/// <summary>
/// Cycles through each of the <paramref name="trainingDatasetEntries"/> and applies the inputs to the <paramref name="network"/>. Afterwards, the <paramref name="network"/> outputs are
/// tested against the expected values from the <paramref name="trainingDatasetEntries"/> to determine if each of the outputs is within the allowed
/// TRAINING_FAULT_TOLERANCE level.
/// </summary>
/// <param name="network">The network that is being tested against.</param>
/// <param name="trainingDatasetEntries">The training entries that test whether the network is trained or not.</param>
/// <returns>Returns true/false indicating the the <paramref name="network"/> is trained according to the <paramref name="trainingDatasetEntries"/>.</returns>
private bool GetIsNetworkTrained(IDFFNeuralNetwork network, IEnumerable <INetworkTrainingIteration> trainingDatasetEntries)
{
foreach (var entry in trainingDatasetEntries)
{
var networkOutputs = network.ApplyInputs(entry.Inputs);
using (var networkOutputsEnumerator = networkOutputs.GetEnumerator())
using (var trainingOutputsEnumerator = entry.Outputs.GetEnumerator())
{
while (networkOutputsEnumerator.MoveNext() && trainingOutputsEnumerator.MoveNext())
{
var networkOutput = networkOutputsEnumerator.Current;
var trainingOutput = trainingOutputsEnumerator.Current;
// If the network output-activation level is {TRAINING_ERROR_ALLOWANCE} away from the expected activation level
// for each output of each dataset entry, the network is considered trained.
if (Math.Abs(trainingOutput.ExpectedActivationLevel - networkOutput.ActivationLevel) > TRAINING_ERROR_ALLOWANCE)
{
return(false);
}
}
}
}
return(true);
}
/// <summary>
/// Fetches and applies the necessary network inputs to the network based on the identified network configuration.
/// </summary>
/// <param name="network">The neural network that the inputs will be applied to.</param>
/// <param name="networkConfig">The NetworkConfiguration to pull inputs from.</param>
/// <param name="companyId">The id of the company that the inputs are chosen for.</param>
/// <param name="testDate">The date of the calculated inputs that will be applied to the <paramref name="network"/>.</param>
/// <returns>Returns the network outputs after the config inputs have been applied.</returns>
private IEnumerable <INetworkOutput> ApplyConfigInputsToNetwork(IDFFNeuralNetwork network, N networkConfig, Q quote)
{
var networkInputLayer = network.Layers?.OfType <IInputLayer>().First();
var inputNeurons = networkInputLayer.Neurons?.OfType <IInputNeuron>().ToList();
var networkInputs = _datasetService.GetNetworkInputs(quote.Id);
MapInputsToInputNeurons(inputNeurons, networkInputs);
// Apply inputs to network and returns network outputs.
var resultingNetworkOutputs = network.ApplyInputs(networkInputs).ToList();
var networkOutputLayer = network.Layers?.OfType <IOutputLayer>().First();
var outputNeurons = networkOutputLayer.Neurons?.OfType <IOutputNeuron>();
return(MapOutputNeuronsToOutputs(outputNeurons, resultingNetworkOutputs));
}
public void ApplyInputs_WithValidNetwork_AssignsActivationLevelsToInputNeurons()
{
// Arrange
_network = new DFFNeuralNetwork(1, 1, 2, 2);
var inputs = new List <INetworkInput>()
{
new NetworkInput()
{
ActivationLevel = .75
}
};
var inputNeuron = _network.Layers.OfType <IInputLayer>().First().Neurons.First();
// Act
_network.ApplyInputs(inputs);
// Assert
Assert.IsTrue(inputNeuron.ActivationLevel == .75);
}