private static SimpleGradientTrainer GetSampleTrainer()
{
var trainer = new SimpleGradientTrainer
{
LearningRate = 0.5,
Momentum = 2,
NumEpochs = 1,
QuadraticRegularization = 0.1,
ShouldInitializeWeights = false
};
return trainer;
}
public void Validate_IfQuadraticRegularizationNegative_Throw()
{
const double bad = -0.1;
var trainer = new SimpleGradientTrainer { LearningRate = 0.1, NumEpochs = 100, QuadraticRegularization = bad };
Action action = () => trainer.Validate();
action.ShouldThrow<NeuralNetworkException>()
.WithMessage($"*Property QuadraticRegularization cannot be negative; was {bad}*");
}
public void Validate_IfNumEpochsIsNotPositive_Throw(int badNumEpochs)
{
var trainer = new SimpleGradientTrainer { LearningRate = 0.1, NumEpochs = badNumEpochs };
Action action = () => trainer.Validate();
action.ShouldThrow<NeuralNetworkException>()
.WithMessage($"*Property NumEpochs must be positive; was {badNumEpochs}*");
}
public void Validate_IfMomentumNegative_Throw()
{
const double badMomentum = -0.2;
var trainer = new SimpleGradientTrainer { LearningRate = 0.1, NumEpochs = 100, Momentum = badMomentum };
Action action = () => trainer.Validate();
action.ShouldThrow<NeuralNetworkException>()
.WithMessage($"*Property Momentum cannot be negative; was {badMomentum}*");
}
public void Validate_IfLearningRateNotPositive_Throw(double badLearnignRate)
{
var trainer = new SimpleGradientTrainer { LearningRate = badLearnignRate, NumEpochs = 100 };
Action action = () => trainer.Validate();
action.ShouldThrow<NeuralNetworkException>()
.WithMessage($"*Property LearningRate must be positive; was {badLearnignRate}*");
}
public void Validate_IfValid_ShouldDoNothing()
{
var config = new SimpleGradientTrainer { LearningRate = 0.1, NumEpochs = 100 };
config.Validate();
}
