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annotate examples/theano_update.py @ 451:d99fefbc9324

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Added a KL-divergence.
author Joseph Turian <turian@gmail.com>
date Thu, 04 Sep 2008 14:46:30 -0400
parents 200a5b0e24ea
children
rev   line source
433
200a5b0e24ea Example showing parameter updates.
Pascal Lamblin <lamblinp@iro.umontreal.ca>
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1 import theano
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2 from theano import tensor
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3
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4 import numpy
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5
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6 # Two scalar symbolic variables
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7 a = tensor.scalar()
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8 b = tensor.scalar()
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9
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10 # Definition of output symbolic variable
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11 c = a * b
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12 # Definition of the function computing it
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13 fprop = theano.function([a,b], [c])
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14
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15 # Initialize numerical variables
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16 a_val = numpy.array(12.)
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17 b_val = numpy.array(2.)
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18 print 'a_val =', a_val
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19 print 'b_val =', b_val
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20
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21 # Numerical value of output is returned by the call to "fprop"
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22 c_val = fprop(a_val, b_val)
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23 print 'c_val =', c_val
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24
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25
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26 # Definition of simple update (increment by one)
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27 new_b = b + 1
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28 update = theano.function([b], [new_b])
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29
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30 # New numerical value of b is returned by the call to "update"
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31 b_val = update(b_val)
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32 print 'new b_val =', b_val
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33 # We can use the new value in "fprop"
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34 c_val = fprop(a_val, b_val)
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35 print 'c_val =', c_val
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36
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37
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38 # Definition of in-place update (increment by one)
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39 re_new_b = tensor.add_inplace(b, 1.)
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40 re_update = theano.function([b], [re_new_b])
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41
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42 # "re_update" can be used the same way as "update"
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43 b_val = re_update(b_val)
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44 print 'new b_val =', b_val
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45 # We can use the new value in "fprop"
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46 c_val = fprop(a_val, b_val)
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47 print 'c_val =', c_val
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48
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49 # It is not necessary to keep the return value when the update is done in place
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50 re_update(b_val)
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51 print 'new b_val =', b_val
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52 c_val = fprop(a_val, b_val)
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53 print 'c_val =', c_val
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