public void BatchNormalizationLayer_CopyLayerForPredictionModel()
{
var batchSize = 1;
var sut = new BatchNormalizationLayer();
sut.Initialize(3, 3, 1, batchSize, Initialization.GlorotUniform, new Random(232));
var layers = new List <ILayer>();
sut.CopyLayerForPredictionModel(layers);
var actual = (BatchNormalizationLayer)layers.Single();
Assert.AreEqual(sut.Width, actual.Width);
Assert.AreEqual(sut.Height, actual.Height);
Assert.AreEqual(sut.Depth, actual.Depth);
MatrixAsserts.AreEqual(sut.Scale, actual.Scale);
MatrixAsserts.AreEqual(sut.Bias, actual.Bias);
MatrixAsserts.AreEqual(sut.MovingAverageMeans, actual.MovingAverageMeans);
MatrixAsserts.AreEqual(sut.MovingAverageVariance, actual.MovingAverageVariance);
Assert.IsNull(actual.BatchColumnMeans);
Assert.IsNull(actual.BatchcolumnVars);
Assert.AreEqual(sut.OutputActivations.RowCount, actual.OutputActivations.RowCount);
Assert.AreEqual(sut.OutputActivations.ColumnCount, actual.OutputActivations.ColumnCount);
}
public void BatchNormalizationLayer_Backward()
{
const int fanIn = 4;
const int batchSize = 2;
var random = new Random(232);
var sut = new BatchNormalizationLayer();
sut.Initialize(1, 1, fanIn, batchSize, Initialization.GlorotUniform, random);
var data = new float[] { 0, 1, -1, 1, 0.5f, 1.5f, -10, 10 };
var input = Matrix <float> .Build.Dense(batchSize, fanIn, data);
Trace.WriteLine(input.ToString());
sut.Forward(input);
var delta = Matrix <float> .Build.Random(batchSize, fanIn, random.Next());
var actual = sut.Backward(delta);
Trace.WriteLine(string.Join(", ", actual.ToColumnMajorArray()));
var expected = Matrix <float> .Build.Dense(batchSize, fanIn, new float[] { -2.600517E-06f, 2.615418E-06f, -1.349278E-06f, 1.349278E-06f, 1.158319E-06f, -1.150868E-06f, -5.639333E-10f, -9.261829E-10f });
MatrixAsserts.AreEqual(expected, actual);
}
public void BatchNormalizationLayer_Forward_SpatialInput()
{
var batchSize = 2;
var filterHeight = 2;
var filterWidth = 2;
var filterDepth = 2;
var stride = 1;
var padding = 0;
var inputWidth = 3;
var inputHeight = 3;
var inputDepth = 3;
var filterGridWidth = ConvUtils.GetFilterGridLength(inputWidth, filterWidth,
stride, padding, BorderMode.Valid);
var filterGridHeight = ConvUtils.GetFilterGridLength(inputHeight, filterHeight,
stride, padding, BorderMode.Valid);
var k = filterDepth;
var input = new float[] { 111, 121, 112, 122, 113, 123, 114, 124, 211, 221, 212, 222, 213, 223, 214, 224 };
var convInput = Matrix <float> .Build.Dense(2, 8, input);
var rowWiseInput = Matrix <float> .Build.Dense(batchSize, k *filterGridWidth *filterGridHeight);
Trace.WriteLine(convInput);
ConvUtils.ReshapeConvolutionsToRowMajor(convInput, inputDepth, inputHeight, inputWidth,
filterHeight, filterWidth, padding, padding, stride, stride,
BorderMode.Valid, rowWiseInput);
Trace.WriteLine(rowWiseInput);
var sut = new BatchNormalizationLayer();
sut.Initialize(filterGridWidth, filterGridHeight, filterDepth, batchSize,
Initialization.GlorotUniform, new Random(232));
var actual = sut.Forward(rowWiseInput);
Trace.WriteLine(string.Join(", ", actual.ToColumnMajorArray()));
Trace.WriteLine(actual);
var expected = Matrix <float> .Build.Dense(batchSize, k *filterGridWidth *filterGridHeight, new float[] { -1.0297426f, 0.9697576f, -1.00974762f, 0.9897526f, -0.9897526f, 1.00974762f, -0.9697576f, 1.0297426f, -1.0297426f, 0.9697576f, -1.00974762f, 0.9897526f, -0.9897526f, 1.00974762f, -0.9697576f, 1.0297426f });
MatrixAsserts.AreEqual(expected, actual);
}
public void BatchNormalizationLayer_Forward()
{
const int fanIn = 4;
const int batchSize = 2;
var sut = new BatchNormalizationLayer();
sut.Initialize(1, 1, fanIn, batchSize, Initialization.GlorotUniform, new Random(232));
var data = new float[] { 0, 1, -1, 1, 0.5f, 1.5f, -10, 10 };
var input = Matrix <float> .Build.Dense(batchSize, fanIn, data);
Trace.WriteLine(input.ToString());
var actual = sut.Forward(input);
Trace.WriteLine(string.Join(", ", actual.ToColumnMajorArray()));
Trace.WriteLine(actual);
var expected = Matrix <float> .Build.Dense(batchSize, fanIn, new float[] { -0.999998f, 0.999998f, -0.9999995f, 0.9999995f, -0.999998f, 0.999998f, -1, 1 });
MatrixAsserts.AreEqual(expected, actual);
}