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);
}
public Dense(int units, IActivation activation, bool useBias = true, IInitialization weightInitializer = null, IInitialization biasInitializer = null)
{
LayerSize = units;
_activation = activation;
_weightInitializer = weightInitializer ?? Initializations.Zero();
_biasInitializer = biasInitializer ?? Initializations.Zero();
_useBias = useBias;
}
public void TestZeroInitialization()
{
var initialization = Initializations.Zero();
var initializedMatrix = initialization.Initialize(2, 2);
var expectedMatrix = Matrix <double> .Build.Dense(2, 2);
Assert.AreEqual(initializedMatrix, expectedMatrix);
}
public void TestFixedInitialization()
{
var initialization = Initializations.Fixed();
var initializedMatrix = initialization.Initialize(2, 2);
var expectedMatrix = Matrix <double> .Build.DenseOfArray(new double[, ] {
{ 0.11, 0.12 }, { 0.21, 0.22 }
});
Assert.AreEqual(initializedMatrix, expectedMatrix);
}
/// <summary>
/// Create a <see cref="For"/> with the specified <see cref="CodeObject"/> in the body.
/// </summary>
public For(Expression initialization, Expression conditional, Expression iteration)
{
if (initialization != null)
{
Initializations.Add(initialization);
}
Conditional = conditional;
if (iteration != null)
{
Iterations.Add(iteration);
}
}
/// <summary>
/// Create a <see cref="For"/> with the specified <see cref="CodeObject"/> in the body.
/// </summary>
public For(Expression initialization, Expression conditional, Expression iteration, CodeObject body)
: base(body, true)
{
if (initialization != null)
{
Initializations.Add(initialization);
}
Conditional = conditional;
if (iteration != null)
{
Iterations.Add(iteration);
}
}
public void TestRandomNormalInitialization()
{
var initialization = Initializations.RandomNormal();
initialization.SetSeed(0);
var initializedMatrix = initialization.Initialize(2, 2);
var expectedMatrix = Matrix <double> .Build.Random(2, 2, new Normal(new Random(0)));
Console.WriteLine(initializedMatrix);
Console.WriteLine(expectedMatrix);
Assert.AreEqual(initializedMatrix, expectedMatrix);
}
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;
}
public void Initialize(VirtualDataWindowFactoryContext factoryContext)
{
Initializations.Add(factoryContext);
}
