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annotate sandbox/simple_autoassociator/model.py @ 393:36baeb7125a4

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Made sandbox directory
author Joseph Turian <turian@gmail.com>
date Tue, 08 Jul 2008 18:46:26 -0400
parents simple_autoassociator/model.py@e2cb8d489908
children 8cc11ac97087
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 class Model:
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17 def __init__(self):
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18 self.parameters = parameters.Parameters(randomly_initialize=True)
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19
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20 def update(self, instance):
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21 """
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22 Update the L{Model} using one training instance.
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23 @param instance: A dict from feature index to (non-zero) value.
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24 @todo: Should assert that nonzero_indices and zero_indices
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25 are correct (i.e. are truly nonzero/zero).
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26 """
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27 x = numpy.zeros(globals.INPUT_DIMENSION)
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28 for idx in instance.keys():
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29 x[idx] = instance[idx]
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30
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31 (y, h, loss, loss_unsummed, gw1, gb1, gw2, gb2, gy) = trainfn(x, self.parameters.w1, self.parameters.b1, self.parameters.w2, self.parameters.b2)
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32 print
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33 print "instance:", instance
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34 print "x:", x
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35 print "OLD y:", y
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36 print "OLD loss (unsummed):", loss_unsummed
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37 print "gy:", gy
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38 print "OLD total loss:", loss
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39 print "gw1:", gw1
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40 print "gb1:", gb1
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41 print "gw2:", gw2
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42 print "gb2:", gb2
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43
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44 # SGD update
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45 self.parameters.w1 -= LR * gw1
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46 self.parameters.b1 -= LR * gb1
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47 self.parameters.w2 -= LR * gw2
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48 self.parameters.b2 -= LR * gb2
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49
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50 # Recompute the loss, to make sure it's descreasing
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51 (y, h, loss, loss_unsummed, gw1, gb1, gw2, gb2, gy) = trainfn(x, self.parameters.w1, self.parameters.b1, self.parameters.w2, self.parameters.b2)
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52 print "NEW y:", y
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53 print "NEW loss (unsummed):", loss_unsummed
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54 print "gy:", gy
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55 print "NEW total loss:", loss
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56 print "h:", h
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57 print self.parameters