Extracting information from Classify

I have used Mathematica’s automated Classify function:

class = Classify[trainlist, Method -> "NeuralNetwork", 
   PerformanceGoal -> "Quality"];

acc = ClassifierMeasurements[class, testlist];

‘trainlist’ has 720 examples and ‘testlist’ has 180 examples. I get 89% accuracy. Great. But now I would like build the network from scratch so I can understand it better. That turns out to be very difficult since Classify[] is like a black box.

The most information I can get about how the network is built is from these two tables. So I tried to build my own from this information:

net = NetChain[{LinearLayer[50, "Input" -> 251], 
   ElementwiseLayer["SELU"], LinearLayer[50, "Input" -> 50], 
   ElementwiseLayer["SELU"], LinearLayer[9, "Input" -> 50], 
   SoftmaxLayer["Input" -> {9}]}]

netin = NetInitialize[net]

nettrain = 
 NetTrain[netin, trainlist[[1 ;; 576]], Method -> "ADAM", 
  MaxTrainingRounds -> 1000, ValidationSet -> trainlist[[577 ;; 720]]]

In all there are about 15500 parameters (I assume they mean weights). But when I check the accuracy, it’s only 0.1111. I have nine classifications so that means it only does as well as random guessing. Why is it so difficult to build a network that does as well as the automated function?