(Python) Gaussian Bernoulli RBM on computing P(v|h)
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Chapters
00:00 (Python) Gaussian Bernoulli Rbm On Computing P(V|H)
00:32 Accepted Answer Score 8
00:55 Thank you
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Full question
https://stackoverflow.com/questions/2069...
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Tags
#python #numpy #machinelearning #neuralnetwork #rbm
#avk47
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Music by Eric Matyas
https://www.soundimage.org
Track title: Life in a Drop
--
Chapters
00:00 (Python) Gaussian Bernoulli Rbm On Computing P(V|H)
00:32 Accepted Answer Score 8
00:55 Thank you
--
Full question
https://stackoverflow.com/questions/2069...
--
Content licensed under CC BY-SA
https://meta.stackexchange.com/help/lice...
--
Tags
#python #numpy #machinelearning #neuralnetwork #rbm
#avk47
ACCEPTED ANSWER
Score 8
The notation X ~ N(μ, σ²) means that X is normally distributed with mean μ and variance σ², so in the RBM training routine, v should be sampled from such a distribution. In NumPy terms, that's
v = sigma * np.random.randn(v_size) + b + sigma * W.dot(h)
Or use scipy.stats.norm for better readable code.