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How to improve a model with high training accuracy but lower testing accuracy?
I have a deep learning model which has high training accuracy(100 %) but very low testing accuracy (10%). How can I improve this model?
I read somewhere that our model gets low testing accuracy because of under fitting. I tried to remove excess neurons from layers but it causes both training and testing accuracy to decrease. I then add regularization such as dropouts and batch normalization but noting improved. I also trained it for more epochs.
Do you have any …!-->