VorontsovIE/quickstart_tutorial.ipynb

## quickstart_tutorial.ipynb
{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/gist/VorontsovIE/33cd40897d884f5cb4ad6d6b8ffe0768/quickstart_tutorial.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "VZ5LVZNmsOzC"
      },
      "outputs": [],
      "source": [
        "# For tips on running notebooks in Google Colab, see\n",
        "# https://pytorch.org/tutorials/beginner/colab\n",
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Hyq25CaesOzH"
      },
      "source": [
        "[Learn the Basics](intro.html) \\|\\| **Quickstart** \\|\\|\n",
        "[Tensors](tensorqs_tutorial.html) \\|\\| [Datasets &\n",
        "DataLoaders](data_tutorial.html) \\|\\|\n",
        "[Transforms](transforms_tutorial.html) \\|\\| [Build\n",
        "Model](buildmodel_tutorial.html) \\|\\|\n",
        "[Autograd](autogradqs_tutorial.html) \\|\\|\n",
        "[Optimization](optimization_tutorial.html) \\|\\| [Save & Load\n",
        "Model](saveloadrun_tutorial.html)\n",
        "\n",
        "Quickstart\n",
        "==========\n",
        "\n",
        "This section runs through the API for common tasks in machine learning.\n",
        "Refer to the links in each section to dive deeper.\n",
        "\n",
        "Working with data\n",
        "-----------------\n",
        "\n",
        "PyTorch has two [primitives to work with\n",
        "data](https://pytorch.org/docs/stable/data.html):\n",
        "`torch.utils.data.DataLoader` and `torch.utils.data.Dataset`. `Dataset`\n",
        "stores the samples and their corresponding labels, and `DataLoader`\n",
        "wraps an iterable around the `Dataset`.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "ZR0fkoUGsOzK"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "from torch import nn\n",
        "from torch.utils.data import DataLoader\n",
        "from torchvision import datasets\n",
        "from torchvision.transforms import ToTensor"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T4lfhCQnsOzL"
      },
      "source": [
        "PyTorch offers domain-specific libraries such as\n",
        "[TorchText](https://pytorch.org/text/stable/index.html),\n",
        "[TorchVision](https://pytorch.org/vision/stable/index.html), and\n",
        "[TorchAudio](https://pytorch.org/audio/stable/index.html), all of which\n",
        "include datasets. For this tutorial, we will be using a TorchVision\n",
        "dataset.\n",
        "\n",
        "The `torchvision.datasets` module contains `Dataset` objects for many\n",
        "real-world vision data like CIFAR, COCO ([full list\n",
        "here](https://pytorch.org/vision/stable/datasets.html)). In this\n",
        "tutorial, we use the FashionMNIST dataset. Every TorchVision `Dataset`\n",
        "includes two arguments: `transform` and `target_transform` to modify the\n",
        "samples and labels respectively.\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/gdrive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uNd8G4cMtkOe",
        "outputId": "64b94cc0-2acc-4c6a-9c0f-4c189a7d9300"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /gdrive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# import os\n",
        "# os.listdir('/gdrive/MyDrive/money_images/')\n"
      ],
      "metadata": {
        "id": "QR-eO8_EuFyc"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "a4ujlWadsOzL"
      },
      "outputs": [],
      "source": [
        "from torchvision import transforms\n",
        "train_transform = transforms.Compose([\n",
        "    transforms.Resize((224, 224)),\n",
        "    transforms.RandomHorizontalFlip(p=0.5),\n",
        "    transforms.RandomVerticalFlip(p=0.5),\n",
        "    transforms.GaussianBlur(kernel_size=(5, 9), sigma=(0.1, 5)),\n",
        "    transforms.RandomRotation(degrees=(30, 70)),\n",
        "    transforms.ToTensor(),\n",
        "    transforms.Normalize(\n",
        "        mean=[0.5, 0.5, 0.5],\n",
        "        std=[0.5, 0.5, 0.5]\n",
        "    )\n",
        "])\n",
        "# the validation transforms\n",
        "valid_transform = transforms.Compose([\n",
        "    transforms.Resize((224, 224)),\n",
        "    transforms.ToTensor(),\n",
        "    transforms.Normalize(\n",
        "        mean=[0.5, 0.5, 0.5],\n",
        "        std=[0.5, 0.5, 0.5]\n",
        "    )\n",
        "])\n",
        "\n",
        "# # Download training data from open datasets.\n",
        "training_data = datasets.ImageFolder('/gdrive/MyDrive/money_images/train/',\n",
        "                                     transform=train_transform,\n",
        "                                     )\n",
        "# datasets.FashionMNIST(\n",
        "#     root=\"data\",\n",
        "#     train=True,\n",
        "#     download=True,\n",
        "#     transform=ToTensor(),\n",
        "# )\n",
        "\n",
        "# # Download test data from open datasets.\n",
        "test_data = datasets.ImageFolder('/gdrive/MyDrive/money_images/train/', transform=valid_transform)  # TODO: fix train -> test\n",
        "# test_data = datasets.FashionMNIST(\n",
        "#     root=\"data\",\n",
        "#     train=False,\n",
        "#     download=True,\n",
        "#     transform=ToTensor(),\n",
        "# )"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7eSowgEFsOzM"
      },
      "source": [
        "We pass the `Dataset` as an argument to `DataLoader`. This wraps an\n",
        "iterable over our dataset, and supports automatic batching, sampling,\n",
        "shuffling and multiprocess data loading. Here we define a batch size of\n",
        "64, i.e. each element in the dataloader iterable will return a batch of\n",
        "64 features and labels.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 486
        },
        "id": "AYJJHHLVsOzM",
        "outputId": "541935ee-5717-4e1f-9d21-af81ad5feba1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape of X [N, C, H, W]: torch.Size([55, 3, 224, 224])\n",
            "Shape of y: torch.Size([55]) torch.int64\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "batch_size = 64\n",
        "\n",
        "# Create data loaders.\n",
        "train_dataloader = DataLoader(training_data, batch_size=batch_size)\n",
        "# test_dataloader = DataLoader(test_data, batch_size=batch_size)\n",
        "\n",
        "for X, y in train_dataloader:\n",
        "    print(f\"Shape of X [N, C, H, W]: {X.shape}\")\n",
        "    print(f\"Shape of y: {y.shape} {y.dtype}\")\n",
        "    npimg = X[0,:].permute(1, 2, 0).numpy()  # Convert tensor to numpy array\n",
        "    plt.imshow(npimg)\n",
        "\n",
        "    break"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "87T9N0posOzN"
      },
      "source": [
        "Read more about [loading data in PyTorch](data_tutorial.html).\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T9gHbDfasOzN"
      },
      "source": [
        "------------------------------------------------------------------------\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VjPA8KTHsOzO"
      },
      "source": [
        "Creating Models\n",
        "===============\n",
        "\n",
        "To define a neural network in PyTorch, we create a class that inherits\n",
        "from\n",
        "[nn.Module](https://pytorch.org/docs/stable/generated/torch.nn.Module.html).\n",
        "We define the layers of the network in the `__init__` function and\n",
        "specify how data will pass through the network in the `forward`\n",
        "function. To accelerate operations in the neural network, we move it to\n",
        "the GPU or MPS if available.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5cfOqcAPsOzP",
        "outputId": "df668afc-9172-4c1c-afa3-a7475a763d71"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Using cpu device\n"
          ]
        }
      ],
      "source": [
        "# Get cpu, gpu or mps device for training.\n",
        "device = (\n",
        "    \"cuda\"\n",
        "    if torch.cuda.is_available()\n",
        "    else \"mps\"\n",
        "    if torch.backends.mps.is_available()\n",
        "    else \"cpu\"\n",
        ")\n",
        "print(f\"Using {device} device\")\n",
        "\n",
        "# # Define model\n",
        "# class NeuralNetwork(nn.Module):\n",
        "#     def __init__(self):\n",
        "#         super().__init__()\n",
        "#         self.flatten = nn.Flatten()\n",
        "#         self.linear_relu_stack = nn.Sequential(\n",
        "#             nn.Linear(28*28, 512),\n",
        "#             nn.ReLU(),\n",
        "#             nn.Linear(512, 512),\n",
        "#             nn.ReLU(),\n",
        "#             nn.Linear(512, 10)\n",
        "#         )\n",
        "\n",
        "#     def forward(self, x):\n",
        "#         x = self.flatten(x)\n",
        "#         logits = self.linear_relu_stack(x)\n",
        "#         return logits\n",
        "\n",
        "# model = NeuralNetwork().to(device)\n",
        "# print(model)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from torchvision import models\n",
        "model = models.vgg16(pretrained=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OwDiagw85z7_",
        "outputId": "f4e0db0c-8acf-4d18-a1f4-5a0e816b2f24"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=VGG16_Weights.IMAGENET1K_V1`. You can also use `weights=VGG16_Weights.DEFAULT` to get the most up-to-date weights.\n",
            "  warnings.warn(msg)\n",
            "Downloading: \"https://download.pytorch.org/models/vgg16-397923af.pth\" to /root/.cache/torch/hub/checkpoints/vgg16-397923af.pth\n",
            "100%|██████████| 528M/528M [00:11<00:00, 46.8MB/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import json\n",
        "from urllib.request import urlopen\n",
        "with urlopen('https://git.io/JvBFb') as f:\n",
        "    LABELS = json.load(f)"
      ],
      "metadata": {
        "id": "8Gun7Aoc79ZD"
      },
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(LABELS)\n",
        "X[0,:].shape\n",
        "# npimg = X[0,:].permute(1, 2, 0).numpy()  # Convert tensor to numpy array\n",
        "# plt.imshow(npimg)\n",
        "\n",
        "import numpy as np\n",
        "LABELS[ np.argmax(model(X[0:1, :]).detach().numpy()) ]\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        },
        "id": "ib6CTenD6qQ7",
        "outputId": "74a6e696-8952-4018-df38-0d1d34d528e5"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['tench', 'goldfish', 'great white shark', 'tiger shark', 'hammerhead shark', 'electric ray', 'stingray', 'cock', 'hen', 'ostrich', 'brambling', 'goldfinch', 'house finch', 'junco', 'indigo bunting', 'American robin', 'bulbul', 'jay', 'magpie', 'chickadee', 'American dipper', 'kite', 'bald eagle', 'vulture', 'great grey owl', 'fire salamander', 'smooth newt', 'newt', 'spotted salamander', 'axolotl', 'American bullfrog', 'tree frog', 'tailed frog', 'loggerhead sea turtle', 'leatherback sea turtle', 'mud turtle', 'terrapin', 'box turtle', 'banded gecko', 'green iguana', 'Carolina anole', 'desert grassland whiptail lizard', 'agama', 'frilled-necked lizard', 'alligator lizard', 'Gila monster', 'European green lizard', 'chameleon', 'Komodo dragon', 'Nile crocodile', 'American alligator', 'triceratops', 'worm snake', 'ring-necked snake', 'eastern hog-nosed snake', 'smooth green snake', 'kingsnake', 'garter snake', 'water snake', 'vine snake', 'night snake', 'boa constrictor', 'African rock python', 'Indian cobra', 'green mamba', 'sea snake', 'Saharan horned viper', 'eastern diamondback rattlesnake', 'sidewinder', 'trilobite', 'harvestman', 'scorpion', 'yellow garden spider', 'barn spider', 'European garden spider', 'southern black widow', 'tarantula', 'wolf spider', 'tick', 'centipede', 'black grouse', 'ptarmigan', 'ruffed grouse', 'prairie grouse', 'peacock', 'quail', 'partridge', 'grey parrot', 'macaw', 'sulphur-crested cockatoo', 'lorikeet', 'coucal', 'bee eater', 'hornbill', 'hummingbird', 'jacamar', 'toucan', 'duck', 'red-breasted merganser', 'goose', 'black swan', 'tusker', 'echidna', 'platypus', 'wallaby', 'koala', 'wombat', 'jellyfish', 'sea anemone', 'brain coral', 'flatworm', 'nematode', 'conch', 'snail', 'slug', 'sea slug', 'chiton', 'chambered nautilus', 'Dungeness crab', 'rock crab', 'fiddler crab', 'red king crab', 'American lobster', 'spiny lobster', 'crayfish', 'hermit crab', 'isopod', 'white stork', 'black stork', 'spoonbill', 'flamingo', 'little blue heron', 'great egret', 'bittern', 'crane (bird)', 'limpkin', 'common gallinule', 'American coot', 'bustard', 'ruddy turnstone', 'dunlin', 'common redshank', 'dowitcher', 'oystercatcher', 'pelican', 'king penguin', 'albatross', 'grey whale', 'killer whale', 'dugong', 'sea lion', 'Chihuahua', 'Japanese Chin', 'Maltese', 'Pekingese', 'Shih Tzu', 'King Charles Spaniel', 'Papillon', 'toy terrier', 'Rhodesian Ridgeback', 'Afghan Hound', 'Basset Hound', 'Beagle', 'Bloodhound', 'Bluetick Coonhound', 'Black and Tan Coonhound', 'Treeing Walker Coonhound', 'English foxhound', 'Redbone Coonhound', 'borzoi', 'Irish Wolfhound', 'Italian Greyhound', 'Whippet', 'Ibizan Hound', 'Norwegian Elkhound', 'Otterhound', 'Saluki', 'Scottish Deerhound', 'Weimaraner', 'Staffordshire Bull Terrier', 'American Staffordshire Terrier', 'Bedlington Terrier', 'Border Terrier', 'Kerry Blue Terrier', 'Irish Terrier', 'Norfolk Terrier', 'Norwich Terrier', 'Yorkshire Terrier', 'Wire Fox Terrier', 'Lakeland Terrier', 'Sealyham Terrier', 'Airedale Terrier', 'Cairn Terrier', 'Australian Terrier', 'Dandie Dinmont Terrier', 'Boston Terrier', 'Miniature Schnauzer', 'Giant Schnauzer', 'Standard Schnauzer', 'Scottish Terrier', 'Tibetan Terrier', 'Australian Silky Terrier', 'Soft-coated Wheaten Terrier', 'West Highland White Terrier', 'Lhasa Apso', 'Flat-Coated Retriever', 'Curly-coated Retriever', 'Golden Retriever', 'Labrador Retriever', 'Chesapeake Bay Retriever', 'German Shorthaired Pointer', 'Vizsla', 'English Setter', 'Irish Setter', 'Gordon Setter', 'Brittany Spaniel', 'Clumber Spaniel', 'English Springer Spaniel', 'Welsh Springer Spaniel', 'Cocker Spaniels', 'Sussex Spaniel', 'Irish Water Spaniel', 'Kuvasz', 'Schipperke', 'Groenendael', 'Malinois', 'Briard', 'Australian Kelpie', 'Komondor', 'Old English Sheepdog', 'Shetland Sheepdog', 'collie', 'Border Collie', 'Bouvier des Flandres', 'Rottweiler', 'German Shepherd Dog', 'Dobermann', 'Miniature Pinscher', 'Greater Swiss Mountain Dog', 'Bernese Mountain Dog', 'Appenzeller Sennenhund', 'Entlebucher Sennenhund', 'Boxer', 'Bullmastiff', 'Tibetan Mastiff', 'French Bulldog', 'Great Dane', 'St. Bernard', 'husky', 'Alaskan Malamute', 'Siberian Husky', 'Dalmatian', 'Affenpinscher', 'Basenji', 'pug', 'Leonberger', 'Newfoundland', 'Pyrenean Mountain Dog', 'Samoyed', 'Pomeranian', 'Chow Chow', 'Keeshond', 'Griffon Bruxellois', 'Pembroke Welsh Corgi', 'Cardigan Welsh Corgi', 'Toy Poodle', 'Miniature Poodle', 'Standard Poodle', 'Mexican hairless dog', 'grey wolf', 'Alaskan tundra wolf', 'red wolf', 'coyote', 'dingo', 'dhole', 'African wild dog', 'hyena', 'red fox', 'kit fox', 'Arctic fox', 'grey fox', 'tabby cat', 'tiger cat', 'Persian cat', 'Siamese cat', 'Egyptian Mau', 'cougar', 'lynx', 'leopard', 'snow leopard', 'jaguar', 'lion', 'tiger', 'cheetah', 'brown bear', 'American black bear', 'polar bear', 'sloth bear', 'mongoose', 'meerkat', 'tiger beetle', 'ladybug', 'ground beetle', 'longhorn beetle', 'leaf beetle', 'dung beetle', 'rhinoceros beetle', 'weevil', 'fly', 'bee', 'ant', 'grasshopper', 'cricket', 'stick insect', 'cockroach', 'mantis', 'cicada', 'leafhopper', 'lacewing', 'dragonfly', 'damselfly', 'red admiral', 'ringlet', 'monarch butterfly', 'small white', 'sulphur butterfly', 'gossamer-winged butterfly', 'starfish', 'sea urchin', 'sea cucumber', 'cottontail rabbit', 'hare', 'Angora rabbit', 'hamster', 'porcupine', 'fox squirrel', 'marmot', 'beaver', 'guinea pig', 'common sorrel', 'zebra', 'pig', 'wild boar', 'warthog', 'hippopotamus', 'ox', 'water buffalo', 'bison', 'ram', 'bighorn sheep', 'Alpine ibex', 'hartebeest', 'impala', 'gazelle', 'dromedary', 'llama', 'weasel', 'mink', 'European polecat', 'black-footed ferret', 'otter', 'skunk', 'badger', 'armadillo', 'three-toed sloth', 'orangutan', 'gorilla', 'chimpanzee', 'gibbon', 'siamang', 'guenon', 'patas monkey', 'baboon', 'macaque', 'langur', 'black-and-white colobus', 'proboscis monkey', 'marmoset', 'white-headed capuchin', 'howler monkey', 'titi', \"Geoffroy's spider monkey\", 'common squirrel monkey', 'ring-tailed lemur', 'indri', 'Asian elephant', 'African bush elephant', 'red panda', 'giant panda', 'snoek', 'eel', 'coho salmon', 'rock beauty', 'clownfish', 'sturgeon', 'garfish', 'lionfish', 'pufferfish', 'abacus', 'abaya', 'academic gown', 'accordion', 'acoustic guitar', 'aircraft carrier', 'airliner', 'airship', 'altar', 'ambulance', 'amphibious vehicle', 'analog clock', 'apiary', 'apron', 'waste container', 'assault rifle', 'backpack', 'bakery', 'balance beam', 'balloon', 'ballpoint pen', 'Band-Aid', 'banjo', 'baluster', 'barbell', 'barber chair', 'barbershop', 'barn', 'barometer', 'barrel', 'wheelbarrow', 'baseball', 'basketball', 'bassinet', 'bassoon', 'swimming cap', 'bath towel', 'bathtub', 'station wagon', 'lighthouse', 'beaker', 'military cap', 'beer bottle', 'beer glass', 'bell-cot', 'bib', 'tandem bicycle', 'bikini', 'ring binder', 'binoculars', 'birdhouse', 'boathouse', 'bobsleigh', 'bolo tie', 'poke bonnet', 'bookcase', 'bookstore', 'bottle cap', 'bow', 'bow tie', 'brass', 'bra', 'breakwater', 'breastplate', 'broom', 'bucket', 'buckle', 'bulletproof vest', 'high-speed train', 'butcher shop', 'taxicab', 'cauldron', 'candle', 'cannon', 'canoe', 'can opener', 'cardigan', 'car mirror', 'carousel', 'tool kit', 'carton', 'car wheel', 'automated teller machine', 'cassette', 'cassette player', 'castle', 'catamaran', 'CD player', 'cello', 'mobile phone', 'chain', 'chain-link fence', 'chain mail', 'chainsaw', 'chest', 'chiffonier', 'chime', 'china cabinet', 'Christmas stocking', 'church', 'movie theater', 'cleaver', 'cliff dwelling', 'cloak', 'clogs', 'cocktail shaker', 'coffee mug', 'coffeemaker', 'coil', 'combination lock', 'computer keyboard', 'confectionery store', 'container ship', 'convertible', 'corkscrew', 'cornet', 'cowboy boot', 'cowboy hat', 'cradle', 'crane (machine)', 'crash helmet', 'crate', 'infant bed', 'Crock Pot', 'croquet ball', 'crutch', 'cuirass', 'dam', 'desk', 'desktop computer', 'rotary dial telephone', 'diaper', 'digital clock', 'digital watch', 'dining table', 'dishcloth', 'dishwasher', 'disc brake', 'dock', 'dog sled', 'dome', 'doormat', 'drilling rig', 'drum', 'drumstick', 'dumbbell', 'Dutch oven', 'electric fan', 'electric guitar', 'electric locomotive', 'entertainment center', 'envelope', 'espresso machine', 'face powder', 'feather boa', 'filing cabinet', 'fireboat', 'fire engine', 'fire screen sheet', 'flagpole', 'flute', 'folding chair', 'football helmet', 'forklift', 'fountain', 'fountain pen', 'four-poster bed', 'freight car', 'French horn', 'frying pan', 'fur coat', 'garbage truck', 'gas mask', 'gas pump', 'goblet', 'go-kart', 'golf ball', 'golf cart', 'gondola', 'gong', 'gown', 'grand piano', 'greenhouse', 'grille', 'grocery store', 'guillotine', 'barrette', 'hair spray', 'half-track', 'hammer', 'hamper', 'hair dryer', 'hand-held computer', 'handkerchief', 'hard disk drive', 'harmonica', 'harp', 'harvester', 'hatchet', 'holster', 'home theater', 'honeycomb', 'hook', 'hoop skirt', 'horizontal bar', 'horse-drawn vehicle', 'hourglass', 'iPod', 'clothes iron', \"jack-o'-lantern\", 'jeans', 'jeep', 'T-shirt', 'jigsaw puzzle', 'pulled rickshaw', 'joystick', 'kimono', 'knee pad', 'knot', 'lab coat', 'ladle', 'lampshade', 'laptop computer', 'lawn mower', 'lens cap', 'paper knife', 'library', 'lifeboat', 'lighter', 'limousine', 'ocean liner', 'lipstick', 'slip-on shoe', 'lotion', 'speaker', 'loupe', 'sawmill', 'magnetic compass', 'mail bag', 'mailbox', 'tights', 'tank suit', 'manhole cover', 'maraca', 'marimba', 'mask', 'match', 'maypole', 'maze', 'measuring cup', 'medicine chest', 'megalith', 'microphone', 'microwave oven', 'military uniform', 'milk can', 'minibus', 'miniskirt', 'minivan', 'missile', 'mitten', 'mixing bowl', 'mobile home', 'Model T', 'modem', 'monastery', 'monitor', 'moped', 'mortar', 'square academic cap', 'mosque', 'mosquito net', 'scooter', 'mountain bike', 'tent', 'computer mouse', 'mousetrap', 'moving van', 'muzzle', 'nail', 'neck brace', 'necklace', 'nipple', 'notebook computer', 'obelisk', 'oboe', 'ocarina', 'odometer', 'oil filter', 'organ', 'oscilloscope', 'overskirt', 'bullock cart', 'oxygen mask', 'packet', 'paddle', 'paddle wheel', 'padlock', 'paintbrush', 'pajamas', 'palace', 'pan flute', 'paper towel', 'parachute', 'parallel bars', 'park bench', 'parking meter', 'passenger car', 'patio', 'payphone', 'pedestal', 'pencil case', 'pencil sharpener', 'perfume', 'Petri dish', 'photocopier', 'plectrum', 'Pickelhaube', 'picket fence', 'pickup truck', 'pier', 'piggy bank', 'pill bottle', 'pillow', 'ping-pong ball', 'pinwheel', 'pirate ship', 'pitcher', 'hand plane', 'planetarium', 'plastic bag', 'plate rack', 'plow', 'plunger', 'Polaroid camera', 'pole', 'police van', 'poncho', 'billiard table', 'soda bottle', 'pot', \"potter's wheel\", 'power drill', 'prayer rug', 'printer', 'prison', 'projectile', 'projector', 'hockey puck', 'punching bag', 'purse', 'quill', 'quilt', 'race car', 'racket', 'radiator', 'radio', 'radio telescope', 'rain barrel', 'recreational vehicle', 'reel', 'reflex camera', 'refrigerator', 'remote control', 'restaurant', 'revolver', 'rifle', 'rocking chair', 'rotisserie', 'eraser', 'rugby ball', 'ruler', 'running shoe', 'safe', 'safety pin', 'salt shaker', 'sandal', 'sarong', 'saxophone', 'scabbard', 'weighing scale', 'school bus', 'schooner', 'scoreboard', 'CRT screen', 'screw', 'screwdriver', 'seat belt', 'sewing machine', 'shield', 'shoe store', 'shoji', 'shopping basket', 'shopping cart', 'shovel', 'shower cap', 'shower curtain', 'ski', 'ski mask', 'sleeping bag', 'slide rule', 'sliding door', 'slot machine', 'snorkel', 'snowmobile', 'snowplow', 'soap dispenser', 'soccer ball', 'sock', 'solar thermal collector', 'sombrero', 'soup bowl', 'space bar', 'space heater', 'space shuttle', 'spatula', 'motorboat', 'spider web', 'spindle', 'sports car', 'spotlight', 'stage', 'steam locomotive', 'through arch bridge', 'steel drum', 'stethoscope', 'scarf', 'stone wall', 'stopwatch', 'stove', 'strainer', 'tram', 'stretcher', 'couch', 'stupa', 'submarine', 'suit', 'sundial', 'sunglass', 'sunglasses', 'sunscreen', 'suspension bridge', 'mop', 'sweatshirt', 'swimsuit', 'swing', 'switch', 'syringe', 'table lamp', 'tank', 'tape player', 'teapot', 'teddy bear', 'television', 'tennis ball', 'thatched roof', 'front curtain', 'thimble', 'threshing machine', 'throne', 'tile roof', 'toaster', 'tobacco shop', 'toilet seat', 'torch', 'totem pole', 'tow truck', 'toy store', 'tractor', 'semi-trailer truck', 'tray', 'trench coat', 'tricycle', 'trimaran', 'tripod', 'triumphal arch', 'trolleybus', 'trombone', 'tub', 'turnstile', 'typewriter keyboard', 'umbrella', 'unicycle', 'upright piano', 'vacuum cleaner', 'vase', 'vault', 'velvet', 'vending machine', 'vestment', 'viaduct', 'violin', 'volleyball', 'waffle iron', 'wall clock', 'wallet', 'wardrobe', 'military aircraft', 'sink', 'washing machine', 'water bottle', 'water jug', 'water tower', 'whiskey jug', 'whistle', 'wig', 'window screen', 'window shade', 'Windsor tie', 'wine bottle', 'wing', 'wok', 'wooden spoon', 'wool', 'split-rail fence', 'shipwreck', 'yawl', 'yurt', 'website', 'comic book', 'crossword', 'traffic sign', 'traffic light', 'dust jacket', 'menu', 'plate', 'guacamole', 'consomme', 'hot pot', 'trifle', 'ice cream', 'ice pop', 'baguette', 'bagel', 'pretzel', 'cheeseburger', 'hot dog', 'mashed potato', 'cabbage', 'broccoli', 'cauliflower', 'zucchini', 'spaghetti squash', 'acorn squash', 'butternut squash', 'cucumber', 'artichoke', 'bell pepper', 'cardoon', 'mushroom', 'Granny Smith', 'strawberry', 'orange', 'lemon', 'fig', 'pineapple', 'banana', 'jackfruit', 'custard apple', 'pomegranate', 'hay', 'carbonara', 'chocolate syrup', 'dough', 'meatloaf', 'pizza', 'pot pie', 'burrito', 'red wine', 'espresso', 'cup', 'eggnog', 'alp', 'bubble', 'cliff', 'coral reef', 'geyser', 'lakeshore', 'promontory', 'shoal', 'seashore', 'valley', 'volcano', 'baseball player', 'bridegroom', 'scuba diver', 'rapeseed', 'daisy', \"yellow lady's slipper\", 'corn', 'acorn', 'rose hip', 'horse chestnut seed', 'coral fungus', 'agaric', 'gyromitra', 'stinkhorn mushroom', 'earth star', 'hen-of-the-woods', 'bolete', 'ear of corn', 'toilet paper']\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'ring binder'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D4H9fxpCsOzR"
      },
      "source": [
        "Read more about [building neural networks in\n",
        "PyTorch](buildmodel_tutorial.html).\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KpFYrE53sOzS"
      },
      "source": [
        "------------------------------------------------------------------------\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ISzNqIFPsOzU"
      },
      "source": [
        "Optimizing the Model Parameters\n",
        "===============================\n",
        "\n",
        "To train a model, we need a [loss\n",
        "function](https://pytorch.org/docs/stable/nn.html#loss-functions) and an\n",
        "[optimizer](https://pytorch.org/docs/stable/optim.html).\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jKidP-cesOzW"
      },
      "outputs": [],
      "source": [
        "loss_fn = nn.CrossEntropyLoss()\n",
        "optimizer = torch.optim.SGD(model.parameters(), lr=1e-3)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oLOlegCTsOzX"
      },
      "source": [
        "In a single training loop, the model makes predictions on the training\n",
        "dataset (fed to it in batches), and backpropagates the prediction error\n",
        "to adjust the model\\'s parameters.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Fw4x8vv9sOzY"
      },
      "outputs": [],
      "source": [
        "def train(dataloader, model, loss_fn, optimizer):\n",
        "    size = len(dataloader.dataset)\n",
        "    model.train()\n",
        "    for batch, (X, y) in enumerate(dataloader):\n",
        "        X, y = X.to(device), y.to(device)\n",
        "\n",
        "        # Compute prediction error\n",
        "        pred = model(X)\n",
        "        loss = loss_fn(pred, y)\n",
        "\n",
        "        # Backpropagation\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "        optimizer.zero_grad()\n",
        "\n",
        "        if batch % 100 == 0:\n",
        "            loss, current = loss.item(), (batch + 1) * len(X)\n",
        "            print(f\"loss: {loss:>7f}  [{current:>5d}/{size:>5d}]\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JMNcyo9ysOza"
      },
      "source": [
        "We also check the model\\'s performance against the test dataset to\n",
        "ensure it is learning.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9x1PayaksOzb"
      },
      "outputs": [],
      "source": [
        "def test(dataloader, model, loss_fn):\n",
        "    size = len(dataloader.dataset)\n",
        "    num_batches = len(dataloader)\n",
        "    model.eval()\n",
        "    test_loss, correct = 0, 0\n",
        "    with torch.no_grad():\n",
        "        for X, y in dataloader:\n",
        "            X, y = X.to(device), y.to(device)\n",
        "            pred = model(X)\n",
        "            test_loss += loss_fn(pred, y).item()\n",
        "            correct += (pred.argmax(1) == y).type(torch.float).sum().item()\n",
        "    test_loss /= num_batches\n",
        "    correct /= size\n",
        "    print(f\"Test Error: \\n Accuracy: {(100*correct):>0.1f}%, Avg loss: {test_loss:>8f} \\n\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kh6K_qRYsOzc"
      },
      "source": [
        "The training process is conducted over several iterations (*epochs*).\n",
        "During each epoch, the model learns parameters to make better\n",
        "predictions. We print the model\\'s accuracy and loss at each epoch;\n",
        "we\\'d like to see the accuracy increase and the loss decrease with every\n",
        "epoch.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BuCMdk_rsOzd"
      },
      "outputs": [],
      "source": [
        "epochs = 5\n",
        "for t in range(epochs):\n",
        "    print(f\"Epoch {t+1}\\n-------------------------------\")\n",
        "    train(train_dataloader, model, loss_fn, optimizer)\n",
        "    test(test_dataloader, model, loss_fn)\n",
        "print(\"Done!\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pbLytaiVsOze"
      },
      "source": [
        "Read more about [Training your model](optimization_tutorial.html).\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mi9iGSZtsOzf"
      },
      "source": [
        "------------------------------------------------------------------------\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HOKCgfEesOzf"
      },
      "source": [
        "Saving Models\n",
        "=============\n",
        "\n",
        "A common way to save a model is to serialize the internal state\n",
        "dictionary (containing the model parameters).\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WjYGG4ohsOzg"
      },
      "outputs": [],
      "source": [
        "torch.save(model.state_dict(), \"model.pth\")\n",
        "print(\"Saved PyTorch Model State to model.pth\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RXusMRgtsOzi"
      },
      "source": [
        "Loading Models\n",
        "==============\n",
        "\n",
        "The process for loading a model includes re-creating the model structure\n",
        "and loading the state dictionary into it.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_OmfQ6QEsOzi"
      },
      "outputs": [],
      "source": [
        "model = NeuralNetwork().to(device)\n",
        "model.load_state_dict(torch.load(\"model.pth\"))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ELPVH7eQsOzj"
      },
      "source": [
        "This model can now be used to make predictions.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GBk-Xx-PsOzl"
      },
      "outputs": [],
      "source": [
        "classes = [\n",
        "    \"T-shirt/top\",\n",
        "    \"Trouser\",\n",
        "    \"Pullover\",\n",
        "    \"Dress\",\n",
        "    \"Coat\",\n",
        "    \"Sandal\",\n",
        "    \"Shirt\",\n",
        "    \"Sneaker\",\n",
        "    \"Bag\",\n",
        "    \"Ankle boot\",\n",
        "]\n",
        "\n",
        "model.eval()\n",
        "x, y = test_data[0][0], test_data[0][1]\n",
        "with torch.no_grad():\n",
        "    x = x.to(device)\n",
        "    pred = model(x)\n",
        "    predicted, actual = classes[pred[0].argmax(0)], classes[y]\n",
        "    print(f'Predicted: \"{predicted}\", Actual: \"{actual}\"')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zbNLeXzSsOzn"
      },
      "source": [
        "Read more about [Saving & Loading your\n",
        "model](saveloadrun_tutorial.html).\n"
      ]
    }
  ],
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