private void обработатьПланшетПреобученнойСетьюToolStripMenuItem_Click(object sender, EventArgs e)
{
BoxerDLL.BoxCut2D cutter = new BoxerDLL.BoxCut2D(7, 10, 0.143f, 0.1f);
if (openImageDialog.ShowDialog() == DialogResult.OK)
{
Bitmap map = new Bitmap(openImageDialog.FileName);
RectangleF[] rects = cutter.Rects1D(map.Width, map.Height);
Color[] colors = new Color[rects.Length];
Color plus = Color.FromArgb(100, Color.Red);
Color minus = Color.FromArgb(100, Color.Green);
var maps = cutter.Enumerator(ref map);
int j = 0;
//List<double[,,]> rgbs = new List<double[,,]>();
do
{
double[,,] output = pretrain.ProcessAutoEncoder(ImageDataConverter.GetRGBArray(new Bitmap(maps.Current, NetworkData.image_size)));
GC.Collect();
colors[j] = output[0, 0, 0] > output[0, 0, 1] ? plus : minus;
j++;
} while (maps.MoveNext());
var bitmap = DrawColoredRects(map, rects, colors);
ImageShow img_show = new ImageShow(bitmap);
img_show.Show();
}
}
private void button1_Click(object sender, EventArgs e)
{
if (openFileDialog1.ShowDialog() == DialogResult.OK)
{
vis = new NeuralNetwork.NetworkVisualizer(NetworkData.network,
ImageDataConverter.GetRGBArray(new Bitmap(new Bitmap(openFileDialog1.FileName), NetworkData.image_size)));
pictureBox2.Image = new Bitmap(new Bitmap(openFileDialog1.FileName), NetworkData.image_size);
}
}
void Translate()
{
Bitmap map = new Bitmap(planes[index]);
RectangleF[] rects = cutter.Rects1D(map.Width, map.Height);
Color[] colors = new Color[rects.Length];
Color plus = Color.FromArgb(100, Color.Red);
Color minus = Color.FromArgb(100, Color.Green);
var maps = cutter.Enumerator(ref map);
int j = 0;
//List<double[,,]> rgbs = new List<double[,,]>();
do
{
double[,,] output = NetworkData.network.GetOutput(ImageDataConverter.GetRGBArray(new Bitmap(maps.Current, NetworkData.image_size)));
GC.Collect();
colors[j] = output[0, 0, 0] > output[0, 0, 1] ? plus : minus;
j++;
} while (maps.MoveNext());
var bitmap = DrawColoredRects(map, rects, colors);
pictureBox1.Image = bitmap;
}
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();
}
}
}
