Binary Classify a Large Data Set using Deep Learning

Question

I tried training this model: All even integers map to T and all odd ones map to F. I used 100 million training examples with Support Vector Machine as the method yet I got garbage for the test cases. Same with all other methods. What am I doing wrong?

Answer 1

This answer is from @GalAster . I simply translate it.

https://www.zhihu.com/question/364113452/answer/996790012

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End-to-end double-layer LSTM can

Simple network two-layer LSTM

Automatic loss function, automatic parameter tuning algorithm, cross-validation 10% per round

The test set is a string of numbers below 10^12, the accuracy is 100%, perfect

makeRule[i_] := IntegerString[i] -> If[OddQ@i, "Odd", "Even"];
trainingData = Flatten@Table[makeRule[i], {i, 0, 10000}];
net = NetChain[
    {
        UnitVectorLayer[],
        LongShortTermMemoryLayer[40],
        LongShortTermMemoryLayer[20],
        SequenceLastLayer[],
        LinearLayer[2],
        SoftmaxLayer[]
    },
    "Input" -> NetEncoder[{"Characters", {DigitCharacter}}],
    "Output" -> NetDecoder[{"Class", {"Odd", "Even"}}]
]
trained = NetTrain[
    net, trainingData, All,
    MaxTrainingRounds -> 25,
    ValidationSet -> Scaled[0.1],
    TargetDevice -> "GPU"
]
eval = trained["TrainedNet"];
eval["123456789101112131415"]
eval["20200202"]
eval["114514"]
eval["19260817"]

testingData = Table[makeRule[i], {i, RandomInteger[10^12, 10^4]}];
NetMeasurements[trained["TrainedNet"], testingData, "Accuracy"]