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.. _sphx_glr_beginner_basics_quickstart_tutorial.py:


`基础知识 <intro.html>`_ ||
**快速入门** ||
`张量 <tensorqs_tutorial.html>`_ ||
`数据集与数据加载器 <data_tutorial.html>`_ ||
`Transforms <transforms_tutorial.html>`_ ||
`构建神经网络 <buildmodel_tutorial.html>`_ ||
`自动微分 <autogradqs_tutorial.html>`_ ||
`优化模型参数 <optimization_tutorial.html>`_ ||
`保存和加载模型 <saveloadrun_tutorial.html>`_

快速入门
==========

本节将介绍机器学习任务中常用的API。想更深入了解各模块内容，可参考每节文末处的链接。

处理数据
-----------------

PyTorch 提供了两个用于 `处理数据的原语<https://pytorch.org/docs/stable/data.html>`:
`torch.utils.data.DataLoader` 和 `torch.utils.data.Dataset`。`Dataset`存储样本及其对应的标签，而`DataLoader`则在`Dataset`外部封装一层，变为可迭代对象。

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.. code-block:: default


    import torch
    from torch import nn
    from torch.utils.data import DataLoader
    from torchvision import datasets
    from torchvision.transforms import ToTensor


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PyTorch 提供了特定领域的库，例如
[TorchText](https://pytorch.org/text/stable/index.html)，
[TorchVision](https://pytorch.org/vision/stable/index.html)，和
[TorchAudio](https://pytorch.org/audio/stable/index.html)，
所有这些库都包含了对应数据集。在本教程中，我们将使用 TorchVision 数据集。

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.. code-block:: default


    # `torchvision.datasets` 模块包含许多现实世界视觉数据`Dataset`，例如 CIFAR、COCO
    # ([数据集列表](https://pytorch.org/vision/stable/datasets.html))。
    # 在本教程中，我们使用 `FashionMNIST`数据集。每个TorchVision `Dataset`包括两个参数：`transform` 和 `target_transform`，分别用于修改样本数据和标签。

    # Download training data from open datasets.
    training_data = datasets.FashionMNIST(
        root="data",
        train=True,
        download=True,
        transform=ToTensor(),
    )

    # Download test data from open datasets.
    test_data = datasets.FashionMNIST(
        root="data",
        train=False,
        download=True,
        transform=ToTensor(),
    )


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我们将 `Dataset` 作为参数传递给 `DataLoader`，在数据集上封装了一个可迭代对象，
支持自动批处理、采样、打乱和多进程数据加载。这里我们定义一个批处理大小为 64，
即 dataloader 每批将返回大小为 64 的特征数据和标签。

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.. code-block:: default


    batch_size = 64

    # Create data loaders.
    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


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获取更多关于 `PyTorch数据加载<data_tutorial.html>`的信息。

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--------------


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创建模型
------------------
要在 PyTorch 中定义一个神经网络，我们需要创建一个继承自 `nn.Module<https://pytorch.org/docs/stable/generated/torch.nn.Module.html> 的类。
我们在 `__init__` 函数中定义网络的层，并在 `forward` 函数中指定数据如何经过网络。为了加速神经网络中的运算，
我们将其移到 GPU 或 MPS（如果可用）上。

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.. code-block:: default


    # Get cpu, gpu or mps device for training.
    device = (
        "cuda"
        if torch.cuda.is_available()
        else "mps"
        if torch.backends.mps.is_available()
        else "cpu"
    )
    print(f"Using {device} device")

    # Define model


    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)


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获取更多关于 `PyTorch构建神经网络<buildmodel_tutorial.html>`的内容。

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--------------


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优化模型参数
----------------------------------------
练一个模型，我们需要一个`损失函数`<https://pytorch.org/docs/stable/nn.html#loss-functions>
和一个`优化器`https://pytorch.org/docs/stable/optim.html>。

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.. code-block:: default



    loss_fn = nn.CrossEntropyLoss()
    optimizer = torch.optim.SGD(model.parameters(), lr=1e-3)



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在单个训练循环中，模型对训练数据集（分批输入）进行预测，并通过反向传播预测误差来调整模型的参数。

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.. code-block:: default


    def train(dataloader, model, loss_fn, optimizer):
        size = len(dataloader.dataset)
        model.train()
        for batch, (X, y) in enumerate(dataloader):
            X, y = X.to(device), y.to(device)

            # Compute prediction error
            pred = model(X)
            loss = loss_fn(pred, y)

            # Backpropagation
            loss.backward()
            optimizer.step()
            optimizer.zero_grad()

            if batch % 100 == 0:
                loss, current = loss.item(), (batch + 1) * len(X)
                print(f"loss: {loss:>7f}  [{current:>5d}/{size:>5d}]")


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我们还需检查模型在测试数据集上的效果，以确保它在持续学习。

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.. code-block:: default



    def test(dataloader, model, loss_fn):
        size = len(dataloader.dataset)
        num_batches = len(dataloader)
        model.eval()
        test_loss, correct = 0, 0
        with torch.no_grad():
            for X, y in dataloader:
                X, y = X.to(device), y.to(device)
                pred = model(X)
                test_loss += loss_fn(pred, y).item()
                correct += (pred.argmax(1) == y).type(torch.float).sum().item()
        test_loss /= num_batches
        correct /= size
        print(
            f"Test Error: \n Accuracy: {(100*correct):>0.1f}%, Avg loss: {test_loss:>8f} \n")


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通过多次迭代（*epochs*）进行训练。在每个迭代过程中，模型通过对参数的学习以提高预测准确性。
我们在每个 epoch 打印模型的准确率和损失；我们希望看到随着每个 epoch 训练，模型预测准确率不断提高，
损失逐渐减少。

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.. code-block:: default



    epochs = 5
    for t in range(epochs):
        print(f"Epoch {t+1}\n-------------------------------")
        train(train_dataloader, model, loss_fn, optimizer)
        test(test_dataloader, model, loss_fn)
    print("Done!")


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Read more about `Training your model <optimization_tutorial.html>`_.
获取更多关于 `训练模型 <optimization_tutorial.html>`的内容。

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--------------


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保存模型
-------------
保存模型的常见方法是将内部状态字典（包含模型参数）序列化。

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.. code-block:: default


    torch.save(model.state_dict(), "model.pth")
    print("Saved PyTorch Model State to model.pth")



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加载模型
----------------------------
加载模型的过程包括重新创建模型结构并加载其内部状态字典。

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.. code-block:: default


    model = NeuralNetwork().to(device)
    model.load_state_dict(torch.load("model.pth"))


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这个模型现在可以用来进行预测了。

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.. code-block:: default


    classes = [
        "T-shirt/top",
        "Trouser",
        "Pullover",
        "Dress",
        "Coat",
        "Sandal",
        "Shirt",
        "Sneaker",
        "Bag",
        "Ankle boot",
    ]

    model.eval()
    x, y = test_data[0][0], test_data[0][1]
    with torch.no_grad():
        x = x.to(device)
        pred = model(x)
        predicted, actual = classes[pred[0].argmax(0)], classes[y]
        print(f'Predicted: "{predicted}", Actual: "{actual}"')



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获取更多有关 `保存和加载模型 <saveloadrun_tutorial.html>`的内容。


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