Mercurial > pylearn

annotate sandbox/sparse_random_autoassociator/main.py @ 672:27b1344a57b1

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Added preprocessing back in
author Joseph Turian <turian@gmail.com>
date Thu, 20 Nov 2008 06:38:06 -0500
parents 36baeb7125a4
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1 #!/usr/bin/python
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2 """
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3 An autoassociator for sparse inputs, using Ronan Collobert + Jason
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4 Weston's sampling trick (2008).
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5
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6 The learned model is::
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7 h = sigmoid(dot(x, w1) + b1)
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8 y = sigmoid(dot(h, w2) + b2)
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9
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10 We assume that most of the inputs are zero, and hence that
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11 we can separate x into xnonzero, x's nonzero components, and
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12 xzero, a sample of the zeros. We sample---randomly without
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13 replacement---ZERO_SAMPLE_SIZE zero columns from x.
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14
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15 The desideratum is that every nonzero entry is separated from every
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16 zero entry by margin at least MARGIN.
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17 For each ynonzero, we want it to exceed max(yzero) by at least MARGIN.
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18 For each yzero, we want it to be exceed by min(ynonzero) by at least MARGIN.
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19 The loss is a hinge loss (linear). The loss is irrespective of the
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20 xnonzero magnitude (this may be a limitation). Hence, all nonzeroes
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21 are equally important to exceed the maximum yzero.
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22
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23 (Alternately, there is a commented out binary xent loss.)
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24
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25 LIMITATIONS:
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26 - Only does pure stochastic gradient (batchsize = 1).
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27 - Loss is irrespective of the xnonzero magnitude.
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28 - We will always use all nonzero entries, even if the training
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29 instance is very non-sparse.
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30 """
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31
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32
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33 import numpy
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34
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35 nonzero_instances = []
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36 nonzero_instances.append({1: 0.1, 5: 0.5, 9: 1})
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37 nonzero_instances.append({2: 0.3, 5: 0.5, 8: 0.8})
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38 nonzero_instances.append({1: 0.2, 2: 0.3, 5: 0.5})
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39
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40 import model
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41 model = model.Model()
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42
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43 for i in xrange(100000):
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44 # Select an instance
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45 instance = nonzero_instances[i % len(nonzero_instances)]
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46
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47 # SGD update over instance
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48 model.update(instance)