sgdr: Stochastic Gradient Descent with Warm Restarts

In GenomeNet/deepG: Deep Learning for Genome Sequence Data

Stochastic Gradient Descent with Warm Restarts

Description

Compute the learning Rate for a given epoch using Stochastic Gradient Descent with Warm Restarts. Implements approach from this paper.

Usage

sgdr(lrmin = 5e-10, lrmax = 0.05, restart = 50, mult = 1, epoch = NULL)

Arguments

lrmin	Lower limit of the range for the learning rate.
lrmax	Upper limit of the range for the learning rate.
restart	Number of epochs until a restart is conducted.
mult	Factor, by which the number of epochs until a restart is increased at every restart.
epoch	Epoch, for which the learning rate shall be calculated.

Value

A numeric value.

Examples

sgdr(lrmin = 5e-10, lrmax = 5e-2, restart = 50,
mult = 1, epoch = 5)

GenomeNet/deepG documentation built on Dec. 24, 2024, 12:11 p.m.