## Problem statement I am probably wrong about those, but I thought to ask here anyway. I was using `mxnet.gluon.probability.Categorical` from master, and two things came to my attention:
1. It requires a `num_events` argument, which feel unnecessary, and possibly could be eliminated? 2. [Possible bug] If providing `logit`, current `log_prob` implementation assumes we are providing `log softmax(x)`. Usually, when providing `logit`, it is the linear output of the net. This is the behavior also in torch/tensorflow, where internally, the logit is scaled by `logsumexp`. ## Proposed solutions 1. Remove `num_events` argument 2. If `logit` provided in constructor, shift it by `logsumexp` ## References - [Tensorflow implementation](https://github.com/tensorflow/probability/blob/v0.11.1/tensorflow_probability/python/distributions/categorical.py#L289) - [Torch implementation](https://github.com/pytorch/pytorch/blob/master/torch/distributions/categorical.py#L55) -- You are receiving this because you are subscribed to this thread. Reply to this email directly or view it on GitHub: https://github.com/apache/incubator-mxnet/issues/19722
