matlab 運行 AlexNet

0. alexnet 工具箱下載

下載地址：Neural Network Toolbox(TM) Model for AlexNet Network

• 需要先注冊（十分簡單），登陸，下載；
• 下載完成之后，windows 是無法運行該文件的；
• 需要打開 matlab，進入到該文件所在的路徑，雙擊運行；（注：需要較久的時間下載安裝 alexnet）

1. demo（十一行代碼）

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. 網絡結構

>> 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