саша - 1 year ago 215

Python Question

I wanted to output the log-probability during learning of the word and doc vectors in gensim. I have taken a look at the implementation of the score function in the "slow plain numpy" version.

`def score_cbow_pair(model, word, word2_indices, l1):`

l2a = model.syn1[word.point] # 2d matrix, codelen x layer1_size

sgn = (-1.0)**word.code # ch function, 0-> 1, 1 -> -1

lprob = -log(1.0 + exp(-sgn*dot(l1, l2a.T)))

return sum(lprob)

The score function should make use of the parameters learned during hierarchical softmax training. But in the calculation of the log-probability there is supposed to be a sigmoid function( word2vec Parameter Learning Explained equation (45)).

So does gensim really calculate the log-probability in

`lprob`

I would have calculated the log-probability as follows:

`-log(1.0/(1.0+exp(-sgn*dot(l1, l2a.T))))`

Is this equation not used because it explodes for values close to zero or is it in general wrong?

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Answer Source

I've overlooked that the logarithm of the sigmoid function can be rewritten: `log(1.0/(1.0+exp(-sgn*dot(l1, l2a.T)))) = log(1)-log(1.0+exp(-sgn*dot(l1, l2a.T))) = -log(1.0+exp(-sgn*dot(l1, l2a.T)))`

So the code does compute the log-likelihood.

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