# Deep Learning in 11 Lines of MATLAB Code

Avi’s pick of the week is Deep Learning in 11 Lines of MATLAB Code by the MathWorks Deep Learning Toolbox Team. This post is follow up to this post by Jiro and provides a more detailed explanation.

If you are  interested in learning more about deep learning or trying out some of latest deep learning research in MATLAB this blog post will walk you through the first steps to getting started. The entry on File Exchange provides everything you need to download one of the most popular deep neural networks and use it to classify images using live video from a webcam. Before you read through the rest of this post, I would highly recommend you watch by Joe Hicklin (pictured below) that illustrates what I will explain in more detail.

This post covers how to download a pre-trained deep convolutional neural network and use it to classify images in a live video stream.

The pre-trained model I’ll use in this post is known as AlexNet. This model was trained to recognize 1000 different categories of objects in images, and was trained using over a million images. The AlexNet model is available as a support package in MATLAB, you can learn more about the AlexNet support package from this blog post. Now let’s load the pre-trained network into MATLAB.

nnet = alexnet; % Will prompt support package install if unavaliable


## View Network Structure

Now let us take a look at the structure of AlexNet model. Notice that the first layer requires a 227×227 RGB image, and the last layer classifies 1000 different objects.

nnet.Layers

ans =

25x1 Layer array with layers:

1   'data'     Image Input                   227x227x3 images with 'zerocenter' normalization
2   'conv1'    Convolution                   96 11x11x3 convolutions with stride [4  4] and padding [0  0]
3   'relu1'    ReLU                          ReLU
4   'norm1'    Cross Channel Normalization   cross channel normalization with 5 channels per element
5   'pool1'    Max Pooling                   3x3 max pooling with stride [2  2] and padding [0  0]
6   'conv2'    Convolution                   256 5x5x48 convolutions with stride [1  1] and padding [2  2]
7   'relu2'    ReLU                          ReLU
8   'norm2'    Cross Channel Normalization   cross channel normalization with 5 channels per element
9   'pool2'    Max Pooling                   3x3 max pooling with stride [2  2] and padding [0  0]
10   'conv3'    Convolution                   384 3x3x256 convolutions with stride [1  1] and padding [1  1]
11   'relu3'    ReLU                          ReLU
12   'conv4'    Convolution                   384 3x3x192 convolutions with stride [1  1] and padding [1  1]
13   'relu4'    ReLU                          ReLU
14   'conv5'    Convolution                   256 3x3x192 convolutions with stride [1  1] and padding [1  1]
15   'relu5'    ReLU                          ReLU
16   'pool5'    Max Pooling                   3x3 max pooling with stride [2  2] and padding [0  0]
17   'fc6'      Fully Connected               4096 fully connected layer
18   'relu6'    ReLU                          ReLU
19   'drop6'    Dropout                       50% dropout
20   'fc7'      Fully Connected               4096 fully connected layer
21   'relu7'    ReLU                          ReLU
22   'drop7'    Dropout                       50% dropout
23   'fc8'      Fully Connected               1000 fully connected layer
24   'prob'     Softmax                       softmax
25   'output'   Classification Output         crossentropyex with 'tench', 'goldfish', and 998 other classes


## Classify Test Images

Before we connect a webcam and try classifying images on a live stream, lets try classifying a single test image.

I = imread('peppers.png');
imshow(I)
title('Input Image')


To classify the input image we use the ‘classify’ function. Since AlexNet expects the input image to be 227×227 let’s resize the input image.

Iin = imresize(I,[227 227]);
figure
label = classify(nnet, Iin);        % Classify the picture
imshow(Iin)     % Show the picture
title(char(label)) % Show the label


You’ll see that the network accurately classifies the image as a bell pepper. You can also see the prediction score or confidence of the prediction for each of the 1000 classes by outputing the score from the classify method.

## Try on Live Video

Now that we’ve tried the deep learning image classifier on a single image lets try doing the same with a live video stream. We connect to a webcam using the webcam object in MATLAB.

camera = webcam;              % Connect to camera
picture = snapshot(camera);   % Take picture
imshow(picture)               % Show picture

% Run live video capture and classification in loop
for n = 1:100
picture = snapshot(camera);             % Take picture
picture = imresize(picture,[227,227]);  % Resize picture
label = classify(nnet, picture);        % Classify picture
imshow(picture);                        % Show picture
title(char(label))                      % Show label
drawnow
end

|

### 댓글

댓글을 남기려면 링크 를 클릭하여 MathWorks 계정에 로그인하거나 계정을 새로 만드십시오.