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11 results

fft_setup.cpp

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  • cnn_dp_bn.py 1.11 KiB
    import torch
    import torch.nn as nn
    import torch.nn.functional as F
    
    # 定义网络
    class ConvNet5(nn.Module):
        def __init__(self):
            super(ConvNet5, 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)
            # dropout
            self.dropout = nn.Dropout(p=.5)
     
        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 = self.dropout(F.relu(self.fc1(x)))
            x = self.fc2(x)
            x = torch.sigmoid(x)
            return x