30 lines
1.8 KiB
Matlab
30 lines
1.8 KiB
Matlab
function nn = nnsetup(architecture)
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%NNSETUP creates a Feedforward Backpropagate Neural Network
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% nn = nnsetup(architecture) returns an neural network structure with n=numel(architecture)
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% layers, architecture being a n x 1 vector of layer sizes e.g. [784 100 10]
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nn.size = architecture;
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nn.n = numel(nn.size);
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nn.activation_function = 'tanh_opt'; % Activation functions of hidden layers: 'sigm' (sigmoid) or 'tanh_opt' (optimal tanh).
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nn.learningRate = 2; % learning rate Note: typically needs to be lower when using 'sigm' activation function and non-normalized inputs.
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nn.momentum = 0.5; % Momentum
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nn.scaling_learningRate = 1; % Scaling factor for the learning rate (each epoch)
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nn.weightPenaltyL2 = 0; % L2 regularization
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nn.nonSparsityPenalty = 0; % Non sparsity penalty
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nn.sparsityTarget = 0.05; % Sparsity target
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nn.inputZeroMaskedFraction = 0; % Used for Denoising AutoEncoders
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nn.dropoutFraction = 0; % Dropout level (http://www.cs.toronto.edu/~hinton/absps/dropout.pdf)
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nn.testing = 0; % Internal variable. nntest sets this to one.
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nn.output = 'sigm'; % output unit 'sigm' (=logistic), 'softmax' and 'linear'
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for i = 2 : nn.n
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% weights and weight momentum
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nn.W{i - 1} = (rand(nn.size(i), nn.size(i - 1)+1) - 0.5) * 2 * 4 * sqrt(6 / (nn.size(i) + nn.size(i - 1)));
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nn.vW{i - 1} = zeros(size(nn.W{i - 1}));
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% average activations (for use with sparsity)
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nn.p{i} = zeros(1, nn.size(i));
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end
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end
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