pytorch_computer_vision.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/KRollins26/learning_pytorch/blob/main/pytorch_computer_vision.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Pytorch Computer Vision\n",
        "\n",
        "https://www.learnpytorch.io/03_pytorch_computer_vision/"
      ],
      "metadata": {
        "id": "Dq1leGW5i32g"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Computer Vision Libraries in PyTorch\n",
        "\n",
        "* `torchvision` - base domain library for PyTorch computer vision\n",
        "https://pytorch.org/vision/stable/index.html\n",
        "\n",
        "* `torchvision.datasets` - get datasets and data loading functions for computer vision\n",
        "\n",
        "* `torchvision.models` - get pretrained computer vision models\n",
        "\n",
        "* `torchvision.transforms` - functions for manipulating your visions data to be suitable for use with an ML model\n",
        "\n",
        "* `torch.utils.data.Dataset` - Base dataset class for Pytorch.\n",
        "\n",
        "* `torch.utils.data.DatLoader` - Creates a Python iterable over a dataset\n"
      ],
      "metadata": {
        "id": "FBHiRQ7as1U7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Import Pytorch\n",
        "import torch\n",
        "from torch import nn\n",
        "\n",
        "# Import torchvisions\n",
        "import torchvision\n",
        "from torchvision import datasets\n",
        "from torchvision import transforms\n",
        "from torchvision.transforms import ToTensor\n",
        "\n",
        "# Import matplotlib for visulization\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Check versions\n",
        "print (torch.__version__)\n",
        "print (torchvision.__version__)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Rg1VEJq0uprs",
        "outputId": "715697c9-aef1-4e20-ad01-2023b21d6165"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2.1.0+cu121\n",
            "0.16.0+cu121\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Getting a dataset\n",
        "\n",
        "The data set that we will be using is the fashion MNIST dataset form torchvision.datasets:\n",
        "\n",
        "https://pytorch.org/vision/stable/datasets.html"
      ],
      "metadata": {
        "id": "QMiRaXmuvsoe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Setup training data\n",
        "train_data = datasets.FashionMNIST(\n",
        "    root = 'data', # where to download the data too\n",
        "    train = True, # do we want the training data set\n",
        "    download = True, #do we want to download yes/no?\n",
        "    transform = torchvision.transforms.ToTensor(), # how do we want to transform the data\n",
        "    target_transform = None # how do we want to transform the labels\n",
        ")\n",
        "\n",
        "test_data = datasets.FashionMNIST(\n",
        "    root = 'data', # where to download the data too\n",
        "    train = False, # do we want the training data set\n",
        "    download = True, #do we want to download yes/no?\n",
        "    transform = ToTensor(), # how do we want to transform the data\n",
        "    target_transform = None # how do we want to transform the labels\n",
        ")"
      ],
      "metadata": {
        "id": "71SuLtANxJVK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "166e5b6f-2619-4fe3-e825-1e5c69c81004"
      },
      "execution_count": 3,
      "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 data/FashionMNIST/raw/train-images-idx3-ubyte.gz\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 26421880/26421880 [00:04<00:00, 6234352.97it/s] \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting data/FashionMNIST/raw/train-images-idx3-ubyte.gz to data/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 data/FashionMNIST/raw/train-labels-idx1-ubyte.gz\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 29515/29515 [00:00<00:00, 38555.62it/s] \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting data/FashionMNIST/raw/train-labels-idx1-ubyte.gz to data/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 data/FashionMNIST/raw/t10k-images-idx3-ubyte.gz\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 4422102/4422102 [00:01<00:00, 3685055.57it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting data/FashionMNIST/raw/t10k-images-idx3-ubyte.gz to data/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 data/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 5148/5148 [00:00<00:00, 19700982.66it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting data/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz to data/FashionMNIST/raw\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(train_data), len(test_data)"
      ],
      "metadata": {
        "id": "8yh7O8oqyYVb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "eb5a41f6-065c-4f70-bc6b-15387dde3dc0"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(60000, 10000)"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# See the first training example\n",
        "\n",
        "image, label = train_data[0]\n",
        "image, label"
      ],
      "metadata": {
        "id": "4Ua7C3kXy9RU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7a0f60bb-ea36-43de-a5a0-7fb7493a23e8"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
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            "text/plain": [
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              "           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, 0.0039, 0.0000, 0.0000, 0.0510,\n",
              "           0.2863, 0.0000, 0.0000, 0.0039, 0.0157, 0.0000, 0.0000, 0.0000,\n",
              "           0.0000, 0.0039, 0.0039, 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, 0.0000, 0.1412, 0.5333,\n",
              "           0.4980, 0.2431, 0.2118, 0.0000, 0.0000, 0.0000, 0.0039, 0.0118,\n",
              "           0.0157, 0.0000, 0.0000, 0.0118],\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, 0.0235, 0.0000, 0.4000, 0.8000,\n",
              "           0.6902, 0.5255, 0.5647, 0.4824, 0.0902, 0.0000, 0.0000, 0.0000,\n",
              "           0.0000, 0.0471, 0.0392, 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, 0.6078, 0.9255,\n",
              "           0.8118, 0.6980, 0.4196, 0.6118, 0.6314, 0.4275, 0.2510, 0.0902,\n",
              "           0.3020, 0.5098, 0.2824, 0.0588],\n",
              "          [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
              "           0.0000, 0.0000, 0.0000, 0.0039, 0.0000, 0.2706, 0.8118, 0.8745,\n",
              "           0.8549, 0.8471, 0.8471, 0.6392, 0.4980, 0.4745, 0.4784, 0.5725,\n",
              "           0.5529, 0.3451, 0.6745, 0.2588],\n",
              "          [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
              "           0.0000, 0.0039, 0.0039, 0.0039, 0.0000, 0.7843, 0.9098, 0.9098,\n",
              "           0.9137, 0.8980, 0.8745, 0.8745, 0.8431, 0.8353, 0.6431, 0.4980,\n",
              "           0.4824, 0.7686, 0.8980, 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, 0.7176, 0.8824, 0.8471,\n",
              "           0.8745, 0.8941, 0.9216, 0.8902, 0.8784, 0.8706, 0.8784, 0.8667,\n",
              "           0.8745, 0.9608, 0.6784, 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, 0.7569, 0.8941, 0.8549,\n",
              "           0.8353, 0.7765, 0.7059, 0.8314, 0.8235, 0.8275, 0.8353, 0.8745,\n",
              "           0.8627, 0.9529, 0.7922, 0.0000],\n",
              "          [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
              "           0.0000, 0.0039, 0.0118, 0.0000, 0.0471, 0.8588, 0.8627, 0.8314,\n",
              "           0.8549, 0.7529, 0.6627, 0.8902, 0.8157, 0.8549, 0.8784, 0.8314,\n",
              "           0.8863, 0.7725, 0.8196, 0.2039],\n",
              "          [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
              "           0.0000, 0.0000, 0.0235, 0.0000, 0.3882, 0.9569, 0.8706, 0.8627,\n",
              "           0.8549, 0.7961, 0.7765, 0.8667, 0.8431, 0.8353, 0.8706, 0.8627,\n",
              "           0.9608, 0.4667, 0.6549, 0.2196],\n",
              "          [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
              "           0.0000, 0.0157, 0.0000, 0.0000, 0.2157, 0.9255, 0.8941, 0.9020,\n",
              "           0.8941, 0.9412, 0.9098, 0.8353, 0.8549, 0.8745, 0.9176, 0.8510,\n",
              "           0.8510, 0.8196, 0.3608, 0.0000],\n",
              "          [0.0000, 0.0000, 0.0039, 0.0157, 0.0235, 0.0275, 0.0078, 0.0000,\n",
              "           0.0000, 0.0000, 0.0000, 0.0000, 0.9294, 0.8863, 0.8510, 0.8745,\n",
              "           0.8706, 0.8588, 0.8706, 0.8667, 0.8471, 0.8745, 0.8980, 0.8431,\n",
              "           0.8549, 1.0000, 0.3020, 0.0000],\n",
              "          [0.0000, 0.0118, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
              "           0.0000, 0.2431, 0.5686, 0.8000, 0.8941, 0.8118, 0.8353, 0.8667,\n",
              "           0.8549, 0.8157, 0.8275, 0.8549, 0.8784, 0.8745, 0.8588, 0.8431,\n",
              "           0.8784, 0.9569, 0.6235, 0.0000],\n",
              "          [0.0000, 0.0000, 0.0000, 0.0000, 0.0706, 0.1725, 0.3216, 0.4196,\n",
              "           0.7412, 0.8941, 0.8627, 0.8706, 0.8510, 0.8863, 0.7843, 0.8039,\n",
              "           0.8275, 0.9020, 0.8784, 0.9176, 0.6902, 0.7373, 0.9804, 0.9725,\n",
              "           0.9137, 0.9333, 0.8431, 0.0000],\n",
              "          [0.0000, 0.2235, 0.7333, 0.8157, 0.8784, 0.8667, 0.8784, 0.8157,\n",
              "           0.8000, 0.8392, 0.8157, 0.8196, 0.7843, 0.6235, 0.9608, 0.7569,\n",
              "           0.8078, 0.8745, 1.0000, 1.0000, 0.8667, 0.9176, 0.8667, 0.8275,\n",
              "           0.8627, 0.9098, 0.9647, 0.0000],\n",
              "          [0.0118, 0.7922, 0.8941, 0.8784, 0.8667, 0.8275, 0.8275, 0.8392,\n",
              "           0.8039, 0.8039, 0.8039, 0.8627, 0.9412, 0.3137, 0.5882, 1.0000,\n",
              "           0.8980, 0.8667, 0.7373, 0.6039, 0.7490, 0.8235, 0.8000, 0.8196,\n",
              "           0.8706, 0.8941, 0.8824, 0.0000],\n",
              "          [0.3843, 0.9137, 0.7765, 0.8235, 0.8706, 0.8980, 0.8980, 0.9176,\n",
              "           0.9765, 0.8627, 0.7608, 0.8431, 0.8510, 0.9451, 0.2549, 0.2863,\n",
              "           0.4157, 0.4588, 0.6588, 0.8588, 0.8667, 0.8431, 0.8510, 0.8745,\n",
              "           0.8745, 0.8784, 0.8980, 0.1137],\n",
              "          [0.2941, 0.8000, 0.8314, 0.8000, 0.7569, 0.8039, 0.8275, 0.8824,\n",
              "           0.8471, 0.7255, 0.7725, 0.8078, 0.7765, 0.8353, 0.9412, 0.7647,\n",
              "           0.8902, 0.9608, 0.9373, 0.8745, 0.8549, 0.8314, 0.8196, 0.8706,\n",
              "           0.8627, 0.8667, 0.9020, 0.2627],\n",
              "          [0.1882, 0.7961, 0.7176, 0.7608, 0.8353, 0.7725, 0.7255, 0.7451,\n",
              "           0.7608, 0.7529, 0.7922, 0.8392, 0.8588, 0.8667, 0.8627, 0.9255,\n",
              "           0.8824, 0.8471, 0.7804, 0.8078, 0.7294, 0.7098, 0.6941, 0.6745,\n",
              "           0.7098, 0.8039, 0.8078, 0.4510],\n",
              "          [0.0000, 0.4784, 0.8588, 0.7569, 0.7020, 0.6706, 0.7176, 0.7686,\n",
              "           0.8000, 0.8235, 0.8353, 0.8118, 0.8275, 0.8235, 0.7843, 0.7686,\n",
              "           0.7608, 0.7490, 0.7647, 0.7490, 0.7765, 0.7529, 0.6902, 0.6118,\n",
              "           0.6549, 0.6941, 0.8235, 0.3608],\n",
              "          [0.0000, 0.0000, 0.2902, 0.7412, 0.8314, 0.7490, 0.6863, 0.6745,\n",
              "           0.6863, 0.7098, 0.7255, 0.7373, 0.7412, 0.7373, 0.7569, 0.7765,\n",
              "           0.8000, 0.8196, 0.8235, 0.8235, 0.8275, 0.7373, 0.7373, 0.7608,\n",
              "           0.7529, 0.8471, 0.6667, 0.0000],\n",
              "          [0.0078, 0.0000, 0.0000, 0.0000, 0.2588, 0.7843, 0.8706, 0.9294,\n",
              "           0.9373, 0.9490, 0.9647, 0.9529, 0.9569, 0.8667, 0.8627, 0.7569,\n",
              "           0.7490, 0.7020, 0.7137, 0.7137, 0.7098, 0.6902, 0.6510, 0.6588,\n",
              "           0.3882, 0.2275, 0.0000, 0.0000],\n",
              "          [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.1569,\n",
              "           0.2392, 0.1725, 0.2824, 0.1608, 0.1373, 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",
              " 9)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class_names = train_data.classes\n",
        "class_names"
      ],
      "metadata": {
        "id": "eoOJguyX0Jet",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "720138b3-b614-49e8-b05a-99ab4dc364a2"
      },
      "execution_count": 6,
      "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": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class_to_idx = train_data.class_to_idx\n",
        "class_to_idx"
      ],
      "metadata": {
        "id": "RLzPvY5h0tQ3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "aebdbc38-9587-4893-8750-586ced3f58ee"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'T-shirt/top': 0,\n",
              " 'Trouser': 1,\n",
              " 'Pullover': 2,\n",
              " 'Dress': 3,\n",
              " 'Coat': 4,\n",
              " 'Sandal': 5,\n",
              " 'Shirt': 6,\n",
              " 'Sneaker': 7,\n",
              " 'Bag': 8,\n",
              " 'Ankle boot': 9}"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_data.targets"
      ],
      "metadata": {
        "id": "vO-OxF5607Lq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a306b79a-fd43-498b-c62e-bc30cf944fe0"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([9, 0, 0,  ..., 3, 0, 5])"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#check the shape of our image\n",
        "print(f\"image shape {image.shape} -> [color_channes, height, width]\")\n",
        "print(f\"image labels: {label}\")\n",
        "\n",
        "# Note this dataset only has one colour channel as the dataset is in grayscale"
      ],
      "metadata": {
        "id": "O68HyJ6C1FBe",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "65b4c49f-1909-44b7-be33-021074c89da1"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "image shape torch.Size([1, 28, 28]) -> [color_channes, height, width]\n",
            "image labels: 9\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Visualizing the Data"
      ],
      "metadata": {
        "id": "CL5r7UKb1KjW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "image, label = train_data[0]\n",
        "print(f\"image shape: {image.shape}\")\n",
        "\n",
        "plt.imshow(image.squeeze(), cmap=\"gray\")\n",
        "plt.title(class_names[label]);"
      ],
      "metadata": {
        "id": "fDfnyrFJ2z-S",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 469
        },
        "outputId": "aab950b6-2e19-4809-b7ab-59bdd59766c8"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "image shape: torch.Size([1, 28, 28])\n"
          ]
        },
        {
          "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": [
        "# Plot more 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",
        "  img, label = train_data[random_idx]\n",
        "  fig.add_subplot(rows, cols, i)\n",
        "  plt.imshow(img.squeeze(), cmap=\"gray\")\n",
        "  plt.title(class_names[label])\n",
        "  plt.axis(False)"
      ],
      "metadata": {
        "id": "jCW_ri_t20bR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 752
        },
        "outputId": "c78f850e-1f88-4b80-fe12-cf3f2765e2f9"
      },
      "execution_count": 11,
      "outputs": [
        {
          "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": [
        "## Prepare the DataLoader\n",
        "\n",
        "Currently data is in the form of Pytorch datasets\n",
        "\n",
        "DataLoader turns datasets into a Python iterable\n",
        "\n",
        "More specifically we want to turn our data into batches / mini batches.\n",
        "\n",
        "1. More computationally efficient (batch of 32).\n",
        "2. It gives the nn more chances to update the gradient per epoch.\n",
        "\n",
        "https://www.youtube.com/watch?v=uFN40asG61g\n",
        "\n"
      ],
      "metadata": {
        "id": "64PhVeqt9N1a"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from torch.utils.data import DataLoader\n",
        "\n",
        "# Set up the batch size hyper parameter\n",
        "\n",
        "BATCH_SIZE = 32\n",
        "\n",
        "train_dataloader = DataLoader(dataset = train_data,\n",
        "                              batch_size=BATCH_SIZE,\n",
        "                              shuffle=True) # Shuffle so model doesnt learn order\n",
        "\n",
        "test_dataloader = DataLoader(dataset = test_data,\n",
        "                             batch_size=BATCH_SIZE,\n",
        "                             shuffle=False)\n",
        "\n",
        "train_dataloader, test_dataloader"
      ],
      "metadata": {
        "id": "9gk_xHqq9Nck",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "17fc5d80-51a3-4c1e-bb8b-43088304691b"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(<torch.utils.data.dataloader.DataLoader at 0x792848ed3f10>,\n",
              " <torch.utils.data.dataloader.DataLoader at 0x79284b7aea40>)"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f'DataLoaders: {train_dataloader, test_dataloader}')\n",
        "print(f'Leng of train_dataloader {len(train_dataloader)} batches of {BATCH_SIZE}')\n",
        "print(f'Leng of test_dataloader {len(test_dataloader)} batches of {BATCH_SIZE}')\n"
      ],
      "metadata": {
        "id": "xpdQOrRr9VaZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "05039ab8-39d1-4e0b-f4b4-d8f2d68ab010"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "DataLoaders: (<torch.utils.data.dataloader.DataLoader object at 0x792848ed3f10>, <torch.utils.data.dataloader.DataLoader object at 0x79284b7aea40>)\n",
            "Leng of train_dataloader 1875 batches of 32\n",
            "Leng of test_dataloader 313 batches of 32\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check out whats inside the train_dataloader\n",
        "\n",
        "train_features_batch, train_labels_batch = next(iter(train_dataloader))\n",
        "train_features_batch.shape, train_labels_batch.shape"
      ],
      "metadata": {
        "id": "DB7MfEu9E1tJ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2a29e1b9-a8de-482d-bf2c-ae4346595177"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(torch.Size([32, 1, 28, 28]), torch.Size([32]))"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Show a sample\n",
        "torch.manual_seed(42)\n",
        "random_idx = torch.randint(0, len(train_features_batch), size=[1]).item()\n",
        "img, label = train_features_batch[random_idx], train_labels_batch[random_idx]\n",
        "plt.imshow(img.squeeze(), cmap='gray')\n",
        "plt.title(class_names[label])\n",
        "plt.axis(False)\n",
        "print(f'Image Size:{img.shape}')\n",
        "print(f'Label: {label}, Label size: {label.shape}')"
      ],
      "metadata": {
        "id": "Id8TEigz9VvO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 463
        },
        "outputId": "639783bc-2fb4-4281-b2e1-2ea73c226a0c"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Image Size:torch.Size([1, 28, 28])\n",
            "Label: 6, Label size: torch.Size([])\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",
      "source": [
        "## Model 0: Build a Baseline Model"
      ],
      "metadata": {
        "id": "OQFbJVmC9V7D"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Creating a flatten layer\n",
        "flatten_model = nn.Flatten()\n",
        "\n",
        "# Get a single sample\n",
        "x = train_features_batch[0]\n",
        "\n",
        "# Flatten the sample\n",
        "output = flatten_model(x) # perform forward pass\n",
        "\n",
        "#print\n",
        "print(f\"Shape before flattening: {x.shape} -> colour channels, height, width\")\n",
        "print(f'Shape after flattening: {output.shape} -> colour channels, height*width\")')\n"
      ],
      "metadata": {
        "id": "51dCenEY9WGy",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b1a949b7-38db-433b-a5ab-ecebd299fd14"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape before flattening: torch.Size([1, 28, 28]) -> colour channels, height, width\n",
            "Shape after flattening: torch.Size([1, 784]) -> colour channels, height*width\")\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from torch import nn\n",
        "class FashionMNISTModelV0(nn.Module):\n",
        "  def __init__(self,\n",
        "               input_shape: int,\n",
        "               hidden_units: int,\n",
        "               output_shape: int):\n",
        "    super().__init__()\n",
        "    self.layer_stack = nn.Sequential(\n",
        "        nn.Flatten(),\n",
        "        nn.Linear(in_features=input_shape,\n",
        "                  out_features=hidden_units),\n",
        "        nn.Linear(in_features=hidden_units,\n",
        "                  out_features=output_shape)\n",
        "    ).to(\"cpu\")\n",
        "\n",
        "  def forward (self, x):\n",
        "    return self.layer_stack(x)"
      ],
      "metadata": {
        "id": "1uFaClAS9WR2"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "torch.manual_seed(42)\n",
        "\n",
        "#setup model with input parameters\n",
        "\n",
        "model_0 = FashionMNISTModelV0(\n",
        "    input_shape = 784, #this is 28*28\n",
        "    hidden_units = 10, # how many units in the hidden layer\n",
        "    output_shape=len(class_names) # one for every class\n",
        ")\n",
        "\n",
        "model_0"
      ],
      "metadata": {
        "id": "wUy9vDED9Wci",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b830e20e-d632-4a34-94e0-f902fe46c651"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "FashionMNISTModelV0(\n",
              "  (layer_stack): Sequential(\n",
              "    (0): Flatten(start_dim=1, end_dim=-1)\n",
              "    (1): Linear(in_features=784, out_features=10, bias=True)\n",
              "    (2): Linear(in_features=10, out_features=10, bias=True)\n",
              "  )\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dummy_x = torch.rand([1,1,28,28])\n",
        "model_0(dummy_x)"
      ],
      "metadata": {
        "id": "wxl5fJa_9Wng",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f4406a6f-d5be-4b71-9a4e-94bdc13fa6a6"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[-0.0315,  0.3171,  0.0531, -0.2525,  0.5959,  0.2112,  0.3233,  0.2694,\n",
              "         -0.1004,  0.0157]], grad_fn=<AddmmBackward0>)"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Setup loss function and optimizer\n",
        "\n",
        "* Loss function will use `nn.CrossEntropyLoss()` as it is a multiclass problem\n",
        "* Optimizer will use `torch.optim.SDG()` (stochastic gradient decent)\n",
        "* Evaluation metric will use the torchmetric accuracy function as out evaluation metric.\n"
      ],
      "metadata": {
        "id": "CdWoJ8IpqRwM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import requests\n",
        "from pathlib import Path\n",
        "\n",
        "# Download helper functions from Learn PyTorch repo (if not already downloaded)\n",
        "if Path(\"helper_functions.py\").is_file():\n",
        "  print(\"helper_functions.py already exists, skipping download\")\n",
        "else:\n",
        "  print(\"Downloading helper_functions.py\")\n",
        "  # Note: you need the \"raw\" GitHub URL for this to work\n",
        "  request = requests.get(\"https://raw.githubusercontent.com/mrdbourke/pytorch-deep-learning/main/helper_functions.py\")\n",
        "  with open(\"helper_functions.py\", \"wb\") as f:\n",
        "    f.write(request.content)"
      ],
      "metadata": {
        "id": "HlpUs6GMxXJU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "72f95d7c-11de-4c63-8bc1-1ef3b1ce2f4f"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading helper_functions.py\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Import accuracy metric\n",
        "from helper_functions import accuracy_fn # Note: could also use torchmetrics.Accuracy(task = 'multiclass', num_classes=len(class_names)).to(device)\n",
        "\n",
        "# Setup loss function and optimizer\n",
        "loss_fn = nn.CrossEntropyLoss() # this is also called \"criterion\"/\"cost function\" in some places\n",
        "optimizer = torch.optim.SGD(params=model_0.parameters(), lr=0.1)"
      ],
      "metadata": {
        "id": "8ETZHG-lyC3U"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Creating a function to time our experiments\n",
        "\n",
        "We'll train this model on the CPU but the next one on the GPU and see what happens.\n",
        "\n",
        "Our timing function will import the `timeit.default_timer()` function from the Python timeit module."
      ],
      "metadata": {
        "id": "NSIqA8ODyC1G"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from timeit import default_timer as timer\n",
        "\n",
        "from timeit import default_timer as timer\n",
        "def print_train_time(start: float,\n",
        "                     end: float,\n",
        "                     device: torch.device = None):\n",
        "    \"\"\"Prints difference between start and end time.\n",
        "\n",
        "    Args:\n",
        "        start (float): Start time of computation (preferred in timeit format).\n",
        "        end (float): End time of computation.\n",
        "        device ([type], optional): Device that compute is running on. Defaults to None.\n",
        "\n",
        "    Returns:\n",
        "        float: time between start and end in seconds (higher is longer).\n",
        "    \"\"\"\n",
        "    total_time = end - start\n",
        "    print(f\"Train time on {device}: {total_time:.3f} seconds\")\n",
        "    return total_time"
      ],
      "metadata": {
        "id": "Rwu30ThgyCy8"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Creating a training loop and training it on batches of data\n",
        "\n",
        "1. Loop through epochs\n",
        "2. Loop through training batches, perform training steps, calculate the training loss *per batch*\n",
        "3. Lopp though testing batches, perform testing steps, calculate the test loss per batch\n",
        "4. Print out what's happening\n",
        "5. Time it all\n"
      ],
      "metadata": {
        "id": "oZxwlJdDyCwu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Import tqdm for progress bar\n",
        "from tqdm.auto import tqdm\n",
        "\n",
        "# Set the seed and start the timer\n",
        "torch.manual_seed(42)\n",
        "train_time_start_on_cpu = timer()\n",
        "\n",
        "# Set the number of epochs (we'll keep this small for faster training times)\n",
        "epochs = 5\n",
        "\n",
        "# Create training and testing loop\n",
        "for epoch in tqdm(range(epochs)):\n",
        "    print(f\"Epoch: {epoch}\\n-------\")\n",
        "    ### Training\n",
        "    train_loss = 0\n",
        "    # Add a loop to loop through training batches\n",
        "    for batch, (X, y) in enumerate(train_dataloader):\n",
        "        model_0.train()\n",
        "        # 1. Forward pass\n",
        "        y_pred = model_0(X)\n",
        "\n",
        "        # 2. Calculate loss (per batch)\n",
        "        loss = loss_fn(y_pred, y)\n",
        "        train_loss += loss # cumulatively add up the loss per epoch\n",
        "\n",
        "        # 3. Optimizer zero grad\n",
        "        optimizer.zero_grad()\n",
        "\n",
        "        # 4. Loss backward\n",
        "        loss.backward()\n",
        "\n",
        "        # 5. Optimizer step\n",
        "        optimizer.step()\n",
        "\n",
        "        # Print out how many samples have been seen\n",
        "        if batch % 400 == 0:\n",
        "            print(f\"Looked at {batch * len(X)}/{len(train_dataloader.dataset)} samples\")\n",
        "\n",
        "    # Divide total train loss by length of train dataloader (average loss per batch per epoch)\n",
        "    train_loss /= len(train_dataloader)\n",
        "\n",
        "    ### Testing\n",
        "    # Setup variables for accumulatively adding up loss and accuracy\n",
        "    test_loss, test_acc = 0, 0\n",
        "    model_0.eval()\n",
        "    with torch.inference_mode():\n",
        "        for X, y in test_dataloader:\n",
        "            # 1. Forward pass\n",
        "            test_pred = model_0(X)\n",
        "\n",
        "            # 2. Calculate loss (accumatively)\n",
        "            test_loss += loss_fn(test_pred, y) # accumulatively add up the loss per epoch\n",
        "\n",
        "            # 3. Calculate accuracy (preds need to be same as y_true)\n",
        "            test_acc += accuracy_fn(y_true=y, y_pred=test_pred.argmax(dim=1))\n",
        "\n",
        "        # Calculations on test metrics need to happen inside torch.inference_mode()\n",
        "        # Divide total test loss by length of test dataloader (per batch)\n",
        "        test_loss /= len(test_dataloader)\n",
        "\n",
        "        # Divide total accuracy by length of test dataloader (per batch)\n",
        "        test_acc /= len(test_dataloader)\n",
        "\n",
        "    ## Print out what's happening\n",
        "    print(f\"\\nTrain loss: {train_loss:.5f} | Test loss: {test_loss:.5f}, Test acc: {test_acc:.2f}%\\n\")\n",
        "\n",
        "# Calculate training time\n",
        "train_time_end_on_cpu = timer()\n",
        "total_train_time_model_0 = print_train_time(start=train_time_start_on_cpu,\n",
        "                                           end=train_time_end_on_cpu,\n",
        "                                           device=str(next(model_0.parameters()).device))\n"
      ],
      "metadata": {
        "id": "CEvcPD0nyCuT",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 934,
          "referenced_widgets": [
            "b96d7fc657284c679fd2c775a5e4e305",
            "055c9b93e5254c2c8aba66e9f25f1e25",
            "8f81008756be45bd8b44814664ac65ad",
            "e18f99bd5393407c9b6f491fa201c188",
            "9d58bf5d010645c4a1e4a318a852aa56",
            "9b3d1ffe3bd3447b9a7f9e073a8cc76f",
            "f92ac863592d4a429737c897f2b74c99",
            "af20e40bd78c4a95a085acaf5c58a4e1",
            "b9c630dbcdb14ff3b7f17e781f4d9878",
            "66110683776d4421ba59d875048a6e52",
            "c509562c256e4ac1b772d5f301cdd4ea"
          ]
        },
        "outputId": "652548b8-997d-4583-97ac-8b6fe72073fd"
      },
      "execution_count": 23,
      "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": "b96d7fc657284c679fd2c775a5e4e305"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 0\n",
            "-------\n",
            "Looked at 0/60000 samples\n",
            "Looked at 12800/60000 samples\n",
            "Looked at 25600/60000 samples\n",
            "Looked at 38400/60000 samples\n",
            "Looked at 51200/60000 samples\n",
            "\n",
            "Train loss: 0.59039 | Test loss: 0.50954, Test acc: 82.04%\n",
            "\n",
            "Epoch: 1\n",
            "-------\n",
            "Looked at 0/60000 samples\n",
            "Looked at 12800/60000 samples\n",
            "Looked at 25600/60000 samples\n",
            "Looked at 38400/60000 samples\n",
            "Looked at 51200/60000 samples\n",
            "\n",
            "Train loss: 0.47633 | Test loss: 0.47989, Test acc: 83.20%\n",
            "\n",
            "Epoch: 2\n",
            "-------\n",
            "Looked at 0/60000 samples\n",
            "Looked at 12800/60000 samples\n",
            "Looked at 25600/60000 samples\n",
            "Looked at 38400/60000 samples\n",
            "Looked at 51200/60000 samples\n",
            "\n",
            "Train loss: 0.45503 | Test loss: 0.47664, Test acc: 83.43%\n",
            "\n",
            "Epoch: 3\n",
            "-------\n",
            "Looked at 0/60000 samples\n",
            "Looked at 12800/60000 samples\n",
            "Looked at 25600/60000 samples\n",
            "Looked at 38400/60000 samples\n",
            "Looked at 51200/60000 samples\n",
            "\n",
            "Train loss: 0.44251 | Test loss: 0.46306, Test acc: 83.75%\n",
            "\n",
            "Epoch: 4\n",
            "-------\n",
            "Looked at 0/60000 samples\n",
            "Looked at 12800/60000 samples\n",
            "Looked at 25600/60000 samples\n",
            "Looked at 38400/60000 samples\n",
            "Looked at 51200/60000 samples\n",
            "\n",
            "Train loss: 0.43582 | Test loss: 0.46869, Test acc: 83.27%\n",
            "\n",
            "Train time on cpu: 57.126 seconds\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Make Predictions and Get Model 0 results"
      ],
      "metadata": {
        "id": "qQJblb1vxiFu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "torch.manual_seed(42)\n",
        "def eval_model(model: torch.nn.Module,\n",
        "               data_loader: torch.utils.data.DataLoader,\n",
        "               loss_fn: torch.nn.Module,\n",
        "               accuracy_fn\n",
        "               ):\n",
        "    \"\"\"Returns a dictionary containing the results of model predicting on data_loader.\n",
        "\n",
        "    Args:\n",
        "        model (torch.nn.Module): A PyTorch model capable of making predictions on data_loader.\n",
        "        data_loader (torch.utils.data.DataLoader): The target dataset to predict on.\n",
        "        loss_fn (torch.nn.Module): The loss function of model.\n",
        "        accuracy_fn: An accuracy function to compare the models predictions to the truth labels.\n",
        "\n",
        "    Returns:\n",
        "        (dict): Results of model making predictions on data_loader.\n",
        "    \"\"\"\n",
        "    loss, acc = 0, 0\n",
        "    model.eval()\n",
        "    with torch.inference_mode():\n",
        "        for X, y in data_loader:\n",
        "            # Make predictions with the model\n",
        "            y_pred = model(X)\n",
        "\n",
        "            # Accumulate the loss and accuracy values per batch\n",
        "            loss += loss_fn(y_pred, y)\n",
        "            acc += accuracy_fn(y_true=y,\n",
        "                                y_pred=y_pred.argmax(dim=1)) # For accuracy, need the prediction labels (logits -> pred_prob -> pred_labels)\n",
        "\n",
        "        # Scale loss and acc to find the average loss/acc per batch\n",
        "        loss /= len(data_loader)\n",
        "        acc /= len(data_loader)\n",
        "\n",
        "    return {\"model_name\": model.__class__.__name__, # only works when model was created with a class\n",
        "            \"model_loss\": loss.item(),\n",
        "            \"model_acc\": acc}\n",
        "\n",
        "# Calculate model 0 results on test dataset\n",
        "model_0_results = eval_model(model=model_0,\n",
        "                             data_loader=test_dataloader,\n",
        "                             loss_fn=loss_fn,\n",
        "                             accuracy_fn=accuracy_fn)\n",
        "model_0_results"
      ],
      "metadata": {
        "id": "LXbpPmylyCms",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5e44f17f-73ad-4153-f6a4-cfbd3ee9c8c8"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'model_name': 'FashionMNISTModelV0',\n",
              " 'model_loss': 0.46869373321533203,\n",
              " 'model_acc': 83.2667731629393}"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Setup device agnostic code\n",
        "import torch\n",
        "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
        "device"
      ],
      "metadata": {
        "id": "GSRgExr1yCkT",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "3f69cb6d-f87c-407b-9a78-865be4917f75"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'cuda'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!nvidia-smi"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "unPlu0Y_xRdX",
        "outputId": "3662cea4-e8ec-47e5-cf1c-080e05d315f8"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fri Jan 26 13:47:28 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   39C    P8               9W /  70W |      3MiB / 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",
            "|  No running processes found                                                           |\n",
            "+---------------------------------------------------------------------------------------+\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a model with non-linear and linear layers\n",
        "class FashionMNISTModelV1(nn.Module):\n",
        "    def __init__(self,\n",
        "                 input_shape: int,\n",
        "                 hidden_units: int,\n",
        "                 output_shape: int\n",
        "                 ):\n",
        "        super().__init__()\n",
        "        self.layer_stack = nn.Sequential(\n",
        "            nn.Flatten(), # flatten inputs into single vector\n",
        "            nn.Linear(in_features=input_shape, out_features=hidden_units),\n",
        "            nn.ReLU(),\n",
        "            nn.Linear(in_features=hidden_units, out_features=output_shape),\n",
        "            nn.ReLU()\n",
        "        )\n",
        "\n",
        "    def forward(self, x: torch.Tensor):\n",
        "        return self.layer_stack(x)"
      ],
      "metadata": {
        "id": "TQw8dc4qxRa2"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "torch.manual_seed(42)\n",
        "model_1 = FashionMNISTModelV1(input_shape=784, # number of input features\n",
        "    hidden_units=10,\n",
        "    output_shape=len(class_names) # number of output classes desired\n",
        ").to(device) # send model to GPU if it's available\n",
        "next(model_1.parameters()).device # check model device"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DrbncBrdxRYd",
        "outputId": "9c20cc48-5c01-45c5-d9bb-ca9e88d62c9d"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "device(type='cuda', index=0)"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Setup loss, optimizer, and evaluation metrics"
      ],
      "metadata": {
        "id": "HJ0hXUMO4M0f"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from helper_functions import accuracy_fn\n",
        "loss_fn = nn.CrossEntropyLoss()\n",
        "optimizer = torch.optim.SGD(params=model_1.parameters(),\n",
        "                            lr=0.1)"
      ],
      "metadata": {
        "id": "R8YvE5HLxRWF"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Functionizing Training and test loops"
      ],
      "metadata": {
        "id": "QABo0oNoxRTm"
      }
    },
    {
      "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,\n",
        "               device: torch.device = device):\n",
        "\n",
        "    train_loss, train_acc = 0, 0\n",
        "    model.to(device)\n",
        "    for batch, (X, y) in enumerate(data_loader):\n",
        "        # Send data to GPU\n",
        "        X, y = X.to(device), y.to(device)\n",
        "\n",
        "        # 1. Forward pass\n",
        "        y_pred = model(X)\n",
        "\n",
        "        # 2. Calculate loss and accuracy per batch\n",
        "        loss = loss_fn(y_pred, y)\n",
        "        train_loss += loss\n",
        "        train_acc += accuracy_fn(y_true=y,\n",
        "                                 y_pred=y_pred.argmax(dim=1)) # Go from logits -> pred labels\n",
        "\n",
        "        # 3. Optimizer zero grad\n",
        "        optimizer.zero_grad()\n",
        "\n",
        "        # 4. Loss backward\n",
        "        loss.backward()\n",
        "\n",
        "        # 5. Optimizer step\n",
        "        optimizer.step()\n",
        "\n",
        "    # Calculate loss and accuracy per epoch and print out what's happening\n",
        "    train_loss /= len(data_loader)\n",
        "    train_acc /= len(data_loader)\n",
        "    print(f\"Train loss: {train_loss:.5f} | Train accuracy: {train_acc:.2f}%\")\n"
      ],
      "metadata": {
        "id": "gJpOgAt6xRO-"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def test_step(data_loader: torch.utils.data.DataLoader,\n",
        "              model: torch.nn.Module,\n",
        "              loss_fn: torch.nn.Module,\n",
        "              accuracy_fn,\n",
        "              device: torch.device = device):\n",
        "\n",
        "    test_loss, test_acc = 0, 0\n",
        "    model.to(device)\n",
        "    model.eval() # put model in eval mode\n",
        "    # Turn on inference context manager\n",
        "    with torch.inference_mode():\n",
        "        for X, y in data_loader:\n",
        "            # Send data to GPU\n",
        "            X, y = X.to(device), y.to(device)\n",
        "\n",
        "            # 1. Forward pass\n",
        "            test_pred = model(X)\n",
        "\n",
        "            # 2. Calculate loss and accuracy\n",
        "            test_loss += loss_fn(test_pred, y)\n",
        "            test_acc += accuracy_fn(y_true=y,\n",
        "                y_pred=test_pred.argmax(dim=1) # Go from logits -> pred labels\n",
        "            )\n",
        "\n",
        "        # Adjust metrics and print out\n",
        "        test_loss /= len(data_loader)\n",
        "        test_acc /= len(data_loader)\n",
        "        print(f\"Test loss: {test_loss:.5f} | Test accuracy: {test_acc:.2f}%\\n\")"
      ],
      "metadata": {
        "id": "eWweNERbxRMs"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "torch.manual_seed(42)\n",
        "\n",
        "# Measure time\n",
        "from timeit import default_timer as timer\n",
        "train_time_start_on_gpu = timer()\n",
        "\n",
        "epochs = 5\n",
        "for epoch in tqdm(range(epochs)):\n",
        "    print(f\"Epoch: {epoch}\\n---------\")\n",
        "    train_step(data_loader=train_dataloader,\n",
        "        model=model_1,\n",
        "        loss_fn=loss_fn,\n",
        "        optimizer=optimizer,\n",
        "        accuracy_fn=accuracy_fn\n",
        "    )\n",
        "    test_step(data_loader=test_dataloader,\n",
        "        model=model_1,\n",
        "        loss_fn=loss_fn,\n",
        "        accuracy_fn=accuracy_fn\n",
        "    )\n",
        "\n",
        "train_time_end_on_gpu = timer()\n",
        "total_train_time_model_1 = print_train_time(start=train_time_start_on_gpu,\n",
        "                                            end=train_time_end_on_gpu,\n",
        "                                            device=device)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 500,
          "referenced_widgets": [
            "d3ec7165b918439896bd4c35002f356c",
            "7ffec731139945f0b0cb80fa67e40266",
            "9570636009c74dd6a97f94395b39d8a6",
            "6b547eca2e91485a83b7093bae5b1805",
            "1c14750c0f3b4161a1084ab03519f7f1",
            "2492cebc5f4a4e07bdeab5cb0b03cb3b",
            "a102b182614b4c10a367a097dbc1cb0a",
            "f703e757c93d4315b74f6d4b97b0ed6f",
            "9dc68a57008c4712a98742561f130a4c",
            "7c4ecc7fae1146b9a27e0e73beed9d73",
            "65f4ed83b7a9422aadbdaa78ec3c124d"
          ]
        },
        "id": "FZ6Y3xRrxRKg",
        "outputId": "3cc5cf14-2896-4983-86cd-0c083a8af0b6"
      },
      "execution_count": 32,
      "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": "d3ec7165b918439896bd4c35002f356c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 0\n",
            "---------\n",
            "Train loss: 1.09199 | Train accuracy: 61.34%\n",
            "Test loss: 0.95636 | Test accuracy: 65.00%\n",
            "\n",
            "Epoch: 1\n",
            "---------\n",
            "Train loss: 0.78101 | Train accuracy: 71.93%\n",
            "Test loss: 0.72227 | Test accuracy: 73.91%\n",
            "\n",
            "Epoch: 2\n",
            "---------\n",
            "Train loss: 0.67027 | Train accuracy: 75.94%\n",
            "Test loss: 0.68500 | Test accuracy: 75.02%\n",
            "\n",
            "Epoch: 3\n",
            "---------\n",
            "Train loss: 0.64384 | Train accuracy: 76.52%\n",
            "Test loss: 0.65079 | Test accuracy: 76.13%\n",
            "\n",
            "Epoch: 4\n",
            "---------\n",
            "Train loss: 0.62892 | Train accuracy: 77.00%\n",
            "Test loss: 0.65277 | Test accuracy: 75.95%\n",
            "\n",
            "Train time on cuda: 60.290 seconds\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "torch.manual_seed(42)\n",
        "def eval_model(model: torch.nn.Module,\n",
        "               data_loader: torch.utils.data.DataLoader,\n",
        "               loss_fn: torch.nn.Module,\n",
        "               accuracy_fn,\n",
        "               device = device\n",
        "               ):\n",
        "    \"\"\"Returns a dictionary containing the results of model predicting on data_loader.\n",
        "\n",
        "    Args:\n",
        "        model (torch.nn.Module): A PyTorch model capable of making predictions on data_loader.\n",
        "        data_loader (torch.utils.data.DataLoader): The target dataset to predict on.\n",
        "        loss_fn (torch.nn.Module): The loss function of model.\n",
        "        accuracy_fn: An accuracy function to compare the models predictions to the truth labels.\n",
        "\n",
        "    Returns:\n",
        "        (dict): Results of model making predictions on data_loader.\n",
        "    \"\"\"\n",
        "    loss, acc = 0, 0\n",
        "    model.eval()\n",
        "    with torch.inference_mode():\n",
        "        for X, y in tqdm(data_loader):\n",
        "            #make device agnostic\n",
        "            X, y = X.to(device), y.to(device)\n",
        "\n",
        "            # Make predictions with the model\n",
        "            y_pred = model(X)\n",
        "\n",
        "            # Accumulate the loss and accuracy values per batch\n",
        "            loss += loss_fn(y_pred, y)\n",
        "            acc += accuracy_fn(y_true=y,\n",
        "                                y_pred=y_pred.argmax(dim=1)) # For accuracy, need the prediction labels (logits -> pred_prob -> pred_labels)\n",
        "\n",
        "        # Scale loss and acc to find the average loss/acc per batch\n",
        "        loss /= len(data_loader)\n",
        "        acc /= len(data_loader)\n",
        "\n",
        "    return {\"model_name\": model.__class__.__name__, # only works when model was created with a class\n",
        "            \"model_loss\": loss.item(),\n",
        "            \"model_acc\": acc}"
      ],
      "metadata": {
        "id": "76PZFE5d-Wrt"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate model 1 results on test dataset\n",
        "model_1_results = eval_model(model=model_1,\n",
        "                             data_loader=test_dataloader,\n",
        "                             loss_fn=loss_fn,\n",
        "                             accuracy_fn=accuracy_fn)\n",
        "model_1_results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 101,
          "referenced_widgets": [
            "d87de08e85914e3a81d110b3b3c1f48d",
            "1980458eb0a64d04b8f710275ee7ee69",
            "79680e08d59a423a949bdcdca2d5c8ff",
            "23baad23df714c0e98ae39157ad372ee",
            "733c61a914af43888bd3bbb42c8fa76a",
            "3073688c2a374b9fbf1e7b3c60fc02de",
            "e45fdfc524114b71a9d0af42e8664468",
            "0fadee6204414e6e9d5fe937e4606e29",
            "3e7028837f3a462589a034a126fda0a0",
            "8d7be9d71a584e14a966ea535979e796",
            "59c7f59396a84eaa85bccf5010f353fe"
          ]
        },
        "id": "ksWgHF6zCkZW",
        "outputId": "a08d5842-40fe-4b00-c2c7-04a86c0a90f9"
      },
      "execution_count": 34,
      "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": "d87de08e85914e3a81d110b3b3c1f48d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'model_name': 'FashionMNISTModelV1',\n",
              " 'model_loss': 0.6527653932571411,\n",
              " 'model_acc': 75.94848242811501}"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_0_results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I8eIcbiREc6m",
        "outputId": "9e679c90-e51c-4e26-bcba-77a05a8a4440"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'model_name': 'FashionMNISTModelV0',\n",
              " 'model_loss': 0.46869373321533203,\n",
              " 'model_acc': 83.2667731629393}"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Model 2: Building a Convolutional Neural Network (CNN)\n",
        "\n",
        "Input layer -> [Convolutional layer -> activation layer -> pooling layer] -> Output layer\n",
        "\n",
        "https://poloclub.github.io/cnn-explainer/"
      ],
      "metadata": {
        "id": "t0WYR3fCEhtu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a convolutional neural network\n",
        "class FashionMNISTModelV2(nn.Module):\n",
        "    \"\"\"\n",
        "    Model architecture copying TinyVGG from:\n",
        "    https://poloclub.github.io/cnn-explainer/\n",
        "    \"\"\"\n",
        "    def __init__(self,\n",
        "                 input_shape: int,\n",
        "                 hidden_units: int,\n",
        "                 output_shape: int):\n",
        "\n",
        "        super().__init__()\n",
        "        self.block_1 = nn.Sequential(\n",
        "            nn.Conv2d(in_channels=input_shape,\n",
        "                      out_channels=hidden_units,\n",
        "                      kernel_size=3, # how big is the square that's going over the image?\n",
        "                      stride=1, # default\n",
        "                      padding=1),# options = \"valid\" (no padding) or \"same\" (output has same shape as input) or int for specific number\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",
        "                         stride=2) # default stride value is same as kernel_size\n",
        "        )\n",
        "        self.block_2 = nn.Sequential(\n",
        "            nn.Conv2d(hidden_units, hidden_units, 3, padding=1),\n",
        "            nn.ReLU(),\n",
        "            nn.Conv2d(hidden_units, hidden_units, 3, padding=1),\n",
        "            nn.ReLU(),\n",
        "            nn.MaxPool2d(2)\n",
        "        )\n",
        "        self.classifier = nn.Sequential(\n",
        "            nn.Flatten(),\n",
        "            # Where did this in_features shape come from?\n",
        "            # It's because each layer of our network compresses and changes the shape of our inputs data.\n",
        "            nn.Linear(in_features=hidden_units*7*7,\n",
        "                      out_features=output_shape)\n",
        "        )\n",
        "\n",
        "    def forward(self, x: torch.Tensor):\n",
        "        x = self.block_1(x)\n",
        "        # print(x.shape)\n",
        "        x = self.block_2(x)\n",
        "        # print(x.shape)\n",
        "        x = self.classifier(x)\n",
        "        # print(x.shape)\n",
        "        return x\n",
        "\n",
        "torch.manual_seed(42)\n",
        "model_2 = FashionMNISTModelV2(input_shape=1,\n",
        "    hidden_units=10,\n",
        "    output_shape=len(class_names)).to(device)\n",
        "model_2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JqotNBtNKIVe",
        "outputId": "8335521c-2bf4-44df-972f-4a87c94de2f7"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "FashionMNISTModelV2(\n",
              "  (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",
              "  (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": 36
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Stepping through nn.Conv2d()\n",
        "\n",
        "* `nn.Conv2d()` -> convolutional layer."
      ],
      "metadata": {
        "id": "JhD-lcWp6XfW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "torch.manual_seed(42)\n",
        "\n",
        "# Create sample batch of random numbers with same size as image batch\n",
        "images = torch.randn(size=(32, 3, 64, 64)) # [batch_size, color_channels, height, width]\n",
        "test_image = images[0] # get a single image for testing\n",
        "print(f\"Image batch shape: {images.shape} -> [batch_size, color_channels, height, width]\")\n",
        "print(f\"Single image shape: {test_image.shape} -> [color_channels, height, width]\")\n",
        "print(f\"Single image pixel values:\\n{test_image}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mQNDyQ1cKIS2",
        "outputId": "12b4578f-4def-4b3e-d462-56d282f8ddaf"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Image batch shape: torch.Size([32, 3, 64, 64]) -> [batch_size, color_channels, height, width]\n",
            "Single image shape: torch.Size([3, 64, 64]) -> [color_channels, height, width]\n",
            "Single image pixel values:\n",
            "tensor([[[ 1.9269,  1.4873,  0.9007,  ...,  1.8446, -1.1845,  1.3835],\n",
            "         [ 1.4451,  0.8564,  2.2181,  ...,  0.3399,  0.7200,  0.4114],\n",
            "         [ 1.9312,  1.0119, -1.4364,  ..., -0.5558,  0.7043,  0.7099],\n",
            "         ...,\n",
            "         [-0.5610, -0.4830,  0.4770,  ..., -0.2713, -0.9537, -0.6737],\n",
            "         [ 0.3076, -0.1277,  0.0366,  ..., -2.0060,  0.2824, -0.8111],\n",
            "         [-1.5486,  0.0485, -0.7712,  ..., -0.1403,  0.9416, -0.0118]],\n",
            "\n",
            "        [[-0.5197,  1.8524,  1.8365,  ...,  0.8935, -1.5114, -0.8515],\n",
            "         [ 2.0818,  1.0677, -1.4277,  ...,  1.6612, -2.6223, -0.4319],\n",
            "         [-0.1010, -0.4388, -1.9775,  ...,  0.2106,  0.2536, -0.7318],\n",
            "         ...,\n",
            "         [ 0.2779,  0.7342, -0.3736,  ..., -0.4601,  0.1815,  0.1850],\n",
            "         [ 0.7205, -0.2833,  0.0937,  ..., -0.1002, -2.3609,  2.2465],\n",
            "         [-1.3242, -0.1973,  0.2920,  ...,  0.5409,  0.6940,  1.8563]],\n",
            "\n",
            "        [[-0.7978,  1.0261,  1.1465,  ...,  1.2134,  0.9354, -0.0780],\n",
            "         [-1.4647, -1.9571,  0.1017,  ..., -1.9986, -0.7409,  0.7011],\n",
            "         [-1.3938,  0.8466, -1.7191,  ..., -1.1867,  0.1320,  0.3407],\n",
            "         ...,\n",
            "         [ 0.8206, -0.3745,  1.2499,  ..., -0.0676,  0.0385,  0.6335],\n",
            "         [-0.5589, -0.3393,  0.2347,  ...,  2.1181,  2.4569,  1.3083],\n",
            "         [-0.4092,  1.5199,  0.2401,  ..., -0.2558,  0.7870,  0.9924]]])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "torch.manual_seed(42)\n",
        "\n",
        "# Create a convolutional layer with same dimensions as TinyVGG\n",
        "# (try changing any of the parameters and see what happens)\n",
        "conv_layer = nn.Conv2d(in_channels=3,\n",
        "                       out_channels=10,\n",
        "                       kernel_size=3,\n",
        "                       stride=1,\n",
        "                       padding=0) # also try using \"valid\" or \"same\" here\n",
        "\n",
        "# Pass the data through the convolutional layer\n",
        "conv_output = conv_layer(test_image)\n",
        "conv_output"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5gKxfMJXKIQg",
        "outputId": "1cec8c2f-3cb3-4df3-80f7-f363cbc159cb"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[[ 1.5396,  0.0516,  0.6454,  ..., -0.3673,  0.8711,  0.4256],\n",
              "         [ 0.3662,  1.0114, -0.5997,  ...,  0.8983,  0.2809, -0.2741],\n",
              "         [ 1.2664, -1.4054,  0.3727,  ..., -0.3409,  1.2191, -0.0463],\n",
              "         ...,\n",
              "         [-0.1541,  0.5132, -0.3624,  ..., -0.2360, -0.4609, -0.0035],\n",
              "         [ 0.2981, -0.2432,  1.5012,  ..., -0.6289, -0.7283, -0.5767],\n",
              "         [-0.0386, -0.0781, -0.0388,  ...,  0.2842,  0.4228, -0.1802]],\n",
              "\n",
              "        [[-0.2840, -0.0319, -0.4455,  ..., -0.7956,  1.5599, -1.2449],\n",
              "         [ 0.2753, -0.1262, -0.6541,  ..., -0.2211,  0.1999, -0.8856],\n",
              "         [-0.5404, -1.5489,  0.0249,  ..., -0.5932, -1.0913, -0.3849],\n",
              "         ...,\n",
              "         [ 0.3870, -0.4064, -0.8236,  ...,  0.1734, -0.4330, -0.4951],\n",
              "         [-0.1984, -0.6386,  1.0263,  ..., -0.9401, -0.0585, -0.7833],\n",
              "         [-0.6306, -0.2052, -0.3694,  ..., -1.3248,  0.2456, -0.7134]],\n",
              "\n",
              "        [[ 0.4414,  0.5100,  0.4846,  ..., -0.8484,  0.2638,  1.1258],\n",
              "         [ 0.8117,  0.3191, -0.0157,  ...,  1.2686,  0.2319,  0.5003],\n",
              "         [ 0.3212,  0.0485, -0.2581,  ...,  0.2258,  0.2587, -0.8804],\n",
              "         ...,\n",
              "         [-0.1144, -0.1869,  0.0160,  ..., -0.8346,  0.0974,  0.8421],\n",
              "         [ 0.2941,  0.4417,  0.5866,  ..., -0.1224,  0.4814, -0.4799],\n",
              "         [ 0.6059, -0.0415, -0.2028,  ...,  0.1170,  0.2521, -0.4372]],\n",
              "\n",
              "        ...,\n",
              "\n",
              "        [[-0.2560, -0.0477,  0.6380,  ...,  0.6436,  0.7553, -0.7055],\n",
              "         [ 1.5595, -0.2209, -0.9486,  ..., -0.4876,  0.7754,  0.0750],\n",
              "         [-0.0797,  0.2471,  1.1300,  ...,  0.1505,  0.2354,  0.9576],\n",
              "         ...,\n",
              "         [ 1.1065,  0.6839,  1.2183,  ...,  0.3015, -0.1910, -0.1902],\n",
              "         [-0.3486, -0.7173, -0.3582,  ...,  0.4917,  0.7219,  0.1513],\n",
              "         [ 0.0119,  0.1017,  0.7839,  ..., -0.3752, -0.8127, -0.1257]],\n",
              "\n",
              "        [[ 0.3841,  1.1322,  0.1620,  ...,  0.7010,  0.0109,  0.6058],\n",
              "         [ 0.1664,  0.1873,  1.5924,  ...,  0.3733,  0.9096, -0.5399],\n",
              "         [ 0.4094, -0.0861, -0.7935,  ..., -0.1285, -0.9932, -0.3013],\n",
              "         ...,\n",
              "         [ 0.2688, -0.5630, -1.1902,  ...,  0.4493,  0.5404, -0.0103],\n",
              "         [ 0.0535,  0.4411,  0.5313,  ...,  0.0148, -1.0056,  0.3759],\n",
              "         [ 0.3031, -0.1590, -0.1316,  ..., -0.5384, -0.4271, -0.4876]],\n",
              "\n",
              "        [[-1.1865, -0.7280, -1.2331,  ..., -0.9013, -0.0542, -1.5949],\n",
              "         [-0.6345, -0.5920,  0.5326,  ..., -1.0395, -0.7963, -0.0647],\n",
              "         [-0.1132,  0.5166,  0.2569,  ...,  0.5595, -1.6881,  0.9485],\n",
              "         ...,\n",
              "         [-0.0254, -0.2669,  0.1927,  ..., -0.2917,  0.1088, -0.4807],\n",
              "         [-0.2609, -0.2328,  0.1404,  ..., -0.1325, -0.8436, -0.7524],\n",
              "         [-1.1399, -0.1751, -0.8705,  ...,  0.1589,  0.3377,  0.3493]]],\n",
              "       grad_fn=<SqueezeBackward1>)"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "conv_output.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S6fcpAXJKIOE",
        "outputId": "ab0eb502-1b57-4334-b956-5c8c55bcbc9f"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([10, 62, 62])"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Stepping through nn.MaxPool2d()\n",
        "\n",
        "* `nn.MaxPool2d()` -> max pooling layer."
      ],
      "metadata": {
        "id": "mZWIjsKB-Wz9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Print out original image shape\n",
        "print(f\"Test image original shape: {test_image.shape}\")\n",
        "\n",
        "# Create a sample nn.MaxPoo2d() layer\n",
        "max_pool_layer = nn.MaxPool2d(kernel_size=2)\n",
        "\n",
        "# Pass data through just the conv_layer\n",
        "test_image_through_conv = conv_layer(test_image)\n",
        "print(f\"Shape after going through conv_layer(): {test_image_through_conv.shape}\")\n",
        "\n",
        "# Pass data through the max pool layer\n",
        "test_image_through_conv_and_max_pool = max_pool_layer(test_image_through_conv)\n",
        "print(f\"Shape after going through conv_layer() and max_pool_layer(): {test_image_through_conv_and_max_pool.shape}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f5PnlZKVKILz",
        "outputId": "fe759dad-507f-41d7-c456-c6d73110dd0e"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test image original shape: torch.Size([3, 64, 64])\n",
            "Shape after going through conv_layer(): torch.Size([10, 62, 62])\n",
            "Shape after going through conv_layer() and max_pool_layer(): torch.Size([10, 31, 31])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "torch.manual_seed(42)\n",
        "# Create a random tensor with a similiar number of dimensions to our images\n",
        "random_tensor = torch.randn(size=(1, 1, 2, 2))\n",
        "print(f\"Random tensor:\\n{random_tensor}\")\n",
        "print(f\"Random tensor shape: {random_tensor.shape}\")\n",
        "\n",
        "# Create a max pool layer\n",
        "max_pool_layer = nn.MaxPool2d(kernel_size=2) # see what happens when you change the kernel_size value\n",
        "\n",
        "# Pass the random tensor through the max pool layer\n",
        "max_pool_tensor = max_pool_layer(random_tensor)\n",
        "print(f\"\\nMax pool tensor:\\n{max_pool_tensor} <- this is the maximum value from random_tensor\")\n",
        "print(f\"Max pool tensor shape: {max_pool_tensor.shape}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XjLQNWc0KIJr",
        "outputId": "92d0c3d6-d127-46b4-d04a-e1ce150a9668"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Random tensor:\n",
            "tensor([[[[0.3367, 0.1288],\n",
            "          [0.2345, 0.2303]]]])\n",
            "Random tensor shape: torch.Size([1, 1, 2, 2])\n",
            "\n",
            "Max pool tensor:\n",
            "tensor([[[[0.3367]]]]) <- this is the maximum value from random_tensor\n",
            "Max pool tensor shape: torch.Size([1, 1, 1, 1])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Setup a loss function and optimizer for model_2\n",
        "\n",
        "We'll use the functions as before, `nn.CrossEntropyLoss()` as the loss function (since we're working with multi-class classification data).\n",
        "\n",
        "And `torch.optim.SGD()` as the optimizer to optimize `model_2.parameters()` with a learning rate of 0.1."
      ],
      "metadata": {
        "id": "MyVZolvE5fFd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Setup loss and optimizer\n",
        "loss_fn = nn.CrossEntropyLoss()\n",
        "optimizer = torch.optim.SGD(params=model_2.parameters(),\n",
        "                             lr=0.1)"
      ],
      "metadata": {
        "id": "RmmFcy4h5e3t"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Training and testing model_2 using our training and test functions\n",
        "\n",
        "We'll use our `train_step()` and `test_step()` functions we created before."
      ],
      "metadata": {
        "id": "VZ9xSyfw8hDJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "torch.manual_seed(42)\n",
        "\n",
        "# Measure time\n",
        "from timeit import default_timer as timer\n",
        "train_time_start_model_2 = timer()\n",
        "\n",
        "# Train and test model\n",
        "epochs = 5\n",
        "for epoch in tqdm(range(epochs)):\n",
        "    print(f\"Epoch: {epoch}\\n---------\")\n",
        "    train_step(data_loader=train_dataloader,\n",
        "        model=model_2,\n",
        "        loss_fn=loss_fn,\n",
        "        optimizer=optimizer,\n",
        "        accuracy_fn=accuracy_fn,\n",
        "        device=device\n",
        "    )\n",
        "    test_step(data_loader=test_dataloader,\n",
        "        model=model_2,\n",
        "        loss_fn=loss_fn,\n",
        "        accuracy_fn=accuracy_fn,\n",
        "        device=device\n",
        "    )\n",
        "\n",
        "train_time_end_model_2 = timer()\n",
        "total_train_time_model_2 = print_train_time(start=train_time_start_model_2,\n",
        "                                           end=train_time_end_model_2,\n",
        "                                           device=device)"
      ],
      "metadata": {
        "id": "-hxGxMVRKIEF",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 500,
          "referenced_widgets": [
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      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/5 [00:00<?, ?it/s]"
            ],
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          "text": [
            "Epoch: 0\n",
            "---------\n",
            "Train loss: 0.59438 | Train accuracy: 78.46%\n",
            "Test loss: 0.40810 | Test accuracy: 85.37%\n",
            "\n",
            "Epoch: 1\n",
            "---------\n",
            "Train loss: 0.36362 | Train accuracy: 86.81%\n",
            "Test loss: 0.35951 | Test accuracy: 86.82%\n",
            "\n",
            "Epoch: 2\n",
            "---------\n",
            "Train loss: 0.32263 | Train accuracy: 88.46%\n",
            "Test loss: 0.31992 | Test accuracy: 88.72%\n",
            "\n",
            "Epoch: 3\n",
            "---------\n",
            "Train loss: 0.30278 | Train accuracy: 88.93%\n",
            "Test loss: 0.32799 | Test accuracy: 88.15%\n",
            "\n",
            "Epoch: 4\n",
            "---------\n",
            "Train loss: 0.29017 | Train accuracy: 89.48%\n",
            "Test loss: 0.31533 | Test accuracy: 88.79%\n",
            "\n",
            "Train time on cuda: 71.269 seconds\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Get model_2 results\n",
        "model_2_results = eval_model(\n",
        "    model=model_2,\n",
        "    data_loader=test_dataloader,\n",
        "    loss_fn=loss_fn,\n",
        "    accuracy_fn=accuracy_fn\n",
        ")\n",
        "model_2_results"
      ],
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        "id": "RiWAl-MaKIB8",
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      "outputs": [
        {
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          "data": {
            "text/plain": [
              "  0%|          | 0/313 [00:00<?, ?it/s]"
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        {
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          "data": {
            "text/plain": [
              "{'model_name': 'FashionMNISTModelV2',\n",
              " 'model_loss': 0.315327525138855,\n",
              " 'model_acc': 88.7879392971246}"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Compare model results and training time"
      ],
      "metadata": {
        "id": "kogbJUv0-VZK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "compare_results = pd.DataFrame([model_0_results, model_1_results, model_2_results])\n",
        "compare_results"
      ],
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        "id": "zA_gVXRsKH_T",
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      "execution_count": 45,
      "outputs": [
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            "text/plain": [
              "            model_name  model_loss  model_acc\n",
              "0  FashionMNISTModelV0    0.468694  83.266773\n",
              "1  FashionMNISTModelV1    0.652765  75.948482\n",
              "2  FashionMNISTModelV2    0.315328  88.787939"
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          "metadata": {},
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        "# Add training times to results comparison\n",
        "compare_results[\"training_time\"] = [total_train_time_model_0,\n",
        "                                    total_train_time_model_1,\n",
        "                                    total_train_time_model_2]\n",
        "compare_results"
      ],
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      "execution_count": 46,
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              "            model_name  model_loss  model_acc  training_time\n",
              "0  FashionMNISTModelV0    0.468694  83.266773      57.125956\n",
              "1  FashionMNISTModelV1    0.652765  75.948482      60.289667\n",
              "2  FashionMNISTModelV2    0.315328  88.787939      71.268739"
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              "      <td>0.468694</td>\n",
              "      <td>83.266773</td>\n",
              "      <td>57.125956</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>FashionMNISTModelV1</td>\n",
              "      <td>0.652765</td>\n",
              "      <td>75.948482</td>\n",
              "      <td>60.289667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>FashionMNISTModelV2</td>\n",
              "      <td>0.315328</td>\n",
              "      <td>88.787939</td>\n",
              "      <td>71.268739</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e1062f21-57eb-4cb6-9980-db64d7efa889')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-e1062f21-57eb-4cb6-9980-db64d7efa889 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-e1062f21-57eb-4cb6-9980-db64d7efa889');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-e6ace695-f868-4e06-b14f-0b29591c1686\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-e6ace695-f868-4e06-b14f-0b29591c1686')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-e6ace695-f868-4e06-b14f-0b29591c1686 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_cc55a032-433a-42c3-9103-9ecae5a71afe\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('compare_results')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_cc55a032-433a-42c3-9103-9ecae5a71afe button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('compare_results');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "source": [
        "# Visualize our model results\n",
        "compare_results.set_index(\"model_name\")[\"model_acc\"].plot(kind=\"barh\")\n",
        "plt.xlabel(\"accuracy (%)\")\n",
        "plt.ylabel(\"model\");"
      ],
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "Czu-7VnG_GEY",
        "outputId": "2af7da1a-4c2d-4731-ae11-966982da7775"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Make and evaluate random predictions with best model"
      ],
      "metadata": {
        "id": "fLxhpwnMA-QJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def make_predictions(model: torch.nn.Module, data: list, device: torch.device = device):\n",
        "    pred_probs = []\n",
        "    model.eval()\n",
        "    with torch.inference_mode():\n",
        "        for sample in data:\n",
        "            # Prepare sample\n",
        "            sample = torch.unsqueeze(sample, dim=0).to(device) # Add an extra dimension and send sample to device\n",
        "\n",
        "            # Forward pass (model outputs raw logit)\n",
        "            pred_logit = model(sample)\n",
        "\n",
        "            # Get prediction probability (logit -> prediction probability)\n",
        "            pred_prob = torch.softmax(pred_logit.squeeze(), dim=0) # note: perform softmax on the \"logits\" dimension, not \"batch\" dimension (in this case we have a batch size of 1, so can perform on dim=0)\n",
        "\n",
        "            # Get pred_prob off GPU for further calculations\n",
        "            pred_probs.append(pred_prob.cpu())\n",
        "\n",
        "    # Stack the pred_probs to turn list into a tensor\n",
        "    return torch.stack(pred_probs)"
      ],
      "metadata": {
        "id": "h4F-qnwj-U5t"
      },
      "execution_count": 48,
      "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 test sample shape and label\n",
        "print(f\"Test sample image shape: {test_samples[0].shape}\\nTest sample label: {test_labels[0]} ({class_names[test_labels[0]]})\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MSF_rc9l-U3j",
        "outputId": "ebaddcc2-2f19-4f42-f61f-27e8bfec6ca7"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test sample image shape: torch.Size([1, 28, 28])\n",
            "Test sample label: 5 (Sandal)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.imshow(test_samples[2].squeeze(), cmap = 'gray')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "AUR3x4u0-U1N",
        "outputId": "fe4a292a-2b76-4438-f601-bc746b4248f7"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7928149e6710>"
            ]
          },
          "metadata": {},
          "execution_count": 50
        },
        {
          "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": [
        "# Make predictions on test samples with model 2\n",
        "pred_probs= make_predictions(model=model_2,\n",
        "                             data=test_samples)\n",
        "\n",
        "# View first two prediction probabilities list\n",
        "pred_probs[:2]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "abGSp47O-Uy6",
        "outputId": "9db42374-ed56-4444-f0ec-c9b339a81a03"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[8.8748e-09, 3.0507e-10, 1.8324e-09, 1.6168e-09, 5.0957e-10, 9.9977e-01,\n",
              "         2.9033e-08, 4.5680e-06, 4.2681e-06, 2.2301e-04],\n",
              "        [1.7498e-03, 9.6836e-01, 2.9980e-05, 6.6500e-03, 1.4094e-02, 5.9372e-06,\n",
              "         9.0318e-03, 6.7898e-05, 1.7967e-06, 4.8466e-06]])"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Turn the prediction probabilities into prediction labels by taking the argmax()\n",
        "pred_classes = pred_probs.argmax(dim=1)\n",
        "pred_classes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2XjmL-e_-Uwb",
        "outputId": "8497ce85-7939-4d88-90ca-e345fb622802"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([5, 1, 7, 4, 3, 0, 4, 7, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Are our predictions in the same form as our test labels?\n",
        "test_labels, pred_classes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VBPtEmpLKJFN",
        "outputId": "9ee130aa-448a-4600-a294-ca6989bd6c0c"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "([5, 1, 7, 4, 3, 0, 4, 7, 1], tensor([5, 1, 7, 4, 3, 0, 4, 7, 1]))"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "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 a subplot\n",
        "  plt.subplot(nrows, ncols, i+1)\n",
        "\n",
        "  # Plot the target image\n",
        "  plt.imshow(sample.squeeze(), cmap=\"gray\")\n",
        "\n",
        "  # Find the prediction label (in text form, e.g. \"Sandal\")\n",
        "  pred_label = class_names[pred_classes[i]]\n",
        "\n",
        "  # Get the truth label (in text form, e.g. \"T-shirt\")\n",
        "  truth_label = class_names[test_labels[i]]\n",
        "\n",
        "  # Create the title text of the plot\n",
        "  title_text = f\"Pred: {pred_label} | Truth: {truth_label}\"\n",
        "\n",
        "  # Check for equality and change title colour accordingly\n",
        "  if pred_label == truth_label:\n",
        "      plt.title(title_text, fontsize=10, c=\"g\") # green text if correct\n",
        "  else:\n",
        "      plt.title(title_text, fontsize=10, c=\"r\") # red text if wrong\n",
        "  plt.axis(False);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 749
        },
        "id": "2-8kh4e1KO8p",
        "outputId": "ac88ee53-9755-470c-ea23-24c8cbdb1605"
      },
      "execution_count": 54,
      "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": [
        "# Making a confusion matrix for further prediction evaluation\n",
        "\n",
        "1. Make predictions with our trained model, `model_2` (a confusion matrix compares predictions to true labels).\n",
        "\n",
        "2. Make a confusion matrix using `torchmetrics.ConfusionMatrix`.\n",
        "https://lightning.ai/docs/torchmetrics/stable/classification/confusion_matrix.html\n",
        "\n",
        "3. Plot the confusion matrix using `mlxtend.plotting.plot_confusion_matrix()`.\n",
        "\n",
        "https://rasbt.github.io/mlxtend/\n"
      ],
      "metadata": {
        "id": "VDWumdfzLRDa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Import tqdm for progress bar\n",
        "from tqdm.auto import tqdm\n",
        "\n",
        "# 1. Make predictions with trained model\n",
        "y_preds = []\n",
        "model_2.eval()\n",
        "with torch.inference_mode():\n",
        "  for X, y in tqdm(test_dataloader, desc=\"Making predictions\"):\n",
        "    # Send data and targets to target device\n",
        "    X, y = X.to(device), y.to(device)\n",
        "    # Do the forward pass\n",
        "    y_logit = model_2(X)\n",
        "    # Turn predictions from logits -> prediction probabilities -> predictions labels\n",
        "    y_pred = torch.softmax(y_logit, dim=1).argmax(dim=1) # note: perform softmax on the \"logits\" dimension, not \"batch\" dimension (in this case we have a batch size of 32, so can perform on dim=1)\n",
        "    # Put predictions on CPU for evaluation\n",
        "    y_preds.append(y_pred.cpu())\n",
        "# Concatenate list of predictions into a tensor\n",
        "y_pred_tensor = torch.cat(y_preds)\n",
        "y_pred_tensor"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 66,
          "referenced_widgets": [
            "850a5b81b24144128c285ebe2040d7ed",
            "d096248364024a6db3bb987d4e4fd380",
            "71813240eec34f40a0b4a148b991eace",
            "d69622b7a27a4e829884390311299f2d",
            "85d4faa701b14452aa7bb74ac7842aae",
            "5d1792a1ec2d4b22b9c48c7075c6bedb",
            "a74ede4761c34e93a57b5ca4ec482b11",
            "bb5947dc17214161bab6caa90babef97",
            "bf9b5bb6d7284de6b4566d9fd858b718",
            "64a99ed126524bcabc11ef4b98207d47",
            "e9c675ea835a42918a75af915c84f180"
          ]
        },
        "id": "b9k1DKMgK0fI",
        "outputId": "23e324eb-afd3-4721-d941-713823bc1697"
      },
      "execution_count": 55,
      "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": "850a5b81b24144128c285ebe2040d7ed"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([9, 2, 1,  ..., 8, 1, 5])"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# See if torchmetrics exists, if not, install it\n",
        "try:\n",
        "    import torchmetrics, mlxtend\n",
        "    print(f\"mlxtend version: {mlxtend.__version__}\")\n",
        "    assert int(mlxtend.__version__.split(\".\")[1]) >= 19, \"mlxtend verison should be 0.19.0 or higher\"\n",
        "except:\n",
        "    !pip install -q torchmetrics -U mlxtend # <- Note: If you're using Google Colab, this may require restarting the runtime\n",
        "    import torchmetrics, mlxtend\n",
        "    print(f\"mlxtend version: {mlxtend.__version__}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Tc_pMDv4LQUJ",
        "outputId": "be2ee053-cd02-4df5-cfef-0a77d1b72834"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/840.2 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m256.0/840.2 kB\u001b[0m \u001b[31m7.6 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m \u001b[32m839.7/840.2 kB\u001b[0m \u001b[31m13.6 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m840.2/840.2 kB\u001b[0m \u001b[31m11.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/1.4 MB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.9/1.4 MB\u001b[0m \u001b[31m25.9 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.4/1.4 MB\u001b[0m \u001b[31m21.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hmlxtend version: 0.23.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Import mlxtend upgraded version\n",
        "import mlxtend\n",
        "print(mlxtend.__version__)\n",
        "assert int(mlxtend.__version__.split(\".\")[1]) >= 19 # should be version 0.19.0 or higher"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GC6foEkzLQR8",
        "outputId": "a133efb2-633c-478b-b1e8-90083d44a090"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.23.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_data.targets"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XoJv9pToEpf5",
        "outputId": "eb170e49-77f2-4702-ebad-361b88fae82a"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([9, 2, 1,  ..., 8, 1, 5])"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "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": 667
        },
        "id": "9tt7dnOxLQPb",
        "outputId": "f185431a-fcc8-49ad-f6b9-089bdb9f70b7"
      },
      "execution_count": 59,
      "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": [
        "# Save and load best performing model\n",
        "\n",
        "`torch.save` - a function to save a whole PyTorch model or a model's `state_dict()`.\n",
        "`torch.load` - a function to load in a saved PyTorch object.\n",
        "`torch.nn.Module.load_state_dict()` - a function to load a saved `state_dict()` into an existing model instance."
      ],
      "metadata": {
        "id": "ENijYKu8FjfT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from pathlib import Path\n",
        "\n",
        "# Create models directory (if it doesn't already exist), see: https://docs.python.org/3/library/pathlib.html#pathlib.Path.mkdir\n",
        "MODEL_PATH = Path(\"models\")\n",
        "MODEL_PATH.mkdir(parents=True, # create parent directories if needed\n",
        "                 exist_ok=True # if models directory already exists, don't error\n",
        ")\n",
        "\n",
        "# Create model save path\n",
        "MODEL_NAME = \"03_pytorch_computer_vision_model_2.pth\"\n",
        "MODEL_SAVE_PATH = MODEL_PATH / MODEL_NAME\n",
        "\n",
        "# Save the model state dict\n",
        "print(f\"Saving model to: {MODEL_SAVE_PATH}\")\n",
        "torch.save(obj=model_2.state_dict(), # only saving the state_dict() only saves the learned parameters\n",
        "           f=MODEL_SAVE_PATH)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JnAI0ivyLQNJ",
        "outputId": "7fa2c5c6-5faf-405b-a8d4-ceb844e5c270"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving model to: models/03_pytorch_computer_vision_model_2.pth\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a new instance of FashionMNISTModelV2 (the same class as our saved state_dict())\n",
        "# Note: loading model will error if the shapes here aren't the same as the saved version\n",
        "loaded_model_2 = FashionMNISTModelV2(input_shape=1,\n",
        "                                    hidden_units=10, # try changing this to 128 and seeing what happens\n",
        "                                    output_shape=10)\n",
        "\n",
        "# Load in the saved state_dict()\n",
        "loaded_model_2.load_state_dict(torch.load(f=MODEL_SAVE_PATH))\n",
        "\n",
        "# Send model to GPU\n",
        "loaded_model_2 = loaded_model_2.to(device)"
      ],
      "metadata": {
        "id": "hz5YNRn_LQKx"
      },
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate loaded model\n",
        "torch.manual_seed(42)\n",
        "\n",
        "loaded_model_2_results = eval_model(\n",
        "    model=loaded_model_2,\n",
        "    data_loader=test_dataloader,\n",
        "    loss_fn=loss_fn,\n",
        "    accuracy_fn=accuracy_fn\n",
        ")\n",
        "\n",
        "loaded_model_2_results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 101,
          "referenced_widgets": [
            "7b6d0f2d34174e76ae8b5acd42c24e47",
            "8976dc81d1194a4a8f8ed6e21c8d50b5",
            "e552bcf8823945aeb135599c17d8233b",
            "92cb8be5ada648b9bdf372012460e369",
            "526940a34cde4378a701aaa95dd36bcb",
            "76b4b906487a45ceaf40bef5b6b7bc5d",
            "5f40403030044d81820d4298f6452ed9",
            "48e6d5a6885d4cd3b06686a39f5556f8",
            "e557c22b0a654aecb8adbbd681790abf",
            "86e1936dcd414abc82e0dd4f38348298",
            "12834e9e272745f285712eec6094de5b"
          ]
        },
        "id": "qqk4zecqGSxv",
        "outputId": "61a55e2b-fe77-4f2b-8bff-9730e7913f30"
      },
      "execution_count": 62,
      "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": "7b6d0f2d34174e76ae8b5acd42c24e47"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'model_name': 'FashionMNISTModelV2',\n",
              " 'model_loss': 0.315327525138855,\n",
              " 'model_acc': 88.7879392971246}"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_2_results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eypFYdOpGSvY",
        "outputId": "b770f083-c808-45df-cf39-0985cfbedc8e"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'model_name': 'FashionMNISTModelV2',\n",
              " 'model_loss': 0.315327525138855,\n",
              " 'model_acc': 88.7879392971246}"
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check to see if results are close to each other (if they are very far away, there may be an error)\n",
        "torch.isclose(torch.tensor(model_2_results[\"model_loss\"]),\n",
        "              torch.tensor(loaded_model_2_results[\"model_loss\"]),\n",
        "              atol=1e-08, # absolute tolerance\n",
        "              rtol=0.0001) # relative tolerance"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SqZmFgQaGStP",
        "outputId": "69851554-9cf9-49e2-e276-516f63f167a4"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor(True)"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "QaTZ8MmOGSrA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "FFoqH9-DGSox"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "7VJfKL4hGSmX"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}