jens
7b34529b24
git-svn-id: http://moon:8086/svn/matlab/trunk@91 801c6759-fa7c-4059-a304-17956f83a07c
105 lines
3.7 KiB
Matlab
105 lines
3.7 KiB
Matlab
%% Convolution Neural Network 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 in building a single.
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% layer convolutional nerual network. In this exercise, you will only
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% need to modify cnnCost.m and cnnminFuncSGD.m. You will not need to
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% modify this file.
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%%======================================================================
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%% STEP 0: Initialize Parameters and Load Data
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% Here we initialize some parameters used for the exercise.
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% Configuration
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imageDim = 28;
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numClasses = 10; % Number of classes (MNIST images fall into 10 classes)
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filterDim = 9; % Filter size for conv layer
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numFilters = 20; % Number of filters for conv layer
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poolDim = 2; % Pooling dimension, (should divide imageDim-filterDim+1)
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% Load MNIST Train
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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,[]);
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labels = loadMNISTLabels('../common/train-labels-idx1-ubyte');
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labels(labels==0) = 10; % Remap 0 to 10
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% Initialize Parameters
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theta = cnnInitParams(imageDim,filterDim,numFilters,poolDim,numClasses);
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%%======================================================================
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%% STEP 1: Implement convNet Objective
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% Implement the function cnnCost.m.
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%%======================================================================
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%% STEP 2: Gradient Check
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% Use the file computeNumericalGradient.m to check the gradient
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% calculation for your cnnCost.m function. You may need to add the
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% appropriate path or copy the file to this directory.
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DEBUG=false; % set this to true to check gradient
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if DEBUG
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% To speed up gradient checking, we will use a reduced network and
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% a debugging data set
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db_numFilters = 2;
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db_filterDim = 9;
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db_poolDim = 5;
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db_images = images(:,:,1:10);
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db_labels = labels(1:10);
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db_theta = cnnInitParams(imageDim,db_filterDim,db_numFilters,...
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db_poolDim,numClasses);
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[cost grad] = cnnCost(db_theta,db_images,db_labels,numClasses,...
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db_filterDim,db_numFilters,db_poolDim);
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% Check gradients
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numGrad = computeNumericalGradient( @(x) cnnCost(x,db_images,...
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db_labels,numClasses,db_filterDim,...
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db_numFilters,db_poolDim), db_theta);
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% Use this to visually compare the gradients side by side
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disp([numGrad grad]);
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diff = norm(numGrad-grad)/norm(numGrad+grad);
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% Should be small. In our implementation, these values are usually
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% less than 1e-9.
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disp(diff);
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assert(diff < 1e-9,...
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'Difference too large. Check your gradient computation again');
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end;
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%%======================================================================
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%% STEP 3: Learn Parameters
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% Implement minFuncSGD.m, then train the model.
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options.epochs = 3;
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options.minibatch = 256;
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options.alpha = 1e-1;
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options.momentum = .95;
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opttheta = minFuncSGD(@(x,y,z) cnnCost(x,y,z,numClasses,filterDim,...
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numFilters,poolDim),theta,images,labels,options);
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%%======================================================================
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%% STEP 4: Test
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% Test the performance of the trained model using the MNIST test set. Your
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% accuracy should be above 97% after 3 epochs of training
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testImages = loadMNISTImages('../common/t10k-images-idx3-ubyte');
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testImages = reshape(testImages,imageDim,imageDim,[]);
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testLabels = loadMNISTLabels('../common/t10k-labels-idx1-ubyte');
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testLabels(testLabels==0) = 10; % Remap 0 to 10
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[~,cost,preds]=cnnCost(opttheta,testImages,testLabels,numClasses,...
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filterDim,numFilters,poolDim,true);
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acc = sum(preds==testLabels)/length(preds);
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% Accuracy should be around 97.4% after 3 epochs
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fprintf('Accuracy is %f\n',acc);
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