Mercurial > pylearn
comparison pylearn/formulas/nnet.py @ 1489:35a3a4e2d999
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 |
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| 1488:440e1afe28a3 | 1489:35a3a4e2d999 |
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| 1 import theano | |
| 2 from theano import tensor | |
| 3 from theano.sandbox import neighbours | |
| 4 | |
| 5 import tags | |
| 6 | |
| 7 | |
| 8 @tags.tags('nnet', 'max pooling', 'inverse') | |
| 9 def inverse_max_pooling(max_pool_out,output_shape,pooling_shape=(2,2), | |
| 10 ignore_borders = True): | |
| 11 """ | |
| 12 Return a symbolic variable representing the inverse of a max pooling | |
| 13 on a given tensor. | |
| 14 | |
| 15 Parameters | |
| 16 ---------- | |
| 17 max_pool_out : 4D tensor | |
| 18 A Theano variable representing the output of a max pooling | |
| 19 output_shape : 4D shape | |
| 20 The shape of the input before pooling | |
| 21 pooling_shape : 2D shape | |
| 22 The shape of the pooling windows | |
| 23 ignore_borders : boolean | |
| 24 Will pad borders with zeros if true | |
| 25 """ | |
| 26 # flatten the input and repeat it | |
| 27 repeated_input = [max_pool_out.flatten()]*(pooling_shape[0]*pooling_shape[1]) | |
| 28 | |
| 29 # concatenate the repeated vectors into | |
| 30 # a 2D matrix in the format neibs2images wants | |
| 31 stacked_conv_neibs = T.stack(*repeated_input).T | |
| 32 | |
| 33 # then get back a stretched version of the stacked neighbours | |
| 34 stretch_unpooling_out = \ | |
| 35 neighbours.neibs2images(stacked_conv_neibs, | |
| 36 pooling_shape, | |
| 37 output_shape, | |
| 38 'ignore_borders' if ignore_borders else 'valid') | |
| 39 return stretch_unpooling_out |