private void metroButton16_Click(object sender, EventArgs e)
{
base.Hide();
TrainerBase ug = new TrainerBase();
ug.Show();
}
public ConvNetSharpNetwork()
{
network = new Net <double>();
network.AddLayer(new InputLayer(50, 52, 1));
network.AddLayer(new ConvLayer(3, 3, 8)
{
Stride = 1, Pad = 2
});
network.AddLayer(new ReluLayer());
network.AddLayer(new PoolLayer(3, 3)
{
Stride = 2
});
network.AddLayer(new ConvLayer(3, 3, 16)
{
Stride = 1, Pad = 2
});
network.AddLayer(new ReluLayer());
network.AddLayer(new PoolLayer(3, 3)
{
Stride = 2
});
network.AddLayer(new ConvLayer(3, 3, 32)
{
Stride = 1, Pad = 2
});
network.AddLayer(new FullyConnLayer(20));
network.AddLayer(new FullyConnLayer(50));
network.AddLayer(new FullyConnLayer(2));
network.AddLayer(new SoftmaxLayer(2));
trainer = GetTrainerForNetwork(network);
}
public void LoadNetworkFromFile(string filePath)
{
var networkJSON = File.ReadAllText(filePath);
network = SerializationExtensions.FromJson <double>(networkJSON);
trainer = GetTrainerForNetwork(network);
}
static void TrainEvaluatePredict(TrainerBase trainer, BostonHousingData newSample)
{
Console.WriteLine("*******************************");
Console.WriteLine($"{ trainer.Name }");
Console.WriteLine("*******************************");
trainer.Fit("..\\Data\\boston_housing.csv");
var modelMetrics = trainer.Evaluate();
Console.WriteLine($"Loss Function: {modelMetrics.LossFunction:0.##}{Environment.NewLine}" +
$"Mean Absolute Error: {modelMetrics.MeanAbsoluteError:#.##}{Environment.NewLine}" +
$"Mean Squared Error: {modelMetrics.MeanSquaredError:#.##}{Environment.NewLine}" +
$"RSquared: {modelMetrics.RSquared:0.##}{Environment.NewLine}" +
$"Root Mean Squared Error: {modelMetrics.RootMeanSquaredError:#.##}");
trainer.Save();
var predictor = new Predictor();
var prediction = predictor.Predict(newSample);
Console.WriteLine("------------------------------");
Console.WriteLine($"Prediction: {prediction.MedianPrice:#.##}");
Console.WriteLine("------------------------------");
}
public TrainingScheme(INet net, TrainerBase trainer, EntryContainer container, string label)
{
this.container = container;
this.label = label;
this.net = net;
this.trainer = trainer;
int n = container.ClassCount;
}
private static void RegressionUpdate(int n, double[] x, TrainerBase trainer, double[] y)
{
var netx = new Volume(1, 1, 1);
var avloss = 0.0;
for (var iters = 0; iters < 50; iters++)
{
for (var ix = 0; ix < n; ix++)
{
netx.Set(0, 0, 0, x[ix]);
trainer.Train(netx, y[ix]);
avloss += trainer.Loss;
}
}
avloss /= n * 50.0;
Console.WriteLine("Loss:" + avloss);
}
private static void Classify2DUpdate(int n, List <double[]> data, TrainerBase trainer, List <int> labels)
{
var netx = new Volume(1, 1, 2);
var avloss = 0.0;
for (var iters = 0; iters < 50; iters++)
{
for (var ix = 0; ix < n; ix++)
{
netx.Set(0, 0, 0, data[ix][0]);
netx.Set(0, 0, 1, data[ix][1]);
trainer.Train(netx, labels[ix]);
avloss += trainer.Loss;
}
}
avloss /= n * 50.0;
Console.WriteLine("Loss:" + avloss);
}
private static void RegressionUpdate(int n, double[] x, TrainerBase <double> trainer, double[] y)
{
var netx = BuilderInstance.Volume.SameAs(new Shape(1, 1, 1, n));
var nety = BuilderInstance.Volume.SameAs(new Shape(1, 1, 1, n));
var avloss = 0.0;
for (var ix = 0; ix < n; ix++)
{
netx.Set(0, 0, 0, ix, x[ix]);
nety.Set(0, 0, 0, ix, y[ix]);
}
for (var iters = 0; iters < 50; iters++)
{
trainer.Train(netx, nety);
avloss += trainer.Loss;
}
avloss /= n * 50.0;
Console.WriteLine("Loss:" + avloss);
}
private static void Classify2DUpdate(int n, List <double[]> data, TrainerBase <double> trainer, List <int> labels)
{
var avloss = 0.0;
//var netx = new Volume(new double[2], new Shape(1, 1, 2, 1));
//for (var iters = 0; iters < 50; iters++)
//{
// for (var ix = 0; ix < n; ix++)
// {
// var hotLabels = new Volume(new double[2], new Shape(1, 1, 2, 1));
// hotLabels.Set(0, 0, labels[ix], 0, 1.0);
// netx.Set(0, 0, 0, data[ix][0]);
// netx.Set(0, 0, 1, data[ix][1]);
// trainer.Train(netx, hotLabels);
// avloss += trainer.Loss;
// }
//}
var netx = new Volume(new double[2 * n], new Shape(1, 1, 2, n));
var hotLabels = new Volume(new double[2 * n], new Shape(1, 1, 2, n));
for (var ix = 0; ix < n; ix++)
{
hotLabels.Set(0, 0, labels[ix], ix, 1.0);
netx.Set(0, 0, 0, ix, data[ix][0]);
netx.Set(0, 0, 1, ix, data[ix][1]);
}
for (var iters = 0; iters < 50; iters++)
{
trainer.Train(netx, hotLabels);
avloss += trainer.Loss;
}
avloss /= 50.0;
Console.WriteLine("Loss:" + avloss);
}
private static void Classify2DUpdate(int n, List <double[]> data, TrainerBase <double> trainer, List <int> labels)
{
var avloss = 0.0;
var netx = BuilderInstance.Volume.SameAs(new Shape(1, 1, 2, n));
var hotLabels = BuilderInstance.Volume.SameAs(new Shape(1, 1, 2, n));
for (var ix = 0; ix < n; ix++)
{
hotLabels.Set(0, 0, labels[ix], ix, 1.0);
netx.Set(0, 0, 0, ix, data[ix][0]);
netx.Set(0, 0, 1, ix, data[ix][1]);
}
for (var iters = 0; iters < 50; iters++)
{
trainer.Train(netx, hotLabels);
avloss += trainer.Loss;
}
avloss /= 50.0;
Console.WriteLine(k++ + " Loss:" + avloss);
}
public ConsoleRunner(TrainerBase <T, TOptions, TReport, TSession> trainer, NeuralNet <T> network)
{
_trainer = trainer;
_network = network;
}
