Performance analysis derivative based updating method
roughly 1e-10 and smaller in absolute value is worrying).If they are you may want to temporarily scale your loss function up by a constant to bring them to a “nicer” range where floats are more dense - ideally on the order of 1.0, where your float exponent is 0. One source of inaccuracy to be aware of during gradient checking is the problem of .
Moreover, a Neural Network with an SVM classifier will contain many more kinks due to Re LUs.If the identity of at least one winner changes when evaluating \(f(x h)\) and then \(f(x-h)\), then a kink was crossed and the numerical gradient will not be exact. One fix to the above problem of kinks is to use fewer datapoints, since loss functions that contain kinks (e.g.