public void TrainNetwork()
{
var network = CreateNetwork();
for (int i = 0; i < 50; i++)
{
RunTrainning((bytes) =>
{
var value = network.Train(bytes.Select(x => (double)x).ToArray(), 0.0005);
}, 20);
}
network.SaveNetwork(NetworkJsonPath);
Dictionary <int, int> groups = new Dictionary <int, int>();
int index = 0;
RunTrainning((bytes) =>
{
var value = network.Process(bytes.Select(x => (double)x).ToArray()).ToList();
var max = value.Max();
groups[index] = value.IndexOf(max);
index++;
}, 10);
network = NeuralNetworkUtils.LoadNetwork(NetworkJsonPath);
groups = new Dictionary <int, int>();
index = 0;
RunTrainning((bytes) =>
{
var value = network.Process(bytes.Select(x => (double)x).ToArray()).ToList();
var max = value.Max();
groups[index] = value.IndexOf(max);
index++;
}, 10);
Assert.AreEqual(10, groups.Count);
}
private static Network CreateNetwork()
{
var network = NeuralNetworkUtils.CreateNeuralNetwork(28, 28, 10);
return(network);
}
