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The plain stochastic gradient descent and momentum stochastic gradientdescent have extremely wide applications in deep learning due to their simplesettings and low computational complexity. The momentum stochastic gradientdescent uses the accumulated gradient as the updated direction of the currentparameters, which has a faster training speed. Because the direction of theplain stochastic gradient descent has not been corrected by the accumulatedgradient. For the parameters that currently need to be updated, it is theoptimal direction, and its update is more accurate. We combine the advantagesof the momentum stochastic gradient descent with fast training speed and theplain stochastic gradient descent with high accuracy, and propose a scalingtransition from momentum stochastic gradient descent to plain stochasticgradient descent(TSGD) method. At the same time, a learning rate that decreaseslinearly with the iterations is used instead of a constant learning rate. TheTSGD algorithm has a larger step size in the early stage to speed up thetraining, and training with a smaller step size in the later stage can steadilyconverge. Our experimental results show that the TSGD algorithm has fastertraining speed, higher accuracy and better stability. Our implementation isavailable at: https://github.com/kunzeng/TSGD.

Scaling transition from momentum stochastic gradient descent to plain stochastic gradient descent