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MLP Activation Function - train vs predict

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