HybridSN 高光谱分类网络的优化

1.HybridSN 

class HybridSN(nn.Module):
    def __init__(self):
        super(HybridSN, self).__init__()
        self.conv1 = nn.Conv3d(1, 8, (7, 3, 3), stride=1, padding=0)
        self.conv2 = nn.Conv3d(8, 16, (5, 3, 3), stride=1, padding=0)
        self.conv3 = nn.Conv3d(16, 32, (3, 3, 3), stride=1, padding=0)
        self.conv4 = nn.Conv2d(576, 64, kernel_size=3, stride=1, padding=0)
        self.bn1 = nn.BatchNorm2d(64)
        self.fc1 = nn.Linear(18496, 256)
        self.dropout1 = nn.Dropout(p=0.4)
        self.fc2 = nn.Linear(256, 128)
        self.dropout2 = nn.Dropout(p=0.4)
        self.fc3 = nn.Linear(128, class_num)

    def forward(self, x):
        out = self.conv1(x)
        out = self.conv2(out)
        out = self.conv3(out)
        #print(batch)
        out = out.reshape(batch, 576, 19, 19)
        out = self.conv4(out)
        out = self.bn1(out)
        out = F.relu(out)
        out = out.view(-1, 64 * 17 * 17)
        out = self.fc1(out)
        out = F.relu(out)
        out = self.dropout1(out)
        out = self.fc2(out)
        out = F.relu(out)
        out = self.dropout2(out)
        out = self.fc3(out)
        return out

2.添加SENet

class SELayer(nn.Module):
    def __init__(self, channel, r=16):
        super(SELayer, self).__init__()
        self.avg_pool = nn.AdaptiveAvgPool2d((1,1))
        self.fc = nn.Sequential(
            nn.Linear(channel, channel // r),
            nn.ReLU(),
            nn.Linear(channel // r, channel),
            nn.Sigmoid()
        )

    def forward(self, x):
        a, b, _, _ = x.size()
        y = self.avg_pool(x).view(a, b)
        y = self.fc(y).view(a, b, 1, 1)
        return x * y.expand_as(x)
class HybridSN(nn.Module):
      def __init__(self, num_classes=16):
    '''
        self.senet = SELayer(64)
    '''
      def forward(self, x):
    '''
        out = self.conv3_2d(out)
        out = self.senet(out)
    '''

3.为什么每次测试结果会不同

Pytorch中，网络有train和eval两种模式

在训练模式model.train() ：启用 BatchNormalization 和 Dropout

在测试模式model.eval() ：不启用 BatchNormalization 和 Dropout

 可以看出训练模式和测试模式是不同的，导致我们在训练好的模型在每次测试中是不同的