我是深度学习的新手，我已经建立了一些基本的CNN，但是这次我试图建立类似于Yolo3的FCN（完全卷积网络）。我的网络包含32个层，其中LeakyRelu作为激活功能和adam优化程序。有680个数据样本，输入图像大小为416x416，与yolo模型相同。我在下面给出了一些代码片段。
我正在使用Pytorch 1.1和cuda 9版本。 我尝试了许多博客中建议的不同学习率，如0.0001、0.000001、0.0000001，还尝试了不同的beta，例如（0.9,0.999），（0.5,0.999）等。我也尝试过训练很长时间，最多200个纪元。
Running loss : 59102027776.0
0%| | 1/630 [00:19<3:24:03, 19.46s/it]Running loss : nan
0%| | 2/630 [00:23<2:34:23, 14.75s/it]Running loss : nan
0%| | 3/630 [00:25<1:53:32, 10.87s/it]Running loss : nan
请仅在图片中考虑等式：7和8。 点击formula picture
的链接masked_pose_loss = torch.mean(
torch.sum(mask * torch.sum(torch.mul(pred - true, pred - true), dim=[1, 2]), dim=[1, 2, 3]))
self.relu1_1 = nn.LeakyReLU(inplace=True)
self.conv1_2 = nn.Conv2d(32, 64, 3, stride=1,padding=1)
self.relu1_2 = nn.LeakyReLU(inplace=True)
self.pool1 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
# conv2
self.conv2_1 = nn.Conv2d(64, 128, 3, stride=1, padding=1)
self.relu2_1 = nn.LeakyReLU(inplace=True)
self.conv2_2 = nn.Conv2d(128, 64, 1, stride=1)
self.relu2_2 = nn.LeakyReLU(inplace=True)
self.pool2 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
# conv3
self.conv3_1 = nn.Conv2d(64, 128, 3, stride=1, padding=1)
self.relu3_1 = nn.LeakyReLU(inplace=True)
self.conv3_2 = nn.Conv2d(128, 256, 3, stride=1, padding=1)
self.relu3_2 = nn.LeakyReLU(inplace=True)
self.conv3_3 = nn.Conv2d(256, 128, 1, stride=1)
self.relu3_3 = nn.LeakyReLU(inplace=True)
self.pool3 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
# conv4
self.conv4_1 = nn.Conv2d(128, 256, 3, stride=1, padding=1)
self.relu4_1 = nn.LeakyReLU(inplace=True)
self.conv4_2 = nn.Conv2d(256, 512, 3, stride=1, padding=1)
self.relu4_2 = nn.LeakyReLU(inplace=True)
self.conv4_3 = nn.Conv2d(512, 256, 1, stride=1)
self.relu4_3 = nn.LeakyReLU(inplace=True)
self.pool4 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
# conv5
self.conv5_1 = nn.Conv2d(256, 512, 3, stride=1, padding=1)
self.relu5_1 = nn.LeakyReLU(inplace=True)
self.conv5_2 = nn.Conv2d(512, 256, 1, stride=1)
self.relu5_2 = nn.LeakyReLU(inplace=True)
self.conv5_3 = nn.Conv2d(256, 512, 3, stride=1, padding=1)
self.relu5_3 = nn.LeakyReLU(inplace=True)
self.pool5 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
# fc6
self.conv6_1 = nn.Conv2d(512, 1024, 3, stride=1, padding=1)
self.relu6_1 = nn.LeakyReLU(inplace=True)
self.conv6_2 = nn.Conv2d(1024, 512, 1, stride=1)
self.relu6_2 = nn.LeakyReLU(inplace=True)
self.conv6_3 = nn.Conv2d(512, 1024, 3, stride=1, padding=1)
self.relu6_3 = nn.LeakyReLU(inplace=True)
self.conv7_1 = nn.Conv2d(1024, 512, 1, stride=1)
self.relu7_1 = nn.LeakyReLU(inplace=True)
self.conv7_2 = nn.Conv2d(512, 1024, 3, stride=1, padding=1)
self.relu7_2 = nn.LeakyReLU(inplace=True)
self.conv7_3 = nn.Conv2d(1024, 1024, 3, stride=1, padding=1)
self.relu7_3 = nn.LeakyReLU(inplace=True)
self.conv8_1 = nn.Conv2d(1024, 1024, 3, stride=1, padding=1)
self.relu8_1 = nn.LeakyReLU(inplace=True)
self.conv8_2 = nn.Conv2d(1024, 1024, 3, stride=1)
self.conv8_2 = nn.ReLU(inplace=True)
self.conv_rout16 = nn.Conv2d(512, 64, 1, stride=1)
self.relu_rout16 = nn.ReLU(inplace=True)enter code here
# Resuming comments: I was implementing last two layer of network:
self.convf_1 = nn.Conv2d(1280, 1024, 3, stride=1, padding=1)
self.reluf_1 = nn.LeakyReLU(inplace=True)
self.convf_2 = nn.Conv2d(1024, self.target_channel_size, 1, stride=1)
self.reluf_2 = nn.LeakyReLU(inplace=True)
非常感谢您的回复。
注意：这是我第一次发布。如果我错过了一些必要的信息，请告诉我。