Basics of keras and GRU, Comparison with LSTM GRU is a model designed to compensate for the high number of parameters in LSTM, i.e., its high computational cost. It combines the operations of memory updating and memory forgetting into a single operation, thereby reducing the computational cost. I’ll spare you the details, as you can find plenty of them by searching. Here is a comparison between GRU and LSTM. github The file in jupyter notebook format is here google colaboratory If you want to run it in google colaboratory here Author’s environment The author’s OS is macOS, and the options are different from Linux and Unix commands.