backpropagation - Hessian matrix not defined in stochastic training -


i learning basics of backpropagation algoritms, , found writing code during process helps me better grasp theoretical concepts.

at moment stuck following statement cannot understand:

"conjugate gradient requires batch training, because hessian matrix defined on full training set"

let me define w network's weights, dw weight adjustment gets calculated after each pattern presentation, , bdw sum of dw across patterns same epoch.

my problem is, don't see how hessian matrix cannot valid in stochastic setting, since have calculate @ every pattern presentation, in order determine dw. whether use dw update w after each pattern presentation (stochastic), or sum update w after patterns have been presented seems me irrelevant.

what missing?


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