1. 首先，你要又并行计算的工具箱，在插件选项里面找到，安装即可

2. 下载训练的数据集，采用matlab演示的材料即可

https://matlabacademy-content.mathworks.com/3.3/R2017b/content/deeplearning_course_files.zip

3. 运行训练脚本：

flower_ds = imageDatastore('Flowers','IncludeSubfolders',true,'LabelSource','foldernames');
[trainImgs,testImgs] = splitEachLabel(flower_ds,0.6);
numClasses = numel(categories(flower_ds.Labels));

net = alexnet;
layers = net.Layers;
layers(end-2) = fullyConnectedLayer(numClasses);
layers(end) = classificationLayer;

options = trainingOptions('sgdm','InitialLearnRate', 0.001);

[flowernet,info] = trainNetwork(trainImgs, layers, options);

testpreds = classify(flowernet,testImgs);

4. 运行报错，GPU内存不够

设置小一点：options = trainingOptions('sgdm','InitialLearnRate', 0.001,'MiniBatchSize', 64);

options = 


  TrainingOptionsSGDM - 属性:


                     Momentum: 0.9000
             InitialLearnRate: 1.0000e-03
    LearnRateScheduleSettings: [1×1 struct]
             L2Regularization: 1.0000e-04
      GradientThresholdMethod: 'l2norm'
            GradientThreshold: Inf
                    MaxEpochs: 30
                MiniBatchSize: 128
                      Verbose: 1
             VerboseFrequency: 50
               ValidationData: []
          ValidationFrequency: 50
           ValidationPatience: 5
                      Shuffle: 'once'
               CheckpointPath: ''
         ExecutionEnvironment: 'auto'
                   WorkerLoad: []
                    OutputFcn: []
                        Plots: 'none'
               SequenceLength: 'longest'

         SequencePaddingValue: 0

5. 结果