Parameters¶

param([options])¶

Retrieves the value of a parameter by name. The parameter is created if it does not already exist.

The following options are supported:

dims

When dims is given, param returns a tensor of dimension dims. In this case dims should be an array.

When dims is omitted, param returns a scalar.

init

A function that computes the initial value of the parameter. The function is passed the dimension of a tensor as its only argument, and should return a tensor of that dimension.

When init is omitted, the parameter is initialized with a draw from the Gaussian distribution described by the mu and sigma options.

mu

The mean of the Gaussian distribution from which the initial parameter value is drawn when init is omitted.

Default: 0

sigma

The standard deviation of the Gaussian distribution from which the initial parameter value is drawn when init is omitted. Specify a standard deviation of 0 to deterministically initialize the parameter to mu.

Default: 0.1

name: The name of the parameter to retrieve. If name is omitted a default name is automatically generated based on the current stack address, relative to the current coroutine.

Examples:

param()
param({name: 'myparam'})
param({mu: 0, sigma: 0.01, name: 'myparam'})
param({dims: [10, 10]})
param({dims: [2, 1], init: function(dims) { return ones(dims); }})

modelParam([options])¶

An analog of param used to create or retrieve a parameter that can be used directly in the model.

Optimizing the ELBO yields maximum likelihood estimation for model parameters. modelParam cannot be used with other inference strategies as it does not have an interpretation in the fully Bayesian setting. Attempting to do so will raise an exception.

modelParam supports the same options as param. See the documentation for param for details.