这一个例子是利用已经训练好的深度学习网络， GoogLeNet， 其具体的解释详见另一篇转载的博客。
GoogLeNet has been trained on over a million images and can classify images into 1000 object categories (such as keyboard, coffee mug, pencil, and many animals). The network has learned rich feature representations for a wide range of images. The network takes an image as input and outputs a label for the object in the image together with the probabilities for each of the object categories.
Step 1.
加载深度学习网络， 并查看输入层的大小。
net = googlenet;
inputSize = net.Layers(1).InputSize
得到
inputSize =
224 224 3
网络层的最后一个层为输出层，随机查看1000种分类中的10个
classNames = net.Layers(end).ClassNames;
numClasses = numel(classNames);
disp(classNames(randperm(numClasses,10)))
得到
'speedboat'
'window screen'
'isopod'
'wooden spoon'
'lipstick'
'drake'
'hyena'
'dumbbell'
'strawberry'
'custard apple'
Step2.
载入图片并重置尺寸
I = imread('peppers.png');
figure
imshow(I)
size(I)
I = imresize(I,inputSize(1:2));
figure
imshow(I)
得到
ans =
384 512 3
原图如下
重置后
Step 3.
分类图片
[label,scores] = classify(net,I);
label
得到
显示图片并计算概率
figure
imshow(I)
title(string(label) + ", " + num2str(100*scores(classNames == label),3) + "%");
显示概率前5的分类结果
[~,idx] = sort(scores,'descend');
idx = idx(5:-1:1);
classNamesTop = net.Layers(end).ClassNames(idx);
scoresTop = scores(idx);
figure
barh(scoresTop)
xlim([0 1])
title('Top 5 Predictions')
xlabel('Probability')
yticklabels(classNamesTop)
总结：
主要学习了 如何 调用已经训练好的 深度学习网络 googlenet， 对于另外的深度学习网络，方法相同
如
