Mercurial > pylearn
comparison simple_autoassociator/model.py @ 389:ec8aadb6694d
Renamed simple AA directory
| author | Joseph Turian <turian@gmail.com> |
|---|---|
| date | Tue, 08 Jul 2008 17:41:45 -0400 |
| parents | simple_autoassociator.py/model.py@98ca97cc9910 |
| children | e2cb8d489908 |
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| 388:98ca97cc9910 | 389:ec8aadb6694d |
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| 1 """ | |
| 2 The model for an autoassociator for sparse inputs, using Ronan Collobert + Jason | |
| 3 Weston's sampling trick (2008). | |
| 4 """ | |
| 5 | |
| 6 from graph import trainfn | |
| 7 import parameters | |
| 8 | |
| 9 import globals | |
| 10 from globals import LR | |
| 11 | |
| 12 import numpy | |
| 13 import random | |
| 14 random.seed(globals.SEED) | |
| 15 | |
| 16 class Model: | |
| 17 def __init__(self): | |
| 18 self.parameters = parameters.Parameters(randomly_initialize=True) | |
| 19 | |
| 20 def update(self, instance): | |
| 21 """ | |
| 22 Update the L{Model} using one training instance. | |
| 23 @param instance: A dict from feature index to (non-zero) value. | |
| 24 @todo: Should assert that nonzero_indices and zero_indices | |
| 25 are correct (i.e. are truly nonzero/zero). | |
| 26 """ | |
| 27 x = numpy.zeros(globals.INPUT_DIMENSION) | |
| 28 for idx in instance.keys(): | |
| 29 x[idx] = instance[idx] | |
| 30 | |
| 31 (y, h, loss, loss_unsummed, gw1, gb1, gw2, gb2) = trainfn(x, self.parameters.w1, self.parameters.b1, self.parameters.w2, self.parameters.b2) | |
| 32 print | |
| 33 print "instance:", instance | |
| 34 print "x:", x | |
| 35 print "OLD y:", y | |
| 36 print "NEW loss (unsummed):", loss_unsummed | |
| 37 print "OLD total loss:", loss | |
| 38 print "gw1:", gw1 | |
| 39 print "gb1:", gb1 | |
| 40 print "gw2:", gw2 | |
| 41 print "gb2:", gb2 | |
| 42 | |
| 43 # SGD update | |
| 44 self.parameters.w1 -= LR * gw1 | |
| 45 self.parameters.b1 -= LR * gb1 | |
| 46 self.parameters.w2 -= LR * gw2 | |
| 47 self.parameters.b2 -= LR * gb2 | |
| 48 | |
| 49 # Recompute the loss, to make sure it's descreasing | |
| 50 (y, h, loss, loss_unsummed, gw1, gb1, gw2, gb2) = trainfn(x, self.parameters.w1, self.parameters.b1, self.parameters.w2, self.parameters.b2) | |
| 51 print "NEW y:", y | |
| 52 print "NEW loss (unsummed):", loss_unsummed | |
| 53 print "NEW total loss:", loss | |
| 54 print h | |
| 55 print self.parameters |