User 2001 | 8/21/2015, 2:53:34 PM
I'm trying to compare different deep learning libraries, and am relatively unfamiliar with graphlab, so I have a few questions!
For weight initialisation, I notice that options are restricted to gaussian and xavier. Am I correct in assuming 'xavier' refers to glorot initialisation, as in 'Xavier Glorot'. If so, is this normal or uniform Glorot initialisation?
Secondly, is optimisation limited to SGD? I'd like to try AdaGrad, AdaDelta, RMSprop, etc., as available in other libraries.
Finally, what loss function does the classifier use for training?
Theano libraies such as Keras and Lasagne allow for other initialisations, including both uniform and glorot uniform in addition to normal. They also include a wider range of optimisers and cost functions. Are we likely to see options such as these in GraphLab in the future?