import torch import torch.nn as nn import torch.nn.functional as F # 定义网络 class ConvNet3(nn.Module): def __init__(self): super(ConvNet3, self).__init__() self.conv1 = nn.Conv1d(1, 16, 16) self.bn1 = nn.BatchNorm1d(16) self.max_pool1 = nn.MaxPool1d(4,4) self.conv2 = nn.Conv1d(16, 32, 8) self.bn2 = nn.BatchNorm1d(32) self.max_pool2 = nn.MaxPool1d(4,4) self.conv3 = nn.Conv1d(32, 64, 8) self.bn3 = nn.BatchNorm1d(64) self.max_pool3 = nn.MaxPool1d(4,4) self.fc1 = nn.Linear(3904, 64) self.fc2 = nn.Linear(64, 1) def forward(self, x): x = self.conv1(x) x = F.relu(self.bn1(x)) x = self.max_pool1(x) x = self.conv2(x) x = F.relu(self.bn2(x)) x = self.max_pool2(x) x = self.conv3(x) x = F.relu(self.bn3(x)) x = self.max_pool3(x) # resize x = x.view(x.size(0), -1) x = F.relu(self.fc1(x)) x = self.fc2(x) x = torch.sigmoid(x) return x