> > c_without_inf = theano.tensor.set_subtensor(c[inf_idx], -999)
may I ask why should we set "inf" to -999 ?? 在 2016年7月30日星期六 UTC+9上午11:57:32，Varglur写道： > > > thanks a lot, Fred. > > I define a theano function as below : > x = T.fmatrix() > y = T.fmatrix() > z = x / y > f = theano.function([x,y],z) > > > and give > a = array([[ 0., 1., 2.], > > [ 3., -4., 5.]], dtype=float32) > > > > *b = array([[ 0., 1., 0.], [ 1., 0., 1.]], > dtype=float32)* > f (a,b) = array([[ nan, 1., inf], > > [ -0., -inf, 5.]], dtype=float32) > > > as can we see from denominator b , there are 0 in b. > > could we modify "z= x/y " to process all the cases?? > > > > > 在 2016年7月30日星期六 UTC+9上午2:40:37，nouiz写道： >> >> Just to be sure, oou want to replace inf/nan with a numerical value in >> Theano? This can be done like this: >> >> import numpy as np >> >> a = np.array([0,0,1,1,2], dtype='float') >> b = np.array([0,1,0,1,3], dtype='float') >> with np.errstate(divide='ignore', invalid='ignore'): >> c = np.true_divide(a,b) >> >> import theano.tensor as T >> import theano >> c=theano.tensor.as_tensor_variable(c) >> inf_mask = T.isinf(c) >> nan_mask = T.isnan(c) >> inf_idx = inf_mask.nonzero() >> nan_idx = nan_mask.nonzero() >> >> c_without_inf = theano.tensor.set_subtensor(c[inf_idx], -999) >> c_without_inf_nan = theano.tensor.set_subtensor(c_without_inf[nan_idx], 0) >> c_without_inf_nan.eval() >> array([ 0.00000000e+00, 0.00000000e+00, -9.99000000e+02, >> 1.00000000e+00, 6.66666667e-01]) >> >> I don't know if it make a difference between inf and -inf. >> >> Fred >> >> >> >> On Thu, Jul 28, 2016 at 11:52 PM Varglur <[email protected]> wrote: >> >>> dear all, >>> >>> similar to problem in numpy here >>> <http://stackoverflow.com/questions/26248654/numpy-return-0-with-divide-by-zero> >>> >>> if element wise divide in theano , are there corresponding function in >>> theano as this answer in numpy ? >>> >>> numpy answer: >>> >>> import numpy as np >>> >>> a = np.array([0,0,1,1,2], dtype='float') >>> b = np.array([0,1,0,1,3], dtype='float') >>> with np.errstate(divide='ignore', invalid='ignore'): >>> c = np.true_divide(a,b) >>> c[c == np.inf] = 0 >>> c = np.nan_to_num(c) >>> print('c: {0}'.format(c)) >>> >>> Output: >>> >>> c: [ 0. 0. 0. 1. 0.66666667] >>> >>> >>> >>> how to process inf and nan problem when 0/0 or 1/0 problem in theano?? >>> >>> thanks >>> >>> >>> >>> -- >>> >>> --- >>> 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. >>> >> -- --- 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.
