Daniel@0: Daniel@0: Daniel@0: Daniel@0: Netlab Reference Manual mlpinit Daniel@0: Daniel@0: Daniel@0: Daniel@0:

mlpinit Daniel@0:

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Daniel@0: Purpose Daniel@0:

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

Daniel@0: Synopsis Daniel@0:

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Daniel@0: net = mlpinit(net, prior)
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Daniel@0: Daniel@0: Daniel@0:

Daniel@0: Description Daniel@0:

Daniel@0: Daniel@0:

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

Daniel@0: See Also Daniel@0:

Daniel@0: mlp, mlpprior, mlppak, mlpunpak

Daniel@0: Pages: Daniel@0: Index Daniel@0:

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