本文实例讲述了Pytorch实现的手写数字mnist识别功能。分享给大家供大家参考，具体如下：

import torch
import torchvision as tv
import torchvision.transforms as transforms
import torch.nn as nn
import torch.optim as optim
import argparse
# 定义是否使用GPU
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# 定义网络结构
class LeNet(nn.Module):
  def __init__(self):
    super(LeNet, self).__init__()
    self.conv1 = nn.Sequential(   #input_size=(1*28*28)
      nn.Conv2d(1, 6, 5, 1, 2), #padding=2保证输入输出尺寸相同
      nn.ReLU(),   #input_size=(6*28*28)
      nn.MaxPool2d(kernel_size=2, stride=2),#output_size=(6*14*14)
    )
    self.conv2 = nn.Sequential(
      nn.Conv2d(6, 16, 5),
      nn.ReLU(),   #input_size=(16*10*10)
      nn.MaxPool2d(2, 2) #output_size=(16*5*5)
    )
    self.fc1 = nn.Sequential(
      nn.Linear(16 * 5 * 5, 120),
      nn.ReLU()
    )
    self.fc2 = nn.Sequential(
      nn.Linear(120, 84),
      nn.ReLU()
    )
    self.fc3 = nn.Linear(84, 10)
  # 定义前向传播过程，输入为x
  def forward(self, x):
    x = self.conv1(x)
    x = self.conv2(x)
    # nn.Linear()的输入输出都是维度为一的值，所以要把多维度的tensor展平成一维
    x = x.view(x.size()[0], -1)
    x = self.fc1(x)
    x = self.fc2(x)
    x = self.fc3(x)
    return x
#使得我们能够手动输入命令行参数，就是让风格变得和Linux命令行差不多
parser = argparse.ArgumentParser()
parser.add_argument('--outf', default='./model/', help='folder to output images and model checkpoints') #模型保存路径
parser.add_argument('--net', default='./model/net.pth', help="path to netG (to continue training)") #模型加载路径
opt = parser.parse_args()
# 超参数设置
EPOCH = 8  #遍历数据集次数
BATCH_SIZE = 64   #批处理尺寸(batch_size)
LR = 0.001    #学习率
# 定义数据预处理方式
transform = transforms.ToTensor()
# 定义训练数据集
trainset = tv.datasets.MNIST(
  root='./data/',
  train=True,
  download=True,
  transform=transform)
# 定义训练批处理数据
trainloader = torch.utils.data.DataLoader(
  trainset,
  batch_size=BATCH_SIZE,
  shuffle=True,
  )
# 定义测试数据集
testset = tv.datasets.MNIST(
  root='./data/',
  train=False,
  download=True,
  transform=transform)
# 定义测试批处理数据
testloader = torch.utils.data.DataLoader(
  testset,
  batch_size=BATCH_SIZE,
  shuffle=False,
  )
# 定义损失函数loss function 和优化方式（采用SGD）
net = LeNet().to(device)
criterion = nn.CrossEntropyLoss() # 交叉熵损失函数，通常用于多分类问题上
optimizer = optim.SGD(net.parameters(), lr=LR, momentum=0.9)
# 训练
if __name__ == "__main__":
  for epoch in range(EPOCH):
    sum_loss = 0.0
    # 数据读取
    for i, data in enumerate(trainloader):
      inputs, labels = data
      inputs, labels = inputs.to(device), labels.to(device)
      # 梯度清零
      optimizer.zero_grad()
      # forward + backward
      outputs = net(inputs)
      loss = criterion(outputs, labels)
      loss.backward()
      optimizer.step()
      # 每训练100个batch打印一次平均loss
      sum_loss += loss.item()
      if i % 100 == 99:
        print('[%d, %d] loss: %.03f'
           % (epoch + 1, i + 1, sum_loss / 100))
        sum_loss = 0.0
    # 每跑完一次epoch测试一下准确率
    with torch.no_grad():
      correct = 0
      total = 0
      for data in testloader:
        images, labels = data
        images, labels = images.to(device), labels.to(device)
        outputs = net(images)
        # 取得分最高的那个类
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum()
      print('第%d个epoch的识别准确率为：%d%%' % (epoch + 1, (100 * correct / total)))
  #torch.save(net.state_dict(), '%s/net_%03d.pth' % (opt.outf, epoch + 1))

希望本文所述对大家Python程序设计有所帮助。