|
private cudnnLRNCrossChannelBackward ( |
||
| handle | ||
| normDesc | ||
| lrnMode | cudnnLRNMode | |
| alpha | double | |
| srcDesc | ||
| srcData | ManagedCuda.BasicTypes.CUdeviceptr | |
| srcDiffDesc | ||
| srcDiffData | ManagedCuda.BasicTypes.CUdeviceptr | |
| destDesc | ||
| destData | ManagedCuda.BasicTypes.CUdeviceptr | |
| beta | double | |
| destDiffDesc | ||
| destDiffData | ManagedCuda.BasicTypes.CUdeviceptr | |
| return | cudnnStatus | |
public void cudnnLRNCrossChannelBackward(
cudnnLRNMode lrnMode,
ref double alpha,
cudnnTensorDescriptor srcDesc,
CUdeviceptr srcData,
cudnnTensorDescriptor srcDiffDesc,
CUdeviceptr srcDiffData,
cudnnTensorDescriptor destDesc,
CUdeviceptr destData,
ref double beta,
cudnnTensorDescriptor destDiffDesc,
CUdeviceptr destDiffData)
{
res = CudaDNNNativeMethods.cudnnLRNCrossChannelBackward(_handle, _desc, lrnMode, ref alpha, srcDesc, srcData, srcDiffDesc, srcDiffData, destDesc, destData, ref beta, destDiffDesc, destDiffData);
Debug.WriteLine(String.Format("{0:G}, {1}: {2}", DateTime.Now, "cudnnLRNCrossChannelBackward", res));
if (res != cudnnStatus.Success)
{
throw new CudaDNNException(res);
}
}
/// <summary>
/// This function performs the backward LRN layer computation.
/// </summary>
/// <param name="lrnMode">LRN layer mode of operation. Currently only
/// CUDNN_LRN_CROSS_CHANNEL_DIM1 is implemented. Normalization is
/// performed along the tensor's dimA[1].</param>
/// <param name="alpha">Pointer to scaling factors (in host memory) used to blend the layer output
/// value with prior value in the destination tensor as follows: dstValue =
/// alpha[0]*resultValue + beta[0]*priorDstValue. Please refer to this section
/// for additional details.</param>
/// <param name="yDesc">Tensor descriptor and pointer in device memory for the bottom layer's
/// data. (Bottom layer is the earlier layer in the computation graph during
/// inference).</param>
/// <param name="y">Tensor descriptor and pointer in device memory for the bottom layer's
/// data. (Bottom layer is the earlier layer in the computation graph during
/// inference).</param>
/// <param name="dyDesc">Tensor descriptor and pointer in device memory for the top layer's
/// cumulative loss differential data (error backpropagation). (Top layer is the
/// later layer in the computation graph during inference).</param>
/// <param name="dy">Tensor descriptor and pointer in device memory for the top layer's
/// cumulative loss differential data (error backpropagation). (Top layer is the
/// later layer in the computation graph during inference).</param>
/// <param name="xDesc">Tensor descriptor and pointer in device memory for the bottom layer's
/// data. (Bottom layer is the earlier layer in the computation graph
/// during inference). Note that these values are not modified during
/// backpropagation.</param>
/// <param name="x">Tensor descriptor and pointer in device memory for the bottom layer's
/// data. (Bottom layer is the earlier layer in the computation graph
/// during inference). Note that these values are not modified during
/// backpropagation.</param>
/// <param name="beta">Pointer to scaling factors (in host memory) used to blend the layer output
/// value with prior value in the destination tensor as follows: dstValue =
/// alpha[0]*resultValue + beta[0]*priorDstValue. Please refer to this section
/// for additional details.</param>
/// <param name="dxDesc">Tensor descriptor and pointer in device memory for the bottom layer's
/// cumulative loss differential data (error backpropagation). (Bottom layer is
/// the earlier layer in the computation graph during inference).</param>
/// <param name="dx">Tensor descriptor and pointer in device memory for the bottom layer's
/// cumulative loss differential data (error backpropagation). (Bottom layer is
/// the earlier layer in the computation graph during inference).</param>
public void cudnnLRNCrossChannelBackward(
cudnnLRNMode lrnMode,
ref double alpha,
cudnnTensorDescriptor yDesc,
CUdeviceptr y,
cudnnTensorDescriptor dyDesc,
CUdeviceptr dy,
cudnnTensorDescriptor xDesc,
CUdeviceptr x,
ref double beta,
cudnnTensorDescriptor dxDesc,
CUdeviceptr dx)
{
res = CudaDNNNativeMethods.cudnnLRNCrossChannelBackward(_handle, _desc, lrnMode, ref alpha, yDesc, y, dyDesc, dy, xDesc, x, ref beta, dxDesc, dx);
Debug.Write(""); //Line(String.Format("{0:G}, {1}: {2}", DateTime.Now, "cudnnLRNCrossChannelBackward", res));
if (res != cudnnStatus.Success)
{
throw new CudaDNNException(res);
}
}
