Mercurial > pylearn
comparison sparse_random_autoassociator/graph.py @ 370:a1bbcde6b456
Moved sparse_random_autoassociator from my repository
| author | Joseph Turian <turian@gmail.com> |
|---|---|
| date | Mon, 07 Jul 2008 01:54:46 -0400 |
| parents | |
| children | e4473d9697d7 |
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| 369:90a29489b5c8 | 370:a1bbcde6b456 |
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| 1 """ | |
| 2 Theano graph for an autoassociator for sparse inputs, which will be trained | |
| 3 using Ronan Collobert + Jason Weston's sampling trick (2008). | |
| 4 @todo: Make nearly everything private. | |
| 5 """ | |
| 6 | |
| 7 from globals import MARGIN | |
| 8 | |
| 9 from pylearn.nnet_ops import sigmoid, crossentropy_softmax_1hot | |
| 10 from theano import tensor as t | |
| 11 from theano.tensor import dot | |
| 12 xnonzero = t.dvector() | |
| 13 w1nonzero = t.dmatrix() | |
| 14 b1 = t.dvector() | |
| 15 w2nonzero = t.dmatrix() | |
| 16 w2zero = t.dmatrix() | |
| 17 b2nonzero = t.dvector() | |
| 18 b2zero = t.dvector() | |
| 19 h = sigmoid(dot(xnonzero, w1nonzero) + b1) | |
| 20 ynonzero = sigmoid(dot(h, w2nonzero) + b2nonzero) | |
| 21 yzero = sigmoid(dot(h, w2zero) + b2zero) | |
| 22 | |
| 23 # May want to weight loss wrt nonzero value? e.g. MARGIN violation for | |
| 24 # 0.1 nonzero is not as bad as MARGIN violation for 0.2 nonzero. | |
| 25 def hingeloss(MARGIN): | |
| 26 return -MARGIN * (MARGIN < 0) | |
| 27 nonzeroloss = hingeloss(ynonzero - t.max(yzero) - MARGIN) | |
| 28 zeroloss = hingeloss(-t.max(-(ynonzero)) - yzero - MARGIN) | |
| 29 # xnonzero sensitive loss: | |
| 30 #nonzeroloss = hingeloss(ynonzero - t.max(yzero) - MARGIN - xnonzero) | |
| 31 #zeroloss = hingeloss(-t.max(-(ynonzero - xnonzero)) - yzero - MARGIN) | |
| 32 loss = t.sum(nonzeroloss) + t.sum(zeroloss) | |
| 33 | |
| 34 (gw1nonzero, gb1, gw2nonzero, gw2zero, gb2nonzero, gb2zero) = t.grad(loss, [w1nonzero, b1, w2nonzero, w2zero, b2nonzero, b2zero]) | |
| 35 | |
| 36 import theano.compile | |
| 37 | |
| 38 inputs = [xnonzero, w1nonzero, b1, w2nonzero, w2zero, b2nonzero, b2zero] | |
| 39 outputs = [ynonzero, yzero, loss, gw1nonzero, gb1, gw2nonzero, gw2zero, gb2nonzero, gb2zero] | |
| 40 trainfn = theano.compile.function(inputs, outputs) |