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();
}
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();
}
public static void Run()
{
var(x, y) = LoadTrain();
var test = LoadTest();
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));
model.EpochEnd += Model_EpochEnd;
model.Compile(SuperNeuro.Engine.OptimizerType.Adam, SuperNeuro.Engine.LossType.MeanSquaredError, SuperNeuro.Engine.MetricType.MAE);
model.Train(new SuperNeuro.Data.DataFrameIter(x, y), 100, 32);
}
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();
}
private static void RunTest()
{
Random Rnd = new Random();
DataFrame trnX_fin = new DataFrame();
DataFrame trnY_fin = new DataFrame();
for (int cc = 0; (cc < 100); cc++)
{
float[] sngLst = new float[100];
for (int indx = 0; (indx < 100); indx++)
{
sngLst[indx] = (float)Rnd.NextDouble();
}
trnX_fin.Add(sngLst);
}
for (int cc = 0; (cc < 100); cc++)
{
float[] sngLst = new float[3];
// fake one hot just for check
sngLst[0] = 0;
sngLst[1] = 1;
sngLst[2] = 0;
trnY_fin.Add(sngLst);
}
XYFrame XYfrm = new XYFrame();
XYfrm.XFrame = trnX_fin;
XYfrm.YFrame = trnY_fin;
// Split
TrainTestFrame trainTestFrame = XYfrm.SplitTrainTest(0.3);
// init some values
int shape_of_input = XYfrm.XFrame.Shape[1];
int embval = 100;
int seed = 2;
Sequential model = new Sequential();
model.Add(new Reshape(Shape.Create(1, embval), Shape.Create(shape_of_input)));
model.Add(new LSTM(64, returnSequence: false, cellDim: 4, weightInitializer: new SiaNet.Model.Initializers.GlorotUniform(0.05, seed), recurrentInitializer: new SiaNet.Model.Initializers.GlorotUniform(0.05, seed), biasInitializer: new SiaNet.Model.Initializers.GlorotUniform(0.05, seed)));
model.Add(new Dense(3, act: "sigmoid", useBias: true, weightInitializer: new SiaNet.Model.Initializers.GlorotUniform(0.05, seed)));
model.Compile(OptOptimizers.Adam, OptLosses.MeanSquaredError, OptMetrics.Accuracy);
model.Train(trainTestFrame.Train, 200, 8, trainTestFrame.Test);
}
public void ConvolutionalMnist()
{
// Load Data
var lines = File.ReadAllLines("..\\..\\..\\..\\..\\datasets\\mnist_train.csv").ToList();
var mnistLables = new List <Matrix>();
var mnistData = new List <Matrix>();
for (var j = 1; j < lines.Count; j++)
{
var t = lines[j];
var data = t.Split(',').ToList();
mnistLables.Add(new Matrix(1, 1).Fill(int.Parse(data[0])));
var mnist = new Matrix(784, 1);
for (var i = 1; i < data.Count; i++)
{
mnist[i - 1, 0] = double.Parse(data[i]);
}
mnistData.Add(mnist);
}
// Create Network
var net = new Sequential(new QuadraticCost(), new NesterovMomentum(0.03));
net.CreateLayer(new Convolutional(784, new Average(), new Tanh()));
net.CreateLayer(new Convolutional(100, new Average(), new Tanh()));
net.CreateLayer(new Dense(100, new Tanh()));
net.CreateLayer(new Output(10, new Softmax()));
net.InitNetwork();
// Train Network
for (var i = 0; i < 800; i++)
{
net.Train(mnistData[i % mnistData.Count], mnistLables[i % mnistData.Count]);
}
// Write Acc Result
// Trace.WriteLine(" Metrics Accuracy: " + acc);
// Assert.IsTrue(acc > 80.0, "Network did not learn MNIST");
}
static void Main(string[] args)
{
Global.UseGpu();
Tensor x = Tensor.FromArray(Global.Device, new float[] { 1, 2, 3, 4, 5, 6, 7, 8, 9 });
x = x.Reshape(3, 3);
var result = TOps.Diag(x);
result.Print();
string datasetFolder = @"C:\dataset\MNIST";
bool useDenseModel = false;
var((trainX, trainY), (valX, valY)) = MNISTParser.LoadDataSet(datasetFolder, trainCount: 60000, testCount: 10000, flatten: useDenseModel);
Console.WriteLine("Train and Test data loaded");
DataFrameIter trainIter = new DataFrameIter(trainX, trainY);
DataFrameIter valIter = new DataFrameIter(valX, valY);
Sequential model = null;
if (useDenseModel)
{
model = BuildFCModel();
}
else
{
model = BuildConvModel();
}
model.Compile(OptimizerType.Adam, LossType.CategorialCrossEntropy, MetricType.Accuracy);
Console.WriteLine("Model compiled.. initiating training");
model.EpochEnd += Model_EpochEnd;
model.Train(trainIter, 10, 32, valIter);
Console.ReadLine();
}
static void Main(string[] args)
{
//Setup Engine
Global.UseEngine(SiaNet.Backend.ArrayFire.SiaNetBackend.Instance, DeviceType.CPU);
//Prep Data
var(x, y) = PrepDataset();
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(100, ActType.ReLU));
model.Add(new Dense(50, ActType.ReLU));
model.Add(new Dense(1, ActType.Sigmoid));
//Compile with Optimizer, Loss and Metric
model.Compile(OptimizerType.Adam, LossType.MeanSquaredError, MetricType.MAE);
// Train for 100 epoch with batch size of 2
model.Train(trainSet, 25, 2);
}
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);
}
static void Main(string[] args)
{
//Setup Engine
Global.UseEngine(SiaNet.Backend.ArrayFire.SiaNetBackend.Instance, DeviceType.CPU);
//Load Train and Test CSV data
var ds = LoadTrain("./train.csv");
//Build Model
var model = new Sequential();
model.EpochEnd += Model_EpochEnd;
model.Add(new Dense(64, activation: ActType.ReLU));
model.Add(new Dense(32, activation: ActType.ReLU));
model.Add(new Dense(1, activation: ActType.Sigmoid));
//Compile with Optimizer, Loss and Metric
model.Compile(OptimizerType.Adam, LossType.MeanSquaredError, MetricType.MAE);
// Train for 100 epoch with batch size of 32
model.Train(ds, 25, 32);
Console.ReadLine();
}
public static void Train()
{
model.Compile(OptOptimizers.Adam, OptLosses.MeanSquaredError, OptMetrics.MSLE);
model.Train(traintest.Train, 500, 32, traintest.Test);
}
public static void Train()
{
//model.Compile(OptOptimizers.SGD, OptLosses.CrossEntropy, OptMetrics.Accuracy);
model.Compile(new SGD(0.01), OptLosses.CrossEntropy, OptMetrics.Accuracy, Regulizers.RegL2(0.01));
model.Train(train, 25, 64, null);
}
public static void Train()
{
model.Compile(OptOptimizers.SGD, OptLosses.CrossEntropy, OptMetrics.Accuracy);
model.Train(trainData, 100, 2);
}
public static void Train()
{
//model.Compile(OptOptimizers.SGD, OptLosses.CrossEntropy, OptMetrics.Accuracy);
model.Compile(new SGD(0.003125), OptLosses.CrossEntropy, OptMetrics.Accuracy);
model.Train(train, 5, 32, null);
}
public static void Train()
{
model.Compile(OptOptimizers.Adam, OptLosses.MeanSquaredError, OptMetrics.MSE);
model.Train(train, 10, 64);
}
public void AdaDeltaTest()
{
var net = new Sequential(new QuadraticCost(), new AdaDelta(0.03));
net.CreateLayer(new Dense(3, new Tanh()));
net.CreateLayer(new Dense(3, new Tanh()));
net.CreateLayer(new Dense(3, new Tanh()));
net.CreateLayer(new Output(1, new Tanh()));
net.InitNetwork();
var inputs = new List <Matrix>();
var outputs = new List <Matrix>();
// 0 0 0 => 0
inputs.Add(new Matrix(new[, ] {
{ 0.0 }, { 0.0 }, { 0.0 }
}));
outputs.Add(new Matrix(new[, ] {
{ 0.0 }
}));
// 0 0 1 => 1
inputs.Add(new Matrix(new[, ] {
{ 0.0 }, { 0.0 }, { 1.0 }
}));
outputs.Add(new Matrix(new[, ] {
{ 1.0 }
}));
// 0 1 0 => 1
inputs.Add(new Matrix(new[, ] {
{ 0.0 }, { 1.0 }, { 0.0 }
}));
outputs.Add(new Matrix(new[, ] {
{ 1.0 }
}));
// 0 1 1 => 0
inputs.Add(new Matrix(new[, ] {
{ 0.0 }, { 1.0 }, { 1.0 }
}));
outputs.Add(new Matrix(new[, ] {
{ 1.0 }
}));
// 1 0 0 => 1
inputs.Add(new Matrix(new[, ] {
{ 1.0 }, { 0.0 }, { 0.0 }
}));
outputs.Add(new Matrix(new[, ] {
{ 1.0 }
}));
// 1 0 1 => 0
inputs.Add(new Matrix(new[, ] {
{ 1.0 }, { 0.0 }, { 1.0 }
}));
outputs.Add(new Matrix(new[, ] {
{ 0.0 }
}));
// 1 1 0 => 0
inputs.Add(new Matrix(new[, ] {
{ 1.0 }, { 1.0 }, { 0.0 }
}));
outputs.Add(new Matrix(new[, ] {
{ 0.0 }
}));
// 1 1 1 => 1
inputs.Add(new Matrix(new[, ] {
{ 1.0 }, { 1.0 }, { 1.0 }
}));
outputs.Add(new Matrix(new[, ] {
{ 1.0 }
}));
for (var i = 0; i < 8; i++)
{
net.Train(inputs[i % 8], outputs[i % 8]);
}
var correct = 0;
for (var i = 0; i < 10; i++)
{
correct += Math.Abs(net.Forward(inputs[0])[0, 0]) < 0.1 ? 1 : 0;
correct += Math.Abs(net.Forward(inputs[1])[0, 0]) - 1 < 0.1 ? 1 : 0;
correct += Math.Abs(net.Forward(inputs[2])[0, 0]) - 1 < 0.1 ? 1 : 0;
correct += Math.Abs(net.Forward(inputs[3])[0, 0]) < 0.1 ? 1 : 0;
correct += Math.Abs(net.Forward(inputs[4])[0, 0]) - 1 < 0.1 ? 1 : 0;
correct += Math.Abs(net.Forward(inputs[5])[0, 0]) < 0.1 ? 1 : 0;
correct += Math.Abs(net.Forward(inputs[6])[0, 0]) < 0.1 ? 1 : 0;
correct += Math.Abs(net.Forward(inputs[7])[0, 0]) - 1 < 0.1 ? 1 : 0;
}
var acc = correct / 80.0 * 100.0;
Trace.WriteLine(" Acc: " + acc);
Assert.IsTrue(acc > 80.0, "Network did not learn XOR");
}
public void DenseMnist()
{
// Load Train Data
var lines = File.ReadAllLines("..\\..\\..\\..\\..\\datasets\\mnist_train.csv").ToList();
var mnistLables = new List <Matrix>();
var mnistData = new List <Matrix>();
for (var j = 1; j < lines.Count; j++)
{
var t = lines[j];
var data = t.Split(',').ToList();
mnistLables.Add(new Matrix(10, 1).Fill(0));
mnistLables[j - 1][int.Parse(data[0]), 0] = 1.0;
var mnist = new Matrix(784, 1);
for (var i = 1; i < data.Count; i++)
{
mnist[i - 1, 0] = double.Parse(data[i]);
}
mnistData.Add(mnist);
}
// Load Test Data
var testlines = File.ReadAllLines("..\\..\\..\\..\\..\\datasets\\mnist_test.csv").ToList();
var mnistTestLables = new List <Matrix>();
var mnistTestData = new List <Matrix>();
for (var j = 1; j < testlines.Count; j++)
{
var t = testlines[j];
var data = t.Split(',').ToList();
mnistTestLables.Add(new Matrix(10, 1).Fill(0));
mnistTestLables[j - 1][int.Parse(data[0]), 0] = 1.0;
var mnist = new Matrix(784, 1);
for (var i = 1; i < data.Count; i++)
{
mnist[i - 1, 0] = double.Parse(data[i]);
}
mnistTestData.Add(mnist);
}
// Create Network
var net = new Sequential(new CategoricalCrossEntropy(), new Adam(0.3), null, 128);
net.CreateLayer(new Dense(784, new Relu()));
net.CreateLayer(new Dense(128, new Relu()));
net.CreateLayer(new Output(10, new Softmax()));
net.InitNetwork();
// Train Network
for (var i = 0; i < mnistData.Count / 100; i++)
{
net.Train(mnistData[i % mnistData.Count], mnistLables[i % mnistData.Count]);
}
// Test Network
for (var i = 0; i < mnistTestData.Count / 100; i++)
{
var mat = net.Forward(mnistTestData[i % mnistTestData.Count]);
var matx = mnistTestLables[i % mnistTestData.Count];
}
Trace.WriteLine(" Metrics Accuracy: ");
//Assert.IsTrue(acc > 80.0, "Network did not learn MNIST");
}
