static bool CatTrain()
{
BitmapCatEnumerator enums = new BitmapCatEnumerator("Sorted", new System.Drawing.Size(50, 25));
Network network = new Network();
network.AddLayer(new Conv2D(new Relu(), 7, 7, 32));
network.AddLayer(new MaxPool2D(new Relu(), 2, 2));
network.AddLayer(new Conv2D(new Relu(), 5, 5, 64));
network.AddLayer(new MaxPool2D(new Relu(), 2, 2));
network.AddLayer(new FullyConnLayar(new Relu(), new Size(1, 1, 256)));
network.AddLayer(new FullyConnLayar(new Sigmoid(), new Size(1, 1, 2)));
network.Compile(new Size(3, 25, 50), true);
network.Normalization();
var pair = enums.GetRandom(ref network);
OneEnumerator one = new OneEnumerator();
one.input = pair.Key;
one.output = pair.Value;
MomentumParallel sgd = new MomentumParallel(network, 0.9, 1e-6);
double[] errors = sgd.TrainBatch(enums, 32, 1000);
return(errors[0] > errors.Last());
}
static bool ConvLearning()
{
int width = 100;
Random r = new Random();
Network network = new Network();
network.AddLayer(new Conv2D(new Relu(), 2, 1, 2));
network.AddLayer(new MaxPool2D(new Relu(), 2, 1));
network.AddLayer(new FullyConnLayar(new Relu(), new Size(1, 1, 1)));
network.Compile(new Size(1, 1, width), true);
double[,,] input = new double[width, width, width];
double[,,] t = new double[1, 1, 1];
for (int i = 0; i < width; i++)
{
input[i, i, i] = (double)r.NextDouble();
}
t[0, 0, 0] = (double)r.NextDouble();
double start_error = network.GetError(input, t);
double last_error = 0;
SGD sgd = new SGD(network, 1e-1f);
OneEnumerator one = new OneEnumerator();
one.input = input;
one.output = t;
for (int i = 0; i < 20; i++)
{
//last_error = network.Learn(input, t, 0.1f);
last_error = sgd.TrainBatch(one, 1, 1)[0];
if (i % 4 == 0)
{
Console.WriteLine(last_error);
}
}
return(start_error > last_error);
}
