torchvision包 包含了目前流行的数据集，模型结构和常用的图片转换工具。

torchvision.datasets中包含了以下数据集

MNIST
COCO（用于图像标注和目标检测）(Captioning and Detection)
LSUN Classification
ImageFolder
Imagenet-12
CIFAR10 and CIFAR100
STL10

torchvision.models

torchvision.models模块的 子模块中包含以下模型结构。
AlexNet
VGG
ResNet
SqueezeNet
DenseNet You can construct a model with random weights by calling its constructor:

pytorch torchvision transform

对PIL.Image进行变换

from __future__ import print_function
import argparse #Python 命令行解析工具
import torch 
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim 
from torchvision import datasets, transforms

class Net(nn.Module):
  def __init__(self):
    super(Net, self).__init__()
    self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
    self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
    self.conv2_drop = nn.Dropout2d()
    self.fc1 = nn.Linear(320, 50)
    self.fc2 = nn.Linear(50, 10)

  def forward(self, x):
    x = F.relu(F.max_pool2d(self.conv1(x), 2))
    x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
    x = x.view(-1, 320)
    x = F.relu(self.fc1(x))
    x = F.dropout(x, training=self.training)
    x = self.fc2(x)
    return F.log_softmax(x, dim=1)

def train(args, model, device, train_loader, optimizer, epoch):
  model.train()
  for batch_idx, (data, target) in enumerate(train_loader):
    data, target = data.to(device), target.to(device)
    optimizer.zero_grad()
    output = model(data)
    loss = F.nll_loss(output, target)
    loss.backward()
    optimizer.step()
    if batch_idx % args.log_interval == 0:
      print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
        epoch, batch_idx * len(data), len(train_loader.dataset),
        100. * batch_idx / len(train_loader), loss.item()))

def test(args, model, device, test_loader):
  model.eval()
  test_loss = 0
  correct = 0
  with torch.no_grad():
    for data, target in test_loader:
      data, target = data.to(device), target.to(device)
      output = model(data)
      test_loss += F.nll_loss(output, target, size_average=False).item() # sum up batch loss
      pred = output.max(1, keepdim=True)[1] # get the index of the max log-probability
      correct += pred.eq(target.view_as(pred)).sum().item()

  test_loss /= len(test_loader.dataset)
  print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
    test_loss, correct, len(test_loader.dataset),
    100. * correct / len(test_loader.dataset)))

def main():
  # Training settings
  parser = argparse.ArgumentParser(description='PyTorch MNIST Example')
  parser.add_argument('--batch-size', type=int, default=64, metavar='N',
            help='input batch size for training (default: 64)')
  parser.add_argument('--test-batch-size', type=int, default=1000, metavar='N',
            help='input batch size for testing (default: 1000)')
  parser.add_argument('--epochs', type=int, default=10, metavar='N',
            help='number of epochs to train (default: 10)')
  parser.add_argument('--lr', type=float, default=0.01, metavar='LR',
            help='learning rate (default: 0.01)')
  parser.add_argument('--momentum', type=float, default=0.5, metavar='M',
            help='SGD momentum (default: 0.5)')
  parser.add_argument('--no-cuda', action='store_true', default=False,
            help='disables CUDA training')
  parser.add_argument('--seed', type=int, default=1, metavar='S',
            help='random seed (default: 1)')
  parser.add_argument('--log-interval', type=int, default=10, metavar='N',
            help='how many batches to wait before logging training status')
  args = parser.parse_args()
  use_cuda = not args.no_cuda and torch.cuda.is_available()

  torch.manual_seed(args.seed)

  device = torch.device("cuda" if use_cuda else "cpu")

  kwargs = {'num_workers': 1, 'pin_memory': True} if use_cuda else {}
  train_loader = torch.utils.data.DataLoader(
    datasets.MNIST('../data', train=True, download=True,
            transform=transforms.Compose([
              transforms.ToTensor(),
              transforms.Normalize((0.1307,), (0.3081,))
            ])),
    batch_size=args.batch_size, shuffle=True, **kwargs)
  test_loader = torch.utils.data.DataLoader(
    datasets.MNIST('../data', train=False, transform=transforms.Compose([
              transforms.ToTensor(),
              transforms.Normalize((0.1307,), (0.3081,))
            ])),
    batch_size=args.test_batch_size, shuffle=True, **kwargs)


  model = Net().to(device)
  optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum)

  for epoch in range(1, args.epochs + 1):
    train(args, model, device, train_loader, optimizer, epoch)
    test(args, model, device, test_loader)


if __name__ == '__main__':
  main()

以上这篇pytorch实现mnist分类的示例讲解就是小编分享给大家的全部内容了，希望能给大家一个参考，也希望大家多多支持python博客。