wolffd@0: wolffd@0: wolffd@0: wolffd@0: Netlab Reference Manual mlpinit wolffd@0: wolffd@0: wolffd@0: wolffd@0:

mlpinit wolffd@0:

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wolffd@0: Purpose wolffd@0:

wolffd@0: Initialise the weights in a 2-layer feedforward network. wolffd@0: wolffd@0:

wolffd@0: Synopsis wolffd@0:

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wolffd@0: net = mlpinit(net, prior)
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wolffd@0: Description wolffd@0:

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net = mlpinit(net, prior) takes a 2-layer feedforward network wolffd@0: net and sets the weights and biases by sampling from a Gaussian wolffd@0: distribution. If prior is a scalar, then all of the parameters wolffd@0: (weights and biases) are sampled from a single isotropic Gaussian with wolffd@0: inverse variance equal to prior. If prior is a data wolffd@0: structure of the kind generated by mlpprior, then the parameters wolffd@0: are sampled from multiple Gaussians according to their groupings wolffd@0: (defined by the index field) with corresponding variances wolffd@0: (defined by the alpha field). wolffd@0: wolffd@0:

wolffd@0: See Also wolffd@0:

wolffd@0: mlp, mlpprior, mlppak, mlpunpak

wolffd@0: Pages: wolffd@0: Index wolffd@0:

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