TrainOptions函數用處如下：
options = trainingOptions(solverName)
options = trainingOptions(solverName,Name,Value)
options = trainingOptions('sgdm',...
'LearnRateSchedule','piecewise',...
'LearnRateDropFactor',0.2,...
'LearnRateDropPeriod',5,...
'MaxEpochs',20,...
'MiniBatchSize',64,...
'Plots','training-progress')
具體可以點擊網頁
而損失函數的用處是和最后一層名字相關 原文說明如下：
Training loss, smoothed training loss, and validation loss — The loss on each mini-batch, its smoothed version, and the loss on the validation set, respectively. If the final layer of your network is a classificationLayer, then the loss function is the cross entropy loss. For more information about loss functions for classification and regression problems, see Output Layers.
所以說 所有網絡中最后有一層是classificationLayer的 都是使用cross entropy交叉熵函數作為損失函數的。