public void RecurrentAddition() { var trainingSet = BinaryIntegers.Addition(10, false).Select(l => l.ToArray()).ToList(); const int HIDDEN_SIZE = 16, NUM_EPOCHS = 100, BATCH_SIZE = 32; var errorMetric = ErrorMetricType.BinaryClassification.Create(); var layerTemplate = new LayerDescriptor(0.1f) { Activation = ActivationType.LeakyRelu, WeightInitialisation = WeightInitialisationType.Gaussian, DecayRate = 0.99f }; var recurrentTemplate = layerTemplate.Clone(); recurrentTemplate.WeightInitialisation = WeightInitialisationType.Gaussian; var trainingDataProvider = _lap.NN.CreateSequentialTrainingDataProvider(trainingSet); var layers = new INeuralNetworkRecurrentLayer[] { _lap.NN.CreateSimpleRecurrentLayer(trainingDataProvider.InputSize, HIDDEN_SIZE, recurrentTemplate), _lap.NN.CreateFeedForwardRecurrentLayer(HIDDEN_SIZE, trainingDataProvider.OutputSize, layerTemplate) }; RecurrentNetwork networkData = null; using (var trainer = _lap.NN.CreateRecurrentBatchTrainer(layers)) { var memory = Enumerable.Range(0, HIDDEN_SIZE).Select(i => 0f).ToArray(); var trainingContext = _lap.NN.CreateTrainingContext(errorMetric, 0.1f, BATCH_SIZE); trainingContext.RecurrentEpochComplete += (tc, rtc) => { Debug.WriteLine(tc.LastTrainingError); }; trainer.Train(trainingDataProvider, memory, NUM_EPOCHS, _lap.NN.CreateRecurrentTrainingContext(trainingContext)); networkData = trainer.NetworkInfo; networkData.Memory = new FloatArray { Data = memory }; } var network = _lap.NN.CreateRecurrent(networkData); foreach (var sequence in trainingSet) { var result = network.Execute(sequence.Select(d => d.Input).ToList()); } }
public static void IntegerAddition() { // generate 1000 random integer additions var dataSet = BinaryIntegers.Addition(1000, false) .Select(l => l.ToArray()) .ToList() ; // split the numbers into training and test sets int split = Convert.ToInt32(dataSet.Count * 0.8); var trainingData = dataSet.Take(split).ToList(); var testData = dataSet.Skip(split).ToList(); // neural network hyper parameters const int HIDDEN_SIZE = 32, NUM_EPOCHS = 25, BATCH_SIZE = 16; const float TRAINING_RATE = 0.001f; var errorMetric = ErrorMetricType.BinaryClassification.Create(); var layerTemplate = new LayerDescriptor(0.3f) { Activation = ActivationType.Relu, WeightInitialisation = WeightInitialisationType.Xavier, WeightUpdate = WeightUpdateType.RMSprop }; var recurrentTemplate = layerTemplate.Clone(); recurrentTemplate.WeightInitialisation = WeightInitialisationType.Gaussian; using (var lap = Provider.CreateLinearAlgebra()) { // create training data providers var trainingDataProvider = lap.NN.CreateSequentialTrainingDataProvider(trainingData); var testDataProvider = lap.NN.CreateSequentialTrainingDataProvider(testData); var layers = new INeuralNetworkRecurrentLayer[] { lap.NN.CreateSimpleRecurrentLayer(trainingDataProvider.InputSize, HIDDEN_SIZE, recurrentTemplate), lap.NN.CreateFeedForwardRecurrentLayer(HIDDEN_SIZE, trainingDataProvider.OutputSize, layerTemplate) }; // train the network RecurrentNetwork networkData = null; using (var trainer = lap.NN.CreateRecurrentBatchTrainer(layers)) { var memory = Enumerable.Range(0, HIDDEN_SIZE).Select(i => 0f).ToArray(); var trainingContext = lap.NN.CreateTrainingContext(errorMetric, TRAINING_RATE, BATCH_SIZE); trainingContext.RecurrentEpochComplete += (tc, rtc) => { var testError = trainer.Execute(testDataProvider, memory, rtc).SelectMany(s => s.Select(d => errorMetric.Compute(d.Output, d.ExpectedOutput))).Average(); Console.WriteLine($"Epoch {tc.CurrentEpoch} - score: {testError:P}"); }; trainer.Train(trainingDataProvider, memory, NUM_EPOCHS, lap.NN.CreateRecurrentTrainingContext(trainingContext)); networkData = trainer.NetworkInfo; networkData.Memory = new FloatArray { Data = memory }; } // evaluate the network on some freshly generated data var network = lap.NN.CreateRecurrent(networkData); foreach (var sequence in BinaryIntegers.Addition(8, true)) { var result = network.Execute(sequence.Select(d => d.Input).ToList()); Console.Write("First: "); foreach (var item in sequence) { _WriteBinary(item.Input[0]); } Console.WriteLine(); Console.Write("Second: "); foreach (var item in sequence) { _WriteBinary(item.Input[1]); } Console.WriteLine(); Console.WriteLine(" --------------------------------"); Console.Write("Expected: "); foreach (var item in sequence) { _WriteBinary(item.Output[0]); } Console.WriteLine(); Console.Write("Predicted: "); foreach (var item in result) { _WriteBinary(item.Output[0]); } Console.WriteLine(); Console.WriteLine(); } } }
public Bidirectional(INeuralNetworkRecurrentLayer forward, INeuralNetworkRecurrentLayer backward = null) { _forward = forward; _backward = backward; }
public INeuralNetworkBidirectionalLayer CreateBidirectionalLayer(INeuralNetworkRecurrentLayer forward, INeuralNetworkRecurrentLayer backward = null) { return(new Training.Layer.Recurrent.Bidirectional(forward, backward)); }