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);
}
private void button1_Click(object sender, EventArgs e)
{
if (openFileDialog1.ShowDialog() == DialogResult.OK)
{
Bitmap map = new Bitmap(openFileDialog1.FileName);
BoxerDLL.BoxCut2D cutter = new BoxCut2D(7, 10, 0.143f, 0.1f);
List <Bitmap> maps = cutter.Cut(map);
List <double[, , ]> rgbs = new List <double[, , ]>();
for (int i = 0; i < maps.Count; i++)
{
maps[i] = new Bitmap(maps[i], new System.Drawing.Size(24, 12));
rgbs.Add(ImageDataConverter.GetRGBArray(maps[i]));
}
RectangleF[] rects = cutter.Rects1D(map.Width, map.Height);
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.AddLayer(new Dropout(new Relu(), 0.05));
network.AddLayer(new Conv2D(new Relu(), 7, 7, 32));
network.AddLayer(new MaxPool2D(new Relu(), 2, 2));
network.AddLayer(new Dropout(new Relu(), 0.05));
network.AddLayer(new Conv2D(new Relu(), 3, 3, 64));
network.AddLayer(new MaxPool2D(new Relu(), 2, 2));
network.AddLayer(new Dropout(new Relu(), 0.05));
network.AddLayer(new FullyConnLayar(new Sigmoid(), new NeuralNetwork.Size(1, 1, 256)));
network.AddLayer(new FullyConnLayar(new Sigmoid(), new NeuralNetwork.Size(1, 1, 2)));
network.Compile(new NeuralNetwork.Size(3, 12, 24), true);
network.Normalization();
pictureBox1.Image = map;
MomentumParallel sgd = new MomentumParallel(network, 0.9, 1e-4);
int last = 0;
for (int i = 0; i < 1000; i++)
{
var errors = sgd.TrainBatchPercent(enums, 32, 1);
if (errors.Last().Value > 90 || i - last > 20)
{
last = i;
Color[] colors = new Color[rects.Length];
Color plus = Color.FromArgb(100, Color.Red);
Color minus = Color.FromArgb(100, Color.Green);
for (int j = 0; j < maps.Count; j++)
{
double[,,] output = network.GetOutput(rgbs[j]);
colors[j] = output[0, 0, 0] > output[0, 0, 1] ? plus : minus;
}
pictureBox1.Image = DrawColoredRects(map, rects, colors);
var validation = network.GetError(val);
label2.Text = "Validation\nError: " + validation.Key + "\nPercent: " + validation.Value;
}
label1.Text = i + "\nError: " + errors.Last().Key + "\nPercent: " + errors.Last().Value;
Update();
}
}
}
static void LinePredictor()
{
StreamReader rd = new StreamReader("data.txt");
string classes_line = rd.ReadLine();
string[] classes = classes_line.Split(' ');
List <double[, , ]> inputs = new List <double[, , ]>();
List <double [, , ]> outputs = new List <double[, , ]>();
while (!rd.EndOfStream)
{
inputs.Add(LineToArr3(rd.ReadLine()));
outputs.Add(LineToArr3(rd.ReadLine()));
}
int output_len = outputs[0].Length;
int nancount = 0;
for (int i = 0; i < inputs.Count; i++)
{
var l = inputs[i];
for (int j = 0; j < l.Length; j++)
{
if (double.IsNaN(l[0, 0, j]) || double.IsInfinity(l[0, 0, j]))
{
nancount++;
inputs.RemoveAt(i);
outputs.RemoveAt(i);
i--;
break;
}
}
}
ArrDataEnumerator enumerator = new ArrDataEnumerator(inputs, outputs);
DataCategoryVisualisation visualisation = new DataCategoryVisualisation(enumerator, 2, 6, 600, classes);
visualisation.ShowDialog();
Network network = new Network();
network.loss = new MaxLoss();
network.AddLayer(new FullyConnLayar(new LeackyRelu(0.01), new NeuralNetwork.Size(1, 1, 64)));
network.AddLayer(new FullyConnLayar(new Sigmoid(), new NeuralNetwork.Size(1, 1, output_len)));
network.Compile(new NeuralNetwork.Size(1, 1, inputs[0].GetLength(2)), true);
network.AddNoise();
Optimizer parallel = new MomentumParallel(network, 0.9, 0.0005);
enumerator.SplitTrainVal(0.8, out ArrDataEnumerator train, out ArrDataEnumerator val);
Learner learner = new Learner()
{
optimizer = parallel,
train_data = train,
val_data = val
};
learner.metrics.Add(new ArgMaxMetrics());
learner.metrics.Add(new ArgmaxCount(0, 0, 4));
learner.metrics.Add(new ArgmaxTrainCount(0, 0, 4));
learner.callbacks.Add(new MetricSaveCallback("val_argmax"));
learner.Learn(8, 50, 10000);
}
