public void NetworkLockedExceptionInit()
{
var net = new Sequential(new QuadraticCost(), new NesterovMomentum(0.03));
net.CreateLayer(new Dense(3, new Tanh()));
net.CreateLayer(new Output(1, new Tanh()));
net.InitNetwork();
net.InitNetwork();
}
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");
}
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");
}
