现在已经知道了一个网络的结构搭建，正向反向传播以及梯度下降的训练方法。那么如何读入一组数据？
首先使用现有的python工具包将训练数据读入存为numpy的形式，之后将numpy转换为pytorch使用的tensor：
-图像：使用Pillow或者OpenCV
-音频：使用scipy或者librosa

在pytorch中提供了一个数据库的读取包torchvision，可以读取并使用一些常用的数据库，例如imagenet，cifar10，mnist等。

下面将以cifar10为例，介绍如何训练一个分类器：
首先使用torchvision工具包下载并调用cifar10数据库：（这一部分将在后面详细介绍，就不在此展开讲）

import torch
import torchvision
import torchvision.transforms as transforms
transform = transforms.Compose(
    [transforms.ToTensor(),
     transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])  

trainset = torchvision.datasets.CIFAR10(root='./data', train=True,
                                        download=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=4,
                                          shuffle=True, num_workers=2)

testset = torchvision.datasets.CIFAR10(root='./data', train=False,
                                       download=True, transform=transform)
testloader = torch.utils.data.DataLoader(testset, batch_size=4,
                                         shuffle=False, num_workers=2)

classes = ('plane', 'car', 'bird', 'cat',
           'deer', 'dog', 'frog', 'horse', 'ship', 'truck')

接下来，定义我们的分类网络：

import torch.nn as nn
import torch.nn.functional as F


class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(3, 6, 5)
        self.pool = nn.MaxPool2d(2, 2)
        self.conv2 = nn.Conv2d(6, 16, 5)
        self.fc1 = nn.Linear(16 * 5 * 5, 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 * 5 * 5)
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x


net = Net()

下面定义一下loss的计算，和反向传播的方法：

import torch.optim as optim

criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)

接下来，开始我们的训练吧：

for epoch in range(2):  # loop over the dataset multiple times

    running_loss = 0.0
    for i, data in enumerate(trainloader, 0):
        # get the inputs
        inputs, labels = data

        # zero the parameter gradients
        optimizer.zero_grad()

        # forward + backward + optimize
        outputs = net(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

        # print statistics
        running_loss += loss.item()
        if i % 2000 == 1999:    # print every 2000 mini-batches
            print('[%d, %5d] loss: %.3f' %
                  (epoch + 1, i + 1, running_loss / 2000))
            running_loss = 0.0

print('Finished Training')

接下来在测试集上进行一下测试：

correct = 0
total = 0
with torch.no_grad():
    for data in testloader:
        images, labels = data
        outputs = net(images)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()

print('Accuracy of the network on the 10000 test images: %d %%' % (
    100 * correct / total))

可以得到输出的结果：

Accuracy of the network on the 10000 test images: 53 %

下面再测试一下每一类的结果：

class_correct = list(0. for i in range(10))
class_total = list(0. for i in range(10))
with torch.no_grad():
    for data in testloader:
        images, labels = data
        outputs = net(images)
        _, predicted = torch.max(outputs, 1)
        c = (predicted == labels).squeeze()
        for i in range(4):
            label = labels[i]
            class_correct[label] += c[i].item()
            class_total[label] += 1


for i in range(10):
    print('Accuracy of %5s : %2d %%' % (
        classes[i], 100 * class_correct[i] / class_total[i]))

输出为：

Accuracy of plane : 60 %
Accuracy of   car : 75 %
Accuracy of  bird : 33 %
Accuracy of   cat : 50 %
Accuracy of  deer : 26 %
Accuracy of   dog : 47 %
Accuracy of  frog : 54 %
Accuracy of horse : 66 %
Accuracy of  ship : 48 %
Accuracy of truck : 70 %

如果使用GPU训练，可以简单的将net，和输入数据都放到GPU上即可。这个matlab的操作类似。
首先使用

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

得到GPU的编号
之后使用

net.to(device)
inputs, labels = inputs.to(device), labels.to(device)

将网络和数据放到GPU上。代码中测试的时候使用

images, labels = images.to(device), labels.to(device)