Mercurial > pylearn
annotate sandbox/weights.py @ 480:1babf35fcef5
merged
author | James Bergstra <bergstrj@iro.umontreal.ca> |
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date | Mon, 27 Oct 2008 17:29:03 -0400 |
parents | 23221eefb70e |
children | 3daabc7f94ff |
rev | line source |
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466
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Joseph Turian <turian@iro.umontreal.ca>
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1 """ |
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2 Routine to initialize weights. |
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3 |
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4 @note: We assume that numpy.random.seed() has already been performed. |
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5 """ |
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6 |
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7 from math import sqrt |
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8 import numpy.random |
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9 def random_weights(nin, nout, scale_by=sqrt(3)): |
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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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12 outputs (cols). |
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13 Each weight is chosen uniformly at random to be in range: |
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14 [-scale_by/sqrt(nin), +scale_by/sqrt(nin)] |
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15 @note: Play with scale_by! |
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16 Ronan derives scale_by=sqrt(3) because that gives variance of |
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17 1 to something (I forget, ask Yoshua for the derivation). However, |
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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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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 |
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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) |