static void FourthNN()
{
var r = new Random();
var data = new Tensor((Matrix) new double[, ] {
{ 0, 0 }, { 0, 1 }, { 1, 0 }, { 1, 1 }
}, true);
var target = new Tensor((Matrix) new double[, ] {
{ 0 }, { 1 }, { 0 }, { 1 }
}, true);
var seq = new Sequential();
seq.Layers.Add(new Linear(2, 3, r));
seq.Layers.Add(new Linear(3, 1, r));
var sgd = new StochasticGradientDescent(seq.Parameters, 0.1f);
var mse = new MeanSquaredError();
for (var i = 0; i < 10; i++)
{
var pred = seq.Forward(data);
var loss = mse.Forward(pred, target);
loss.Backward(new Tensor(Matrix.Ones(loss.Data.X, loss.Data.Y)));
sgd.Step();
Console.WriteLine($"Epoch: {i} Loss: {loss}");
}
}
// Start is called before the first frame update
IEnumerator Start()
{
var r = new System.Random(2);
var x = (Matrix) new double[1000, 1];
Matrix.MatrixLoop((i, j) =>
{
x[i, 0] = i;
}, x.X, x.Y);
var y = (Matrix) new double[1000, 1];
Matrix.MatrixLoop((i, j) =>
{
y[i, 0] = i * 12 + 15 + r.Next(10);
}, x.X, x.Y);
// var x = new double[,] { { 0, 0 }, { 0, 1 }, { 1, 0 }, { 1, 1 } };
// var y = new double[,] { { 0 }, { 1 }, { 0 }, { 1 } };
var X = new Tensor(x, true);
var Y = new Tensor(y, true);
var seq = new Sequential();
seq.Layers.Add(new Linear(1, 1, r));
var sgd = new StochasticGradientDescent(seq.Parameters, 0.001);
var mse = new MeanSquaredError();
for (var i = 0; i < 10000; i++)
{
yield return(null);
var pred = seq.Forward(X);
print(pred.Data.Size);
var loss = mse.Forward(pred, Y);
loss.Backward();
sgd.Step();
print($"Epoch: {i} Loss: {loss.Data[0, 0]}");
print(Y);
print(pred);
}
print(seq.Forward(new Tensor(x)));
}
static void Main(string[] args)
{
Operations K = new Operations();
//Load array to the tensor
NDArray x = new NDArray(3, 3);
x.Load(2, 4, 6, 1, 3, 5, 2, 3, 5);
x.Print("Load X Values");
NDArray y = new NDArray(3, 1);
y.Load(20, 15, 15);
y.Print("Load Y Values");
//Create two layers, one with 6 neurons and another with 1
FullyConnected fc1 = new FullyConnected(3, 6, "relu");
FullyConnected fc2 = new FullyConnected(6, 1, "relu");
//Connect input by passing data from one layer to another
fc1.Forward(x);
fc2.Forward(fc1.Output);
var preds = fc2.Output;
preds.Print("Predictions");
//Calculate the mean square error cost between the predicted and expected values
BaseCost cost = new MeanSquaredError();
var costValues = cost.Forward(preds, y);
costValues.Print("MSE Cost");
//Calculate the mean absolute metric value for the predicted vs expected values
BaseMetric metric = new MeanAbsoluteError();
var metricValues = metric.Calculate(preds, y);
metricValues.Print("MAE Metric");
Console.ReadLine();
}
private void Train()
{
var array = new int[totalInputList.Count];
for (int i = 0; i < totalInputList.Count; i++)
{
array[i] = i;
}
System.Array.Sort <int>(array, new System.Comparison <int>(
(i1, i2) => totalInputList[i2].Count.CompareTo(totalInputList[i1].Count)));
var newArray = new int[getFirst];
var regCount = 0;
for (int i = 0; i < getFirst; i++)
{
newArray[i] = i;
regCount += totalOutputList[i].Count;
}
var MatrixX = new double[regCount, 4];
var MatrixY = new double[regCount, 1];
var x = 0;
for (int i = 0; i < newArray.Length; i++)
{
for (int j = 0; j < totalInputList[i].Count; j++)
{
MatrixX[x, 0] = totalInputList[newArray[i]][j][0, 0];
MatrixX[x, 1] = totalInputList[newArray[i]][j][0, 1];
MatrixX[x, 2] = totalInputList[newArray[i]][j][0, 2];
MatrixX[x, 3] = totalInputList[newArray[i]][j][0, 3];
MatrixY[x, 0] = totalOutputList[newArray[i]][j][0, 0];
x++;
}
}
var X = new Tensor(MatrixX, true);
var Y = new Tensor(MatrixY, true);
for (var i = 0; i < fittingEpoch; i++)
{
var pred = seq.Forward(X);
var loss = mse.Forward(pred, Y);
loss.Backward();
sgd.Step();
if (double.IsNaN(loss.Data[0, 0]))
{
Debug.LogError("LOSS IS NAN");
Debug.Break();
}
print("Epoch: " + i + " Loss: " + loss.Data[0, 0]);
}
totalInputList.Clear();
totalOutputList.Clear();
}