public void DenseLayer_Backward()
{
const int fanIn = 5;
const int batchSize = 2;
const int neuronCount = 3;
var random = new Random(232);
var sut = new DenseLayer(neuronCount, Activation.Undefined);
sut.Initialize(5, 1, 1, batchSize, Initialization.GlorotUniform, random);
var input = Matrix <float> .Build.Random(batchSize, fanIn, random.Next());
sut.Forward(input);
var delta = Matrix <float> .Build.Random(batchSize, neuronCount, random.Next());
var actual = sut.Backward(delta);
Trace.WriteLine(string.Join(", ", actual.ToColumnMajorArray()));
var expected = Matrix <float> .Build.Dense(batchSize, fanIn, new float[] { 0.001748383f, -0.2615477f, -0.6422306f, -0.01443626f, 0.4605991f, -0.7384186f, -0.6931117f, 0.1083627f, -0.6230267f, -1.20742f });
MatrixAsserts.AreEqual(expected, actual);
}
public void DenseLayer_CopyLayerForPredictionModel()
{
var batchSize = 1;
var random = new Random(232);
var neuronCount = 5;
var sut = new DenseLayer(neuronCount, Activation.Undefined);
sut.Initialize(5, 1, 1, batchSize, Initialization.GlorotUniform, random);
var layers = new List <ILayer>();
sut.CopyLayerForPredictionModel(layers);
var actual = (DenseLayer)layers.Single();
Assert.AreEqual(sut.Width, actual.Width);
Assert.AreEqual(sut.Height, actual.Height);
Assert.AreEqual(sut.Depth, actual.Depth);
MatrixAsserts.AreEqual(sut.Weights, actual.Weights);
MatrixAsserts.AreEqual(sut.Bias, actual.Bias);
Assert.AreEqual(sut.OutputActivations.RowCount, actual.OutputActivations.RowCount);
Assert.AreEqual(sut.OutputActivations.ColumnCount, actual.OutputActivations.ColumnCount);
}
public void DenseLayer_MultipleBackwardsPasses()
{
const int fanIn = 5;
const int batchSize = 2;
const int neuronCount = 3;
var random = new Random(232);
var sut = new DenseLayer(neuronCount, Activation.Undefined);
sut.Initialize(5, 1, 1, batchSize, Initialization.GlorotUniform, random);
var input = Matrix <float> .Build.Random(batchSize, fanIn, random.Next());
sut.Forward(input);
var delta = Matrix <float> .Build.Dense(batchSize, neuronCount, 1.0f);
var expected = Matrix <float> .Build.Dense(batchSize, fanIn);
sut.Backward(delta).CopyTo(expected);
for (int i = 0; i < 20; i++)
{
var actual = sut.Backward(delta);
Assert.AreEqual(expected, actual);
}
}
public void EvaluateTest()
{
var layer = new DenseLayer(6, 3, new IdentityActivation(), new Distance());
layer.Initialize();
var input = NNArray.Random(6);
layer.Evaluate(input);
}
public void DenseLayer_Forward()
{
const int fanIn = 5;
const int batchSize = 2;
const int neuronCount = 3;
var random = new Random(232);
var sut = new DenseLayer(neuronCount, Activation.Undefined);
sut.Initialize(5, 1, 1, batchSize, Initialization.GlorotUniform, random);
var input = Matrix <float> .Build.Random(batchSize, fanIn, random.Next());
var actual = sut.Forward(input);
Trace.WriteLine(string.Join(", ", actual.ToColumnMajorArray()));
var expected = Matrix <float> .Build.Dense(batchSize, neuronCount, new float[] { 0.9898463f, 0.4394523f, 0.4259368f, -1.051275f, -0.5012454f, 0.08094172f });
MatrixAsserts.AreEqual(expected, actual);
}
public void LinearRegressionTest()
{
// y = ax + b
double a = 1, b = -2;
int count = 20;
double[] input = new double[count];
double[] expectedOutput = new double[count];
for (int i = 1; i < count; i++)
{
input[i] = i;
expectedOutput[i] = a * i + b;
}
var layer = new DenseLayer(1, 1, new IdentityActivation(), new Distance());
layer.Initialize();
layer.Biases[0] = 0;
layer.Weights[0, 0] = 2;
int epoc = 0;
double error = 100;
while (++epoc < 10000 && error > 0.01)
{
error = layer.Train(new double[] { input[1] }, new double[] { expectedOutput[1] }, 0.01);
error = layer.Train(new double[] { input[2] }, new double[] { expectedOutput[2] }, 0.01);
error = layer.Train(new double[] { input[3] }, new double[] { expectedOutput[3] }, 0.01);
}
for (int n = 0; n < 20; n++)
{
for (int i = 1; i < 20; i++)
{
}
}
double bias = layer.Biases[0];
double coef = layer.Weights[0, 0];
}
