Mercurial > pylearn
annotate sandbox/sparse_random_autoassociator/parameters.py @ 464:121cc6db4481
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author | Joseph Turian <turian@iro.umontreal.ca> |
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date | Wed, 15 Oct 2008 17:00:26 -0400 |
parents | 36baeb7125a4 |
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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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Joseph Turian <turian@gmail.com>
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9 """ |
a1bbcde6b456
Moved sparse_random_autoassociator from my repository
Joseph Turian <turian@gmail.com>
parents:
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10 Parameters used by the L{Model}. |
a1bbcde6b456
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Joseph Turian <turian@gmail.com>
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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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Moved sparse_random_autoassociator from my repository
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14 Initialize L{Model} parameters. |
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15 @param randomly_initialize: If True, then randomly initialize |
a1bbcde6b456
Moved sparse_random_autoassociator from my repository
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parents:
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16 according to the given seed. If False, then just use zeroes. |
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Joseph Turian <turian@gmail.com>
parents:
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17 """ |
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Joseph Turian <turian@gmail.com>
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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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parents:
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22 self.b1 = numpy.zeros(hidden_dimension) |
a1bbcde6b456
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parents:
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23 self.b2 = numpy.zeros(input_dimension) |
a1bbcde6b456
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Joseph Turian <turian@gmail.com>
parents:
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24 else: |
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parents:
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25 self.w1 = numpy.zeros((input_dimension, hidden_dimension)) |
a1bbcde6b456
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parents:
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26 self.w2 = numpy.zeros((hidden_dimension, input_dimension)) |
a1bbcde6b456
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parents:
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27 self.b1 = numpy.zeros(hidden_dimension) |
a1bbcde6b456
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parents:
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28 self.b2 = numpy.zeros(input_dimension) |