# SoftmaxTransform

Source: https://www.tensorplay.cn/docs/generated/tensorplay.distributions.SoftmaxTransform.html

```python
class tensorplay.distributions.SoftmaxTransform(cache_size: int = 0)
```

Transform from unconstrained space to the simplex via $y = \exp(x)$ then
normalizing.

This is not bijective and cannot be used for HMC. However this acts mostly
coordinate-wise (except for the final normalization), and thus is
appropriate for coordinate-wise optimization algorithms.

```python
property inv: Transform
```

Returns the inverse [Transform](/docs/generated/tensorplay.distributions.Transform.html#tensorplay.distributions.Transform) of this transform.
This should satisfy t.inv.inv is t.

```python
log_abs_det_jacobian(x, y)
```

Computes the log det jacobian log |dy/dx| given input and output.

```python
property sign: int
```

Returns the sign of the determinant of the Jacobian, if applicable.
In general this only makes sense for bijective transforms.