I'm a bit of a CNN newbie, and I'm trying to train one to image classify pictures of pretty similar looking particles. I'm making the inputs and labels by hand from a set of 48x48 grayscale images, and labeling them with a one-hot vector based on their position in the sequence (for example, the 400/1000th image might have a one-hot in the 4th position if I have 10 categories in the run). I'm using sigmoidal output activation and categorical cross entropy loss. I've played around with a few different optimizers, as well. I'm implementing in python keras.
Unfortunately, although I have pretty good accuracy numbers for the training and validation, when I actually look at the outputs being produced, it generally gives multiple categories, which is not at all what I want. For example, if I have 6 categories and a label of 3, it might give the following probability vector:
[ .99 .98 1.0 .99 0.02 0.05 ]
It was my understanding that categorical cross entropy would not allow this type of categorization, and yet it is prevalent in my code. I am under the impression that I'm doing something fundamentally wrong, but I cant figure out what. Any help would be appreciated.