matlab 运行 AlexNet

0. alexnet 工具箱下载

下载地址：Neural Network Toolbox(TM) Model for AlexNet Network

• 需要先注册（十分简单），登陆，下载；
• 下载完成之后，windows 是无法运行该文件的；
• 需要打开 matlab，进入到该文件所在的路径，双击运行；（注：需要较久的时间下载安装 alexnet）

1. demo（十一行代码）

deep-learning-in-11-lines-of-matlab-code

clear
camera = webcam;
nnet = alexnet;
while true
    picture = camera.snapshot;
    picture = imresize(picture, [227, 227]);
    label = classify(nnet, picture);
    image(picture);
    title(char(label));
end

2. 网络结构

>> nnet = alexnet;
>> nnet.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         cross-entropy with \'tench\', \'goldfish\', and 998 other classes