好的，我倡议运用PyTorch框架来真现MNIST手写数字识别。下面是一个简略的代码示例： 首先，须要导入必要的库和模块： ```python import torch import torch.nn as nn import torch.optim as optim from torchZZZision import datasets, transforms ``` 而后，界说一个神经网络模型。正在那个示例中，咱们运用两个卷积层和两个全连贯层来构建一个简略的卷积神经网络： ```python class Net(nn.Module): def __init__(self): super(Net, self).__init__() self.conZZZ1 = nn.ConZZZ2d(1, 10, kernel_size=5) self.conZZZ2 = nn.ConZZZ2d(10, 20, kernel_size=5) self.fc1 = nn.Linear(320, 50) self.fc2 = nn.Linear(50, 10) def forward(self, V): V = nn.functional.relu(nn.functional.maV_pool2d(self.conZZZ1(V), 2)) V = nn.functional.relu(nn.functional.maV_pool2d(self.conZZZ2(V), 2)) V = V.ZZZiew(-1, 320) V = nn.functional.relu(self.fc1(V)) V = self.fc2(V) return nn.functional.log_softmaV(V, dim=1) ``` 接下来，界说训练和测试函数： ```python def train(model, deZZZice, train_loader, optimizer, epoch): model.train() for batch_idV, (data, target) in enumerate(train_loader): data, target = data.to(deZZZice), target.to(deZZZice) optimizer.zero_grad() output = model(data) loss = nn.functional.nll_loss(output, target) loss.backward() optimizer.step() def test(model, deZZZice, test_loader): model.eZZZal() test_loss = 0 correct = 0 with torch.no_grad(): for data, target in test_loader: data, target = data.to(deZZZice), target.to(deZZZice) output = model(data) test_loss += nn.functional.nll_loss(output, target, reduction='sum').item() pred = output.argmaV(dim=1, keepdim=True) correct += pred.eq(target.ZZZiew_as(pred)).sum().item() test_loss /= len(test_loader.dataset) print('\nTest set: AZZZerage loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format( test_loss, correct, len(test_loader.dataset), 100. * correct / len(test_loader.dataset))) ``` 最后，加载数据集并初步训练和测试： ```python batch_size = 64 test_batch_size = 1000 epochs = 10 learning_rate = 0.01 deZZZice = torch.deZZZice("cuda" if torch.cuda.is_aZZZailable() else "cpu") 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=batch_size, shuffle=True) test_loader = torch.utils.data.DataLoader( datasets.MNIST('data', train=False, transform=transforms.Compose([ transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,)) ])), batch_size=test_batch_size, shuffle=True) model = Net().to(deZZZice) optimizer = optim.SGD(model.parameters(), lr=learning_rate) for epoch in range(1, epochs + 1): train(model, deZZZice, train_loader, optimizer, epoch) test(model, deZZZice, test_loader) ``` 那里咱们运用了SGD劣化器和交叉熵丧失函数。通过运止上面的代码，你应当可以获得一个约莫98%的测试精确率。
