public ((NDarray, NDarray), (NDarray, NDarray)) BuildDataSets()
{
List <string> Labels = imageDatas.GetLabels();
if (Labels == null || Labels.Count == 0)
{
throw new Exception("Invalid Dataset Labels!");
}
List <byte[]> train = new List <byte[]>();
List <int> train_labels = new List <int>();
List <byte[]> test = new List <byte[]>();
List <int> test_labels = new List <int>();
if (BuildSets(train, train_labels, test, test_labels, Labels) != imageDatas.Count)
{
throw new Exception(string.Format("Error creating data sets: Expected:{0}", imageDatas.Count));
}
(NDarray, NDarray)tuple1 = GetDataSets(train, train_labels);
(NDarray, NDarray)tuple2 = GetDataSets(test, test_labels);
return(tuple1, tuple2);
}
// Performs predictions on a supplied dataset and loaded neural model:
// Calculates prediction accuracies and errors relative to the expected labeling
public ImageDatas Predict(bool isCNN)
{
if (model_loaded == null)
{
throw new Exception("Invalid Model!");
}
if (dataSets == null || !dataSets.isImageDatas())
{
throw new Exception("Invalid Dataset!");
}
ImageDatas ids = dataSets.GetImageDatas();
ImageDatas idf = new ImageDatas();
NDarray x_data = dataSets.BuildDataSet();
List <string> labels = ids.GetLabels();
if (isCNN)
{
x_data = (K.ImageDataFormat() == "channels_first")
? x_data.reshape(x_data.shape[0], 1, height, width)
: x_data.reshape(x_data.shape[0], height, width, 1);
}
Console.WriteLine("Predicting {0} Images", ids.Count);
NDarray y = model_loaded.Predict(x_data, verbose: 2);
int index;
NDarray result;
for (int i = 0; i < y.len; i++)
{
result = y[i];
result = result.argmax();
index = result.asscalar <int>();
if (ids[i].Label != labels[index])
{
ids[i].Index = labels.IndexOf(ids[i].Label) + 1;
idf.Add(ids[i]);
}
}
double accuracy = Math.Round(((y.len - idf.Count) * 100) / (double)y.len, 2);
idf.SetResults(string.Format("Predicted:{0} Correct: {1} Incorrect:{2} Accuracy:{3}", y.len, y.len - idf.Count, idf.Count, accuracy));
return(idf);
}