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