jens
7b34529b24
git-svn-id: http://moon:8086/svn/matlab/trunk@91 801c6759-fa7c-4059-a304-17956f83a07c
39 lines
1.4 KiB
Matlab
39 lines
1.4 KiB
Matlab
function [Wc, Wd, bc, bd] = cnnParamsToStack(theta,imageDim,filterDim,...
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numFilters,poolDim,numClasses)
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% Converts unrolled parameters for a single layer convolutional neural
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% network followed by a softmax layer into structured weight
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% tensors/matrices and corresponding biases
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%
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% Parameters:
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% theta - unrolled parameter vectore
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% imageDim - height/width of image
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% filterDim - dimension of convolutional filter
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% numFilters - number of convolutional filters
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% poolDim - dimension of pooling area
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% numClasses - number of classes to predict
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%
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%
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% Returns:
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% Wc - filterDim x filterDim x numFilters parameter matrix
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% Wd - numClasses x hiddenSize parameter matrix, hiddenSize is
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% calculated as numFilters*((imageDim-filterDim+1)/poolDim)^2
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% bc - bias for convolution layer of size numFilters x 1
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% bd - bias for dense layer of size hiddenSize x 1
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outDim = (imageDim - filterDim + 1)/poolDim;
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hiddenSize = outDim^2*numFilters;
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%% Reshape theta
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indS = 1;
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indE = filterDim^2*numFilters;
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Wc = reshape(theta(indS:indE),filterDim,filterDim,numFilters);
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indS = indE+1;
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indE = indE+hiddenSize*numClasses;
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Wd = reshape(theta(indS:indE),numClasses,hiddenSize);
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indS = indE+1;
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indE = indE+numFilters;
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bc = theta(indS:indE);
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bd = theta(indE+1:end);
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end |