Formulation The learning rate is annealed using a cosine schedule over the course of learning of n_total total steps with an initial warmup period of n_warmup steps. Hence, the learning rate at step i is computed as: Learning rate will be changed as: Usage # optimizer, warmup_epochs, warmup_lr, num_epochs, base_lr, final_lr, iter_per_epoch lr_scheduler = LR_Scheduler( optimizer, args.warmup_epochs, args.warmup_lr

formulae formulae is a Python library that implements Wilkinson’s formulas for mixed-effects models. The main difference with other implementations like Patsy or formulaic is that formulae can work with formulas describing a model with both common and group specific effects (a.k.a. fixed and random effects, respectively). This package has been written to make it easier to specify models with group effects in Bambi, a package that makes it easy to work with Bayesian GLMMs in Python, but it could be […]