# LostTech.TensorFlow : API Documentation

Type RelaxedOneHotCategorical

Namespace tensorflow.contrib.distributions

Parent TransformedDistribution

Interfaces IRelaxedOneHotCategorical

RelaxedOneHotCategorical distribution with temperature and logits.

The RelaxedOneHotCategorical is a distribution over random probability vectors, vectors of positive real values that sum to one, which continuously approximates a OneHotCategorical. The degree of approximation is controlled by a temperature: as the temperature goes to 0 the RelaxedOneHotCategorical becomes discrete with a distribution described by the `logits` or `probs` parameters, as the temperature goes to infinity the RelaxedOneHotCategorical becomes the constant distribution that is identically the constant vector of (1/event_size,..., 1/event_size).

The RelaxedOneHotCategorical distribution was concurrently introduced as the Gumbel-Softmax (Jang et al., 2016) and Concrete (Maddison et al., 2016) distributions for use as a reparameterized continuous approximation to the `Categorical` one-hot distribution. If you use this distribution, please cite both papers.

#### Examples

Creates a continuous distribution, which approximates a 3-class one-hot categorical distribution. The 2nd class is the most likely to be the largest component in samples drawn from this distribution. Creates a continuous distribution, which approximates a 3-class one-hot categorical distribution. The 2nd class is the most likely to be the largest component in samples drawn from this distribution. Creates a continuous distribution, which approximates a 3-class one-hot categorical distribution. Because the temperature is very low, samples from this distribution are almost discrete, with one component almost 1 and the others nearly 0. The 2nd class is the most likely to be the largest component in samples drawn from this distribution. Creates a continuous distribution, which approximates a 3-class one-hot categorical distribution. Because the temperature is very high, samples from this distribution are usually close to the (1/3, 1/3, 1/3) vector. The 2nd class is still the most likely to be the largest component in samples drawn from this distribution. Eric Jang, Shixiang Gu, and Ben Poole. Categorical Reparameterization with Gumbel-Softmax. 2016.

Chris J. Maddison, Andriy Mnih, and Yee Whye Teh. The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables. 2016.
Show Example
```temperature = 0.5
p = [0.1, 0.5, 0.4]
dist = RelaxedOneHotCategorical(temperature, probs=p) ```