48 lines
1.6 KiB
Matlab
48 lines
1.6 KiB
Matlab
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
|
|
end
|
|
end
|