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 ConfigureModel(string configstring, string algorithm, string dataset, string filename)
{
await Clients.All.SendAsync("ReceiveMessage", "Configuring model...");
model = new AI.ML.CNN.Model();
model.Configure(configstring);
await Clients.All.SendAsync("ReceiveMessage", "Initializing dataset...");
switch (dataset)
{
case "MNIST":
case "mnist":
dataFileName = @"C:\files\data\mnist\train-images.idx3-ubyte";
labelFileName = @"C:\files\data\mnist\train-labels.idx1-ubyte";
dataList = (List <AI.Core.fData>)AI.Core.UByteLoader.ReadGrayImage(dataFileName, 1, 0.0, 1.0, labelFileName, 0.0, 1.0);
dataSet = new AI.Core.fDataSet();
dataSet.fData = dataList;
await Clients.All.SendAsync("SetCookie", dataset, "dataset");
break;
case "CIFAR":
case "cifar":
dataFileName = @"C:\files\data\cifar\cifar-10-batches-bin\data_batch_1.bin";
dataList = (List <AI.Core.fData>)AI.Core.UByteLoader.ReadColorImage(dataFileName, 1, 0.0, 1.0, 0.0, 1.0);
dataSet = new AI.Core.fDataSet();
dataSet.fData = dataList;
await Clients.All.SendAsync("SetCookie", dataset, "dataset");
break;
default:
dataSet = new AI.Core.fDataSet();
dataSet.Deserializer(dataset);
await Clients.All.SendAsync("SetCookie", dataset, "dataset");
break;
}
await Clients.All.SendAsync("ReceiveMessage", "Initializing training...");
//parse trainer string
AI.ML.CNN.Trainers.DeltaRule deltaRule = new AI.ML.CNN.Trainers.DeltaRule();
deltaRule.Configure <AI.ML.CNN.Lossfunc.CategoricalCrossEntropy>(model, epochs, dataSet, learningRate, momentum);
await Clients.All.SendAsync("ReceiveMessage", deltaRule.NextVerbose());
filename = @"C:\files\model\" + filename;
AI.Core.ModelSerializer modelSerializer = new AI.Core.ModelSerializer();
path = modelSerializer.Serialize(configstring, model, filename);
await Clients.All.SendAsync("SetCookie", path, "model");
await Clients.All.SendAsync("ReceiveMessage", "done");
}
