public void When_querying_the_network_after_training_for_the_XOR_problem_it_should_return_the_correct_output()
{
// Arrange
const int numberOfEpochs = 10000;
const double learningRate = 0.3;
const double momentum = 0.1;
var network = new NeuralNetworkBuilder()
.Using(new XORNetworkLayout())
.Build();
network.Train(new[]
{
new TrainingExample(new[] { 0.0, 0.0 }, new[] { 0.0 }),
new TrainingExample(new[] { 0.0, 1.0 }, new[] { 1.0 }),
new TrainingExample(new[] { 1.0, 0.0 }, new[] { 1.0 }),
new TrainingExample(new[] { 1.0, 1.0 }, new[] { 0.0 })
}, numberOfEpochs, learningRate, momentum);
// Act
double output_0_0 = network.Query(new[] { 0.0, 0.0 }).Single();
double output_0_1 = network.Query(new[] { 0.0, 1.0 }).Single();
double output_1_0 = network.Query(new[] { 1.0, 0.0 }).Single();
double output_1_1 = network.Query(new[] { 1.0, 1.0 }).Single();
// Assert
output_0_0.Should().BeApproximately(0.0, 0.05);
output_0_1.Should().BeApproximately(1.0, 0.05);
output_1_0.Should().BeApproximately(1.0, 0.05);
output_1_1.Should().BeApproximately(0.0, 0.05);
}
public void When_querying_the_network_after_training_it_should_yield_the_correct_output()
{
// Arrange
var network = new NeuralNetworkBuilder()
.Using(new XORNetworkLayout())
.Build();
network.Train(new[]
{
new TrainingExample(new[] { 0.0, 0.0 }, new[] { 0.0 }),
new TrainingExample(new[] { 0.0, 1.0 }, new[] { 1.0 }),
new TrainingExample(new[] { 1.0, 0.0 }, new[] { 1.0 }),
new TrainingExample(new[] { 1.0, 1.0 }, new[] { 0.0 })
}, 10000, 0.3, 0.1);
// Act
double output_0_0 = network.Query(new[] { 0.0, 0.0 }).Single();
double output_0_1 = network.Query(new[] { 0.0, 1.0 }).Single();
double output_1_0 = network.Query(new[] { 1.0, 0.0 }).Single();
double output_1_1 = network.Query(new[] { 1.0, 1.0 }).Single();
// Assert
output_0_0.Should().BeApproximately(0.0, 0.05);
output_0_1.Should().BeApproximately(1.0, 0.05);
output_1_0.Should().BeApproximately(1.0, 0.05);
output_1_1.Should().BeApproximately(0.0, 0.05);
}
public void When_querying_a_network_that_has_known_weights_and_biases_it_should_yield_the_expected_output()
{
// Arrange
var network = new NeuralNetworkBuilder()
.Using(new TwoLayerNetworkProvider())
.Build();
// Act
double[] networkOutput = network.Query(new[] { 1.0, -2.0, 3.0 });
// Assert
networkOutput[0].Should().BeApproximately(0.5164, 0.00005);
networkOutput[1].Should().BeApproximately(0.5172, 0.00005);
}
public void When_querying_a_network_with_known_weights_and_biases_it_should_return_the_expected_output()
{
// Arrange
var network = new NeuralNetworkBuilder()
.Using(new TwoLayerNetworkProvider())
.Build();
// Act
double[] networkOutput = network.Query(new[] { 0.1, 0.2, 0.3 });
// Assert
networkOutput.Should().HaveCount(2);
networkOutput[0].Should().BeApproximately(0.6918, 0.00005);
networkOutput[1].Should().BeApproximately(0.8596, 0.00005);
}
public void When_querying_the_network_after_training_it_should_return_the_correct_output()
{
// Arrange
const int numberOfEpochs = 20000;
const double learningRate = 0.3;
const double momentum = 0.1;
var network = new NeuralNetworkBuilder()
.Using(new TwoLayerNetworkProvider())
.Build();
network.Train(new[]
{
new TrainingExample(new[] { 0.1, 0.2, 0.3 }, new[] { 1.0, 0.0 })
}, numberOfEpochs, learningRate, momentum);
// Act
double[] output = network.Query(new[] { 0.1, 0.2, 0.3 });
// Assert
output[0].Should().BeApproximately(1.0, 0.005);
output[1].Should().BeApproximately(0.0, 0.005);
}
