快速入门
译者：Daydaylight
项目地址：https://pytorch.apachecn.org/2.0/tutorials/beginner/basics/quickstart_tutorial
原始地址：https://pytorch.org/tutorials/beginner/basics/quickstart_tutorial.html
本节介绍机器学习中常见任务的 API。请参考每个部分中的链接，以便进一步深入。
处理数据
PyTorch有两个处理数据的基本操作：torch.utils.data.DataLoader和torch.utils.data.Dataset。Dataset用于存储样本及其对应的标签，而DataLoader则围绕Dataset包装了一个可迭代的数据加载器。
import torch
from torch import nn
from torch.utils.data import DataLoader
from torchvision import datasets
from torchvision.transforms import ToTensor
PyTorch 提供特定于领域的库，如 TorchText, TorchVision 和 TorchAudio，所有这些库都包含数据集。对于本教程，我们将使用 TorchVision 数据集。
torchvision.datasets模块包含了许多真实世界视觉数据的 Dataset对象，比如 CIFAR、 COCO (完整列表在这里)。在本教程中，我们使用 FashionMNIST 数据集。每个 TorchVision Dataset都包含两个参数: transform 和 target_transform，分别用于转换样本和标签。
# 从开源数据集下载训练数据。
training_data = datasets.FashionMNIST(
root="data",
train=True,
download=True,
transform=ToTensor(),
)
# 从开源数据集下载测试数据。
test_data = datasets.FashionMNIST(
root="data",
train=False,
download=True,
transform=ToTensor(),
)
Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz
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
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Extracting data/FashionMNIST/raw/train-images-idx3-ubyte.gz to data/FashionMNIST/raw
Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz
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
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Extracting data/FashionMNIST/raw/train-labels-idx1-ubyte.gz to data/FashionMNIST/raw
Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz
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
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Extracting data/FashionMNIST/raw/t10k-images-idx3-ubyte.gz to data/FashionMNIST/raw
Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz
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
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Extracting data/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz to data/FashionMNIST/raw
We pass the Dataset as an argument to DataLoader. This wraps an iterable over our dataset, and supports
automatic batching, sampling, shuffling and multiprocess data loading. Here we define a batch size of 64, i.e. each element
in the dataloader iterable will return a batch of 64 features and labels.
我们将Dataset作为参数传递给DataLoader。这将在我们的数据集上包装一个迭代器，并支持自动批处理、采样、随机打乱和多进程数据加载。
在这里，我们定义了一个大小为64的批处理，即 DataLoader 迭代器中的每个元素都会返回一个由64个特征和标签组成的批次数据。
batch_size = 64
# 创建数据加载器
train_dataloader = DataLoader(training_data, batch_size=batch_size)
test_dataloader = DataLoader(test_data, batch_size=batch_size)
for X, y in test_dataloader:
print(f"Shape of X [N, C, H, W]: {X.shape}")
print(f"Shape of y: {y.shape} {y.dtype}")
break
阅读有关在 PyTorch 中加载数据的更多信息。
创建模型
To define a neural network in PyTorch, we create a class that inherits
from nn.Module. We define the layers of the network
in the __init__ function and specify how data will pass through the network in the forward function. To accelerate
operations in the neural network, we move it to the GPU or MPS if available.
为了在 PyTorch 中定义一个神经网络，我们需要创建了一个继承自 nn.Module 的自定义类。我们在 __init__ 方法中定义网络的层次结构，并在 forward 方法中指定数据将如何通过网络的各个层。为了加速神经网络中的操作，我们将其移动到 GPU 或 MPS (如果有的话)。
# 获取 cpu, gpu 或 mps 设备用于加速训练.
device = (
"cuda"
if torch.cuda.is_available()
else "mps"
if torch.backends.mps.is_available()
else "cpu"
)
print(f"Using {device} device")
# 定义神经网络
class NeuralNetwork(nn.Module):
def __init__(self):
super().__init__()
self.flatten = nn.Flatten()
self.linear_relu_stack = nn.Sequential(
nn.Linear(28*28, 512),
nn.ReLU(),
nn.Linear(512, 512),
nn.ReLU(),
nn.Linear(512, 10)
)
def forward(self, x):
x = self.flatten(x)
logits = self.linear_relu_stack(x)
return logits
model = NeuralNetwork().to(device)
print(model)
输出：
Using cuda device
NeuralNetwork(
(flatten): Flatten(start_dim=1, end_dim=-1)
(linear_relu_stack): Sequential(
(0): Linear(in_features=784, out_features=512, bias=True)
(1): ReLU()
(2): Linear(in_features=512, out_features=512, bias=True)
(3): ReLU()
(4): Linear(in_features=512, out_features=10, bias=True)
)
)
