PyCaffe output layer for testing binary classification model
SigmoidWithLoss layer outputs a single number per batch representing the loss w.r.t the ground truth labels.
On the other hand, Sigmoid layer outputs a probability value for each input in the batch. This output does not require the ground truth labels to be computed.
If you are looking for the probability per input, you should be looking at the output of the Sigmoid layer
I fine tunes vgg-16 for binary classification. I used sigmoidLoss layer as the loss function.
To test the model, I coded a python file in which I loaded the model with an image and got the output using :
out = net.forward()
My doubt is should I take the output from Sigmoid or SigmoidLoss layer.
And What is the difference between 2 layers.
My output will actually be the probability of input image being class 1.**