问题
I am finetuning using Caffe on an image dataset on a Tesla K40. Using a batch size=47, solver_type=SGD, base_lr=0.001, lr_policy="step", momentum=0.9, gamma=0.1, the training loss decreases and test accuracy goes from 2%-50% in 100 iterations which is quite good.
When using other optimisers such as RMSPROP, ADAM and ADADELTA, the training loss remains almost the same even and no improvement in test accuracy after 1000 iterations.
For RMSPROP, I have changed the respective parameters as mentioned here.
For ADAM, I have changed the respective parameters as mentioned here
For ADADELTA, I have changed the respective parameters as mentioned here
Can someone please tell me what i am doing wrong?
回答1:
I saw similar results to pir: Adam would diverge when given the same base_lr that SGD used. When I reduced base_lr to 1/100 of its original value, Adam suddenly converged, and gave good results.
来源：https://stackoverflow.com/questions/32909986/rmsprop-adam-adadelta-test-accuracy-does-not-improve-using-caffe