jens
7b34529b24
git-svn-id: http://moon:8086/svn/matlab/trunk@91 801c6759-fa7c-4059-a304-17956f83a07c
73 lines
2.3 KiB
Matlab
73 lines
2.3 KiB
Matlab
function convolvedFeatures = cnnConvolve(filterDim, numFilters, images, W, b)
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%cnnConvolve Returns the convolution of the features given by W and b with
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%the given images
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%
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% Parameters:
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% filterDim - filter (feature) dimension
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% numFilters - number of feature maps
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% images - large images to convolve with, matrix in the form
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% images(r, c, image number)
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% W, b - W, b for features from the sparse autoencoder
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% W is of shape (filterDim,filterDim,numFilters)
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% b is of shape (numFilters,1)
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%
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% Returns:
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% convolvedFeatures - matrix of convolved features in the form
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% convolvedFeatures(imageRow, imageCol, featureNum, imageNum)
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numImages = size(images, 3);
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imageDim = size(images, 1);
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convDim = imageDim - filterDim + 1;
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convolvedFeatures = zeros(convDim, convDim, numFilters, numImages);
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% Instructions:
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% Convolve every filter with every image here to produce the
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% (imageDim - filterDim + 1) x (imageDim - filterDim + 1) x numFeatures x numImages
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% matrix convolvedFeatures, such that
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% convolvedFeatures(imageRow, imageCol, featureNum, imageNum) is the
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% value of the convolved featureNum feature for the imageNum image over
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% the region (imageRow, imageCol) to (imageRow + filterDim - 1, imageCol + filterDim - 1)
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%
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% Expected running times:
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% Convolving with 100 images should take less than 30 seconds
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% Convolving with 5000 images should take around 2 minutes
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% (So to save time when testing, you should convolve with less images, as
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% described earlier)
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for imageNum = 1:numImages
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for filterNum = 1:numFilters
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% convolution of image with feature matrix
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convolvedImage = zeros(convDim, convDim);
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% Obtain the feature (filterDim x filterDim) needed during the convolution
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%%% YOUR CODE HERE %%%
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% Flip the feature matrix because of the definition of convolution, as explained later
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filter = rot90(squeeze(filter),2);
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% Obtain the image
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im = squeeze(images(:, :, imageNum));
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% Convolve "filter" with "im", adding the result to convolvedImage
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% be sure to do a 'valid' convolution
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%%% YOUR CODE HERE %%%
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% Add the bias unit
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% Then, apply the sigmoid function to get the hidden activation
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%%% YOUR CODE HERE %%%
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convolvedFeatures(:, :, filterNum, imageNum) = convolvedImage;
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end
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end
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end
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