Thanks again for the clarifications Sebastian! Keras has a Scikit-learn API with the KeraRegressor which implements the Scikit-Learn MLPRegressor interface:
https://keras.io/scikit-learn-api/ Is it possible to change the loss function in KerasRegressor? I don't have time right now to experiment with hyperparameters of new ANN architectures. I am in urgent need to reproduce in Keras the results obtained with MLPRegressor and the set of hyperparameters that I have optimized for my problem and later change the loss function. On 13 September 2017 at 18:14, Sebastian Raschka <se.rasc...@gmail.com> wrote: > > 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 > -- ====================================================================== 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/
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