I adapted the example workflow for image-classification with the keras integration but for multiple classes, such that my output is an array of the N class probabilities returned by a softmax layer.
It works fine but for prediction I get the probabilities as either a collection column (see below with N=2 classes, the collection column next to the ground truth Inflated/Elongated) or as separate columns called Probabilities.
The probabilities are ordered according to the order of columns fed to the corresponding trainer as far as I understood, but I was wondering if there is a more straightforward way to recover the names of the class and the associated probabilities.
Like for other predictor nodes like random forest… which yield columns named like
I know it’s not as straightforward since the keras network executor needs to account for all possible kind of output and scenario.
Also the example here is for N=2 classes which I could classify as in the example workflow with a sigmoid layer instead but the workflow is designed to accept any number of classes, hence the softmax.