  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.
