# mlpinit

## Purpose

Initialise the weights in a 2-layer feedforward network.

## Synopsis

    net = mlpinit(net, prior)

## Description

`net = mlpinit(net, prior)` takes a 2-layer feedforward network `net` and sets the weights and biases by sampling from a Gaussian distribution. If `prior` is a scalar, then all of the parameters (weights and biases) are sampled from a single isotropic Gaussian with inverse variance equal to `prior`. If `prior` is a data structure of the kind generated by `mlpprior`, then the parameters are sampled from multiple Gaussians according to their groupings (defined by the `index` field) with corresponding variances (defined by the `alpha` field).

## See Also

`mlp`, `mlpprior`, `mlppak`, `mlpunpak`

Pages: Index

Copyright (c) Ian T Nabney (1996-9)