jens
7b34529b24
git-svn-id: http://moon:8086/svn/matlab/trunk@91 801c6759-fa7c-4059-a304-17956f83a07c
107 lines
3.8 KiB
Matlab
107 lines
3.8 KiB
Matlab
%% Convolution and Pooling Exercise
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% Instructions
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% ------------
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%
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% This file contains code that helps you get started on the
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% convolution and pooling exercise. In this exercise, you will only
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% need to modify cnnConvolve.m and cnnPool.m. You will not need to modify
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% this file.
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%%======================================================================
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%% STEP 0: Initialization and Load Data
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% Here we initialize some parameters used for the exercise.
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imageDim = 28; % image dimension
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filterDim = 8; % filter dimension
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numFilters = 100; % number of feature maps
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numImages = 60000; % number of images
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poolDim = 3; % dimension of pooling region
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% Here we load MNIST training images
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addpath ../common/;
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images = loadMNISTImages('../common/train-images-idx3-ubyte');
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images = reshape(images,imageDim,imageDim,numImages);
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W = randn(filterDim,filterDim,numFilters);
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b = rand(numFilters);
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%%======================================================================
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%% STEP 1: Implement and test convolution
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% In this step, you will implement the convolution and test it on
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% on a small part of the data set to ensure that you have implemented
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% this step correctly.
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%% STEP 1a: Implement convolution
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% Implement convolution in the function cnnConvolve in cnnConvolve.m
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%% Use only the first 8 images for testing
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convImages = images(:, :, 1:8);
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% NOTE: Implement cnnConvolve in cnnConvolve.m first!
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convolvedFeatures = cnnConvolve(filterDim, numFilters, convImages, W, b);
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%% STEP 1b: Checking your convolution
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% To ensure that you have convolved the features correctly, we have
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% provided some code to compare the results of your convolution with
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% activations from the sparse autoencoder
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% For 1000 random points
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for i = 1:1000
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filterNum = randi([1, numFilters]);
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imageNum = randi([1, 8]);
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imageRow = randi([1, imageDim - filterDim + 1]);
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imageCol = randi([1, imageDim - filterDim + 1]);
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patch = convImages(imageRow:imageRow + filterDim - 1, imageCol:imageCol + filterDim - 1, imageNum);
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feature = sum(sum(patch.*W(:,:,filterNum)))+b(filterNum);
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feature = 1./(1+exp(-feature));
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if abs(feature - convolvedFeatures(imageRow, imageCol,filterNum, imageNum)) > 1e-9
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fprintf('Convolved feature does not match test feature\n');
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fprintf('Filter Number : %d\n', filterNum);
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fprintf('Image Number : %d\n', imageNum);
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fprintf('Image Row : %d\n', imageRow);
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fprintf('Image Column : %d\n', imageCol);
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fprintf('Convolved feature : %0.5f\n', convolvedFeatures(imageRow, imageCol, filterNum, imageNum));
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fprintf('Test feature : %0.5f\n', feature);
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error('Convolved feature does not match test feature');
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end
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end
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disp('Congratulations! Your convolution code passed the test.');
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%%======================================================================
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%% STEP 2: Implement and test pooling
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% Implement pooling in the function cnnPool in cnnPool.m
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%% STEP 2a: Implement pooling
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% NOTE: Implement cnnPool in cnnPool.m first!
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pooledFeatures = cnnPool(poolDim, convolvedFeatures);
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%% STEP 2b: Checking your pooling
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% To ensure that you have implemented pooling, we will use your pooling
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% function to pool over a test matrix and check the results.
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testMatrix = reshape(1:64, 8, 8);
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expectedMatrix = [mean(mean(testMatrix(1:4, 1:4))) mean(mean(testMatrix(1:4, 5:8))); ...
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mean(mean(testMatrix(5:8, 1:4))) mean(mean(testMatrix(5:8, 5:8))); ];
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testMatrix = reshape(testMatrix, 8, 8, 1, 1);
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pooledFeatures = squeeze(cnnPool(4, testMatrix));
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if ~isequal(pooledFeatures, expectedMatrix)
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disp('Pooling incorrect');
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disp('Expected');
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disp(expectedMatrix);
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disp('Got');
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disp(pooledFeatures);
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else
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disp('Congratulations! Your pooling code passed the test.');
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end
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