Deep neural network models have been successfully applied to many tasks such as image labeling and speech recognition. Mini-batch stochastic gradient descent is the most prevalent method for training these models. A critical part of successfully applying this method is choosing appropriate initial values, as well as local and global learning rate scheduling algorithms. In this paper, we present a method which is less sensitive to choice of initial values, works better than popular learning rate adjustment algorithms, and speeds convergence on model parameters. We show that using the Self-stabilized DNN method, we no longer require initial learning rate tuning and training converges quickly with a fixed global learning rate. The proposed method provides promising results over conventional DNN structure with better convergence rate.<\/p>\n","protected":false},"excerpt":{"rendered":"
Deep neural network models have been successfully applied to many tasks such as image labeling and speech recognition. Mini-batch stochastic gradient descent is the most prevalent method for training these models. A critical part of successfully applying this method is choosing appropriate initial values, as well as local and global learning rate scheduling algorithms. 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