使用 PyTorch 训练
译者：Fadegentle
项目地址：https://pytorch.apachecn.org/2.0/tutorials/beginner/introyt/trainingyt
原始地址：https://pytorch.org/tutorials/beginner/introyt/trainingyt.html
请跟随下面的视频或在 youtube 上观看。
入门
在过去的视频中，我们讨论并演示了
- 使用 torch.nn 模块的神经网络层和功能构建模型
- 自动计算梯度的机制，这是基于梯度的模型训练的核心
- 使用 TensorBoard 可视化训练进度和其他活动
在本视频中，我们将为您的清单添加一些新工具：
- 我们将熟悉数据集和数据加载器抽象，以及它们如何简化在训练循环中向模型输入数据的过程
- 我们将讨论特定的损失函数以及何时使用它们
- 我们将了解 PyTorch 优化器，它实现了根据损失函数的结果调整模型权重的算法
最后，我们将把所有这些内容整合在一起，看看一个完整的 PyTorch 训练循环是如何运行的。
Dataset 和 DataLoader
Dataset 和 DataLoader 类封装了从存储中提取数据并将其批量供给训练循环的过程。
Dataset 负责访问和处理单个数据实例。
DataLoader 从 Dataset 中提取数据实例(可自动提取，也可使用您定义的采样器)，分批收集，并返回给训练循环使用。DataLoader 可以处理各种数据集，无论其中数据类型如何。
在本教程中，我们将使用 TorchVision 提供的 Fashion-MNIST 数据集。我们使用 torchvision.transforms.Normalize() 对图块内容的分布进行零中心化和归一化处理，并下载训练和验证数据分片。
import torch
import torchvision
import torchvision.transforms as transforms
# PyTorch TensorBoard support
from torch.utils.tensorboard import SummaryWriter
from datetime import datetime
transform = transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize((0.5,), (0.5,))])
# Create datasets for training & validation, download if necessary
training_set = torchvision.datasets.FashionMNIST('./data', train=True, transform=transform, download=True)
validation_set = torchvision.datasets.FashionMNIST('./data', train=False, transform=transform, download=True)
# Create data loaders for our datasets; shuffle for training, not for validation
training_loader = torch.utils.data.DataLoader(training_set, batch_size=4, shuffle=True)
validation_loader = torch.utils.data.DataLoader(validation_set, batch_size=4, shuffle=False)
# Class labels
classes = ('T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',
'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle Boot')
# Report split sizes
print('Training set has {} instances'.format(len(training_set)))
print('Validation set has {} instances'.format(len(validation_set)))
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]
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100%|##########| 26421880/26421880 [00:02<00:00, 13077679.87it/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
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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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100%|##########| 4422102/4422102 [00:00<00:00, 5939129.50it/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
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Extracting ./data/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz to ./data/FashionMNIST/raw
Training set has 60000 instances
Validation set has 10000 instances
和往常一样，让我们把数据可视化，检查其是否合理：
import matplotlib.pyplot as plt
import numpy as np
# Helper function for inline image display
def matplotlib_imshow(img, one_channel=False):
if one_channel:
img = img.mean(dim=0)
img = img / 2 + 0.5 # unnormalize
npimg = img.numpy()
if one_channel:
plt.imshow(npimg, cmap="Greys")
else:
plt.imshow(np.transpose(npimg, (1, 2, 0)))
dataiter = iter(training_loader)
images, labels = next(dataiter)
# Create a grid from the images and show them
img_grid = torchvision.utils.make_grid(images)
matplotlib_imshow(img_grid, one_channel=True)
print(' '.join(classes[labels[j]] for j in range(4)))
输出：
模型
本示例中使用的模型是 LeNet-5 的变体，如果您看过本系列的前几期视频，应该对它不陌生。
import torch.nn as nn
import torch.nn.functional as F
# PyTorch models inherit from torch.nn.Module
class GarmentClassifier(nn.Module):
def __init__(self):
super(GarmentClassifier, self).__init__()
self.conv1 = nn.Conv2d(1, 6, 5)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(6, 16, 5)
self.fc1 = nn.Linear(16 * 4 * 4, 120)
self.fc2 = nn.Linear(120, 84)
self.fc3 = nn.Linear(84, 10)
def forward(self, x):
x = self.pool(F.relu(self.conv1(x)))
x = self.pool(F.relu(self.conv2(x)))
x = x.view(-1, 16 * 4 * 4)
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = self.fc3(x)
return x
model = GarmentClassifier()
损失函数
本例中，我们将使用交叉熵损失。为了演示，我们将创建一批假输出和标签值，通过损失函数运行它们，并检查结果。
loss_fn = torch.nn.CrossEntropyLoss()
# NB: Loss functions expect data in batches, so we're creating batches of 4
# Represents the model's confidence in each of the 10 classes for a given input
dummy_outputs = torch.rand(4, 10)
# Represents the correct class among the 10 being tested
dummy_labels = torch.tensor([1, 5, 3, 7])
print(dummy_outputs)
print(dummy_labels)
loss = loss_fn(dummy_outputs, dummy_labels)
print('Total loss for this batch: {}'.format(loss.item()))
输出：
tensor([[0.7026, 0.1489, 0.0065, 0.6841, 0.4166, 0.3980, 0.9849, 0.6701, 0.4601,
0.8599],
[0.7461, 0.3920, 0.9978, 0.0354, 0.9843, 0.0312, 0.5989, 0.2888, 0.8170,
0.4150],
[0.8408, 0.5368, 0.0059, 0.8931, 0.3942, 0.7349, 0.5500, 0.0074, 0.0554,
0.1537],
[0.7282, 0.8755, 0.3649, 0.4566, 0.8796, 0.2390, 0.9865, 0.7549, 0.9105,
0.5427]])
tensor([1, 5, 3, 7])
Total loss for this batch: 2.428950071334839
优化器
本例中，我们将使用简单的动量随机梯度下降法。
尝试这种优化方案的一些变式可能会有所启发：
- 学习率决定了优化器的步长。不同的学习率会对训练结果的准确性和收敛时间产生什么影响？
- 动量(Momentum)使优化器在多个步骤中朝着梯度最大的方向前进。改变这个值对结果有什么影响？
- 尝试一些不同的优化算法，如平均 SGD、Adagrad 或 Adam。结果有何不同？
# Optimizers specified in the torch.optim package
optimizer = torch.optim.SGD(model.parameters(), lr=0.001, momentum=0.9)
训练循环
下面，我们有一个执行一个训练周期的函数。它从 DataLoader 中枚举数据，并在循环的每次传递中执行以下操作：
- 从 DataLoader 中获取一批训练数据
- 将优化器的梯度清零
- 执行推理，即从输入批次的模型中获取预测结果
- 计算这组预测与数据集标签的损失
- 计算学习权重的后向梯度
- 告诉优化器执行一个学习步骤，即根据我们选择的优化算法和观察到的梯度调整模型的学习权重。
- 报告每 1000 个批次的损失。
- 最后，它会报告最近 1000 个批次的平均每批次损失，以便与验证运行进行比较
def train_one_epoch(epoch_index, tb_writer):
running_loss = 0.
last_loss = 0.
# Here, we use enumerate(training_loader) instead of
# iter(training_loader) so that we can track the batch
# index and do some intra-epoch reporting
for i, data in enumerate(training_loader):
# Every data instance is an input + label pair
inputs, labels = data
# Zero your gradients for every batch!
optimizer.zero_grad()
# Make predictions for this batch
outputs = model(inputs)
# Compute the loss and its gradients
loss = loss_fn(outputs, labels)
loss.backward()
# Adjust learning weights
optimizer.step()
# Gather data and report
running_loss += loss.item()
if i % 1000 == 999:
last_loss = running_loss / 1000 # loss per batch
print(' batch {} loss: {}'.format(i + 1, last_loss))
tb_x = epoch_index * len(training_loader) + i + 1
tb_writer.add_scalar('Loss/train', last_loss, tb_x)
running_loss = 0.
return last_loss
周期活动
有几件事我们需要在每个周期进行一次：
- 在一组未用于训练的数据上，验证相对损失，并报告结果
- 保存模型副本
在这里，我们用 TensorBoard 进行报告。这需要通过命令行启动 TensorBoard，并在另一个浏览器标签页中打开它。
# Initializing in a separate cell so we can easily add more epochs to the same run
timestamp = datetime.now().strftime('%Y%m%d_%H%M%S')
writer = SummaryWriter('runs/fashion_trainer_{}'.format(timestamp))
epoch_number = 0
EPOCHS = 5
best_vloss = 1_000_000.
for epoch in range(EPOCHS):
print('EPOCH {}:'.format(epoch_number + 1))
# Make sure gradient tracking is on, and do a pass over the data
model.train(True)
avg_loss = train_one_epoch(epoch_number, writer)
running_vloss = 0.0
# Set the model to evaluation mode, disabling dropout and using population
# statistics for batch normalization.
model.eval()
# Disable gradient computation and reduce memory consumption.
with torch.no_grad():
for i, vdata in enumerate(validation_loader):
vinputs, vlabels = vdata
voutputs = model(vinputs)
vloss = loss_fn(voutputs, vlabels)
running_vloss += vloss
avg_vloss = running_vloss / (i + 1)
print('LOSS train {} valid {}'.format(avg_loss, avg_vloss))
# Log the running loss averaged per batch
# for both training and validation
writer.add_scalars('Training vs. Validation Loss',
{ 'Training' : avg_loss, 'Validation' : avg_vloss },
epoch_number + 1)
writer.flush()
# Track best performance, and save the model's state
if avg_vloss < best_vloss:
best_vloss = avg_vloss
model_path = 'model_{}_{}'.format(timestamp, epoch_number)
torch.save(model.state_dict(), model_path)
epoch_number += 1
输出：
EPOCH 1:
batch 1000 loss: 1.6334228584356607
batch 2000 loss: 0.8325267538074403
batch 3000 loss: 0.7359380583595484
batch 4000 loss: 0.6198329215242994
batch 5000 loss: 0.6000315657821484
batch 6000 loss: 0.555109024874866
batch 7000 loss: 0.5260250487388112
batch 8000 loss: 0.4973462742221891
batch 9000 loss: 0.4781935699362075
batch 10000 loss: 0.47880298678041433
batch 11000 loss: 0.45598648857555235
batch 12000 loss: 0.4327470133750467
batch 13000 loss: 0.41800182418141046
batch 14000 loss: 0.4115047634313814
batch 15000 loss: 0.4211296908891527
LOSS train 0.4211296908891527 valid 0.414460688829422
EPOCH 2:
batch 1000 loss: 0.3879808729066281
batch 2000 loss: 0.35912817339546743
batch 3000 loss: 0.38074520684120944
batch 4000 loss: 0.3614532373107213
batch 5000 loss: 0.36850082185724753
batch 6000 loss: 0.3703581801643886
batch 7000 loss: 0.38547042514081115
batch 8000 loss: 0.37846584360170527
batch 9000 loss: 0.3341486988377292
batch 10000 loss: 0.3433013284947956
batch 11000 loss: 0.35607743899174965
batch 12000 loss: 0.3499939931873523
batch 13000 loss: 0.33874178926000603
batch 14000 loss: 0.35130289171106416
batch 15000 loss: 0.3394507191307202
LOSS train 0.3394507191307202 valid 0.3581162691116333
EPOCH 3:
batch 1000 loss: 0.3319729989422485
batch 2000 loss: 0.29558994361863006
batch 3000 loss: 0.3107374766407593
batch 4000 loss: 0.3298987646112146
batch 5000 loss: 0.30858693152241906
batch 6000 loss: 0.33916381367447684
batch 7000 loss: 0.3105102765217889
batch 8000 loss: 0.3011080777524912
batch 9000 loss: 0.3142058177240979
batch 10000 loss: 0.31458891937109
batch 11000 loss: 0.31527258940579483
batch 12000 loss: 0.31501667268342864
batch 13000 loss: 0.3011875962628328
batch 14000 loss: 0.30012811454350596
batch 15000 loss: 0.31833117976446373
LOSS train 0.31833117976446373 valid 0.3307691514492035
EPOCH 4:
batch 1000 loss: 0.2786161053752294
batch 2000 loss: 0.27965198021690596
batch 3000 loss: 0.28595415444140965
batch 4000 loss: 0.292985666413857
batch 5000 loss: 0.3069892351147719
batch 6000 loss: 0.29902250939945224
batch 7000 loss: 0.2863366014406201
batch 8000 loss: 0.2655441066541243
batch 9000 loss: 0.3045048695363293
batch 10000 loss: 0.27626545656517554
batch 11000 loss: 0.2808379335970967
batch 12000 loss: 0.29241049340573955
batch 13000 loss: 0.28030834131941446
batch 14000 loss: 0.2983542350126445
batch 15000 loss: 0.3009556676162611
LOSS train 0.3009556676162611 valid 0.41686952114105225
EPOCH 5:
batch 1000 loss: 0.2614263167564495
batch 2000 loss: 0.2587047562422049
batch 3000 loss: 0.2642477260621345
batch 4000 loss: 0.2825975873669813
batch 5000 loss: 0.26987933717705165
batch 6000 loss: 0.2759250026817317
batch 7000 loss: 0.26055969463163275
batch 8000 loss: 0.29164007206353565
batch 9000 loss: 0.2893096504513578
batch 10000 loss: 0.2486029507305684
batch 11000 loss: 0.2732803234480907
batch 12000 loss: 0.27927226484491985
batch 13000 loss: 0.2686819267635074
batch 14000 loss: 0.24746483912148323
batch 15000 loss: 0.27903492261294194
LOSS train 0.27903492261294194 valid 0.31206756830215454
加载模型的已保存版本：
加载模型后，它就可以用于任何用途——更多训练、推理或分析。
请注意，如果您的模型有影响模型结构的构造函数参数，您需要提供这些参数，并将模型配置成和保存时相同的状态。
其它资源
- pytorch.org 上关于数据工具(包括 Dataset 和 DataLoader)的文档
- 关于在 GPU 训练中使用固定内存的说明
- 关于 TorchVision、TorchText 和 TorchAudio 中可用数据集的文档
- 关于 PyTorch 中可用损失函数的文档
- 有关 torch.optim 包的文档，其中包括优化器和相关工具，如学习率调度等
- 保存和加载模型的详细教程
- pytorch.org 的教程部分包含各种训练任务的教程，包括不同领域的分类、生成对抗网络、强化学习等。
