[Numpy-discussion] numpy.random.randn
I've some neural network code in NumPy that I'd like to compare with a Scala based implementation. My problem is currently random initialization of the neural net parameters. I'd like to be able to get the same results from both implementations when using the same random seed. One approach I've though of would be to use the NumPy random generator also with the Scala implementation, but unfortunately the linear algebra library I'm using doesn't provide an equivalent for this. Could someone give pointers to implementing numpy.random.randn? Or alternatively, is there an equivalent random generator for Scala or Java? marko ___ NumPy-Discussion mailing list NumPy-Discussion@python.org https://mail.python.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] ANN: SfePy 2018.1
I am pleased to announce release 2018.1 of SfePy. Description --- SfePy (simple finite elements in Python) is a software for solving systems of coupled partial differential equations by the finite element method or by the isogeometric analysis (limited support). It is distributed under the new BSD license. Home page: http://sfepy.org Mailing list: https://mail.python.org/mm3/mailman3/lists/sfepy.python.org/ Git (source) repository, issue tracker: https://github.com/sfepy/sfepy Highlights of this release -- - major update of time-stepping solvers and solver handling - Newmark and Bathe elastodynamics solvers - interface to MUMPS linear solver - new examples: - iron plate impact problem (elastodynamics) - incompressible Mooney-Rivlin material model (hyperelasticity) as a script For full release notes see http://docs.sfepy.org/doc/release_notes.html#id1 (rather long and technical). Cheers, Robert Cimrman --- Contributors to this release in alphabetical order: Robert Cimrman Jan Heczko Jan Kopacka Vladimir Lukes ___ NumPy-Discussion mailing list NumPy-Discussion@python.org https://mail.python.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] numpy.random.randn
On Tue, Mar 6, 2018 at 1:39 AM, Marko Asplund wrote: > > I've some neural network code in NumPy that I'd like to compare with a Scala based implementation. > My problem is currently random initialization of the neural net parameters. > I'd like to be able to get the same results from both implementations when using the same random seed. > > One approach I've though of would be to use the NumPy random generator also with the Scala implementation, but unfortunately the linear algebra library I'm using doesn't provide an equivalent for this. > > Could someone give pointers to implementing numpy.random.randn? > Or alternatively, is there an equivalent random generator for Scala or Java? I would just recommend using one of the codebases to initialize the network, save the network out to disk, and load up the initialized network in each of the different codebases for training. That way you are sure that they are both starting from the same exact network parameters. Even if you do rewrite a precisely equivalent np.random.randn() for Scala/Java, you ought to write the code to serialize the initialized network anyways so that you can test that the two initialization routines are equivalent. But if you're going to do that, you might as well take my recommended approach. -- Robert Kern ___ NumPy-Discussion mailing list NumPy-Discussion@python.org https://mail.python.org/mailman/listinfo/numpy-discussion
