jens
7b34529b24
git-svn-id: http://moon:8086/svn/matlab/trunk@91 801c6759-fa7c-4059-a304-17956f83a07c
69 lines
2.4 KiB
Matlab
69 lines
2.4 KiB
Matlab
function features = feedfowardRICA(filterDim, poolDim, numFilters, images, W)
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% feedfowardRICA Returns the convolution of the features given by W with
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% the given images. It should be very similar to cnnConvolve.m+cnnPool.m
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% in the CNN exercise, except that there is no bias term b, and the pooling
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% is RICA-style square-square-root pooling instead of average pooling.
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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 - W should be the weights learnt using RICA
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% W is of shape (filterDim,filterDim,numFilters)
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%
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% Returns:
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% features - matrix of convolved and pooled features in the form
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% features(imageRow, imageCol, featureNum, imageNum)
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global params;
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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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features = zeros(convDim / poolDim, ...
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convDim / poolDim, numFilters, numImages);
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poolMat = ones(poolDim);
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% Instructions:
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% Convolve every filter with every image just like what you did in
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% cnnConvolve.m to get a response.
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% Then perform square-square-root pooling on the response with 3 steps:
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% 1. Square every element in the response
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% 2. Sum everything in each pooling region
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% 3. add params.epsilon to every element before taking element-wise square-root
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% (Hint: use poolMat similarly as in cnnPool.m)
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for imageNum = 1:numImages
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if mod(imageNum,500)==0
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fprintf('forward-prop image %d\n', imageNum);
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end
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for filterNum = 1:numFilters
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filter = zeros(8,8); % You should replace this
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% Form W, obtain the feature (filterDim x filterDim) needed during the
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% 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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resp = zeros(convDim, convDim); % You should replace this
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% Convolve "filter" with "im" to find "resp"
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% be sure to do a 'valid' convolution
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%%% YOUR CODE HERE %%%
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% Then, apply square-square-root pooling on "resp" to get the hidden
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% activation "act"
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act = zeros(convDim / poolDim, convDim / poolDim); % You should replace this
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%%% YOUR CODE HERE %%%
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features(:, :, filterNum, imageNum) = act;
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end
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end
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end
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