Mercurial > pylearn

annotate sandbox/weights.py @ 476:8fcd0f3d9a17

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added a few algorithms
author Olivier Breuleux <breuleuo@iro.umontreal.ca>
date Mon, 27 Oct 2008 17:26:00 -0400
parents 23221eefb70e
children 3daabc7f94ff
rev   line source
466
23221eefb70e Added pylearn.sandbox.weights.random_weights
Joseph Turian <turian@iro.umontreal.ca>
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1 """
23221eefb70e Added pylearn.sandbox.weights.random_weights
Joseph Turian <turian@iro.umontreal.ca>
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2 Routine to initialize weights.
23221eefb70e Added pylearn.sandbox.weights.random_weights
Joseph Turian <turian@iro.umontreal.ca>
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3
23221eefb70e Added pylearn.sandbox.weights.random_weights
Joseph Turian <turian@iro.umontreal.ca>
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4 @note: We assume that numpy.random.seed() has already been performed.
23221eefb70e Added pylearn.sandbox.weights.random_weights
Joseph Turian <turian@iro.umontreal.ca>
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5 """
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Joseph Turian <turian@iro.umontreal.ca>
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6
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Joseph Turian <turian@iro.umontreal.ca>
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7 from math import sqrt
23221eefb70e Added pylearn.sandbox.weights.random_weights
Joseph Turian <turian@iro.umontreal.ca>
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8 import numpy.random
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Joseph Turian <turian@iro.umontreal.ca>
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9 def random_weights(nin, nout, scale_by=sqrt(3)):
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Joseph Turian <turian@iro.umontreal.ca>
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10 """
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11 Generate an initial weight matrix with nin inputs (rows) and nout
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Joseph Turian <turian@iro.umontreal.ca>
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12 outputs (cols).
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Joseph Turian <turian@iro.umontreal.ca>
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13 Each weight is chosen uniformly at random to be in range:
23221eefb70e Added pylearn.sandbox.weights.random_weights
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14 [-scale_by/sqrt(nin), +scale_by/sqrt(nin)]
23221eefb70e Added pylearn.sandbox.weights.random_weights
Joseph Turian <turian@iro.umontreal.ca>
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15 @note: Play with scale_by!
23221eefb70e Added pylearn.sandbox.weights.random_weights
Joseph Turian <turian@iro.umontreal.ca>
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16 Ronan derives scale_by=sqrt(3) because that gives variance of
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Joseph Turian <turian@iro.umontreal.ca>
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17 1 to something (I forget, ask Yoshua for the derivation). However,
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Joseph Turian <turian@iro.umontreal.ca>
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18 Ronan got better results by accidentally using scale_by=1. Yoshua
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19 hypothesizes this is because the variance will get telescopically
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Joseph Turian <turian@iro.umontreal.ca>
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20 smaller as we go up the layers [need more explanation of this
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21 argument].
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22 @note: Things may get even trickier if the same weights are being
23221eefb70e Added pylearn.sandbox.weights.random_weights
Joseph Turian <turian@iro.umontreal.ca>
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23 shared in multiple places.
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24 """
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25 return (numpy.random.rand(nin, nout) * 2.0 - 1) * scale_by / sqrt(nin)