Mercurial > pylearn

annotate sandbox/sparse_random_autoassociator/model.py @ 395:70019965f888

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Basic, broken RBM implementation
author Joseph Turian <turian@gmail.com>
date Tue, 08 Jul 2008 20:14:21 -0400
parents 36baeb7125a4
children
rev   line source
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1 """
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2 The model for an autoassociator for sparse inputs, using Ronan Collobert + Jason
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3 Weston's sampling trick (2008).
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4 """
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5
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6 from graph import trainfn
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7 import parameters
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8
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9 import globals
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10 from globals import LR
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11
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12 import numpy
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13 import random
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14 random.seed(globals.SEED)
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15
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16 def _select_indices(instance):
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17 """
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18 Choose nonzero and zero indices (feature columns) of the instance.
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19 We select B{all} nonzero indices.
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20 We select L{globals.ZERO_SAMPLE_SIZE} zero indices randomly,
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21 without replacement.
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22 @bug: If there are not ZERO_SAMPLE_SIZE zeroes, we will enter
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23 an endless loop.
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24 @return: (nonzero_indices, zero_indices)
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25 """
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26 # Get the nonzero indices
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27 nonzero_indices = instance.keys()
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28 nonzero_indices.sort()
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29
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30 # Get the zero indices
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31 # @bug: If there are not ZERO_SAMPLE_SIZE zeroes, we will enter an endless loop.
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32 zero_indices = []
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33 while len(zero_indices) < globals.ZERO_SAMPLE_SIZE:
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34 idx = random.randint(0, globals.INPUT_DIMENSION - 1)
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35 if idx in nonzero_indices or idx in zero_indices: continue
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36 zero_indices.append(idx)
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37 zero_indices.sort()
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38
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39 return (nonzero_indices, zero_indices)
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40
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41 class Model:
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42 def __init__(self):
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43 self.parameters = parameters.Parameters(randomly_initialize=True)
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44
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45 def update(self, instance):
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46 """
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47 Update the L{Model} using one training instance.
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48 @param instance: A dict from feature index to (non-zero) value.
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49 @todo: Should assert that nonzero_indices and zero_indices
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50 are correct (i.e. are truly nonzero/zero).
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51 """
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52 (nonzero_indices, zero_indices) = _select_indices(instance)
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53 # No update if there aren't any non-zeros.
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54 if len(nonzero_indices) == 0: return
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55 xnonzero = numpy.asarray([instance[idx] for idx in nonzero_indices])
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56 print
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57 print "xnonzero:", xnonzero
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58
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59 (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])
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60 print "OLD ynonzero:", ynonzero
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61 print "OLD yzero:", yzero
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62 print "OLD total loss:", loss
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63
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64 # SGD update
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65 self.parameters.w1[nonzero_indices, :] -= LR * gw1nonzero
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66 self.parameters.b1 -= LR * gb1
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67 self.parameters.w2[:, nonzero_indices] -= LR * gw2nonzero
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68 self.parameters.w2[:, zero_indices] -= LR * gw2zero
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69 self.parameters.b2[nonzero_indices] -= LR * gb2nonzero
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70 self.parameters.b2[zero_indices] -= LR * gb2zero
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71
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72 # Recompute the loss, to make sure it's descreasing
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73 (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])
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74 print "NEW ynonzero:", ynonzero
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75 print "NEW yzero:", yzero
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76 print "NEW total loss:", loss