Hey, so my question was for the developers.
In the docs, it says that training is done using the sigmoid activation function. However, when you use predict it uses y = 1.7159*tanh(2/3 * x).
Can I just have a little clarification please? I don't understand why they train and predict on two different equations. Not only am I just genuinely curious why they are different, but I also need responses to have the range [0,1]. I could normalize the responses, however a better solution is to insure my values in the feature vector are positive (which does not cause a loss in data for my case), this insures the sigmoid responses are between [0,1]. This works for the training side, however not for the testing side since they use different equations and responses will now be between [0,1.7]. I would rather avoid another normalization, if possible.
Thanks for the time! -Tim