I am using cosine annealing scheduler, but it is adjusting and such a small rate.
This is how it is initialized:
lrs = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max = len(train_loader))
and this is how it is being called:
`for i in range(epoch):`
`trn_corr = 0`
`tst_corr = 0`
`#lrs.step()`
`#adjust the learning rate after 30 epochs`
`#adjust_learning_rate(optimizer, i, learning_rate)`
`#Run training for one epoch`
`train(train_loader, MobileNet, criterion, optimizer, i,trn_corr)`
`#evaluate the validation/test`
`test(test_loader, MobileNet, criterion, i, epoch,tst_corr)`
`lrs.step()`
inside the training loop the optimizer is being adjusted like so:
`# Update parameters`
`optimizer.zero_grad()`
`loss.backward()`
`optimizer.step()`