var layerConfig = new int[]{ 784, 32, 32, 10 };
var network = Network.CreateNetworkInitRandom( layerConfig, new SigmoidActivation() );

string jsonData = "{ ... }";
var network = Network.CreateNetworkFromJSON( jsonData ); 

string json = network.ExportToJSON();

float[] input = { ... };
ComputeDevice computeDevice = ComputeDeviceFactory.GetFirstOpenCLDevice(); //Get an OpenCL device
float[] results = network.Compute( input, computeDevice );

ComputeDevice computeDevice = ComputeDeviceFactory.GetFirstOpenCLDevice(); //Get an OpenCL device
List<TrainingSuite.TrainingData> trainingData = new List<TrainingSuite.TrainingData>();

//Set up training examples
for (int i = 0; i < 10000; ++i)
{
    float[] trainingInput = ...;
    float[] desiredOutput = ...;
    trainingData.Add( new TrainingSuite.TrainingData( trainingInput, desiredOutput ) );
}

TrainingSuite trainingSuite = new TrainingSuite( trainingData );

//Set up training configuration
trainingSuite.config.epochs = 100;
trainingSuite.config.shuffleTrainingData = true;
trainingSuite.config.miniBatchSize = 50;
trainingSuite.config.learningRate = 0.005f;
trainingSuite.config.costFunction = new CrossEntropyErrorFunction();
trainingSuite.config.regularization = TrainingSuite.TrainingConfig.Regularization.L2;
trainingSuite.config.regularizationLambda = 0.5f;

var trainingPromise = network.Train( trainingSuite, computeDevice );

trainingPromise.Await();