public void Filler_GaussianDense(double meanParam, double stdParam)
{
var blob = new Tensor(2, 3, 4, 5);
var config = new GaussianFillerConfiguration(meanParam, stdParam);
var filler = new GaussianFiller(config);
filler.Fill(blob);
double mean = 0;
double var = 0;
int count = blob.Count;
using (var blobCpu = blob.OnCpu())
{
for (int i = 0; i < count; i++)
{
mean += blobCpu.DataAt(i);
var += (blobCpu.DataAt(i) - config.Mean) * (blobCpu.DataAt(i) - config.Mean);
}
mean /= count;
var /= count;
Assert.True(mean >= config.Mean - config.Std * 5);
Assert.True(mean <= config.Mean + config.Std * 5);
double targetVar = config.Std * config.Std;
Assert.True(var >= (targetVar / 5.0d));
Assert.True(var <= (targetVar * 5.0d));
}
}
public void Filler_GaussianDense(double meanParam, double stdParam)
{
var blob = new Tensor(2, 3, 4, 5);
var config = new GaussianFillerConfiguration(meanParam, stdParam);
var filler = new GaussianFiller(config);
filler.Fill(blob);
double mean = 0;
double var = 0;
int count = blob.Count;
using (var blobCpu = blob.OnCpu())
{
for (int i = 0; i < count; i++)
{
mean += blobCpu.DataAt(i);
var += (blobCpu.DataAt(i) - config.Mean) * (blobCpu.DataAt(i) - config.Mean);
}
mean /= count;
var /= count;
Assert.True(mean >= config.Mean - config.Std * 5);
Assert.True(mean <= config.Mean + config.Std * 5);
double targetVar = config.Std * config.Std;
Assert.True(var >= (targetVar / 5.0d));
Assert.True(var <= (targetVar * 5.0d));
}
}
public EuclideanLossLayerTests()
{
var filler = new GaussianFiller();
blobBottomData = new Tensor(10, 5, 1, 1);
filler.Fill(blobBottomData);
bottom.Add(blobBottomData);
blobBottomLabel = new Tensor(10, 5, 1, 1);
filler.Fill(blobBottomLabel);
bottom.Add(blobBottomLabel);
}
public EuclideanLossLayerTests()
{
var filler = new GaussianFiller();
blobBottomData = new Tensor(10, 5, 1, 1);
filler.Fill(blobBottomData);
bottom.Add(blobBottomData);
blobBottomLabel = new Tensor(10, 5, 1, 1);
filler.Fill(blobBottomLabel);
bottom.Add(blobBottomLabel);
}
public SoftmaxLossLayerTests()
{
var filler = new GaussianFiller();
filler.Fill(bottom);
Random rnd = new Random(1000);
using (var labelsCpu = labels.OnCpu())
{
var labelData = labelsCpu.Data;
for (int i = 0; i < labelData.Count; i++)
labelData[i] = rnd.Next(5);
}
}
public SoftmaxLossLayerTests()
{
var filler = new GaussianFiller();
filler.Fill(bottom);
Random rnd = new Random(1000);
using (var labelsCpu = labels.OnCpu())
{
var labelData = labelsCpu.Data;
for (int i = 0; i < labelData.Count; i++)
{
labelData[i] = rnd.Next(5);
}
}
}
public void Filler_GaussianSparse(double meanParam, double stdParam)
{
var blob = new Tensor(2, 3, 4, 5);
var config = new GaussianFillerConfiguration(meanParam, stdParam)
{
IsSparse = true
};
var filler = new GaussianFiller(config);
filler.Fill(blob);
double mean = 0;
double var = 0;
using (var blobCpu = blob.OnCpu())
{
int count = blob.Count;
int zeroes = 0;
for (int i = 0; i < count; i++)
{
if (blobCpu.DataAt(i) == 0.0d)
{
zeroes++;
}
else
{
mean += blobCpu.DataAt(i);
var += (blobCpu.DataAt(i) - config.Mean) * (blobCpu.DataAt(i) - config.Mean);
}
}
mean /= (count - zeroes);
var /= (count - zeroes);
Assert.True(mean >= config.Mean - config.Std * 5);
Assert.True(mean <= config.Mean + config.Std * 5);
double targetVar = config.Std * config.Std;
Assert.True(var >= (targetVar / 5.0d));
Assert.True(var <= (targetVar * 5.0d));
}
}
public MaxPoolingLayerTests()
{
var filler = new GaussianFiller();
filler.Fill(bottom);
}
public BnllLayerTests()
{
var filler = new GaussianFiller();
filler.Fill(bottom);
}
public SoftmaxLayerTests()
{
var filler = new GaussianFiller();
filler.Fill(bottom);
}
public AveragePoolingLayerTests()
{
var filler = new GaussianFiller();
filler.Fill(bottom);
}
public SigmoidLayerTests()
{
var filler = new GaussianFiller();
filler.Fill(bottom);
}
public DropoutLayerTests()
{
var filler = new GaussianFiller();
filler.Fill(bottom);
}
public ThresholdLayerTests()
{
var filler = new GaussianFiller();
filler.Fill(bottom);
}
public void Filler_GaussianSparse(double meanParam, double stdParam)
{
var blob = new Tensor(2, 3, 4, 5);
var config = new GaussianFillerConfiguration(meanParam, stdParam) { IsSparse = true };
var filler = new GaussianFiller(config);
filler.Fill(blob);
double mean = 0;
double var = 0;
using (var blobCpu = blob.OnCpu())
{
int count = blob.Count;
int zeroes = 0;
for (int i = 0; i < count; i++)
{
if (blobCpu.DataAt(i) == 0.0d)
{
zeroes++;
}
else
{
mean += blobCpu.DataAt(i);
var += (blobCpu.DataAt(i) - config.Mean) * (blobCpu.DataAt(i) - config.Mean);
}
}
mean /= (count - zeroes);
var /= (count - zeroes);
Assert.True(mean >= config.Mean - config.Std * 5);
Assert.True(mean <= config.Mean + config.Std * 5);
double targetVar = config.Std * config.Std;
Assert.True(var >= (targetVar / 5.0d));
Assert.True(var <= (targetVar * 5.0d));
}
}
public SoftmaxLayerTests()
{
var filler = new GaussianFiller();
filler.Fill(bottom);
}
public DropoutLayerTests()
{
var filler = new GaussianFiller();
filler.Fill(bottom);
}
public ThresholdLayerTests()
{
var filler = new GaussianFiller();
filler.Fill(bottom);
}
public TanhLayerTests()
{
var filler = new GaussianFiller();
filler.Fill(bottom);
}
public SigmoidLayerTests()
{
var filler = new GaussianFiller();
filler.Fill(bottom);
}
