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.ipynb_checkpoints/intro-checkpoint.ipynb
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.ipynb_checkpoints/intro-checkpoint.ipynb
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"%matplotlib inline"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"\n**Learn the Basics** ||\n`Quickstart <quickstart_tutorial.html>`_ ||\n`Tensors <tensorqs_tutorial.html>`_ ||\n`Datasets & DataLoaders <data_tutorial.html>`_ ||\n`Transforms <transforms_tutorial.html>`_ ||\n`Build Model <buildmodel_tutorial.html>`_ ||\n`Autograd <autogradqs_tutorial.html>`_ ||\n`Optimization <optimization_tutorial.html>`_ ||\n`Save & Load Model <saveloadrun_tutorial.html>`_\n\nLearn the Basics\n===================\n\nAuthors:\n`Suraj Subramanian <https://github.com/suraj813>`_,\n`Seth Juarez <https://github.com/sethjuarez/>`_,\n`Cassie Breviu <https://github.com/cassieview/>`_,\n`Dmitry Soshnikov <https://soshnikov.com/>`_,\n`Ari Bornstein <https://github.com/aribornstein/>`_\n\nMost machine learning workflows involve working with data, creating models, optimizing model\nparameters, and saving the trained models. This tutorial introduces you to a complete ML workflow\nimplemented in PyTorch, with links to learn more about each of these concepts.\n\nWe'll use the FashionMNIST dataset to train a neural network that predicts if an input image belongs\nto one of the following classes: T-shirt/top, Trouser, Pullover, Dress, Coat, Sandal, Shirt, Sneaker,\nBag, or Ankle boot.\n\n`This tutorial assumes a basic familiarity with Python and Deep Learning concepts.`\n\n\nRunning the Tutorial Code\n------------------\nYou can run this tutorial in a couple of ways:\n\n- **In the cloud**: This is the easiest way to get started! Each section has a \"Run in Microsoft Learn\" link at the top, which opens an integrated notebook in Microsoft Learn with the code in a fully-hosted environment.\n- **Locally**: This option requires you to setup PyTorch and TorchVision first on your local machine (`installation instructions <https://pytorch.org/get-started/locally/>`_). Download the notebook or copy the code into your favorite IDE.\n\n\nHow to Use this Guide\n-----------------\nIf you're familiar with other deep learning frameworks, check out the `0. Quickstart <quickstart_tutorial.html>`_ first\nto quickly familiarize yourself with PyTorch's API.\n\nIf you're new to deep learning frameworks, head right into the first section of our step-by-step guide: `1. Tensors <tensor_tutorial.html>`_.\n\n\n.. include:: /beginner_source/basics/qs_toc.txt\n\n.. toctree::\n :hidden:\n\n\n"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.13"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 0
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}
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.ipynb_checkpoints/quickstart_tutorial-checkpoint.ipynb
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.ipynb_checkpoints/quickstart_tutorial-checkpoint.ipynb
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"%matplotlib inline"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"\n`Learn the Basics <intro.html>`_ ||\n**Quickstart** ||\n`Tensors <tensorqs_tutorial.html>`_ ||\n`Datasets & DataLoaders <data_tutorial.html>`_ ||\n`Transforms <transforms_tutorial.html>`_ ||\n`Build Model <buildmodel_tutorial.html>`_ ||\n`Autograd <autogradqs_tutorial.html>`_ ||\n`Optimization <optimization_tutorial.html>`_ ||\n`Save & Load Model <saveloadrun_tutorial.html>`_\n\nQuickstart\n===================\nThis section runs through the API for common tasks in machine learning. Refer to the links in each section to dive deeper.\n\nWorking with data\n-----------------\nPyTorch has two `primitives to work with data <https://pytorch.org/docs/stable/data.html>`_:\n``torch.utils.data.DataLoader`` and ``torch.utils.data.Dataset``.\n``Dataset`` stores the samples and their corresponding labels, and ``DataLoader`` wraps an iterable around\nthe ``Dataset``.\n\n\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"import torch\nfrom torch import nn\nfrom torch.utils.data import DataLoader\nfrom torchvision import datasets\nfrom torchvision.transforms import ToTensor, Lambda, Compose\nimport matplotlib.pyplot as plt"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"PyTorch offers domain-specific libraries such as `TorchText <https://pytorch.org/text/stable/index.html>`_,\n`TorchVision <https://pytorch.org/vision/stable/index.html>`_, and `TorchAudio <https://pytorch.org/audio/stable/index.html>`_,\nall of which include datasets. For this tutorial, we will be using a TorchVision dataset.\n\nThe ``torchvision.datasets`` module contains ``Dataset`` objects for many real-world vision data like\nCIFAR, COCO (`full list here <https://pytorch.org/vision/stable/datasets.html>`_). In this tutorial, we\nuse the FashionMNIST dataset. Every TorchVision ``Dataset`` includes two arguments: ``transform`` and\n``target_transform`` to modify the samples and labels respectively.\n\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"# Download training data from open datasets.\ntraining_data = datasets.FashionMNIST(\n root=\"data\",\n train=True,\n download=True,\n transform=ToTensor(),\n)\n\n# Download test data from open datasets.\ntest_data = datasets.FashionMNIST(\n root=\"data\",\n train=False,\n download=True,\n transform=ToTensor(),\n)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"We pass the ``Dataset`` as an argument to ``DataLoader``. This wraps an iterable over our dataset, and supports\nautomatic batching, sampling, shuffling and multiprocess data loading. Here we define a batch size of 64, i.e. each element\nin the dataloader iterable will return a batch of 64 features and labels.\n\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"batch_size = 64\n\n# Create data loaders.\ntrain_dataloader = DataLoader(training_data, batch_size=batch_size)\ntest_dataloader = DataLoader(test_data, batch_size=batch_size)\n\nfor X, y in test_dataloader:\n print(\"Shape of X [N, C, H, W]: \", X.shape)\n print(\"Shape of y: \", y.shape, y.dtype)\n break"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
|
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"Read more about `loading data in PyTorch <data_tutorial.html>`_.\n\n\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
|
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"--------------\n\n\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Creating Models\n------------------\nTo define a neural network in PyTorch, we create a class that inherits\nfrom `nn.Module <https://pytorch.org/docs/stable/generated/torch.nn.Module.html>`_. We define the layers of the network\nin the ``__init__`` function and specify how data will pass through the network in the ``forward`` function. To accelerate\noperations in the neural network, we move it to the GPU if available.\n\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"# Get cpu or gpu device for training.\ndevice = \"cuda\" if torch.cuda.is_available() else \"cpu\"\nprint(\"Using {} device\".format(device))\n\n# Define model\nclass NeuralNetwork(nn.Module):\n def __init__(self):\n super(NeuralNetwork, self).__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 nn.ReLU()\n )\n\n def forward(self, x):\n x = self.flatten(x)\n logits = self.linear_relu_stack(x)\n return logits\n\nmodel = NeuralNetwork().to(device)\nprint(model)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Read more about `building neural networks in PyTorch <buildmodel_tutorial.html>`_.\n\n\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"--------------\n\n\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Optimizing the Model Parameters\n----------------------------------------\nTo train a model, we need a `loss function <https://pytorch.org/docs/stable/nn.html#loss-functions>`_\nand an `optimizer <https://pytorch.org/docs/stable/optim.html>`_.\n\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"loss_fn = nn.CrossEntropyLoss()\noptimizer = torch.optim.SGD(model.parameters(), lr=1e-3)"
|
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"In a single training loop, the model makes predictions on the training dataset (fed to it in batches), and\nbackpropagates the prediction error to adjust the model's parameters.\n\n"
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]
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},
|
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"def train(dataloader, model, loss_fn, optimizer):\n size = len(dataloader.dataset)\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 optimizer.zero_grad()\n loss.backward()\n optimizer.step()\n\n if batch % 100 == 0:\n loss, current = loss.item(), batch * len(X)\n print(f\"loss: {loss:>7f} [{current:>5d}/{size:>5d}]\")"
|
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]
|
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},
|
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"We also check the model's performance against the test dataset to ensure it is learning.\n\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"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\")"
|
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]
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},
|
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{
|
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"cell_type": "markdown",
|
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"metadata": {},
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"source": [
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"The training process is conducted over several iterations (*epochs*). During each epoch, the model learns\nparameters to make better predictions. We print the model's accuracy and loss at each epoch; we'd like to see the\naccuracy increase and the loss decrease with every epoch.\n\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"epochs = 5\nfor 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)\nprint(\"Done!\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Read more about `Training your model <optimization_tutorial.html>`_.\n\n\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"--------------\n\n\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Saving Models\n-------------\nA common way to save a model is to serialize the internal state dictionary (containing the model parameters).\n\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"torch.save(model.state_dict(), \"model.pth\")\nprint(\"Saved PyTorch Model State to model.pth\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Loading Models\n----------------------------\n\nThe process for loading a model includes re-creating the model structure and loading\nthe state dictionary into it.\n\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"model = NeuralNetwork()\nmodel.load_state_dict(torch.load(\"model.pth\"))"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"This model can now be used to make predictions.\n\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"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\nmodel.eval()\nx, y = test_data[0][0], test_data[0][1]\nwith torch.no_grad():\n pred = model(x)\n predicted, actual = classes[pred[0].argmax(0)], classes[y]\n print(f'Predicted: \"{predicted}\", Actual: \"{actual}\"')"
|
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]
|
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},
|
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{
|
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"cell_type": "markdown",
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"metadata": {},
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"source": [
|
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"Read more about `Saving & Loading your model <saveloadrun_tutorial.html>`_.\n\n\n"
|
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]
|
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}
|
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],
|
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.13"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 0
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}
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.ipynb_checkpoints/test-checkpoint.ipynb
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{
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"cells": [],
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"metadata": {},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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quickstart_tutorial.ipynb
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false,
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"jupyter": {
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"outputs_hidden": false
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}
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},
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"outputs": [],
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"source": [
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"%matplotlib inline"
|
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"\n",
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"`Learn the Basics <intro.html>`_ ||\n",
|
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"**Quickstart** ||\n",
|
||||
"`Tensors <tensorqs_tutorial.html>`_ ||\n",
|
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"`Datasets & DataLoaders <data_tutorial.html>`_ ||\n",
|
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"`Transforms <transforms_tutorial.html>`_ ||\n",
|
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"`Build Model <buildmodel_tutorial.html>`_ ||\n",
|
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"`Autograd <autogradqs_tutorial.html>`_ ||\n",
|
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"`Optimization <optimization_tutorial.html>`_ ||\n",
|
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"`Save & Load Model <saveloadrun_tutorial.html>`_\n",
|
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"\n",
|
||||
"Quickstart\n",
|
||||
"===================\n",
|
||||
"This section runs through the API for common tasks in machine learning. Refer to the links in each section to dive deeper.\n",
|
||||
"\n",
|
||||
"Working with data\n",
|
||||
"-----------------\n",
|
||||
"PyTorch has two `primitives to work with data <https://pytorch.org/docs/stable/data.html>`_:\n",
|
||||
"``torch.utils.data.DataLoader`` and ``torch.utils.data.Dataset``.\n",
|
||||
"``Dataset`` stores the samples and their corresponding labels, and ``DataLoader`` wraps an iterable around\n",
|
||||
"the ``Dataset``.\n",
|
||||
"\n",
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"metadata": {
|
||||
"collapsed": false,
|
||||
"jupyter": {
|
||||
"outputs_hidden": false
|
||||
}
|
||||
},
|
||||
"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, Lambda, Compose\n",
|
||||
"import matplotlib.pyplot as plt"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"PyTorch offers domain-specific libraries such as `TorchText <https://pytorch.org/text/stable/index.html>`_,\n",
|
||||
"`TorchVision <https://pytorch.org/vision/stable/index.html>`_, and `TorchAudio <https://pytorch.org/audio/stable/index.html>`_,\n",
|
||||
"all of which include datasets. For this tutorial, we will be using a TorchVision dataset.\n",
|
||||
"\n",
|
||||
"The ``torchvision.datasets`` module contains ``Dataset`` objects for many real-world vision data like\n",
|
||||
"CIFAR, COCO (`full list here <https://pytorch.org/vision/stable/datasets.html>`_). In this tutorial, we\n",
|
||||
"use the FashionMNIST dataset. Every TorchVision ``Dataset`` includes two arguments: ``transform`` and\n",
|
||||
"``target_transform`` to modify the samples and labels respectively.\n",
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"metadata": {
|
||||
"collapsed": false,
|
||||
"jupyter": {
|
||||
"outputs_hidden": false
|
||||
}
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"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"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"application/vnd.jupyter.widget-view+json": {
|
||||
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|
||||
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|
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|
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|
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|
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"metadata": {},
|
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"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"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"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"application/vnd.jupyter.widget-view+json": {
|
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|
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|
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"metadata": {},
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|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"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"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"application/vnd.jupyter.widget-view+json": {
|
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|
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|
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|
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|
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|
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|
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"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"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"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"application/vnd.jupyter.widget-view+json": {
|
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"model_id": "418ca86b3df24c84979a54ca66cebe56",
|
||||
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|
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|
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]
|
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},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Extracting data/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz to data/FashionMNIST/raw\n",
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"/home/ta180m/.local/lib/python3.9/site-packages/torchvision/datasets/mnist.py:498: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /build/python-pytorch/src/pytorch-1.9.0-opt/torch/csrc/utils/tensor_numpy.cpp:174.)\n",
|
||||
" return torch.from_numpy(parsed.astype(m[2], copy=False)).view(*s)\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Download training data from open datasets.\n",
|
||||
"training_data = 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.FashionMNIST(\n",
|
||||
" root=\"data\",\n",
|
||||
" train=False,\n",
|
||||
" download=True,\n",
|
||||
" transform=ToTensor(),\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"We pass the ``Dataset`` as an argument to ``DataLoader``. This wraps an iterable over our dataset, and supports\n",
|
||||
"automatic batching, sampling, shuffling and multiprocess data loading. Here we define a batch size of 64, i.e. each element\n",
|
||||
"in the dataloader iterable will return a batch of 64 features and labels.\n",
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"metadata": {
|
||||
"collapsed": false,
|
||||
"jupyter": {
|
||||
"outputs_hidden": false
|
||||
}
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Shape of X [N, C, H, W]: torch.Size([64, 1, 28, 28])\n",
|
||||
"Shape of y: torch.Size([64]) torch.int64\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"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 test_dataloader:\n",
|
||||
" print(\"Shape of X [N, C, H, W]: \", X.shape)\n",
|
||||
" print(\"Shape of y: \", y.shape, y.dtype)\n",
|
||||
" break"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Read more about `loading data in PyTorch <data_tutorial.html>`_.\n",
|
||||
"\n",
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"--------------\n",
|
||||
"\n",
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Creating Models\n",
|
||||
"------------------\n",
|
||||
"To define a neural network in PyTorch, we create a class that inherits\n",
|
||||
"from `nn.Module <https://pytorch.org/docs/stable/generated/torch.nn.Module.html>`_. We define the layers of the network\n",
|
||||
"in the ``__init__`` function and specify how data will pass through the network in the ``forward`` function. To accelerate\n",
|
||||
"operations in the neural network, we move it to the GPU if available.\n",
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"metadata": {
|
||||
"collapsed": false,
|
||||
"jupyter": {
|
||||
"outputs_hidden": false
|
||||
}
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Using cpu device\n",
|
||||
"NeuralNetwork(\n",
|
||||
" (flatten): Flatten(start_dim=1, end_dim=-1)\n",
|
||||
" (linear_relu_stack): Sequential(\n",
|
||||
" (0): Linear(in_features=784, out_features=512, bias=True)\n",
|
||||
" (1): ReLU()\n",
|
||||
" (2): Linear(in_features=512, out_features=512, bias=True)\n",
|
||||
" (3): ReLU()\n",
|
||||
" (4): Linear(in_features=512, out_features=10, bias=True)\n",
|
||||
" (5): ReLU()\n",
|
||||
" )\n",
|
||||
")\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Get cpu or gpu device for training.\n",
|
||||
"device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
|
||||
"print(\"Using {} device\".format(device))\n",
|
||||
"\n",
|
||||
"# Define model\n",
|
||||
"class NeuralNetwork(nn.Module):\n",
|
||||
" def __init__(self):\n",
|
||||
" super(NeuralNetwork, self).__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",
|
||||
" nn.ReLU()\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": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Read more about `building neural networks in PyTorch <buildmodel_tutorial.html>`_.\n",
|
||||
"\n",
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"--------------\n",
|
||||
"\n",
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Optimizing the Model Parameters\n",
|
||||
"----------------------------------------\n",
|
||||
"To train a model, we need a `loss function <https://pytorch.org/docs/stable/nn.html#loss-functions>`_\n",
|
||||
"and an `optimizer <https://pytorch.org/docs/stable/optim.html>`_.\n",
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"metadata": {
|
||||
"collapsed": false,
|
||||
"jupyter": {
|
||||
"outputs_hidden": false
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"loss_fn = nn.CrossEntropyLoss()\n",
|
||||
"optimizer = torch.optim.SGD(model.parameters(), lr=1e-3)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"In a single training loop, the model makes predictions on the training dataset (fed to it in batches), and\n",
|
||||
"backpropagates the prediction error to adjust the model's parameters.\n",
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"metadata": {
|
||||
"collapsed": false,
|
||||
"jupyter": {
|
||||
"outputs_hidden": false
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def train(dataloader, model, loss_fn, optimizer):\n",
|
||||
" size = len(dataloader.dataset)\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",
|
||||
" optimizer.zero_grad()\n",
|
||||
" loss.backward()\n",
|
||||
" optimizer.step()\n",
|
||||
"\n",
|
||||
" if batch % 100 == 0:\n",
|
||||
" loss, current = loss.item(), batch * len(X)\n",
|
||||
" print(f\"loss: {loss:>7f} [{current:>5d}/{size:>5d}]\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"We also check the model's performance against the test dataset to ensure it is learning.\n",
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"metadata": {
|
||||
"collapsed": false,
|
||||
"jupyter": {
|
||||
"outputs_hidden": false
|
||||
}
|
||||
},
|
||||
"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": {},
|
||||
"source": [
|
||||
"The training process is conducted over several iterations (*epochs*). During each epoch, the model learns\n",
|
||||
"parameters to make better predictions. We print the model's accuracy and loss at each epoch; we'd like to see the\n",
|
||||
"accuracy increase and the loss decrease with every epoch.\n",
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 11,
|
||||
"metadata": {
|
||||
"collapsed": false,
|
||||
"jupyter": {
|
||||
"outputs_hidden": false
|
||||
}
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Epoch 1\n",
|
||||
"-------------------------------\n",
|
||||
"loss: 1.758146 [ 0/60000]\n",
|
||||
"loss: 1.820034 [ 6400/60000]\n",
|
||||
"loss: 1.846449 [12800/60000]\n",
|
||||
"loss: 1.975245 [19200/60000]\n",
|
||||
"loss: 1.612495 [25600/60000]\n",
|
||||
"loss: 1.748993 [32000/60000]\n",
|
||||
"loss: 1.628008 [38400/60000]\n",
|
||||
"loss: 1.655061 [44800/60000]\n",
|
||||
"loss: 1.770255 [51200/60000]\n",
|
||||
"loss: 1.654287 [57600/60000]\n",
|
||||
"Test Error: \n",
|
||||
" Accuracy: 37.7%, Avg loss: 1.749445 \n",
|
||||
"\n",
|
||||
"Epoch 2\n",
|
||||
"-------------------------------\n",
|
||||
"loss: 1.670408 [ 0/60000]\n",
|
||||
"loss: 1.743051 [ 6400/60000]\n",
|
||||
"loss: 1.773547 [12800/60000]\n",
|
||||
"loss: 1.924395 [19200/60000]\n",
|
||||
"loss: 1.529726 [25600/60000]\n",
|
||||
"loss: 1.692361 [32000/60000]\n",
|
||||
"loss: 1.559834 [38400/60000]\n",
|
||||
"loss: 1.593531 [44800/60000]\n",
|
||||
"loss: 1.712157 [51200/60000]\n",
|
||||
"loss: 1.605115 [57600/60000]\n",
|
||||
"Test Error: \n",
|
||||
" Accuracy: 38.1%, Avg loss: 1.694516 \n",
|
||||
"\n",
|
||||
"Epoch 3\n",
|
||||
"-------------------------------\n",
|
||||
"loss: 1.607648 [ 0/60000]\n",
|
||||
"loss: 1.684907 [ 6400/60000]\n",
|
||||
"loss: 1.716139 [12800/60000]\n",
|
||||
"loss: 1.888849 [19200/60000]\n",
|
||||
"loss: 1.474264 [25600/60000]\n",
|
||||
"loss: 1.652733 [32000/60000]\n",
|
||||
"loss: 1.514825 [38400/60000]\n",
|
||||
"loss: 1.549373 [44800/60000]\n",
|
||||
"loss: 1.670293 [51200/60000]\n",
|
||||
"loss: 1.571395 [57600/60000]\n",
|
||||
"Test Error: \n",
|
||||
" Accuracy: 39.0%, Avg loss: 1.653676 \n",
|
||||
"\n",
|
||||
"Epoch 4\n",
|
||||
"-------------------------------\n",
|
||||
"loss: 1.561757 [ 0/60000]\n",
|
||||
"loss: 1.640771 [ 6400/60000]\n",
|
||||
"loss: 1.669458 [12800/60000]\n",
|
||||
"loss: 1.862879 [19200/60000]\n",
|
||||
"loss: 1.435348 [25600/60000]\n",
|
||||
"loss: 1.623189 [32000/60000]\n",
|
||||
"loss: 1.482370 [38400/60000]\n",
|
||||
"loss: 1.515045 [44800/60000]\n",
|
||||
"loss: 1.638349 [51200/60000]\n",
|
||||
"loss: 1.545919 [57600/60000]\n",
|
||||
"Test Error: \n",
|
||||
" Accuracy: 39.9%, Avg loss: 1.621615 \n",
|
||||
"\n",
|
||||
"Epoch 5\n",
|
||||
"-------------------------------\n",
|
||||
"loss: 1.525517 [ 0/60000]\n",
|
||||
"loss: 1.604991 [ 6400/60000]\n",
|
||||
"loss: 1.630397 [12800/60000]\n",
|
||||
"loss: 1.841878 [19200/60000]\n",
|
||||
"loss: 1.406707 [25600/60000]\n",
|
||||
"loss: 1.599460 [32000/60000]\n",
|
||||
"loss: 1.456716 [38400/60000]\n",
|
||||
"loss: 1.485950 [44800/60000]\n",
|
||||
"loss: 1.612476 [51200/60000]\n",
|
||||
"loss: 1.525381 [57600/60000]\n",
|
||||
"Test Error: \n",
|
||||
" Accuracy: 40.7%, Avg loss: 1.595456 \n",
|
||||
"\n",
|
||||
"Done!\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"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": {},
|
||||
"source": [
|
||||
"Read more about `Training your model <optimization_tutorial.html>`_.\n",
|
||||
"\n",
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"--------------\n",
|
||||
"\n",
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Saving Models\n",
|
||||
"-------------\n",
|
||||
"A common way to save a model is to serialize the internal state dictionary (containing the model parameters).\n",
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 12,
|
||||
"metadata": {
|
||||
"collapsed": false,
|
||||
"jupyter": {
|
||||
"outputs_hidden": false
|
||||
}
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Saved PyTorch Model State to model.pth\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"torch.save(model.state_dict(), \"model.pth\")\n",
|
||||
"print(\"Saved PyTorch Model State to model.pth\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Loading Models\n",
|
||||
"----------------------------\n",
|
||||
"\n",
|
||||
"The process for loading a model includes re-creating the model structure and loading\n",
|
||||
"the state dictionary into it.\n",
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 13,
|
||||
"metadata": {
|
||||
"collapsed": false,
|
||||
"jupyter": {
|
||||
"outputs_hidden": false
|
||||
}
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"<All keys matched successfully>"
|
||||
]
|
||||
},
|
||||
"execution_count": 13,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"model = NeuralNetwork()\n",
|
||||
"model.load_state_dict(torch.load(\"model.pth\"))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"This model can now be used to make predictions.\n",
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 17,
|
||||
"metadata": {
|
||||
"collapsed": false,
|
||||
"jupyter": {
|
||||
"outputs_hidden": false
|
||||
}
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Predicted: \"Ankle boot\", Actual: \"Ankle boot\"\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"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",
|
||||
" pred = model(x)\n",
|
||||
" predicted, actual = classes[pred[0].argmax(0)], classes[y]\n",
|
||||
" print(f'Predicted: \"{predicted}\", Actual: \"{actual}\"')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Read more about `Saving & Loading your model <saveloadrun_tutorial.html>`_.\n",
|
||||
"\n",
|
||||
"\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3 (ipykernel)",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.9.6"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 4
|
||||
}
|
||||
78
test.ipynb
Normal file
78
test.ipynb
Normal file
|
|
@ -0,0 +1,78 @@
|
|||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"id": "5a2a1e37-f4db-4963-a0ac-7b39353f826f",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"tensor([[0.2226, 0.2227, 0.1347],\n",
|
||||
" [0.7975, 0.3823, 0.9395],\n",
|
||||
" [0.1675, 0.9185, 0.4175],\n",
|
||||
" [0.0476, 0.1244, 0.6878],\n",
|
||||
" [0.5177, 0.7245, 0.2723]])\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"import torch\n",
|
||||
"x = torch.rand(5, 3)\n",
|
||||
"print(x)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"id": "53469c52-9438-4d62-aa2a-e93fb3fef23b",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"False"
|
||||
]
|
||||
},
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"torch.cuda.is_available()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "f13c7010-acb0-4870-aa6f-1294701cfc7e",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3 (ipykernel)",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.9.6"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
Loading…
Reference in a new issue