public void ForwardBackwardTest()
{
var fl = new FullLayer(3);
var weights = TensorOld.Ones(4, 3);
var bias = TensorOld.Values(0.5, 1, 3);
var input = new TensorOld(new double[] { 1, 2, 3, 4, 5, 6, 7, 8 }, 2, 4);
var error = new TensorOld(new double[] { -1, 0.8, 1.5, 1, -1, 1 }, 2, 3);
var forward = new TensorOld(new double[] { 10.5, 10.5, 10.5, 26.5, 26.5, 26.5 }, 2, 3);
var backward = new TensorOld(new double[] { 1.3, 1.3, 1.3, 1.3, 1, 1, 1, 1 }, 2, 4);
var biasGradient = new TensorOld(new double[] { 0, -0.2, 2.5 }, 1, 3);
var weightsGradient = new TensorOld(new double[, ]
{
{ 4, -4.2, 6.5 },
{ 4, -4.4, 9 },
{ 4, -4.6, 11.5 },
{ 4, -4.8, 14 },
});
fl.PrepareTrain(input);
fl.SetWeights(weights);
fl.SetBias(bias);
fl.Forward(input);
fl.Backward(error);
Assert.Equal(forward, fl.ForwardOutput);
Assert.Equal(backward, fl.BackwardOutput);
MyAssert.ApproximatelyEqual(biasGradient, fl.BiasGradient);
Assert.Equal(weightsGradient, fl.WeightsGradient);
}
public void Play()
{
var data = DataEmulator.Instance.RandomArray(100000);
var tensor = new TensorOld(data, 500, 200);
var full = new FullLayer(100);
full.PrepareTrain(tensor);
var start1 = DateTime.Now;
var output = full.Forward(tensor);
var ts1 = DateTime.Now - start1;
Console.WriteLine($"Forward Time:{ts1.TotalMilliseconds}");
}
public void FullLayerStorageTest()
{
var weights = new TensorOld(new double[] { 1, 2, 3, 4, 5, 6, 7, 8 }, 4, 2);
var bias = new TensorOld(new double[] { 3, 4 }, 1, 2);
var xml = new XmlDocument();
var layer = new FullLayer(2);
layer.SetWeights(weights);
layer.SetBias(bias);
var el = XmlStorage.SaveToEl(xml, layer);
var test = XmlStorage.LoadFromNode <ILayer>(el);
Assert.True(test is FullLayer);
var full = test as FullLayer;
Assert.Equal(2, full.UnitCount);
Assert.Equal(weights, full.Weights);
Assert.Equal(bias, full.Bias);
}
