32 lines
1.2 KiB
Python
32 lines
1.2 KiB
Python
import torch
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import torch.nn as nn
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import torch.optim as optim
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class Simple1DCNN(nn.Module):
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def __init__(self, input_size, num_classes):
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super(Simple1DCNN, self).__init__()
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self.conv1 = nn.Conv1d(in_channels=1, out_channels=32, kernel_size=3, stride=1, padding=1)
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self.relu = nn.ReLU()
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self.pool = nn.MaxPool1d(kernel_size=2, stride=2)
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self.conv2 = nn.Conv1d(in_channels=32, out_channels=64, kernel_size=3, stride=1, padding=1)
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self.fc1 = nn.Linear(64 * (input_size // 4), 128) # 假设经过两次池化后，长度减半两次
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self.fc2 = nn.Linear(128, num_classes)
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def forward(self, x):
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x = self.pool(self.relu(self.conv1(x)))
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x = self.pool(self.relu(self.conv2(x)))
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x = x.view(-1, 64 * (self.input_size // 4)) # 展平特征图
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x = self.relu(self.fc1(x))
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x = self.fc2(x)
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return x
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# 实例化模型
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input_size = 100 # 假设n=100
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num_classes = 10 # 假设有10个类别
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model = Simple1DCNN(input_size, num_classes)
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# 定义损失函数和优化器
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criterion = nn.CrossEntropyLoss()
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optimizer = optim.Adam(model.parameters(), lr=0.001)
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# 训练和评估模型的代码与之前类似，这里不再赘述。 |