public void Main()
{
WriteLine("Reading data");
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;
// Create network model
int inputDim = 784;
int outputDim = 10;
NDShape inputShape = new int[] { 28, 28, 1 };
var outputShape = new NDShape(1, outputDim);
Variable features = Variable.InputVariable(inputShape, DataType.Float);
Variable label = Variable.InputVariable(outputShape, DataType.Float);
// 28x28x1 -> 14x14x4, window 3x3
var ConvParam1 = new Parameter(new int[] { 3, 3, 1, 4 }, DataType.Float, CNTKLib.GlorotUniformInitializer(0.26, -1, 2), device);
var Layer1 = CNTKLib.ReLU(CNTKLib.Convolution(ConvParam1, features, new int[] { 1, 1, 1 }));
var Layer1Pool = CNTKLib.Pooling(Layer1, PoolingType.Max, new int[] { 3, 3 }, new int[] { 2, 2 }, true.AsArray());
// 14x14x4 -> 7x7x8, window 3
var ConvParam2 = new Parameter(new int[] { 3, 3, 4, 8 }, DataType.Float, CNTKLib.GlorotUniformInitializer(0.26, -1, 2), device);
var Layer2 = CNTKLib.ReLU(CNTKLib.Convolution(ConvParam2, Layer1Pool, new int[] { 1, 1, 4 }));
var Layer2Pool = CNTKLib.Pooling(Layer2, PoolingType.Max, new int[] { 3, 3 }, new int[] { 2, 2 }, new bool[] { true });
var ConvOut = CNTKLib.Reshape(Layer2Pool, (7 * 7 * 8).AsArray());
var W = new Parameter(new int[] { outputDim, 7 * 7 * 8 }, DataType.Float, CNTKLib.GlorotUniformInitializer(), device, "w2");
var b = new Parameter(new int[] { outputDim }, DataType.Float, 0, device, "b2");
var z = CNTKLib.Times(W, ConvOut) + 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 = 10000;
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);
// 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}");
}
}
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);
}
/// <summary>
/// Creates a new batch iterator.
/// </summary>
/// <param name="batchSource">
/// The delegate that returns collections to iterate over.
/// Each collection represents a "batch" of data.
/// </param>
public BatchIterator(BatchSource <T> batchSource)
{
this.batchDataSource = batchSource;
}
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();
}
