| public static GetSample1HiddenLayerPerceptron ( ) : MultilayerPerceptron | ||
| return | MultilayerPerceptron | |
public void FeedForward_IfInputLengthDoesNotMatchNumInputs_Throw()
{
var nn = SampleInputs.GetSample1HiddenLayerPerceptron();
Action action = () => nn.FeedForward(new[] { 0.0 });
action.ShouldThrow <NeuralNetworkException>()
.WithMessage($"*Argument 'inputs' should have width {nn.NumInputs}; was 1.*");
}
public void IfTargetLengthDoesNotMatchNumOutputs_Throw()
{
var nn = SampleInputs.GetSample1HiddenLayerPerceptron();
Action action = () => nn.CalculateGradients(SampleInputs.GetSampleInputs(), new[] { 0.0 });
action.ShouldThrow <NeuralNetworkException>()
.WithMessage($"*Argument 'targets' should have width {nn.NumOutputs}; was 1.*");
}
public void ShouldIncludeInputsWithBias()
{
var inputs = SampleInputs.GetSampleInputs();
var nn = SampleInputs.GetSample1HiddenLayerPerceptron();
var result = nn.FeedForward(inputs);
var expected = new double[inputs.Length + 1];
Array.Copy(inputs, expected, inputs.Length);
expected[inputs.Length] = 1;
result.InputWithBias.Should().Equal(expected);
}
public void ShouldCalculateCorrectly_OneHiddenLayer()
{
var nn = SampleInputs.GetSample1HiddenLayerPerceptron();
var result = nn.FeedForward(SampleInputs.GetSampleInputs());
result.HiddenLayers.Should().HaveCount(1);
var expectedHidden = new[] { 0.336375544336332, 0.347853774202261, 0.359228665810271, 1.0 };
result.HiddenLayers[0].ShouldApproximatelyEqual(expectedHidden, 1e-12);
var expectedOutput = new[] { 0.449090580148409, 0.550909419851591 };
result.Output.ShouldApproximatelyEqual(expectedOutput, 1e-12);
}
public void ShouldCalculateGrads_OneHiddenLayerCase()
{
// See MultilayerPerceptronTests.xlsx in this folder for calculations of
// expected outputs.
var nn = SampleInputs.GetSample1HiddenLayerPerceptron();
var result = nn.CalculateGradients(SampleInputs.GetSampleInputs(), SampleInputs.GetSampleTargets());
result.Should().NotBeNullOrEmpty();
result.Should().HaveCount(2);
var expectedHiddenGrads = new[] { 0.00488574841598145, 0.00977149683196289, 0.0488574841598144, 0.00484248141480251, 0.00968496282960501, 0.0484248141480251, 0.00479817184666825, 0.0095963436933365, 0.0479817184666825 };
var expectedOutputGrads = new[] { -0.185312455982592, -0.191635920938954, -0.197902455875597, -0.550909419851591, 0.185312455982592, 0.191635920938954, 0.197902455875597, 0.550909419851591 };
result[1].ShouldApproximatelyEqual(expectedOutputGrads, 1e-12);
result[0].ShouldApproximatelyEqual(expectedHiddenGrads, 1e-12);
}