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Large Batch Training of Convolutional Networks with Linear-wise Adaptive Rate Scaling
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Boris Ginsburg (NVIDIA) - CS Special Guest Talk
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Thursday, 19.10.2017, 14:30
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Room 337 Taub Bld.
A common way to speed up training of large convolutional networks is to add computational units. Training is then performed using data-parallel synchronous Stochastic Gradient Descent (SGD) with a mini-batch divided between computational units. With an increase in the number of nodes, the batch size grows. However, training with a large batch often results in lower model accuracy. We argue that the current recipe for large batch training (linear learning rate scaling with warm-up) is not general enough and training may diverge. To overcome these optimization difficulties, we propose a new training algorithm based on Layer-wise Adaptive Rate Scaling (LARS). Using LARS, we scaled AlexNet and ResNet-50 to a batch size of 16K without loss in accuracy
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