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annotate pylearn/formulas/nnet.py @ 1489:35a3a4e2d999

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added formulas/nnet.py with inverse_max_pooling(...)
author Emmanuel Bengio <bengioe@iro.umontreal.ca>
date Wed, 27 Jul 2011 10:37:41 -0400
parents
children 9d2323513092
rev   line source
1489
35a3a4e2d999 added formulas/nnet.py with inverse_max_pooling(...)
Emmanuel Bengio <bengioe@iro.umontreal.ca>
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1 import theano
35a3a4e2d999 added formulas/nnet.py with inverse_max_pooling(...)
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2 from theano import tensor
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3 from theano.sandbox import neighbours
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4
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5 import tags
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6
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7
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8 @tags.tags('nnet', 'max pooling', 'inverse')
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9 def inverse_max_pooling(max_pool_out,output_shape,pooling_shape=(2,2),
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10 ignore_borders = True):
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11 """
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12 Return a symbolic variable representing the inverse of a max pooling
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13 on a given tensor.
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14
35a3a4e2d999 added formulas/nnet.py with inverse_max_pooling(...)
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15 Parameters
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16 ----------
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17 max_pool_out : 4D tensor
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18 A Theano variable representing the output of a max pooling
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19 output_shape : 4D shape
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20 The shape of the input before pooling
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21 pooling_shape : 2D shape
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22 The shape of the pooling windows
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23 ignore_borders : boolean
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24 Will pad borders with zeros if true
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25 """
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26 # flatten the input and repeat it
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27 repeated_input = [max_pool_out.flatten()]*(pooling_shape[0]*pooling_shape[1])
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28
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29 # concatenate the repeated vectors into
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30 # a 2D matrix in the format neibs2images wants
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31 stacked_conv_neibs = T.stack(*repeated_input).T
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32
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33 # then get back a stretched version of the stacked neighbours
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34 stretch_unpooling_out = \
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35 neighbours.neibs2images(stacked_conv_neibs,
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36 pooling_shape,
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37 output_shape,
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38 'ignore_borders' if ignore_borders else 'valid')
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39 return stretch_unpooling_out