Mercurial > pylearn
comparison sandbox/sparse_random_autoassociator/parameters.py @ 393:36baeb7125a4
Made sandbox directory
author | Joseph Turian <turian@gmail.com> |
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date | Tue, 08 Jul 2008 18:46:26 -0400 |
parents | sparse_random_autoassociator/parameters.py@a1bbcde6b456 |
children |
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392:e2cb8d489908 | 393:36baeb7125a4 |
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1 """ | |
2 Parameters (weights) used by the L{Model}. | |
3 """ | |
4 | |
5 import numpy | |
6 import globals | |
7 | |
8 class Parameters: | |
9 """ | |
10 Parameters used by the L{Model}. | |
11 """ | |
12 def __init__(self, input_dimension=globals.INPUT_DIMENSION, hidden_dimension=globals.HIDDEN_DIMENSION, randomly_initialize=False, seed=globals.SEED): | |
13 """ | |
14 Initialize L{Model} parameters. | |
15 @param randomly_initialize: If True, then randomly initialize | |
16 according to the given seed. If False, then just use zeroes. | |
17 """ | |
18 if randomly_initialize: | |
19 numpy.random.seed(seed) | |
20 self.w1 = (numpy.random.rand(input_dimension, hidden_dimension)-0.5)/input_dimension | |
21 self.w2 = (numpy.random.rand(hidden_dimension, input_dimension)-0.5)/hidden_dimension | |
22 self.b1 = numpy.zeros(hidden_dimension) | |
23 self.b2 = numpy.zeros(input_dimension) | |
24 else: | |
25 self.w1 = numpy.zeros((input_dimension, hidden_dimension)) | |
26 self.w2 = numpy.zeros((hidden_dimension, input_dimension)) | |
27 self.b1 = numpy.zeros(hidden_dimension) | |
28 self.b2 = numpy.zeros(input_dimension) |