private void button8_Click(object sender, EventArgs e)
{
nn = new NeuralUtility();
lLog2.Items.Add("Loading... wait...");
nn.LoadNetwork("net1.txt");
lLog2.Items.Add("Loaded from net.txt");
lLog.Items.Clear();
nn.LogFunction = Log;
nn.ShowDefinition();
nn.LogFunction = Log2;
}
private void button3_Click(object sender, EventArgs e)
{
Clear();
nn = new NeuralUtility();
nn.activation = ACTIVATION.SIGMOID;
nn.loss = LOSS.MSE;
nn.connection = CONNECTIONS.ALL_IN_ALL;
nn.bias = 0.000;
nn.learnFactor = 0.01;
nn.LoadFromFile(@"C:\Users\Jonathan\Documents\Visual Studio 2012\Projects\Train_MNIST\Images\0.png", true);
nn.AddLayer(196);
nn.AddLayer(49);
nn.activation = ACTIVATION.SOFTMAX;
nn.loss = LOSS.CROSS_ENTROPY;
nn.AddLayer(10);
nn.Randomize(2, 9, -1, true);
// entry
/*
* nn.AddLayer(2);
*
* //hidden
* nn.activation = ACTIVATION.SIGMOID;
* nn.AddLayer(2);
*
* //output
* nn.AddLayer(1);
*
* nn.Randomize(2, 9, -1, true);
*
* List<Tuple<double, double, double>> trainList = new List<Tuple<double, double, double>>();
* Tuple<double, double, double> value;
*
* value = new Tuple<double, double, double>(0.00, 0.00, 0.00);
* trainList.Add(value);
*
* value = new Tuple<double, double, double>(0.00, 1.00, 1.00);
* trainList.Add(value);
*
* value = new Tuple<double, double, double>(1.00, 0.00, 1.00);
* trainList.Add(value);
*
* value = new Tuple<double, double, double>(1.00, 1.00, 0.00);
* trainList.Add(value);
*
*
* //train
* for (int i = 0; i < 10000; i++)
* {
* foreach (Tuple<double, double, double> item in trainList)
* {
* nn.entry.Clear();
* nn.entry.Add(item.Item1);
* nn.entry.Add(item.Item2);
*
* nn.expected.Clear();
* nn.expected.Add(item.Item3);
*
* nn.Process();
* nn.Learn();
* }
* }
*
*
* lLog2.Items.Clear();
*
* foreach (Tuple<double, double, double> item in trainList)
* {
* nn.entry.Clear();
* nn.entry.Add(item.Item1);
* nn.entry.Add(item.Item2);
* nn.Process();
*
* lLog2.Items.Add("Test: " + ((int)item.Item1).ToString() + " - " + ((int)item.Item2).ToString());
* lLog2.Items.Add(" result: " + nn.output[0].ToString("0.0000"));
* }
*/
start = 1;
finish = 40000;
test = 1000;
nn.LogFunction = Log;
nn.ShowDefinition();
}
