我有一个来自十个类别各100个共1000个的信号数据，每个数据有512个特征点，存储为(1000,512)的tensor张量，现在我想将其输入一个最新的CNN网络训练分类模型用于分类这些类别，请使用pytorch实现

以下是一个基本的PyTorch实现：

import torch
import torch.nn as nn
import torch.optim as optim

# 定义模型结构
class SignalNet(nn.Module):
    def __init__(self):
        super(SignalNet, self).__init__()
        self.conv1 = nn.Conv1d(in_channels=1, out_channels=16, kernel_size=3)
        self.conv2 = nn.Conv1d(in_channels=16, out_channels=32, kernel_size=3)
        self.pool = nn.MaxPool1d(kernel_size=2)
        self.fc1 = nn.Linear(in_features=32*126, out_features=128)
        self.fc2 = nn.Linear(in_features=128, out_features=10)
    
    def forward(self, x):
        x = self.conv1(x)
        x = nn.functional.relu(x)
        x = self.pool(x)
        x = self.conv2(x)
        x = nn.functional.relu(x)
        x = self.pool(x)
        x = torch.flatten(x, start_dim=1)
        x = self.fc1(x)
        x = nn.functional.relu(x)
        x = self.fc2(x)
        return x

# 创建模型实例和优化器
model = SignalNet()
optimizer = optim.Adam(model.parameters(), lr=0.001)

# 定义损失函数和训练循环
criterion = nn.CrossEntropyLoss()

def train(model, optimizer, criterion, data_loader, num_epochs):
    for epoch in range(num_epochs):
        running_loss = 0.0
        for i, data in enumerate(data_loader, 0):
            inputs, labels = data
            inputs = inputs.unsqueeze(1)
            optimizer.zero_grad()
            outputs = model(inputs)
            loss = criterion(outputs, labels)
            loss.backward()
            optimizer.step()
            running_loss += loss.item()
        print(f"Epoch {epoch+1}, loss: {running_loss/len(data_loader)}")

# 加载数据并进行训练
train_dataset = torch.utils.data.TensorDataset(signal_data, label_data)
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=32, shuffle=True)
train(model, optimizer, criterion, train_loader, num_epochs=10)

在上面的代码中，我们定义了一个包含两个卷积层和两个全连接层的CNN模型，并使用Adam优化器进行训练。我们使用交叉熵损失函数来计算分类损失，并使用PyTorch的DataLoader将数据转换为批处理。在训练循环中，我们使用了反向传播和优化器来更新模型参数，并输出每个epoch的平均损失。