jens
7b34529b24
git-svn-id: http://moon:8086/svn/matlab/trunk@91 801c6759-fa7c-4059-a304-17956f83a07c
45 lines
1.5 KiB
Matlab
45 lines
1.5 KiB
Matlab
function theta = cnnInitParams(imageDim,filterDim,numFilters,...
|
|
poolDim,numClasses)
|
|
% Initialize parameters for a single layer convolutional neural
|
|
% network followed by a softmax layer.
|
|
%
|
|
% Parameters:
|
|
% imageDim - height/width of image
|
|
% filterDim - dimension of convolutional filter
|
|
% numFilters - number of convolutional filters
|
|
% poolDim - dimension of pooling area
|
|
% numClasses - number of classes to predict
|
|
%
|
|
%
|
|
% Returns:
|
|
% theta - unrolled parameter vector with initialized weights
|
|
|
|
%% Initialize parameters randomly based on layer sizes.
|
|
assert(filterDim < imageDim,'filterDim must be less that imageDim');
|
|
|
|
Wc = 1e-1*randn(filterDim,filterDim,numFilters);
|
|
|
|
outDim = imageDim - filterDim + 1; % dimension of convolved image
|
|
|
|
% assume outDim is multiple of poolDim
|
|
assert(mod(outDim,poolDim)==0,...
|
|
'poolDim must divide imageDim - filterDim + 1');
|
|
|
|
outDim = outDim/poolDim;
|
|
hiddenSize = outDim^2*numFilters;
|
|
|
|
% we'll choose weights uniformly from the interval [-r, r]
|
|
r = sqrt(6) / sqrt(numClasses+hiddenSize+1);
|
|
Wd = rand(numClasses, hiddenSize) * 2 * r - r;
|
|
|
|
bc = zeros(numFilters, 1);
|
|
bd = zeros(numClasses, 1);
|
|
|
|
% Convert weights and bias gradients to the vector form.
|
|
% This step will "unroll" (flatten and concatenate together) all
|
|
% your parameters into a vector, which can then be used with minFunc.
|
|
theta = [Wc(:) ; Wd(:) ; bc(:) ; bd(:)];
|
|
|
|
end
|
|
|