word2vec - log in the objective softmax function I'm reading a TensorFlow tutorial on Word2Vec models and got confused with the objective function. The base softmax function is the following:

$P(w_t|h) = softmax(score(w_t, h) = \frac{exp[score(w_t, h)]}{\Sigma_v exp[score(w',h)]}$, where $score$ computes the compatibility of word $w_t$ with the context $h$ (a dot product is commonly used). We train this model by maximizing its log-likelihood on the training set, i.e. by maximizing $ J_{ML} = \log P(w_t|h) = score(w_t,h) - \log \bigl(\Sigma_v exp[score(w',h)\bigr)$

But why $\log$ disappeared from the $score(w_t,h)$ term?
No, the logartihm doesn't disappear. From the equation
 ,
When you want to calculate
 ,
it essentially means calculating ,  
Now , 
So ,


as .