prune_input_snn.m

来自「神经网络的工具箱, 神经网络的工具箱,」· M 代码 · 共 31 行

function [dX, varargout] = prune_input_snn(net, data, units, F_change_inv, X)
%PRUNE_INPUT_SNN

%
% [dX, dFinv] = prune_input_snn(net, data, units)
% [dX, dFinv] = prune_input_snn(net, data, units, F_change_inv, X)

if (nargin <= 3)
   
   F_change = fisher_nc_snn(net, data);
   options.disp = 0;
   l_max = eigs(F_change,1,options);
   F_change_inv = inv(F_change + 1e-7 * eye(size(F_change,1)));

   X = getx_snn(net);

end

% units(i) = index for input
D = [];
for i = 1:prod(size(units))
    S1 = size(net.weights{1}, 1);
    D = [D [(units(i)-1)*S1+1:units(i)*S1]];
end

if (nargout <= 1)
   dX = prune_snn(F_change_inv, X, D);
else
   [dX, varargout{1:nargout-1}] = prune_snn(F_change_inv, X, D);
end