03_Pytorch_Computer_Vision_Exercises.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/MathewS424/DeepLearning_with_PyTorch/blob/main/03_Pytorch_Computer_Vision_Exercises.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 03. PyTorch Computer Vision Exercises\n",
        "\n",
        "The following is a collection of exercises based on computer vision fundamentals in PyTorch.\n",
        "\n",
        "They're a bunch of fun.\n",
        "\n",
        "You're going to get to write plenty of code!\n",
        "\n",
        "## Resources\n",
        "\n",
        "1. These exercises are based on [notebook 03 of the Learn PyTorch for Deep Learning course](https://www.learnpytorch.io/03_pytorch_computer_vision/).\n",
        "2. See a live [walkthrough of the solutions (errors and all) on YouTube](https://youtu.be/_PibmqpEyhA).\n",
        "  * **Note:** Going through these exercises took me just over 3 hours of solid coding, so you should expect around the same.\n",
        "3. See [other solutions on the course GitHub](https://github.com/mrdbourke/pytorch-deep-learning/tree/main/extras/solutions)."
      ],
      "metadata": {
        "id": "Vex99np2wFVt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Check for GPU\n",
        "!nvidia-smi"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GaeYzOTLwWh2",
        "outputId": "a3a6e7c1-4a0e-4baa-863a-50ec37038302"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mon Dec 23 04:03:01 2024       \n",
            "+---------------------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 535.104.05             Driver Version: 535.104.05   CUDA Version: 12.2     |\n",
            "|-----------------------------------------+----------------------+----------------------+\n",
            "| GPU  Name                 Persistence-M | Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp   Perf          Pwr:Usage/Cap |         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                                         |                      |               MIG M. |\n",
            "|=========================================+======================+======================|\n",
            "|   0  Tesla T4                       Off | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   73C    P0              32W /  70W |    171MiB / 15360MiB |      0%      Default |\n",
            "|                                         |                      |                  N/A |\n",
            "+-----------------------------------------+----------------------+----------------------+\n",
            "                                                                                         \n",
            "+---------------------------------------------------------------------------------------+\n",
            "| Processes:                                                                            |\n",
            "|  GPU   GI   CI        PID   Type   Process name                            GPU Memory |\n",
            "|        ID   ID                                                             Usage      |\n",
            "|=======================================================================================|\n",
            "+---------------------------------------------------------------------------------------+\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Import torch\n",
        "import torch\n",
        "\n",
        "# Exercises require PyTorch > 1.10.0\n",
        "print(torch.__version__)\n",
        "\n",
        "# TODO: Setup device agnostic code\n",
        "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
        "device"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "id": "DNwZLMbCzJLk",
        "outputId": "58cb5d6b-a0b3-4aa1-a3d8-67d330e3531b"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2.5.1+cu121\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'cuda'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 1. What are 3 areas in industry where computer vision is currently being used?\n",
        "\n",
        "1. Self-driving cars, such as Tesla using computer vision to percieve what's happening on the road. See Tesla AI day for more - https://youtu.be/j0z4FweCy4M\n",
        "2. Healthcare imaging, such as using computer vision to help interpret X-rays. Google also uses computer vision for detecting polyps in the intenstines - https://ai.googleblog.com/2021/08/improved-detection-of-elusive-polyps.html\n",
        "3. Security, computer vision can be used to detect whether someone is invading your home or not - https://store.google.com/au/product/nest_cam_battery?hl=en-GB"
      ],
      "metadata": {
        "id": "FSFX7tc1w-en"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2. Search \"what is overfitting in machine learning\" and write down a sentence about what you find.\n",
        "\n",
        "In machine learning, overfitting occurs when a model learns the patterns and noise in the training data too well, resulting in poor performance on new data:\n",
        "Explanation\n",
        "An overfitted model is overly specialized in the training data, and fails to generalize to new data. This can happen when a model has too many parameters, or when it learns the noise in the data as if it were an underlying model structure.\n",
        "Example\n",
        "A model might have 97% accuracy on the training data, but only 50% accuracy on new data.\n",
        "Causes\n",
        "Overfitting can be caused by too little data, or data that is too homogenous.\n",
        "Solutions\n",
        "Some ways to prevent overfitting include:\n",
        "Cross-validation\n",
        "Training with more data\n",
        "Removing features\n",
        "Early stopping\n",
        "Regularization\n",
        "Ensembling\n",
        "Regularization is a collection of techniques that try to reduce overfitting by grading features based on importance. For example, L1 regularization encourages sparsity in coefficients, while L2 regularization maintains all coefficients small but not zero."
      ],
      "metadata": {
        "id": "oBK-WI6YxDYa"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 3. Search \"ways to prevent overfitting in machine learning\", write down 3 of the things you find and a sentence about each.\n",
        "> **Note:** there are lots of these, so don't worry too much about all of them, just pick 3 and start with those."
      ],
      "metadata": {
        "id": "XeYFEqw8xK26"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### ANSWER\n",
        "Use more data\n",
        "Increasing the amount of data in a model makes it harder for the model to memorize exact patterns. This forces the model to find solutions that are more flexible. However, it's important to use relevant and clean data.\n",
        "Early stopping\n",
        "Pause the training process before the model learns noise in the data. However, it's important to get the timing right.\n",
        "Regularization\n",
        "Use techniques to reduce overfitting by grading features based on importance. For example, you can prune a decision tree or use dropout on a neural network.\n"
      ],
      "metadata": {
        "id": "ytux47Mmfj26"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 4. Spend 20-minutes reading and clicking through the [CNN Explainer website](https://poloclub.github.io/cnn-explainer/).\n",
        "\n",
        "* Upload your own example image using the \"upload\" button on the website and see what happens in each layer of a CNN as your image passes through it."
      ],
      "metadata": {
        "id": "DKdEEFEqxM-8"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "TqZaJIRMbFtS"
      },
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 5. Load the [`torchvision.datasets.MNIST()`](https://pytorch.org/vision/stable/generated/torchvision.datasets.MNIST.html#torchvision.datasets.MNIST) train and test datasets."
      ],
      "metadata": {
        "id": "lvf-3pODxXYI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Import PyTorch\n",
        "import torch\n",
        "from torch import nn\n",
        "\n",
        "# Import torchvision\n",
        "import torchvision\n",
        "from torchvision import datasets\n",
        "from torchvision import transforms\n",
        "from torchvision.transforms import ToTensor\n",
        "\n",
        "# Import matplotlib\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "#Check version\n",
        "print(torch.__version__)\n",
        "print(torchvision.__version__)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9YIsoxsniFBj",
        "outputId": "6246f936-2182-4138-b468-7ae348f1f0b6"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2.5.1+cu121\n",
            "0.20.1+cu121\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Setup training and testing data\n",
        "from torchvision import datasets\n",
        "train_data = datasets.MNIST(\n",
        "    root=\"data\",\n",
        "    train=True,\n",
        "    download=True,\n",
        "    transform=torchvision.transforms.ToTensor(),\n",
        "    target_transform=None\n",
        ")\n",
        "test_data = datasets.MNIST(\n",
        "    root=\"data\",\n",
        "    train=False,\n",
        "    download=True,\n",
        "    transform=ToTensor(),\n",
        "    target_transform=None\n",
        ")"
      ],
      "metadata": {
        "id": "SHjeuN81bHza"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 6. Visualize at least 5 different samples of the MNIST training dataset."
      ],
      "metadata": {
        "id": "qxZW-uAbxe_F"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "CODE:\n",
        "\n",
        "`import matplotlib.pyplot as plt\n",
        "\n",
        "torch.manual_seed(42)\n",
        "\n",
        "\n",
        "fig = plt.figure(figsize=(9, 9))\n",
        "\n",
        "rows, cols = 4, 4\n",
        "\n",
        "for i in range(1, rows*cols + 1):\n",
        "   \n",
        "  \n",
        "  random_idx = torch.randint(0, len(train_data), size=[1])\n",
        "  \n",
        "  print(f\"1:{random_idx}\")\n",
        "  \n",
        "  random_idx = torch.randint(0, len(train_data), size=[1]).item()\n",
        "  \n",
        "  print(f\"2:{random_idx}\")`\n",
        "\n",
        "\n",
        "OUTPUT:\n",
        "\n",
        "`1:tensor([37542])\n",
        "2:46067\n",
        "1:tensor([46876])\n",
        "2:46414\n",
        "1:tensor([10026])\n",
        "2:27335\n",
        "1:tensor([38620])\n",
        "2:11924\n",
        "1:tensor([14950])\n",
        "2:57113\n",
        "1:tensor([31378])\n",
        "2:29014\n",
        "1:tensor([47210])\n",
        "2:18954\n",
        "1:tensor([18231])\n",
        "2:47572\n",
        "1:tensor([29315])\n",
        "2:48295\n",
        "1:tensor([48567])\n",
        "2:24706\n",
        "1:tensor([29749])\n",
        "2:5876\n",
        "1:tensor([51073])\n",
        "2:41111\n",
        "1:tensor([33899])\n",
        "2:13213\n",
        "1:tensor([49541])\n",
        "2:44769\n",
        "1:tensor([32287])\n",
        "2:16219\n",
        "1:tensor([36372])\n",
        "2:2880\n",
        "<Figure size 900x900 with 0 Axes>`"
      ],
      "metadata": {
        "id": "9doZcuQxkR1L"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class_names = train_data.classes\n",
        "class_names"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "umGJWcPIldRD",
        "outputId": "5084eefb-3ad5-4f4e-8337-63e015fa03db"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['0 - zero',\n",
              " '1 - one',\n",
              " '2 - two',\n",
              " '3 - three',\n",
              " '4 - four',\n",
              " '5 - five',\n",
              " '6 - six',\n",
              " '7 - seven',\n",
              " '8 - eight',\n",
              " '9 - nine']"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class_to_idx = train_data.class_to_idx\n",
        "class_to_idx"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W5pidI38li8b",
        "outputId": "f057afd6-ef3c-4982-b13d-4f05db9efd77"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'0 - zero': 0,\n",
              " '1 - one': 1,\n",
              " '2 - two': 2,\n",
              " '3 - three': 3,\n",
              " '4 - four': 4,\n",
              " '5 - five': 5,\n",
              " '6 - six': 6,\n",
              " '7 - seven': 7,\n",
              " '8 - eight': 8,\n",
              " '9 - nine': 9}"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_data.targets"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "omsvcaevlmrU",
        "outputId": "5a907aa7-80e1-426c-d574-8a3bf20e51ed"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([5, 0, 4,  ..., 5, 6, 8])"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# See the first training example\n",
        "image, label = train_data[0]\n",
        "print(f\"Image: \\n{image}\")\n",
        "print(f\"Label: \\n{label}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uW60axFQlrmL",
        "outputId": "c05960ba-19f4-4d49-8b38-5d6b8a0d73da"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Image: \n",
            "tensor([[[0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
            "          0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
            "          0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
            "          0.0000, 0.0000, 0.0000, 0.0000],\n",
            "         [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
            "          0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
            "          0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
            "          0.0000, 0.0000, 0.0000, 0.0000],\n",
            "         [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
            "          0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
            "          0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
            "          0.0000, 0.0000, 0.0000, 0.0000],\n",
            "         [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
            "          0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
            "          0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
            "          0.0000, 0.0000, 0.0000, 0.0000],\n",
            "         [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
            "          0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
            "          0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
            "          0.0000, 0.0000, 0.0000, 0.0000],\n",
            "         [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
            "          0.0000, 0.0000, 0.0000, 0.0000, 0.0118, 0.0706, 0.0706, 0.0706,\n",
            "          0.4941, 0.5333, 0.6863, 0.1020, 0.6510, 1.0000, 0.9686, 0.4980,\n",
            "          0.0000, 0.0000, 0.0000, 0.0000],\n",
            "         [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
            "          0.1176, 0.1412, 0.3686, 0.6039, 0.6667, 0.9922, 0.9922, 0.9922,\n",
            "          0.9922, 0.9922, 0.8824, 0.6745, 0.9922, 0.9490, 0.7647, 0.2510,\n",
            "          0.0000, 0.0000, 0.0000, 0.0000],\n",
            "         [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.1922,\n",
            "          0.9333, 0.9922, 0.9922, 0.9922, 0.9922, 0.9922, 0.9922, 0.9922,\n",
            "          0.9922, 0.9843, 0.3647, 0.3216, 0.3216, 0.2196, 0.1529, 0.0000,\n",
            "          0.0000, 0.0000, 0.0000, 0.0000],\n",
            "         [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0706,\n",
            "          0.8588, 0.9922, 0.9922, 0.9922, 0.9922, 0.9922, 0.7765, 0.7137,\n",
            "          0.9686, 0.9451, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
            "          0.0000, 0.0000, 0.0000, 0.0000],\n",
            "         [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
            "          0.3137, 0.6118, 0.4196, 0.9922, 0.9922, 0.8039, 0.0431, 0.0000,\n",
            "          0.1686, 0.6039, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
            "          0.0000, 0.0000, 0.0000, 0.0000],\n",
            "         [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
            "          0.0000, 0.0549, 0.0039, 0.6039, 0.9922, 0.3529, 0.0000, 0.0000,\n",
            "          0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
            "          0.0000, 0.0000, 0.0000, 0.0000],\n",
            "         [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
            "          0.0000, 0.0000, 0.0000, 0.5451, 0.9922, 0.7451, 0.0078, 0.0000,\n",
            "          0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
            "          0.0000, 0.0000, 0.0000, 0.0000],\n",
            "         [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
            "          0.0000, 0.0000, 0.0000, 0.0431, 0.7451, 0.9922, 0.2745, 0.0000,\n",
            "          0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
            "          0.0000, 0.0000, 0.0000, 0.0000],\n",
            "         [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
            "          0.0000, 0.0000, 0.0000, 0.0000, 0.1373, 0.9451, 0.8824, 0.6275,\n",
            "          0.4235, 0.0039, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
            "          0.0000, 0.0000, 0.0000, 0.0000],\n",
            "         [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
            "          0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.3176, 0.9412, 0.9922,\n",
            "          0.9922, 0.4667, 0.0980, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
            "          0.0000, 0.0000, 0.0000, 0.0000],\n",
            "         [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
            "          0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.1765, 0.7294,\n",
            "          0.9922, 0.9922, 0.5882, 0.1059, 0.0000, 0.0000, 0.0000, 0.0000,\n",
            "          0.0000, 0.0000, 0.0000, 0.0000],\n",
            "         [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
            "          0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0627,\n",
            "          0.3647, 0.9882, 0.9922, 0.7333, 0.0000, 0.0000, 0.0000, 0.0000,\n",
            "          0.0000, 0.0000, 0.0000, 0.0000],\n",
            "         [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
            "          0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
            "          0.0000, 0.9765, 0.9922, 0.9765, 0.2510, 0.0000, 0.0000, 0.0000,\n",
            "          0.0000, 0.0000, 0.0000, 0.0000],\n",
            "         [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
            "          0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.1804, 0.5098,\n",
            "          0.7176, 0.9922, 0.9922, 0.8118, 0.0078, 0.0000, 0.0000, 0.0000,\n",
            "          0.0000, 0.0000, 0.0000, 0.0000],\n",
            "         [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
            "          0.0000, 0.0000, 0.0000, 0.0000, 0.1529, 0.5804, 0.8980, 0.9922,\n",
            "          0.9922, 0.9922, 0.9804, 0.7137, 0.0000, 0.0000, 0.0000, 0.0000,\n",
            "          0.0000, 0.0000, 0.0000, 0.0000],\n",
            "         [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
            "          0.0000, 0.0000, 0.0941, 0.4471, 0.8667, 0.9922, 0.9922, 0.9922,\n",
            "          0.9922, 0.7882, 0.3059, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
            "          0.0000, 0.0000, 0.0000, 0.0000],\n",
            "         [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
            "          0.0902, 0.2588, 0.8353, 0.9922, 0.9922, 0.9922, 0.9922, 0.7765,\n",
            "          0.3176, 0.0078, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
            "          0.0000, 0.0000, 0.0000, 0.0000],\n",
            "         [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0706, 0.6706,\n",
            "          0.8588, 0.9922, 0.9922, 0.9922, 0.9922, 0.7647, 0.3137, 0.0353,\n",
            "          0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
            "          0.0000, 0.0000, 0.0000, 0.0000],\n",
            "         [0.0000, 0.0000, 0.0000, 0.0000, 0.2157, 0.6745, 0.8863, 0.9922,\n",
            "          0.9922, 0.9922, 0.9922, 0.9569, 0.5216, 0.0431, 0.0000, 0.0000,\n",
            "          0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
            "          0.0000, 0.0000, 0.0000, 0.0000],\n",
            "         [0.0000, 0.0000, 0.0000, 0.0000, 0.5333, 0.9922, 0.9922, 0.9922,\n",
            "          0.8314, 0.5294, 0.5176, 0.0627, 0.0000, 0.0000, 0.0000, 0.0000,\n",
            "          0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
            "          0.0000, 0.0000, 0.0000, 0.0000],\n",
            "         [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
            "          0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
            "          0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
            "          0.0000, 0.0000, 0.0000, 0.0000],\n",
            "         [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
            "          0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
            "          0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
            "          0.0000, 0.0000, 0.0000, 0.0000],\n",
            "         [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
            "          0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
            "          0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
            "          0.0000, 0.0000, 0.0000, 0.0000]]])\n",
            "Label: \n",
            "5\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check the shape of our image\n",
        "print(f\"Image shape: {image.shape} -> [color_channels, height, width]\")\n",
        "print(f\"Image Label -> {label} : {class_names[label]}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ji8EjkaMlpdE",
        "outputId": "5c137fe5-34e1-444c-8187-fdc567f6c769"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Image shape: torch.Size([1, 28, 28]) -> [color_channels, height, width]\n",
            "Image Label -> 5 : 5 - five\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "# Visulaize images\n",
        "torch.manual_seed(42)\n",
        "fig = plt.figure(figsize=(9, 9))\n",
        "rows, cols = 4, 4\n",
        "for i in range(1, rows*cols + 1):\n",
        "  random_idx = torch.randint(0, len(train_data), size=[1]).item()\n",
        "  images, labels = train_data[random_idx]\n",
        "  fig.add_subplot(rows, cols, i)\n",
        "  plt.imshow(images.squeeze(), cmap=\"gray\")\n",
        "  plt.title(class_names[labels])\n",
        "  plt.axis(False)\n",
        "  print(random_idx)"
      ],
      "metadata": {
        "id": "QVFsYi1PbItE",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "f901fb82-cbc4-44f9-cb49-56111dce3371"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "37542\n",
            "46067\n",
            "46876\n",
            "46414\n",
            "10026\n",
            "27335\n",
            "38620\n",
            "11924\n",
            "14950\n",
            "57113\n",
            "31378\n",
            "29014\n",
            "47210\n",
            "18954\n",
            "18231\n",
            "47572\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 900x900 with 16 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 7. Turn the MNIST train and test datasets into dataloaders using `torch.utils.data.DataLoader`, set the `batch_size=32`."
      ],
      "metadata": {
        "id": "JAPDzW0wxhi3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from pickle import TRUE\n",
        "from torch.utils.data import DataLoader\n",
        "\n",
        "BATCH_SIZE = 32\n",
        "\n",
        "# Turn datasets into iterables (batches)\n",
        "train_dataloader = DataLoader(dataset=train_data,\n",
        "                              batch_size=BATCH_SIZE,\n",
        "                              shuffle=True)\n",
        "test_dataloader = DataLoader(dataset=test_data,\n",
        "                             batch_size=BATCH_SIZE,\n",
        "                             shuffle=False)\n",
        "print(f\"DataLoader-> Train: {train_dataloader}, Test: {test_dataloader}\")"
      ],
      "metadata": {
        "id": "ALA6MPcFbJXQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3989e940-d23f-4d2a-8a5d-c9fdb8f17d04"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "DataLoader-> Train: <torch.utils.data.dataloader.DataLoader object at 0x7841f0ed64d0>, Test: <torch.utils.data.dataloader.DataLoader object at 0x7841f0ed7dc0>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 8. Recreate `model_2` used in notebook 03 (the same model from the [CNN Explainer website](https://poloclub.github.io/cnn-explainer/), also known as TinyVGG) capable of fitting on the MNIST dataset."
      ],
      "metadata": {
        "id": "bCCVfXk5xjYS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a convolutional nueral network\n",
        "class MNISTModelV0(nn.Module):\n",
        "  \"\"\"\n",
        "  Model architecture that replicates the TinyVGG\n",
        "  model from CNN explainer website.\n",
        "  \"\"\"\n",
        "  def __init__(self, input_shape: int, hidden_units: int, output_shape: int):\n",
        "    super().__init__()\n",
        "    self.conv_block_1 = nn.Sequential(\n",
        "        nn.Conv2d(in_channels=input_shape,\n",
        "                  out_channels=output_shape,\n",
        "                  kernel_size=3,\n",
        "                  stride=1,\n",
        "                  padding=1), # values we can set ourselves in our NN's are called hyperparameter\n",
        "        nn.ReLU(),\n",
        "        nn.Conv2d(in_channels=hidden_units,\n",
        "                  out_channels=hidden_units,\n",
        "                  kernel_size=3,\n",
        "                  stride=1,\n",
        "                  padding=1),\n",
        "        nn.ReLU(),\n",
        "        nn.MaxPool2d(kernel_size=2)\n",
        "    )\n",
        "    self.conv_block_2 = nn.Sequential(\n",
        "        nn.Conv2d(in_channels=hidden_units,\n",
        "                  out_channels=hidden_units,\n",
        "                  kernel_size=3,\n",
        "                  stride=1,\n",
        "                  padding=1),\n",
        "        nn.ReLU(),\n",
        "        nn.Conv2d(in_channels=hidden_units,\n",
        "                  out_channels=hidden_units,\n",
        "                  kernel_size=3,\n",
        "                  stride=1,\n",
        "                  padding=1),\n",
        "        nn.ReLU(),\n",
        "        nn.MaxPool2d(kernel_size=2)\n",
        "    )\n",
        "    self.classifier = nn.Sequential(\n",
        "        nn.Flatten(),\n",
        "        nn.Linear(in_features=hidden_units*7*7, # there is a trick to calculating this...\n",
        "                  out_features=output_shape)\n",
        "    )\n",
        "\n",
        "  def forward(self, x):\n",
        "    x = self.conv_block_1(x)\n",
        "    #print(x.shape)\n",
        "    x = self.conv_block_2(x)\n",
        "    #print(x.shape)\n",
        "    x = self.classifier(x)\n",
        "    #print(x.shape)\n",
        "    return x\n",
        "    # return self.classifier(self.conv_block_2(self.conv_block_1(x)))"
      ],
      "metadata": {
        "id": "5IKNF22XbKYS"
      },
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "torch.manual_seed(42)\n",
        "model_0 = MNISTModelV0(input_shape=1, # Grayscale image channel = 1\n",
        "                              hidden_units=10,\n",
        "                              output_shape=len(class_names)).to(device)"
      ],
      "metadata": {
        "id": "InGDbVTWndR8"
      },
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "loss_fn = nn.CrossEntropyLoss()\n",
        "optimizer = torch.optim.SGD(params=model_0.parameters(),\n",
        "                            lr=0.1)"
      ],
      "metadata": {
        "id": "XUZ1Oi1unwYt"
      },
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from timeit import default_timer as timer\n",
        "def print_train_time(start: float,\n",
        "                     end: float,\n",
        "                     device: torch.device = None):\n",
        "  total_time = end - start\n",
        "  print(f\"Train time on {device}: {total_time:.3f} seconds\")\n",
        "  return total_time"
      ],
      "metadata": {
        "id": "EL46lhBNoZOk"
      },
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def accuracy_fn(y_true, y_pred):\n",
        "    \"\"\"Calculates accuracy between truth labels and predictions.\n",
        "\n",
        "    Args:\n",
        "        y_true (torch.Tensor): Truth labels for predictions.\n",
        "        y_pred (torch.Tensor): Predictions to be compared to predictions.\n",
        "\n",
        "    Returns:\n",
        "        [torch.float]: Accuracy value between y_true and y_pred, e.g. 78.45\n",
        "    \"\"\"\n",
        "    correct = torch.eq(y_true, y_pred).sum().item()\n",
        "    acc = (correct / len(y_pred)) * 100\n",
        "    return acc"
      ],
      "metadata": {
        "id": "XRT3aaLppNgk"
      },
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def train_step(model: torch.nn.Module,\n",
        "               data_loader: torch.utils.data.DataLoader,\n",
        "               loss_fn: torch.nn.Module,\n",
        "               optimizer:torch.optim.Optimizer,\n",
        "               accuracy_fn,device: torch.device = device):\n",
        "  \"\"\"Performs a training with model trying to learn on data_loader.\"\"\"\n",
        "  ## Training\n",
        "  train_loss, train_acc = 0, 0\n",
        "  model.train()   # By Default it is in train mode\n",
        "  #Add a loop to loop through the training batches\n",
        "  for batch, (X, y) in enumerate(data_loader):\n",
        "    # Put data on target device\n",
        "    X, y = X.to(device), y.to(device)\n",
        "\n",
        "    # 1. Forward Pass\n",
        "    y_pred = model(X)\n",
        "\n",
        "    # 2. Calculate loss (per batch)\n",
        "    loss = loss_fn(y_pred, y)\n",
        "    train_loss += loss\n",
        "    train_acc += accuracy_fn(y_true=y, y_pred=y_pred.argmax(dim=1))\n",
        "\n",
        "    # 3. Optimizer Zero Grad\n",
        "    optimizer.zero_grad()\n",
        "\n",
        "    # 4. Loss Backward\n",
        "    loss.backward()\n",
        "\n",
        "    # 5. Optimizer step (update the model's parameters *once per batch*)\n",
        "    optimizer.step()\n",
        "\n",
        "  # Divide total train loss and acc by length of train dataloader\n",
        "  train_loss /= len(data_loader)\n",
        "  train_acc /= len(data_loader)\n",
        "  print(f\"Train Loss: {train_loss: .5f} || Train acc: {train_acc: .2f}%\")\n",
        "\n",
        "def test_step(model: torch.nn.Module,\n",
        "              data_loader: torch.utils.data.DataLoader,\n",
        "              loss_fn: torch.nn.Module,\n",
        "              accuracy_fn,\n",
        "              device: torch.device = device):\n",
        "  \"\"\"Performs a testing loop step on model going over data_loader.\"\"\"\n",
        "   ### Testing\n",
        "  test_loss, test_acc = 0, 0\n",
        "\n",
        "  #Put the model in eval mode\n",
        "  model.eval()\n",
        "\n",
        "  #Turn on inference mode context manager\n",
        "  with torch.inference_mode():\n",
        "    for X_test, y_test in data_loader:\n",
        "\n",
        "      #Send the data to the target device\n",
        "      X_test, y_test =  X_test.to(device), y_test.to(device)\n",
        "\n",
        "      # 1. Forward Pass (outputs raw logits)\n",
        "      test_pred = model(X_test)\n",
        "\n",
        "      # 2. Calculate the loss (accumulatively)\n",
        "      test_loss += loss_fn(test_pred, y_test)\n",
        "\n",
        "      # 3. Calculate the accuracy\n",
        "      test_acc += accuracy_fn(y_true=y_test, y_pred=test_pred.argmax(dim=1))\n",
        "\n",
        "    # Calculate the test loss average per batch\n",
        "    test_loss /= len(test_dataloader)\n",
        "\n",
        "    # Calculate the test acc average per batch\n",
        "    test_acc /= len(test_dataloader)\n",
        "\n",
        "    # Print\n",
        "    print(f\"\\nTest loss: {test_loss:.5f}, Test acc: {test_acc:.2f}%\\n\")"
      ],
      "metadata": {
        "id": "qUcAP6U2n94U"
      },
      "execution_count": 60,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 9. Train the model you built in exercise 8. for 5 epochs on CPU and GPU and see how long it takes on each."
      ],
      "metadata": {
        "id": "sf_3zUr7xlhy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# CUDA\n",
        "torch.manual_seed(42)\n",
        "\n",
        "# Measure time\n",
        "from timeit import default_timer as timer\n",
        "from tqdm.auto import tqdm\n",
        "train_time_start_on_gpu = timer()\n",
        "\n",
        "# Set epochs\n",
        "epochs = 5\n",
        "\n",
        "# Create a optimization and evaluation loop using train_step() and test_step()\n",
        "for epoch in tqdm(range(epochs)):\n",
        "  print(f\"Epoch: {epoch}\\n------------------------------------------------------\")\n",
        "  train_step(model=model_0,\n",
        "             data_loader=train_dataloader,\n",
        "             loss_fn=loss_fn,\n",
        "             optimizer=optimizer,\n",
        "             accuracy_fn=accuracy_fn,\n",
        "             device=device)\n",
        "  test_step(model=model_0,\n",
        "            data_loader=test_dataloader,\n",
        "            loss_fn=loss_fn,\n",
        "            accuracy_fn=accuracy_fn,\n",
        "            device=device)\n",
        "\n",
        "\n",
        "train_time_end_on_gpu = timer()\n",
        "total_train_time_model_0_gpu = print_train_time(start=train_time_start_on_gpu,\n",
        "                                            end=train_time_end_on_gpu,\n",
        "                                            device=device)\n"
      ],
      "metadata": {
        "id": "jSo6vVWFbNLD",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 587,
          "referenced_widgets": [
            "81422dfa932f4d0f8adc94a9af398b65",
            "7b425003e8584be8ab6e623f8a5b28b9",
            "2709ff8a5059413e900ec2da55a28029",
            "91dc7be7ebd645458bffd0fb1d6362bb",
            "17b8917bb6b14fa29e48e178940186ad",
            "60aab5ce8dda4e00844d42f559330d2a",
            "095840876fe44434b67530ebdb79864d",
            "e9a90b4038ea4457bafb1d094f71bacd",
            "d78d46efc6284fdaaa1761113391c7d0",
            "a0cf75fc65a74901a622e0c32f1ddb81",
            "a673c334ebfb43cf97d17f8f428847d9"
          ]
        },
        "outputId": "7c1d3617-1c8e-40bc-f8f3-41c5a9705eb8"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/5 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "81422dfa932f4d0f8adc94a9af398b65"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 0\n",
            "------------------------------------------------------\n",
            "Train Loss:  0.31573 || Train acc:  89.38%\n",
            "\n",
            "Test loss: 0.08005, Test acc: 97.68%\n",
            "\n",
            "Epoch: 1\n",
            "------------------------------------------------------\n",
            "Train Loss:  0.08246 || Train acc:  97.45%\n",
            "\n",
            "Test loss: 0.07260, Test acc: 97.74%\n",
            "\n",
            "Epoch: 2\n",
            "------------------------------------------------------\n",
            "Train Loss:  0.06406 || Train acc:  98.01%\n",
            "\n",
            "Test loss: 0.05783, Test acc: 98.03%\n",
            "\n",
            "Epoch: 3\n",
            "------------------------------------------------------\n",
            "Train Loss:  0.05583 || Train acc:  98.22%\n",
            "\n",
            "Test loss: 0.05162, Test acc: 98.32%\n",
            "\n",
            "Epoch: 4\n",
            "------------------------------------------------------\n",
            "Train Loss:  0.04921 || Train acc:  98.44%\n",
            "\n",
            "Test loss: 0.05075, Test acc: 98.20%\n",
            "\n",
            "Train time on cuda: 63.240 seconds\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# CPU\n",
        "torch.manual_seed(42)\n",
        "model_0_cpu = MNISTModelV0(input_shape=1, # Grayscale image channel = 1\n",
        "                              hidden_units=10,\n",
        "                              output_shape=len(class_names)).to(\"cpu\")\n",
        "loss_fn_cpu = nn.CrossEntropyLoss()\n",
        "optimizer_cpu = torch.optim.SGD(params=model_0_cpu.parameters(),\n",
        "                            lr=0.1)\n",
        "torch.manual_seed(42)\n",
        "\n",
        "# Measure time\n",
        "from timeit import default_timer as timer\n",
        "from tqdm.auto import tqdm\n",
        "train_time_start_on_cpu = timer()\n",
        "\n",
        "# Set epochs\n",
        "epochs = 5\n",
        "\n",
        "# Create a optimization and evaluation loop using train_step() and test_step()\n",
        "for epoch in tqdm(range(epochs)):\n",
        "  print(f\"Epoch: {epoch}\\n------------------------------------------------------\")\n",
        "  train_step(model=model_0_cpu,\n",
        "             data_loader=train_dataloader,\n",
        "             loss_fn=loss_fn_cpu,\n",
        "             optimizer=optimizer_cpu,\n",
        "             accuracy_fn=accuracy_fn,\n",
        "             device=\"cpu\")\n",
        "  test_step(model=model_0_cpu,\n",
        "            data_loader=test_dataloader,\n",
        "            loss_fn=loss_fn_cpu,\n",
        "            accuracy_fn=accuracy_fn,\n",
        "            device=\"cpu\")\n",
        "\n",
        "\n",
        "train_time_end_on_cpu = timer()\n",
        "total_train_time_model_0_cpu = print_train_time(start=train_time_start_on_cpu,\n",
        "                                            end=train_time_end_on_cpu,\n",
        "                                            device=\"cpu\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 587,
          "referenced_widgets": [
            "858e51ec7add4ec293e80446667824c4",
            "55246af32e904bf19b6af0b4dfa25817",
            "ffbb0ca8b7ad4e0ea2677d3c10dbe2ac",
            "e099558243814eba8615777ab7fdedec",
            "d5a98140aa19484685e280a8699e9603",
            "89f41b5c599d49e78bd111bf3e39f661",
            "547d8d7a9a4b4672b9c42b471326f996",
            "6ab0bb2da71243e7a65433f38d6f8880",
            "5146bce322f04084911564d061378330",
            "6c04c1a6a8f7497c988f83e6dac5477a",
            "caa1d9644542407a9c126c5253a9cff7"
          ]
        },
        "id": "K3oSf46zqQEE",
        "outputId": "06c92f05-4619-4602-eb39-32882794e0b9"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/5 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "858e51ec7add4ec293e80446667824c4"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 0\n",
            "------------------------------------------------------\n",
            "Train Loss:  0.32017 || Train acc:  89.23%\n",
            "\n",
            "Test loss: 0.07544, Test acc: 97.72%\n",
            "\n",
            "Epoch: 1\n",
            "------------------------------------------------------\n",
            "Train Loss:  0.08204 || Train acc:  97.48%\n",
            "\n",
            "Test loss: 0.06162, Test acc: 97.92%\n",
            "\n",
            "Epoch: 2\n",
            "------------------------------------------------------\n",
            "Train Loss:  0.06394 || Train acc:  98.02%\n",
            "\n",
            "Test loss: 0.05525, Test acc: 98.10%\n",
            "\n",
            "Epoch: 3\n",
            "------------------------------------------------------\n",
            "Train Loss:  0.05469 || Train acc:  98.27%\n",
            "\n",
            "Test loss: 0.04741, Test acc: 98.38%\n",
            "\n",
            "Epoch: 4\n",
            "------------------------------------------------------\n",
            "Train Loss:  0.04825 || Train acc:  98.47%\n",
            "\n",
            "Test loss: 0.04637, Test acc: 98.35%\n",
            "\n",
            "Train time on cpu: 186.889 seconds\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 10. Make predictions using your trained model and visualize at least 5 of them comparing the prediciton to the target label."
      ],
      "metadata": {
        "id": "w1CsHhPpxp1w"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def make_prediction(model: torch.nn.Module,\n",
        "                    data: list,\n",
        "                    device: torch.device = device):\n",
        "  pred_probs = []\n",
        "  model.eval()\n",
        "  with torch.inference_mode():\n",
        "    for sample in data:\n",
        "      # Prepare the sample (add a batch dimention and pass to target device)\n",
        "      sample = torch.unsqueeze(sample, dim=0).to(device)\n",
        "\n",
        "      # Forward pass (model ouputs raw logits)\n",
        "      pred_logit = model(sample)\n",
        "\n",
        "      # Get prediction probability (logit -> prediction probability)\n",
        "      preb_prob = torch.softmax(pred_logit.squeeze(), dim=0)\n",
        "\n",
        "      # Get preb_prob off the GPU for further calculations\n",
        "      pred_probs.append(preb_prob.cpu())\n",
        "  # Stack the preb_probs to turn list into a tensor\n",
        "  return torch.stack(pred_probs)"
      ],
      "metadata": {
        "id": "_YGgZvSobNxu"
      },
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import random\n",
        "random.seed(42)\n",
        "test_samples = []\n",
        "test_labels = []\n",
        "for sample, label in random.sample(list(test_data), k=9):\n",
        "  test_samples.append(sample)\n",
        "  test_labels.append(label)\n",
        "\n",
        "# View the first sample shape\n",
        "test_samples[0].shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WB3H1FTtuhEt",
        "outputId": "b5dea87b-4bb1-41b2-dbf8-a1444f3fd076"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([1, 28, 28])"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.imshow(test_samples[0].squeeze(), cmap=\"gray\")\n",
        "plt.title(class_names[test_labels[0]])\n",
        "# Make predictions\n",
        "pred_probs = make_prediction(model=model_0,\n",
        "                             data=test_samples)\n",
        "\n",
        "# View first two prediction probabilities\n",
        "pred_probs[:2]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 521
        },
        "id": "i0O_ozWrunoc",
        "outputId": "059e2d58-22df-4673-9f0c-45416b412666"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[1.5812e-11, 6.2927e-07, 1.0000e+00, 7.7068e-08, 7.6474e-17, 4.8357e-16,\n",
              "         4.2665e-15, 1.3463e-09, 8.8702e-08, 7.3944e-13],\n",
              "        [5.2357e-07, 9.8288e-01, 2.1057e-05, 6.8598e-07, 9.0941e-06, 3.1339e-03,\n",
              "         1.0675e-02, 5.5533e-06, 3.1703e-03, 9.9874e-05]])"
            ]
          },
          "metadata": {},
          "execution_count": 65
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Convert prediction probabilities to labels\n",
        "pred_classes = pred_probs.argmax(dim=1)\n",
        "pred_classes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i5fpgCrQu3YU",
        "outputId": "da34ec37-871c-46c4-ddf4-405907c09ac5"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([2, 1, 2, 4, 6, 6, 4, 9, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_labels"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "64jonz75u5GN",
        "outputId": "f98d82ce-98ce-4d6d-fcdd-a23023853a85"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[2, 1, 2, 4, 6, 6, 4, 9, 1]"
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot predictions\n",
        "plt.figure(figsize=(9, 9))\n",
        "nrows = 3\n",
        "ncols = 3\n",
        "for i, sample in enumerate(test_samples):\n",
        "  # Create subplot\n",
        "  plt.subplot(nrows, ncols, i + 1)\n",
        "\n",
        "  # Plot the target image\n",
        "  plt.imshow(sample.squeeze(), cmap=\"grey\")\n",
        "\n",
        "  # Find the prediction (in text form, e.g \"Sandal\" )\n",
        "  pred_label = class_names[pred_classes[i]]\n",
        "\n",
        "  # Get the truth label (in text form)\n",
        "  truth_label = class_names[test_labels[i]]\n",
        "\n",
        "  # Creat a title for plot\n",
        "  title_text = f\"Pred: {pred_label} | Truth : {truth_label}\"\n",
        "\n",
        "  # Check for equality blw pred and truth and change color of title text\n",
        "  if pred_label == truth_label:\n",
        "    plt.title(title_text, fontsize=10, c=\"g\") # green text if prediction same as truth\n",
        "  else:\n",
        "    plt.title(title_text, fontsize=10, c=\"r\")\n",
        "  plt.axis(False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 749
        },
        "id": "qryty2y8ulr0",
        "outputId": "40c8189c-d0f7-4c66-b922-a56e6f91d5ad"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 900x900 with 9 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 11. Plot a confusion matrix comparing your model's predictions to the truth labels."
      ],
      "metadata": {
        "id": "qQwzqlBWxrpG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Import tqdm.auto\n",
        "from tqdm.auto import tqdm\n",
        "\n",
        "# 1. Make predictions with trained model\n",
        "y_preds = []\n",
        "model_0.eval()\n",
        "with torch.inference_mode():\n",
        "  for X, y in tqdm(test_dataloader, desc=\"Making predictions...\"):\n",
        "    # Send the data and targets to target device\n",
        "    X, y = X.to(device), y.to(device)\n",
        "    # Forward pass\n",
        "    y_logit = model_0(X)\n",
        "    # Turn predictions from logits -> prediction proabilities -> prediction labels\n",
        "    y_pred = torch.softmax(y_logit.squeeze(), dim=0).argmax(dim=1)\n",
        "    # Put prediction on CPU for evaluation\n",
        "    y_preds.append(y_pred.cpu())\n",
        "\n",
        "# Concatenate list of predictions into a tensor\n",
        "y_pred_tensor = torch.cat(y_preds)\n",
        "y_pred_tensor\n"
      ],
      "metadata": {
        "id": "vSrXiT_AbQ6e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 66,
          "referenced_widgets": [
            "0d76812bf1014e938a41b7f95efc179c",
            "aa81b194efb241e5b3760e763e840622",
            "61861eae7b8042db916444d17678f3ee",
            "4c30245d47e54272aefbccd62f5674c9",
            "ec3270cf77364ab882b3bdf2aa7f061b",
            "973ae7168c764cf98409e1c73761c550",
            "0bed6e3baf734960b81d18fa90bfd833",
            "d1cbcfef50e6461f84021017f974b6a1",
            "1129774d597b4e1193f41a016024d610",
            "91994c051a0944d29e68f25b536005cd",
            "7417cef80fe440a4b1a53737cd7738e7"
          ]
        },
        "outputId": "88e51067-f89a-4ede-edad-636eac8485f5"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Making predictions...:   0%|          | 0/313 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0d76812bf1014e938a41b7f95efc179c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([7, 2, 1,  ..., 4, 5, 6])"
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# See if required packages are installed and if not, install them..\n",
        "!pip install torchmetrics -U mlxtend"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_LKk8NiPvce0",
        "outputId": "e9ae66b5-5568-4b6a-a88c-b8804864f6ea"
      },
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting torchmetrics\n",
            "  Downloading torchmetrics-1.6.0-py3-none-any.whl.metadata (20 kB)\n",
            "Requirement already satisfied: mlxtend in /usr/local/lib/python3.10/dist-packages (0.23.3)\n",
            "Requirement already satisfied: numpy>1.20.0 in /usr/local/lib/python3.10/dist-packages (from torchmetrics) (1.26.4)\n",
            "Requirement already satisfied: packaging>17.1 in /usr/local/lib/python3.10/dist-packages (from torchmetrics) (24.2)\n",
            "Requirement already satisfied: torch>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from torchmetrics) (2.5.1+cu121)\n",
            "Collecting lightning-utilities>=0.8.0 (from torchmetrics)\n",
            "  Downloading lightning_utilities-0.11.9-py3-none-any.whl.metadata (5.2 kB)\n",
            "Requirement already satisfied: scipy>=1.2.1 in /usr/local/lib/python3.10/dist-packages (from mlxtend) (1.13.1)\n",
            "Requirement already satisfied: pandas>=0.24.2 in /usr/local/lib/python3.10/dist-packages (from mlxtend) (2.2.2)\n",
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            "Downloading torchmetrics-1.6.0-py3-none-any.whl (926 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m926.4/926.4 kB\u001b[0m \u001b[31m52.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading lightning_utilities-0.11.9-py3-none-any.whl (28 kB)\n",
            "Installing collected packages: lightning-utilities, torchmetrics\n",
            "Successfully installed lightning-utilities-0.11.9 torchmetrics-1.6.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from torchmetrics import ConfusionMatrix\n",
        "from mlxtend.plotting import plot_confusion_matrix\n",
        "\n",
        "# 2. Setup confusion matrix instance and compare predictions to targets\n",
        "confmat = ConfusionMatrix(num_classes=len(class_names), task='multiclass')\n",
        "confmat_tensor = confmat(preds=y_pred_tensor,\n",
        "                         target=test_data.targets)\n",
        "\n",
        "# 3. Plot the confusion matrix\n",
        "fig, ax = plot_confusion_matrix(\n",
        "    conf_mat=confmat_tensor.numpy(), # matplotlib likes working with NumPy\n",
        "    class_names=class_names, # turn the row and column labels into class names\n",
        "    figsize=(10, 7)\n",
        ");"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 660
        },
        "id": "N-fr8kXbvZVt",
        "outputId": "b5644576-a846-444e-bc28-1859c71b20fa"
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x700 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 12. Create a random tensor of shape `[1, 3, 64, 64]` and pass it through a `nn.Conv2d()` layer with various hyperparameter settings (these can be any settings you choose), what do you notice if the `kernel_size` parameter goes up and down?"
      ],
      "metadata": {
        "id": "lj6bDhoWxt2y"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "random_tensor = torch.rand([1, 3, 64, 64])\n",
        "random_tensor.shape"
      ],
      "metadata": {
        "id": "leCTsqtSbR5P",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7fd91da0-2cb1-438a-d330-4d2fedf25ce5"
      },
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([1, 3, 64, 64])"
            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "conv_layer = nn.Conv2d(in_channels=3,\n",
        "                       out_channels=64,\n",
        "                       kernel_size=3,\n",
        "                       stride=2,\n",
        "                       padding=1)\n",
        "\n",
        "print(f\"Random tensor original shape: {random_tensor.shape}\")\n",
        "random_tensor_through_conv_layer = conv_layer(random_tensor)\n",
        "print(f\"Random tensor through conv layer shape: {random_tensor_through_conv_layer.shape}\")\n",
        ""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J5oRRa3LwF1d",
        "outputId": "d6736491-b207-48c7-cbe8-8553bca89a35"
      },
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Random tensor original shape: torch.Size([1, 3, 64, 64])\n",
            "Random tensor through conv layer shape: torch.Size([1, 64, 32, 32])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 13. Use a model similar to the trained `model_2` from notebook 03 to make predictions on the test [`torchvision.datasets.FashionMNIST`](https://pytorch.org/vision/main/generated/torchvision.datasets.FashionMNIST.html) dataset.\n",
        "* Then plot some predictions where the model was wrong alongside what the label of the image should've been.\n",
        "* After visualing these predictions do you think it's more of a modelling error or a data error?\n",
        "* As in, could the model do better or are the labels of the data too close to each other (e.g. a \"Shirt\" label is too close to \"T-shirt/top\")?"
      ],
      "metadata": {
        "id": "VHS20cNTxwSi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Download FashionMNIST train & test\n",
        "from torchvision import datasets\n",
        "from torchvision import transforms\n",
        "\n",
        "fashion_mnist_train = datasets.FashionMNIST(root=\".\",\n",
        "                                            download=True,\n",
        "                                            train=True,\n",
        "                                            transform=transforms.ToTensor())\n",
        "\n",
        "fashion_mnist_test = datasets.FashionMNIST(root=\".\",\n",
        "                                           train=False,\n",
        "                                           download=True,\n",
        "                                           transform=transforms.ToTensor())\n",
        "\n",
        "len(fashion_mnist_train), len(fashion_mnist_test)"
      ],
      "metadata": {
        "id": "78a8LjtdbSZj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "552a8d1e-c392-4659-dfad-11626051909c"
      },
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz to ./FashionMNIST/raw/train-images-idx3-ubyte.gz\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 26.4M/26.4M [00:02<00:00, 11.1MB/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ./FashionMNIST/raw/train-images-idx3-ubyte.gz to ./FashionMNIST/raw\n",
            "\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz to ./FashionMNIST/raw/train-labels-idx1-ubyte.gz\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 29.5k/29.5k [00:00<00:00, 169kB/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ./FashionMNIST/raw/train-labels-idx1-ubyte.gz to ./FashionMNIST/raw\n",
            "\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz to ./FashionMNIST/raw/t10k-images-idx3-ubyte.gz\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 4.42M/4.42M [00:01<00:00, 3.28MB/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ./FashionMNIST/raw/t10k-images-idx3-ubyte.gz to ./FashionMNIST/raw\n",
            "\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz to ./FashionMNIST/raw/t10k-labels-idx1-ubyte.gz\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 5.15k/5.15k [00:00<00:00, 8.30MB/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ./FashionMNIST/raw/t10k-labels-idx1-ubyte.gz to ./FashionMNIST/raw\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(60000, 10000)"
            ]
          },
          "metadata": {},
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Get the class names of the Fashion MNIST dataset\n",
        "fashion_mnist_class_names = fashion_mnist_train.classes\n",
        "fashion_mnist_class_names\n",
        ""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zp-lGhwjwQKG",
        "outputId": "f7896720-5e71-4366-ef64-2e9ef0e50bb3"
      },
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['T-shirt/top',\n",
              " 'Trouser',\n",
              " 'Pullover',\n",
              " 'Dress',\n",
              " 'Coat',\n",
              " 'Sandal',\n",
              " 'Shirt',\n",
              " 'Sneaker',\n",
              " 'Bag',\n",
              " 'Ankle boot']"
            ]
          },
          "metadata": {},
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Turn FashionMNIST datasets into dataloaders\n",
        "from torch.utils.data import DataLoader\n",
        "\n",
        "fashion_mnist_train_dataloader = DataLoader(fashion_mnist_train,\n",
        "                                            batch_size=32,\n",
        "                                            shuffle=True)\n",
        "\n",
        "fashion_mnist_test_dataloader = DataLoader(fashion_mnist_test,\n",
        "                                           batch_size=32,\n",
        "                                           shuffle=False)\n",
        "\n",
        "len(fashion_mnist_train_dataloader), len(fashion_mnist_test_dataloader)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZM7ir5KvwTJs",
        "outputId": "8a45cee1-ab92-4d27-a61b-fa054cb3df91"
      },
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1875, 313)"
            ]
          },
          "metadata": {},
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# model_2 is the same architecture as MNIST_model\n",
        "model_2 = MNISTModelV0(input_shape=1,\n",
        "                      hidden_units=10,\n",
        "                      output_shape=10).to(device)\n",
        "model_2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HQQo5LnuwdFk",
        "outputId": "adb8b485-d3ad-4ad0-eae3-64000e81fccd"
      },
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "MNISTModelV0(\n",
              "  (conv_block_1): Sequential(\n",
              "    (0): Conv2d(1, 10, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (1): ReLU()\n",
              "    (2): Conv2d(10, 10, 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",
              "  )\n",
              "  (conv_block_2): Sequential(\n",
              "    (0): Conv2d(10, 10, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (1): ReLU()\n",
              "    (2): Conv2d(10, 10, 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",
              "  )\n",
              "  (classifier): Sequential(\n",
              "    (0): Flatten(start_dim=1, end_dim=-1)\n",
              "    (1): Linear(in_features=490, out_features=10, bias=True)\n",
              "  )\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Setup loss and optimizer\n",
        "from torch import nn\n",
        "loss_fn = nn.CrossEntropyLoss()\n",
        "optimizer = torch.optim.SGD(model_2.parameters(), lr=0.01)"
      ],
      "metadata": {
        "id": "XaSJWP3xwjOk"
      },
      "execution_count": 79,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Setup metrics\n",
        "from tqdm.auto import tqdm\n",
        "from torchmetrics import Accuracy\n",
        "\n",
        "acc_fn = Accuracy(task = 'multiclass', num_classes=len(fashion_mnist_class_names)).to(device)\n",
        "\n",
        "# Setup training/testing loop\n",
        "epochs = 5\n",
        "for epoch in tqdm(range(epochs)):\n",
        "  train_loss, test_loss_total = 0, 0\n",
        "  train_acc, test_acc = 0, 0\n",
        "\n",
        "  ### Training\n",
        "  model_2.train()\n",
        "  for batch, (X_train, y_train) in enumerate(fashion_mnist_train_dataloader):\n",
        "    X_train, y_train = X_train.to(device), y_train.to(device)\n",
        "\n",
        "    # Forward pass and loss\n",
        "    y_pred = model_2(X_train)\n",
        "    loss = loss_fn(y_pred, y_train)\n",
        "    train_loss += loss\n",
        "    train_acc += acc_fn(y_pred, y_train)\n",
        "\n",
        "    # Backprop and gradient descent\n",
        "    optimizer.zero_grad()\n",
        "    loss.backward()\n",
        "    optimizer.step()\n",
        "\n",
        "  # Adjust the loss/acc (find the loss/acc per epoch)\n",
        "  train_loss /= len(fashion_mnist_train_dataloader)\n",
        "  train_acc /= len(fashion_mnist_train_dataloader)\n",
        "\n",
        "  ### Testing\n",
        "  model_2.eval()\n",
        "  with torch.inference_mode():\n",
        "    for batch, (X_test, y_test) in enumerate(fashion_mnist_test_dataloader):\n",
        "      X_test, y_test = X_test.to(device), y_test.to(device)\n",
        "\n",
        "      # Forward pass and loss\n",
        "      y_pred_test = model_2(X_test)\n",
        "      test_loss = loss_fn(y_pred_test, y_test)\n",
        "      test_loss_total += test_loss\n",
        "\n",
        "      test_acc += acc_fn(y_pred_test, y_test)\n",
        "\n",
        "    # Adjust the loss/acc (find the loss/acc per epoch)\n",
        "    test_loss /= len(fashion_mnist_test_dataloader)\n",
        "    test_acc /= len(fashion_mnist_test_dataloader)\n",
        "\n",
        "  # Print out what's happening\n",
        "  print(f\"Epoch: {epoch} | Train loss: {train_loss:.3f} | Train acc: {train_acc:.2f} | Test loss: {test_loss_total:.3f} | Test acc: {test_acc:.2f}\")\n",
        ""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136,
          "referenced_widgets": [
            "e5277db8e76b485796a4a3460d3edf7f",
            "a1bf23ac1f2e477dbeaa35f92429e018",
            "fe7183eda0254c1692fb2316a86d0ce8",
            "6949b37b2fb643b783f1961245dc8953",
            "74b9b6eb7f2a46188ac8488dccfa39df",
            "3d644b23a30c401fb0d806de291ebc84",
            "897c5ca3ff424b509e68b65a5ded2d3a",
            "594a2b470d404232b638ea598221e5fb",
            "db0ed751f4c14b5aaffb2d6809912012",
            "d8e5336a147f4075a2c0c602e83fcf2c",
            "e0d9d10fdebc4f50b85353814485a356"
          ]
        },
        "id": "OMllPBK-w1kF",
        "outputId": "9fdbb395-98cf-4bdb-9d8e-b5b8cbc45425"
      },
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/5 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e5277db8e76b485796a4a3460d3edf7f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 0 | Train loss: 1.300 | Train acc: 0.53 | Test loss: 211.276 | Test acc: 0.76\n",
            "Epoch: 1 | Train loss: 0.566 | Train acc: 0.79 | Test loss: 156.334 | Test acc: 0.82\n",
            "Epoch: 2 | Train loss: 0.454 | Train acc: 0.84 | Test loss: 139.499 | Test acc: 0.84\n",
            "Epoch: 3 | Train loss: 0.413 | Train acc: 0.85 | Test loss: 133.493 | Test acc: 0.85\n",
            "Epoch: 4 | Train loss: 0.384 | Train acc: 0.86 | Test loss: 121.090 | Test acc: 0.86\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Make predictions with trained model_2\n",
        "test_preds = []\n",
        "model_2.eval()\n",
        "with torch.inference_mode():\n",
        "  for X_test, y_test in tqdm(fashion_mnist_test_dataloader):\n",
        "    y_logits = model_2(X_test.to(device))\n",
        "    y_pred_probs = torch.softmax(y_logits, dim=1)\n",
        "    y_pred_labels = torch.argmax(y_pred_probs, dim=1)\n",
        "    test_preds.append(y_pred_labels)\n",
        "test_preds = torch.cat(test_preds).cpu() # matplotlib likes CPU\n",
        "test_preds[:10], len(test_preds)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 66,
          "referenced_widgets": [
            "e0a4b3956dee4708b27106fc1fc0acd6",
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            "5c9e12750f6b4260b697fbbb7d54c587",
            "27f4bfc8dc2d45ba89a2de1be1a4aac0"
          ]
        },
        "id": "juxXZRrTw3U3",
        "outputId": "274f0bb2-f575-43ed-d8e8-e4de0b8f2fa5"
      },
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/313 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e0a4b3956dee4708b27106fc1fc0acd6"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(tensor([9, 2, 1, 1, 6, 1, 4, 6, 5, 7]), 10000)"
            ]
          },
          "metadata": {},
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Get wrong prediction indexes\n",
        "import numpy as np\n",
        "wrong_pred_indexes = np.where(test_preds != fashion_mnist_test.targets)[0]\n",
        "len(wrong_pred_indexes)\n",
        ""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7BWzAFTHxE9d",
        "outputId": "5e1862e7-570a-4a04-9252-8021c79d6b10"
      },
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1376"
            ]
          },
          "metadata": {},
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Select random 9 wrong predictions and plot them\n",
        "import random\n",
        "random_selection = random.sample(list(wrong_pred_indexes), k=9)\n",
        "\n",
        "plt.figure(figsize=(10, 10))\n",
        "for i, idx in enumerate(random_selection):\n",
        "  # Get true and pred labels\n",
        "  true_label = fashion_mnist_class_names[fashion_mnist_test[idx][1]]\n",
        "  pred_label = fashion_mnist_class_names[test_preds[idx]]\n",
        "\n",
        "  # Plot the wrong prediction with its original label\n",
        "  plt.subplot(3, 3, i+1)\n",
        "  plt.imshow(fashion_mnist_test[idx][0].squeeze(), cmap=\"gray\")\n",
        "  plt.title(f\"True: {true_label} | Pred: {pred_label}\", c=\"r\")\n",
        "  plt.axis(False);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 829
        },
        "id": "iZ5arBskxHVE",
        "outputId": "0b80ebcf-1bf8-40c3-d993-1d0d739f0f1e"
      },
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 9 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}