public void TrainCorrectThreePasses()
{
Layer layer = new Layer(new double[, ] {
{ 1, 1 }
});
List <TrainingVector> trainingVectors = new List <TrainingVector>
{
new TrainingVector(
new NetworkVector(new double[] { 0, 0 }),
new NetworkVector(new double[] { 1 })
),
new TrainingVector(
new NetworkVector(new double[] { 1, 0 }),
new NetworkVector(new double[] { 0 })
),
new TrainingVector(
new NetworkVector(new double[] { 0, 1 }),
new NetworkVector(new double[] { 0 })
),
new TrainingVector(
new NetworkVector(new double[] { 1, 1 }),
new NetworkVector(new double[] { 1 })
)
};
OnlineTrainer trainer = new OnlineTrainer(layer, trainingVectors);
trainer.Train();
trainer.Train();
trainer.Train();
double[,] weightsCheck = new double[, ] {
{ 3, 7 }
};
double[] biasesCheck = new double[] { -1 };
for (int i = 0; i < layer.NumberOfOutputs; i++)
{
Assert.AreEqual(biasesCheck[i], layer.State.Biases[i]);
for (int j = 0; j < layer.NumberOfInputs; j++)
{
Assert.AreEqual(weightsCheck[i, j], layer.State.Weights[i, j]);
}
}
}
public void CanRunOnlineTrainer()
{
Layer layer = new Layer(new double[, ] {
{ 1, 1 }
});
List <TrainingVector> trainingVectors = new List <TrainingVector>
{
new TrainingVector(
new NetworkVector(new double[] { 0, 0 }),
new NetworkVector(new double[] { 1 })
)
};
OnlineTrainer trainer = new OnlineTrainer(layer, trainingVectors);
trainer.Train();
}