public static string MakeUntilDoneGradientTrainer(
[ExcelArgument(Description = "Name of trainer object to create.")] string name,
[ExcelArgument(Description = "Maximum 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 = "Portion of the training set to use as validation set to check whether " +
"training has improved performance of the neural network. " +
"Must be between 0 and 1.")] double validationSetFraction,
[ExcelArgument(Description = "Training will abort after there is no error improvement on validation set after " +
"this number of backpropagation steps.")] int maxEpochsWithoutImprovement,
[ExcelArgument(Description = "Number of backpropagation steps before checking for error improvement on " +
"validation set.")] int epochsBetweenValidations,
[ExcelArgument(Description = "Seed for random number generation.")] int seed)
{
var config = new UntilDoneGradientTrainer
{
NumEpochs = numEpochs,
LearningRate = learningRate,
Momentum = momentum,
QuadraticRegularization = quadraticRegularization,
BatchSize = batchSize,
ValidationSetFraction = validationSetFraction,
MaxEpochsWithoutImprovement = maxEpochsWithoutImprovement,
EpochsBetweenValidations = epochsBetweenValidations,
Seed = seed,
};
config.Validate();
ObjectStore.Add(name, config);
return(name);
}
public void ShouldReturnValidationSetFraction()
{
var trainer = new UntilDoneGradientTrainer {
ValidationSetFraction = 0.3
};
trainer.GetValidationSetFraction().Should().Be(0.3);
}
public ValidateTests()
{
_trainer = new UntilDoneGradientTrainer
{
LearningRate = 0.1,
NumEpochs = 100,
ValidationSetFraction = 0.3,
MaxEpochsWithoutImprovement = 10,
};
}
private static UntilDoneGradientTrainer GetTrainer()
{
var trainer = new UntilDoneGradientTrainer
{
LearningRate = 0.5,
Momentum = 2,
NumEpochs = 1,
QuadraticRegularization = 0.1,
ShouldInitializeWeights = false,
MaxEpochsWithoutImprovement = 100,
ValidationSetFraction = 0.5
};
return(trainer);
}
