(Python) Gaussian Bernoulli RBM on computing P(v|h)
machine-learning, neural-network, numpy, python, rbm
Solution
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.
Problem
Context: I am implementing Gaussian Bernoulli RBM, it is like the popular RBM but with real-valued visible units. True that the procedure of sampling hidden values `p(h=1|v)` are the same for both, i.e. Problem: My problem is in coding (using Python) `p(v|h)`, which is, I am a little bit confused as to how N() works. Do I simply add Gaussian noise using the data's standard deviation to `b + sigma * W.dot(h)`? Thank you in advance.