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Deep Neural Networks are generally trained using iterative gradient updates.Magnitudes of gradients are affected by many factors, including choice ofactivation functions and initialization. More importantly, gradient magnitudescan greatly differ across layers, with some layers receiving much smallergradients than others. causing some layers to train slower than others andtherefore slowing down the overall convergence. We analytically explain thisdisproportionality. Then we propose to explicitly train all layers at the samespeed, by scaling the gradient w.r.t. every trainable tensor to be proportionalto its current value. In particular, at every batch, we want to update alltrainable tensors, such that the relative change of the L1-norm of the tensorsis the same, across all layers of the network, throughout training time.Experiments on MNIST show that our method appropriately scales gradients, suchthat the relative change in trainable tensors is approximately equal acrosslayers. In addition, measuring the test accuracy with training time, shows thatour method trains faster than other methods, giving higher test accuracy givensame budget of training steps.

Proportionate gradient updates with PercentDelta