTnRBDYLITYgjjkt7i4uIQEhKCgIAABAYGwuPxePWRZAYnVVnaBveffPKJaLt69apo06qypPNpG1Y67bkWGhoq2iIiIkRbTEyM7bi2lZAmazmV7KTr6XQbJy0OTcJ0EodWoaZdT0ma9YZUaalVYEoSoCbXjVtnf++99zBnzpzxHoYQMsnwazwhhjCuZHe5XFiyZAmys7PhdrsnKiZCyCQwrq/xH374IaKjo3H16lUUFRVh4cKFyM/Pf+Bn3G736C+Crq6u8ZyOEDIOxvVkj46OBgCEh4dj+fLlqK+vf+hnKioq4PF44PF41IUsQsjk4jjZ+/v70dvbO/rvt99+G2lpaRMWGCFkYnH8Nb6zsxPLly8HcE9C+tWvfoWf/exnXv2cNMqT0KSOhoYG0XblyhXRpsk4kvyjNTzUZLLIyEjRpjU2vHjxomiz+3YF6FJeUlKSaIuNjRVt3/nOd0SbJOdpEqtWKadJb5qMFhQUNOY4tGpKp1t9aZJuf3+/7fiXX34p+iQmJtqOa3Ku42SPj4/H6dOnnboTQnwMpTdCDIHJToghMNkJMQQmOyGGwGQnxBAem73eNCSZZMaMGaKPE6kD0KULSWJrbm4WfTSpRpPeUlJSRNvg4KBoa2xstB3XKtskH0CWrgBg4cKFok2SYcPDw0UfTULTZC0ne+1pPloc2txrsu25c+dEm3Rfffzxx6KPJL3dvHlT9OGTnRBDYLITYghMdkIMgclOiCEw2QkxBJ9v/6StdGp+dmgrxVrBjRbDggULRJtUeHPkyBHRR1tF1opk7pcP2/HVV1+Jtnnz5tmOL168WPTRClC01V2tWEda7da219JsmkripABFuz+0ohttNV5TPLTPJvW8O3v2rOhz/fr1MZ+HT3ZCDIHJToghMNkJMQQmOyGGwGQnxBCY7IQYgs8LYSTJQ5PDJNlFK2bQZCFt+ydNPpGKDzSp5oc//KFoKywsFG1ZWVmiTSvykfqWaXOVmZkp2rRebR0dHaJNup6SZATo8ppTOUyS5bRzaZ9Z25JJ621448YN0SYVFK1YsUL0OXPmjO24dp35ZCfEEJjshBgCk50QQ2CyE2IITHZCDIHJTogheJXeysvL8dZbbyE8PByfffYZAKCnpwerV69Ga2sr4uLicODAAcyaNcvrySzLEmUjrXJJqsrSZDKtIk6Tf7S+X7Nnz7Yd16QaTdaSpDxAl7XCwsJEW3BwsO24Nh+anKTNlbbtlSRtaRKr1q9Pm2NNlpP8tPvNaYyaBHj58mXRJvUw1CrzpK23tPi8Ptmff/551NXVPTBWVVWFwsJCNDU1obCwEFVVVd4OQwjxM16TPT8//6HfIrW1tSgrKwMAlJWV4dChQ5MTHSFkwnD0N3tnZyeioqIA3GuH3NnZOaFBEUImnnG/LutyudS/LdxuN9xuNwDg2rVr4z0dIcQhjp7sERERowtIHR0dauP/iooKeDweeDwecYGLEDL5OEr24uJiVFdXAwCqq6uxbNmyCQ2KEDLxeP0a/9xzz+HYsWPo7u5GTEwMtm/fjm3btmHVqlXYt28fYmNjceDAgUc62a1bt9SthiRu375tO65VtknNIQGoawyanCRJVJp0JcXuDS1GqeIJkKurtMaR77//vmhzIvMB9+RZO5xWtmlo10ySerVzaTF2dXWJNu3PWU2a/uc//2k7rkmAc+fOtR3XYvea7DU1Nbbj7777rjdXQshjBN+gI8QQmOyEGAKTnRBDYLITYghMdkIMwacNJ/v6+nD8+HFb20RXvWlNJefPny/atDikyjEtjhMnTog27Y1CTRr64osvRJtEZGSkaLtw4YJoCw0NFW3Tpk0TbZIs6qQ5pDebJm9OZINTQJYUAb2KUdufT7p/+vv7xxyHKm2KFkLItwomOyGGwGQnxBCY7IQYApOdEENgshNiCD6V3lwul9gQz0mFj1ZHn5GRIdrmzZsn2rQCHycVVOfOnRNt58+fF22xsbGiTZNXJFlRm98ZM2aINk3+0fYVk+RSLXbJB9ClN00e1KQyJ+eSGj0CelNMrSIuJibGdlzr/yCdS5OB+WQnxBCY7IQYApOdEENgshNiCEx2QgzBp6vxwcHB4nZIWh8xaVUyOztb9HnyySdF29NPPy3atJVpqX+etrWS9rm0QhitcEJb9Y2IiLAdLygoEH20oiFt2yInhSva9kRaQYvW5+/73/++aDt69KjtuNSrDwBSU1NFm3ZfacqFpq5I59PuRelzjWv7J0LItwMmOyGGwGQnxBCY7IQYApOdEENgshNiCF6lt/Lycrz11lsIDw/HZ599BgCorKzE3r17RwtUduzYgaVLl3o9WVBQEBISEmxtt27dEv2kXmearKXJSZqkUV5ePuZjahKUVpig2bTP5qRgRJOuvvvd74q2p556SrRpSAUv2lxp/em0AporV66ItvsbkH6T7u5u0WfhwoWiTZM9tfi17Z/ee+8923GtUEq6B7Q88vpkf/7551FXV/fQ+ObNm9HY2IjGxsZHSnRCiH/xmuz5+flqWR8h5P8Hjv9m37VrFzIyMlBeXo7r169PZEyEkEnAUbJv3LgRzc3NaGxsRFRUFLZu3Sr+rNvtRk5ODnJycvhLgRA/4ijZIyIiEBAQgClTpmDDhg2or68Xf7aiogIejwcej0ddpCCETC6Okv3rK5wHDx5EWlrahAVECJkcvEpvzz33HI4dO4bu7m7ExMRg+/btOHbsGBobG+FyuRAXF4fXXnvtkU42MjKCvr4+W5vWo0uSa5qamkSf+zKhHQsWLBBt+fn5oi0kJES0SWgy2c2bN0WbJuNER0eLNqnXnHYuTZbTPrPmp0lsElovP+14Fy9eFG1tbW2241rsLS0tok2be60iTqselKRgTT6WrrMmUXpN9pqamofG1q9f782NEPKYwTfoCDEEJjshhsBkJ8QQmOyEGAKTnRBD8GnDye7ubrz++uu2Nk1aiYyMtB3XKsO06qSoqCjRph1TqlLTZENpyygAaGhoEG1dXV2iTWuwmJiYaDuuSWjaXGk2bUspSXLU5krbTkqbR00qk+ZDa+ip3QNaJdrPf/5z0aZJsJLkmJKSIvpI9we3fyKEMNkJMQUmOyGGwGQnxBCY7IQYApOdEEPwqfQWGhqKJUuW2Nr+/ve/i369vb2241qVkdZKKzQ0VLRpe4CFhYWJNgmtkis5OVm0SfvbAcC8efNEm9RwMDg4WPTRYtSqqDSbE6SKSG9ITSUBudGj1nBSqzjUZE9tPrRr1traOubjSXvHadV8fLITYghMdkIMgclOiCEw2QkxBCY7IYbg09X4gIAAcZV89erVot+BAwdsx7XigunTp4s2bSX2/pZWdkgr2lqxiFb4oXXb1dQEbcVVUi4CA+VLra3Ga4UV2lZDUsfh48ePiz5an7aZM2eKNq0v3I9+9CPb8SNHjog+7777rmjTimS0Oda2HJOumaYySIVB2go+n+yEGAKTnRBDYLITYghMdkIMgclOiCEw2QkxBK/SW1tbG9atW4fOzk64XC5UVFRg06ZN6OnpwerVq9Ha2oq4uDgcOHDA68aNg4ODYg8vrfDjBz/4ge349773PdFHk940qUwrrpH6lmn987S+ak7RJB4p/qCgIEfn0opT/vrXv4q2V1991Xb89u3boo+2LZcms2pS6hdffGE7PmfOHNFH662nnUuTKZubm0WbVHij+UgypTa/Xp/sgYGBeOWVV3DmzBmcPHkSu3fvxpkzZ1BVVYXCwkI0NTWhsLAQVVVV3g5FCPEjXpM9KioKixYtAnCvQ2lycjLa29tRW1uLsrIyAEBZWRkOHTo0uZESQsbFmP5mb21txalTp5CXl4fOzs7RlsyRkZHo7OyclAAJIRPDI78u29fXh5KSEuzcufOh5g8ul0t8LdTtdsPtdo8egxDiHx7pyT40NISSkhKsWbMGK1asAABERESMvrvb0dGB8PBwW9+Kigp4PB54PB71/WBCyOTiNdkty8L69euRnJyMLVu2jI4XFxejuroaAFBdXY1ly5ZNXpSEkHHj9Wv8Rx99hP379yM9PR1ZWVkAgB07dmDbtm1YtWoV9u3bh9jYWLEy7YGTBQaKEptWQSX1fps/f77oo1WbadJVT0/PmP00WUiT5bTqNc2mxS9JL1rfPa0/3d/+9jfR9pe//EW0JSUl2Y6vXbtW9ImIiBBtGmfPnhVt+/btsx3XqtcyMzNFmyalanKvtm2UdD2lOQSA69ev245rFYxek/2ZZ54RdWmtFJAQ8njBN+gIMQQmOyGGwGQnxBCY7IQYApOdEEPwacPJoaEhXLlyxdYWExMj+kkyVEJCguijSRBaVZO29Y8k50kNAwG9CkmT0DSJR5PzpPNJDQoBoKmpSbS98cYbou3+69J2/Pa3v7Udb2hoEH32798v2rStt5599lnR9uKLL9qO7927V/TR0Bo6Svc2oDcQlZpzalKeVPGpVTfyyU6IITDZCTEEJjshhsBkJ8QQmOyEGAKTnRBD8Kn0Njw8jJs3b9ratCo1qRBHk9C0BpbTpk0TbVp1mNTMUavW0qQQTXrTmmJqFVv9/f224x9//LHos2fPHtGmdSBat26daJMaIv773/8WfTQJU5MH//e//4m2yspK2/Hc3FzRR2sqmZ6eLto0WU67v6UKNq3ZiyRFavcNn+yEGAKTnRBDYLITYghMdkIMgclOiCH4dDU+KChIfIG/vb1d9JNWz3ft2iX6dHV1iTaty622/ZPkN3fuXNFHK+DQtiCaOXOmaNNWXC9dumQ7fvr0adFHmyvts8XGxoo2adslDadbVEkKBAAcPnzYdrygoED0qampEW3aFmfaPGo9FqWiLa0PoXTvaAoPn+yEGAKTnRBDYLITYghMdkIMgclOiCEw2QkxBK/SW1tbG9atW4fOzk64XC5UVFRg06ZNqKysxN69e0elmR07dmDp0qXqsSzLErdKamtrG3PwUgEBoBcz3LhxY8znAmSJROt3pxVAaH6axKMdUyrG0HraaXKjJuVo8qCTXnhanzZtWy5NipTuA01SvHr1qmiTCnwAICQkRLQdO3ZMtF24cMF2XCuwknolanPhNdkDAwPxyiuvYNGiRejt7UV2djaKiooAAJs3b8bvfvc7b4cghDwGeE32qKio0S6iISEhSE5OVl+AIYQ8nozpb/bW1lacOnUKeXl5AO69wZaRkYHy8nL1KzUhxP88crL39fWhpKQEO3fuRGhoKDZu3Ijm5mY0NjYiKioKW7dutfVzu93IyclBTk6O2pyAEDK5PFKyDw0NoaSkBGvWrMGKFSsA3Fs8CAgIwJQpU7Bhwwax0X1FRQU8Hg88Ho+6gEEImVy8JrtlWVi/fj2Sk5OxZcuW0fGOjo7Rfx88eBBpaWmTEyEhZELwukD30UcfYf/+/UhPT0dWVhaAezJbTU0NGhsb4XK5EBcXh9dee83ryQYGBnDu3Dlb2wcffCD6SXKStrXSE0884cimSUMSmoSmSSGahKb1mXOybZS2ZZT2mbXtsDS/6Oho23HtTzntumjzoflpfeEktGt29uxZR3FoflIFmyaJtra22o5r18trsj/zzDO2H96bpk4IebzgG3SEGAKTnRBDYLITYghMdkIMgclOiCH4tOFkX18fjh8/bh+IIidJDSenT58u+jitNtOqq6SmgVrsWhza9lXa1j9aBZs0V1ocUiUioDdz/OSTT0Tb/WKpb6JtvXX+/HnRpr2QpcUvVY5pn0vaogwATp48Kdq0z/b000+Ltp/85Ce245qM1tzcbDuuyX98shNiCEx2QgyByU6IITDZCTEEJjshhsBkJ8QQfCq9TZ06FTExMbY2rdJIkqg06cpJZRig778mdeMJDg52dC6tEk2TXZygVahpcWj7jTU0NIg2SU564YUXRJ89e/aINk1e0xpVLlu2zHb8P//5j+ij3YvafnTaHoKanCf5LVy4UPSR8qi2tlb04ZOdEENgshNiCEx2QgyByU6IITDZCTEEJjshhuBT6W1kZERsHOik0aMmGWlVXk73NpNsmrzmVJbTJB6tak86piZdac0cNQlQO+ahQ4dsx5999lnRZ/fu3aLtypUrok2rNjtx4oTteF1dneijzb02V5r01tLSItqkCrb58+eLPpGRkbbj2v3LJzshhsBkJ8QQmOyEGAKTnRBDYLITYgheV+MHBgaQn5+PwcFB3L17FytXrsT27dvR0tKC0tJSXLt2DdnZ2di/f79aNAEAs2fPxtq1a21tWlGLZNNWs7UVa22lXtt7XupPN2/ePNFH26JKi0PrJaatkEsKhXY8LQ7Npm2tJH3u7u5u0Sc2Nla0Sb3kAODixYuibe7cubbjGzZsEH20fnfaPSedC9ALimbPnm07rhX4SP0Xx9WDLigoCEePHsXp06fR2NiIuro6nDx5En/4wx+wefNmfPnll5g1axb27dvn7VCEED/iNdldLteofjg0NIShoSG4XC4cPXoUK1euBACUlZWJuioh5PHgkf5mHx4eRlZWFsLDw1FUVISEhASEhYWNCvgxMTHq119CiP95pGQPCAhAY2MjLl26hPr6enX72W/idruRk5ODnJwctYCfEDK5jGk1PiwsDAUFBThx4gRu3LgxukBz6dIlcT/uiooKeDweeDwezJw5c/wRE0Ic4TXZu7q6cOPGDQD3VljfeecdJCcno6CgAP/4xz8AANXV1WL7H0LI44HL0hpuAfjvf/+LsrIyDA8PY2RkBKtWrcIf//hHnD9/HqWlpejp6cFTTz2FN954Qy0gAICcnBx4PJ4J/QATzeXLl0VbU1OT7bjUDwzQt5Ny2utMkyklOUwr4NAKg7TiDm2dRopf+8xaYZP2mTU/qRBJ88nKyhJtmiz3OKDlmFedPSMjA6dOnXpoPD4+HvX19eOPjhDiE/gGHSGGwGQnxBCY7IQYApOdEENgshNiCF6lt4lkzpw5iIuLA3BPv9eqhHwF42Ac36Y4Wltb5cpCy09kZ2f769QPwDgehHE8yLcpDn6NJ8QQmOyEGEJAZWVlpb9Onp2d7a9TPwDjeBDG8SDfljh8ukBHCPEf/BpPiCH4Jdnr6uqwYMECJCYmoqqqyh8hAADi4uKQnp6OrKws5OTk+Oy85eXlCA8PR1pa2uhYT08PioqKkJSUhKKiIly/ft0vcVRWViI6OhpZWVnIysrCv/71r0mPo62tDQUFBUhJSUFqaipeffVVAL6fEykOX8/JwMAAFi9ejMzMTKSmpuKll14CcG8Lqby8PCQmJmL16tVj3zJt3Ov5Y+Tu3btWfHy81dzcbA0ODloZGRnW559/7uswLMuyrNjYWKurq8vn533//fetTz/91EpNTR0de/HFF62XX37ZsizLevnll63f//73fonjpZdesv785z9P+rm/zuXLl61PP/3UsizL+uqrr6ykpCTr888/9/mcSHH4ek5GRkas3t5ey7Is686dO9bixYutEydOWL/85S+tmpoay7Is6ze/+Y21Z8+eMR3X50/2+vp6JCYmIj4+HlOnTkVpaSlqa2t9HYZfyc/Pf6hNcG1tLcrKygD4roGnXRz+ICoqCosWLQJwr148OTkZ7e3tPp8TKQ5fM1lNXn2e7O3t7Q/sTunPZpUulwtLlixBdnY23G63X2K4T2dnJ6KiogDc26Gzs7PTb7Hs2rULGRkZKC8v98mfE1+ntbUVp06dQl5enl/n5OtxAL6fk8lo8mr0At2HH36IhoYGHDlyBLt378bx48f9HRKAe7+EtM0ZJpONGzeiubkZjY2NiIqKwtatW3127r6+PpSUlGDnzp0IDQ19wObLOflmHP6Yk/E0eZXwebJHR0ejra1t9P9as0pfxAIA4eHhWL58uV8770RERKCjowMA0NHRgfDwcL/FERAQgClTpmDDhg0+m5OhoSGUlJRgzZo1WLFixWgsvp4TKQ5/zAngrMmrhM+TPTc3F01NTWhpacGdO3fw5ptvori42NdhoL+/H729vaP/fvvttx9YlfY1xcXFqK6uBuDfBp73kwsADh486JM5sSwL69evR3JyMrZs2TI67us5keLw9ZxMWpPXCV5IfCQOHz5sJSUlWfHx8daf/vQnf4RgNTc3WxkZGVZGRoaVkpLi0zhKS0utyMhIKzAw0IqOjrZef/11q7u72/rxj39sJSYmWoWFhda1a9f8EsfatWuttLQ0Kz093frFL35hXb58edLj+OCDDywAVnp6upWZmWllZmZahw8f9vmcSHH4ek5Onz5tZWVlWenp6VZqaqq1fft2y7Lu3bO5ublWQkKCtXLlSmtgYGBMx+UbdIQYgtELdISYBJOdEENgshNiCEx2QgyByU6IITDZCTEEJjshhsBkJ8QQ/g9BuVxQiDJQOgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "GT5bNsj2Jp_V",
        "outputId": "432639a4-5f33-42f3-9522-e3dbbbaea9cd"
      },
      "source": [
        "img, label = test_dataset[6153]\n",
        "plt.imshow(img.permute(1, 2, 0))\n",
        "print('Label:', dataset.classes[label], ', Predicted:', predict_image(img, model))"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Label: frog , Predicted: frog\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yqZv8qKPJp_V"
      },
      "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",
        "\n",
        "As a final step, let's also look at the overall loss and accuracy of the model on the test set, and record using `jovian`. We expect these values to be similar to those for the validation set. If not, we might need a better validation set that has similar data and distribution as the test set (which often comes from real world data)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-1hjzFhUJp_V",
        "outputId": "28f5bdc4-b6cb-4f55-ce35-51eb469e64ea"
      },
      "source": [
        "test_loader = DeviceDataLoader(DataLoader(test_dataset, batch_size*2), device)\n",
        "result = evaluate(model, test_loader)\n",
        "result"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'val_acc': 0.775585949420929, 'val_loss': 0.8247837424278259}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ACxJDNG9Jp_V",
        "outputId": "b7e34099-aa8f-45f9-9752-b029b0fa9e8e"
      },
      "source": [
        "jovian.log_metrics(test_loss=result['val_loss'], test_acc=result['val_acc'])"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[jovian] Metrics logged.\u001b[0m\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Smk8znXkJp_W"
      },
      "source": [
        "## Saving and loading the model\n",
        "\n",
        "Since we've trained our model for a long time and achieved a resonable accuracy, it would be a good idea to save the weights of the model to disk, so that we can reuse the model later and avoid retraining from scratch. Here's how you can save the model."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WLLm-pIyJp_W"
      },
      "source": [
        "torch.save(model.state_dict(), 'cifar10-cnn.pth')"
      ],
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WsxZJi5fJp_W"
      },
      "source": [
        "The `.state_dict` method returns an `OrderedDict` containing all the weights and bias matrices mapped to the right attributes of the model. To load the model weights, we can redefine the model with the same structure, and use the `.load_state_dict` method."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Mb9KOT7GJp_W"
      },
      "source": [
        "model2 = to_device(Cifar10CnnModel(), device)"
      ],
      "execution_count": 62,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TFCWPlalJp_W",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f66f6b2e-1276-4ec0-9e23-92b2b12f551b"
      },
      "source": [
        "model2.load_state_dict(torch.load('cifar10-cnn.pth'))"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<All keys matched successfully>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o53KprzHJp_W"
      },
      "source": [
        "Just as a sanity check, let's verify that this model has the same loss and accuracy on the test set as before."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YAtV9ZOqJp_W",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b36600e0-a17e-48a5-ec83-be09a330300e"
      },
      "source": [
        "evaluate(model2, test_loader)"
      ],
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'val_acc': 0.775585949420929, 'val_loss': 0.8247837424278259}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nrqd6TsvJp_X"
      },
      "source": [
        "Let's make one final commit using `jovian`."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ANPiy0tfJp_X",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        },
        "outputId": "40368958-6854-4c67-81c3-364851a4d5bc"
      },
      "source": [
        "jovian.commit(project=project_name)"
      ],
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[jovian] Detected Colab notebook...\u001b[0m\n",
            "[jovian] Uploading colab notebook to Jovian...\u001b[0m\n",
            "[jovian] Capturing environment..\u001b[0m\n",
            "[jovian] Attaching records (metrics, hyperparameters, dataset etc.)\u001b[0m\n",
            "[jovian] Committed successfully! https://jovian.ai/rahulgarg95/05-cifar10-cnn\u001b[0m\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'https://jovian.ai/rahulgarg95/05-cifar10-cnn'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J3mWqJcOJp_X"
      },
      "source": [
        "Check out the **Files** tab on the project page to view or download the trained model weights. You can also download all the files together using the *Download Zip* option in the *Clone* dropdown.\n",
        "\n",
        "Data science work is often fragmented across many different platforms (Git for code, Dropbox/S3 for datasets & artifacts, spreadsheets for hyperparameters, metrics etc.) which can make it difficult to share and reproduce experiments. Jovian.ml solves this by capturing everyting related to a data science project on a single platform, while providing a seamless workflow for capturing, sharing and reproducting your work. To learn what you can do with Jovian.ml, check out the docs: [https://docs.jovian.ml](https://docs.jovian.ml)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uAESHPLiJp_X"
      },
      "source": [
        "## Summary and Further Reading/Exercises\n",
        "\n",
        "We've covered a lot of ground in this tutorial. Here's quick recap of the topics:\n",
        "* Introduction to the CIFAR10 dataset for image classification\n",
        "* Downloading, extracing and loading an image dataset using `torchvision`\n",
        "* Show random batches of images in a grid using `torchvision.utils.make_grid`\n",
        "* Creating a convolutional neural network using with `nn.Conv2d` and `nn.MaxPool2d` layers\n",
        "* Capturing dataset information, metrics and hyperparameters using the `jovian` library\n",
        "* Training a convolutional neural network and visualizing the losses and errors\n",
        "* Understanding overfitting and the strategies for avoiding it (more on this later)\n",
        "* Generating predictions on single images from the test set\n",
        "* Saving and loading the model weights, and attaching them to the eperiment snaptshot using `jovian`\n",
        "\n",
        "There's a lot of scope to experiment here, and I encourage you to use the interactive nature of Jupyter to play around with the various parameters. Here are a few ideas:\n",
        "* Try chaging the hyperparameters to achieve a higher accuracy within fewer epochs. You use the comparison table on the Jovian.ml project page to compare your experiments.\n",
        "* Try adding more convolutional layers, or increasing the number of channels in each convolutional layer\n",
        "* Try using a feedforward neural network and see what's the maximum accuracy you can achieve\n",
        "* Read about some of the startegies mentioned above for reducing overfitting and achieving better results, and try to implement them by looking into the PyTorch docs.\n",
        "* Modify this notebook to train a model for a different dataset (e.g. CIFAR100 or ImageNet)\n",
        "\n",
        "In the next tutorial, we will continue to improve our model's accuracy using techniques like data augmentation, batch normalization and dropout. We will also learn about residual networks (or ResNets), a small but critical change to the model architecture that will significantly boost the performance of our model. Stay tuned!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ViVX5iIxJp_X"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}