public static void SimpleLogisticRegression()
{
//const int N = 8;
//const int D = 5;
//const int P = 3;
//const double learn = 0.001;
const int N = 100;
const int D = 784;
const int P = 10;
const double learn = 0.00005;
var input = Variable <double>();
var label = Variable <double>();
var weights = Parameter(0.01 * RandomUniform <double>(Shape.Create(D, P)));
var pred = Dot(input, weights);
var loss = L2Loss(pred, label);
var ctx = Context.GpuContext(0);
var opt = new GradientDescentOptimizer(ctx, loss, learn);
// set some data
var inputData = new double[N, D];
var matA = new double[D, P];
var matB = new double[N, P];
NormalRandomArray(inputData);
NormalRandomArray(matA);
NormalRandomArray(matB);
var labelData = Dot(inputData, matA).Add(matB.Mul(0.1));
opt.AssignTensor(input, inputData.AsTensor());
opt.AssignTensor(label, labelData.AsTensor());
opt.Initalize();
for (var i = 0; i < 800; ++i)
{
opt.Forward();
opt.Backward();
opt.Optimize();
if (i % 20 == 0)
{
Console.WriteLine($"loss = {opt.GetTensor(loss).ToScalar()}");
}
}
}
