76 lines
3.5 KiB
Mathematica
76 lines
3.5 KiB
Mathematica
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classdef DenoisingAutoEncoder < AutoEncoder
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%DenoisingAutoEncoder
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% This object mimics the behavior of a Denoising Auto Encoder network,
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% which is an Auto Encoder that receives noised input data and tries
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% to denoise it
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% This object has elastic habilities, being able to grow and prune
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% nodes automatically.
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% TODO: Provide the paper or study material for the Denoising Auto Encoder
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methods (Access = public)
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% function self = DenoisingAutoEncoder(nInput, nHiddenNodes)
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% %DenoisingAutoEncoder Construct an instance of this class
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% % nInput (integer)
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% % Number of input nodes
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% % nHiddenNodes (integer)
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% % Number of nodes at the hidden layer
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% self@AutoEncoder(nInput, nHiddenNodes);
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% end
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function self = DenoisingAutoEncoder(layers)
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%DenoisingAutoEncoder Construct an instance of this class
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self@AutoEncoder(layers);
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end
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function train(self, X, noiseRatio, nWeight)
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% train
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% See train@NeuralNetwork
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% X (matrix)
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% Input and output data
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% noiseRatio (double)
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% Value between 0.0 and 1.0
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% It indicates the percentage of noise that will be
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% applied on the input datapreparing the network for
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% another kind of data.
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% nWeight (integer) [optional]
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% You has the ability to define which weight and bias you
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% want to update using backpropagation. This method will
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% update only that weight and bias, even if there is
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% weights and biases on layers before and after that.
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% The number of the weight and bias you want to update.
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% Remember that 1 indicates the weight and bias that get
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% out of the input layer.
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if nargin == 3
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train@AutoEncoder(self, X, noiseRatio)
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elseif nargin == 4
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train@AutoEncoder(self, X, noiseRatio, nWeight);
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end
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end
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function greddyLayerWiseTrain(self, X, nEpochs, noiseRatio)
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%greddyLayerWiseTrain
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% Performs Greedy Layer Wise train
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% TODO: Provide the paper or study material for the Greedy
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% layer Wise train
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% X (matrix)
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% Input and output data
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% nEpochs (integer)
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% The number of epochs which the greedy layer wise train
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% will occurs. If you are running a single pass model,
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% you want this to be equal one.
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% noiseRatio (double)
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% Value between 0.0 and 1.0
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% It indicates the percentage of noise that will be
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% isTiedWeight (bool) [optional]
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% On a Tied Weight training, after the train the weights
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% after the middle layer will be a transpose version of
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% the weights before the middle layer. The bias is still
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% kept. This make the network find it hard to train, and
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% that's is good when we are preparing the network for
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% another kind of data.
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greddyLayerWiseTrain@AutoEncoder(self, X, nEpochs, noiseRatio);
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end
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end
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end
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