本文是《PyTorch官方教程中文版》系列文章之一，目录链接：[翻译]PyTorch官方教程中文版：目录

本文翻译自PyTorch官方网站，链接地址：Optimization

优化模型参数

现在我们有了模型和数据，是时候进行训练了。训练模型是一个迭代的过程，每次迭代，模型首先执行推理并输出推理结果，然后计算推理结果的误差，最后使用梯度优化模型的参数。有关这一过程的更多细节请观看视频“backpropagation from 3Blue1Brown”。

已有代码

我们将使用在《数据集和数据加载器》和《构建神经网络》这两篇文章中的已有代码。

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

training_data = datasets.FashionMNIST(
    root="data",
    train=True,
    download=True,
    transform=ToTensor()
)

test_data = datasets.FashionMNIST(
    root="data",
    train=False,
    download=True,
    transform=ToTensor()
)

train_dataloader = DataLoader(training_data, batch_size=64)
test_dataloader = DataLoader(test_data, batch_size=64)

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()

上述代码输出：

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

  0%|          | 0/26421880 [00:00<?, ?it/s]
  0%|          | 65536/26421880 [00:00<01:11, 368255.79it/s]
  1%|          | 229376/26421880 [00:00<00:38, 688549.60it/s]
  3%|3         | 819200/26421880 [00:00<00:11, 2313186.02it/s]
  6%|6         | 1703936/26421880 [00:00<00:06, 3617627.02it/s]
 15%|#5        | 3964928/26421880 [00:00<00:02, 8602348.57it/s]
 23%|##2       | 5963776/26421880 [00:00<00:02, 9951096.66it/s]
 33%|###2      | 8617984/26421880 [00:01<00:01, 13917394.47it/s]
 41%|####      | 10715136/26421880 [00:01<00:01, 13488072.19it/s]
 50%|#####     | 13303808/26421880 [00:01<00:00, 16300023.87it/s]
 59%|#####8    | 15564800/26421880 [00:01<00:00, 15297545.00it/s]
 69%|######8   | 18153472/26421880 [00:01<00:00, 17670252.93it/s]
 77%|#######7  | 20414464/26421880 [00:01<00:00, 16207901.21it/s]
 87%|########6 | 22970368/26421880 [00:01<00:00, 18274314.00it/s]
 96%|#########5| 25264128/26421880 [00:01<00:00, 16669617.45it/s]
100%|##########| 26421880/26421880 [00:01<00:00, 13254361.16it/s]
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

  0%|          | 0/29515 [00:00<?, ?it/s]
100%|##########| 29515/29515 [00:00<00:00, 329995.61it/s]
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

  0%|          | 0/4422102 [00:00<?, ?it/s]
  1%|1         | 65536/4422102 [00:00<00:11, 365836.10it/s]
  5%|5         | 229376/4422102 [00:00<00:06, 687195.68it/s]
 14%|#4        | 622592/4422102 [00:00<00:02, 1706700.21it/s]
 32%|###1      | 1409024/4422102 [00:00<00:01, 3000999.19it/s]
 66%|######5   | 2916352/4422102 [00:00<00:00, 6200376.74it/s]
100%|##########| 4422102/4422102 [00:00<00:00, 5457885.10it/s]
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

  0%|          | 0/5148 [00:00<?, ?it/s]
100%|##########| 5148/5148 [00:00<00:00, 40059883.10it/s]
Extracting data/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz to data/FashionMNIST/raw

超参数

超参数是用于控制模型优化过程的可调整参数，设置不同的超参数值会影响模型的训练效果和收敛率（关于调整超参数的更多信息请阅读《Hyperparameter tuning with Ray Tune》）。

我们定义了下列超参数：

周期数 - 整个数据集的迭代次数
批次大小 - 每次处理的数据个数(译者注：越大越耗内存/显存，但整体速度更快)
学习率 - 在每个批次/周期中调整模型参数的速率。较小的值会导致学习速度变慢，而较大的值可能会导致训练期间不可预测的行为。

learning_rate = 1e-3
batch_size = 64
epochs = 5

优化循环

设置好了我们的超参数，就可以使用优化循环来训练模型了，优化循环的每次迭代称为一个周期(epoch)。

每个周期主要包含下列两项主要工作：

训练循环 - 遍历训练集并优化参数，尝试找到最优参数。
校验/测试循环 - 遍历测试集，检查模型性能是否正在提高。

让我们简单地熟悉一下训练循环中使用的一些概念。

损失函数

在训练数据上进行推理时，未训练的神经网络可能无法给出正确的答案。损失函数计算推理的结果与正确答案的差异程度，这就是损失函数的作用。为了计算损失，我们将给定样本数据输入神经网络，并将神经网络的输出与标签值进行比较。

常见的损失函数有用于回归任务的 nn.MSELoss，以及用于分类的 nn.NLLLoss，合并了 nn.LogSoftmax 和 nn.NLLLoss 的 nn.CrossEntropyLoss等。

我们把模型的输出数据传递给 nn.CrossEntropyLoss 函数，它会计算误差。

# Initialize the loss function
loss_fn = nn.CrossEntropyLoss()

优化器

优化是通过模型误差调整模型参数的过程，优化算法决定了如何执行这一过程(在本例中，我们使用随机梯度下降算法(Stochastic Gradient Descent))。所有优化逻辑都封装在优化器对象中，本文使用 SGD 优化器。PyTorch中还有许多不同的优化器，例如 ADAM 和 RMSProp等，它们适用于其他类型的模型和数据。

我们通过注册需要训练的模型参数，并传入学习率超参数来初始化优化器。

optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)

在训练循环中，需要让优化器执行下列三个步骤：

调用 optimizer.zero_grad() 重置模型参数的梯度。默认情况下梯度是叠加的，为了防止重复计算，我们每次迭代时都明确的将其归零。
通过调用 loss.backward() 函数反向传播误差(loss)，PyTorch自动计算每个参数关于误差的梯度。
计算得到梯度后，我们调用 optimizer.step()，利用梯度来调整模型的参数。

完整实现

我们定义了 train_loop 函数作为优化循环，定义了 test_loop 函数来评估模型性能。

def train_loop(dataloader, model, loss_fn, optimizer):
    size = len(dataloader.dataset)
    # Set the model to training mode - important for batch normalization and dropout layers
    # Unnecessary in this situation but added for best practices
    model.train()
    for batch, (X, y) in enumerate(dataloader):
        # Compute prediction and loss
        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}]")


def test_loop(dataloader, model, loss_fn):
    # Set the model to evaluation mode - important for batch normalization and dropout layers
    # Unnecessary in this situation but added for best practices
    model.eval()
    size = len(dataloader.dataset)
    num_batches = len(dataloader)
    test_loss, correct = 0, 0

    # Evaluating the model with torch.no_grad() ensures that no gradients are computed during test mode
    # also serves to reduce unnecessary gradient computations and memory usage for tensors with requires_grad=True
    with torch.no_grad():
        for X, y in dataloader:
            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")

我们初始化了损失函数和优化器，并将其传递给train_loop和test_loop函数。你可以尝试着随意增加或减少周期数，观察模型的性能的变化情况。

loss_fn = nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)

epochs = 10
for t in range(epochs):
    print(f"Epoch {t+1}\n-------------------------------")
    train_loop(train_dataloader, model, loss_fn, optimizer)
    test_loop(test_dataloader, model, loss_fn)
print("Done!")

上述代码输出：

Epoch 1
-------------------------------
loss: 2.298730  [   64/60000]
loss: 2.289123  [ 6464/60000]
loss: 2.273286  [12864/60000]
loss: 2.269406  [19264/60000]
loss: 2.249603  [25664/60000]
loss: 2.229407  [32064/60000]
loss: 2.227368  [38464/60000]
loss: 2.204261  [44864/60000]
loss: 2.206193  [51264/60000]
loss: 2.166651  [57664/60000]
Test Error:
 Accuracy: 50.9%, Avg loss: 2.166725

Epoch 2
-------------------------------
loss: 2.176750  [   64/60000]
loss: 2.169595  [ 6464/60000]
loss: 2.117500  [12864/60000]
loss: 2.129272  [19264/60000]
loss: 2.079674  [25664/60000]
loss: 2.032928  [32064/60000]
loss: 2.050115  [38464/60000]
loss: 1.985236  [44864/60000]
loss: 1.987887  [51264/60000]
loss: 1.907162  [57664/60000]
Test Error:
 Accuracy: 55.9%, Avg loss: 1.915486

Epoch 3
-------------------------------
loss: 1.951612  [   64/60000]
loss: 1.928685  [ 6464/60000]
loss: 1.815709  [12864/60000]
loss: 1.841552  [19264/60000]
loss: 1.732467  [25664/60000]
loss: 1.692914  [32064/60000]
loss: 1.701714  [38464/60000]
loss: 1.610632  [44864/60000]
loss: 1.632870  [51264/60000]
loss: 1.514263  [57664/60000]
Test Error:
 Accuracy: 58.8%, Avg loss: 1.541525

Epoch 4
-------------------------------
loss: 1.616448  [   64/60000]
loss: 1.582892  [ 6464/60000]
loss: 1.427595  [12864/60000]
loss: 1.487950  [19264/60000]
loss: 1.359332  [25664/60000]
loss: 1.364817  [32064/60000]
loss: 1.371491  [38464/60000]
loss: 1.298706  [44864/60000]
loss: 1.336201  [51264/60000]
loss: 1.232145  [57664/60000]
Test Error:
 Accuracy: 62.2%, Avg loss: 1.260237

Epoch 5
-------------------------------
loss: 1.345538  [   64/60000]
loss: 1.327798  [ 6464/60000]
loss: 1.153802  [12864/60000]
loss: 1.254829  [19264/60000]
loss: 1.117322  [25664/60000]
loss: 1.153248  [32064/60000]
loss: 1.171765  [38464/60000]
loss: 1.110263  [44864/60000]
loss: 1.154467  [51264/60000]
loss: 1.070921  [57664/60000]
Test Error:
 Accuracy: 64.1%, Avg loss: 1.089831

Epoch 6
-------------------------------
loss: 1.166889  [   64/60000]
loss: 1.170514  [ 6464/60000]
loss: 0.979435  [12864/60000]
loss: 1.113774  [19264/60000]
loss: 0.973411  [25664/60000]
loss: 1.015192  [32064/60000]
loss: 1.051113  [38464/60000]
loss: 0.993591  [44864/60000]
loss: 1.039709  [51264/60000]
loss: 0.971077  [57664/60000]
Test Error:
 Accuracy: 65.8%, Avg loss: 0.982440

Epoch 7
-------------------------------
loss: 1.045165  [   64/60000]
loss: 1.070583  [ 6464/60000]
loss: 0.862304  [12864/60000]
loss: 1.022265  [19264/60000]
loss: 0.885213  [25664/60000]
loss: 0.919528  [32064/60000]
loss: 0.972762  [38464/60000]
loss: 0.918728  [44864/60000]
loss: 0.961629  [51264/60000]
loss: 0.904379  [57664/60000]
Test Error:
 Accuracy: 66.9%, Avg loss: 0.910167

Epoch 8
-------------------------------
loss: 0.956964  [   64/60000]
loss: 1.002171  [ 6464/60000]
loss: 0.779057  [12864/60000]
loss: 0.958409  [19264/60000]
loss: 0.827240  [25664/60000]
loss: 0.850262  [32064/60000]
loss: 0.917320  [38464/60000]
loss: 0.868384  [44864/60000]
loss: 0.905506  [51264/60000]
loss: 0.856353  [57664/60000]
Test Error:
 Accuracy: 68.3%, Avg loss: 0.858248

Epoch 9
-------------------------------
loss: 0.889765  [   64/60000]
loss: 0.951220  [ 6464/60000]
loss: 0.717035  [12864/60000]
loss: 0.911042  [19264/60000]
loss: 0.786085  [25664/60000]
loss: 0.798370  [32064/60000]
loss: 0.874939  [38464/60000]
loss: 0.832796  [44864/60000]
loss: 0.863254  [51264/60000]
loss: 0.819742  [57664/60000]
Test Error:
 Accuracy: 69.5%, Avg loss: 0.818780

Epoch 10
-------------------------------
loss: 0.836395  [   64/60000]
loss: 0.910220  [ 6464/60000]
loss: 0.668506  [12864/60000]
loss: 0.874338  [19264/60000]
loss: 0.754805  [25664/60000]
loss: 0.758453  [32064/60000]
loss: 0.840451  [38464/60000]
loss: 0.806153  [44864/60000]
loss: 0.830360  [51264/60000]
loss: 0.790281  [57664/60000]
Test Error:
 Accuracy: 71.0%, Avg loss: 0.787271

Done!

进一步阅读：

芸芸小站首发，阅读原文：http://www.xiaoyunyun.net/index.php/archives/317.html

[翻译]PyTorch官方教程中文版：优化模型

优化模型参数

已有代码

超参数

优化循环

损失函数

优化器

完整实现

进一步阅读：

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