public static void Main(string[] args)
{
NeuralNetwork network = new NeuralNetwork(ExecMode.Learning, 1 * Math.Pow(10, -6));
string imageFilePath = @"C:\Users\cleist\source\repos\LicensPlateRecognition\LicensPlateRecognition\LicensPlateRecognition\Image\";
string[] trainingData = Directory.GetFiles(imageFilePath + "TrainingData", "*");
string[] testData = Directory.GetFiles(imageFilePath + "TestData", "*");
// key value pairs for training or test input and desired output
Dictionary <string, double[]> keyValuePairs = new Dictionary <string, double[]>();
// Declare network layers: declare in order of traversion! Since it will be the order of the layers list in network class
InputLayer inputLayer = new InputLayer(28, 28, 1, network);
ConvolutionLayer convLayer1 = new ConvolutionLayer(new Filter(5, 5, inputLayer.Depth), 20, 1, network);
//ConvolutionLayer convLayer2 = new ConvolutionLayer(new Filter(5, 5, convLayer1.Filters.Count), 20, 1, network);
PoolingLayer pooling1 = new PoolingLayer(network);
ConvolutionLayer convLayer3 = new ConvolutionLayer(new Filter(5, 5, convLayer1.Filters.Count), 40, 1, network);
//ConvolutionLayer convLayer4 = new ConvolutionLayer(new Filter(3, 3, convLayer3.Filters.Count), 40, 1, network);
PoolingLayer pooling2 = new PoolingLayer(network);
FullyConnectedLayer fullyConnectedLayer1 = new FullyConnectedLayer(network);
//FullyConnectedLayer fullyConnectedLayer2 = new FullyConnectedLayer(network);
OutputLayer outputLayer = new OutputLayer(network);
// Declare Output Classes
int outClass = 10;
// ------------------------ MNIST Dataset ------------------------
MNIST mnist = new MNIST();
// ------------------------ MNIST Dataset ------------------------
if (network.ExecMode == ExecMode.Learning)
{
// create a csv with tuple of image and class value
//network.CreateCSV(imageFilePath, trainingData, "training.csv");
//network.LoadCSV(imageFilePath, keyValuePairs, "training.csv", outClass);
// ------------------------ MNIST Dataset ------------------------
mnist.ReadTrainMNIST();
// ------------------------ MNIST Dataset ------------------------
var epochs = 59;
// must be divisible through number of training data
var miniBatchSize = 10;
network.Learning(keyValuePairs, outClass, epochs, miniBatchSize, imageFilePath, mnist /* Mnist */);
Console.WriteLine("Press any key to continue...");
Console.ReadKey();
}
if (network.ExecMode == ExecMode.Testing)
{
// create a csv with tuple of image and class value
//network.CreateCSV(imageFilePath, testData, "testing.csv");
//network.LoadCSV(imageFilePath, keyValuePairs, "testing.csv", outClass);
// ------------------------ MNIST Dataset ------------------------
mnist.ReadTestMNIST();
// ------------------------ MNIST Dataset ------------------------
network.Testing(outClass, keyValuePairs, mnist /* Mnist */);
Console.WriteLine("Press any key to continue...");
Console.ReadKey();
}
if (network.ExecMode == ExecMode.Normal)
{
while (true)
{
Console.WriteLine("Please Insert an image filepath...");
try
{
string image = Console.ReadLine();
double[] output = network.ForwardPass(outClass, image, null);
for (int i = 0; i < output.Length; i++)
{
Console.Write("{0} ", output[i]);
}
Console.WriteLine();
}
catch
{
Console.WriteLine("No image or supported image format!");
}
}
}
}