Mercurial > pylearn

annotate sandbox/sparse_random_autoassociator/parameters.py @ 419:43d9aa93934e

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added other_ops.py to nnet_ops; added basic tests, no docs.
author James Bergstra <bergstrj@iro.umontreal.ca>
date Mon, 14 Jul 2008 16:48:02 -0400
parents 36baeb7125a4
children
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a1bbcde6b456 Moved sparse_random_autoassociator from my repository
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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 import globals
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7
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8 class Parameters:
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9 """
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10 Parameters used by the L{Model}.
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11 """
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12 def __init__(self, input_dimension=globals.INPUT_DIMENSION, hidden_dimension=globals.HIDDEN_DIMENSION, randomly_initialize=False, seed=globals.SEED):
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13 """
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14 Initialize L{Model} parameters.
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15 @param randomly_initialize: If True, then randomly initialize
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16 according to the given seed. If False, then just use zeroes.
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17 """
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18 if randomly_initialize:
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19 numpy.random.seed(seed)
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20 self.w1 = (numpy.random.rand(input_dimension, hidden_dimension)-0.5)/input_dimension
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21 self.w2 = (numpy.random.rand(hidden_dimension, input_dimension)-0.5)/hidden_dimension
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22 self.b1 = numpy.zeros(hidden_dimension)
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23 self.b2 = numpy.zeros(input_dimension)
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24 else:
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25 self.w1 = numpy.zeros((input_dimension, hidden_dimension))
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26 self.w2 = numpy.zeros((hidden_dimension, input_dimension))
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27 self.b1 = numpy.zeros(hidden_dimension)
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28 self.b2 = numpy.zeros(input_dimension)