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[P] Learning Rate Dropout in PyTorch

https://github.com/noahgolmant/pytorch-lr-dropout

I just implemented learning rate dropout using PyTorch! This technique applies dropout to the weight update at each iteration instead of the weights themselves.

I welcome any and all feedback! I ran four trials with a ResNet34 model on CIFAR-10 using both the baseline optimizer (SGD with momentum) and this variant. I wasn’t able to achieve the numbers reported in the paper, though. Feel free to double-check the masking logic or hyperparameters in case that explains the difference.

submitted by /u/noahgolm
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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.