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The Cyclical optimizer uses a global learning rate that cycles between the lower and upper bound over a designated period while also decaying the upper bound by a factor at each step. Cyclical learning rates have been shown to help escape bad local minima and saddle points of the gradient.


# Name Default Type Description
1 lower 0.001 float The lower bound on the learning rate.
2 upper 0.006 float The upper bound on the learning rate.
3 steps 100 int The number of steps in every half cycle.
4 decay 0.99994 float The exponential decay factor to decrease the learning rate by every step.


use Rubix\ML\NeuralNet\Optimizers\Cyclical;

$optimizer = new Cyclical(0.001, 0.005, 1000);


  1. L. N. Smith. (2017). Cyclical Learning Rates for Training Neural Networks.