Yes, overriding the adam loss with a partial of the adam loss with the eps worked for me:
learn.opt_func = partial(learn.opt_func, eps=1e-4)
Yes, overriding the adam loss with a partial of the adam loss with the eps worked for me:
learn.opt_func = partial(learn.opt_func, eps=1e-4)
any problem is seeing model converge?
I always find with fp16 model is not getting converged and metrics are getting worst…
i just use below peace of code
learn1 = Learner(data,
md_ef,
loss_func=loss_func,
metrics = [qk,r2_score,exp_rmspe],
path='.').to_fp16()
learn1.model.half()
Mi making any mistake some where