public static void TestOpenCLTrainingWithConfig(IErrorFunction errorFunc, TrainingSuite.TrainingConfig.Regularization regularization, float regularizationLambda, float learningRate)
{
List <int> layerConfig = new List <int>();
layerConfig.Add(10);
layerConfig.Add(512);
layerConfig.Add(12);
layerConfig.Add(3);
layerConfig.Add(51);
layerConfig.Add(30);
Network networkReference = Network.CreateNetworkInitRandom(layerConfig.ToArray(), new SigmoidActivation());
var jsonData = networkReference.ExportToJSON();
Network networkCpuTrained = Network.CreateNetworkFromJSON(jsonData);
Network networkOpenCLTrained = Network.CreateNetworkFromJSON(jsonData);
var cpuCalculator = ComputeDeviceFactory.CreateFallbackComputeDevice();
var openCLCalculator = GetFirstOpenCLDevice();
var rnd = new Random();
List <TrainingSuite.TrainingData> trainingData = new List <TrainingSuite.TrainingData>();
for (int i = 0; i < 1000; i++)
{
float[] input = new float[layerConfig[0]];
float[] output = new float[layerConfig[layerConfig.Count - 1]];
var idx = rnd.Next(0, input.Length);
input[rnd.Next(0, input.Length)] = 1.0f;
for (int j = 0; j < 10; j++)
{
output[j * 3 + 0] = idx * 0.1f;
output[j * 3 + 1] = 1.0f - (idx * 0.1f);
output[j * 3 + 2] = idx * 0.05f;
}
trainingData.Add(new TrainingSuite.TrainingData(input, output));
}
TrainingSuite suite = new TrainingSuite(trainingData);
suite.config.epochs = 1;
suite.config.shuffleTrainingData = false;
suite.config.miniBatchSize = 13;
suite.config.costFunction = errorFunc;
suite.config.regularization = regularization;
suite.config.regularizationLambda = regularizationLambda;
suite.config.learningRate = learningRate;
var promise1 = networkCpuTrained.Train(suite, cpuCalculator);
var promise2 = networkOpenCLTrained.Train(suite, openCLCalculator);
promise1.Await();
promise2.Await();
Assert.IsTrue(promise1.IsReady() && promise2.IsReady());
float[] testInput = new float[layerConfig[0]];
var cpuTrainedOutput = networkCpuTrained.Compute(testInput, cpuCalculator);
var openCLTrainedOutput = networkOpenCLTrained.Compute(testInput, cpuCalculator);
CheckNetworkError(cpuTrainedOutput, openCLTrainedOutput);
}
public static void TestTraining(Network network, float[] referenceOutput, IErrorFunction errorFunc, TrainingSuite.TrainingConfig.Regularization regularization, float regularizationLambda, float learningRate)
{
List <int> layerConfig = new List <int>();
layerConfig.Add(5);
layerConfig.Add(33);
layerConfig.Add(12);
layerConfig.Add(51);
layerConfig.Add(5);
#region Training
List <TrainingSuite.TrainingData> trainingData = new List <TrainingSuite.TrainingData>();
for (int i = 0; i < 1000; i++)
{
float[] input = new float[layerConfig[0]];
float[] desiredOutput = new float[layerConfig[layerConfig.Count - 1]];
input[(i * 13426) % 5] = 1.0f;
desiredOutput[(i * 13426) % 5] = 1.0f;
trainingData.Add(new TrainingSuite.TrainingData(input, desiredOutput));
}
TrainingSuite suite = new TrainingSuite(trainingData);
suite.config.epochs = 2;
suite.config.shuffleTrainingData = false;
suite.config.miniBatchSize = 13;
suite.config.costFunction = errorFunc;
suite.config.regularization = regularization;
suite.config.regularizationLambda = regularizationLambda;
suite.config.learningRate = learningRate;
var promise = network.Train(suite, ComputeDeviceFactory.CreateFallbackComputeDevice());
promise.Await();
#endregion
float[] testInput = new float[] { 0.3f, 0.4f, 0.6f, 0.1f, 0.5f };
var result = network.Compute(testInput, ComputeDeviceFactory.CreateFallbackComputeDevice());
Utils.CheckNetworkError(referenceOutput, result);
}
