public void LogisticRegression()
{
var X = Matrix <double> .Build.DenseOfArray(new double[, ] {
{ 1 }, { 2 }
});
var Y = Matrix <double> .Build.DenseOfArray(new double[, ] {
{ 1 }
});
var model = new DeepCat.DeepCat();
model.Add(new Dense(1, Activations.Sigmoid(), weightInitializer: Initializations.Fixed()));
model.Compile(X.RowCount, LossFunctions.CrossEntropy(), Optimizers.GradientDescent(0.02));
model.Fit(X, Y, 1);
var a = model.Predict(X);
a[0, 0] = Math.Round(a[0, 0], 8);
var expectedResult = Matrix <double> .Build.DenseOfArray(new double[, ] {
{ 0.59859297 }
});
Assert.AreEqual(a, expectedResult);
}
static void Main(string[] args)
{
var X = Matrix <double> .Build.Random(5, 100);
var Y = Matrix <double> .Build.Random(1, 100);
var test = Matrix <double> .Build.Random(5, 1);
var model = new DeepCat();
model.Add(new Dense(5, Activations.Relu(), weightInitializer: Initializations.RandomNormal()));
model.Add(new Dense(5, Activations.Relu(), weightInitializer: Initializations.RandomNormal()));
model.Add(new Dense(1, Activations.Sigmoid()));
model.Compile(X.RowCount, LossFunctions.CrossEntropy(), Optimizers.GradientDescent(0.002));
model.Fit(X, Y, 100);
model.Predict(test);
var x = 1;
}
