I am building a neural network that consists of an LSTM, dense and dropout layers using Keras to forecast 8 continuous values in the future. I unfortunately have a very small data-set of 56 observations. I divided my data-set into 48 observations for training and 8 observations for testing. I ran my code 5 times in order to split my data-set differently each time. Within each run, I also fit the model 100 times and use the maximum test R-squared to pick the best performing model in each run. Therefore, I have the following r-squared values: 0.89, 0.90, 0.93, 0.91, 0.87. If I want to report an r-squared value, which one should I use? How to be sure that I reached the global optimum r-squared value? Also, would the RMSE benefit me in taking such decision?