static void Main(string[] args)
{
WriteLine("Execution begins");
var fn = @"c:\DEMO\Data\train.csv";
var f = File.ReadLines(fn);
var data = from t in f.Skip(1)
let zz = t.Split(',').Select(float.Parse)
select new Digit
{
Label = (int)zz.First(),
Image = zz.Skip(1).Select(x => x / 256f).ToArray()
};
var train = data.Take(40000).ToArray();
var test = data.Skip(40000).Take(1000).ToArray();
WriteLine("Creating network");
DeviceDescriptor device = DeviceDescriptor.CPUDevice;
int inputDim = 784;
int outputDim = 10;
var inputShape = new NDShape(1, inputDim);
var outputShape = new NDShape(1, outputDim);
Variable features = Variable.InputVariable(inputShape, DataType.Float);
Variable label = Variable.InputVariable(outputShape, DataType.Float);
var W = new Parameter(new [] { outputDim, inputDim }, DataType.Float, CNTKLib.GlorotUniformInitializer(), device, "w");
var b = new Parameter(new [] { outputDim }, DataType.Float, 0, device, "b");
var z = CNTKLib.Times(W, features) + b;
var loss = CNTKLib.CrossEntropyWithSoftmax(z, label);
var evalError = CNTKLib.ClassificationError(z, label);
CNTK.TrainingParameterScheduleDouble learningRatePerSample = new CNTK.TrainingParameterScheduleDouble(0.02, 1);
IList <Learner> parameterLearners =
new List <Learner>()
{
Learner.SGDLearner(z.Parameters(), learningRatePerSample)
};
var trainer = Trainer.CreateTrainer(z, loss, evalError, parameterLearners);
int minibatchSize = 64;
int numMinibatchesToTrain = 500;
var feat = new BatchSource <float[]>((from x in train select x.Image).ToArray(), minibatchSize);
var labl = new BatchSource <float[]>((from x in train select x.Label.ToOneHot10(10).ToFloatArray()).ToArray(), minibatchSize);
var gr = new List <float>();
// train the model
for (int ep = 0; ep < numMinibatchesToTrain; ep++)
{
Value ft, lb;
feat.MoveNext(); labl.MoveNext();
ft = Value.CreateBatchOfSequences <float>(inputShape, feat.Current, device);
lb = Value.CreateBatchOfSequences <float>(outputShape, labl.Current, device);
trainer.TrainMinibatch(
new Dictionary <Variable, Value>()
{
{ features, ft }, { label, lb }
}, device);
if (ep % 50 == 0)
{
var _loss = trainer.PreviousMinibatchLossAverage();
var _eval = trainer.PreviousMinibatchEvaluationAverage();
WriteLine($"Epoch={ep}, loss={_loss}, eval={_eval}");
gr.Add((float)_loss);
}
}
var G = new GraphLib();
G.Plot(gr, System.Windows.Forms.DataVisualization.Charting.SeriesChartType.Line);
int count = 0, correct = 0;
// Test the model
foreach (var x in test)
{
var imap = new Dictionary <Variable, Value> {
{ features, Value.CreateBatch(inputShape, x.Image, device) }
};
var omap = new Dictionary <Variable, Value> {
{ z, null }
};
z.Evaluate(imap, omap, device);
var o = omap[z].GetDenseData <float>(z).First();
var res = o.MaxIndex();
WriteLine("{0} => {1}", x.Label, res);
if (x.Label == res)
{
correct++;
}
count++;
}
WriteLine("Done, {0} of {1} correct ({2}%)", correct, count, (double)correct / (double)count * 100);
Console.ReadKey();
}
public void Main()
{
WriteLine("Starting");
var G = new GraphLib();
WriteLine("Generating sample data");
// Создаём случайные точки двух типов
float cx1 = -1.0f, cy1 = -1.0f;
float cx2 = 1.0f, cy2 = 1.0f;
var Rnd = new Random();
var x = new List <float>();
var y = new List <float>();
var lab = new List <float>();
var lab_oh = new List <float[]>();
for (int i = 0; i < 200; i++)
{
x.Add(cx1 + Rnd.Next(-2.0, 2.0)); y.Add(cy1 + Rnd.Next(-2.0, 2.0));
lab.Add(-1.0f); lab_oh.Add(new float[] { 1.0f, 0.0f });
x.Add(cx2 + Rnd.Next(-2.0, 2.0)); y.Add(cy2 + Rnd.Next(-2.0, 2.0));
lab.Add(1.0f); lab_oh.Add(new float[] { 0.0f, 1.0f });
}
G.Plot(x, y);
WriteLine("Doing data split");
var x_train = x.Take(150).ToArray();
var y_train = y.Take(150).ToArray();
var l_train = lab.Take(150).ToArray();
var l_oh_train = lab_oh.Take(150).ToArray();
var x_test = x.Skip(150).ToArray();
var y_test = y.Skip(150).ToArray();
var l_test = lab.Skip(150).ToArray();
var l_oh_test = lab_oh.Skip(150).ToArray();
WriteLine("Creating network");
DeviceDescriptor device = DeviceDescriptor.CPUDevice;
// Create network model
int inputDim = 2;
int outputDim = 2;
Variable features = Variable.InputVariable(inputDim.AsArray(), DataType.Float);
Variable label = Variable.InputVariable(outputDim.AsArray(), DataType.Float);
var W = new Parameter(new int[] { outputDim, inputDim }, DataType.Float, 1, device, "w");
var b = new Parameter(new int[] { outputDim }, DataType.Float, 0, device, "b");
var z = CNTKLib.Times(W, features) + b;
var loss = CNTKLib.CrossEntropyWithSoftmax(z, label);
var evalError = CNTKLib.ClassificationError(z, label);
// prepare for training
CNTK.TrainingParameterScheduleDouble learningRatePerSample = new CNTK.TrainingParameterScheduleDouble(0.02, 1);
IList <Learner> parameterLearners =
new List <Learner>()
{
Learner.SGDLearner(z.Parameters(), learningRatePerSample)
};
var trainer = Trainer.CreateTrainer(z, loss, evalError, parameterLearners);
int minibatchSize = 64;
int numMinibatchesToTrain = 1000;
int k = 0; // current position in dataset
// train the model
for (int ep = 0; ep < numMinibatchesToTrain; ep++)
{
Value f, l;
var fa = new float[minibatchSize * inputDim];
var la = new float[minibatchSize * outputDim];
for (int j = 0; j < minibatchSize; j++)
{
fa[j * inputDim] = x_train[k];
fa[j * inputDim + 1] = y_train[k];
la[j * outputDim] = l_oh_train[k][0];
la[j * outputDim + 1] = l_oh_train[k][1];
k++;
if (k == x_train.Length)
{
k = 0;
}
}
f = Value.CreateBatch <float>(inputDim.AsArray(), fa, device);
l = Value.CreateBatch <float>(outputDim.AsArray(), la, device);
trainer.TrainMinibatch(
new Dictionary <Variable, Value>()
{
{ features, f }, { label, l }
}, device);
if (ep % 50 == 0)
{
var _loss = trainer.PreviousMinibatchLossAverage();
var _eval = trainer.PreviousMinibatchEvaluationAverage();
WriteLine($"Epoch={ep}, loss={_loss}, eval={_eval}");
}
}
WriteLine("Press any key to exit");
Console.ReadKey();
}