LostTech.TensorFlow : API Documentation

OneHotCategorical distribution.

The categorical distribution is parameterized by the log-probabilities of a set of classes. The difference between OneHotCategorical and Categorical distributions is that OneHotCategorical is a discrete distribution over one-hot bit vectors whereas Categorical is a discrete distribution over positive integers. OneHotCategorical is equivalent to Categorical except Categorical has event_dim=() while OneHotCategorical has event_dim=K, where K is the number of classes.

This class provides methods to create indexed batches of OneHotCategorical distributions. If the provided `logits` or `probs` is rank 2 or higher, for every fixed set of leading dimensions, the last dimension represents one single OneHotCategorical distribution. When calling distribution functions (e.g. `dist.prob(x)`), `logits` and `x` are broadcast to the same shape (if possible). In all cases, the last dimension of `logits,x` represents single OneHotCategorical distributions.

#### Examples

Creates a 3-class distribution, with the 2nd class, the most likely to be drawn from. Creates a 3-class distribution, with the 2nd class the most likely to be drawn from, using logits. Creates a 3-class distribution, with the 3rd class is most likely to be drawn.