ML 22: Learning Rate and Batch Size Are One Knob, Until the Rate Hits a Wall
Disclosure: this post was written with the assistance of an AI system (Claude), which wrote the analysis code, ran the experiments and drafted the text. The topic, the constraints, the data choices and the final review are the author’s. Plain SGD on a two-layer network on the digits, with 20 epochs’ worth of examples at every batch size, swept over learning rates from 0.003 to 6.144. Away from divergence, runs with the same ratio of learning rate to batch size reached the same training loss to within 2....