static void ReberPrediction()
{
// generate 500 extended reber grammar training examples
var grammar = new ReberGrammar();
var sequences = grammar.GetExtended(10, 16).Take(500).ToList();
// split the data into training and test sets
var data = ReberGrammar.GetOneHot(sequences).Split(0);
using var lap = BrightWireProvider.CreateLinearAlgebra();
var graph = new GraphFactory(lap);
// binary classification rounds each output to either 0 or 1
var errorMetric = graph.ErrorMetric.BinaryClassification;
// configure the network properties
graph.CurrentPropertySet.Use(graph.GradientDescent.RmsProp).
Use(graph.WeightInitialisation.Xavier);
// create the engine
var trainingData = graph.CreateDataSource(data.Training);
var testData = trainingData.CloneWith(data.Test);
var engine = graph.CreateTrainingEngine(trainingData, learningRate: 0.1f, batchSize: 32);
// build the network
const int HIDDEN_LAYER_SIZE = 32, TRAINING_ITERATIONS = 30;
graph.Connect(engine).AddGru(HIDDEN_LAYER_SIZE).AddFeedForward(engine.DataSource.OutputSize).
Add(graph.SigmoidActivation()).AddBackpropagationThroughTime(errorMetric);
engine.Train(TRAINING_ITERATIONS, testData, errorMetric);
// generate a sample sequence using the learned state transitions
var networkGraph = engine.Graph;
var executionEngine = graph.CreateEngine(networkGraph);
Console.WriteLine("Generating new reber sequences from the observed state probabilities...");
for (var z = 0; z < 3; z++)
{
// prepare the first input
var input = new float[ReberGrammar.Size];
input[ReberGrammar.GetIndex('B')] = 1f;
Console.Write("B");
int index = 0, eCount = 0;
using var executionContext = graph.CreateExecutionContext();
var result = executionEngine.ExecuteSequential(index++, input, executionContext,
MiniBatchSequenceType.SequenceStart);
for (var i = 0; i < 32; i++)
{
var next = result.Output[0].Data.Select((v, j) => ((double)v, j)).
Where(d => d.Item1 >= 0.1f).ToList();
var distribution = new Categorical(next.Select(d => d.Item1).ToArray());
var nextIndex = next[distribution.Sample()].Item2;
Console.Write(ReberGrammar.GetChar(nextIndex));
if (nextIndex == ReberGrammar.GetIndex('E') && ++eCount == 2)
{
break;
}
Array.Clear(input, 0, ReberGrammar.Size);
input[nextIndex] = 1f;
result = executionEngine.ExecuteSequential(index++, input, executionContext,
MiniBatchSequenceType.Standard);
}
Console.WriteLine();
}
}
static void ReberPrediction()
{
var grammar = new ReberGrammar(false);
var sequences = grammar.GetExtended(10).Take(500).ToList();
var data = ReberGrammar.GetOneHot(sequences).Split(0);
using (var lap = BrightWireProvider.CreateLinearAlgebra(false)) {
var graph = new GraphFactory(lap);
var errorMetric = graph.ErrorMetric.BinaryClassification;
var propertySet = graph.CurrentPropertySet
.Use(graph.GradientDescent.RmsProp)
.Use(graph.WeightInitialisation.Xavier)
;
// create the engine
var trainingData = graph.CreateDataSource(data.Training);
var testData = trainingData.CloneWith(data.Test);
var engine = graph.CreateTrainingEngine(trainingData, 0.1f, 32);
// build the network
const int HIDDEN_LAYER_SIZE = 64;
var memory = new float[HIDDEN_LAYER_SIZE];
var network = graph.Connect(engine)
//.AddSimpleRecurrent(graph.ReluActivation(), memory)
.AddGru(memory)
//.AddRan(memory)
//.AddLstm(memory)
.AddFeedForward(engine.DataSource.OutputSize)
.Add(graph.TanhActivation())
.AddBackpropagationThroughTime(errorMetric)
;
engine.Train(30, testData, errorMetric);
// generate sample sequence
var networkGraph = engine.Graph;
using (var executionContext = graph.CreateExecutionContext()) {
var executionEngine = graph.CreateEngine(networkGraph);
Console.WriteLine("Generating a new reber sequence...");
var input = new float[ReberGrammar.Size];
input[ReberGrammar.GetIndex('B')] = 1f;
Console.Write("B");
int index = 0, eCount = 0;
var result = executionEngine.ExecuteSequential(index++, input, executionContext, MiniBatchSequenceType.SequenceStart);
for (var i = 0; i < 32; i++)
{
var nextIndex = result.Output[0].Data
.Select((v, j) => (v, j))
.Where(d => d.Item1 >= 0.5f)
.Select(d => d.Item2)
.Shuffle()
.FirstOrDefault()
;
if (index == 0)
{
break;
}
Console.Write(ReberGrammar.GetChar(nextIndex));
if (nextIndex == ReberGrammar.GetIndex('E') && ++eCount == 2)
{
break;
}
input = new float[ReberGrammar.Size];
input[nextIndex] = 1f;
result = executionEngine.ExecuteSequential(index++, input, executionContext, MiniBatchSequenceType.Standard);
}
Console.WriteLine();
}
}
}