TrainOptions函数用处如下：
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')
具体可以点击网页
而损失函数的用处是和最后一层名字相关 原文说明如下：
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.
所以说 所有网络中最后有一层是classificationLayer的 都是使用cross entropy交叉熵函数作为损失函数的。