function de_dx = gradff_snn(X, net, data)%GRADFF_SNN Derivative of cost function with respect to weights and%           biases.%% Syntax%%  dE_dX = gradff_snn(net, data)%  dE_dX = gradff_snn(X, net, data)%% Description%%  GRADFF_SNN takes%    net    - a net_struct%    data   - the data for net.costFcn.name%    X      - a vector containing connection weights and biases (optional; %             default these will be taken from net)%  and returns%    dE_dX  - dE_dX (where E is cost function)%%%  See also%%    GETX_SNN%%#function lintf_snn exptf_snn logsigtf_snn radbastf_snn tansigtf_snn %#function dlintf_snn dexptf_snn dlogsigtf_snn dradbastf_snn dtansigtf_snn %#function wcf_snn dwcf_snn if (nargin == 2)         % gradff_snn(net, data)   de_dx = gradff_all(X, net);elseif (nargin == 3)   net = setx_snn(net, X);   de_dx = gradff_all(net, data);end%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%function de_dx = gradff_all(net, data)M = net.numLayers;N{1} = net.weights{1}*data.P + repmat(net.biases{1}, 1, size(data.P,2));V{1} = feval(net.transferFcn{1}, N{1});if (M > 1)    for m = 2:M       N{m} = net.weights{m}*V{m-1} + repmat(net.biases{m}, 1, size(data.P,2));       V{m} = feval(net.transferFcn{m}, N{m});    endenddelta{M} = feval(feval(net.transferFcn{M}, 'deriv'), N{M}, V{M}) .* ...           feval(feval(net.costFcn.name, 'deriv'), net, data, V{M});for m = [(M-1):-1:1]    delta{m} = feval(feval(net.transferFcn{m}, 'deriv'), N{m}, V{m}) .* ...               (net.weights{m+1}'*delta{m+1});endde_db{1} = sum(delta{1},2);de_dw{1} = delta{1}*data.P';if (M > 1)   for m = 2:M      de_db{m} = sum(delta{m},2);      de_dw{m} = delta{m}*(V{m-1}');   endendind_1 = 1;for m = 1:M   [rows, columns] = size(de_dw{m});   ind_2 = ind_1 + rows*columns;   de_dx([ind_1:(ind_2-1)],1) = reshape(de_dw{m}, rows*columns, 1);   ind_1 = ind_2 + rows;   de_dx([ind_2:(ind_1-1)],1) = de_db{m};end