646 lines
18 KiB
Text
646 lines
18 KiB
Text
{
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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",
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"`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",
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"Quickstart\n",
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"===================\n",
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"This section runs through the API for common tasks in machine learning. Refer to the links in each section to dive deeper.\n",
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"\n",
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"Working with data\n",
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"-----------------\n",
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"PyTorch has two `primitives to work with data <https://pytorch.org/docs/stable/data.html>`_:\n",
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"``torch.utils.data.DataLoader`` and ``torch.utils.data.Dataset``.\n",
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"``Dataset`` stores the samples and their corresponding labels, and ``DataLoader`` wraps an iterable around\n",
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"the ``Dataset``.\n",
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"\n",
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"\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": 1,
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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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"import torch\n",
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"from torch import nn\n",
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"from torch.utils.data import DataLoader\n",
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"from torchvision import datasets\n",
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"from torchvision.transforms import ToTensor, Lambda, Compose\n",
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"import 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",
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"`TorchVision <https://pytorch.org/vision/stable/index.html>`_, and `TorchAudio <https://pytorch.org/audio/stable/index.html>`_,\n",
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"all of which include datasets. For this tutorial, we will be using a TorchVision dataset.\n",
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"\n",
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"The ``torchvision.datasets`` module contains ``Dataset`` objects for many real-world vision data like\n",
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"CIFAR, COCO (`full list here <https://pytorch.org/vision/stable/datasets.html>`_). In this tutorial, we\n",
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"use the FashionMNIST dataset. Every TorchVision ``Dataset`` includes two arguments: ``transform`` and\n",
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"``target_transform`` to modify the samples and labels respectively.\n",
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"\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": 2,
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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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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/home/ta180m/git/PyTorch/.venv/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 /pytorch/torch/csrc/utils/tensor_numpy.cpp:180.)\n",
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" return torch.from_numpy(parsed.astype(m[2], copy=False)).view(*s)\n"
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]
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}
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],
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"source": [
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"# Download training data from open datasets.\n",
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"training_data = datasets.FashionMNIST(\n",
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" root=\"data\",\n",
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" train=True,\n",
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" download=True,\n",
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" transform=ToTensor(),\n",
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")\n",
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"\n",
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"# Download test data from open datasets.\n",
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"test_data = datasets.FashionMNIST(\n",
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" root=\"data\",\n",
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" train=False,\n",
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" download=True,\n",
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" transform=ToTensor(),\n",
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")"
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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\n",
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"automatic batching, sampling, shuffling and multiprocess data loading. Here we define a batch size of 64, i.e. each element\n",
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"in the dataloader iterable will return a batch of 64 features and labels.\n",
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"\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": 3,
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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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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Shape of X [N, C, H, W]: torch.Size([64, 1, 28, 28])\n",
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"Shape of y: torch.Size([64]) torch.int64\n"
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]
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}
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],
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"source": [
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"batch_size = 64\n",
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"\n",
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"# Create data loaders.\n",
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"train_dataloader = DataLoader(training_data, batch_size=batch_size)\n",
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"test_dataloader = DataLoader(test_data, batch_size=batch_size)\n",
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"\n",
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"for X, y in test_dataloader:\n",
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" print(\"Shape of X [N, C, H, W]: \", X.shape)\n",
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" print(\"Shape of y: \", y.shape, y.dtype)\n",
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" 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",
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"\n",
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"\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",
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"\n",
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"\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",
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"------------------\n",
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"To define a neural network in PyTorch, we create a class that inherits\n",
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"from `nn.Module <https://pytorch.org/docs/stable/generated/torch.nn.Module.html>`_. We define the layers of the network\n",
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"in the ``__init__`` function and specify how data will pass through the network in the ``forward`` function. To accelerate\n",
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"operations in the neural network, we move it to the GPU if available.\n",
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"\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": 4,
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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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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Using cpu device\n",
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"NeuralNetwork(\n",
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" (flatten): Flatten(start_dim=1, end_dim=-1)\n",
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" (linear_relu_stack): Sequential(\n",
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" (0): Linear(in_features=784, out_features=512, bias=True)\n",
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" (1): ReLU()\n",
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" (2): Linear(in_features=512, out_features=512, bias=True)\n",
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" (3): ReLU()\n",
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" (4): Linear(in_features=512, out_features=10, bias=True)\n",
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" (5): ReLU()\n",
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" )\n",
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")\n"
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]
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}
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],
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"source": [
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"# Get cpu or gpu device for training.\n",
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"device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
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"print(\"Using {} device\".format(device))\n",
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"\n",
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"# Define model\n",
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"class NeuralNetwork(nn.Module):\n",
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" def __init__(self):\n",
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" super(NeuralNetwork, self).__init__()\n",
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" self.flatten = nn.Flatten()\n",
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" self.linear_relu_stack = nn.Sequential(\n",
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" nn.Linear(28*28, 512),\n",
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" nn.ReLU(),\n",
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" nn.Linear(512, 512),\n",
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" nn.ReLU(),\n",
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" nn.Linear(512, 10),\n",
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" nn.ReLU()\n",
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" )\n",
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"\n",
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" def forward(self, x):\n",
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" x = self.flatten(x)\n",
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" logits = self.linear_relu_stack(x)\n",
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" return logits\n",
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"\n",
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"model = NeuralNetwork().to(device)\n",
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"print(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",
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"\n",
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"\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",
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"\n",
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"\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",
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"----------------------------------------\n",
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"To train a model, we need a `loss function <https://pytorch.org/docs/stable/nn.html#loss-functions>`_\n",
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"and an `optimizer <https://pytorch.org/docs/stable/optim.html>`_.\n",
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"\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": 5,
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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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"loss_fn = nn.CrossEntropyLoss()\n",
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"optimizer = 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\n",
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"backpropagates the prediction error to adjust the model's parameters.\n",
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"\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": 6,
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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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"def train(dataloader, model, loss_fn, optimizer):\n",
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" size = len(dataloader.dataset)\n",
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" for batch, (X, y) in enumerate(dataloader):\n",
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" X, y = X.to(device), y.to(device)\n",
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"\n",
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" # Compute prediction error\n",
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" pred = model(X)\n",
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" loss = loss_fn(pred, y)\n",
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"\n",
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" # Backpropagation\n",
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" optimizer.zero_grad()\n",
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" loss.backward()\n",
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" optimizer.step()\n",
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"\n",
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" if batch % 100 == 0:\n",
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" loss, current = loss.item(), batch * len(X)\n",
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" 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",
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"\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": 7,
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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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"def test(dataloader, model, loss_fn):\n",
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" size = len(dataloader.dataset)\n",
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" num_batches = len(dataloader)\n",
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" model.eval()\n",
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" test_loss, correct = 0, 0\n",
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" with torch.no_grad():\n",
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" for X, y in dataloader:\n",
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" X, y = X.to(device), y.to(device)\n",
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" pred = model(X)\n",
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" test_loss += loss_fn(pred, y).item()\n",
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" correct += (pred.argmax(1) == y).type(torch.float).sum().item()\n",
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" test_loss /= num_batches\n",
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" correct /= size\n",
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" 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\n",
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"parameters to make better predictions. We print the model's accuracy and loss at each epoch; we'd like to see the\n",
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"accuracy increase and the loss decrease with every epoch.\n",
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"\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": 8,
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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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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Epoch 1\n",
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"-------------------------------\n",
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"loss: 2.300270 [ 0/60000]\n",
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"loss: 2.290948 [ 6400/60000]\n",
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"loss: 2.280627 [12800/60000]\n",
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"loss: 2.283042 [19200/60000]\n",
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"loss: 2.268817 [25600/60000]\n",
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"loss: 2.262104 [32000/60000]\n",
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"loss: 2.248093 [38400/60000]\n",
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"loss: 2.233517 [44800/60000]\n",
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"loss: 2.234299 [51200/60000]\n",
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"loss: 2.234841 [57600/60000]\n",
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"Test Error: \n",
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" Accuracy: 51.1%, Avg loss: 2.221716 \n",
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"\n",
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"Epoch 2\n",
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"-------------------------------\n",
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"loss: 2.203925 [ 0/60000]\n",
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"loss: 2.200477 [ 6400/60000]\n",
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"loss: 2.180246 [12800/60000]\n",
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"loss: 2.213840 [19200/60000]\n",
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"loss: 2.155768 [25600/60000]\n",
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"loss: 2.154045 [32000/60000]\n",
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"loss: 2.130811 [38400/60000]\n",
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"loss: 2.101326 [44800/60000]\n",
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"loss: 2.118105 [51200/60000]\n",
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"loss: 2.116674 [57600/60000]\n",
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"Test Error: \n",
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" Accuracy: 51.0%, Avg loss: 2.093179 \n",
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"\n",
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"Epoch 3\n",
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"-------------------------------\n",
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"loss: 2.054594 [ 0/60000]\n",
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"loss: 2.047193 [ 6400/60000]\n",
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"loss: 2.009665 [12800/60000]\n",
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"loss: 2.093936 [19200/60000]\n",
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"loss: 1.965194 [25600/60000]\n",
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"loss: 1.976750 [32000/60000]\n",
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"loss: 1.938592 [38400/60000]\n",
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"loss: 1.889513 [44800/60000]\n",
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"loss: 1.942611 [51200/60000]\n",
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"loss: 1.936963 [57600/60000]\n",
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"Test Error: \n",
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" Accuracy: 51.2%, Avg loss: 1.900150 \n",
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"\n",
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"Epoch 4\n",
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"-------------------------------\n",
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"loss: 1.837173 [ 0/60000]\n",
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"loss: 1.822518 [ 6400/60000]\n",
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"loss: 1.775139 [12800/60000]\n",
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"loss: 1.925843 [19200/60000]\n",
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"loss: 1.731390 [25600/60000]\n",
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"loss: 1.778743 [32000/60000]\n",
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"loss: 1.714922 [38400/60000]\n",
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"loss: 1.670009 [44800/60000]\n",
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"loss: 1.755909 [51200/60000]\n",
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"loss: 1.763030 [57600/60000]\n",
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"Test Error: \n",
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" Accuracy: 52.5%, Avg loss: 1.711389 \n",
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"\n",
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"Epoch 5\n",
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"-------------------------------\n",
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"loss: 1.621799 [ 0/60000]\n",
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"loss: 1.615258 [ 6400/60000]\n",
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"loss: 1.567131 [12800/60000]\n",
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"loss: 1.768921 [19200/60000]\n",
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"loss: 1.539987 [25600/60000]\n",
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"loss: 1.627408 [32000/60000]\n",
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"loss: 1.533756 [38400/60000]\n",
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"loss: 1.510703 [44800/60000]\n",
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"loss: 1.603583 [51200/60000]\n",
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"loss: 1.636089 [57600/60000]\n",
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"Test Error: \n",
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" Accuracy: 53.2%, Avg loss: 1.568043 \n",
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"\n",
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"Done!\n"
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]
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}
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],
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"source": [
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"epochs = 5\n",
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"for t in range(epochs):\n",
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" print(f\"Epoch {t+1}\\n-------------------------------\")\n",
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" 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": "PyTorch",
|
|
"language": "python",
|
|
"name": "pytorch"
|
|
},
|
|
"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
|
|
}
|