Hi! Let me elaborate a liitle bit more my “goodness” probe:
As input of my network I have images in tensor dimension C,H,W with for instance C=5, H=W=64;  the output is a vector of nbins length which reprensent the p.d.f of some variable ‘y’: p(y). I have train & test set with y_true which is a float vector in [0,1]. My favorite variables are statistical summary of the distribution dy = (y_predic-y_true)/(1+y_true)   with y_predict is the weighted mean using the probability p(y) and the location of bins center value. So, my statistical summary is the sigma ~ 1 and the fraction of outliers (eta) defined as |y_predic - y_true| > 5.
Now comparing different seed run: example
seed=0 sigma=11, eta=1;    <== OK
seed=1 ~ seed=0
seed=2 sigma=15, eta = 11    <===== Not OK
Now, I have used other seeds and some are ok, other no.
So I was wandering if there are “good” seeds as it generrate a good random number generation unbiased, and some other seeds which produce biased random series.
best.