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annotate sandbox/rbm/parameters.py @ 442:b3315b252824

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Finished derivative of softmax gradient.
author Pascal Lamblin <lamblinp@iro.umontreal.ca>
date Fri, 22 Aug 2008 15:53:34 -0400
parents c2e6a8fcc35e
children
rev   line source
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1 """
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2 Parameters (weights) used by the L{Model}.
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3 """
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4
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5 import numpy
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6
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7 class Parameters:
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8 """
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9 Parameters used by the L{Model}.
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10 """
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11 def __init__(self, input_dimension, hidden_dimension, randomly_initialize, random_seed):
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12 """
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13 Initialize L{Model} parameters.
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14 @param randomly_initialize: If True, then randomly initialize
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15 according to the given random_seed. If False, then just use zeroes.
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16 """
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17 if randomly_initialize:
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18 numpy.random.random_seed(random_seed)
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19 self.w = (numpy.random.rand(input_dimension, hidden_dimension)-0.5)/input_dimension
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20 self.b = numpy.zeros((1, hidden_dimension))
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21 self.c = numpy.zeros((1, input_dimension))
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22 else:
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23 self.w = numpy.zeros((input_dimension, hidden_dimension))
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24 self.b = numpy.zeros((1, hidden_dimension))
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25 self.c = numpy.zeros((1, input_dimension))
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26
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27 def __str__(self):
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28 s = ""
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29 s += "w: %s\n" % self.w
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30 s += "b: %s\n" % self.b
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31 s += "c: %s\n" % self.c
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32 return s