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annotate sandbox/sparse_random_autoassociator/graph.py @ 536:c6563c629984

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Moved word preprocessing out
author Joseph Turian <turian@gmail.com>
date Thu, 20 Nov 2008 06:11:52 -0500
parents 36baeb7125a4
children
rev   line source
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1 """
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2 Theano graph for an autoassociator for sparse inputs, which will be trained
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3 using Ronan Collobert + Jason Weston's sampling trick (2008).
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4 @todo: Make nearly everything private.
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5 """
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6
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7 from globals import MARGIN
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8
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9 from pylearn.nnet_ops import sigmoid, binary_crossentropy
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10 from theano import tensor as t
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11 from theano.tensor import dot
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12 xnonzero = t.dvector()
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13 w1nonzero = t.dmatrix()
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14 b1 = t.dvector()
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15 w2nonzero = t.dmatrix()
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16 w2zero = t.dmatrix()
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17 b2nonzero = t.dvector()
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18 b2zero = t.dvector()
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19 h = sigmoid(dot(xnonzero, w1nonzero) + b1)
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20 ynonzero = sigmoid(dot(h, w2nonzero) + b2nonzero)
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21 yzero = sigmoid(dot(h, w2zero) + b2zero)
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22
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23 # May want to weight loss wrt nonzero value? e.g. MARGIN violation for
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24 # 0.1 nonzero is not as bad as MARGIN violation for 0.2 nonzero.
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25 def hingeloss(MARGIN):
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26 return -MARGIN * (MARGIN < 0)
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27 nonzeroloss = hingeloss(ynonzero - t.max(yzero) - MARGIN)
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28 zeroloss = hingeloss(-t.max(-(ynonzero)) - yzero - MARGIN)
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29 # xnonzero sensitive loss:
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30 #nonzeroloss = hingeloss(ynonzero - t.max(yzero) - MARGIN - xnonzero)
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31 #zeroloss = hingeloss(-t.max(-(ynonzero - xnonzero)) - yzero - MARGIN)
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32 loss = t.sum(nonzeroloss) + t.sum(zeroloss)
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33
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34 #loss = t.sum(binary_crossentropy(ynonzero, xnonzero)) + t.sum(binary_crossentropy(yzero, t.constant(0)))
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35
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36 (gw1nonzero, gb1, gw2nonzero, gw2zero, gb2nonzero, gb2zero) = t.grad(loss, [w1nonzero, b1, w2nonzero, w2zero, b2nonzero, b2zero])
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37
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38 import theano.compile
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39
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40 inputs = [xnonzero, w1nonzero, b1, w2nonzero, w2zero, b2nonzero, b2zero]
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41 outputs = [ynonzero, yzero, loss, gw1nonzero, gb1, gw2nonzero, gw2zero, gb2nonzero, gb2zero]
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42 trainfn = theano.compile.function(inputs, outputs)