  -The fluctuation effect of gradient expectation and variance caused by
parameter update between consecutive iterations is neglected or confusing by
current mainstream gradient optimization algorithms. The work in this paper
remedy this issue by introducing a novel unbiased stratified statistic \
$\bar{G}_{mst}$\ , a sufficient condition of fast convergence for \
$\bar{G}_{mst}$\ also is established. A novel algorithm named MSSG designed
based on \ $\bar{G}_{mst}$\ outperforms other sgd-like algorithms. Theoretical
conclusions and experimental evidence strongly suggest to employ MSSG when
training deep model.
