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

class Simple1DCNN(nn.Module):
    def __init__(self, input_size, num_classes):
        super(Simple1DCNN, self).__init__()
        self.conv1 = nn.Conv1d(in_channels=1, out_channels=32, kernel_size=3, stride=1, padding=1)
        self.relu = nn.ReLU()
        self.pool = nn.MaxPool1d(kernel_size=2, stride=2)
        self.conv2 = nn.Conv1d(in_channels=32, out_channels=64, kernel_size=3, stride=1, padding=1)
        self.fc1 = nn.Linear(64 * (input_size // 4), 128)  # 假设经过两次池化后，长度减半两次
        self.fc2 = nn.Linear(128, num_classes)

    def forward(self, x):
        x = self.pool(self.relu(self.conv1(x)))
        x = self.pool(self.relu(self.conv2(x)))
        x = x.view(-1, 64 * (self.input_size // 4))  # 展平特征图
        x = self.relu(self.fc1(x))
        x = self.fc2(x)
        return x

# 实例化模型
input_size = 100  # 假设n=100
num_classes = 10  # 假设有10个类别
model = Simple1DCNN(input_size, num_classes)

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

# 训练和评估模型的代码与之前类似，这里不再赘述。