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。
转载于:https://www.cnblogs.com/Caelum/p/9240568.html
相关资源：JAVA上百实例源码以及开源项目