/// <summary>
/// Trains the <paramref name="network"/> using <paramref name="trainingDatasetEntries"/> and return the average training cost of the final iteration.
/// </summary>
/// <param name="network">The neural network that is to be trained.</param>
/// <param name="trainingDatasetEntries">The training iterations to train the network with.</param>
/// <returns>Returns the average training cost of the final iteration.</returns>
private IDFFNeuralNetwork TrainNetwork(IDFFNeuralNetwork network, IList <INetworkTrainingIteration> trainingDatasetEntries)
{
var priorTrainingCost = 999.0;
var trainingIteration = 0;
while (true)
{
trainingDatasetEntries.Shuffle();
// TODO: Implement Clone() method for NeuralNetwork and store the BEST network.
var trainingCost = network.Train(trainingDatasetEntries).Average(i => i.TrainingCost);
Console.WriteLine($"{trainingIteration} : {trainingCost}");
// Check the training cost every 500 iterations to ensure it's continuing to improve
if (trainingIteration % 500 == 0)
{
if ((priorTrainingCost - trainingCost) < .01)
{
break;
}
else
{
priorTrainingCost = trainingCost;
}
}
trainingIteration++;
}
return(network);
}
public void Train_WithNullDataset_ThrowsException()
{
// Act
_network.Train(null);
}