.
. , , .
.
, . 10x10. 100 .
.
́ , . ( = .) (: 6, 2, 6, 6, 8, 9, 9, 9, 10; = 6 9). 100 , , . . , 16777216 . , . , , . , , , 16 . . , , Windows , , , 16, .
, , .
. 2^100 . , . Ascii. , , , , 2.6.
2.6: .
, - . : , .
, , #, . , . :
|
|
|
1:
| . | - () |
| , . , # () | |
| (( ) | |
| ) ( ) | |
| _ () |
| 0 () | |
| , . , () | |
| , | / () |
| , | \ ( ) |
| , | | ( ) |
. : , .
, . 770 ( 70 ).
.
c#. : , . , , . . , , .
- , : , , , , , , .
3.1:
, , , .
, . .
, .
( ) .
.
.
|
|
|
.
3.2: .
INeuroObject . . System.Object.
INeuralNetwork .
ILayer
ISingleLayer .
IMultiLayer ISingleLayer
.
3.3: .
ILearn . INeuralNetwork , .
ILearningConfig , . , .
INeuroRandom . , , . , , [-5,5] .
.
3.4: .
IFunction , , IErrorFunction IActivation .
IDifferentiableActivation (,, ). .
IPartialErrorFunction , . , .
IFullErrorFunction . , , .
.
. , (, , ) . , System.Double.
:
2 : , .
100 [0,1]. 10 . , . . 7 . . . . . . 317 . 0,3265. , 15% , 93,5%. 770 .
:
|
|
|
ISingleLayer<double>[] layers = new ISingleLayer<double>[2];
layers[0] = new SingleLayer(100, 7, new Neuro.MLP.ActivateFunction.Sigmoid(), new Random());
layers[1] = new SingleLayer(7, 10, new Neuro.MLP.ActivateFunction.Relu(), new Random());
MultiLayer mLayer = new MultiLayer(layers);
DifferintiableLearningConfig config = new DifferintiableLearningConfig(new Neuro.MLP.ErrorFunction.HalfEuclid());
config.Step = 0.1;
config.OneImageMinError = 0.01;
config.MinError = 0.4;
config.MinChangeError = 0.0000001;
config.UseRandomShuffle = true;
config.MaxEpoch = 10000;
SimpleBackPropogation learn = new SimpleBackPropogation(config);
MultiLayerNeuralNetwork network = new MultiLayerNeuralNetwork(mLayer, learn);
network.DefaultInitialise();
network.Train(learningSet, testingSet);
network.Save("fsdf" +".json");
. , 16 . , 16 . 6 . 24 , RGB , 24 . , . 561 0,4543. , 15% , 97%. 600 .
:
ISingleLayer<double>[] layers = new ISingleLayer<double>[2];
layers[0] = new SingleLayer(24, 6, new Neuro.MLP.ActivateFunction.Sigmoid(), new Random());
layers[1] = new SingleLayer(6, 16, new Neuro.MLP.ActivateFunction.Relu(), new Random());
MultiLayer mLayer = new MultiLayer(layers);
DifferintiableLearningConfig config = new DifferintiableLearningConfig(new Neuro.MLP.ErrorFunction.HalfEuclid());
config.Step = 0.1;
config.OneImageMinError = 0.01;
config.MinError = 0.5;
config.MinChangeError = 0.0000001;
config.UseRandomShuffle = true;
config.MaxEpoch = 10000;
SimpleBackPropogation learn = new SimpleBackPropogation(config);
MultiLayerNeuralNetwork network = new MultiLayerNeuralNetwork(mLayer, learn);
network.DefaultInitialise();
network.Train(learningSet, testingSet);
network.Save("fsdf" +".json");
( ():
3.5:
, , : , , , .
, .
1) System.Drawing.Bitmap. ..
2) . .
3) . . . . .
4) , , . , .
5) ,
|
|
|
6) , , .
7)
8) n x m, , 10 x 10, .
9) .
10)
11) .
12) , .
13) {' ','#','(',')','\','/','|','_','-','0',\n}, # .
14) {, , , , , , ,,, , , , , , , }
:
:
1 : .
, . 2. .
2 :
. . . , 2.1. . , 2.2. 3 7. .
2.1 2 .
2.2 - . 2, , 2.
3 : . .
4 : . . . . .
5 : GO.
. . .
6 : .
.
7 : .
, .
:
, , , .
.:
, 1, .( 3.6)
, 5. . .( 3.7)
, 4. , .
, 2. , .
3.6:
3.7:
:
C1
2
3
4
5 5
6
7 .
1 1
2 4
3 2
4 7
5 . 5 .
6 2.2 , 2
7 ( 3 6 )
:
:
, , , .
, BitmapWorker.
|
|
|
System.Drawing.Bitmap
, , . , , . , Image System.Windows.Forms.PictureBox, . .
, , . Compute MultiLayerNeyralNetwork, .
16 , , RGB . , char.
. \n , , , , \n. , Model.
:
ColorTo , 24 8 .
ColorToGray ( ). , ColorTo
ut .
RecalculateSize , .
Resize
Round , , .
Zoom
Colors . , .
DefaultZoom , . , ( 1600 , , , )
Doublicate , . , , , .
3.7: .
ImagePath , .
Images < , >
IsChanged , . . .
SixteenColorMode . . , windows , .
3.8:
Text . , .
UsePreworking . , . . , , .
3.9: .
WidthValue , .
. , , .
Content , , .
ChangeImage . . , .
ClearContent . .
PutImage , , . , , .
AssignEmptySimbol AssignFillSymbol . , .
FromColor ToColor RGB , .
3.10: # █ .
.
:
:
, 600
#
:
| 
|
| 
|
| 
|
. .
,
| 
|
| 
|
| 
|
, . , . . , - .
, . , .
| 
|
| 
|
| 
|
. , -, , - , , , . .
.
4.1: -
4.2:
4.3:
4.4: .
4.5: , .
. , , . : , , .
ascii art. , , , . , . . : .
ascii art. , , , , .
, , . , . , .
, , .
:
1) .. : ,
2) . habrahabr.
3) .. . . , , 2003.
[DataContract]
public class SimpleBackPropogation: DifferintiableLearningConfig, ILearn<double>
{
public SimpleBackPropogation(DifferintiableLearningConfig config)
: base(config)
{
}
/// <summary>
///
/// </summary>
/// <param name="network"></param>
/// <param name="data"></param>
/// <param name="check"></param>
public void Train(INeuralNetwork<double> network, IList<NeuroImage<double>> data, IList<NeuroImage<double>> check)
{
//
MultiLayer net = network.Layer as MultiLayer;
this.CurrentEpoch = 0;
this.Succes = false;
double lastError = 0;
double[][] y = new double[data.Count][];
do
{
lastError = CurrentError;
//
if (UseRandomShuffle == true)
{
RandomShuffle(ref data);
}
for (int curr = 0; curr < data.Count; curr++)
{
//
y[curr] = network.Compute(data[curr].Input);
// , ,
// .
//
if (Compare(data[curr].Output, network.Layer[net.Layer.Length - 1].LastPulse) == true)
{
continue;
}
//
OutputLayerError(data, net, curr);
//
HiddenlayerError(net);
//
ModifyParametrs(data, net, curr);
}
//
ComputeError(data, network,y);
//
ComputeRegularizationError(data, net);
System.Console.WriteLine("Eposh #" + CurrentEpoch.ToString() +
" finished; current error is " + CurrentError.ToString()
);
CurrentEpoch++;
}
while (CurrentEpoch < MaxEpoch && CurrentError > MinError &&
Math.Abs(CurrentError - lastError) > MinChangeError);
ComputeRecognizeError(check, network);
}
private void ComputeRecognizeError(IList<NeuroImage<double>> check, INeuralNetwork<double> network)
{
int accept = 0;
double LastError = 0;
RecogniseError = 0;
for (int i = 0; i < check.Count; i++)
{
double[] realOutput = network.Compute(check[i].Input);
double[] result = new double[realOutput.Length];
LastError = ErrorFunction.Compute(check[i].Output, realOutput);
RecogniseError += LastError;
double max = realOutput.Max();
int index = realOutput.ToList().IndexOf(max);
result[index] = 1;
if (ArrayCompare(result, check[i].Output) == true)
{
accept++;
}
}
RecognisePercent = (double)accept / (double)check.Count;
RecogniseError /= 2;
}
public static bool ArrayCompare(double[] a, double[] b)
{
if (a.Length == b.Length)
{
for (int i = 0; i < a.Length; i++)
{
if (a[i]!= b[i]) { return false; };
}
return true;
}
return false;
}
/// <summary>
///
/// </summary>
/// <param name="data"> </param>
/// <param name="net"></param>
private void ComputeRegularizationError(IList<NeuroImage<double>> data, MultiLayer net)
{
if (Math.Abs(Regularization - 0d) > Double.Epsilon)
{
double reg = 0;
for (int layerIndex = 0; layerIndex < net.Layer.Length; layerIndex++)
{
for (int neuronIndex = 0; neuronIndex < net.Layer[layerIndex].Neuron.Length; neuronIndex++)
{
for (int weightIndex = 0; weightIndex < net.Layer[layerIndex].Neuron[neuronIndex].Weights.Length; weightIndex++)
{
reg += net.Layer[layerIndex].Neuron[neuronIndex].Weights[weightIndex] *
net.Layer[layerIndex].Neuron[neuronIndex].Weights[weightIndex];
}
}
}
CurrentError += Regularization * reg / (2 * data.Count);
}
}
/// <summary>
///
/// </summary>
/// <param name="data"> </param>
/// <param name="network"></param>
private void ComputeError(IList<NeuroImage<double>> data, INeuralNetwork<double> network, double[][] realOutput)
{
int accept = 0;
double LastError = 0;
CurrentError = 0;
for (int i = 0; i < data.Count; i++)
{
LastError = ErrorFunction.Compute(data[i].Output, realOutput[i]);
CurrentError += LastError;
}
TeachPercent = (double)accept / (double)data.Count;
CurrentError /= 2;
}
/// <summary>
///
/// </summary>
/// <param name="data"> </param>
/// <param name="net"></param>
/// <param name="curr"> </param>
private void ModifyParametrs(IList<NeuroImage<double>> data, MultiLayer net, int curr)
{
for (int i = net.Layer.Length - 1; i >= 0; i--)//
{
for (int j = 0; j < net[i].Neuron.Length; j++)//
{
double temp = Step * net[i].Neuron[j].CurrentError
* net[i].Neuron[j].ActivationFunction.Derivative(net[i].Neuron[j].LastPulse);
net[i].Neuron[j].Offset += temp;
for (int k = 0; k < net[i].Neuron[j].Weights.Length; k++)//
{
if (i == 0)
{
net[i].Neuron[j].Weights[k] -= temp
* data[curr].Input[k] + Regularization * net[i].Neuron[j].Weights[k] / data.Count;
}
else
{
net[i].Neuron[j].Weights[k] -= temp
* net[i - 1].Neuron[k].LastState + Regularization * net[i].Neuron[j].Weights[k] / data.Count;
}
}
}
}
}
/// <summary>
///
/// </summary>
/// <param name="net"></param>
private static void HiddenlayerError(MultiLayer net)
{
for (int k = net.Length - 2; k >= 0; k--)
{
//
for (int j = 0; j < net[k].Neuron.Length; j++)
{
net.Layer[k].Neuron[j].CurrentError = 0;
//
for (int i = 0; i < net[k + 1].Neuron.Length; i++)
{
//errorj = sum_for_i(errori * F'(Sj) * wij)
net.Layer[k].Neuron[j].CurrentError += net.Layer[k + 1].Neuron[i].CurrentError
* net.Layer[k + 1].Neuron[i].Weights[j] *
net.Layer[k + 1].Neuron[i].ActivationFunction.Derivative(net.Layer[k + 1].Neuron[i].LastPulse);
}
}
}
}
/// <summary>
///
/// </summary>
/// <param name="data"> </param>
/// <param name="net"></param>
/// <param name="curr"> </param>
///
private void OutputLayerError(IList<NeuroImage<double>> data, MultiLayer net, int curr)
{
int last = net.Layer.Length - 1;
//
for (int j = 0; j < net.Layer[last].Neuron.Length; j++)
{
net.Layer[last].Neuron[j].CurrentError = 0;
net.Layer[last].Neuron[j].CurrentError = ErrorFunction.Derivative(net.Layer[last].Neuron[j].LastState, data[curr].Output[j]);
}
}
private void RandomShuffle(ref IList<NeuroImage<double>> data)
{
Random gen = new Random();
int ind1, ind2;
for (int i = 0; i < data.Count; i++)
{
ind1 = gen.Next(0, data.Count);
ind2 = gen.Next(0, data.Count);
Swap(data, ind1, ind2);
}
}
private void Swap(IList<NeuroImage<double>> data, int ind1, int ind2)
{
NeuroImage<double> temp = data[ind1];
data[ind1] = data[ind2];
data[ind2] = temp;
}
private bool Compare(double[] p1, double[] p2)
{
double error = 0.0;
for (int i = 0; i < p1.Length; i++)
{
error += (p1[i] - p2[i]) * (p1[i] - p2[i]);
}
error /= 2;
if (error < OneImageMinError)
return true;
else
return false;
}
}
:
private void cloudButton1_Click(object sender, EventArgs e)
{
UpdateParametrizedSymbol();
toolStripProgressBar1.ProgressBar.Show();
Application.DoEvents();
source.Colors.Clear();
source.Text = null;
Task.Run(
() =>
{
Bitmap image = new Bitmap(pictureBox1.Image);
if (System.IO.File.Exists("SymbolRecognise.json") == false)
{
MessageBox.Show("! SymbolRecognise.json");
return;
}
if (System.IO.File.Exists("ColorRecognise.json") == false)
{
MessageBox.Show("! ColorRecognise.json");
return;
}
var SymbolNetwork = Neuro.MLP.MultiLayerNeuralNetwork.Load("SymbolRecognise.json");
var ColorNetwork = Neuro.MLP.MultiLayerNeuralNetwork.Load("ColorRecognise.json");
System.Drawing.Size blockSize = new Size(10, 10);
System.Drawing.Size metrics = Work.BitmapWorker.Round(image.Size, blockSize);
if (source.DefaultZoom == true)
{
metrics = Work.BitmapWorker.RecalculateSize(image.Size, source.WidthValue);
image = Work.BitmapWorker.Zoom(image, metrics);
}
if (image.Height % blockSize.Height!= 0)
{
metrics = Work.BitmapWorker.Round(image.Size, blockSize);
image = Work.BitmapWorker.Resize(image, metrics);
}
int blockPerLine = Convert.ToInt32((float)image.Width / (float)blockSize.Width);
int BlockAmount = Convert.ToInt32((float)image.Width / (float)blockSize.Width * (float)image.Height / (float)blockSize.Height);
double[] input = new double[100];
Bitmap ImageToEncoding;
if (source.UsePreworking == true)
ImageToEncoding = Work.BitmapWorker.ColorToGray(image);
else
ImageToEncoding = image;
Bitmap temp, encodeTemp;
Rectangle rect;
for (int i = 0; i < BlockAmount; i++)
{
rect = GetBlockRect(image, i);
temp = Work.BitmapWorker.Cut(image, rect);
encodeTemp = Work.BitmapWorker.Cut(ImageToEncoding, rect);
if (source.SixteenColorMode == true)
{
source.Colors.Add(ColorNetwork.Compute(temp));
}
else
{
source.Colors.Add(temp.GetPixel(5, 5));
}
input = CreateInput(encodeTemp);
var res = SymbolNetwork.Compute(input);
double max = res.Max();
int index = res.ToList().IndexOf(max);
source.Text += source.Images[index];
if (source.Doublicate)
{
source.Text += source.Images[index];
source.Colors.Add(source.Colors.Last());
}
if (i % blockPerLine == blockPerLine - 1)
source.Text += '\n';
}
Form2 dlg = new Form2(source.Text, source.Colors);
toolStripProgressBar1.ProgressBar.InvokeIfRequired(() =>
{
// Do anything you want with the control here
toolStripProgressBar1.ProgressBar.Hide();
});
dlg.ShowDialog();
}
);
}
