Depends what kind of performance are you measuring it on and what optimizer you are using? Is it training or validation/test performance and are you using any adaptive method (RMSProp, Adam etc..)?
On Sunday, 28 May 2017 03:03:42 UTC+1, Ji Qiujia wrote: > > Recently I am doing mnist image classification using resnet. And I found > something strange, or interesting. First, though it's usually said that we > should do early stopping, I found it's always better to run more epochs > with the initial learning rate, which I set to 0.1 or 0.01, and then > downscale learning rate quickly. For example, my learning rate strategy is > to begin with 0.1 and is scaled down by 0.1 at the 200th, 210th, 220th > epoch with batchsize of 64 and totally 230 epochs. I also found the last > downscaling of learning rate usually degrade performance. Am I doing > anything wrong?You are welcomed to share your parameter adjusting > experience. > -- --- You received this message because you are subscribed to the Google Groups "theano-users" group. To unsubscribe from this group and stop receiving emails from it, send an email to [email protected]. For more options, visit https://groups.google.com/d/optout.
