Different Accuracy Between Python Keras And Keras In R

I build a image classification model in R by keras for R. Got about 98% accuracy, while got terrible accuracy in python. Keras version for R is 2.1.3, and 2.1.5 in python following

Solution 1:

That is a dramatic difference so perhaps there's a bug in the code or something unexpected in the data but reproducing Keras results from R in Python is more difficult than it may seem since setting the seed on the R side is insufficient. Instead of set.seed you should use use_session_with_seed, which comes with the R libraries for tensorflow and keras. Note that for full reproducibility you need to use_session_with_seed(..., disable_gpu=TRUE, disable_parallel_cpu=TRUE). See also stack and tf docs. Also, here is an example using the github version of kerasformula and a public dataset. Also, watch out for functions like layer_dropout that accept seed as a parameter.