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介绍 || 张量 || 自动微分 || 构建模型 || TensorBoard 支持 || 训练模型 || 理解模型

使用 PyTorch 进行训练¶

按照下面的视频或在 youtube 上一起学习。

介绍¶

在之前的视频中，我们讨论并演示了

使用 torch.nn 模块的神经网络层和函数构建模型
自动梯度计算的机制，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，我们将向您的工具库添加一些新工具

我们将熟悉数据集和数据加载器抽象，以及它们如何在训练循环期间将数据馈送到模型的过程
我们将讨论特定的损失函数以及何时使用它们
我们将查看 PyTorch 优化器，它们实现了算法来根据损失函数的结果调整模型权重

最后，我们将把所有这些整合在一起，并查看一个完整的 PyTorch 训练循环。

数据集和数据加载器¶

Dataset 和 DataLoader 类封装了从存储中提取数据并以批次形式将其提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（自动或使用您定义的采样器），将它们收集到批次中，并返回以供您的训练循环使用。 DataLoader 可与所有类型的数据集一起使用，无论它们包含的数据类型。

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() 来对图像块内容的分布进行零中心化和归一化，并下载训练和验证数据拆分。

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)))

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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100%|##########| 26.4M/26.4M [00:01<00:00, 19.3MB/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%|##########| 4.42M/4.42M [00:01<00:00, 2.85MB/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

与往常一样，让我们将数据可视化作为完整性检查。

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)))

Sandal  Sneaker  Coat  Sneaker

模型¶

我们将在本例中使用的模型是 LeNet-5 的变体 - 如果你观看过本系列中的先前视频，你应该很熟悉它。

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¶

在本例中，我们将使用交叉熵损失。出于演示目的，我们将创建一批虚拟输出和标签值，并将它们通过损失函数，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()))

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¶

在本例中，我们将使用简单的随机梯度下降，并带有动量。

尝试对这种优化方案进行一些变体会很有启发。

学习率决定了优化器采取的步骤的大小。不同的学习率对您的训练结果（在准确率和收敛时间方面）有什么影响？
动量在多个步骤中推动优化器朝最强梯度的方向前进。更改此值对您的结果有什么影响？
尝试一些不同的优化算法，例如平均 SGD、Adagrad 或 Adam。您的结果有何不同？

# 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轮次的函数。它枚举了来自 DataLoader 的数据，并且在循环的每次迭代中执行以下操作

从 DataLoader 获取一批训练数据
将优化器的梯度置零
执行推理 - 也就是说，获取模型对输入批次的预测
计算该组预测与数据集上的标签的损失
计算学习权重的反向梯度
告诉优化器执行一个学习步骤 - 也就是说，根据观察到的批次梯度，根据我们选择的优化算法调整模型的学习权重
它报告每 1000 批的损失。
最后，它报告最后 1000 批的平均每批损失，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

每个轮次活动¶

我们每个轮次需要做几件事。

通过检查我们在未用于训练的数据集上的相对损失来执行验证，并报告此损失。
保存模型的副本。

在这里，我们将在 TensorBoard 中进行报告。这将需要转到命令行启动 TensorBoard，并在另一个浏览器选项卡中打开它。

# 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

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

要加载模型的保存版本

saved_model = GarmentClassifier()
saved_model.load_state_dict(torch.load(PATH))

加载模型后，它就可以满足您的任何需求 - 更多训练、推理或分析。

请注意，如果您的模型具有影响模型结构的构造函数参数，则需要提供它们并配置模型以与保存状态完全一致。

其他资源¶

有关数据实用程序（包括 Dataset 和 DataLoader）的文档，请访问 pytorch.org。
有关使用固定内存进行 GPU 训练的说明
有关 TorchVision、TorchText 和 TorchAudio 中可用数据集的文档
有关 PyTorch 中可用的损失函数的文档
有关 torch.optim 包的文档，其中包括优化器和相关工具，例如学习率调度
有关保存和加载模型的详细教程
pytorch.org 的教程部分包含关于各种训练任务的教程，包括不同领域中的分类、生成对抗网络、强化学习等等。

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