[D] How to pick a learning rate scheduler?

• RAdam

  4 2,520 0.0 Python

  On the Variance of the Adaptive Learning Rate and Beyond

common practice is to include some type of annealing (cosine, linear, etc.), which makes intuitive sense. for adam/adamw, it's generally a good idea to include a warmup in the lr schedule, as the gradient distribution without the warmup can be distorted, leading to the optimizer being trapped in a bad local min. see this paper. there are also introduced in this paper and subsequent works (radam, ranger, and variants) that don't require a warmup stage to stabilize the gradients. i would say in general, if you're using adam/adamw, include a warmup and some annealing, either linear or cosine. if you're using radam/ranger/variants, you can skip the warmup. how many steps to use for warmup/annealing are probably problem specific, and require some hyperparam tuning to get optimimal results

• InfluxDB

  www.influxdata.com featured

  Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.

  

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