pytorch教程实现mnist手写数字识别代码示例

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.size() = 28*28*1
  x = F.relu(F.max_pool2d(self.conv1(x), 2))
  x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))  #x.size() =12*12*10 
  x = x.view(-1, 320)  #x.size() =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)

model = Net()  #调用写好的网络
if args.cuda:  #如果有GPU使用CPU
 model.cuda()
optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum)#设置SGD随机梯度下降算法
def train(epoch):
 model.train() 
 for batch_idx, (data, target) in enumerate(train_loader):
  if args.cuda:
data, target = data.cuda(), target.cuda()
  data, target = Variable(data), Variable(target)
  optimizer.zero_grad()#梯度初始化为O
  output = model(data) 
  loss = F.nll_loss(output, target) #简历loss function
  loss.backward()#反向传播，计算梯度
  optimizer.step()  #更新权重
  if batch_idx % args.log_interval == 0:  #输出信息
print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
 epoch, batch_idx * len(data), len(train_loader.dataset),
 100. * batch_idx / len(train_loader), loss.data[0]))

def test():
 model.eval()
 test_loss = 0
 correct = 0
 for data, target in test_loader:
  if args.cuda:
data, target = data.cuda(), target.cuda()
  data, target = Variable(data, volatile=True), Variable(target)
  output = model(data)
  test_loss += F.nll_loss(output, target, size_average=False).data[0] # sum up batch loss
  pred = output.data.max(1, keepdim=True)[1] # get the index of the max log-probability
  correct += pred.eq(target.data.view_as(pred)).long().cpu().sum()
  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)))

for epoch in range(1, args.epochs + 1):
 train(epoch)  #训练N个epoch
 test()  #检验在测试集上的表现

# -*- coding: utf-8 -*-
from __future__ import print_function
import argparse
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
from torch.autograd import Variable
# 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()
args.cuda = not args.no_cuda and torch.cuda.is_available()
 
torch.manual_seed(args.seed)
if args.cuda:
 torch.cuda.manual_seed(args.seed) 
kwargs = {'num_workers': 1, 'pin_memory': True} if args.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)
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):
  print (x.size())
  x = F.relu(F.max_pool2d(self.conv1(x), 2))
  print(x.size())
  x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
  print(x.size())
  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)
 model = Net()  #调用写好的网络
if args.cuda:  #如果有GPU使用CPU
 model.cuda()
optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum)#设置SGD随机梯度下降算法
def train(epoch):
 model.train()
 for batch_idx, (data, target) in enumerate(train_loader):
  if args.cuda:
data, target = data.cuda(), target.cuda()
  data, target = Variable(data), Variable(target)
  optimizer.zero_grad()#梯度初始化为O
  output = model(data)
  loss = F.nll_loss(output, target) #简历loss function
  loss.backward()#反向传播，计算梯度
  optimizer.step()  #更新权重
  if batch_idx % args.log_interval == 0:  #输出信息
print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
 epoch, batch_idx * len(data), len(train_loader.dataset),
 100. * batch_idx / len(train_loader), loss.data[0]))
def test():
 model.eval()
 test_loss = 0
 correct = 0
 for data, target in test_loader:
  if args.cuda:
data, target = data.cuda(), target.cuda()
  data, target = Variable(data, volatile=True), Variable(target)
  output = model(data)
  test_loss += F.nll_loss(output, target, size_average=False).data[0] # sum up batch loss
  pred = output.data.max(1, keepdim=True)[1] # get the index of the max log-probability
  correct += pred.eq(target.data.view_as(pred)).long().cpu().sum()
 
 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))) 
for epoch in range(1, args.epochs + 1):
 train(epoch)
 test()