pytorch手写数字识别

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_d.pth' % (opt.outf, epoch + 1))

参考链接：https://blog.csdn.net/sunqiande88/article/details/80089941