AI 智能体应用工程师核心知识模拟试题与解析

import torch import torch.nn as nn import torch.optim as optim from torchvision import datasets, transforms # 1. 数据预处理 transform = transforms.Compose([ transforms.ToTensor(), transforms.Normalize((0.5,), (0.5,)) ]) # 2. 加载 MNIST 数据集 trainset = datasets.MNIST( root='./data', train=True, download=True, transform=transform ) trainloader = torch.utils.data.DataLoader(trainset, batch_size=64, shuffle=True) testset = datasets.MNIST( root='./data', train=False, download=True, transform=transform ) testloader = torch.utils.data.DataLoader(testset, batch_size=64, shuffle=False) # 3. 定义神经网络模型 class Net(nn.Module): def __init__(self): super(Net, self).__init__() self.fc1 = nn.Linear(784, 128) self.fc2 = nn.Linear(128, 10) def forward(self, x): x = x.view(-1, 784) # 将图像展平 x = torch.relu(self.fc1(x)) x = self.fc2(x) return x net = Net() # 4. 定义损失函数和优化器 criterion = nn.CrossEntropyLoss() optimizer = optim.SGD(net.parameters(), lr=0.01, momentum=0.5) # 5. 训练模型 for epoch in range(2): running_loss = 0.0 for i, data in enumerate(trainloader, 0): inputs, labels = data optimizer.zero_grad() # 清空之前的梯度 outputs = net(inputs) loss = criterion(outputs, labels) loss.backward() # 反向传播计算梯度 optimizer.step() # 更新模型参数 running_loss += loss.item() if i % 2000 == 1999: # 每 2000 个 mini-batches 打印一次 print('[%d, %5d] loss: %.3f' % (epoch + 1, i + 1, running_loss / 2000)) running_loss = 0.0 print('Finished Training') # 6. 测试模型 correct = 0 total = 0 with torch.no_grad(): for data in testloader: images, labels = data outputs = net(images) _, predicted = torch.max(outputs.data, 1) total += labels.size(0) correct += (predicted == labels).sum().item() print('Accuracy of the network on the 10000 test images: %d %%' % (100 * correct / total))

AI 智能体应用工程师核心知识模拟试题与解析