public void Run()
{
Stopwatch watch = Stopwatch.StartNew();
NeuralNetwork network = new NeuralNetwork();
network.AddLayer("input", new InputLayer(2), BaseLayer.TYPE.INPUT);
network.AddLayer("hidden", new CoreLayer(8, ACTIVATION.SIGMOID, BaseLayer.TYPE.HIDDEN), BaseLayer.TYPE.HIDDEN);
network.AddLayer("output", new CoreLayer(1, ACTIVATION.SIGMOID, BaseLayer.TYPE.OUTPUT), BaseLayer.TYPE.OUTPUT);
network.AddConnection("input", "hidden", Connection.INIT.GLOROT_UNIFORM);
network.AddConnection("hidden", "output", Connection.INIT.GLOROT_UNIFORM);
/*
* Optimizer optimizer = new BackProp(network, 1e-5f, 0.99f, true)
* {
* Alpha = 0.1f
* };
*/
Optimizer optimizer = new RMSProp(network)
{
Alpha = 0.1f
};
optimizer.InitBatchMode(4);
Vector[] input = new Vector[4];
Vector[] target = new Vector[4];
//Vector output = null;
input[0] = Vector.Build(2, new float[] { 0f, 0f });
input[1] = Vector.Build(2, new float[] { 0f, 1f });
input[2] = Vector.Build(2, new float[] { 1f, 0f });
input[3] = Vector.Build(2, new float[] { 1f, 1f });
target[0] = Vector.Build(1, new float[] { 0f });
target[1] = Vector.Build(1, new float[] { 1f });
target[2] = Vector.Build(1, new float[] { 1f });
target[3] = Vector.Build(1, new float[] { 0f });
for (int e = 0; e < 200; e++)
{
//Console.Write("Start ");
//BasePool.Instance.Check();
float err = 0;
for (int i = 0; i < 4; i++)
{
err += optimizer.Train(input[i], target[i]);
}
Console.WriteLine(err);
//Console.Write("End ");
//BasePool.Instance.Check();
}
Console.WriteLine();
for (int i = 0; i < 4; i++)
{
Console.WriteLine(network.Activate(input[i])[0]);
Vector.Release(input[i]);
Vector.Release(target[i]);
}
optimizer.Dispose();
Console.Write("Finish ");
BasePool.Instance.Check();
watch.Stop();
Console.WriteLine(watch.ElapsedMilliseconds);
}
