I’m using a CNN with PyKeras as a classifier to separate signal/background events, and I’m getting very good results after training based on my ROC curve and Training+Testing Response plot. However, when I look at the weight file (the .xml file after training is complete), its showing ±3.40282347e+38 for Min and Max values for all my variables, which I know are the values represented by a 32-bit float number. So what went wrong with the saved weights?
Here’s more details on my CNN model: I’ve slighlty edited the TMVA_CNN_Classification.py file in the TMVA tutorials for my particular input. My input has 360 variables as 20 X 18 X 1 image. The input variables are energy deposition values, which is a typical HEP image classifier problem. The signal events used for training are clean, single tracks, while the background events are multiple intersecting/heavy interaction tracks. Most of the values will be zeros, but there will be variables with non-zero values that represent the reconstructed energy deposition in the image. The results show a strong separation and I was very happy with the results, so I’m confused as to why the weights look that way.