The vector p t represents a probability distribution on V with each coordinate p (i) t defined as: Following the definition of softmax function: Intuitively, to maximize the targeted caption's probability, we can directly use its negative log probability (5) as a loss function.
the inputs of the RNN are the first N − 1 words of the targeted Applying (5) to (2), the formulation of targeted caption method given a targeted caption S is: Alternatively, using the definition of the softmax function, (3) can be simplified as as large as possible, it is sufficient to require the target word S t to attain the largest (top-1) logit (or probability) among all the words in the vocabulary at position t. we aim to minimize the difference between the maximum logit except S t , denoted by max k∈V,k =St {z (k) t }, and the logit of S t , denoted by z (St) t .