public void TrainXOR()
{
try {
//Load train data
float[,] testX = new float[, ] {
{ 0, 1 },
};
float[,] x = new float[, ] {
{ 0, 0 }, { 0, 1 }, { 1, 0 }, { 1, 1 }
};
float[] y = new float[] { 0, 1, 1, 0 };
//Build sequential model
var model = new Sequential();
model.Add(new Dense(32, activation: "relu", input_shape: new Shape(2)));
model.Add(new Dense(32, activation: "relu"));
model.Add(new Dropout(0.1d));
model.Add(new Dense(1, activation: "sigmoid"));
//Compile and train
var optimizer = new Adam();
model.Compile(optimizer: optimizer, loss: "mse", metrics: new string[] { "accuracy" });
model.Fit(x, y, batch_size: 2, epochs: 1000, verbose: 1);
float[] predicts;
predicts = model.Predict(x).GetData <float>();
predicts = model.PredictOnBatch(x).GetData <float>();
predicts = model.Predict(x).GetData <float>();
predicts = model.PredictOnBatch(x).GetData <float>();
predicts = model.Predict(x).GetData <float>();
predicts = model.PredictOnBatch(x).GetData <float>();
Stopwatch watch = new Stopwatch();
watch.Restart();
for (int i = 0; i < 5; ++i)
{
predicts = model.PredictOnBatch(testX).GetData <float>();
}
watch.Stop();
string batchMs = watch.GetElapsedMilliseconds().ToString();
watch.Restart();
for (int i = 0; i < 5; ++i)
{
predicts = model.Predict(testX).GetData <float>();
}
watch.Stop();
//MainWindow.Instance.Dispatcher.BeginInvoke(new Action(() => {
// MainWindow.Instance.DebugTextBox.Text = batchMs + " / " + watch.GetElapsedMilliseconds().ToString();
//}));
} catch (Exception ex) {
//MainWindow.Instance.Dispatcher.BeginInvoke(new Action(() => {
// MainWindow.Instance.DebugTextBox.Text = ex.ToString();
//}));
}
}
