使用cnn网络对一维序列进行预测，要求序列有20000条左右，pytorch实现

以下是使用PyTorch实现CNN对一维序列进行预测的代码示例：

import torch
import torch.nn as nn

class CNN1d(nn.Module):
    def __init__(self):
        super(CNN1d, self).__init__()
        self.conv1 = nn.Conv1d(in_channels=1, out_channels=16, kernel_size=3, stride=1, padding=1)
        self.pool1 = nn.MaxPool1d(kernel_size=2, stride=2)
        self.conv2 = nn.Conv1d(in_channels=16, out_channels=32, kernel_size=3, stride=1, padding=1)
        self.pool2 = nn.MaxPool1d(kernel_size=2, stride=2)
        self.fc1 = nn.Linear(8000, 128)
        self.fc2 = nn.Linear(128, 1)

    def forward(self, x):
        x = self.conv1(x)
        x = torch.relu(x)
        x = self.pool1(x)
        x = self.conv2(x)
        x = torch.relu(x)
        x = self.pool2(x)
        x = x.view(-1, 8000)
        x = self.fc1(x)
        x = torch.relu(x)
        x = self.fc2(x)
        return x

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

    def __getitem__(self, index):
        x = self.data[index, :-1]
        y = self.data[index, -1:]
        return x, y

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

# 加载数据
data = torch.randn(20000, 101)
dataset = MyDataset(data)
dataloader = torch.utils.data.DataLoader(dataset, batch_size=128, shuffle=True)

# 定义损失函数和优化器
model = CNN1d()
criterion = nn.MSELoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)

# 训练模型
for epoch in range(100):
    for i, (inputs, labels) in enumerate(dataloader):
        optimizer.zero_grad()
        outputs = model(inputs.unsqueeze(1))
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
    print('Epoch: {}, Loss: {}'.format(epoch+1, loss.item()))

这里使用了一个带有100个特征的随机数据集，每个样本包含100个特征和1个标签。模型使用了两个卷积层和两个池化层，最后经过两个全连接层进行预测。训练过程中使用MSE损失函数和Adam优化器。