/// <summary>
/// Backpropagate "errors" to previous layer for future use
/// </summary>
protected override void DoBackprop(DeepLayerBase prevLayer, double[][,] prevValues, double[][,] prevErrors, double[][,] errors)
{
if (prevLayer == null)
{
throw new MLException("Prev layer is null");
}
for (int p = 0; p < m_InputDepth; p++)
{
for (int y = 0; y < m_WindowHeight; y++)
{
for (int x = 0; x < m_WindowWidth; x++)
{
var g = 0.0D;
for (int q = 0; q < m_OutputDepth; q++)
{
var idx = x + y * m_WindowWidth + p * m_KernelParamCount + q * m_FeatureMapParamCount;
g += errors[q][0, 0] * m_Weights[idx]; // Kernel(q, p, y, x)
}
var value = prevValues[p][y, x];
var deriv = (prevLayer.ActivationFunction != null) ? prevLayer.ActivationFunction.DerivativeFromValue(value) : 1;
prevErrors[p][y, x] = g * deriv;
}
}
}
}
protected override void DoBackprop(DeepLayerBase prevLayer, double[][,] prevValues, double[][,] prevErrors, double[][,] errors)
{
for (int i = 0; i < prevErrors.Length; i++)
{
Array.Clear(prevErrors[i], 0, prevErrors[i].Length);
}
var maxPos = MaxIndexPositions;
// backpropagate "errors" to previous layer for future use
for (int q = 0; q < m_OutputDepth; q++)
{
for (int i = 0; i < m_OutputHeight; i++)
{
for (int j = 0; j < m_OutputWidth; j++)
{
var xmaxIdx = maxPos.Value[q][i, j, 0];
var ymaxIdx = maxPos.Value[q][i, j, 1];
var value = prevValues[q][ymaxIdx, xmaxIdx];
var deriv = (prevLayer.ActivationFunction != null) ? prevLayer.ActivationFunction.DerivativeFromValue(value) : 1;
prevErrors[q][ymaxIdx, xmaxIdx] += errors[q][i, j] * deriv;
}
}
}
}
/// <summary>
/// Backpropagate "errors" to previous layer for future use
/// </summary>
/// <param name="prevLayer">Previous layer</param>
/// <param name="errors">Current layer gradient "errors"</param>
/// <param name="updates">Previous layer gradient "errors"</param>
public void Backprop(DeepLayerBase prevLayer, double[][,] prevValues, double[][,] prevErrors, double[][,] errors)
{
if (!m_IsTraining)
{
throw new MLException("Backpropagation can not run in test mode");
}
DoBackprop(prevLayer, prevValues, prevErrors, errors);
}
/// <summary>
/// Backpropagate "errors" to previous layer for future use
/// </summary>
protected override void DoBackprop(DeepLayerBase prevLayer, double[][,] prevValues, double[][,] prevErrors, double[][,] errors)
{
if (prevLayer == null)
{
throw new MLException("Prev layer is null");
}
for (int p = 0; p < m_InputDepth; p++)
{
for (int i = 0; i < m_InputHeight; i++)
{
for (int j = 0; j < m_InputWidth; j++)
{
var g = 0.0D;
for (int q = 0; q < m_OutputDepth; q++)
{
var y = i + m_PaddingHeight + m_StrideHeight;
for (int k = 0; k < m_OutputHeight; k++)
{
y -= m_StrideHeight;
if (y >= m_WindowHeight)
{
continue;
}
if (y < 0)
{
break;
}
var x = j + m_PaddingWidth + m_StrideWidth;
for (int m = 0; m < m_OutputWidth; m++)
{
x -= m_StrideWidth;
if (x >= m_WindowWidth)
{
continue;
}
if (x < 0)
{
break;
}
var idx = x + y * m_WindowWidth + p * m_KernelParamCount + q * m_FeatureMapParamCount;
g += errors[q][k, m] * m_Weights[idx]; // Kernel(q, p, y, x)
}
}
}
var value = prevValues[p][i, j];
var deriv = (prevLayer.ActivationFunction != null) ? prevLayer.ActivationFunction.DerivativeFromValue(value) : 1;
prevErrors[p][i, j] = g * deriv;
}
}
}
}
protected override void DoBackprop(DeepLayerBase prevLayer, double[][,] prevValues, double[][,] prevError, double[][,] errors)
{
for (int p = 0; p < m_OutputDepth; p++)
{
for (int i = 0; i < m_OutputHeight; i++)
{
for (int j = 0; j < m_OutputWidth; j++)
{
var value = prevValues[p][i, j];
var deriv = (prevLayer.ActivationFunction != null) ? prevLayer.ActivationFunction.DerivativeFromValue(value) : 1;
prevError[p][i, j] = errors[p][i, j] * deriv;
}
}
}
}
/// <summary>
/// Backpropagate "errors" to previous layer for future use
/// </summary>
protected override void DoBackprop(DeepLayerBase prevLayer, double[][,] prevValues, double[][,] prevError, double[][,] errors)
{
if (prevLayer == null)
{
throw new MLException("Prev layer is null");
}
var plen = m_InputDepth + 1;
for (int p = 0; p < m_InputDepth; p++)
{
var g = 0.0D;
for (int q = 0; q < m_OutputDepth; q++)
{
g += errors[q][0, 0] * m_Weights[p + q * plen]; // Kernel(q, p, 0, 0)
}
var value = prevValues[p][0, 0];
var deriv = (prevLayer.ActivationFunction != null) ? prevLayer.ActivationFunction.DerivativeFromValue(value) : 1;
prevError[p][0, 0] = g * deriv;
}
}
protected override void DoBackprop(DeepLayerBase prevLayer, double[][,] prevValues, double[][,] prevErrors, double[][,] errors)
{
throw new NotImplementedException(); //TODO
}
protected abstract void DoBackprop(DeepLayerBase prevLayer, double[][,] prevValues, double[][,] prevError, double[][,] errors);
protected override void DoBackprop(DeepLayerBase prevLayer, double[][,] prevValues, double[][,] prevError, double[][,] errors)
{
throw new NotSupportedException();
}