private void MnistDemo()
{
// Load data
this.training = MnistReader.Load(@"..\..\Mnist\train-labels.idx1-ubyte", @"..\..\Mnist\train-images.idx3-ubyte");
this.testing = MnistReader.Load(@"..\..\Mnist\t10k-labels.idx1-ubyte", @"..\..\Mnist\t10k-images.idx3-ubyte");
if (this.training.Count == 0 || this.testing.Count == 0)
{
Console.WriteLine("Missing Mnist training/testing files.");
Console.ReadKey();
return;
}
// Create network
this.net = new Net();
this.net.AddLayer(new InputLayer(24, 24, 1));
this.net.AddLayer(new ConvLayer(5, 5, 8) { Stride = 1, Pad = 2, Activation = Activation.Relu });
this.net.AddLayer(new PoolLayer(2, 2) { Stride = 2 });
this.net.AddLayer(new ConvLayer(5, 5, 16) { Stride = 1, Pad = 2, Activation = Activation.Relu });
this.net.AddLayer(new PoolLayer(3, 3) { Stride = 3 });
this.net.AddLayer(new SoftmaxLayer(10));
this.trainer = new Trainer(this.net)
{
BatchSize = 20,
L2Decay = 0.001,
TrainingMethod = Trainer.Method.Adadelta
};
do
{
var sample = this.SampleTrainingInstance();
this.Step(sample);
} while (!Console.KeyAvailable);
}
private static void Main(string[] args)
{
// species a 2-layer neural network with one hidden layer of 20 neurons
var net = new Net();
// input layer declares size of input. here: 2-D data
// ConvNetJS works on 3-Dimensional volumes (width, height, depth), but if you're not dealing with images
// then the first two dimensions (width, height) will always be kept at size 1
net.AddLayer(new InputLayer(1, 1, 2));
// declare 20 neurons, followed by ReLU (rectified linear unit non-linearity)
net.AddLayer(new FullyConnLayer(20, Activation.Relu));
// declare the linear classifier on top of the previous hidden layer
net.AddLayer(new SoftmaxLayer(10));
// forward a random data point through the network
var x = new Volume(new[] {0.3, -0.5});
var prob = net.Forward(x);
// prob is a Volume. Volumes have a property Weights that stores the raw data, and WeightGradients that stores gradients
Console.WriteLine("probability that x is class 0: " + prob.Weights[0]); // prints e.g. 0.50101
var trainer = new Trainer(net) {LearningRate = 0.01, L2Decay = 0.001};
trainer.Train(x, 0); // train the network, specifying that x is class zero
var prob2 = net.Forward(x);
Console.WriteLine("probability that x is class 0: " + prob2.Weights[0]);
// now prints 0.50374, slightly higher than previous 0.50101: the networks
// weights have been adjusted by the Trainer to give a higher probability to
// the class we trained the network with (zero)
}
private static void Regression1DDemo()
{
var net = new Net();
net.AddLayer(new InputLayer(1, 1, 1));
net.AddLayer(new FullyConnLayer(20, Activation.Relu));
net.AddLayer(new FullyConnLayer(20, Activation.Sigmoid));
net.AddLayer(new RegressionLayer(1));
var trainer = new Trainer(net) { LearningRate = 0.01, Momentum = 0.0, BatchSize = 1, L2Decay = 0.001 };
// Function we want to learn
double[] x = { 0.0, 0.5, 1.0 };
double[] y = { 0.0, 0.1, 0.2 };
var n = x.Length;
// Training
do
{
RegressionUpdate(n, x, trainer, y);
} while (!Console.KeyAvailable);
// Testing
var netx = new Volume(1, 1, 1);
for (var ix = 0; ix < n; ix++)
{
netx.Weights = new[] { x[ix] };
var result = net.Forward(netx);
}
}
private static void Classify2DDemo()
{
var net = new Net();
net.AddLayer(new InputLayer(1, 1, 2));
net.AddLayer(new FullyConnLayer(6, Activation.Tanh));
net.AddLayer(new FullyConnLayer(2, Activation.Tanh));
net.AddLayer(new SoftmaxLayer(2));
var trainer = new Trainer(net) { LearningRate = 0.01, Momentum = 0.0, BatchSize = 10, L2Decay = 0.001 };
// Data
var data = new List<double[]>();
var labels = new List<int>();
data.Add(new[] { -0.4326, 1.1909 });
labels.Add(1);
data.Add(new[] { 3.0, 4.0 });
labels.Add(1);
data.Add(new[] { 0.1253, -0.0376 });
labels.Add(1);
data.Add(new[] { 0.2877, 0.3273 });
labels.Add(1);
data.Add(new[] { -1.1465, 0.1746 });
labels.Add(1);
data.Add(new[] { 1.8133, 1.0139 });
labels.Add(0);
data.Add(new[] { 2.7258, 1.0668 });
labels.Add(0);
data.Add(new[] { 1.4117, 0.5593 });
labels.Add(0);
data.Add(new[] { 4.1832, 0.3044 });
labels.Add(0);
data.Add(new[] { 1.8636, 0.1677 });
labels.Add(0);
data.Add(new[] { 0.5, 3.2 });
labels.Add(1);
data.Add(new[] { 0.8, 3.2 });
labels.Add(1);
data.Add(new[] { 1.0, -2.2 });
labels.Add(1);
var n = labels.Count;
// Training
do
{
Classify2DUpdate(n, data, trainer, labels);
} while (!Console.KeyAvailable);
// Testing
var netx = new Volume(1, 1, 1);
for (var ix = 0; ix < n; ix++)
{
netx.Weights = data[ix];
var result = net.Forward(netx);
var c = net.GetPrediction();
bool accurate = c == labels[ix];
}
}
private static void Classify2DUpdate(int n, List<double[]> data, Trainer trainer, List<int> labels)
{
var netx = new Volume(1, 1, 1);
var avloss = 0.0;
for (var iters = 0; iters < 50; iters++)
{
for (var ix = 0; ix < n; ix++)
{
netx.Weights = data[ix];
trainer.Train(netx, labels[ix]);
avloss += trainer.Loss;
}
}
avloss /= n * 50.0;
Console.WriteLine("Loss:" + avloss);
}
private static void RegressionUpdate(int n, double[] x, Trainer trainer, double[] y)
{
var netx = new Volume(1, 1, 1);
var avloss = 0.0;
for (var iters = 0; iters < 50; iters++)
{
for (var ix = 0; ix < n; ix++)
{
netx.Weights = new[] { x[ix] };
trainer.Train(netx, y[ix]);
avloss += trainer.Loss;
}
}
avloss /= n * 50.0;
Console.WriteLine("Loss:" + avloss);
}