03_pytorch_computer_vision_CNN.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/ErikLarssonDev/PyTorch/blob/main/03_pytorch_computer_vision_CNN.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"
      ],
      "metadata": {
        "id": "PvuQw8rEDaZS"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 0. Computer vision libraries in PyToch\n",
        "\n",
        "* `torchvision` - Base domain library for PyTorch computer vision\n",
        "* `torchvision.datasets` - Get datasets and data loading functions for computer vision\n",
        "* `tochvision.models` - Get pretrained computer vision models that you can leverage for own problems\n",
        "* `torchvision.transforms` - Functions for manipulating your vision data (images) to be suitable for use with a ML model\n",
        "* `torch.utils.data.Dataset` - Base dataset class for PyTorch\n",
        "* `torch.utils.data.DataLoader`  Creates a Python iterable over a dataset"
      ],
      "metadata": {
        "id": "0rnSgY6ONDvr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Import PyTorch\n",
        "import torch\n",
        "from torch import nn\n",
        "\n",
        "# Import torchvision\n",
        "import torchvision\n",
        "from torchvision import datasets\n",
        "from torchvision import transforms\n",
        "from torchvision.transforms import ToTensor\n",
        "\n",
        "# Import matplotlib for visualization\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Check versions\n",
        "print(torch.__version__)\n",
        "print(torchvision.__version__)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g5yCHjZYOCxK",
        "outputId": "eed0bb05-a074-4d44-8d10-d08a57778d7a"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2.0.1+cu118\n",
            "0.15.2+cu118\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 1. Getting a dataset\n",
        "\n",
        "The dataset we'll be using is the Fashion-MNIST dataset."
      ],
      "metadata": {
        "id": "q9jL-WqtPDMa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Setup training data\n",
        "train_data = datasets.FashionMNIST(\n",
        "    root=\"data\", # Where to download data to\n",
        "    train=True, # Do we want the training dataset? Else, test dataset.\n",
        "    download=True,\n",
        "    transform=ToTensor(),\n",
        "    target_transform=None\n",
        ")\n",
        "\n",
        "# Setup test data\n",
        "test_data = datasets.FashionMNIST(\n",
        "    root=\"data\",\n",
        "    train=False,\n",
        "    download=True,\n",
        "    transform=ToTensor(),\n",
        "    target_transform=None\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DgLWbjhgPUb7",
        "outputId": "1e701001-fb50-4a53-9e84-5b2d46f5d988"
      },
      "execution_count": 4,
      "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 [00:01<00:00, 16348512.04it/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, 303756.09it/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 [00:00<00:00, 5571995.32it/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, 10888692.38it/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": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y4a6VLVMRIT-",
        "outputId": "3f0c2d79-e0d4-419a-bd5a-26dc810c7377"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(60000, 10000)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# See the first training data example\n",
        "image, label = train_data[0]\n",
        "image, label"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LV7Uzj-DRfyq",
        "outputId": "257b61d8-d273-4246-ece6-f347eaecc7c9"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
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              " 9)"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class_names = train_data.classes\n",
        "class_names"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8rPWPOp1RkdJ",
        "outputId": "db65be13-ae30-446d-8c8a-b2b46136a023"
      },
      "execution_count": 7,
      "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": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_data.class_to_idx"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8ZSvPqBuSIQP",
        "outputId": "877ec406-ac56-490f-f365-20d9c2f7da7e"
      },
      "execution_count": 8,
      "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": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_data.targets"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KZfBEVXdSMgW",
        "outputId": "43ff477f-ba6b-414c-c6fc-fd120c7fa190"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([9, 0, 0,  ..., 3, 0, 5])"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check the shape of our image and label (does not have a shape)\n",
        "print(f\"Image shape: {image.shape} -> [color_channels (only 1 -> black/white), height width]\")\n",
        "print(f\"Image label: {class_names[label]}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AA3w8wcYSTME",
        "outputId": "f45d6d78-1994-4cbf-c861-9800b22cd7ec"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Image shape: torch.Size([1, 28, 28]) -> [color_channels (only 1 -> black/white), height width]\n",
            "Image label: Ankle boot\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 1.2 Visualize our data"
      ],
      "metadata": {
        "id": "pbangndDSXsi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "image, label = train_data[0]\n",
        "\n",
        "print(f\"Image shape: {image.shape}\")\n",
        "plt.imshow(image.squeeze())\n",
        "plt.title(label)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "td7r3TqpUGZB",
        "outputId": "caa11558-f576-4290-f93b-24a226a35906"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Image shape: torch.Size([1, 28, 28])\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, '9')"
            ]
          },
          "metadata": {},
          "execution_count": 11
        },
        {
          "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": [
        "plt.imshow(image.squeeze(), cmap=\"gray\")\n",
        "plt.title(class_names[label])\n",
        "plt.axis(False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 446
        },
        "id": "VmZilxhKUfAe",
        "outputId": "95fdb1af-de09-4ded-9a4c-f5d241587c91"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(-0.5, 27.5, 27.5, -0.5)"
            ]
          },
          "metadata": {},
          "execution_count": 12
        },
        {
          "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": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 752
        },
        "id": "t1usjGMAUrxq",
        "outputId": "67359ed9-8452-45ad-cc4e-d54b5b8b58a7"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 900x900 with 16 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_data, test_data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JswNpQc4W52a",
        "outputId": "e45b9e1f-2b4c-4437-8cd4-8d33492453ae"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(Dataset FashionMNIST\n",
              "     Number of datapoints: 60000\n",
              "     Root location: data\n",
              "     Split: Train\n",
              "     StandardTransform\n",
              " Transform: ToTensor(),\n",
              " Dataset FashionMNIST\n",
              "     Number of datapoints: 10000\n",
              "     Root location: data\n",
              "     Split: Test\n",
              "     StandardTransform\n",
              " Transform: ToTensor())"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2. Prepare DataLoader\n",
        "\n",
        "Right now, our data is in the form of PyTorch Datasets.\n",
        "\n",
        "DataLoader turns our data into a Python iterable.\n",
        "\n",
        "More specifically, we want to turn our data into batches (or mini-batches).\n",
        "\n",
        "Why should we do this?\n",
        "1. It is more computationally efficient, as in, your computing hardware may not be able to look (store in memory) at 60000 images in one hit. So we break it down to 32 images at a time (batch size of 32).\n",
        "2. It gives our neural network more chances to update its gradients per epoch."
      ],
      "metadata": {
        "id": "E1hXIKk4VHrc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from torch.utils.data import DataLoader\n",
        "\n",
        "# Setup the batch size hyperparameter\n",
        "BATCH_SIZE = 32\n",
        "\n",
        "# Turn datasets into iterables (batches)\n",
        "train_dataloader = DataLoader(dataset=train_data,\n",
        "                              batch_size=BATCH_SIZE,\n",
        "                              shuffle=True)\n",
        "test_dataloader = DataLoader(dataset=test_data,\n",
        "                              batch_size=BATCH_SIZE,\n",
        "                              shuffle=False) # Order does not matter as much for test data, can be easier to compare experiments if it is the same order every time\n",
        "train_dataloader, test_dataloader"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mfOh7ustW5Sa",
        "outputId": "2d3870cf-fafc-4972-bd99-b28ad00d6a89"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(<torch.utils.data.dataloader.DataLoader at 0x7fc81d3fcaf0>,\n",
              " <torch.utils.data.dataloader.DataLoader at 0x7fc81d3ff700>)"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Let's check out what we've created\n",
        "print(f\"DataLoader: {train_dataloader, test_dataloader}\")\n",
        "print(f\"Length of train_dataloader: {len(train_dataloader)} batches of {BATCH_SIZE}...\")\n",
        "print(f\"Length of test_dataloader: {len(test_dataloader)} batches of {BATCH_SIZE}...\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sJ-ScVPNZG5e",
        "outputId": "ebcd8b56-c3b2-41e2-a6f4-dc64b2a09946"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "DataLoader: (<torch.utils.data.dataloader.DataLoader object at 0x7fc81d3fcaf0>, <torch.utils.data.dataloader.DataLoader object at 0x7fc81d3ff700>)\n",
            "Length of train_dataloader: 1875 batches of 32...\n",
            "Length of test_dataloader: 313 batches of 32...\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check out what's inside the training dataloader\n",
        "train_features_batch, train_labels_batch = next(iter(train_dataloader))\n",
        "train_features_batch.shape, train_labels_batch.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AkLsJ5evZ8BY",
        "outputId": "c1004e25-a24a-442b-afab-f4312014583c"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(torch.Size([32, 1, 28, 28]), torch.Size([32]))"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "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.size}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 464
        },
        "id": "i9dpvHglZTaF",
        "outputId": "9cc5fd7a-0037-4a75-a600-6d83571af1a6"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Image size: torch.Size([1, 28, 28])\n",
            "Label: 3, label size: <built-in method size of Tensor object at 0x7fc83bfd76f0>\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": [
        "## 3. Model 0: Build a baseline model"
      ],
      "metadata": {
        "id": "iHCOdws7ahub"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create 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 our what happened\n",
        "print(f\"Shape before flattening: {x.shape}\") # [color_channels, height, width]\n",
        "print(f\"Shape after flattening: {output.shape}\") # [color_channels, height*width]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pvL6TKhrbeh-",
        "outputId": "2d5dc49a-8dc0-4dd4-eb6d-0d56988d1d20"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape before flattening: torch.Size([1, 28, 28])\n",
            "Shape after flattening: torch.Size([1, 784])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from torch import nn\n",
        "class FashionMNISTModel_0(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(),\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",
        "    )\n",
        "\n",
        "  def forward(self, x):\n",
        "    return self.layer_stack(x)"
      ],
      "metadata": {
        "id": "NOjkX3Jwb828"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "torch.manual_seed(42)\n",
        "\n",
        "# Setup model with input parameters\n",
        "model_0 = FashionMNISTModel_0(\n",
        "    input_shape=784, # 28*28\n",
        "    hidden_units=10,\n",
        "    output_shape=len(class_names) # One for every class\n",
        ").to(\"cpu\")\n",
        "model_0"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-cFmS3zZdsoG",
        "outputId": "fb02e458-e627-462c-eb60-171f3bcd2d0e"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "FashionMNISTModel_0(\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": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dummy_x = torch.rand([1, 1, 28, 28])\n",
        "model_0(dummy_x).shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LtxyHRDHeKn2",
        "outputId": "c21bfc24-9b1c-48f6-b537-f841b1de8215"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([1, 10])"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 3.1 Setup loss, optimizer and evaluation metrics"
      ],
      "metadata": {
        "id": "82PyQLG6e_6X"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install torchmetrics"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Clf_wUlwhGsp",
        "outputId": "99f68f3f-2f03-45ef-a582-ded8f95712b7"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: torchmetrics in /usr/local/lib/python3.10/dist-packages (1.0.1)\n",
            "Requirement already satisfied: numpy>1.20.0 in /usr/local/lib/python3.10/dist-packages (from torchmetrics) (1.22.4)\n",
            "Requirement already satisfied: torch>=1.8.1 in /usr/local/lib/python3.10/dist-packages (from torchmetrics) (2.0.1+cu118)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from torchmetrics) (23.1)\n",
            "Requirement already satisfied: lightning-utilities>=0.7.0 in /usr/local/lib/python3.10/dist-packages (from torchmetrics) (0.9.0)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.10/dist-packages (from lightning-utilities>=0.7.0->torchmetrics) (4.7.1)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch>=1.8.1->torchmetrics) (3.12.2)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch>=1.8.1->torchmetrics) (1.11.1)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch>=1.8.1->torchmetrics) (3.1)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch>=1.8.1->torchmetrics) (3.1.2)\n",
            "Requirement already satisfied: triton==2.0.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.8.1->torchmetrics) (2.0.0)\n",
            "Requirement already satisfied: cmake in /usr/local/lib/python3.10/dist-packages (from triton==2.0.0->torch>=1.8.1->torchmetrics) (3.25.2)\n",
            "Requirement already satisfied: lit in /usr/local/lib/python3.10/dist-packages (from triton==2.0.0->torch>=1.8.1->torchmetrics) (16.0.6)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.8.1->torchmetrics) (2.1.3)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.8.1->torchmetrics) (1.3.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Import accuracy metric\n",
        "from torchmetrics import Accuracy\n",
        "\n",
        "# Setup metric\n",
        "accuracy_fn = Accuracy(task=\"multiclass\", num_classes=10) #.to(device)"
      ],
      "metadata": {
        "id": "Eg6RopwRfoUE"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Setup loss function and optimizer\n",
        "loss_fn = nn.CrossEntropyLoss()\n",
        "optimizer = torch.optim.SGD(params=model_0.parameters(),\n",
        "                            lr=0.1)"
      ],
      "metadata": {
        "id": "5rJPHCRphQvs"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 3.2 Creating a function to time our experiments"
      ],
      "metadata": {
        "id": "kkiyy0aghoXe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from timeit import default_timer as timer\n",
        "def print_train_time(start: float,\n",
        "                     end: float,\n",
        "                     device: torch.device = None):\n",
        "  \"\"\"Prints difference between start and end time.\"\"\"\n",
        "  total_time = end - start\n",
        "  print(f\"Train time on {device}: {total_time:.3f} seconds\")\n",
        "  return total_time"
      ],
      "metadata": {
        "id": "082ta0Zlh6z-"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 3.3 Creating a training loop and training a model on batches of data\n",
        "\n",
        "1. Loop through epochs.\n",
        "2. Loop through training batches, perform training steps, calculate the train loss *per batch*.\n",
        "3. Loop through testing batches, perform testing steps, calculate the test loss *per batch*.\n",
        "4. Print out what's happening.\n",
        "5. Time it all."
      ],
      "metadata": {
        "id": "UFMQRDHammH9"
      }
    },
    {
      "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 time)\n",
        "epochs = 3\n",
        "\n",
        "# Create training and test 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 the 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 # accumulate train loss\n",
        "\n",
        "    # 3. Optimizer zero grad\n",
        "    optimizer.zero_grad()\n",
        "\n",
        "    # 4. Loss backward\n",
        "    loss.backward()\n",
        "\n",
        "    # 5. Optimizer step (update the model's parameters once *per batch*)\n",
        "    optimizer.step()\n",
        "\n",
        "    # Print out what's happening\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\n",
        "  train_loss /= len(train_dataloader)\n",
        "\n",
        "  ### Testing\n",
        "  test_loss, test_acc = 0, 0\n",
        "  model_0.eval()\n",
        "  with torch.inference_mode():\n",
        "    for X_test, y_test in test_dataloader:\n",
        "      # 1. Forward pass\n",
        "      test_pred = model_0(X_test)\n",
        "\n",
        "      # 2. Calculate loss (accumulatively)\n",
        "      test_loss += loss_fn(test_pred, y_test)\n",
        "\n",
        "      # 3. Calculate accuracy\n",
        "      test_acc += accuracy_fn(test_pred.argmax(dim=1), y_test)\n",
        "\n",
        "    # Calculate the test loss average per batch\n",
        "    test_loss /= len(test_dataloader)\n",
        "\n",
        "    # Calculate the test acc average per batch\n",
        "    test_acc /= len(test_dataloader)\n",
        "\n",
        "  # Print out what's happening\n",
        "  print(f\"\\nTrain loss: {train_loss:.4f} | Test loss: {test_loss:.4f}, Test acc: {test_acc:.4f}\")\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",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 549,
          "referenced_widgets": [
            "ab1f9615a375484eada3e855a361ed4d",
            "3d37de6631d14276b85fdd3ef51106f9",
            "95ff02a525f14220bd4644b4e7d403bf",
            "3ecc358f53de41deab328a233c4f9bfb",
            "d72f5b3244084f21b8752f55d9e3b11a",
            "61522d4ccacd432ba5aa7a4c1a40ca48",
            "4f3970e280494de9bf93053893a4157a",
            "02d39a778b2549f2a866c7caf927024a",
            "e2605d37fea74b31b1fbdb20e0547c53",
            "23d5586a14984c79b01da3cd95894052",
            "984619a1abfc40288ead3ede5bbadd04"
          ]
        },
        "id": "VTh2TIcKnp9v",
        "outputId": "b125264b-10a1-4d53-a6ce-9b1cd0de10ef"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/3 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ab1f9615a375484eada3e855a361ed4d"
            }
          },
          "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: 0.5904 | Test loss: 0.5095, Test acc: 0.8204\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: 0.4763 | Test loss: 0.4799, Test acc: 0.8320\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: 0.4550 | Test loss: 0.4766, Test acc: 0.8343\n",
            "Train time on cpu: 63.597 seconds\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 4. Make predictions and get Model 0 results"
      ],
      "metadata": {
        "id": "hRAdob9SsDRk"
      }
    },
    {
      "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):\n",
        "  \"\"\"Returns a dictionary containing the results of model predicting on data_loader.\"\"\"\n",
        "  loss, acc = 0, 0\n",
        "  model.eval()\n",
        "  with torch.inference_mode():\n",
        "    for X, y in tqdm(data_loader):\n",
        "      X, y = X.to(device), y.to(device)\n",
        "\n",
        "      # Make predictions\n",
        "      y_pred = model(X)\n",
        "\n",
        "      # Accumulate the loss and acc values per batch\n",
        "      loss += loss_fn(y_pred, y)\n",
        "      acc += accuracy_fn(y_pred.argmax(dim=1), y)\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.item()\n",
        "          }\n",
        "\n",
        "# Calculate model 0 results on test dataset\n",
        "model_0_results = eval_model(model_0,\n",
        "                             test_dataloader,\n",
        "                             loss_fn,\n",
        "                             accuracy_fn,\n",
        "                             device=\"cpu\")\n",
        "model_0_results\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 103,
          "referenced_widgets": [
            "c7296910e208429c85b1976456ac36e3",
            "9be5d10309e644ee9b4334ecc64b9ca4",
            "ae70365d098a4525826694b8b18ff1f0",
            "981697f0e7224a5fb7a3997f02aa00f4",
            "a31ac70d579d40d5b8312944439ba5aa",
            "a93b70b317d5423980b103b36487693c",
            "1c6c409b61234c2f9efdf9537d551452",
            "5137415ee7bd4787a800cb47600ec0a9",
            "55062bd7b81945ce8d3ae2ce5bdf4c11",
            "bd9ab7a3779f4ce288c190a8f9f935df",
            "148645821bee45d3a7dce3eab7dbbd7b"
          ]
        },
        "id": "GXC8BEjnuHYr",
        "outputId": "034015b3-9fb7-4a50-b381-e89c90ad3899"
      },
      "execution_count": 28,
      "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": "c7296910e208429c85b1976456ac36e3"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'model_name': 'FashionMNISTModel_0',\n",
              " 'model_loss': 0.47663894295692444,\n",
              " 'model_acc': 0.8342651724815369}"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 5. Setup device agnostic-code (for using a GPU if there is one)"
      ],
      "metadata": {
        "id": "ibbHyHPKvfTR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!nvidia-smi"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IDOICKhLwKPB",
        "outputId": "fe905a2f-0f89-4aa9-c984-8354620827c1"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/bin/bash: line 1: nvidia-smi: command not found\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "torch.cuda.is_available()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y7-yyhzxwL30",
        "outputId": "3197f71d-dee9-41ba-bbea-44decb2ab622"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Setup device-agnostic code\n",
        "import torch\n",
        "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
        "device"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "6JZJ0DCuwlQE",
        "outputId": "78adad14-aad5-462a-8c6f-c9487db38c21"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'cpu'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 6. Model 1: Building a better model with non-linearity"
      ],
      "metadata": {
        "id": "4JUWlgH3wu-l"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a model with non-linear and linear layers\n",
        "class FashionMNISTModel_1(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(),\n",
        "        nn.Linear(in_features=input_shape,\n",
        "                  out_features=hidden_units),\n",
        "        nn.ReLU(),\n",
        "        nn.Linear(in_features=hidden_units,\n",
        "                  out_features=output_shape),\n",
        "        nn.ReLU()\n",
        "    )\n",
        "\n",
        "  def forward(self, x):\n",
        "    return self.layer_stack(x)"
      ],
      "metadata": {
        "id": "sbsKKfrGSU6D"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create an instance of model_1\n",
        "torch.manual_seed(42)\n",
        "model_1 = FashionMNISTModel_1(input_shape=784, # 28*28\n",
        "                              hidden_units=10,\n",
        "                              output_shape=len(class_names)\n",
        "                              ).to(device)\n",
        "next(model_1.parameters()).device"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OayPxgjHTE5d",
        "outputId": "312eca53-11a3-4f12-bd64-31a95372a381"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "device(type='cpu')"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Import accuracy metric\n",
        "from torchmetrics import Accuracy\n",
        "\n",
        "# Setup metric\n",
        "accuracy_fn = Accuracy(task=\"multiclass\", num_classes=10).to(device)"
      ],
      "metadata": {
        "id": "AJjku1wGUHgl"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Setup loss function and optimizer\n",
        "loss_fn = nn.CrossEntropyLoss()\n",
        "optimizer = torch.optim.SGD(params=model_1.parameters(),\n",
        "                            lr=0.1)"
      ],
      "metadata": {
        "id": "ne1-1SMgTT1O"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Functionizing training and evaluation loops"
      ],
      "metadata": {
        "id": "YTD4zzFCUS_C"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "### Training function\n",
        "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",
        "  \"\"\"Performs a training with model trying to learn on data_loader.\"\"\"\n",
        "  train_loss, train_acc = 0, 0\n",
        "\n",
        "  # Put model into training mode\n",
        "  model.train()\n",
        "\n",
        "  # Add a loop to loop through the training batches\n",
        "  for batch, (X, y) in enumerate(data_loader):\n",
        "    # Put data on target device\n",
        "    X, y = X.to(device), y.to(device)\n",
        "\n",
        "    # 1. Forward pass (outputs the raw logits from the model)\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 # accumulate train loss\n",
        "    train_acc += accuracy_fn(y_pred.argmax(dim=1), y) # go from logits -> prediction labels\n",
        "\n",
        "    # 3. Optimizer zero grad\n",
        "    optimizer.zero_grad()\n",
        "\n",
        "    # 4. Loss backward\n",
        "    loss.backward()\n",
        "\n",
        "    # 5. Optimizer step (update the model's parameters once *per batch*)\n",
        "    optimizer.step()\n",
        "\n",
        "  # Divide total train loss and acc by length of train dataloader\n",
        "  train_loss /= len(data_loader)\n",
        "  train_acc /= len(data_loader)\n",
        "  print(f\"Train loss: {train_loss:.5f} | Train acc: {train_acc*100:.2f}%\")"
      ],
      "metadata": {
        "id": "0RAMsrwbW1Vq"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "### Testing function\n",
        "def test_step(model: torch.nn.Module,\n",
        "              data_loader: torch.utils.data.DataLoader,\n",
        "              loss_fn: torch.nn.Module,\n",
        "              accuracy_fn,\n",
        "              device: torch.device = device):\n",
        "  \"\"\"Performs a testing loop step on model going over data_loader.\"\"\"\n",
        "  test_loss, test_acc = 0, 0\n",
        "\n",
        "  # Put the model in eval mode\n",
        "  model.eval()\n",
        "\n",
        "  # Turn on inference mode context manager\n",
        "  with torch.inference_mode():\n",
        "    for X, y in data_loader:\n",
        "      # Send the data to the target device\n",
        "      X, y = X.to(device), y.to(device)\n",
        "\n",
        "      # 1. Forward pass (outputs raw logits)\n",
        "      test_pred = model(X)\n",
        "\n",
        "      # 2. Calculuate the loss/acc\n",
        "      test_loss += loss_fn(test_pred, y)\n",
        "      test_acc += accuracy_fn(test_pred.argmax(dim=1), y) # go from logits -> prediction labels\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 acc: {test_acc*100:.2f}%\\n\")"
      ],
      "metadata": {
        "id": "DWPEFfGHYZ2p"
      },
      "execution_count": 37,
      "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",
        "# Set epochs\n",
        "epochs = 3\n",
        "\n",
        "# Create a optimization and evaluation loop using train_step() and test_step()\n",
        "for epoch in tqdm(range(epochs)):\n",
        "  print(f\"Epoch: {epoch}\\n----------\")\n",
        "  train_step(model=model_1,\n",
        "             data_loader=train_dataloader,\n",
        "             loss_fn=loss_fn,\n",
        "             optimizer=optimizer,\n",
        "             accuracy_fn=accuracy_fn,\n",
        "             device=device)\n",
        "  test_step(model=model_1,\n",
        "            data_loader=test_dataloader,\n",
        "            loss_fn=loss_fn,\n",
        "            accuracy_fn=accuracy_fn,\n",
        "            device=device)\n",
        "train_time_end_on_gpu = timer()\n",
        "total_train_time_model_1 = print_train_time(train_time_start_on_gpu,\n",
        "                                            train_time_end_on_gpu,\n",
        "                                            device=device)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 335,
          "referenced_widgets": [
            "62213c362d0045f783888b0676ff6c02",
            "7490ab4b3f0e4d52813cfce40bc855e4",
            "4346f9ee2d3a41e7bfcee5bbb8467b6e",
            "b3fc93d21e9a454ebaf335367ed122e0",
            "483c464cb5984ab3b4608e05a369351c",
            "6feb1adfdc3c4969bf87631ccbd8c11f",
            "30d436d34754410e91dbda4c91dc9f6e",
            "514a0791038a462e9197664ed13f796d",
            "ac635112e3ba45b6a7e4cafae5ab46df",
            "f75eb793a7e2482ab7434a1273fd7766",
            "1e4fdad2e8b64d4ab295defbb46fd97c"
          ]
        },
        "id": "WPYdl0tEZ6R1",
        "outputId": "23f36eb5-0798-4777-d285-718318376eb6"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/3 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "62213c362d0045f783888b0676ff6c02"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 0\n",
            "----------\n",
            "Train loss: 1.09199 | Train acc: 61.34%\n",
            "Test loss: 0.95636 | Test acc: 65.00%\n",
            "\n",
            "Epoch: 1\n",
            "----------\n",
            "Train loss: 0.78101 | Train acc: 71.93%\n",
            "Test loss: 0.72227 | Test acc: 73.91%\n",
            "\n",
            "Epoch: 2\n",
            "----------\n",
            "Train loss: 0.67027 | Train acc: 75.94%\n",
            "Test loss: 0.68500 | Test acc: 75.02%\n",
            "\n",
            "Train time on cpu: 67.136 seconds\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_0_results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "55s0Wp27bE_N",
        "outputId": "275dd782-6511-467b-e4e3-45f64003dac2"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'model_name': 'FashionMNISTModel_0',\n",
              " 'model_loss': 0.47663894295692444,\n",
              " 'model_acc': 0.8342651724815369}"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "total_train_time_model_0"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_cdmqYevbgOe",
        "outputId": "76f89ea9-5e8b-4120-f164-8f7e1bf9eb3a"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "63.59670714599997"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Note:** Sometimes, depending on your data/hardware you might find that your model trains faster on CPU than GPU.\n",
        "\n",
        "1. It could be that the overhead for copying data/model to and from the GPU outweighs the compute benefits offered by the GPU.\n",
        "2. The hardware you're using has a better CPU in terms of compute capability than the GPU.\n",
        "\n",
        "For more on how to make models compute faster: https://horace.io/brrr_intro.html"
      ],
      "metadata": {
        "id": "lZhBm-hkblcO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Get model_1 results dictionary\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",
        "                             device=device)\n",
        "model_1_results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 103,
          "referenced_widgets": [
            "444eb826cffe4a77b5843b05fad9f142",
            "44312126a94b4ba3b5ba462d7b7e0c7e",
            "688601ec3dd24c608f2178eebb54da66",
            "3755f8e33f0741bf8ad87ea569d954fd",
            "326d4fd4016241a3aff831a049334d3b",
            "6da187f9a2e4499a9982af02fd5aa3f3",
            "4806bc62f0984734890b6327d4809a5d",
            "44558eea79b747dd941a59c40671ec6d",
            "34fd2b4823d34046bba286fa472f1635",
            "916693dbc7f94de49b6d7aa3a7bc2dd2",
            "8a3c3349717e469aa747515dbd90a88f"
          ]
        },
        "id": "gP2sgIhcfiDT",
        "outputId": "27946d0f-59bd-434c-c0ce-6805f667fa06"
      },
      "execution_count": 41,
      "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": "444eb826cffe4a77b5843b05fad9f142"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'model_name': 'FashionMNISTModel_1',\n",
              " 'model_loss': 0.6850009560585022,\n",
              " 'model_acc': 0.7501996755599976}"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_0_results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bP0t9JzRgG3y",
        "outputId": "0b1383e8-1e0e-44e2-cda4-1264f9c79ae8"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'model_name': 'FashionMNISTModel_0',\n",
              " 'model_loss': 0.47663894295692444,\n",
              " 'model_acc': 0.8342651724815369}"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Model 2: Building a Convolutional Neural Network (CNN)"
      ],
      "metadata": {
        "id": "bl2fJ2DciJd1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a convolutional neural network\n",
        "class FashionMNISTModel_2(nn.Module):\n",
        "  \"\"\"\n",
        "  Model architecture that replicates the TinyVGG\n",
        "  model from CNN explainer website.\n",
        "  \"\"\"\n",
        "  def __init__(self, input_shape: int, hidden_units: int, output_shape: int):\n",
        "    super().__init__()\n",
        "    self.conv_block_1 = nn.Sequential(\n",
        "\n",
        "        nn.Conv2d(in_channels=input_shape,\n",
        "                  out_channels=hidden_units,\n",
        "                  kernel_size=3,\n",
        "                  stride=1,\n",
        "                  padding=1),\n",
        "        nn.ReLU(),\n",
        "        nn.Conv2d(in_channels=hidden_units,\n",
        "                  out_channels=hidden_units,\n",
        "                  kernel_size=3,\n",
        "                  stride=1,\n",
        "                  padding=1),\n",
        "        nn.ReLU(),\n",
        "        nn.MaxPool2d(kernel_size=2),\n",
        "    )\n",
        "    self.conv_block_2 = nn.Sequential(\n",
        "        nn.Conv2d(in_channels=hidden_units,\n",
        "                out_channels=hidden_units,\n",
        "                kernel_size=3,\n",
        "                stride=1,\n",
        "                padding=1),\n",
        "        nn.ReLU(),\n",
        "        nn.Conv2d(in_channels=hidden_units,\n",
        "                  out_channels=hidden_units,\n",
        "                  kernel_size=3,\n",
        "                  stride=1,\n",
        "                  padding=1),\n",
        "        nn.ReLU(),\n",
        "        nn.MaxPool2d(kernel_size=2),\n",
        "    )\n",
        "    self.classifier = nn.Sequential(\n",
        "        nn.Flatten(),\n",
        "        nn.Linear(in_features=hidden_units*7*7, # There is a trick to calculate this, see prints below\n",
        "                  out_features=output_shape)\n",
        "    )\n",
        "  def forward(self, x):\n",
        "    x = self.conv_block_1(x)\n",
        "    # print(f\"Output shape of conv_block_1: {x.shape}\")\n",
        "    x = self.conv_block_2(x)\n",
        "    # print(f\"Output shape of conv_block_2: {x.shape}\")\n",
        "    x = self.classifier(x)\n",
        "    # print(f\"Output shape of classifier: {x.shape}\")\n",
        "    return x"
      ],
      "metadata": {
        "id": "MVSQenOCiTU4"
      },
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "torch.manual_seed(42)\n",
        "model_2 = FashionMNISTModel_2(input_shape=1, # Black and white, only one color channel\n",
        "                              hidden_units=10,\n",
        "                              output_shape=len(class_names)).to(device)\n",
        "model_2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P2fGjkfYrbfz",
        "outputId": "8aa81c55-cf52-4e82-c0be-350bc5294abc"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "FashionMNISTModel_2(\n",
              "  (conv_block_1): Sequential(\n",
              "    (0): Conv2d(1, 10, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (1): ReLU()\n",
              "    (2): Conv2d(10, 10, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (3): ReLU()\n",
              "    (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "  )\n",
              "  (conv_block_2): Sequential(\n",
              "    (0): Conv2d(10, 10, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (1): ReLU()\n",
              "    (2): Conv2d(10, 10, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (3): ReLU()\n",
              "    (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "  )\n",
              "  (classifier): Sequential(\n",
              "    (0): Flatten(start_dim=1, end_dim=-1)\n",
              "    (1): Linear(in_features=490, out_features=10, bias=True)\n",
              "  )\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Pass image through model\n",
        "model_2(image.unsqueeze(0).to(device))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I3BvYFMI-xQs",
        "outputId": "4beda006-4ae6-4acf-b544-9dad4e74879d"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[ 0.0246, -0.0644,  0.0551, -0.0360, -0.0139,  0.0113, -0.0014, -0.0075,\n",
              "          0.0213,  0.0076]], grad_fn=<AddmmBackward0>)"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.imshow(image.squeeze(), cmap=\"gray\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        },
        "id": "S1uEDSuj-hgO",
        "outputId": "47ca59f4-02a7-46aa-dbe0-41c58224c83c"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7fc81d17ebf0>"
            ]
          },
          "metadata": {},
          "execution_count": 46
        },
        {
          "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": [
        "### 7.1 Stepping through `nn.Conv2d()`"
      ],
      "metadata": {
        "id": "HmtZZ0k1rdQH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "torch.manual_seed(42)\n",
        "\n",
        "# Create a batch of images\n",
        "images = torch.randn(size=(32, 3, 64, 64))\n",
        "test_image = images[0]\n",
        "\n",
        "print(f\"Image batch shape: {images.shape}\")\n",
        "print(f\"Single image shape: {test_image.shape}\")\n",
        "print(f\"Test image:\\n {test_image}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ac3z9fBvtjSP",
        "outputId": "a2be8e43-8768-442f-d346-f6f67c899efb"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Image batch shape: torch.Size([32, 3, 64, 64])\n",
            "Single image shape: torch.Size([3, 64, 64])\n",
            "Test image:\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 single conv2d layer\n",
        "conv_layer = nn.Conv2d(in_channels=3, # 3 color channels\n",
        "                       out_channels=10, # Hidden units\n",
        "                       kernel_size=3,\n",
        "                       stride=1,\n",
        "                       padding=1)\n",
        "\n",
        "# Pass the data through the convolutional layer\n",
        "conv_output = conv_layer(test_image.unsqueeze(dim=0))\n",
        "conv_output.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0L3eYVFDt8SN",
        "outputId": "7bf0c711-efdc-437f-df7f-b54b159ab01f"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([1, 10, 64, 64])"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 7.2 Stepping through `nn.MaxPool2d`"
      ],
      "metadata": {
        "id": "9Z04x0zg33s2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Print out original image shape without unsqueezed dimension\n",
        "print(f\"Test image orginal shape: {test_image.shape}\")\n",
        "print(f\"Test image with unsqueezed dimension: {test_image.unsqueeze(0).shape}\")\n",
        "\n",
        "# Create a smaple nn.MaxPool2d layer\n",
        "max_pool_layer = nn.MaxPool2d(kernel_size=2)\n",
        "\n",
        "# Pass data through just the convolutional layer\n",
        "test_image_through_conv = conv_layer(test_image.unsqueeze(dim=0))\n",
        "print(f\"Shape after coing 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 coing through conv_layer() and max_pool_layer(): {test_image_through_conv_and_max_pool.shape}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U-bD1heQ48AF",
        "outputId": "0917535e-d045-480e-915d-382aaf6997b4"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test image orginal shape: torch.Size([3, 64, 64])\n",
            "Test image with unsqueezed dimension: torch.Size([1, 3, 64, 64])\n",
            "Shape after coing through conv_layer(): torch.Size([1, 10, 64, 64])\n",
            "Shape after coing through conv_layer() and max_pool_layer(): torch.Size([1, 10, 32, 32])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "torch.manual_seed(42)\n",
        "\n",
        "# Create a random tensor with a similar number of dimensions to our images\n",
        "random_tensor = torch.randn(size=(1, 1, 2, 2))\n",
        "print(f\"\\nRandom tensor:\\n {random_tensor}\")\n",
        "print(f\"Random tensor shape: {random_tensor.shape}\")\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}\")\n",
        "print(f\"Max pool tensor shape: {max_pool_tensor.shape}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8mSUnyI551fd",
        "outputId": "0477e827-234d-4c0a-9c23-f4c5c1538b0a"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "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]]]])\n",
            "Max pool tensor shape: torch.Size([1, 1, 1, 1])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 7.3 Setup a loss function and optimizer for `model_2`"
      ],
      "metadata": {
        "id": "hhCbe2Ee84Jm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Setup loss function/eval metrics/optimizer\n",
        "# Import accuracy metric\n",
        "from torchmetrics import Accuracy\n",
        "\n",
        "# Setup accuracy metric\n",
        "accuracy_fn = Accuracy(task=\"multiclass\", num_classes=10).to(device)\n",
        "\n",
        "# Setup loss function and optimizer\n",
        "loss_fn = nn.CrossEntropyLoss()\n",
        "optimizer = torch.optim.SGD(params=model_2.parameters(),\n",
        "                            lr=0.1)"
      ],
      "metadata": {
        "id": "wL65CgV8BXvQ"
      },
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 7.4 Training and testing `model_2` using our training and test functions"
      ],
      "metadata": {
        "id": "h-DQJb8YBjUU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "torch.manual_seed(42)\n",
        "torch.cuda.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 = 3\n",
        "for epoch in tqdm(range(epochs)):\n",
        "  print(f\"\\n----------\\nEpoch: {epoch}\")\n",
        "  train_step(model=model_2,\n",
        "             data_loader=train_dataloader,\n",
        "             loss_fn=loss_fn,\n",
        "             optimizer=optimizer,\n",
        "             accuracy_fn=accuracy_fn,\n",
        "             device=device)\n",
        "  test_step(model=model_2,\n",
        "            data_loader=test_dataloader,\n",
        "            loss_fn=loss_fn,\n",
        "            accuracy_fn=accuracy_fn,\n",
        "            device=device)\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": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424,
          "referenced_widgets": [
            "b205535d85d14f51aa33f9fc7a262188",
            "88f0bbac2793487daa287e829967fd11",
            "3f64f0662df34955915db435b0058d6a",
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            "6b68edbf01fe4a72b76f06fffe794e54",
            "4c965c1777814bf2a020715cb24ebf5e",
            "9a7e67a734964a56be6b73173d3b9165",
            "c3fea6a28c4e4ed28152b38fe5d4d3a0",
            "b1a92ae6d5244d01b8d1dc02ecc8fd52",
            "19489a188f364abca64829391f10d696",
            "d84977adcbfa40ae9a49a530ea518388"
          ]
        },
        "id": "EOB-IinoB_Ny",
        "outputId": "4b6f9a3a-c097-42fd-88dd-be1861ec1277"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/3 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b205535d85d14f51aa33f9fc7a262188"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "----------\n",
            "Epoch: 0\n",
            "Train loss: 0.59518 | Train acc: 78.38%\n",
            "Test loss: 0.39500 | Test acc: 85.99%\n",
            "\n",
            "Train time on cpu: 94.812 seconds\n",
            "\n",
            "----------\n",
            "Epoch: 1\n",
            "Train loss: 0.36536 | Train acc: 86.90%\n",
            "Test loss: 0.35244 | Test acc: 86.91%\n",
            "\n",
            "Train time on cpu: 181.933 seconds\n",
            "\n",
            "----------\n",
            "Epoch: 2\n",
            "Train loss: 0.32588 | Train acc: 88.13%\n",
            "Test loss: 0.32719 | Test acc: 88.07%\n",
            "\n",
            "Train time on cpu: 277.262 seconds\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Get model_2 results\n",
        "model_2_results = eval_model(model=model_2,\n",
        "                             data_loader=test_dataloader,\n",
        "                             loss_fn=loss_fn,\n",
        "                             accuracy_fn=accuracy_fn,\n",
        "                             device=device)\n",
        "model_2_results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 103,
          "referenced_widgets": [
            "2a7639aeb1b840e6a60ca386338bbbb6",
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            "d47781f3d34947f8b51f8948918cc8f2",
            "c5aba6c2afe64905b3bf1dabc991a745",
            "a64263a008374e2eab8726c1e1723a15",
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            "284c7df9c2894e718fa36e1b0e9662c6",
            "de562a3c693444448a1175528f720c75",
            "f9c9488b299e4b99890d0fdb837fafdd",
            "31fdcd5431cd473ca8b3d8d418911b89",
            "de8de1a438c04e43a6ab8c77db6d7d74"
          ]
        },
        "id": "QiCW_8gOC6_b",
        "outputId": "063a96d8-8e26-406d-9d06-9aa5f52b64bb"
      },
      "execution_count": 53,
      "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": "2a7639aeb1b840e6a60ca386338bbbb6"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'model_name': 'FashionMNISTModel_2',\n",
              " 'model_loss': 0.32718968391418457,\n",
              " 'model_acc': 0.88069087266922}"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_0_results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3zCA86p0DeOG",
        "outputId": "d953a380-49f0-42ea-c673-8e7a906fd601"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'model_name': 'FashionMNISTModel_0',\n",
              " 'model_loss': 0.47663894295692444,\n",
              " 'model_acc': 0.8342651724815369}"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 8. Compare model results and training time"
      ],
      "metadata": {
        "id": "eY3xcAgbEVTD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "compare_results = pd.DataFrame([model_0_results,\n",
        "                                model_1_results,\n",
        "                                model_2_results])\n",
        "compare_results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "Slxqa8FZEhzj",
        "outputId": "f6e7e7a2-9ba9-412f-f7b7-9fd3e4916cab"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            model_name  model_loss  model_acc\n",
              "0  FashionMNISTModel_0    0.476639   0.834265\n",
              "1  FashionMNISTModel_1    0.685001   0.750200\n",
              "2  FashionMNISTModel_2    0.327190   0.880691"
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-a2c202fc-5d90-4c80-a1ae-871bc9daf4bd\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>model_name</th>\n",
              "      <th>model_loss</th>\n",
              "      <th>model_acc</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>FashionMNISTModel_0</td>\n",
              "      <td>0.476639</td>\n",
              "      <td>0.834265</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>FashionMNISTModel_1</td>\n",
              "      <td>0.685001</td>\n",
              "      <td>0.750200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>FashionMNISTModel_2</td>\n",
              "      <td>0.327190</td>\n",
              "      <td>0.880691</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "    domScope.querySelector('#df-fd2b0389-31c5-4ac9-8121-62c6a1e9f117 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-a2c202fc-5d90-4c80-a1ae-871bc9daf4bd 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-a2c202fc-5d90-4c80-a1ae-871bc9daf4bd');\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",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Add training time 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"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "Wi1HExcuEprJ",
        "outputId": "0a68a293-5216-4806-80c6-06799728cb4e"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            model_name  model_loss  model_acc  training_time\n",
              "0  FashionMNISTModel_0    0.476639   0.834265      63.596707\n",
              "1  FashionMNISTModel_1    0.685001   0.750200      67.135748\n",
              "2  FashionMNISTModel_2    0.327190   0.880691     277.262130"
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-730094ea-e6dc-4c0a-9ebb-179cf06ea480\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>model_name</th>\n",
              "      <th>model_loss</th>\n",
              "      <th>model_acc</th>\n",
              "      <th>training_time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>FashionMNISTModel_0</td>\n",
              "      <td>0.476639</td>\n",
              "      <td>0.834265</td>\n",
              "      <td>63.596707</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>FashionMNISTModel_1</td>\n",
              "      <td>0.685001</td>\n",
              "      <td>0.750200</td>\n",
              "      <td>67.135748</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>FashionMNISTModel_2</td>\n",
              "      <td>0.327190</td>\n",
              "      <td>0.880691</td>\n",
              "      <td>277.262130</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-730094ea-e6dc-4c0a-9ebb-179cf06ea480')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-33837dd0-7aa2-4bff-8af3-02562aa328ba\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-33837dd0-7aa2-4bff-8af3-02562aa328ba')\"\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",
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              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\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-quickchart: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-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-33837dd0-7aa2-4bff-8af3-02562aa328ba button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-730094ea-e6dc-4c0a-9ebb-179cf06ea480 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-730094ea-e6dc-4c0a-9ebb-179cf06ea480');\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",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "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\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "5mC2pdg9FYtz",
        "outputId": "9a7a631d-df63-4d79-d275-b364564966bf"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'model')"
            ]
          },
          "metadata": {},
          "execution_count": 57
        },
        {
          "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": [
        "## 9. Make and evaluate random predictions with best model"
      ],
      "metadata": {
        "id": "Em3aJMFDGQr5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def make_predictions(model: torch.nn.Module,\n",
        "                     data: list,\n",
        "                     device: torch.device = device):\n",
        "  pred_probs = []\n",
        "  model.to(device)\n",
        "  model.eval()\n",
        "  with torch.inference_mode():\n",
        "    for sample in data:\n",
        "      # Prepare the sample\n",
        "      sample = torch.unsqueeze(sample, dim=0).to(device)\n",
        "\n",
        "      # Foward pass (model outputs raw logits)\n",
        "      pred_logit = model(sample)\n",
        "\n",
        "      # Get prediction probability\n",
        "      pred_prob = torch.softmax(pred_logit.squeeze(), dim=0)\n",
        "\n",
        "      # Get pred_prob off the 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": "lex0b5d4IqMV"
      },
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import random\n",
        "# random.seed(42)\n",
        "\n",
        "test_samples = []\n",
        "test_labels = []\n",
        "\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 sampel shape\n",
        "test_samples[0].shape\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rSckLBQlJm6y",
        "outputId": "519a5a6c-3f24-4d7d-bd0a-d143ee866865"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([1, 28, 28])"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.imshow(test_samples[0].squeeze(), cmap=\"gray\")\n",
        "plt.title(class_names[test_labels[0]])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 470
        },
        "id": "owfhSNWHJ60E",
        "outputId": "98bcc536-7dd3-4401-a910-26bc6658e541"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Dress')"
            ]
          },
          "metadata": {},
          "execution_count": 60
        },
        {
          "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\n",
        "pred_probs = make_predictions(model=model_2,\n",
        "                              data=test_samples)\n",
        "\n",
        "# View first two prediction probabilites\n",
        "pred_probs[:2]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1DP1K5LwKayW",
        "outputId": "c94bc406-f40a-4874-ccaf-cb0d912eb909"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[8.4333e-03, 5.3090e-02, 1.2773e-04, 8.9786e-01, 3.1229e-02, 2.2148e-04,\n",
              "         1.2746e-03, 2.2426e-03, 4.9441e-03, 5.7917e-04],\n",
              "        [7.9783e-01, 1.5420e-04, 1.4323e-01, 1.2040e-03, 1.2694e-03, 4.0531e-06,\n",
              "         5.5383e-02, 2.7765e-05, 8.9460e-04, 1.2013e-05]])"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Convert prediction probabilities to labels\n",
        "pred_classes = pred_probs.argmax(dim=1)\n",
        "pred_classes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Nna4hVnRKrJt",
        "outputId": "19c04320-26e4-4b7e-b1e5-7488e204a628"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([3, 0, 7, 2, 8, 0, 4, 3, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot predictions\n",
        "plt.figure(figsize=(9, 9))\n",
        "nrows = 3\n",
        "ncols = 3\n",
        "for i, sample in enumerate(test_samples):\n",
        "  # Create subplot\n",
        "  plt.subplot(nrows, ncols, i+1)\n",
        "\n",
        "  # Plot the target image\n",
        "  plt.imshow(sample.squeeze(), cmap=\"gray\")\n",
        "\n",
        "  # Find the prediction (in text form)\n",
        "  pred_label = class_names[pred_classes[i]]\n",
        "\n",
        "  # Get the truth label (in text form)\n",
        "  truth_label = class_names[test_labels[i]]\n",
        "\n",
        "  # Create a title for the plot\n",
        "  title_text = f\"Pred: {pred_label} | Truth: {truth_label}\"\n",
        "\n",
        "  # Chech for equality between pred and truth and change color of title text\n",
        "  if pred_label == truth_label:\n",
        "    plt.title(title_text, fontsize=10, c=\"g\") # Green text if pred is correct\n",
        "  else:\n",
        "    plt.title(title_text, fontsize=10, c=\"r\") # Red text if pred is correct\n",
        "  plt.axis(False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 749
        },
        "id": "eJ7WmcToK0eO",
        "outputId": "5f75a5e0-1d70-4068-e0d4-1cf3dac3437e"
      },
      "execution_count": 63,
      "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": [
        "## 10. Making a confusion matrix for further prediction evaluation"
      ],
      "metadata": {
        "id": "kHOpCFx5Svlm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Import tqdm.auto for progress bar\n",
        "from tqdm.auto import tqdm\n",
        "\n",
        "# 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 the 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 to prediction probabilities to prediction labels\n",
        "    y_pred = torch.softmax(y_logit.squeeze(), dim=0).argmax(dim=1)\n",
        "    # Put predictions on CPU for evaluation\n",
        "    y_preds.append(y_pred.cpu())\n",
        "\n",
        "# Concatinate list of predictions into a tensor\n",
        "y_pred_tensor = torch.cat(y_preds)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "ab8762d27cfb40db99dfb26a65492a46",
            "303e2ae90fe9438fbd960e897c47c0fb",
            "41d0aaf0aa294429a9750c9c4b6b97db",
            "5f4d8ab063e04d5e961fb9e80afdeaee",
            "7e51db22d66947dea5995f1b7190371f",
            "0e596ba0c0ea43f9be2edd7cdb0e4bf0",
            "0095f191d63f445a92bca8d45df7bb19",
            "ab2cceb09d024934bb535d47c95f2875",
            "33e2e427c4504231afc69a05870e8890",
            "4716439429f047658b29577e786e4147",
            "aefbb8b263f34c369bcd32e11d451db3"
          ]
        },
        "id": "7sh7wEipXTOJ",
        "outputId": "3d9c2141-0abe-4d7c-b3f0-99e398f8075e"
      },
      "execution_count": 64,
      "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": "ab8762d27cfb40db99dfb26a65492a46"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# See if required packages are installed and if not, install them...\n",
        "try:\n",
        "  import torchmetrics, mlxtend\n",
        "  print(f\"mlxtend version: {mlxtend.__version__}\")\n",
        "  assert int(mlxtend.__version__.split(\".\")[1]) >= 19, \"mlxtend version shoud be 0.19.0 or hihger\"\n",
        "except:\n",
        "  !pip install -q torchmetrics -U mlxtend\n",
        "  import torchmetrics, mlxtend\n",
        "  print(f\"mlxtend version: {mlxtend.__version__}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4XY8DNcoT4HJ",
        "outputId": "7cc99e92-5bde-4e69-b0b6-fe169bdaff09"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "mlxtend version: 0.22.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from torchmetrics import ConfusionMatrix\n",
        "from mlxtend.plotting import plot_confusion_matrix\n",
        "\n",
        "# 2. Setup confusion instance and compare predictions to targets\n",
        "confmat = ConfusionMatrix(task=\"multiclass\",\n",
        "                          num_classes=len(class_names))\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 to work with numpy\n",
        "    class_names=class_names,\n",
        "    figsize=(10, 5)\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "id": "9SnVTSvaXLU1",
        "outputId": "65672ab2-7a49-4728-af7a-2341345d48eb"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 11. Save and load best performing model"
      ],
      "metadata": {
        "id": "yJJvencqXM8v"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from pathlib import Path\n",
        "\n",
        "# Create model directory path\n",
        "MODEL_PATH = Path(\"models\")\n",
        "MODEL_PATH.mkdir(parents=True,\n",
        "                 exist_ok=True)\n",
        "\n",
        "# Create model save\n",
        "MODEL_NAME = \"03_pytorch_computer_vision_CNN_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(),\n",
        "           f=MODEL_SAVE_PATH)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2lj9ZDNgcaKO",
        "outputId": "04ff0952-f895-4e39-94c5-5c687655553c"
      },
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving model to: models/03_pytorch_computer_vision_CNN_model_2.pth\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a new instance\n",
        "torch.manual_seed(42)\n",
        "\n",
        "loaded_model_2 = FashionMNISTModel_2(input_shape=1,\n",
        "                                     hidden_units=10,\n",
        "                                     output_shape=len(class_names))\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 target device\n",
        "loaded_model_2.to(device)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kpuHQEGgc5QH",
        "outputId": "a877030f-3584-4f9c-bdf7-a57223665d11"
      },
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "FashionMNISTModel_2(\n",
              "  (conv_block_1): Sequential(\n",
              "    (0): Conv2d(1, 10, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (1): ReLU()\n",
              "    (2): Conv2d(10, 10, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (3): ReLU()\n",
              "    (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "  )\n",
              "  (conv_block_2): Sequential(\n",
              "    (0): Conv2d(10, 10, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (1): ReLU()\n",
              "    (2): Conv2d(10, 10, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (3): ReLU()\n",
              "    (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "  )\n",
              "  (classifier): Sequential(\n",
              "    (0): Flatten(start_dim=1, end_dim=-1)\n",
              "    (1): Linear(in_features=490, out_features=10, bias=True)\n",
              "  )\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_2_results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-OLHu1Oxd4H8",
        "outputId": "2e470f9b-ec26-43e7-ef41-4a257919e494"
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'model_name': 'FashionMNISTModel_2',\n",
              " 'model_loss': 0.32718968391418457,\n",
              " 'model_acc': 0.88069087266922}"
            ]
          },
          "metadata": {},
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate loaded model\n",
        "torch.manual_seed(42)\n",
        "\n",
        "loaded_model_2_results = eval_model(model=loaded_model_2,\n",
        "                                    data_loader=test_dataloader,\n",
        "                                    loss_fn=loss_fn,\n",
        "                                    accuracy_fn=accuracy_fn,\n",
        "                                    device=device)\n",
        "loaded_model_2_results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 103,
          "referenced_widgets": [
            "0f58f7cf665c43a39391ebe67775cb68",
            "253ad6c97f0b4fe0907e1bd1e071730a",
            "e564b72c04e24069814413d01a59cdcd",
            "8d45723fabdc4ed3a59058d4773236e8",
            "aabd8bdcb5a74657bb2b88f81c592410",
            "31c872c0d33c4559b1327f1254bfd41f",
            "b1dbaf4a322b436d84ac6fcee24ae2cc",
            "f186abb24fd14561a413c15fca8fb1e5",
            "af309b07aa40457cb771c5205092bdb6",
            "59411e55712c4077a28ed3b1faa8c15e",
            "6db35a5c189a4c3185401543d2231d32"
          ]
        },
        "id": "-DnxGDT5d5xJ",
        "outputId": "4778d455-8dba-4c92-f059-c474ad000b61"
      },
      "execution_count": 76,
      "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": "0f58f7cf665c43a39391ebe67775cb68"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'model_name': 'FashionMNISTModel_2',\n",
              " 'model_loss': 0.32718968391418457,\n",
              " 'model_acc': 0.88069087266922}"
            ]
          },
          "metadata": {},
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check if model results are close to each other\n",
        "torch.isclose(torch.tensor(model_2_results[\"model_loss\"]),\n",
        "              torch.tensor(loaded_model_2_results[\"model_loss\"]),\n",
        "              atol=1e-08) # Checking that we are close within 8 decimal points"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "veniyNb-eOK2",
        "outputId": "3f1d1250-c05d-4899-fa81-296f5e091769"
      },
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor(True)"
            ]
          },
          "metadata": {},
          "execution_count": 79
        }
      ]
    }
  ]
}