NN代码解析

function nn = nnbp(nn)%NNBP performs backpropagation% nn = nnbp(nn) returns an neural network structure with updated weights         n = nn.n;    sparsityError = 0;    switch nn.output        case 'sigm'            d{n} = - nn.e .* (nn.a{n} .* (1 - nn.a{n}));        case {'softmax','linear'}            d{n} = - nn.e;    end    for i = (n - 1) : -1 : 2        % Derivative of the activation function        switch nn.activation_function             case 'sigm'                d_act = nn.a{i} .* (1 - nn.a{i});            case 'tanh_opt'                d_act = 1.7159 * 2/3 * (1 - 1/(1.7159)^2 * nn.a{i}.^2);        end                if(nn.nonSparsityPenalty>0)            pi = repmat(nn.p{i}, size(nn.a{i}, 1), 1);            sparsityError = [zeros(size(nn.a{i},1),1) nn.nonSparsityPenalty * (-nn.sparsityTarget ./ pi + (1 - nn.sparsityTarget) ./ (1 - pi))];        end                % Backpropagate first derivatives        if i+1==n % in this case in d{n} there is not the bias term to be removed                         d{i} = (d{i + 1} * nn.W{i} + sparsityError) .* d_act; % Bishop (5.56)        else % in this case in d{i} the bias term has to be removed            d{i} = (d{i + 1}(:,2:end) * nn.W{i} + sparsityError) .* d_act;        end                if(nn.dropoutFraction>0)            d{i} = d{i} .* [ones(size(d{i},1),1) nn.dropOutMask{i}];        end    end    for i = 1 : (n - 1)        if i+1==n            nn.dW{i} = (d{i + 1}' * nn.a{i}) / size(d{i + 1}, 1);        else            nn.dW{i} = (d{i + 1}(:,2:end)' * nn.a{i}) / size(d{i + 1}, 1);              end    endend