public void TrainXOR()
{
try {
//Load train data
float[,] testX = new float[, ] {
{ 0, 1 },
};
float[,] x = new float[, ] {
{ 0, 0 }, { 0, 1 }, { 1, 0 }, { 1, 1 }
};
float[] y = new float[] { 0, 1, 1, 0 };
//Build sequential model
var model = new Sequential();
model.Add(new Dense(32, activation: "relu", input_shape: new Shape(2)));
model.Add(new Dense(32, activation: "relu"));
model.Add(new Dropout(0.1d));
model.Add(new Dense(1, activation: "sigmoid"));
//Compile and train
var optimizer = new Adam();
model.Compile(optimizer: optimizer, loss: "mse", metrics: new string[] { "accuracy" });
model.Fit(x, y, batch_size: 2, epochs: 1000, verbose: 1);
float[] predicts;
predicts = model.Predict(x).GetData <float>();
predicts = model.PredictOnBatch(x).GetData <float>();
predicts = model.Predict(x).GetData <float>();
predicts = model.PredictOnBatch(x).GetData <float>();
predicts = model.Predict(x).GetData <float>();
predicts = model.PredictOnBatch(x).GetData <float>();
Stopwatch watch = new Stopwatch();
watch.Restart();
for (int i = 0; i < 5; ++i)
{
predicts = model.PredictOnBatch(testX).GetData <float>();
}
watch.Stop();
string batchMs = watch.GetElapsedMilliseconds().ToString();
watch.Restart();
for (int i = 0; i < 5; ++i)
{
predicts = model.Predict(testX).GetData <float>();
}
watch.Stop();
//MainWindow.Instance.Dispatcher.BeginInvoke(new Action(() => {
// MainWindow.Instance.DebugTextBox.Text = batchMs + " / " + watch.GetElapsedMilliseconds().ToString();
//}));
} catch (Exception ex) {
//MainWindow.Instance.Dispatcher.BeginInvoke(new Action(() => {
// MainWindow.Instance.DebugTextBox.Text = ex.ToString();
//}));
}
}
static void Main(string[] args)
{
SaveRateToFileTrainData("USD");
SaveRateToFileTestData("USD");
Global.UseEngine(SiaNet.Backend.ArrayFire.SiaNetBackend.Instance, DeviceType.CUDA, true);
var train = PreparingExchangeRateData.LoadTrain();
var test = PreparingExchangeRateData.LoadTest();
var model = new Sequential();
model.EpochEnd += Model_EpochEnd;
model.Add(new Dense(60, ActType.Sigmoid));
model.Add(new Dense(60, ActType.Sigmoid));
model.Add(new Dense(1, ActType.Linear));
//Compile with Optimizer, Loss and Metric
model.Compile(OptimizerType.SGD, LossType.MeanSquaredError, MetricType.MSE);
// Train for 1000 epoch with batch size of 2
model.Train(train, epochs: 1000, batchSize: 32);
//Create prediction data to evaluate
DataFrame2D predX = new DataFrame2D(2);
predX.Load(0, 0, 0, 1, 1, 0, 1, 1); //Result should be 0, 1, 1, 0
var rawPred = model.Predict(test);
Console.ReadLine();
}
static void Main(string[] args)
{
//Setup Engine
Global.UseEngine(SiaNet.Backend.MxNetLib.SiaNetBackend.Instance, DeviceType.CPU);
//Prep Data
var(x, y) = PrepDataset();
x.Head();
DataFrameIter trainSet = new DataFrameIter(x, y);
//Build model with simple fully connected layers
var model = new Sequential();
model.EpochEnd += Model_EpochEnd;
model.Add(new Dense(64, ActType.ReLU));
model.Add(new Dense(1, ActType.Sigmoid));
//Compile with Optimizer, Loss and Metric
model.Compile(OptimizerType.SGD, LossType.MeanSquaredError, MetricType.BinaryAccurary);
// Train for 100 epoch with batch size of 2
model.Train(trainSet, 1000, 2);
//Create prediction data to evaluate
DataFrame2D predX = new DataFrame2D(2);
predX.Load(0, 0, 0, 1); //Result should be 0 and 1
var rawPred = model.Predict(predX);
Console.ReadLine();
}
public NDarray Predict(string imgPath)
{
NDarray x = Utils.Normalize(imgPath);
x = x.reshape(1, x.shape[0], x.shape[1], x.shape[2]);
return(_model.Predict(x));
}
static (NDarray, NDarray) GenerateFakeGeneratorSamples(Sequential generatorModel, int latentDim, int sampleCount)
{
var xInput = GenerateLatentPoints(latentDim, sampleCount);
var x = generatorModel.Predict(xInput);
var y = np.zeros(new int[] { sampleCount, 1 });
return(x, y);
}
/// <summary>
/// Teaches an agent whose model is represented by a direct distribution network (non-recurrent). Used when the model operates only with the current state of the environment, not taking into account previous states.
/// </summary>
/// <param name="agent">Agent for training, a network of a given architecture</param>
/// <param name="iterationCount">Number of learning iterations (eras)</param>
/// <param name="rolloutCount">The number of runs (in the case of a game - passing the level until the end of the game <seealso cref="Environment.IsTerminated"/> ), which will be completed before the weights are updated.
/// It can be interpreted as the amount of training data for one era.</param>
/// <param name="minibatchSize">Minibatch size for training</param>
/// <param name="actionPerIteration">The arbitrary action that each epoch requires. Allows you to interrupt the training process. Input parameters: era, loss error, evaluation error.
/// Weekend: true - interrupt the training process, false - continue the training.
/// Used for logging, displaying the learning process, saving intermediate model checkpoints, etc.</param>
/// <param name="gamma">Reward attenuation coefficient (reward) when calculating Discounted reward</param>
/// <returns></returns>
public Sequential <T> Teach(Sequential <T> agent, int iterationCount, int rolloutCount, int minibatchSize, Func <int, double, double, bool> actionPerIteration = null, double gamma = 0.99)
{
for (int iteration = 0; iteration < iterationCount; iteration++)
{
var data = new LinkedList <(int rollout, int actionNumber, T[] state, T[] action, T reward)>();
for (int rolloutNumber = 0; rolloutNumber < rolloutCount; rolloutNumber++)
{
int actionNumber = 0;
while (!Environment.IsTerminated)
{
var currentState = Environment.GetCurrentState <T>();
var action = agent.Predict(currentState, Device);
var reward = Environment.PerformAction(action);
data.AddLast((rolloutNumber, ++actionNumber, currentState, action, reward));
}
Environment.Reset();
}
var discountedRewards = new T[data.Count];
foreach (var rollout in data.GroupBy(p => p.rollout))
{
var steps = rollout.ToList();
steps.Sort((a, b) => a.actionNumber > b.actionNumber ? 1 : a.actionNumber < b.actionNumber ? -1 : 0); //ascending actionNumber
for (int i = 0; i < steps.Count; i++)
{
var remainingRewards = steps.GetRange(i, steps.Count - i)
.Select(p => Environment.HasRewardOnlyForRollout ? steps[steps.Count - 1].reward : p.reward)
.ToArray();
discountedRewards[i] = CalculateDiscountedReward(remainingRewards, gamma);
}
}
var features = data.Select(p => p.state);
var labels = data.Zip(discountedRewards, (d, reward) => Multiply(d.action, reward));
var dataset = features.Zip(labels, (f, l) => f.Concat(l).ToArray()).ToArray();
var inputDim = features.FirstOrDefault().Length;
var fitResult = agent.Fit(dataset,
inputDim,
minibatchSize,
GetLoss()[0],
GetEvalLoss()[0],
GetOptimizer()[0],
1,
false,
Device);
data.Clear();
var needStop = actionPerIteration?.Invoke(iteration, fitResult.LossError, fitResult.EvaluationError);
if (needStop.HasValue && needStop.Value)
{
break;
}
}
return(agent);
}
public int Classify(Bitmap bitmap)
{
if (_framework == "keras")
{
var pred = ClassFromNumpy(_model.Predict(np.array(ImagePixels(bitmap))));
return pred;
}
else
{
_trainer.Net.Forward(BuilderInstance.Volume.From(ImagePixels(bitmap), new Shape(80, 60, 1)));
var predictedClass = _trainer.Net.GetPrediction();
return predictedClass[0];
}
}
public static void Run()
{
var(x, y) = LoadTrain();
Sequential model = new Sequential();
model.Add(new Dense(32, activation: SuperNeuro.Engine.ActType.ReLU));
model.Add(new Dense(32, activation: SuperNeuro.Engine.ActType.ReLU));
model.Add(new Dense(1, activation: SuperNeuro.Engine.ActType.Sigmoid));
model.EpochEnd += Model_EpochEnd;
model.Compile(SuperNeuro.Engine.OptimizerType.Adam, SuperNeuro.Engine.LossType.BinaryCrossEntropy, SuperNeuro.Engine.MetricType.BinaryAccurary);
model.Train(new SuperNeuro.Data.DataFrameIter(x, y), 100, 32);
var test = model.Predict(LoadTest());
test.Head();
}
static void Main(string[] args)
{
//Setup Engine
Global.UseEngine(SiaNet.Backend.ArrayFire.SiaNetBackend.Instance, DeviceType.Default);
var train = LoadTrain(); //Load train data
var test = LoadTest(); //Load test data
var model = new Sequential();
model.EpochEnd += Model_EpochEnd;
model.Add(new Dense(128, ActType.ReLU));
model.Add(new Dense(64, ActType.ReLU));
model.Add(new Dense(1, ActType.Sigmoid));
//Compile with Optimizer, Loss and Metric
model.Compile(OptimizerType.Adam, LossType.BinaryCrossEntropy, MetricType.BinaryAccurary);
// Perform training with train and val dataset
model.Train(train, epochs: 100, batchSize: 200);
var prediction = model.Predict(test);
}
/// <summary>
/// Teaches an agent whose model is represented by a recurrent network. It is used when the model operates with a chain of environmental states.
/// </summary>
/// <param name="agent">Agent for training, a network of a given architecture</param>
/// <param name="iterationCount">Number of learning iterations (eras)</param>
/// <param name="rolloutCount">The number of runs (in the case of a game - passing the level until the end of the game <seealso cref="Environment.IsTerminated"/> ), which will be completed before the weights are updated.
/// It can be interpreted as the amount of training data for one era.</param>
/// <param name="minibatchSize">Minibatch size for training</param>
/// <param name="sequenceLength">Sequence length: a chain of previous state environments on each action.</param>
/// <param name="actionPerIteration">The arbitrary action that each epoch requires. Allows you to interrupt the training process. Input parameters: era, loss error, evaluation error.
/// Weekend: true - interrupt the training process, false - continue the training.
/// Used for logging, displaying the learning process, saving intermediate model checkpoints, etc.</param>
/// <param name="gamma">Reward attenuation coefficient (reward) when calculating Discounted reward</param>
/// <returns></returns>
public Sequential <T> Teach(Sequential <T> agent, int iterationCount, int rolloutCount, int minibatchSize, int sequenceLength, Func <int, double, double, bool> actionPerIteration = null, double gamma = 0.99)
{
for (int iteration = 0; iteration < iterationCount; iteration++)
{
var data = new List <(int rollout, int actionNumber, T[] state, T[] action, T reward)>();
for (int rolloutNumber = 0; rolloutNumber < rolloutCount; rolloutNumber++)
{
int actionNumber = 0;
while (!Environment.IsTerminated)
{
var currentState = Environment.GetCurrentState <T>();
var sequence = actionNumber < sequenceLength
? data.GetRange(data.Count - actionNumber, actionNumber)
: data.GetRange(data.Count - sequenceLength - 1, sequenceLength - 1);
var sequenceStates = sequence
.Select(p => p.state)
.ToList();
sequenceStates.Add(currentState);
var action = agent.Predict(sequenceStates, Device);
var reward = Environment.PerformAction(action);
data.Add((rolloutNumber, ++actionNumber, currentState, action, reward));
}
Environment.Reset();
}
var discountedRewards = new T[data.Count];
foreach (var rollout in data.GroupBy(p => p.rollout))
{
var steps = rollout.ToList();
for (int i = 0; i < steps.Count; i++)
{
var remainingRewards = steps.GetRange(i, steps.Count - i)
.Select(p => Environment.HasRewardOnlyForRollout ? steps[steps.Count - 1].reward : p.reward)
.ToArray();
discountedRewards[i] = CalculateDiscountedReward(remainingRewards, gamma);
}
}
var features = new List <IList <T[]> >();
var labels = new List <T[]>();
var dataWithDiscountedReward = data.Zip(discountedRewards, (dat, reward) => (dat, reward)).GroupBy(p => p.dat.rollout);
foreach (var rollout in dataWithDiscountedReward)
{
var steps = rollout.ToList();
steps.Sort((a, b) => a.dat.actionNumber > b.dat.actionNumber ? 1 : a.dat.actionNumber < b.dat.actionNumber ? -1 : 0); //ascending actionNumber
for (int i = 0; i < steps.Count; i++)
{
if (i < sequenceLength)
{
features.Add(steps.GetRange(0, i + 1).Select(p => p.dat.state).ToArray());
}
else
{
features.Add(steps.GetRange(i - sequenceLength, sequenceLength).Select(p => p.dat.state).ToArray());
}
labels.Add(Multiply(steps[i].dat.action, steps[i].reward));
}
}
var fitResult = agent.Fit(features,
labels,
minibatchSize,
GetLoss()[0],
GetEvalLoss()[0],
GetOptimizer()[0],
1,
Device);
data.Clear();
var needStop = actionPerIteration?.Invoke(iteration, fitResult.LossError, fitResult.EvaluationError);
if (needStop.HasValue && needStop.Value)
{
break;
}
}
return(agent);
}
