public Task TrainAsync(MLPointCollection points)
{
return(Task.Factory.StartNew(() =>
{
_modelDispatcher.Invoke(() =>
{
Log.Info("Training");
var xydata = _dataGenerator.GetPointsXYData(points);
var x = np.array(xydata.x.ToArray());
var y = np.array(xydata.y.ToArray());
var count = (int)(xydata.x.Count / (decimal)xydata.dataItemsPerX);
x = x.reshape(count, xydata.dataItemsPerX);
y = y.reshape(count, 3);
//Tensorflow.InvalidArgumentError: 'In[0] mismatch In[1] shape: 28 vs. 1120: [5,28] [1120,60] 0 0'
/*_model = keras.Sequential(
* new List<ILayer>
* {
* new Flatten(new FlattenArgs
* {
* InputShape = new TensorShape(xydata.dataItemsPerX)
* }),
* //keras.layers.Flatten(),
* keras.layers.Dense(xydata.dataItemsPerX, activation: "relu"),//, input_shape: new TensorShape(-1, xydata.dataItemsPerX)),
* keras.layers.Dense(60, activation: "relu"),
* keras.layers.Dense(40, activation: "relu"),
* keras.layers.Dense(3, activation: "softmax"),
* });
*
* _model.compile(keras.optimizers.SGD(0.01F), keras.losses.CategoricalCrossentropy(from_logits: true),
*/
var numberOfClasses = 3;
_model = keras.Sequential(
new List <ILayer>
{
new Flatten(new FlattenArgs
{
InputShape = new TensorShape(xydata.dataItemsPerX)
}),
//keras.layers.Flatten(),
keras.layers.Dense(xydata.dataItemsPerX, activation: "relu"), //, input_shape: new TensorShape(-1, xydata.dataItemsPerX)),
keras.layers.Dropout(0.2F),
keras.layers.Dense(12, activation: "relu"),
keras.layers.Dropout(0.2F),
keras.layers.Dense(6, activation: "relu"),
keras.layers.Dense(numberOfClasses, activation: "softmax"),
});
//var loss = new SGD(0.05F);
//var optimiser = new SparseCategoricalCrossentropy();
//model.compile(loss, optimiser, new[] { "accuracy" });
//model.compile(new SGD(0.1F), new SparseCategoricalCrossentropy(), new[] { "accuracy" });
// logits and labels must have the same first dimension, got logits shape [5,3] and labels shape [15]'
_model.compile(
keras.optimizers.Adam(0.01F),
keras.losses.CategoricalCrossentropy(),
new[] { "acc" });
//here // SparseCategoricalCrossentropy? Validation set? More generated data?
_model.fit(x, y, 5, 100, 1, validation_split: 0.1F);
Log.Info("Training complete");
});
}));//, TaskCreationOptions.LongRunning);
}
public (List <float> x, List <float> y, List <Candle> candlesUsed) GetPointXYData(MLPoint p, MLPointCollection points)
{
var candlesLookup = GetCandlesLookup(points);
return(GetPointXYData(p, candlesLookup));
}
