linfam=NetInitialize[LinearLayer[9,"Input"->16]];​​famNet=NetChain[{linfam,SoftmaxLayer[]}];​​trainingdatafam={{1.170257379182556`,0.8834800056332996`,-1.8880447863270255`,1.7383080896524223`,0.8541655906922596`,-1.9001396949705738`,1.2523122147743493`,-1.3560268699842641`,-1.2098043477355072`,-1.7649789916289649`,-0.3498844228769027`,1.373553046598066`,-0.01475918494208539`,0.5780407607895546`,0.23229752072848786`,-0.6405965608551494`}{0.009011841640902891`,0.7612245040392964`,0.08053001026135634`,0.0316771294453021`,0.10223535908072512`,0.007826905942510036`,0.0014664568001033702`,0.0016700044455327`,0.004357788344271065`},{1.1626658151383369`,1.628863047357016`,0.33000095351321246`,0.7235765020706708`,-0.6458716911669997`,-1.3273231288684153`,0.9334774670736019`,-1.654368994361037`,-1.596099508963544`,-0.1326712734471327`,-1.2731785809118872`,0.27199406428857387`,-1.829943867356878`,-0.727436404726955`,1.754074239183951`,0.4823567966087854`}{0.19565731967495337`,0.0067116291709780895`,0.03151031854938959`,0.13159053401739096`,0.046363543251618136`,0.019420760159229573`,0.17229987462992097`,0.09759464753753087`,0.29885137300898845`},{0.9118484603267802`,-1.6933518707622932`,1.7004203017294568`,-0.22211856674624197`,-1.5819070686210273`,-1.9732771933437308`,0.959053008859871`,-1.6178422504643377`,-1.2916773418665723`,-1.5507134873845478`,-0.26072346331771`,1.7527346479580412`,1.5559104364379852`,0.6283660844941377`,0.7665025478049792`,-1.1198610230355968`}{0.05035986870689777`,0.02359541207413506`,0.7473542364096804`,0.04284044574731035`,0.07321710764915496`,0.028799035579096046`,0.02082153216768656`,0.007860901511417676`,0.005151460154621098`},{1.556184186842386`,0.5592338450347496`,-1.1710945588106796`,1.1630759191575963`,-1.5273715963225047`,1.2261398262762562`,-1.4677154523465639`,-0.43469621680493997`,-1.7340232992942077`,-0.36203638845865527`,0.8374072982447744`,0.7018128772397185`,-1.3887777155183514`,1.5148597900487726`,1.9270770406431668`,1.0634844974210491`}{0.01997601633446644`,0.019772433364016944`,0.06012125987708762`,0.013507297619945461`,0.029903866993918726`,0.004279657365862622`,0.08855150680345733`,0.16479558889445195`,0.5990923727467927`},{0.6489614551993323`,0.6215145419846646`,0.21401068388040745`,0.12625948339415416`,-1.5951747111601122`,-0.24613799225813437`,0.7800105950979423`,0.46618532215181574`,1.527966824071842`,1.361755760652148`,-0.20945020753647192`,1.0181934555133019`,0.5779603127317179`,0.10813874399151313`,-0.2592557771454862`,1.5804149524849942`}{0.021544031042071452`,0.010220643321522496`,0.13003891743006907`,0.021942223256673735`,0.6337970972157173`,0.02831112402294383`,0.0135773044889661`,0.1249098753633126`,0.01565878385872343`}};​​famTrained=NetTrain[famNet,trainingdatafam];
NetTrain
:Batch #1 will be skipped, because one or or more inputs provided to port Output was invalid: input is not an integer between 1 and 9. This batch will be ignored in subsequent training rounds. More information can be obtained via the SkippedTrainingData property.
In[]:=
famTrained=NetTrain[famNet,trainingdatafam,CrossEntropyLossLayer["Probabilities"]]
NetTrain
:Property specification CrossEntropyLossLayer[TypeCrossEntropyLoss,3,OutputsLoss22[1],1] is not All, a valid property, or a list of valid properties. Valid properties include all keys available to TrainingProgressFunction.
Out[]=
$Failed