if you launch ipython and try something like this: import numpy as np import theano import theano.tensor as tt from scipy.special import erfinv
z = tt.vector() y = tt.erfinv(z) f = theano.function([z], y) f.trust_input = True x = np.linspace(-1,1,1000000) %timeit f(x) %timeit erfinv(x) You'll notice the theano version takes about 60-90 times longer than the call to scipy.special.erfinv. Since theano does not have a c code implementation for erfinv, it uses the same scipy.special,erfinv function. I've also tried modifying the Erfinv Op in theano.scalar.basic_scipy to provide a c code implementation (taken from here: https://stackoverflow.com/questions/27229371/inverse-error-function-in-c). This speeds up the resulting function slightly, but it is still around 50 times slower than just calling scipy.special.erfinv. So my question is, where does this massive overhead come from? Anyn ideas on how to sidestep this issue? Note that this is for the CPU version. On GPU everything is fine. -- Juan Camilo -- --- You received this message because you are subscribed to the Google Groups "theano-users" group. To unsubscribe from this group and stop receiving emails from it, send an email to [email protected]. For more options, visit https://groups.google.com/d/optout.
