private void MnistDemo()
{
BuilderInstance.Volume = new VolumeBuilder();
var datasets = new DataSets();
if (!datasets.Load(100))
{
return;
}
// Create network
this._net = new Net <float>();
this._net.AddLayer(new InputLayer(28, 28, 1));
this._net.AddLayer(new ConvLayer(5, 5, 8)
{
Stride = 1, Pad = 2
});
this._net.AddLayer(new ReluLayer());
this._net.AddLayer(new PoolLayer(2, 2)
{
Stride = 2
});
this._net.AddLayer(new ConvLayer(5, 5, 16)
{
Stride = 1, Pad = 2
});
this._net.AddLayer(new ReluLayer());
this._net.AddLayer(new PoolLayer(3, 3)
{
Stride = 3
});
this._net.AddLayer(new FullyConnLayer(10));
this._net.AddLayer(new SoftmaxLayer(10));
// Fluent version
// this._net = FluentNet<float>.Create(24, 24, 1)
// .Conv(5, 5, 8).Stride(1).Pad(2)
// .Relu()
// .Pool(2, 2).Stride(2)
// .Conv(5, 5, 16).Stride(1).Pad(2)
// .Relu()
// .Pool(3, 3).Stride(3)
// .FullyConn(10)
// .Softmax(10)
// .Build();
this._trainer = new SgdTrainer(this._net)
{
LearningRate = 0.01f,
BatchSize = 1024,
L2Decay = 0.001f,
Momentum = 0.9f
};
Console.WriteLine("Convolutional neural network learning...[Press any key to stop]");
do
{
var trainSample = datasets.Train.NextBatch(this._trainer.BatchSize);
Train(trainSample.Item1, trainSample.Item2, trainSample.Item3);
var testSample = datasets.Test.NextBatch(this._trainer.BatchSize);
Test(testSample.Item1, testSample.Item3, this._testAccWindow);
Console.WriteLine("Loss: {0} Train accuracy: {1}% Test accuracy: {2}%", this._trainer.Loss,
Math.Round(this._trainAccWindow.Items.Average() * 100.0, 2),
Math.Round(this._testAccWindow.Items.Average() * 100.0, 2));
Console.WriteLine("Example seen: {0} Fwd: {1}ms Bckw: {2}ms Updt: {3}ms", this._stepCount,
Math.Round(this._trainer.ForwardTimeMs, 2),
Math.Round(this._trainer.BackwardTimeMs, 2),
Math.Round(this._trainer.UpdateWeightsTimeMs, 2));
} while (!Console.KeyAvailable);
}
private void MnistDemo()
{
BuilderInstance <float> .Volume = new ConvNetSharp.Volume.GPU.Single.VolumeBuilder();
var datasets = new DataSets();
if (!datasets.Load(100))
{
return;
}
// Create network
this._net = new Net <float>();
this._net.AddLayer(new InputLayer <float>());
this._net.AddLayer(new ConvLayer <float>(5, 5, 8)
{
Stride = 1, Pad = 2, BiasPref = 0.1f
});
this._net.AddLayer(new ReluLayer <float>());
this._net.AddLayer(new PoolLayer <float>(2, 2)
{
Stride = 2
});
this._net.AddLayer(new ConvLayer <float>(5, 5, 16)
{
Stride = 1, Pad = 2, BiasPref = 0.1f
});
this._net.AddLayer(new ReluLayer <float>());
this._net.AddLayer(new PoolLayer <float>(3, 3)
{
Stride = 3
});
this._net.AddLayer(new FullyConnLayer <float>(10));
this._net.AddLayer(new SoftmaxLayer <float>());
// Fluent version
//this._net = Net<float>.Create()
// .Conv(5, 5, 8).Stride(1).Pad(2)
// .Relu()
// .Pool(2, 2).Stride(2)
// .Conv(5, 5, 16).Stride(1).Pad(2)
// .Relu()
// .Pool(3, 3).Stride(3)
// .FullyConn(10)
// .Softmax()
// .Build();
this._trainer = new SgdTrainer <float>(this._net, 0.01f)
{
BatchSize = 1024,
//L2Decay = 0.001f,
//Momentum = 0.9f
};
if (File.Exists("loss.csv"))
{
File.Delete("loss.csv");
}
Console.WriteLine("Convolutional neural network learning...[Press any key to stop]");
do
{
var trainSample = datasets.Train.NextBatch(this._trainer.BatchSize);
Train(trainSample.Item1, trainSample.Item2, trainSample.Item3);
var testSample = datasets.Test.NextBatch(this._trainer.BatchSize);
Test(testSample.Item1, testSample.Item3, this._testAccWindow);
Console.WriteLine("Loss: {0} Train accuracy: {1}% Test accuracy: {2}%", this._trainer.Loss,
Math.Round(this._trainAccWindow.Items.Average() * 100.0, 2),
Math.Round(this._testAccWindow.Items.Average() * 100.0, 2));
Console.WriteLine("Example seen: {0} Fwd: {1}ms Bckw: {2}ms Updt: {3}ms", this._stepCount,
Math.Round(this._trainer.ForwardTimeMs, 2),
Math.Round(this._trainer.BackwardTimeMs, 2),
Math.Round(this._trainer.UpdateWeightsTimeMs, 2));
File.AppendAllLines("loss.csv", new[] { $"{this._stepCount}, {this._trainer.Loss}, { Math.Round(this._trainAccWindow.Items.Average() * 100.0, 2)}, {Math.Round(this._testAccWindow.Items.Average() * 100.0, 2)}" });
} while (!Console.KeyAvailable);
// Display graph
//var vm = new ViewModel<float>(_net.Op);
//var app = new Application();
//app.Run(new GraphControl { DataContext = vm });
this._net.Dispose();
this._trainer.Dispose();
}