AdaBatch: Adaptive Batch Sizes for Training Deep Neural NetworksAdaBatch: Adaptive Batch Sizes for Training Deep Neural NetworksAditya Devarakonda and Maxim Naumov and Michael Garland2017
Paper summaryevgeniizh**TL;DR**: You can increase batch size in advanced phases of training without hurting accuracy and gaining some speedup. You should multiply the learning rate by the same value you multiplied batch size.
**Long version**: Authors propose to increase batch size gradually, starting with a small batch size $r$, and then progressively increase the batch size while adapting the learning rate $\alpha$ so that the ratio $\alpha/r$ remains constant (without taking in account scheduled LR reduce). In this paper, they double the batch size by schedule. At the same time, learning rate is decayed and then multiplied by 2 to compensate batch size increase: if in baseline lr is multiplied by $0.375$, it is multiplied by $0.75$ now.
The experiments on CIFAR-100 dataset show that the gradual increase of batch size allows to converge to the same values as constant small batch size. However, bigger batches allow faster training, providing $\times 1.5$ speedup on AlexNet, and around $\times 1.2$ speedup on ResNet and VGG, for both forward and backward passes on single GPU.
On multiple GPUs the approach allows to further increase batchsize. On fortunate setups authors manage to get up to $\times 1.6$ speedup compared to constant batch size equal to initial value, while the error is almost unchanged. For bigger batch sizes lr warmup is used.
For ImageNet, same behavior is shown for accuracy: gradual increase of batch size converges to same values as setup with initial batch size. Since authors haven't access to a system capable of processing large batches on ImageNet, no performance results are reported.
First published: 2017/12/06 (1 year ago) Abstract: Training deep neural networks with Stochastic Gradient Descent, or its
variants, requires careful choice of both learning rate and batch size. While
smaller batch sizes generally converge in fewer training epochs, larger batch
sizes offer more parallelism and hence better computational efficiency. We have
developed a new training approach that, rather than statically choosing a
single batch size for all epochs, adaptively increases the batch size during
the training process. Our method delivers the convergence rate of small batch
sizes while achieving performance similar to large batch sizes. We analyse our
approach using the standard AlexNet, ResNet, and VGG networks operating on the
popular CIFAR-10, CIFAR-100, and ImageNet datasets. Our results demonstrate
that learning with adaptive batch sizes can improve performance by factors of
up to 6.25 on 4 NVIDIA Tesla P100 GPUs while changing accuracy by less than 1%
relative to training with fixed batch sizes.