# 对训练集进行预测 train_preds = [] for i in range(len(train_dataset)): inputs, _ = train_dataset[i] inputs = inputs.unsqueeze(0) outputs = model(inputs) _, preds = torch.max(outputs, 1) train_preds.append(preds.item()) # 对验证集进行预测 val_preds = [] for i in range(len(val_dataset)): inputs, _ = val_dataset[i] inputs = inputs.unsqueeze(0) outputs = model(inputs) _, preds = torch.max(outputs, 1) val_preds.append(preds.item()) import numpy as np # 计算训练集准确率 train_labels = np.array([label for _, label in train_dataset]) train_acc = np.mean(np.array(train_preds) == train_labels) print('训练集准确率：{:.2f}%'.format(train_acc * 100)) # 计算验证集准确率 val_labels = np.array([label for _, label in val_dataset]) val_acc = np.mean(np.array(val_preds) == val_labels) print('验证集准确率：{:.2f}%'.format(val_acc * 100))，可以在这里添加一段拟合的过程吗

这段代码是对训练集和验证集使用训练好的模型进行预测，并计算准确率的过程。如果你想添加拟合过程，可以在这段代码之前先进行模型训练，例如：

import torch
import torch.nn as nn
import torch.optim as optim
import torchvision.transforms as transforms
import torchvision.datasets as datasets

# 加载数据集
train_dataset = datasets.CIFAR10(root='./data', train=True, transform=transforms.ToTensor(), download=True)
val_dataset = datasets.CIFAR10(root='./data', train=False, transform=transforms.ToTensor(), download=True)

# 定义模型
class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(3, 32, kernel_size=3, padding=1)
        self.conv2 = nn.Conv2d(32, 64, kernel_size=3, padding=1)
        self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
        self.fc1 = nn.Linear(64 * 8 * 8, 128)
        self.fc2 = nn.Linear(128, 10)
    
    def forward(self, x):
        x = self.pool(torch.relu(self.conv1(x)))
        x = self.pool(torch.relu(self.conv2(x)))
        x = x.view(-1, 64 * 8 * 8)
        x = torch.relu(self.fc1(x))
        x = self.fc2(x)
        return x

# 初始化模型和优化器
model = Net()
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.001, momentum=0.9)

# 训练模型
for epoch in range(10):
    running_loss = 0.0
    for i, data in enumerate(train_dataset, 0):
        inputs, labels = data
        optimizer.zero_grad()
        outputs = model(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
        running_loss += loss.item()
        if i % 100 == 99:
            print('[Epoch %d, Batch %d] loss: %.3f' % (epoch + 1, i + 1, running_loss / 100))
            running_loss = 0.0

这段代码定义了一个简单的卷积神经网络模型，并使用CIFAR10数据集进行训练。你可以根据需要修改模型结构、训练次数、学习率等参数。在模型训练完成后，你可以使用之前的预测过程对训练集和验证集进行评估，计算准确率并输出。