private static void DQN(IEnumerable <BatchInputWrapper> trainData, IEnumerable <BatchInputWrapper> cvData)
{
using (MathOperationManager mathManager = new MathOperationManager(MathType.GPU))
{
var hiddenLayers = new List <int>();
hiddenLayers.Add(10);
hiddenLayers.Add(5);
DQNNeuralNetworkConfiguration config = new DQNNeuralNetworkConfiguration(5, hiddenLayers, 12);
config.LossFunction = LossFunctionType.BellmanError;
config.Epochs = 20;
config.StepSize = (float)0.1;
using (DQN nn = new DQN(mathManager, config))
{
nn.MiniBatchStochasticGradientDescent(trainData, cvData);
}
}
}
private static void DQN(IEnumerable<BatchInputWrapper> trainData, IEnumerable<BatchInputWrapper> cvData)
{
using (MathOperationManager mathManager = new MathOperationManager(MathType.GPU))
{
var hiddenLayers = new List<int>();
hiddenLayers.Add(10);
hiddenLayers.Add(5);
DQNNeuralNetworkConfiguration config = new DQNNeuralNetworkConfiguration(5, hiddenLayers, 12);
config.LossFunction = LossFunctionType.BellmanError;
config.Epochs = 20;
config.StepSize = (float)0.1;
using (DQN nn = new DQN(mathManager, config))
{
nn.MiniBatchStochasticGradientDescent(trainData, cvData);
}
}
}
