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When using Alex's version of RMSProp to train a network, I found it sometimes update some network weights to NaN.
After that, I found the RMSProp implementation is a little bit different from Alex's paper. In equation 40, the epsilon should be under the square root.
I guess I got
NaN
becausestate_n - state_g * state_g
can sometimes be something like-1e-20
(because of floating point arithmetic error). Indeed, after I applied this patch, it stops update network weights toNaN
.