idx = randperm(n); trainIdx = idx(1:round(0.8*n)); testIdx = idx(round(0.8*n)+1:end); trainData = table(features(trainIdx), labels(trainIdx)); testData = table(features(testIdx), labels(testIdx));之后怎么构建cnn

构建CNN的步骤如下：

1.导入所需的库和模块，例如PyTorch、torchvision等。
2.定义模型结构，包括卷积层、池化层、全连接层等。可以根据需求自定义层数和参数。
3.定义损失函数和优化器。常用的损失函数是交叉熵损失函数，优化器可以选择Adam、SGD等。
4.训练模型。将训练数据输入CNN，计算loss并进行反向传播，更新参数。
5.测试模型。将测试数据输入CNN，计算模型准确率等指标。

下面是一个简单的CNN的代码示例：

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

# 定义CNN模型
class CNN(nn.Module):
    def __init__(self):
        super(CNN, self).__init__()
        self.conv1 = nn.Conv2d(3, 16, kernel_size=3, stride=1, padding=1)
        self.conv2 = nn.Conv2d(16, 32, kernel_size=3, stride=1, padding=1)
        self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
        self.fc1 = nn.Linear(32 * 8 * 8, 128)
        self.fc2 = nn.Linear(128, 10)

    def forward(self, x):
        x = self.pool(nn.functional.relu(self.conv1(x)))
        x = self.pool(nn.functional.relu(self.conv2(x)))
        x = x.view(-1, 32 * 8 * 8)
        x = nn.functional.relu(self.fc1(x))
        x = self.fc2(x)
        return x

# 加载数据集
transform = transforms.Compose([transforms.Resize((32, 32)), transforms.ToTensor()])
train_data = datasets.CIFAR10(root='data', train=True, transform=transform, download=True)
test_data = datasets.CIFAR10(root='data', train=False, transform=transform, download=True)
train_loader = torch.utils.data.DataLoader(train_data, batch_size=64, shuffle=True)
test_loader = torch.utils.data.DataLoader(test_data, batch_size=64, shuffle=False)

# 定义损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)

# 训练模型
for epoch in range(10):
    running_loss = 0.0
    for i, data in enumerate(train_loader, 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('[%d, %5d] loss: %.3f' % (epoch + 1, i + 1, running_loss / 100))
            running_loss = 0.0

# 测试模型
correct = 0
total = 0
with torch.no_grad():
    for data in test_loader:
        images, labels = data
        outputs = model(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))

这个CNN模型包括两个卷积层、两个池化层和两个全连接层，使用CIFAR-10数据集进行训练和测试。你可以根据自己的需求修改模型参数和数据集。