That's a good question since the outputs would be differently scaled if the logistic sigmoid vs the softmax is used in the output layer. I think you don't need to worry about setting anything though, since the "activation" only applies to the hidden layers, and the softmax is, regardless of "activation," automatically used in the output layer.
Best, Sebastian > On Apr 18, 2018, at 3:15 PM, Daniel Baláček <daniel.bala...@gmail.com> wrote: > > Hello everyone > > I have a question regarding MLPClassifier in sklearn. In the documentation in > section 1.17. Neural network models (supervised) - 1.17.2 Classification it > is stated that "MLPClassifier supports multi-class classification by > applying Softmax as the output function." > However it is not clear how to apply the Softmax function. > > The way I think (or hope) this works is that if a parameter activation is set > to activation = 'logistic' Softmax function should be automatically applied > whenever there are more than two classes. Is this right or does one have to > explicitly specify the use of Softmax function in some way? > > I am sorry if this is a nonsense question. I am new to scikit-learn and > machine learning in general and I was not sure about this one. Thank you for > any answers in advance. > > With regards, > D. B. > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn _______________________________________________ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn
