Pretrained network for multi-class classification


#1

Hi,

I am very new to MXNet and Gluon. I took the “Transfering knowledge through finetuning” example from http://gluon.mxnet.io/chapter08_computer-vision/fine-tuning.html and I am trying to do use it on my own data for multi-class classification. My labels are n-hot. I just substituted loss = gluon.loss.SoftmaxCrossEntropyLoss() with loss = gluon.loss.SigmoidBinaryCrossEntropyLoss() However the error loss doesn’t go down. Is this the way to go for multi-class classification? Here is my code: https://gist.github.com/mongoose54/8d47be1359691bae3b2470dfab60fc00