cnn_dp_bn.py

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