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the last run.
DeleteOldModel(ModelPath)
# Find and save optimal epochs.
CallBack=CheckPoint(CheckPointName)
# Build DNN regression model.
DNNModel=BuildModel(Normalizer)
DNNModel.summary()
DNNHistory=DNNModel.fit(TrainX,
TrainY,
epochs=FitEpoch,
# batch_size=BatchSize,
verbose=1,
callbacks=CallBack,
validation_split=ValFrac)
# Draw error image.
LossPlot(DNNHistory)
# Optimize the model based on optimal epoch.
DNNModel=BestEpochIntoModel(CheckPointPath,DNNModel)
# Predict test set data.
TestPrediction=DNNModel.predict(TestX).flatten()
# Draw Test image.
TestPlot(TestY,TestPrediction)
# Verify the accuracy and draw error hist image.
AccuracyResult=AccuracyVerification(TestY,TestPrediction)
PearsonR,R2,RMSE=AccuracyResult[0],AccuracyResult[1],AccuracyResult[2]
# Save model and key parameters.
DNNModel.save(ModelPath)
WriteAccuracy(ParameterPath,PearsonR,R2,RMSE,TrainFrac,RandomSeed,CheckPointMethod,
','.join('%s' %i for i in HiddenLayer),RegularizationFactor,
ActivationMethod,','.join('%s' %i for i in DropoutValue),OutputLayerActMethod,
LossMethod,LearnRate,LearnDecay,FitEpoch,BatchSize,ValFrac,BestEpochOptMethod)
??至此，大功告成。
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