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 TestFullConnect()
{
ArrDataEnumerator datas = new ArrDataEnumerator();
//and test
double[,,] temp = new double[1, 1, 2];
double[,,] otemp = new double[1, 1, 1];
datas.AddSample(temp, otemp);
temp = new double[1, 1, 2];
otemp = new double[1, 1, 1];
temp[0, 0, 0] = 1;
datas.AddSample(temp, otemp);
temp = new double[1, 1, 2];
otemp = new double[1, 1, 1];
temp[0, 0, 1] = 1;
datas.AddSample(temp, otemp);
temp = new double[1, 1, 2];
otemp = new double[1, 1, 1];
temp[0, 0, 0] = 1;
temp[0, 0, 1] = 1;
otemp[0, 0, 0] = 1;
datas.AddSample(temp, otemp);
Network network = new Network();
network.AddLayer(new TSConv2D(new Sigmoid(), 2, 1, 1, 10, 1));
network.AddLayer(new FullyConnLayar(new Sigmoid(), new Size(1, 1, 1)));
//network.AddLayer(new FullyConnLayar(new Sigmoid(), new Size(1, 1, 20)));
//network.AddLayer(new FullyConnLayar(new Sigmoid(), new Size(1, 1, 1)));
network.Compile(new Size(1, 1, 2), true);
network.Normalization();
//network.Normalization();
MomentumParallel mom = new MomentumParallel(network, 0.9, 1e-2);
mom.TrainBatch(datas, 320, 3000);
return(true);
}
static bool PreTrain()
{
BitmapCatEnumerator enums = new BitmapCatEnumerator("Sorted", new System.Drawing.Size(24, 12));
BitmapCatEnumerator val = new BitmapCatEnumerator("Val", new System.Drawing.Size(24, 12));
Network network = new Network();
//network.LoadJSON(System.IO.File.ReadAllText("pretrained_2.neural"));
//network.CompileOnlyError();
network.AddLayer(new Conv2D(new Relu(), 3, 3, 10));
network.AddLayer(new MaxPool2D(new Relu(), 2, 2));
network.AddLayer(new Conv2D(new Relu(), 5, 5, 30));
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, 12, 24), true);
network.Normalization();
network.Normalization();
MomentumParallel sgd = new MomentumParallel(network, 0.9, 1e-4);
sgd.need_max = false;
var pair = PretrainAutoEncoder.Action(network, sgd, enums, val, 2000, 32);
Console.WriteLine("{0}\n{1}", pair.Key, pair.Value);
Console.WriteLine("Start train");
sgd = new MomentumParallel(network, 0, 1e-5);
DateTime start = DateTime.Now;
sgd.TrainBatch(enums, 256, 1);
for (int i = 0; i < 100000; i++)
{
double[] errors = sgd.TrainBatchContinue(enums, 256, 1);
if ((DateTime.Now - start).TotalMinutes > 5)
{
System.IO.File.WriteAllText("train_" + i + ".neural", network.SaveJSON());
start = DateTime.Now;
Console.WriteLine("Saved at " + "train_" + i + ".neural");
}
}
return(true);
}