public static void XOR()
{
using (var lap = BrightWireProvider.CreateLinearAlgebra()) {
// Create some training data that the network will learn. The XOR pattern looks like:
// 0 0 => 0
// 1 0 => 1
// 0 1 => 1
// 1 1 => 0
var data = Xor.Get();
// create the graph
var graph = new GraphFactory(lap);
var errorMetric = graph.ErrorMetric.CrossEntropy;
graph.CurrentPropertySet
// use rmsprop gradient descent optimisation
.Use(graph.GradientDescent.RmsProp)
// and xavier weight initialisation
.Use(graph.WeightInitialisation.Gaussian)
;
// create the engine
var testData = graph.CreateDataSource(data);
var engine = graph.CreateTrainingEngine(testData, 0.1f, 4);
// create the network
const int HIDDEN_LAYER_SIZE = 6;
graph.Connect(engine)
// create a feed forward layer with sigmoid activation
.AddFeedForward(HIDDEN_LAYER_SIZE)
.Add(graph.SigmoidActivation())
// create a second feed forward layer with sigmoid activation
.AddFeedForward(engine.DataSource.OutputSize)
.Add(graph.SigmoidActivation())
// backpropagate the error signal at the end of the graph
.AddBackpropagation(errorMetric)
;
// train the network
var executionContext = graph.CreateExecutionContext();
for (var i = 0; i < 1000; i++)
{
var trainingError = engine.Train(executionContext);
if (i % 100 == 0)
{
engine.Test(testData, errorMetric);
}
}
engine.Test(testData, errorMetric);
// create a new network to execute the learned network
var networkGraph = engine.Graph;
var executionEngine = graph.CreateEngine(networkGraph);
var output = executionEngine.Execute(testData);
Console.WriteLine(output.Average(o => o.CalculateError(errorMetric)));
// print the learnt values
foreach (var item in output)
{
foreach (var index in item.MiniBatchSequence.MiniBatch.Rows)
{
var row = data.GetRow(index);
var result = item.Output[index];
Console.WriteLine($"{row.GetField<int>(0)} XOR {row.GetField<int>(1)} = {result.Data[0]}");
}
}
}
}
protected override void OnCreate(Bundle savedInstanceState)
{
base.OnCreate(savedInstanceState);
var sb = new StringBuilder();
using (var lap = BrightWireProvider.CreateLinearAlgebra()) {
// Create some training data that the network will learn. The XOR pattern looks like:
// 0 0 => 0
// 1 0 => 1
// 0 1 => 1
// 1 1 => 0
var data = Xor.Get();
// create the graph
var graph = new GraphFactory(lap);
var errorMetric = graph.ErrorMetric.CrossEntropy;
graph.CurrentPropertySet
// use rmsprop gradient descent optimisation
.Use(graph.GradientDescent.RmsProp)
// and xavier weight initialisation
.Use(graph.WeightInitialisation.Gaussian)
;
// create the engine
var testData = graph.CreateDataSource(data);
var engine = graph.CreateTrainingEngine(testData, 0.1f, 4);
// create the network
const int HIDDEN_LAYER_SIZE = 6;
graph.Connect(engine)
// create a feed forward layer with sigmoid activation
.AddFeedForward(HIDDEN_LAYER_SIZE)
.Add(graph.SigmoidActivation())
// create a second feed forward layer with sigmoid activation
.AddFeedForward(engine.DataSource.OutputSize)
.Add(graph.SigmoidActivation())
// backpropagate the error signal at the end of the graph
.AddBackpropagation(errorMetric)
;
// train the network
var executionContext = graph.CreateExecutionContext();
for (var i = 0; i < 1000; i++)
{
var trainingError = engine.Train(executionContext);
if (i % 100 == 0)
{
engine.Test(testData, errorMetric);
}
}
engine.Test(testData, errorMetric);
// create a new network to execute the learned network
var networkGraph = engine.Graph;
var executionEngine = graph.CreateEngine(networkGraph);
var output = executionEngine.Execute(testData);
// print the learnt values
foreach (var item in output)
{
foreach (var index in item.MiniBatchSequence.MiniBatch.Rows)
{
var row = data.GetRow(index);
var result = item.Output[index];
sb.AppendLine($"{row.GetField<int>(0)} XOR {row.GetField<int>(1)} = {result.Data[0]}");
}
}
}
// Set our view from the "main" layout resource
SetContentView(Resource.Layout.Main);
var outputText = FindViewById <TextView>(Resource.Id.outputText);
outputText.Text = sb.ToString();
}
public void Xor_RegisterOperands_ResultIsCorrect()
{
var prev = new MutableState().Set(Register.A, 6).Set(Register.B, 5);
var state = new Xor(new Reg(Register.A), new Reg(Register.B)).Apply(prev);
Assert.AreEqual(0x3, state.Get(Register.A));
}
