private void LearnSession(int bestof)
{
if (bestNet == null)
{
bestNet = new NeuralNet(11, 6, NUM_NEURONS, 1, new Random());
}
bestNet.LearnSession(lessonplan, bestof);
Console.WriteLine("-\t-\t- FINAL -\t-\t-\n\nSUCCESS\t\tACC [MIN " + NeuralNet.ACC_THRESHOLD.ToString("f2") + "]");
Console.WriteLine(bestNet.Test(new float[] { 4, 3, 2, 3, 4, 5 }, new float[] { 0 }));
Console.WriteLine(bestNet.Test(new float[] { 4, 4, 2, 2, 3, 3 }, new float[] { 0 }));
Console.WriteLine(bestNet.Test(new float[] { 9, 9, 9, 9, 9, 9 }, new float[] { 0 }));
Console.WriteLine(bestNet.Test(new float[] { 7, 8, 2, 3, 4, 5 }, new float[] { 1 }));
Console.WriteLine(bestNet.Test(new float[] { 0, 8, 2, 3, 4, 1 }, new float[] { 1 }));
Console.WriteLine(bestNet.Test(new float[] { 5, 3, 2, 0, 9, 1 }, new float[] { 1 }));
Console.WriteLine(bestNet.Test(new float[] { 1, 2, 9, 8, 4, 5 }, new float[] { 1 }));
Console.WriteLine(bestNet.Test(new float[] { 1, 0, 2, 3, 4, 6 }, new float[] { 1 }));
Console.WriteLine(bestNet.Test(new float[] { 8, 7, 7, 8, 8, 7 }, new float[] { 0 }));
Console.WriteLine("\nDone! Type 'help' for commands.\n");
string cmd;
while ((cmd = Console.ReadLine().ToLower()) != "")
{
string[] args = cmd.Split(' ');
Commands(args);
}
}
private void Commands(string[] args)
{
string path = "";
switch (args[0].ToLower())
{
case "new":
bestNet = null;
//net.bestScore = 0;
Console.WriteLine("Best network cleared.");
break;
case "test":
float[] vals = new float[args.Length - 1];
bool failed = false;
for (int i = 0; i < vals.Length; i++)
{
if (failed = !float.TryParse(args[i + 1], out vals[i]))
{
break;
}
}
if (failed)
{
Console.WriteLine("Parsing inputs failed.");
break;
}
float[] result = bestNet.Test(vals);
Console.Write("Result: ");
for (int i = 0; i < result.Length; i++)
{
Console.WriteLine(result[i]);
}
break;
case "info":
if (bestNet == null)
{
Console.WriteLine("Neural network is empty.");
break;
}
Console.Write("\nInputs: {0}\nOutputs: {1}\nLayers: ", bestNet.array[0].array.Length, bestNet.array[bestNet.array.Length - 1].array.Length);
for (int i = 1; i < bestNet.array.Length - 2; i++)
{
Console.Write(bestNet.array[i].array.Length + " ");
}
Console.WriteLine("\nCurrent best score: {0}\nCurrent %ACC: {1}", bestNet.bestScore, bestNet.learnLen != 0 ? (bestNet.bestScore / (bestNet.learnLen * 2) * 100) + "%" : "Unknown");
break;
case "save":
if (args.Length < 2)
{
Console.WriteLine("Please specify a file path. Example: C:\\dir\\MyNeuralNet");
break;
}
if (bestNet == null)
{
Console.WriteLine("Cannot save an empty network.");
break;
}
args[0] = "";
path = string.Join(" ", args).Trim();
if (!path.EndsWith(".znn"))
{
path += ".znn";
}
bestNet.SaveNet(path);
Console.WriteLine("Saved to file {0}\n", path);
break;
case "load":
args[0] = "";
bool noBest = false;
if (args[1] == "--noscore" || args[1] == "-ns")
{
noBest = true;
args[1] = "";
}
path = string.Join(" ", args).Trim();
if (!path.EndsWith(".znn"))
{
path += ".znn";
}
if (NeuralNet.LoadNet(ref bestNet, path))
{
if (noBest)
{
bestNet.bestScore = 0;
}
Console.WriteLine(path + " loaded.\n");
}
else
{
Console.WriteLine("File does not exist! {0}\n", path);
}
break;
case "learn":
int bestof;
Console.WriteLine("Best of " + args[1]);
if (int.TryParse(args[1], out bestof))
{
LearnSession(bestof);
}
break;
case "help":
Console.WriteLine("\nhelp\n- Shows this help dialog.\n\n" +
"new\n- Clears the current best neural network so a new one can be created.\n\n" +
"save [file]\n- Saves the 'best' neural network to [file].\n\n" +
"load [-ns] [file]\n- Loads the 'best' neural network from [file].\n-ns : Load without the 'best score.'\n\n" +
"test [inputs]\n- Tests the neural network against the inputs and returns its outputs. Missing inputs are 0. Excess inputs are truncated.\n\n" +
"learn [x]\n- Sends the current neural network through [x] training sessions, or creates one.\n\n" +
"info\n- Gives information about the current neural network.\n\n" +
"exit\n- Quits the program.\n");
break;
case "exit":
Environment.Exit(0);
break;
default:
Console.WriteLine("Unknown command.");
break;
}
}