annotate cost.py @ 479:fbfd3932fd00

typo
author James Bergstra <bergstrj@iro.umontreal.ca>
date Mon, 27 Oct 2008 17:28:48 -0400
parents d99fefbc9324
children 3daabc7f94ff
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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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6 """
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7
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8 import theano.tensor as T
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9 from xlogx import xlogx
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10
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11 def quadratic(target, output, axis=1):
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12 return T.mean(T.sqr(target - output), axis)
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14 def cross_entropy(target, output, axis=1):
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15 """
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16 @todo: This is essentially duplicated as nnet_ops.binary_crossentropy
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17 @warning: OUTPUT and TARGET are reversed in nnet_ops.binary_crossentropy
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18 """
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19 return -T.mean(target * T.log(output) + (1 - target) * T.log(1 - output), axis=axis)
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20
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21 def KL_divergence(target, output):
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22 """
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23 @note: We do not compute the mean, because if target and output have
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24 different shapes then the result will be garbled.
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25 """
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26 return -(target * T.log(output) + (1 - target) * T.log(1 - output)) \
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27 + (xlogx(target) + xlogx(1 - target))
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28 # return cross_entropy(target, output, axis) - cross_entropy(target, target, axis)