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