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，存储为一个(300,1,512)的torch.tensor张量，现在我想将其输入一个深度DenseNet网络训练分类模型用于分类这些信号，请使用pytorch实现

以下是一个简单的实现：

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

# 定义深度DenseNet网络
class DenseNet(nn.Module):
    def __init__(self):
        super(DenseNet, self).__init__()

        self.conv1 = nn.Conv1d(1, 64, kernel_size=3, padding=1)
        self.dense1 = nn.Sequential(
            nn.BatchNorm1d(64),
            nn.ReLU(),
            nn.Conv1d(64, 128, kernel_size=3, padding=1),
            nn.BatchNorm1d(128),
            nn.ReLU(),
            nn.Conv1d(128, 32, kernel_size=1),
            nn.BatchNorm1d(32),
            nn.ReLU()
        )
        self.dense2 = nn.Sequential(
            nn.BatchNorm1d(96),
            nn.ReLU(),
            nn.Conv1d(96, 128, kernel_size=3, padding=1),
            nn.BatchNorm1d(128),
            nn.ReLU(),
            nn.Conv1d(128, 32, kernel_size=1),
            nn.BatchNorm1d(32),
            nn.ReLU()
        )
        self.dense3 = nn.Sequential(
            nn.BatchNorm1d(128),
            nn.ReLU(),
            nn.Conv1d(128, 128, kernel_size=3, padding=1),
            nn.BatchNorm1d(128),
            nn.ReLU(),
            nn.Conv1d(128, 32, kernel_size=1),
            nn.BatchNorm1d(32),
            nn.ReLU()
        )
        self.dense4 = nn.Sequential(
            nn.BatchNorm1d(160),
            nn.ReLU(),
            nn.Conv1d(160, 128, kernel_size=3, padding=1),
            nn.BatchNorm1d(128),
            nn.ReLU(),
            nn.Conv1d(128, 32, kernel_size=1),
            nn.BatchNorm1d(32),
            nn.ReLU()
        )
        self.dense5 = nn.Sequential(
            nn.BatchNorm1d(192),
            nn.ReLU(),
            nn.Conv1d(192, 128, kernel_size=3, padding=1),
            nn.BatchNorm1d(128),
            nn.ReLU(),
            nn.Conv1d(128, 32, kernel_size=1),
            nn.BatchNorm1d(32),
            nn.ReLU()
        )
        self.dense6 = nn.Sequential(
            nn.BatchNorm1d(224),
            nn.ReLU(),
            nn.Conv1d(224, 128, kernel_size=3, padding=1),
            nn.BatchNorm1d(128),
            nn.ReLU(),
            nn.Conv1d(128, 32, kernel_size=1),
            nn.BatchNorm1d(32),
            nn.ReLU()
        )
        self.pool = nn.AdaptiveMaxPool1d(1)
        self.fc = nn.Linear(32, 10)

    def forward(self, x):
        x = self.conv1(x)
        x1 = self.dense1(x)
        x1 = torch.cat([x, x1], 1)
        x2 = self.dense2(x1)
        x2 = torch.cat([x1, x2], 1)
        x3 = self.dense3(x2)
        x3 = torch.cat([x2, x3], 1)
        x4 = self.dense4(x3)
        x4 = torch.cat([x3, x4], 1)
        x5 = self.dense5(x4)
        x5 = torch.cat([x4, x5], 1)
        x6 = self.dense6(x5)
        x6 = torch.cat([x5, x6], 1)
        x = self.pool(x6)
        x = x.view(x.size(0), -1)
        x = self.fc(x)
        return x

# 定义数据集
class SignalDataset(torch.utils.data.Dataset):
    def __init__(self, data):
        self.data = data

    def __getitem__(self, index):
        return self.data[index], index // 100

    def __len__(self):
        return len(self.data)

# 加载数据
data = torch.load('data.pt')
dataset = SignalDataset(data)
dataloader = torch.utils.data.DataLoader(dataset, batch_size=32, shuffle=True)

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

# 训练模型
for epoch in range(10):
    running_loss = 0.0
    for i, (inputs, labels) in enumerate(dataloader, 0):
        optimizer.zero_grad()

        outputs = densenet(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

        running_loss += loss.item()
        if i % 10 == 9:
            print('[%d, %5d] loss: %.3f' % (epoch + 1, i + 1, running_loss / 10))
            running_loss = 0.0

注意，这只是一个简单的实现，您可能需要根据您的数据进行一些调整。此外，您可能还需要使用更复杂的DenseNet结构，以获得更好的分类性能。