  We introduce Gravity, another algorithm for gradient-based optimization. In
this paper, we explain how our novel idea change parameters to reduce the deep
learning model's loss. It has three intuitive hyper-parameters that the best
values for them are proposed. Also, we propose an alternative to moving
average. To compare the performance of the Gravity optimizer with two common
optimizers, Adam and RMSProp, five standard datasets were trained on two VGGNet
models with a batch size of 128 for 100 epochs. Gravity hyper-parameters did
not need to be tuned for different models. As will be explained more in the
paper, to investigate the direct impact of the optimizer itself on loss
reduction no overfitting prevention technique was used. The obtained results
show that the Gravity optimizer has more stable performance than Adam and
RMSProp and gives greater values of validation accuracy for datasets with more
output classes like CIFAR-100 (Fine).
