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(); }