public static void Kaggle_Test()
{
//DigitImage[] training = CSV_Helper.Read(DataSet.Training);
DigitImage[] testing = CSV_Helper.Read(DataSet.Testing);
int dialogResult;
CNN cnn = CreateCNN(out dialogResult);
DigitImage[] digitImagesDatas = testing;
Stopwatch stopwatch = new Stopwatch();
stopwatch.Start();
int testing_count = digitImagesDatas.Length;
int correct_count = 0;
double timeLimit = 0, error = 0;
Matrix[] input, targets;
Console.WriteLine("System is getting tested. You will see the results when it is done...\n");
if (cursorTopTesting == 0)
{
cursorTopTesting = Console.CursorTop;
}
InitializeInputAndTarget(out input, out targets);
timeLimit = stopwatch.ElapsedMilliseconds;
string submissionCSV = "ImageId,Label" + Environment.NewLine;
for (int i = 0; i < testing_count; i++)
{
for (int j = 0; j < 28; j++)
{
for (int k = 0; k < 28; k++)
{
input[0][j, k] = digitImagesDatas[i].pixels[j][k];
}
}
input[0].Normalize(0f, 255f, 0f, 1f);
Matrix ans = cnn.Predict(input);
submissionCSV += (i + 1).ToString() + "," + ans.GetMaxRowIndex() + Environment.NewLine;
if (stopwatch.ElapsedMilliseconds > timeLimit)
{
// every 0.5 sec update error
timeLimit += 500;
error = cnn.GetError();
}
int val = Map(0, testing_count, 0, 100, i);
ProgressBar(val, i, testing_count, error, stopwatch.ElapsedMilliseconds / 1000.0, cursorTopTesting);
}
CSV_Helper.Write(submissionCSV);
}
public JobCNNClassify(GRasterLayer featureRasterLayer, GRasterLayer labelRasterLayer, int epochs, int model, int width, int height, int channel)
{
_t = new Thread(() => {
ImageClassifyEnv env = new ImageClassifyEnv(featureRasterLayer, labelRasterLayer);
CNN cnn = new CNN(new int[] { channel, width, height }, env.ActionNum);
//training
Summary = "模型训练中";
for (int i = 0; i < epochs; i++)
{
int batchSize = cnn.BatchSize;
var(states, labels) = env.RandomEval(batchSize);
double[][] inputX = new double[batchSize][];
for (int j = 0; j < batchSize; j++)
{
inputX[j] = states[j];
}
double loss = cnn.Train(inputX, labels);
Process = (double)i / epochs;
}
//classify
Summary = "分类应用中";
IRasterLayerCursorTool pRasterLayerCursorTool = new GRasterLayerCursorTool();
pRasterLayerCursorTool.Visit(featureRasterLayer);
//GDI graph
Bitmap classificationBitmap = new Bitmap(featureRasterLayer.XSize, featureRasterLayer.YSize);
Graphics g = Graphics.FromImage(classificationBitmap);
//
int seed = 0;
int totalPixels = featureRasterLayer.XSize * featureRasterLayer.YSize;
//应用dqn对图像分类
for (int i = 0; i < featureRasterLayer.XSize; i++)
{
for (int j = 0; j < featureRasterLayer.YSize; j++)
{
//get normalized input raw value
double[] normal = pRasterLayerCursorTool.PickNormalValue(i, j);
//}{debug
double[] action = cnn.Predict(normal);
//convert action to raw byte value
int gray = env.RandomSeedKeys[NP.Argmax(action)];
//后台绘制，报告进度
Color c = Color.FromArgb(gray, gray, gray);
Pen p = new Pen(c);
SolidBrush brush = new SolidBrush(c);
g.FillRectangle(brush, new Rectangle(i, j, 1, 1));
//report progress
Process = (double)(seed++) / totalPixels;
}
}
//保存结果至tmp
string fullFileName = Directory.GetCurrentDirectory() + @"\tmp\" + DateTime.Now.ToFileTimeUtc() + ".png";
classificationBitmap.Save(fullFileName);
//complete
Summary = "CNN训练分类完成";
Complete = true;
OnTaskComplete?.Invoke(Name, fullFileName);
});
}
public static void CNN_OverfittingTest()
{
CNN cnn = new CNN();
DigitImage[] digitImages = MNIST_Parser.ReadFromFile(DataSet.Testing, 100);
Stopwatch stopwatch = new Stopwatch();
stopwatch.Start();
int iteration_count = 10;
for (int i = 0; i < iteration_count; i++)
{
for (int j = 0; j < digitImages.Length; ++j)
{
Matrix[] input = new Matrix[1];
input[0] = new Matrix(digitImages[j].pixels);
Matrix target = new Matrix(10, 1);
target[(int)digitImages[j].label, 0] = 1f;
cnn.Train(input, target);
double error = cnn.GetError();
int val = (int)((i - 0) / (double)(iteration_count - 1 - 0) * (100 - 0) + 0);
ProgressBar(val, i, iteration_count, error, stopwatch.ElapsedMilliseconds / 1000.0);
}
}
for (int j = 0; j < digitImages.Length; ++j)
{
Matrix[] input = new Matrix[1];
input[0] = new Matrix(digitImages[j].pixels);
Matrix output = cnn.Predict(input);
Console.WriteLine(output.ToString());
Console.WriteLine(digitImages[j].ToString());
Console.ReadLine();
}
}
public void ClassificationByCNN()
{
//loss
double _loss = 1.0;
//training epochs
int epochs = 100;
//
GRasterLayer featureLayer = new GRasterLayer(featureFullFilename);
GRasterLayer labelLayer = new GRasterLayer(trainFullFilename);
//create environment for agent exploring
IEnv env = new ImageClassifyEnv(featureLayer, labelLayer);
//assume 18dim equals 3x6 (image)
CNN cnn = new CNN(new int[] { 1, 3, 6 }, env.ActionNum);
//training
for (int i = 0; i < epochs; i++)
{
int batchSize = cnn.BatchSize;
var(states, labels) = env.RandomEval(batchSize);
double[][] inputX = new double[batchSize][];
for (int j = 0; j < batchSize; j++)
{
inputX[j] = states[j];
}
_loss = cnn.Train(inputX, labels);
}
//in general, loss is less than 5
Assert.IsTrue(_loss < 5.0);
//apply cnn to classify featureLayer
IRasterLayerCursorTool pRasterLayerCursorTool = new GRasterLayerCursorTool();
pRasterLayerCursorTool.Visit(featureLayer);
//get normalized input raw value
double[] normal = pRasterLayerCursorTool.PickNormalValue(50, 50);
double[] action = cnn.Predict(normal);
int landCoverType = env.RandomSeedKeys[NP.Argmax(action)];
//do something as you need. i.e. draw landCoverType to bitmap at position ( i , j )
//the classification results are not stable because of the training epochs are too few.
Assert.IsTrue(landCoverType >= 0);
}
public static void CNN_Testing(DigitImage[] digitImagesDatas, bool predictionIsOn, CNN cnn, int iterationCount)
{
Stopwatch stopwatch = new Stopwatch();
stopwatch.Start();
int testing_count = digitImagesDatas.Length;
int correct_count = 0;
double timeLimit = 0, error = 0;
Matrix[] input, targets;
Console.WriteLine("System is getting tested. You will see the results when it is done...\n");
if (cursorTopTesting == 0)
{
cursorTopTesting = Console.CursorTop;
}
InitializeInputAndTarget(out input, out targets);
timeLimit = stopwatch.ElapsedMilliseconds;
for (int i = 0; i < testing_count; i++)
{
for (int j = 0; j < 28; j++)
{
for (int k = 0; k < 28; k++)
{
input[0][j, k] = digitImagesDatas[i].pixels[j][k];
}
}
input[0].Normalize(0f, 255f, 0f, 1f);
Matrix ans = null;
if (predictionIsOn)
{
ans = cnn.Predict(input);
}
else
{
cnn.Train(input, targets[digitImagesDatas[i].label]);
ans = cnn.Layers[cnn.Layers.Length - 1].Output[0];
}
if (ans.GetMaxRowIndex() == digitImagesDatas[i].label)
{
correct_count++;
}
if (stopwatch.ElapsedMilliseconds > timeLimit)
{
// every 0.5 sec update error
timeLimit += 500;
error = cnn.GetError();
}
int val = Map(0, testing_count, 0, 100, i);
ProgressBar(val, i, testing_count, error, stopwatch.ElapsedMilliseconds / 1000.0, cursorTopTesting);
}
double accuracy = (correct_count * 1f / testing_count) * 100.0;
Console.WriteLine("\nIteration Count:" + iterationCount);
Console.WriteLine("\nTime :" + (stopwatch.ElapsedMilliseconds / 1000.0).ToString("F4"));
Console.WriteLine("\nAccuracy: %{0:F2}\n", accuracy);
Console.WriteLine("Correct/All: {0}/{1}", correct_count, testing_count);
cursorTopTesting = Console.CursorTop;
if (accuracy >= 95 && iterationCount != -1)
{
string name = accuracy.ToString("F2") + "__" + iterationCount + "__";
Random random = new Random();
int length = 6;
for (int i = 0; i < length; i++)
{
char c = (char)random.Next('A', 'F' + 1);
name += c;
}
cnn.SaveData(name + ".json");
}
}
