一、实现过程
本文对经典手写数字数据集进行多分类，损失函数采用交叉熵，激活函数采用ReLU，优化器采用带有动量的mini-batchSGD算法。
所有代码如下：
0、导包
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import torchfrom torchvision import transforms,datasetsfrom torch.utils.data import DataLoaderimport torch.nn.functional as Fimport torch.optim as optim |
1、准备数据
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batch_size = 64transform = transforms.Compose([ transforms.ToTensor(), transforms.Normalize((0.1307,),(0.3081,))])# 训练集train_dataset = datasets.MNIST(root='G:/datasets/mnist',train=True,download=False,transform=transform)train_loader = DataLoader(train_dataset,shuffle=True,batch_size=batch_size)# 测试集test_dataset = datasets.MNIST(root='G:/datasets/mnist',train=False,download=False,transform=transform)test_loader = DataLoader(test_dataset,shuffle=False,batch_size=batch_size) |
2、设计模型
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class Net(torch.nn.Module): def __init__(self): super(Net, self).__init__() self.l1 = torch.nn.Linear(784, 512) self.l2 = torch.nn.Linear(512, 256) self.l3 = torch.nn.Linear(256, 128) self.l4 = torch.nn.Linear(128, 64) self.l5 = torch.nn.Linear(64, 10) def forward(self, x): x = x.view(-1, 784) x = F.relu(self.l1(x)) x = F.relu(self.l2(x)) x = F.relu(self.l3(x)) x = F.relu(self.l4(x)) return self.l5(x)model = Net()# 模型加载到GPU上device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")model.to(device) |
3、构造损失函数和优化器
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criterion = torch.nn.CrossEntropyLoss()optimizer = optim.SGD(model.parameters(),lr=0.01,momentum=0.5) |
4、训练和测试
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def train(epoch): running_loss = 0.0 for batch_idx, data in enumerate(train_loader, 0): inputs, target = data optimizer.zero_grad() # forward+backward+update outputs = model(inputs.to(device)) loss = criterion(outputs, target.to(device)) loss.backward() optimizer.step() running_loss += loss.item() if batch_idx % 300 == 299: print('[%d,%d] loss: %.3f' % (epoch + 1, batch_idx + 1, running_loss / 300)) running_loss = 0.0def test(): correct = 0 total = 0 with torch.no_grad(): for data in test_loader: images, labels = data outputs = model(images.to(device)) _, predicted = torch.max(outputs.data, dim=1) total += labels.size(0) correct += (predicted.cpu() == labels).sum().item() print('Accuracy on test set: %d %%' % (100 * correct / total))for epoch in range(10): train(epoch) test() |
运行结果如下：
[1,300] loss: 2.166
[1,600] loss: 0.797
[1,900] loss: 0.405
Accuracy on test set: 90 %
[2,300] loss: 0.303
[2,600] loss: 0.252
[2,900] loss: 0.218
Accuracy on test set: 94 %
[3,300] loss: 0.178
[3,600] loss: 0.168
[3,900] loss: 0.142
Accuracy on test set: 95 %
[4,300] loss: 0.129
[4,600] loss: 0.119
[4,900] loss: 0.110
Accuracy on test set: 96 %
[5,300] loss: 0.094
[5,600] loss: 0.092
[5,900] loss: 0.091
Accuracy on test set: 96 %
[6,300] loss: 0.077
[6,600] loss: 0.070
[6,900] loss: 0.075
Accuracy on test set: 97 %
[7,300] loss: 0.061
[7,600] loss: 0.058
[7,900] loss: 0.058
Accuracy on test set: 97 %
[8,300] loss: 0.043
[8,600] loss: 0.051
[8,900] loss: 0.050
Accuracy on test set: 97 %
[9,300] loss: 0.041
[9,600] loss: 0.038
[9,900] loss: 0.043
Accuracy on test set: 97 %
[10,300] loss: 0.030
[10,600] loss: 0.032
[10,900] loss: 0.033
Accuracy on test set: 97 %
二、参考文献
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原文链接：https://blog.csdn.net/weixin_43821559/article/details/123337335
