[Retracted] Gradient Descent Optimization in Deep Learning Model Training Based on Multistage and Method Combination Strategy

Table 13

Performance of the proposed method, SGD with Momentum and decay.

	ResNet-20 on Cafri-10		LSTM on IMDB
	Val-loss	Val-acc	Val-loss	Val-acc
(SGD + M + d) + SGD	0.6324	0.8340	0.3736	0.8366
(SGD + M + d) + (SGD + M)	0.8533	0.7737	0.3770	0.8369
(SGD + M + d) + (SGD + d)	0.6485	0.8278	0.3970	0.8234
(SGD + M + d) + (SGD + M + d)	0.8289	0.7868	0.4274	0.8036
(SGD + M + d) + RMSprop	0.9795	0.7593	0.4949	0.8245
(SGD + M + d) + (RMSprop + d)	0.8371	0.7884	0.4587	0.8279
(SGD + M + d) + Adam	1.2722	0.7058	0.8569	0.8091
(SGD + M + d) + (Adam + d)	0.7481	0.8112	0.9056	0.8070

The bold values represent the best results.