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];
}
}
void Start()
{
for (var i = 0; i < pixels.GetLength(0); ++i)
{
for (var j = 0; j < pixels.GetLength(1); ++j)
{
pixels [i, j] = Instantiate(pixel, new Vector3(i, j, 0), new Quaternion()) as GameObject;
}
}
net = new Net();
trainer = new Trainer(net);
net.AddLayer(new InputLayer(1, 1, 2));
net.AddLayer(new FullyConnLayer(2, Activation.Sigmoid));
net.AddLayer(new FullyConnLayer(1, Activation.Sigmoid));
net.AddLayer(new RegressionLayer(1));
}
// Use this for initialization
public QLearning()
{
exp = new Experience[experienceSize];
expi = 0;
expn = 0;
t = 0;
r0 = -99f;
// species a 2-layer neural network with one hidden layer of 20 neurons
net = new Net();
// input layer declares size of input. here: 2-D data
// ConvNetSharp 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, numStates));
// declare 20 neurons, followed by ReLU (rectified linear unit non-linearity)
net.AddLayer(new FullyConnLayer(hiddenNeurons - 10, Activation.Relu));
//snet.AddLayer(new FullyConnLayer(hiddenNeurons/4, Activation.Relu));
// declare the linear classifier on top of the previous hidden layer
net.AddLayer(new RegressionLayer(numActions));
Debug.Log("Network initialized");
// species a 2-layer neural network with one hidden layer of 20 neurons
netClassify = new Net();
// input layer declares size of input. here: 2-D data
// ConvNetSharp 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
netClassify.AddLayer(new InputLayer(1, 1, 2));
// declare 20 neurons, followed by ReLU (rectified linear unit non-linearity)
netClassify.AddLayer(new FullyConnLayer(4, Activation.Relu));
//snet.AddLayer(new FullyConnLayer(hiddenNeurons/4, Activation.Relu));
// declare the linear classifier on top of the previous hidden layer
netClassify.AddLayer(new SoftmaxLayer(2));
Debug.Log("Network Classify initialized");
/*
* List<double> list = new List<double>();
*
* list = netToList(net);
*
* outputList(list, "agent1");
*
*
* ListToNet(net, list);
*
* List<double> list2 = new List<double>();
*
* list2 = netToList(net);
*
* list2[1] = 0.5f;
*
* outputList(list2, "agent2");
*
*/
//double[] weights = { 0.3, -0.5, 0.1, 0.9, 0.6 };
// forward a random data point through the network
//var x = new Volume(weights);
//var prob = net.Forward(x);
// prob is a Volume. Volumes have a property Weights that stores the raw data, and WeightGradients that stores gradients
//Debug.Log("probability that x is class 0: " + prob.Weights[0]); // prints e.g. 0.50101
trainer = new SgdTrainer(net)
{
LearningRate = 0.01, L2Decay = 0.001, Momentum = 0.0, BatchSize = 5
};
//trainer.Train(x, 0); // train the network, specifying that x is class zero
// Volume prob2 = net.Forward(x);
//Debug.Log("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)
e = new Entropy();
q = new Quartiles();
double[] arr = new double[8] {
5, 6, 7, 2, 1, 8, 4, 3
};
double[] ascOrderedArray = (from i in arr orderby i ascending select i).ToArray();
Debug.Log(q.umidmean(ascOrderedArray));
Debug.Log(q.lmidmean(ascOrderedArray));
}
