搭建一个ReXNet网络，写出完整代码

首先，我们需要导入必要的库：import torch import torchvision from torchvision import transforms from torch.utils.data import DataLoader from torchvision.models import ReXNet from torch import nn, optim 接下来，我们需要定义数据预处理：transform = transforms.Compose([transforms.Resize(256), transforms.CenterCrop(224), transforms.ToTensor(), transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])]) 接着，我们可以加载数据：dataset = torchvision.datasets.ImageFolder(root='/path/to/data', transform=transform) dataloader = DataLoader(dataset, batch_size=32, shuffle=True) 接下来，我们可以定义模型：model = ReXNet(num_classes=100) 接着，我们可以定义损失函数和优化器：criterion = nn.CrossEntropyLoss() optimizer = optim.Adam(model.parameters(), lr=0.001) 最后，我们可以开始训练：for epoch in range(num_epochs): for data in dataloader: inputs, labels = data inputs, labels = inputs.to(device), labels.to(device) optimizer.zero_grad() outputs = model(inputs) loss = criterion(outputs, labels) loss.backward() optimizer.step()