public void SingleIterationOfLearnWithActivationFunctionReturnCorrectResult()
{
// Arrange
var activationFunction = ActivationFunction.Sigmoid;
var model = CreateFullyConnectedNeuralNetworkModel(activationFunction);
var batch = CreateBatch();
var expectedResult = new FullyConnectedNeuralNetworkModel
{
ActivationCountsPerLayer = new[] { 4, 3 },
ActivationFunction = activationFunction,
BiasLayers = new[]
{
new[] { -1.2943006116609224, 0.77069126468580773, 0.41209730715748505 }
},
WeightLayers = new[]
{
new[, ]
{
{ -0.80537379400399278, 0.41910423130779234, -0.97124654040321312 },
{ 0.40789139000539582, -0.89706697984869466, 0.46692747025766013 },
{ -0.7938561825726973, -0.74708704168834117, -0.0907913437911862 },
{ 0.536301613044516, 0.1534224913289616, -0.76938811941200691 }
}
}
};
mockExecuter.Setup(m => m.Execute(It.IsAny <FullyConnectedNeuralNetworkModel>(), It.Is <double[]>(it => it.Equals(batch[0].Inputs))))
.Returns <FullyConnectedNeuralNetworkModel, double[]>((arg1, arg2) => new double[][] { arg2, new[] { 0.23878872335077425, 0.41971501950211154, 0.66660062117031826 } });
mockExecuter.Setup(m => m.Execute(It.IsAny <FullyConnectedNeuralNetworkModel>(), It.Is <double[]>(it => it.Equals(batch[1].Inputs))))
.Returns <FullyConnectedNeuralNetworkModel, double[]>((arg1, arg2) => new double[][] { arg2, new[] { 0.51112549346515468, 0.53561880887592039, 0.61608786703901575 } });
mockSigmoidActivationFunction.Setup(m => m.CalculateDerivative(It.IsAny <double>()))
.Returns <double>(arg1 => 0.5 * arg1);
sut.Initialize(model);
// Act
sut.Learn(batch);
// Assert
mockExecuter.Verify(m => m.Execute(It.IsAny <FullyConnectedNeuralNetworkModel>(), It.Is <double[]>(it => it.Equals(batch[0].Inputs))), Times.Once);
mockExecuter.Verify(m => m.Execute(It.IsAny <FullyConnectedNeuralNetworkModel>(), It.Is <double[]>(it => it.Equals(batch[1].Inputs))), Times.Once);
mockSigmoidActivationFunction.Verify(m => m.CalculateDerivative(It.IsAny <double>()), Times.Exactly(model.BiasLayers.Sum(l => l.Length) * batch.Length));
sut.Model.Should().BeEquivalentTo(expectedResult);
}
public void InitializeSetsTheModelCorrectly()
{
// Arrange
var model = new FullyConnectedNeuralNetworkModel();
// Act
sut.Initialize(model);
// Assert
sut.Model.Should().BeSameAs(model);
}