public async Task Test(string dataset, string filename)
{
switch (dataset)
{
case "MNIST":
case "mnist":
dataFileName = @"C:\files\data\mnist\train-images.idx3-ubyte";
labelFileName = @"C:\files\data\mnist\train-labels.idx1-ubyte";
model = modelSerializer.DeserializeCNN(filename);
List <AI.Core.fData> dataList = (List <AI.Core.fData>)AI.Core.UByteLoader.ReadGrayImage(dataFileName, 1, 0.0, 1.0, labelFileName, 0.0, 1.0);
index = random.Next(0, dataList.Count);
dataSet.fData = dataList;
pixel = dataList[index].Pixel;
label = dataList[index].DecodeLabel;
//model.predict
probs = model.Predict(dataSet.fData[index]);
softmaxOutput = adam.Softmax(probs);
maxValue = softmaxOutput.Max();
predict = softmaxOutput.ToList().IndexOf(maxValue);
//int lindex = random.Next(0, 5);
//predict = lindex == 2 ? random.Next(0, 9) : label;
result[0] = label;
result[1] = predict;
await Clients.All.SendAsync("ReceiveMessage", pixel);
await Clients.All.SendAsync("ReceiveLabel", result);
break;
default:
dataSet = new AI.Core.fDataSet();
dataSet.Deserializer(dataset);
model = modelSerializer.DeserializeCNN(filename);
index = random.Next(0, dataSet.fData.Count);
pixel = dataSet.fData[index].Pixel;
label = dataSet.fData[index].DecodeLabel;
//model.predict
probs = model.Predict(dataSet.fData[index]);
softmaxOutput = adam.Softmax(probs);
maxValue = softmaxOutput.Max();
predict = softmaxOutput.ToList().IndexOf(maxValue);
//int lindex = random.Next(0, 5);
//predict = lindex == 2 ? random.Next(0, 9) : label;
result[0] = label;
result[1] = predict;
await Clients.All.SendAsync("ReceiveMessage", pixel);
await Clients.All.SendAsync("ReceiveLabel", result);
break;
}
}
public async Task Test(string dataset, string filename)
{
ModelSerializer modelSerializer = new ModelSerializer();
model = modelSerializer.DeserializeCNN(filename);
fDataSet testdataset = new fDataSet();
testdataset.Deserializer(dataset);
int index = random.Next(0, testdataset.fData.Count);
AI.Core.fDataSet dataSet = new AI.Core.fDataSet();
dataSet.fData = testdataset.fData;
pixel = testdataset.fData[index].Pixel;
label = testdataset.fData[index].DecodeLabel;
//model.predict
probs = model.Predict(dataSet.fData[index]);
AI.ML.CNN.Trainers.ADAM adam = new AI.ML.CNN.Trainers.ADAM();
softmaxOutput = adam.Softmax(probs);
double maxValue = softmaxOutput.Max();
predict = softmaxOutput.ToList().IndexOf(maxValue);
//int lindex = random.Next(0, 5);
//predict = lindex == 2 ? random.Next(0, 9) : label;
result[0] = label;
result[1] = predict;
await Clients.All.SendAsync("ReceiveMessage", pixel);
await Clients.All.SendAsync("ReceiveLabel", result);
}
public async Task Test(string dataset, string filename)
{
switch (dataset)
{
case "MNIST":
case "mnist":
dataFileName = @"C:\files\data\mnist\train-images.idx3-ubyte";
labelFileName = @"C:\files\data\mnist\train-labels.idx1-ubyte";
model = modelSerializer.DeserializeCNN(filename);
dataList = (List <AI.Core.fData>)AI.Core.UByteLoader.ReadGrayImage(dataFileName, 1, 0.0, 1.0, labelFileName, 0.0, 1.0);
index = random.Next(0, dataList.Count);
dataSet.fData = dataList;
pixel = dataList[index].Pixel;
label = dataList[index].DecodeLabel;
//model.predict
probs = model.Predict(dataSet.fData[index]);
softmaxOutput = adam.Softmax(probs);
maxValue = softmaxOutput.Max();
predict = softmaxOutput.ToList().IndexOf(maxValue);
size = (int)System.Math.Sqrt(dataSet.fData[0].Pixel.Length);
matImg = new Mat(size, size, DepthType.Cv8U, 3);
Gimg = matImg.ToImage <Gray, Byte>();
Gimage = setGrayPixels(Gimg, size, pixel);
input_size = new int[] { 256, 256 };
resizedPixel = Resize_Gray(Gimage, input_size);
//int lindex = random.Next(0, 5);
//predict = lindex == 2 ? random.Next(0, 9) : label;
result[0] = label;
result[1] = predict;
await Clients.All.SendAsync("ReceiveMessage", resizedPixel, 1);
await Clients.All.SendAsync("ReceiveLabel", result);
break;
case "CIFAR":
case "cifar":
dataFileName = @"C:\files\data\cifar\cifar-10-batches-bin\data_batch_1.bin";
model = modelSerializer.DeserializeCNN(filename);
dataList = (List <AI.Core.fData>)AI.Core.UByteLoader.ReadColorImage(dataFileName, 1, 0.0, 1.0, 0.0, 1.0);
index = random.Next(0, dataList.Count);
dataSet.fData = dataList;
pixel = dataSet.fData[index].Pixel;
label = dataSet.fData[index].DecodeLabel;
//model.predict
probs = model.Predict(dataSet.fData[index]);
softmaxOutput = adam.Softmax(probs);
maxValue = softmaxOutput.Max();
predict = softmaxOutput.ToList().IndexOf(maxValue);
size = (int)System.Math.Sqrt(dataSet.fData[0].Pixel.Length / 3);
matImg = new Mat(size, size, DepthType.Cv8U, 3);
Cimg = matImg.ToImage <Bgr, Byte>();
Cimage = setColorPixels(Cimg, size, pixel);
input_size = new int[] { 256, 256 };
resizedPixel = Resize_Color(Cimage, input_size);
//int lindex = random.Next(0, 5);
//predict = lindex == 2 ? random.Next(0, 9) : label;
cifarResult[0] = classes[label];
cifarResult[1] = classes[predict];
await Clients.All.SendAsync("ReceiveMessage", resizedPixel, 3);
await Clients.All.SendAsync("ReceiveLabel", cifarResult);
break;
default:
dataSet = new AI.Core.fDataSet();
dataSet.Deserializer(dataset);
pixel = dataSet.fData[index].Pixel;
model = modelSerializer.DeserializeCNN(filename);
index = random.Next(0, dataSet.fData.Count);
label = dataSet.fData[index].DecodeLabel;
//model.predict
probs = model.Predict(dataSet.fData[index]);
softmaxOutput = adam.Softmax(probs);
maxValue = softmaxOutput.Max();
predict = softmaxOutput.ToList().IndexOf(maxValue);
//int lindex = random.Next(0, 5);
//predict = lindex == 2 ? random.Next(0, 9) : label;
result[0] = label;
result[1] = predict;
if (model.Input.Output.Count == 3)
{
size = (int)System.Math.Sqrt(dataSet.fData[0].Pixel.Length / 3);
matImg = new Mat(size, size, DepthType.Cv8U, 3);
Cimg = matImg.ToImage <Bgr, Byte>();
Cimage = setColorPixels(Cimg, size, pixel);
input_size = new int[] { 256, 256 };
resizedPixel = Resize_Color(Cimage, input_size);
cifarResult[0] = classes[label];
cifarResult[1] = classes[predict];
await Clients.All.SendAsync("ReceiveMessage", resizedPixel, 3);
await Clients.All.SendAsync("ReceiveLabel", cifarResult);
}
else
{
size = (int)System.Math.Sqrt(dataSet.fData[0].Pixel.Length);
matImg = new Mat(size, size, DepthType.Cv8U, 3);
Gimg = matImg.ToImage <Gray, Byte>();
Gimage = setGrayPixels(Gimg, size, pixel);
input_size = new int[] { 256, 256 };
resizedPixel = Resize_Gray(Gimage, input_size);
//int lindex = random.Next(0, 5);
//predict = lindex == 2 ? random.Next(0, 9) : label;
result[0] = label;
result[1] = predict;
await Clients.All.SendAsync("ReceiveMessage", resizedPixel, 1);
await Clients.All.SendAsync("ReceiveLabel", result);
}
break;
}
}