Mercurial > pylearn
view cost.py @ 439:45879c1ecde7
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author | Joseph Turian <turian@iro.umontreal.ca> |
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date | Tue, 19 Aug 2008 18:41:39 -0400 |
parents | 0f366ecb11ee |
children | 0961d4b56ec5 |
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""" Cost functions. @note: All of these functions return one cost per example. So it is your job to perform a tensor.sum over the individual example losses. """ import theano.tensor as T def quadratic(target, output, axis=1): return T.mean(T.sqr(target - output), axis) def cross_entropy(target, output, axis=1): return -T.mean(target * T.log(output) + (1 - target) * T.log(1 - output), axis=axis)