public void Validate_IfValid_ShouldDoNothing()
{
var config = new SimpleGradientTrainer {
LearningRate = 0.1, NumEpochs = 100
};
config.Validate();
}
public void Validate_IfNumEpochsIsNotPositive_Throw(int badNumEpochs)
{
var trainer = new SimpleGradientTrainer {
LearningRate = 0.1, NumEpochs = badNumEpochs
};
Action action = () => trainer.Validate();
action.ShouldThrow <NeuralNetworkException>()
.WithMessage($"*Property NumEpochs must be positive; was {badNumEpochs}*");
}
public void Validate_IfLearningRateNotPositive_Throw(double badLearnignRate)
{
var trainer = new SimpleGradientTrainer {
LearningRate = badLearnignRate, NumEpochs = 100
};
Action action = () => trainer.Validate();
action.ShouldThrow <NeuralNetworkException>()
.WithMessage($"*Property LearningRate must be positive; was {badLearnignRate}*");
}
public void Validate_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($"*Property QuadraticRegularization cannot be negative; was {bad}*");
}
public void Validate_IfMomentumNegative_Throw()
{
const double badMomentum = -0.2;
var trainer = new SimpleGradientTrainer {
LearningRate = 0.1, NumEpochs = 100, Momentum = badMomentum
};
Action action = () => trainer.Validate();
action.ShouldThrow <NeuralNetworkException>()
.WithMessage($"*Property Momentum cannot be negative; was {badMomentum}*");
}
private static SimpleGradientTrainer GetSampleTrainer()
{
var trainer = new SimpleGradientTrainer
{
LearningRate = 0.5,
Momentum = 2,
NumEpochs = 1,
QuadraticRegularization = 0.1,
ShouldInitializeWeights = false
};
return(trainer);
}
public static string MakeSimpleGradientTrainer(
[ExcelArgument(Description = "Name of trainer object to create.")] string name,
[ExcelArgument(Description = "Number of backpropagation steps to run. Each step may be on-line or a batch step.")] int numEpochs,
[ExcelArgument(Description = "Impact of each backpropagation step on weight adjustment.")] double learningRate,
[ExcelArgument(Description = "Impact of previous backpropagation stepts each step's adjustment.")] double momentum,
[ExcelArgument(Description = "Higher numbers help with keeping weights from becoming too large.")] double quadraticRegularization,
[ExcelArgument(Description = "Number of training samples to evaluate for each backpropagation step.")] int batchSize,
[ExcelArgument(Description = "Seed for random number generation.")] int seed)
{
var config = new SimpleGradientTrainer
{
NumEpochs = numEpochs,
LearningRate = learningRate,
Momentum = momentum,
QuadraticRegularization = quadraticRegularization,
BatchSize = batchSize,
Seed = seed,
};
config.Validate();
ObjectStore.Add(name, config);
return(name);
}
