Mercurial > pylearn
view cost.py @ 451:d99fefbc9324
Added a KL-divergence.
author | Joseph Turian <turian@gmail.com> |
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date | Thu, 04 Sep 2008 14:46:30 -0400 |
parents | 2bb67e978c28 |
children | 3daabc7f94ff |
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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 from xlogx import xlogx def quadratic(target, output, axis=1): return T.mean(T.sqr(target - output), axis) def cross_entropy(target, output, axis=1): """ @todo: This is essentially duplicated as nnet_ops.binary_crossentropy @warning: OUTPUT and TARGET are reversed in nnet_ops.binary_crossentropy """ return -T.mean(target * T.log(output) + (1 - target) * T.log(1 - output), axis=axis) def KL_divergence(target, output): """ @note: We do not compute the mean, because if target and output have different shapes then the result will be garbled. """ return -(target * T.log(output) + (1 - target) * T.log(1 - output)) \ + (xlogx(target) + xlogx(1 - target)) # return cross_entropy(target, output, axis) - cross_entropy(target, target, axis)