static Matrix feedForwardAlgirithm(String[] words)
{
try
{
if (words.Length == 1)
{
Console.WriteLine("Feed Forward must have some parameters!");
return(null);
}
float[] input = new float[words.Length - 1];
for (int i = 0; i < words.Length - 1; i++)
{
input[i] = float.Parse(words[i + 1]);
}
if (nn == null)
{
Console.WriteLine("You must first initialize a Neural Network");
return(null);
}
return(nn.feedforward(input));
} catch (Exception e)
{
Console.WriteLine("Feed Forward Failed!");
printError(e);
return(null);
}
}
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!");
}
