> What about the SVR? Is it possible to change the loss function there? Here you would have the same problem; SVR is a constrained optimization problem and you would have to change the calculation of the loss gradient then. Since SVR is a "1-layer" neural net, if you change the cost function to something else, it's not really a SVR anymore.
> Could you please clarify what the "x" and "x'" parameters in the default > Kernel functions mean? Is "x" a NxM array, where N is the number of > observations and M the number of features? Both x and x' should be denoting training examples. The kernel matrix is symmetric (N x N). Best, Sebastian > On Sep 13, 2017, at 5:25 AM, Thomas Evangelidis <teva...@gmail.com> wrote: > > Thanks Sebastian. Exploring Tensorflow capabilities was in my TODO list, but > now it's in my immediate plans. > What about the SVR? Is it possible to change the loss function there? Could > you please clarify what the "x" and "x'" parameters in the default Kernel > functions mean? Is "x" a NxM array, where N is the number of observations and > M the number of features? > > http://scikit-learn.org/stable/modules/svm.html#kernel-functions > > > > On 12 September 2017 at 00:37, Sebastian Raschka <se.rasc...@gmail.com> wrote: > Hi Thomas, > > > For the MLPRegressor case so far my conclusion was that it is not possible > > unless you modify the source code. > > Also, I suspect that this would be non-trivial. I haven't looked to closely > at how the MLPClassifier/MLPRegressor are implemented but since you perform > the weight updates based on the gradient of the cost function wrt the > weights, the modification would be non-trivial if the partial derivatives are > not computed based on some autodiff implementation -- you would have to edit > all the partial d's along the backpropagation up to the first hidden layer. > While I think that scikit-learn is by far the best library out there for > machine learning, I think if you want an easy solution, you probably won't > get around TensorFlow or PyTorch or equivalent, here, for your specific MLP > problem unless you want to make your life extra hard :P (seriously, you can > pick up any of the two in about an hour and have your MLPRegressor up and > running so that you can then experiment with your cost function). > > Best, > Sebastian > > > On Sep 11, 2017, at 6:13 PM, Thomas Evangelidis <teva...@gmail.com> wrote: > > > > Greetings, > > > > I know this is a recurrent question, but I would like to use my own loss > > function either in a MLPRegressor or in an SVR. For the MLPRegressor case > > so far my conclusion was that it is not possible unless you modify the > > source code. On the other hand, for the SVR I was looking at setting custom > > kernel functions. But I am not sure if this is the same thing. Could > > someone please clarify this to me? Finally, I read about the "scoring" > > parameter is cross-validation, but this is just to select a Regressor that > > has been trained already with the default loss function, so it would be > > harder to find one that minimizes my own loss function. > > > > For the record, my loss function is the centered root mean square error. > > > > Thanks in advance for any advice. > > > > > > > > -- > > ====================================================================== > > Dr Thomas Evangelidis > > Post-doctoral Researcher > > CEITEC - Central European Institute of Technology > > Masaryk University > > Kamenice 5/A35/2S049, > > 62500 Brno, Czech Republic > > > > email: tev...@pharm.uoa.gr > > teva...@gmail.com > > > > website: https://sites.google.com/site/thomasevangelidishomepage/ > > > > > > _______________________________________________ > > 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 > > > > -- > ====================================================================== > Dr Thomas Evangelidis > Post-doctoral Researcher > CEITEC - Central European Institute of Technology > Masaryk University > Kamenice 5/A35/2S049, > 62500 Brno, Czech Republic > > email: tev...@pharm.uoa.gr > teva...@gmail.com > > website: https://sites.google.com/site/thomasevangelidishomepage/ > > > _______________________________________________ > 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
