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These parameters included the learning rate, learning rate decay, non-linear function and pooling function.

Learning rate was one of the most important DNN hyperparameters.

Jacobs, R. A. Increased rates of convergence through learning rate adaptation.

j Learning rate.

Learning rate: 1.   6.

Learning rate, α: 0.7.

where a is learning rate.

First is the optimization of learning rate.

Learning rate (LR) and momentum (M): We continue the analysis with the learning rate and momentum.

and Equation 2 being a fixed value, learning rate μ becomes a self-adaptive learning rate.

Fig. 21 a Learning rate with accuracy of model.