61 lines
1.8 KiB
Matlab
61 lines
1.8 KiB
Matlab
function nn = nnff(nn, x, y)
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%NNFF performs a feedforward pass
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% nn = nnff(nn, x, y) returns an neural network structure with updated
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% layer activations, error and loss (nn.a, nn.e and nn.L)
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n = nn.n;
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m = size(x, 1);
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x = [ones(m,1) x];
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nn.a{1} = x;
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%feedforward pass
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for i = 2 : n-1
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switch nn.activation_function
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case 'sigm'
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% Calculate the unit's outputs (including the bias term)
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nn.a{i} = sigm(nn.a{i - 1} * nn.W{i - 1}');
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case 'tanh_opt'
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nn.a{i} = tanh_opt(nn.a{i - 1} * nn.W{i - 1}');
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end
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%dropout
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if(nn.dropoutFraction > 0)
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if(nn.testing)
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nn.a{i} = nn.a{i}.*(1 - nn.dropoutFraction);
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else
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nn.dropOutMask{i} = (rand(size(nn.a{i}))>nn.dropoutFraction);
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nn.a{i} = nn.a{i}.*nn.dropOutMask{i};
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end
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end
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%calculate running exponential activations for use with sparsity
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if(nn.nonSparsityPenalty>0)
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nn.p{i} = 0.99 * nn.p{i} + 0.01 * mean(nn.a{i}, 1);
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end
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%Add the bias term
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nn.a{i} = [ones(m,1) nn.a{i}];
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end
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switch nn.output
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case 'sigm'
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nn.a{n} = sigm(nn.a{n - 1} * nn.W{n - 1}');
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case 'linear'
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nn.a{n} = nn.a{n - 1} * nn.W{n - 1}';
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case 'softmax'
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nn.a{n} = nn.a{n - 1} * nn.W{n - 1}';
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nn.a{n} = exp(bsxfun(@minus, nn.a{n}, max(nn.a{n},[],2)));
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nn.a{n} = bsxfun(@rdivide, nn.a{n}, sum(nn.a{n}, 2));
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end
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%error and loss
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nn.e = y - nn.a{n};
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switch nn.output
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case {'sigm', 'linear'}
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nn.L = 1/2 * sum(sum(nn.e .^ 2)) / m;
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case 'softmax'
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nn.L = -sum(sum(y .* log(nn.a{n}))) / m;
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end
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end
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