Mercurial > pylearn
diff sandbox/sparse_random_autoassociator/model.py @ 393:36baeb7125a4
Made sandbox directory
author | Joseph Turian <turian@gmail.com> |
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date | Tue, 08 Jul 2008 18:46:26 -0400 |
parents | sparse_random_autoassociator/model.py@42cc94cf6c12 |
children |
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/sandbox/sparse_random_autoassociator/model.py Tue Jul 08 18:46:26 2008 -0400 @@ -0,0 +1,76 @@ +""" +The model for an autoassociator for sparse inputs, using Ronan Collobert + Jason +Weston's sampling trick (2008). +""" + +from graph import trainfn +import parameters + +import globals +from globals import LR + +import numpy +import random +random.seed(globals.SEED) + +def _select_indices(instance): + """ + Choose nonzero and zero indices (feature columns) of the instance. + We select B{all} nonzero indices. + We select L{globals.ZERO_SAMPLE_SIZE} zero indices randomly, + without replacement. + @bug: If there are not ZERO_SAMPLE_SIZE zeroes, we will enter + an endless loop. + @return: (nonzero_indices, zero_indices) + """ + # Get the nonzero indices + nonzero_indices = instance.keys() + nonzero_indices.sort() + + # Get the zero indices + # @bug: If there are not ZERO_SAMPLE_SIZE zeroes, we will enter an endless loop. + zero_indices = [] + while len(zero_indices) < globals.ZERO_SAMPLE_SIZE: + idx = random.randint(0, globals.INPUT_DIMENSION - 1) + if idx in nonzero_indices or idx in zero_indices: continue + zero_indices.append(idx) + zero_indices.sort() + + return (nonzero_indices, zero_indices) + +class Model: + def __init__(self): + self.parameters = parameters.Parameters(randomly_initialize=True) + + def update(self, instance): + """ + Update the L{Model} using one training instance. + @param instance: A dict from feature index to (non-zero) value. + @todo: Should assert that nonzero_indices and zero_indices + are correct (i.e. are truly nonzero/zero). + """ + (nonzero_indices, zero_indices) = _select_indices(instance) + # No update if there aren't any non-zeros. + if len(nonzero_indices) == 0: return + xnonzero = numpy.asarray([instance[idx] for idx in nonzero_indices]) + print + print "xnonzero:", xnonzero + + (ynonzero, yzero, loss, gw1nonzero, gb1, gw2nonzero, gw2zero, gb2nonzero, gb2zero) = trainfn(xnonzero, self.parameters.w1[nonzero_indices, :], self.parameters.b1, self.parameters.w2[:, nonzero_indices], self.parameters.w2[:, zero_indices], self.parameters.b2[nonzero_indices], self.parameters.b2[zero_indices]) + print "OLD ynonzero:", ynonzero + print "OLD yzero:", yzero + print "OLD total loss:", loss + + # SGD update + self.parameters.w1[nonzero_indices, :] -= LR * gw1nonzero + self.parameters.b1 -= LR * gb1 + self.parameters.w2[:, nonzero_indices] -= LR * gw2nonzero + self.parameters.w2[:, zero_indices] -= LR * gw2zero + self.parameters.b2[nonzero_indices] -= LR * gb2nonzero + self.parameters.b2[zero_indices] -= LR * gb2zero + + # Recompute the loss, to make sure it's descreasing + (ynonzero, yzero, loss, gw1nonzero, gb1, gw2nonzero, gw2zero, gb2nonzero, gb2zero) = trainfn(xnonzero, self.parameters.w1[nonzero_indices, :], self.parameters.b1, self.parameters.w2[:, nonzero_indices], self.parameters.w2[:, zero_indices], self.parameters.b2[nonzero_indices], self.parameters.b2[zero_indices]) + print "NEW ynonzero:", ynonzero + print "NEW yzero:", yzero + print "NEW total loss:", loss