annotate cost.py @ 494:02a331ba833b

merge
author Joseph Turian <turian@gmail.com>
date Tue, 28 Oct 2008 11:40:56 -0400
parents 94a4c5b7293b
children f13847478c6d
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1 """
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2 Cost functions.
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3
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4 @note: All of these functions return one cost per example. So it is your
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5 job to perform a tensor.sum over the individual example losses.
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7 @todo: It would be nice to implement a hinge loss, with a particular margin.
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8 """
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9
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10 import theano.tensor as T
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11 from xlogx import xlogx
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12
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13 def quadratic(target, output, axis=1):
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14 return T.mean(T.sqr(target - output), axis=axis)
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16 def cross_entropy(target, output, axis=1):
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17 """
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18 @todo: This is essentially duplicated as nnet_ops.binary_crossentropy
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19 @warning: OUTPUT and TARGET are reversed in nnet_ops.binary_crossentropy
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20 """
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21 return -T.mean(target * T.log(output) + (1 - target) * T.log(1 - output), axis=axis)
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22
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23 def KL_divergence(target, output):
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24 """
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25 @note: We do not compute the mean, because if target and output have
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26 different shapes then the result will be garbled.
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27 """
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28 return -(target * T.log(output) + (1 - target) * T.log(1 - output)) \
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29 + (xlogx(target) + xlogx(1 - target))
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30 # return cross_entropy(target, output, axis) - cross_entropy(target, target, axis)