New trained model added (best performance yet)

Some trained models added and their .pkl data added ...

Minor change in the rnn test function

Smarter checkpoints ...

Going back to earlier last checkpoint if persormance was better than at end of training
Scalers now work on on 2d and 3d arrays
full_load and full_save added which save and restore the rnn with all parameters
get_rnn_folder added which is a helper function for full_load and provides the folder name for an architecture with the correct notation
Also dataset added

Standard scaler (and inverse) added for 2d and 3d arrays of all shapes ...

Min Max scaler (and inverse) added for 2d and 3d arrays of all shapes
Scalers now also work on test set
Huge performance increase
Finetuning for patience trigger for scalers missing
Trained model for newest version added and old version discarded

Loss normalized ...

Early stopping with patience improved and fine tuned
Improved checkpoints
Bugfixes
Plot of loss added
Trained save of rnn_lstm_5l_[50,40,30,20,10] added