/// <summary>
///
/// </summary>
/// <param name="inputWidth"></param>
/// <param name="inputHeight"></param>
/// <param name="inputDepth"></param>
/// <param name="batchSize"></param>
/// <param name="initializtion"></param>
/// <param name="random"></param>
public void Initialize(int inputWidth, int inputHeight, int inputDepth, int batchSize, Initialization initializtion, Random random)
{
var fans = WeightInitialization.GetFans(this, inputWidth, inputHeight, inputDepth);
var distribution = WeightInitialization.GetWeightDistribution(initializtion, fans, random);
Weights = Matrix <float> .Build.Random(fans.FanIn, fans.FanOut, distribution);
Bias = Vector <float> .Build.Dense(fans.FanOut, 0.0f);
WeightsGradients = Matrix <float> .Build.Dense(fans.FanIn, fans.FanOut);
BiasGradients = Vector <float> .Build.Dense(fans.FanOut);
OutputActivations = Matrix <float> .Build.Dense(batchSize, fans.FanOut);
m_delta = Matrix <float> .Build.Dense(batchSize, fans.FanIn);
}
/// <summary>
///
/// </summary>
/// <param name="inputWidth"></param>
/// <param name="inputHeight"></param>
/// <param name="inputDepth"></param>
/// <param name="batchSize"></param>
/// <param name="initializtion"></param>
/// <param name="random"></param>
public void Initialize(int inputWidth, int inputHeight, int inputDepth, int batchSize,
Initialization initializtion, Random random)
{
InputHeight = inputHeight;
InputWidth = inputWidth;
InputDepth = inputDepth;
var filterGridWidth = ConvUtils.GetFilterGridLength(InputWidth, FilterWidth,
m_stride, m_padWidth, BorderMode);
var filterGridHeight = ConvUtils.GetFilterGridLength(InputHeight, FilterHeight,
m_stride, m_padHeight, BorderMode);
// Calculations of dimensions based on:
// Nvidia, cuDNN: Efficient Primitives for Deep Learning: https://arxiv.org/pdf/1410.0759.pdf
var filterCubeSize = InputDepth * FilterWidth * FilterHeight;
var filterGridSize = filterGridWidth * filterGridHeight;
Height = filterGridHeight;
Width = filterGridWidth;
Depth = FilterCount;
var fans = WeightInitialization.GetFans(this, InputWidth, InputHeight, inputDepth);
var distribution = WeightInitialization.GetWeightDistribution(initializtion, fans, random);
Weights = Matrix <float> .Build.Random(FilterCount, filterCubeSize, distribution);
WeightsGradients = Matrix <float> .Build.Dense(FilterCount, filterCubeSize);
Bias = Vector <float> .Build.Dense(FilterCount, 0.0f);
BiasGradients = Vector <float> .Build.Dense(FilterCount);
Im2Cols = Matrix <float> .Build.Dense(filterCubeSize, filterGridSize *batchSize);
Conv = Matrix <float> .Build.Dense(FilterCount, filterGridSize *batchSize);
OutputActivations = Matrix <float> .Build.Dense(batchSize, FilterCount *filterGridSize);
m_deltaInReshape = Matrix <float> .Build.Dense(FilterCount, filterGridSize *batchSize);
var fanIn = inputWidth * inputHeight * inputDepth;
m_delta = Matrix <float> .Build.Dense(batchSize, fanIn);
}