Cyclical#
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.
Parameters#
# | 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. |
Example#
use Rubix\ML\NeuralNet\Optimizers\Cyclical;
$optimizer = new Cyclical(0.001, 0.005, 1000);
References#
-
L. N. Smith. (2017). Cyclical Learning Rates for Training Neural Networks. ↩
Last update:
2021-03-03