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MATLAB预训练好的Alexnet各层如下：

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 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 0 0]

6 'conv2' Convolution 256 5x5x48 convolutions with stride [1 1] and padding [2 2 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 0 0]

10 'conv3' Convolution 384 3x3x256 convolutions with stride [1 1] and padding [1 1 1 1]

11 'relu3' ReLU ReLU

12 'conv4' Convolution 384 3x3x192 convolutions with stride [1 1] and padding [1 1 1 1]

13 'relu4' ReLU ReLU

14 'conv5' Convolution 256 3x3x192 convolutions with stride [1 1] and padding [1 1 1 1]

15 'relu5' ReLU ReLU

16 'pool5' Max Pooling 3x3 max pooling with stride [2 2] and padding [0 0 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' and 999 other classes

你需要用MATLAB自带的深度学习工具箱函数创建和上面各层一致的CNN框架，然后用你的数据训练网络，可以参考：https://ww2.mathworks.cn/help/deeplearning/examples/create-simple-deep-learning-network-for-classification.html