Pytorch implementation of Learning Rate Dropout.

Related tags

Deep Learning Learning-Rate-Dropout
Overview

Learning-Rate-Dropout

Pytorch implementation of Learning Rate Dropout.

Paper Link: https://arxiv.org/pdf/1912.00144.pdf

Train ResNet-34 for Cifar10:

run:

python main.py --model=resnet --optim=adam_lrd --lr=0.001 --LRD_p=0.5

python main.py --model=resnet --optim=adam --lr=0.001

python main.py --model=resnet --optim=sgd_lrd --lr=0.1 --LRD_p=0.5

python main.py --model=resnet --optim=sgd --lr=0.1 

python main.py --model=resnet --optim=rmsprop_lrd --lr=0.001 --LRD_p=0.5

python main.py --model=resnet --optim=rmsprop --lr=0.001

After training, run "plot.py" to show the learning curves.

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Comments
  • MNIST setup

    MNIST setup

    I noticed that on mnist most optimizers do not reach 100% train accuracy (or oscillate) while in my implementation I get 100% train accuracy after like 10 epochs.

    Is it possible to publish the code/setup for the mnist experiments as well?

    opened by ifeherva 0
Owner
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GitHub
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