jens
7b34529b24
git-svn-id: http://moon:8086/svn/matlab/trunk@91 801c6759-fa7c-4059-a304-17956f83a07c
136 lines
5.0 KiB
Matlab
136 lines
5.0 KiB
Matlab
function [cost, grad, preds] = cnnCost(theta,images,labels,numClasses,...
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filterDim,numFilters,poolDim,pred)
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% Calcualte cost and gradient for a single layer convolutional
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% neural network followed by a softmax layer with cross entropy
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% objective.
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%
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% Parameters:
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% theta - unrolled parameter vector
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% images - stores images in imageDim x imageDim x numImges
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% array
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% numClasses - number of classes to predict
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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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% pred - boolean only forward propagate and return
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% predictions
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%
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%
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% Returns:
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% cost - cross entropy cost
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% grad - gradient with respect to theta (if pred==False)
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% preds - list of predictions for each example (if pred==True)
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if ~exist('pred','var')
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pred = false;
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end;
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imageDim = size(images,1); % height/width of image
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numImages = size(images,3); % number of images
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%% Reshape parameters and setup gradient matrices
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% Wc is filterDim x filterDim x numFilters parameter matrix
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% bc is the corresponding bias
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% Wd is numClasses x hiddenSize parameter matrix where hiddenSize
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% is the number of output units from the convolutional layer
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% bd is corresponding bias
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[Wc, Wd, bc, bd] = cnnParamsToStack(theta,imageDim,filterDim,numFilters,...
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poolDim,numClasses);
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% Same sizes as Wc,Wd,bc,bd. Used to hold gradient w.r.t above params.
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Wc_grad = zeros(size(Wc));
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Wd_grad = zeros(size(Wd));
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bc_grad = zeros(size(bc));
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bd_grad = zeros(size(bd));
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%%======================================================================
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%% STEP 1a: Forward Propagation
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% In this step you will forward propagate the input through the
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% convolutional and subsampling (mean pooling) layers. You will then use
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% the responses from the convolution and pooling layer as the input to a
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% standard softmax layer.
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%% Convolutional Layer
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% For each image and each filter, convolve the image with the filter, add
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% the bias and apply the sigmoid nonlinearity. Then subsample the
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% convolved activations with mean pooling. Store the results of the
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% convolution in activations and the results of the pooling in
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% activationsPooled. You will need to save the convolved activations for
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% backpropagation.
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convDim = imageDim-filterDim+1; % dimension of convolved output
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outputDim = (convDim)/poolDim; % dimension of subsampled output
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% convDim x convDim x numFilters x numImages tensor for storing activations
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activations = zeros(convDim,convDim,numFilters,numImages);
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% outputDim x outputDim x numFilters x numImages tensor for storing
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% subsampled activations
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activationsPooled = zeros(outputDim,outputDim,numFilters,numImages);
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%%% YOUR CODE HERE %%%
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% Reshape activations into 2-d matrix, hiddenSize x numImages,
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% for Softmax layer
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activationsPooled = reshape(activationsPooled,[],numImages);
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%% Softmax Layer
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% Forward propagate the pooled activations calculated above into a
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% standard softmax layer. For your convenience we have reshaped
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% activationPooled into a hiddenSize x numImages matrix. Store the
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% results in probs.
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% numClasses x numImages for storing probability that each image belongs to
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% each class.
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probs = zeros(numClasses,numImages);
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%%% YOUR CODE HERE %%%
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%%======================================================================
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%% STEP 1b: Calculate Cost
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% In this step you will use the labels given as input and the probs
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% calculate above to evaluate the cross entropy objective. Store your
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% results in cost.
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cost = 0; % save objective into cost
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%%% YOUR CODE HERE %%%
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% Makes predictions given probs and returns without backproagating errors.
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if pred
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[~,preds] = max(probs,[],1);
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preds = preds';
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grad = 0;
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return;
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end;
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%%======================================================================
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%% STEP 1c: Backpropagation
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% Backpropagate errors through the softmax and convolutional/subsampling
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% layers. Store the errors for the next step to calculate the gradient.
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% Backpropagating the error w.r.t the softmax layer is as usual. To
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% backpropagate through the pooling layer, you will need to upsample the
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% error with respect to the pooling layer for each filter and each image.
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% Use the kron function and a matrix of ones to do this upsampling
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% quickly.
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%%% YOUR CODE HERE %%%
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%%======================================================================
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%% STEP 1d: Gradient Calculation
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% After backpropagating the errors above, we can use them to calculate the
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% gradient with respect to all the parameters. The gradient w.r.t the
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% softmax layer is calculated as usual. To calculate the gradient w.r.t.
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% a filter in the convolutional layer, convolve the backpropagated error
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% for that filter with each image and aggregate over images.
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%%% YOUR CODE HERE %%%
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%% Unroll gradient into grad vector for minFunc
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grad = [Wc_grad(:) ; Wd_grad(:) ; bc_grad(:) ; bd_grad(:)];
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end
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