COSMOS_ML_05_AlexNet.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/sungjuGit/COSMOS_Ju/blob/main/COSMOS_ML_05_AlexNet.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NF2IR5u8-0xo"
      },
      "source": [
        "# AlexNet\n",
        "\n",
        "We will now implement a modified version of [AlexNet](https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf), a neural network model that uses convolutional neural network (CNN) layers and was designed for the [ImageNet challenge](http://www.image-net.org/challenges/LSVRC/). AlexNet is famous for winning the ImageNet challenge in 2012 by beating the second place competitor by over 10% accuracy and kickstarting the interest in deep learning for computer vision.\n",
        "\n",
        "The image below shows the architecture of AlexNet.\n",
        "\n",
        "![](https://github.com/bentrevett/pytorch-image-classification/blob/master/assets/alexnet.png?raw=1)\n",
        "\n",
        "We are now moving on from the MNIST dataset and from now on we will be using the [CIFAR10](https://www.cs.toronto.edu/~kriz/cifar.html) dataset. CIFAR10 consists of 60000 32x32 colour images in 10 classes, with 6000 images per class. The classes are: airplane, automobile, bird, cat, deer, dog, frog, horse, ship, truck.\n",
        "\n",
        "![](https://github.com/bentrevett/pytorch-image-classification/blob/master/assets/cifar10.png?raw=1)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "## Set Things Up"
      ],
      "metadata": {
        "id": "wbDkOjxAW7EH"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XYaxtUEgIafG",
        "cellView": "form"
      },
      "outputs": [],
      "source": [
        "# @title Import Modules\n",
        "\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "import torch.optim as optim\n",
        "import torch.utils.data as data\n",
        "\n",
        "import torchvision.transforms as transforms\n",
        "import torchvision.datasets as datasets\n",
        "from torchvision import models\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "import random\n",
        "import time\n",
        "\n",
        "SEED = 1234\n",
        "\n",
        "torch.manual_seed(SEED)\n",
        "torch.cuda.manual_seed(SEED)\n",
        "torch.backends.cudnn.deterministic = True"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uecfsf1f-0xw"
      },
      "source": [
        "We calculate the mean and standard deviation of our data so we can normalize it.\n",
        "\n",
        "Our dataset is made up of color images but three color channels (red, green and blue), compared to MNIST's black and white images with a single color channel. To normalize our data we need to calculate the means and standard deviations for each of the color channels independently.\n",
        "\n",
        "To do this we pass a tuple containing the axes we want to take the means and standard deviations over to the `mean` and `std` functions and we receive a list of means and standard deviations for each of the three color channels."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IprUgI0fIafN",
        "outputId": "b03e69dc-8471-470e-dcb0-0e1014092c41"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to .data/cifar-10-python.tar.gz\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 170498071/170498071 [00:03<00:00, 45543522.14it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting .data/cifar-10-python.tar.gz to .data\n",
            "Calculated means: [0.49139968 0.48215841 0.44653091]\n",
            "Calculated stds: [0.24703223 0.24348513 0.26158784]\n"
          ]
        }
      ],
      "source": [
        "ROOT = '.data'\n",
        "\n",
        "train_data = datasets.CIFAR10(root=ROOT,\n",
        "                              train=True,\n",
        "                              download=True)\n",
        "\n",
        "means = train_data.data.mean(axis=(0, 1, 2)) / 255\n",
        "stds = train_data.data.std(axis=(0, 1, 2)) / 255\n",
        "\n",
        "print(f'Calculated means: {means}')\n",
        "print(f'Calculated stds: {stds}')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ajVaWn_7-0x0"
      },
      "source": [
        "Next up is defining the transforms for data augmentation.\n",
        "\n",
        "The images in the CIFAR10 dataset are significantly more complex than the MNIST dataset. They are larger, have three times the amount of pixels and are more cluttered. This makes them harder to learn and consequently means we should use less augmentation.\n",
        "\n",
        "A new transform we use is `RandomHorizontalFlip`. This, with a probability of `0.5` as specified, flips the image horizontally. So an image of a horse facing to the right will be flipped so it will face to the left. We couldn't do this in the MNIST dataset as we are not expecting our test set to contain any flipped digits, however natural images, such as those in the CIFAR10 dataset, can potentially be flipped as they still make visual sense.\n",
        "\n",
        "As our `means` and `stds` are now already in lists we do not need to enclose them in lists as we did for the single channel images in the MNIST dataset."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tfzKdrpfIafR"
      },
      "outputs": [],
      "source": [
        "train_transforms = transforms.Compose([\n",
        "                           transforms.RandomRotation(5),\n",
        "                           transforms.RandomHorizontalFlip(0.5),\n",
        "                           transforms.RandomCrop(32, padding=2),\n",
        "                           transforms.ToTensor(),\n",
        "                           transforms.Normalize(mean=means,\n",
        "                                                std=stds)\n",
        "                       ])\n",
        "\n",
        "test_transforms = transforms.Compose([\n",
        "                           transforms.ToTensor(),\n",
        "                           transforms.Normalize(mean=means,\n",
        "                                                std=stds)\n",
        "                       ])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TPLqEffl-0x3"
      },
      "source": [
        "Next, as standard, we'll load the dataset with our transforms..."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vqxev2wBIafU",
        "outputId": "bb57c0a3-23f4-48f8-d433-4b0dfcdf4e06"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Files already downloaded and verified\n",
            "Files already downloaded and verified\n"
          ]
        }
      ],
      "source": [
        "train_data = datasets.CIFAR10(ROOT,\n",
        "                              train=True,\n",
        "                              download=True,\n",
        "                              transform=train_transforms)\n",
        "\n",
        "test_data = datasets.CIFAR10(ROOT,\n",
        "                             train=False,\n",
        "                             download=True,\n",
        "                             transform=test_transforms)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "## Check Out Data"
      ],
      "metadata": {
        "id": "Tr90OAjgXI3s"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yjflicvF-0yB"
      },
      "source": [
        "We print out the number of examples in each set of data to ensure everything has gone OK so far."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "enX-chu-Iafc",
        "outputId": "3bcef7ea-c0a8-4435-cd6c-a880d07772fc"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of training examples: 50000\n",
            "Number of testing examples: 10000\n"
          ]
        }
      ],
      "source": [
        "print(f'Number of training examples: {len(train_data)}')\n",
        "print(f'Number of testing examples: {len(test_data)}')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eQq6BaNr-0yE"
      },
      "source": [
        "Now, we'll create a function to plot some images in our dataset to see what they actually look like.\n",
        "\n",
        "Note that by default PyTorch handles images that are arranged `[channel, height, width]`, but `matplotlib` expects images to be `[height, width, channel]`, hence we need to `permute` our images before plotting them.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gVDM7PTCswBx",
        "cellView": "form"
      },
      "outputs": [],
      "source": [
        "# @title Normalize Image Function to avoid saturation\n",
        "def normalize_image(image):\n",
        "    image_min = image.min()\n",
        "    image_max = image.max()\n",
        "    image.clamp_(min=image_min, max=image_max)\n",
        "    image.add_(-image_min).div_(image_max - image_min + 1e-5)\n",
        "    return image"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KVwZrsXpcUSB",
        "cellView": "form"
      },
      "outputs": [],
      "source": [
        "# @title Plot Images Function\n",
        "def plot_images(images, labels, classes, normalize=False):\n",
        "\n",
        "    n_images = len(images)\n",
        "\n",
        "    rows = int(np.sqrt(n_images))\n",
        "    cols = int(np.sqrt(n_images))\n",
        "\n",
        "    fig = plt.figure(figsize=(10, 10))\n",
        "\n",
        "    for i in range(rows*cols):\n",
        "\n",
        "        ax = fig.add_subplot(rows, cols, i+1)\n",
        "\n",
        "        image = images[i]\n",
        "\n",
        "        if normalize:\n",
        "          image = normalize_image(image)\n",
        "\n",
        "        ax.imshow(image.permute(1, 2, 0).cpu().numpy())\n",
        "        ax.set_title(classes[labels[i]])\n",
        "        ax.axis('off')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tXe4GSO7-0yH"
      },
      "source": [
        "Then, we'll actually plot the images.\n",
        "\n",
        "We get both the images and the labels from the training set and convert the labels, which are originally stored as integers, into their human readable class by using the data's `classes` dictionary.\n",
        "\n",
        "When we plot them we see lots of warnings. This is because `matplotlib` is expecting the values of every pixel to be between $[0, 1]$, however our normalization will cause them to be outside this range. By default `matplotlib` will then clip these values into the $[0,1]$ range. This clipping causes all the images to look a bit weird - all the colors are oversaturated."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 829
        },
        "id": "A6piM5oUcZIY",
        "outputId": "776b90c8-07d5-4484-fbe3-3fd6646a3010"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 25 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "N_IMAGES = 25\n",
        "\n",
        "images, labels = zip(*[(image, label) for image, label in\n",
        "                       [train_data[i] for i in range(N_IMAGES)]])\n",
        "\n",
        "classes = test_data.classes\n",
        "\n",
        "plot_images(images, labels, classes, normalize=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SMrfzrD0-0yQ"
      },
      "source": [
        "As before, we'll check what images look like with Sobel filters applied to them."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "apk0CzvWoZkw",
        "cellView": "form"
      },
      "outputs": [],
      "source": [
        "# @title Plot Filter Function\n",
        "def plot_filter(images, filter, normalize=True):\n",
        "\n",
        "    images = torch.cat([i.unsqueeze(0) for i in images], dim=0).cpu()\n",
        "    filter = torch.FloatTensor(filter).unsqueeze(0).unsqueeze(0).cpu()\n",
        "    filter = filter.repeat(3, 3, 1, 1)\n",
        "\n",
        "    n_images = images.shape[0]\n",
        "\n",
        "    filtered_images = F.conv2d(images, filter)\n",
        "\n",
        "    images = images.permute(0, 2, 3, 1)\n",
        "    filtered_images = filtered_images.permute(0, 2, 3, 1)\n",
        "\n",
        "    fig = plt.figure(figsize=(25, 5))\n",
        "\n",
        "    for i in range(n_images):\n",
        "\n",
        "        image = images[i]\n",
        "\n",
        "        if normalize:\n",
        "            image = normalize_image(image)\n",
        "\n",
        "        ax = fig.add_subplot(2, n_images, i+1)\n",
        "        ax.imshow(image)\n",
        "        ax.set_title('Original')\n",
        "        ax.axis('off')\n",
        "\n",
        "        image = filtered_images[i]\n",
        "\n",
        "        if normalize:\n",
        "            image = normalize_image(image)\n",
        "\n",
        "        ax = fig.add_subplot(2, n_images, n_images+i+1)\n",
        "        ax.imshow(image)\n",
        "        ax.set_title('Filtered')\n",
        "        ax.axis('off')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X5mBc7Zy-0yS"
      },
      "source": [
        "The filters are still 2-dimensional but they are expanded to a depth of three dimensions inside the `plot_filter` function.\n",
        "\n",
        "Below is a filter which detects horizontal lines."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 397
        },
        "id": "s37UHTS-oV2l",
        "outputId": "58c6f84b-59a5-4f2b-db5c-2a58eb41fd00"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2500x500 with 20 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ],
      "source": [
        "N_IMAGES = 10\n",
        "\n",
        "images = [image for image, label in [train_data[i] for i in range(N_IMAGES)]]\n",
        "\n",
        "horizontal_filter = [[-1, -2, -1],\n",
        "                     [ 0,  0,  0],\n",
        "                     [ 1,  2,  1]]\n",
        "\n",
        "plot_filter(images, horizontal_filter)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CYT28zhp-0yW"
      },
      "source": [
        "Here's a filter that detects vertical lines."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 397
        },
        "id": "6i229QYDq0ai",
        "outputId": "4353fb19-7850-42ef-96ee-6990675e9295"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2500x500 with 20 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ],
      "source": [
        "vertical_filter = [[-1, 0, 1],\n",
        "                   [-2, 0, 2],\n",
        "                   [-1, 0, 1]]\n",
        "\n",
        "plot_filter(images, vertical_filter)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B6SjLzwv-0yY"
      },
      "source": [
        "We'll also do the same for subsampling/pooling."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Ar1EwXXOsQi6",
        "cellView": "form"
      },
      "outputs": [],
      "source": [
        "# @title Plot Subsample Function\n",
        "def plot_subsample(images, pool_type, pool_size, normalize=True):\n",
        "\n",
        "    images = torch.cat([i.unsqueeze(0) for i in images], dim=0).cpu()\n",
        "\n",
        "    if pool_type.lower() == 'max':\n",
        "        pool = F.max_pool2d\n",
        "    elif pool_type.lower() in ['mean', 'avg']:\n",
        "        pool = F.avg_pool2d\n",
        "    else:\n",
        "        raise ValueError(f'pool_type must be either max or mean, got: {pool_type}')\n",
        "\n",
        "    n_images = images.shape[0]\n",
        "\n",
        "    pooled_images = pool(images, kernel_size=pool_size)\n",
        "\n",
        "    images = images.permute(0, 2, 3, 1)\n",
        "    pooled_images = pooled_images.permute(0, 2, 3, 1)\n",
        "\n",
        "    fig = plt.figure(figsize=(25, 5))\n",
        "\n",
        "    for i in range(n_images):\n",
        "\n",
        "        image = images[i]\n",
        "\n",
        "        if normalize:\n",
        "            image = normalize_image(image)\n",
        "\n",
        "        ax = fig.add_subplot(2, n_images, i+1)\n",
        "        ax.imshow(image)\n",
        "        ax.set_title('Original')\n",
        "        ax.axis('off')\n",
        "\n",
        "        image = pooled_images[i]\n",
        "\n",
        "        if normalize:\n",
        "            image = normalize_image(image)\n",
        "\n",
        "        ax = fig.add_subplot(2, n_images, n_images+i+1)\n",
        "        ax.imshow(image)\n",
        "        ax.set_title('Subsampled')\n",
        "        ax.axis('off')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3bSOuZcS-0yb"
      },
      "source": [
        "As before, the higher filter sizes in the pooling layers means more information is lost, i.e. the image becomes lower resolution."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 397
        },
        "id": "-aP2y7Yqsc0f",
        "outputId": "e968c865-bc2d-4760-8b17-baa75e02447f"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2500x500 with 20 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ],
      "source": [
        "plot_subsample(images, 'max', 2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 397
        },
        "id": "tD48HviKtYIZ",
        "outputId": "745d900a-25f7-4fc6-f250-5eaeb101fc41"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2500x500 with 20 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ],
      "source": [
        "plot_subsample(images, 'max', 3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 397
        },
        "id": "AK-j6-E8teSe",
        "outputId": "8ef31aba-140c-48f5-b493-a889312f38c7"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2500x500 with 20 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ],
      "source": [
        "plot_subsample(images, 'avg', 2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 397
        },
        "id": "FSl6M9cvtkB3",
        "outputId": "4d41de4f-dab5-4282-f717-abd03e6b26ea"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2500x500 with 20 Axes>"
            ],
            "image/png": 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      ],
      "source": [
        "plot_subsample(images, 'avg', 3)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BL_2X7ZF-0yl"
      },
      "source": [
        "The final bit of the data processing is creating the iterators.\n",
        "\n",
        "We use a much larger batch size here than in previous models. Generally, when using a GPU, a larger batch size means our model trains faster. Our model has significantly more parameters and the images it is training on are much larger, than the previous notebook, so will generally take longer. We offset this as much as we can by using a batch size of 256 instead of 64."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "o3rhhD0HIaff"
      },
      "outputs": [],
      "source": [
        "BATCH_SIZE = 256\n",
        "\n",
        "train_loader = data.DataLoader(train_data,\n",
        "                                 shuffle=True,\n",
        "                                 batch_size=BATCH_SIZE)\n",
        "\n",
        "test_loader = data.DataLoader(test_data,\n",
        "                                batch_size=BATCH_SIZE)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "98q8mpRN-0yp"
      },
      "source": [
        "---\n",
        "## Define the Model\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Next up is defining the model.\n",
        "\n",
        "The actual model itself is no more difficult to understand than the previous model, LeNet. It is made up of convolutional layers, pooling layers and ReLU activation functions. See the previous notebook for a refresher on these concepts.\n",
        "\n",
        "Dropout is a form of [*regularization*](https://en.wikipedia.org/wiki/Regularization_(mathematics)). As our models get larger, to perform more accurately on richer datasets, they start having a significantly higher number of parameters. The problem with lots of parameters is that our models begin to *overfit*. That is, they do not learn general image features whilst learning to classify images but instead simply memorize images within the training set. This is bad as it will cause our model to achieve poor performance on the validation/testing set. To solve this overfitting problem, we use regularization. Dropout is just one method of regularization, other common ones are *L1 regularization*, *L2 regularization* and *weight decay*.\n",
        "\n",
        "Dropout works by randomly setting a certain fraction, 0.5 here, of the neurons in a layer to zero. This effectively adds noise to the training of the neural network and causes neurons to learn with \"less\" data as they are only getting half of the information from a previous layer with dropout applied. It can also be thought of as causing your model to learn multiple smaller models with less parameters.\n",
        "\n",
        "Dropout is only applied when the model is training. It needs to be \"turned off\" when validating, testing or using the model for inference.\n",
        "\n",
        "One last thing to mention is that the very first convolutional layer has an `in_channel` of three. That is because we are handling color images that have three channels (red, green and blue) instead of the single channel grayscale images from the MNIST dataset. This doesn't change the way any of the convolutional filter works, it just means the first filter has a depth of three instead of a depth of one."
      ],
      "metadata": {
        "id": "sSJDoptFXYYo"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sQq-LE22Iafk"
      },
      "outputs": [],
      "source": [
        "OUTPUT_DIM = 10\n",
        "\n",
        "model = nn.Sequential(\n",
        "\n",
        "            nn.Conv2d(3, 64, 3, 2, 1),  # in_channels, out_channels, kernel_size, stride, padding\n",
        "            nn.MaxPool2d(2),  # kernel_size\n",
        "            nn.ReLU(inplace=True),\n",
        "            nn.Conv2d(64, 192, 3, padding=1),\n",
        "            nn.MaxPool2d(2),\n",
        "            nn.ReLU(inplace=True),\n",
        "            nn.Conv2d(192, 384, 3, padding=1),\n",
        "            nn.ReLU(inplace=True),\n",
        "            nn.Conv2d(384, 256, 3, padding=1),\n",
        "            nn.ReLU(inplace=True),\n",
        "            nn.Conv2d(256, 256, 3, padding=1),\n",
        "            nn.MaxPool2d(2),\n",
        "            nn.ReLU(inplace=True),\n",
        "\n",
        "            nn.Flatten(),\n",
        "\n",
        "            nn.Dropout(0.5),\n",
        "            nn.Linear(256 * 2 * 2, 4096),\n",
        "            nn.ReLU(inplace=True),\n",
        "            nn.Dropout(0.5),\n",
        "            nn.Linear(4096, 4096),\n",
        "            nn.ReLU(inplace=True),\n",
        "            nn.Linear(4096, OUTPUT_DIM),\n",
        "        )\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O9Dkc_uBXVoy"
      },
      "source": [
        "Then we'll see how many trainable parameters our model has.\n",
        "\n",
        "Our LeNet architecture had ~44k, but here we have 23.2M parameters - and AlexNet is a relatively small model for computer vision."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3g-pV7t2Iafs",
        "outputId": "c0372a06-1912-4a61-8077-29d7bcc445ad"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The model has 23,272,266 trainable parameters\n"
          ]
        }
      ],
      "source": [
        "def count_parameters(model):\n",
        "    return sum(p.numel() for p in model.parameters() if p.requires_grad)\n",
        "\n",
        "\n",
        "print(f'The model has {count_parameters(model):,} trainable parameters')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n99m5MPZXVo1"
      },
      "source": [
        "---\n",
        "## Initialize the Parameters of the Model (Optional)\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "PyTorch's default initialization is usually fine. However, by manually trying different initialization schemes we can usually squeeze out a slight performance improvement. For our convolutional layers, we'll initialize the weights using *Kaiming Normal*, also known as *He Normal*. See the [paper](https://arxiv.org/abs/1502.01852) to learn more about how it was devised. For the linear layers we initialize the weights using *Xavier Normal*, also known as *Glorot Normal*. See the [paper](http://proceedings.mlr.press/v9/glorot10a/glorot10a.pdf) for the theory behind it.\n",
        "\n",
        "For both types of layer we initialize the bias terms to zeros."
      ],
      "metadata": {
        "id": "hyuHUcBNV1es"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "I4NbFid9Iafw"
      },
      "outputs": [],
      "source": [
        "def initialize_parameters(m):\n",
        "    if isinstance(m, nn.Conv2d):\n",
        "        nn.init.kaiming_normal_(m.weight.data, nonlinearity='relu')\n",
        "        nn.init.constant_(m.bias.data, 0)\n",
        "    elif isinstance(m, nn.Linear):\n",
        "        nn.init.xavier_normal_(m.weight.data, gain=nn.init.calculate_gain('relu'))\n",
        "        nn.init.constant_(m.bias.data, 0)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8SkSdnytXVo3"
      },
      "source": [
        "We apply the initialization by using the model's `apply` method. This will call the given function on every module and sub-module within the model."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Bve8IsHsIafz",
        "outputId": "eed3650e-7641-4b4f-ebbe-0153b7413920"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Sequential(\n",
              "  (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n",
              "  (1): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "  (2): ReLU(inplace=True)\n",
              "  (3): Conv2d(64, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "  (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "  (5): ReLU(inplace=True)\n",
              "  (6): Conv2d(192, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "  (7): ReLU(inplace=True)\n",
              "  (8): Conv2d(384, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "  (9): ReLU(inplace=True)\n",
              "  (10): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "  (11): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "  (12): ReLU(inplace=True)\n",
              "  (13): Flatten(start_dim=1, end_dim=-1)\n",
              "  (14): Dropout(p=0.5, inplace=False)\n",
              "  (15): Linear(in_features=1024, out_features=4096, bias=True)\n",
              "  (16): ReLU(inplace=True)\n",
              "  (17): Dropout(p=0.5, inplace=False)\n",
              "  (18): Linear(in_features=4096, out_features=4096, bias=True)\n",
              "  (19): ReLU(inplace=True)\n",
              "  (20): Linear(in_features=4096, out_features=10, bias=True)\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ],
      "source": [
        "model.apply(initialize_parameters)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9HoBM4KEXVpS"
      },
      "source": [
        "---\n",
        "## Train and Test the Model\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The rest of the notebook is pretty similar to the previous notebooks from these tutorials.\n",
        "\n",
        "We define a function to calculate accuracy..."
      ],
      "metadata": {
        "id": "ntBrg0OWXmgA"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "P6hTusQNIagD"
      },
      "outputs": [],
      "source": [
        "optimizer = optim.Adam(model.parameters(), lr=1e-3)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "loss_fn = nn.CrossEntropyLoss()"
      ],
      "metadata": {
        "id": "q9E0GfeaA_X9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# @title Use GPU if available\n",
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "\n",
        "model = model.to(device)\n",
        "loss_fn = loss_fn.to(device)"
      ],
      "metadata": {
        "cellView": "form",
        "id": "06HxAfyMAyxg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TJj9fQ5WIagI",
        "cellView": "form"
      },
      "outputs": [],
      "source": [
        "# @title Calculate Accuracy\n",
        "def calculate_accuracy(y_pred, y):\n",
        "    top_pred = y_pred.argmax(1, keepdim=True)\n",
        "    correct = top_pred.eq(y.view_as(top_pred)).sum()\n",
        "    acc = correct.float() / y.shape[0]\n",
        "    return acc"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WGjesEGDXVpV"
      },
      "source": [
        "...and a function to implement our training loop.\n",
        "\n",
        "As we are using dropout we need to make sure to \"turn it on\" when training by using `model.train()`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "e65mN1cGIagK"
      },
      "outputs": [],
      "source": [
        "def train(model, loader, optimizer, loss_fn, device):\n",
        "\n",
        "    epoch_loss = 0\n",
        "    epoch_acc = 0\n",
        "\n",
        "    model.train()\n",
        "\n",
        "    for (x, y) in loader:\n",
        "\n",
        "        x = x.to(device)\n",
        "        y = y.to(device)\n",
        "\n",
        "        optimizer.zero_grad()\n",
        "\n",
        "        y_pred = model(x)\n",
        "\n",
        "        loss = loss_fn(y_pred, y)\n",
        "\n",
        "        acc = calculate_accuracy(y_pred, y)\n",
        "\n",
        "        loss.backward()\n",
        "\n",
        "        optimizer.step()\n",
        "\n",
        "        epoch_loss += loss.item()\n",
        "        epoch_acc += acc.item()\n",
        "\n",
        "    return epoch_loss / len(loader), epoch_acc / len(loader)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MkkzHBcoXVpa"
      },
      "source": [
        "Next, we define a function to tell us how long an epoch takes."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RX-Mwze-IagO"
      },
      "outputs": [],
      "source": [
        "def epoch_time(start_time, end_time):\n",
        "    elapsed_time = end_time - start_time\n",
        "    elapsed_mins = int(elapsed_time / 60)\n",
        "    elapsed_secs = int(elapsed_time - (elapsed_mins * 60))\n",
        "    return elapsed_mins, elapsed_secs"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "53srBoLAXVpe"
      },
      "source": [
        "Then, finally, we train our model."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mN02GOocIagR",
        "outputId": "a832ae47-5688-4112-d88c-b053ff25c5af"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 01 | Epoch Time: 0m 35s\n",
            "\tTrain Loss: 1.804 | Train Acc: 29.77%\n",
            "Epoch: 02 | Epoch Time: 0m 34s\n",
            "\tTrain Loss: 1.443 | Train Acc: 46.74%\n",
            "Epoch: 03 | Epoch Time: 0m 34s\n",
            "\tTrain Loss: 1.269 | Train Acc: 54.40%\n",
            "Epoch: 04 | Epoch Time: 0m 33s\n",
            "\tTrain Loss: 1.159 | Train Acc: 58.83%\n",
            "Epoch: 05 | Epoch Time: 0m 33s\n",
            "\tTrain Loss: 1.077 | Train Acc: 62.29%\n",
            "Epoch: 06 | Epoch Time: 0m 34s\n",
            "\tTrain Loss: 1.025 | Train Acc: 64.12%\n",
            "Epoch: 07 | Epoch Time: 0m 33s\n",
            "\tTrain Loss: 0.961 | Train Acc: 66.25%\n",
            "Epoch: 08 | Epoch Time: 0m 34s\n",
            "\tTrain Loss: 0.918 | Train Acc: 68.21%\n",
            "Epoch: 09 | Epoch Time: 0m 34s\n",
            "\tTrain Loss: 0.886 | Train Acc: 69.40%\n",
            "Epoch: 10 | Epoch Time: 0m 33s\n",
            "\tTrain Loss: 0.845 | Train Acc: 70.66%\n",
            "Epoch: 11 | Epoch Time: 0m 34s\n",
            "\tTrain Loss: 0.812 | Train Acc: 71.89%\n",
            "Epoch: 12 | Epoch Time: 0m 33s\n",
            "\tTrain Loss: 0.787 | Train Acc: 72.71%\n",
            "Epoch: 13 | Epoch Time: 0m 34s\n",
            "\tTrain Loss: 0.764 | Train Acc: 73.79%\n",
            "Epoch: 14 | Epoch Time: 0m 34s\n",
            "\tTrain Loss: 0.741 | Train Acc: 74.49%\n",
            "Epoch: 15 | Epoch Time: 0m 33s\n",
            "\tTrain Loss: 0.723 | Train Acc: 75.19%\n",
            "Epoch: 16 | Epoch Time: 0m 34s\n",
            "\tTrain Loss: 0.705 | Train Acc: 75.78%\n",
            "Epoch: 17 | Epoch Time: 0m 33s\n",
            "\tTrain Loss: 0.685 | Train Acc: 76.25%\n",
            "Epoch: 18 | Epoch Time: 0m 33s\n",
            "\tTrain Loss: 0.680 | Train Acc: 76.47%\n",
            "Epoch: 19 | Epoch Time: 0m 33s\n",
            "\tTrain Loss: 0.658 | Train Acc: 77.10%\n",
            "Epoch: 20 | Epoch Time: 0m 33s\n",
            "\tTrain Loss: 0.642 | Train Acc: 77.99%\n",
            "Epoch: 21 | Epoch Time: 0m 34s\n",
            "\tTrain Loss: 0.633 | Train Acc: 78.15%\n",
            "Epoch: 22 | Epoch Time: 0m 34s\n",
            "\tTrain Loss: 0.633 | Train Acc: 78.20%\n",
            "Epoch: 23 | Epoch Time: 0m 33s\n",
            "\tTrain Loss: 0.616 | Train Acc: 78.60%\n",
            "Epoch: 24 | Epoch Time: 0m 34s\n",
            "\tTrain Loss: 0.597 | Train Acc: 79.27%\n",
            "Epoch: 25 | Epoch Time: 0m 33s\n",
            "\tTrain Loss: 0.597 | Train Acc: 79.63%\n"
          ]
        }
      ],
      "source": [
        "EPOCHS = 25\n",
        "\n",
        "for epoch in range(EPOCHS):\n",
        "\n",
        "    start_time = time.monotonic()\n",
        "\n",
        "    train_loss, train_acc = train(model, train_loader, optimizer, loss_fn, device)\n",
        "\n",
        "    end_time = time.monotonic()\n",
        "\n",
        "    epoch_mins, epoch_secs = epoch_time(start_time, end_time)\n",
        "\n",
        "    print(f'Epoch: {epoch+1:02} | Epoch Time: {epoch_mins}m {epoch_secs}s')\n",
        "    print(f'\\tTrain Loss: {train_loss:.3f} | Train Acc: {train_acc*100:.2f}%')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "torch.save(model.state_dict(), 'AlexNet-model.pt')"
      ],
      "metadata": {
        "id": "NH-1skeQPgMX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.load_state_dict(torch.load('AlexNet-model.pt'))"
      ],
      "metadata": {
        "id": "FI-pParbPj4D",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bef688f9-0d66-455b-8d08-3f831f8488b4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<All keys matched successfully>"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Hmm, so how does our model do?\n",
        "\n",
        "Some of the images are hard to tell if for us humans.  Still, there may be hilarious errors by our (arguably very simple CNN) model."
      ],
      "metadata": {
        "id": "FumrHHBsRCj9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "test_idx = random.randint(0,len(test_data)-1)\n",
        "\n",
        "test_img = test_data[test_idx][0]\n",
        "\n",
        "test_img_plt = normalize_image(test_img)\n",
        "plt.imshow(test_img_plt.permute(1, 2, 0).cpu().numpy())\n",
        "\n",
        "model.eval()\n",
        "with torch.no_grad():\n",
        "  test_pred = model(test_img.unsqueeze_(0).to(device))\n",
        "  prob = torch.softmax(test_pred, dim=-1)\n",
        "  print(classes[torch.argmax(prob, 1)])\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        },
        "id": "TEJqO2KSCK2O",
        "outputId": "772035d5-e570-40c2-d51b-e3f72dc21f61"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "cat\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U0usLljsXVpY"
      },
      "source": [
        "We also define an evaluation loop, making sure to \"turn off\" dropout with `model.eval()`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gnUhGD9MIagM"
      },
      "outputs": [],
      "source": [
        "def evaluate(model, loader, loss_fn, device):\n",
        "\n",
        "    epoch_loss = 0\n",
        "    epoch_acc = 0\n",
        "\n",
        "    model.eval()\n",
        "\n",
        "    with torch.no_grad():\n",
        "\n",
        "        for (x, y) in loader:\n",
        "\n",
        "            x = x.to(device)\n",
        "            y = y.to(device)\n",
        "\n",
        "            y_pred = model(x)\n",
        "\n",
        "            loss = loss_fn(y_pred, y)\n",
        "\n",
        "            acc = calculate_accuracy(y_pred, y)\n",
        "\n",
        "            epoch_loss += loss.item()\n",
        "            epoch_acc += acc.item()\n",
        "\n",
        "    return epoch_loss / len(loader), epoch_acc / len(loader)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_loss, test_acc = evaluate(model, test_loader, loss_fn, device)\n",
        "print(f'Test Loss: {test_loss:.3f} | Test Acc: {test_acc*100:.2f}%')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Yx9QuzvjBe8U",
        "outputId": "47d949c5-82c4-4e0a-d8d0-904a17eeb17b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Loss: 0.701 | Test Acc: 76.81%\n"
          ]
        }
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "machine_shape": "hm",
      "provenance": [],
      "collapsed_sections": [
        "wbDkOjxAW7EH",
        "Tr90OAjgXI3s",
        "98q8mpRN-0yp",
        "n99m5MPZXVo1",
        "9HoBM4KEXVpS"
      ],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.3"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}