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首页> 美国卫生研究院文献>Entropy >Mutual Information Based Learning Rate Decay for Stochastic Gradient Descent Training of Deep Neural Networks
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Mutual Information Based Learning Rate Decay for Stochastic Gradient Descent Training of Deep Neural Networks

机译：基于互动信息的学习速率衰减用于深神经网络的随机梯度血统训练

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This paper demonstrates a novel approach to training deep neural networks using a Mutual Information (MI)-driven, decaying Learning Rate (LR), Stochastic Gradient Descent (SGD) algorithm. MI between the output of the neural network and true outcomes is used to adaptively set the LR for the network, in every epoch of the training cycle. This idea is extended to layer-wise setting of LR, as MI naturally provides a layer-wise performance metric. A LR range test determining the operating LR range is also proposed. Experiments compared this approach with popular alternatives such as gradient-based adaptive LR algorithms like Adam, RMSprop, and LARS. Competitive to better accuracy outcomes obtained in competitive to better time, demonstrate the feasibility of the metric and approach.

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