static void Train(String[] words)
{
Layer[] layer = new Layer[]
{
new ConvolutionalLayer(3, 3, 3, 0, 1),
new ConvolutionalLayer(3, 3, 3, 0, 1), //26
new Lib.Layers.Convolutional.LeakyReluLayer(),
new ConvolutionalLayer(2, 2, 3, 0, 1), //24
new Lib.Layers.Convolutional.LeakyReluLayer(),
new ConvolutionalLayer(2, 2, 3, 0, 1), //22
new Lib.Layers.Convolutional.LeakyReluLayer(),
new FullyConnectedLayer(1728),
new SoftmaxLayer(10)
};
nn = new Lib.NeuralNetwork(Lib.NeuralNetwork.random, layer);
if (nn == null)
{
Console.WriteLine("you must first initialize a Neural Network.");
return;
}
TrainingData[] data = Loader.loadTestMNSIT(@"C:\Users\drumm\Desktop\MNIST");
Trainer trainer = new Trainer(data);
Matrix.table(nn.feedforward(data[0].inputs));
Matrix.table(data[0].labels);
trainer.Train(nn);
Matrix.table(data[0].labels);
Matrix.table(nn.feedforward(data[0].inputs));
Console.WriteLine("Training is completed succesfully!");
}
static void createNeuralNetwork(String[] words)
{
Random r = Lib.NeuralNetwork.random;
try
{
if (Char.IsDigit(words[1].ToCharArray()[0]))
{
int[] nodes = new int[words.Length - 1];
for (int i = 0; i < nodes.Length; i++)
{
nodes[i] = int.Parse(words[i + 1]);
}
nn = new Lib.NeuralNetwork(r, nodes);
}
else
{
Layer[] layers = new Layer[(words.Length - 1) / 2];
for (int i = 1; i < words.Length; i += 2)
{
if (words[i].Equals("relu"))
{
int nodes = int.Parse(words[i + 1]);
layers[(i - 1) / 2] = new ReluLayer(nodes);
}
else if (words[i].Equals("do"))
{
int nodes = int.Parse(words[i + 1]);
layers[(i - 1) / 2] = new DropoutLayer(nodes);
}
else if (words[i].Equals("lrelu"))
{
int nodes = int.Parse(words[i + 1]);
layers[(i - 1) / 2] = new Lib.Layers.LeakyReluLayer(nodes);
}
else
{
Console.WriteLine("There is no layer type like \"" + words[i] + "\"");
return;
}
}
for (int i = 0; i < layers.Length; i++)
{
if (i != layers.Length - 1)
{
layers[i].weights = new Matrix(layers[i + 1].nodes, layers[i].nodes);
layers[i].weights.randomize(r);
}
layers[i].bias = new Matrix(layers[i].nodes, 1);
layers[i].bias.randomize(r);
}
nn = new Lib.NeuralNetwork(Lib.NeuralNetwork.random, layers);
}
} catch (Exception e)
{
printError(e);
return;
}
Console.WriteLine("Neural Network was created succesfully!");
}
public static NeuralNetwork loadNetwork(String file_name)
{
String text;
String[] sections;
Layer[] layers;
using (StreamReader streamReader = new StreamReader("models\\" + file_name + ".network", Encoding.UTF8))
{
text = streamReader.ReadToEnd();
sections = text.Split("#".ToCharArray());
}
String[] values = sections[0].Split(";".ToCharArray());
layers = new Layer[values.Length];
for (int i = 0; i < sections.Length; i++)
{
if (i == 0)
{
for (int j = 0; j < layers.Length; j++)
{
String[] curText = values[j].Split(" ".ToCharArray());
if (curText[0] == "relu")
{
int layer_nodes = int.Parse(curText[1]);
layers[j] = new ReluLayer(layer_nodes);
}
else if (curText[0] == "do")
{
int layer_nodes = int.Parse(curText[1]);
layers[j] = new DropoutLayer(layer_nodes);
}
else if (curText[0] == "lrelu")
{
int layer_nodes = int.Parse(curText[1]);
layers[j] = new LeakyReluLayer(layer_nodes);
}
if (j == layers.Length - 1)
{
layers[j].weights = new Matrix(1, 1);
}
}
}
else if (layers[i - 1].weights == null)
{
int index = i - 1;
values = sections[i].Split(";".ToCharArray());
int rows = values.Length;
int cols = values[0].Split(" ".ToCharArray()).Length;
Matrix m = new Matrix(rows, cols);
for (int j = 0; j < values.Length; j++)
{
String[] comp = values[j].Split(" ".ToCharArray());
for (int k = 0; k < comp.Length; k++)
{
m.data[j, k] = float.Parse(comp[k]);
}
}
Matrix.table(m);
layers[index].weights = m;
}
}
NeuralNetwork nn = new NeuralNetwork(layers);
return(nn);
}
