http://www.ninadthakoor.com/2016/07/25/getting-started-with-matconvnet/
Available at: http://www.vlfeat.org/matconvnet/
My goal is to use this toolbox to classify cars into four classes: Sedan, Minivan, SUV and pickup. I already have the data from my prior work (http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6558793).
My plan is to first use transfer learning i. e. to use one of the deep networks pre-trained with imagenet data to extract features and use an SVM classifier to do the actual classification. I plan to do this with Matlab 2016a on Windows 10 PC equipped with GeForce GTX 960. I am aware that Matlab also has deep learning support in its Neural Network toolbox, I am going with MatConNet in hopes that it will stay more cutting edge.
The code below is derived from http://www.mathworks.com/company/newsletters/articles/deep-learning-for-computer-vision-with-matlab.html and http://www.vlfeat.org/matconvnet/quick/
|
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64 |
clear; close all; clc;%Load the pre-trained netnet = load('imagenet-vgg-f.mat');net = vl_simplenn_tidy(net) ;%Remove the last layer (softmax layer)net.layers = net.layers(1 : end - 1);%% This deals with reading the data and getting the ground truth class labels%All files are are inside the root root = 'C:\Users\ninad\Dropbox\Ninad\Lasagne\data\c200x200\';Files = dir(fullfile(root, '*.bmp'));%Load the map which stores the class informationload MakeModels.matfor i = 1 : length(Files) waitbar (i/ length(Files)); % Read class from the map Q = Files(i).name(end - 18 : end - 4); Qout = MakeModels(Q); Files(i).class = Qout.Type; % Preprocess the data and get it ready for the CNN im = imread(fullfile(root, Files(i).name)); im_ = single(im); % note: 0-255 range im_ = imresize(im_, net.meta.normalization.imageSize(1:2)); im_ = bsxfun(@minus, im_, net.meta.normalization.averageImage); % run the CNN to compute the features feats = vl_simplenn(net, im_) ; Files(i).feats = squeeze(feats(end).x);end%% Classifier training%Select training data fractiontrainFraction = 0.5;randomsort = randperm(length(Files));trainSamples = randomsort(1 : trainFraction * length(Files));testSamples = randomsort(trainFraction * length(Files)+1 : end);Labels = [Files.class];Features = [Files.feats];trainingFeatures = Features( :, trainSamples);trainingLabels = Labels( :, trainSamples);classifier = fitcecoc(trainingFeatures', trainingLabels);%% Carry out the validation with rest of the datatestFeatures = Features( :, testSamples);testLabels = Labels( :, testSamples);predictedLabels = predict(classifier, testFeatures');confMat = confusionmat(testLabels, predictedLabels);% Convert confusion matrix into percentage formconfMat = bsxfun(@rdivide,confMat,sum(confMat,2))% Display the mean accuracymean(diag(confMat)) |
'Deep Learning > Matconvnet' 카테고리의 다른 글
| How to generate confusion matrix in MatConvNet (0) | 2017.05.15 |
|---|---|
| assertition failed vl_simplenn.m (0) | 2017.05.13 |
Hi,
After run cnn_mnist.m, I have few net-epoch-n.mat models.
To predict one image on mnist , following the example in http://www.vlfeat.org/matconvnet/pretrained
When calling
net=load('./data/mnist-baseline/net-epoch-5.mat');
res=vl_simplenn(net.net, im); % im is just one mnist image whose size is 28*28
I got the error:
Error using vl_nnsoftmaxloss (line 42) Assertion failed.
Error in vl_simplenn (line 164)
res(i+1).x = vl_nnsoftmaxloss(res(i).x, l.class) ;