static void Main(string[] args)
{
//var net = MNIST_Train();
//net.SaveModel("MNIST_model.json");
//net.SaveModel("../MNIST_model.json");
var net = new SimpleNeuralNet(784, 200, 10);
net.Initialize();
net.LoadModel("../MNIST_model.json");
Kaggle_MNIST_Test(net);
}
private static SimpleNeuralNet MNIST_Train(int epochs = 10)
{
var lines = File.ReadAllLines("../mnist_train.csv");
Vector <double>[] train_data = new Vector <double> [lines.Length];
Vector <double>[] target_data = new Vector <double> [lines.Length];
var j = -1;
foreach (var line in lines)
{
var words = line.Split(',');
string label = words[0];
double[] nums = new double[words.Length - 1];
for (int i = 1; i < words.Length; i++)
{
nums[i - 1] = Convert.ToDouble(words[i]);
nums[i - 1] = (nums[i - 1] / 255.0) * 0.99 + 0.01; // normalize the nums to be between 0.01 - 1 (inclusive)
}
train_data[++j] = DenseVector.OfArray(nums);
var target = new double[10];
for (int i = 0; i < 10; i++)
{
target[i] = 0.01;
}
target[Convert.ToInt32(label)] = 0.99;
target_data[j] = DenseVector.OfArray(target);
}
Console.WriteLine("Initialize the network...");
var net = new SimpleNeuralNet(train_data[0].Count, 200, 10);
net.Initialize();
for (int k = 0; k < epochs; k++)
{
Console.WriteLine("start training: epoch[" + (k + 1) + "]");
for (int i = 0; i < train_data.Length; i++)
{
net.Train(train_data[i], target_data[i]);
}
}
return(net);
}