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Diffstat (limited to 'sourcecodes/bnt-master/netlab3.3/netinit.m')
| -rw-r--r-- | sourcecodes/bnt-master/netlab3.3/netinit.m | 45 |
1 files changed, 45 insertions, 0 deletions
diff --git a/sourcecodes/bnt-master/netlab3.3/netinit.m b/sourcecodes/bnt-master/netlab3.3/netinit.m new file mode 100644 index 00000000..f94e30b3 --- /dev/null +++ b/sourcecodes/bnt-master/netlab3.3/netinit.m @@ -0,0 +1,45 @@ +function net = netinit(net, prior) +%NETINIT Initialise the weights in a network. +% +% Description +% +% NET = NETINIT(NET, PRIOR) takes a network data structure 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 +% MLPPRIOR, NETUNPAK, RBFPRIOR +% + +% Copyright (c) Ian T Nabney (1996-2001) + +if isstruct(prior) + if (isfield(net, 'mask')) + if find(sum(prior.index, 2)) ~= find(net.mask) + error('Index does not match mask'); + end + sig = sqrt(prior.index*prior.alpha); + % Weights corresponding to zeros in mask will not be used anyway + % Set their priors to one to avoid division by zero + sig = sig + (sig == 0); + sig = 1./sqrt(sig); + else + sig = 1./sqrt(prior.index*prior.alpha); + end + w = sig'.*randn(1, net.nwts); +elseif size(prior) == [1 1] + w = randn(1, net.nwts).*sqrt(1/prior); +else + error('prior must be a scalar or a structure'); +end + +if (isfield(net, 'mask')) + w = w(logical(net.mask)); +end +net = netunpak(net, w); + |