public void CreateAndCompileModel3()
{
Console.WriteLine("Creating model");
GlobalRandom.InitializeRandom();
int imgSize = 75;
ReluActivation reluActivation = new ReluActivation();
SoftmaxActivation softmaxActivation = new SoftmaxActivation();
model = new ConvolutionalNeuralNetwork(imgSize, "rgb");
model.Add(new ConvolutionalLayer(5, 5, reluActivation, "valid"));
model.Add(new MaxPoolingLayer());
model.Add(new ConvolutionalLayer(5, 3, reluActivation, "valid"));
model.Add(new MaxPoolingLayer());
model.Add(new DropoutLayer(0.2));
model.Add(new FlattenLayer());
model.Add(new DropoutLayer(0.5));
model.Add(new DenseLayer(26, softmaxActivation));
Console.WriteLine("Model created");
model.Compile();
Console.WriteLine("Model compiled");
}
public void ReluActivation_Derivative()
{
var actual = Matrix <float> .Build.Random(5, 5, 23);
var sut = new ReluActivation();
sut.Derivative(actual.Data(), actual.Data());
var expected = Matrix <float> .Build.Dense(5, 5, new float[] { 1f, 1f, 1f, 0f, 1f, 1f, 1f, 1f, 0f, 0f, 0f, 0f, 0f, 0f, 1f, 0f, 0f, 0f, 1f, 0f, 1f, 0f, 1f, 1f, 1 });
Assert.AreEqual(expected.ToString(), actual.ToString());
}
public void ReluActivation_Activiation()
{
var actual = Matrix <float> .Build.Random(5, 5, 23);
var sut = new ReluActivation();
sut.Activation(actual.Data());
var expected = Matrix <float> .Build.Dense(5, 5, new float[] { 1.602584f, 0.4367149f, 1.206761f, 0f, 0.9932324f, 1.745592f, 0.6927068f, 0.546773f, 0f, 0f, 0f, 0f, 0f, 0f, 0.2088782f, 0f, 0f, 0f, 1.082098f, 0f, 0.2817991f, 0f, 0.1463978f, 0.3077979f, 0.8454311f });
Assert.AreEqual(expected.ToString(), actual.ToString());
}