cudnnLRNCrossChannelForward() private méthode

private cudnnLRNCrossChannelForward ( cudnnHandle handle, cudnnLRNDescriptor normDesc, cudnnLRNMode lrnMode, double &alpha, cudnnTensorDescriptor srcDesc, ManagedCuda.BasicTypes.CUdeviceptr srcData, double &beta, cudnnTensorDescriptor destDesc, ManagedCuda.BasicTypes.CUdeviceptr destData ) : cudnnStatus
handle cudnnHandle
normDesc cudnnLRNDescriptor
lrnMode cudnnLRNMode
alpha double
srcDesc cudnnTensorDescriptor
srcData ManagedCuda.BasicTypes.CUdeviceptr
beta double
destDesc cudnnTensorDescriptor
destData ManagedCuda.BasicTypes.CUdeviceptr
Résultat cudnnStatus
CudaDNNNativeMethods Class Documentation
Exemple #1
0
 public void cudnnLRNCrossChannelForward(
     cudnnLRNMode lrnMode,
     double alpha,
     cudnnTensorDescriptor srcDesc,
     CUdeviceptr srcData,
     double beta,
     cudnnTensorDescriptor destDesc,
     CUdeviceptr destData)
 {
     res = CudaDNNNativeMethods.cudnnLRNCrossChannelForward(_handle, _desc, lrnMode, ref alpha, srcDesc, srcData, ref beta, destDesc, destData);
     Debug.WriteLine(String.Format("{0:G}, {1}: {2}", DateTime.Now, "cudnnLRNCrossChannelForward", res));
     if (res != cudnnStatus.Success)
     {
         throw new CudaDNNException(res);
     }
 }
Exemple #2
0
 /// <summary>
 /// This function performs the forward 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="xDesc">Tensor descriptor objects for the input and output tensors.</param>
 /// <param name="x">Input tensor data pointer in device memory.</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="yDesc">Tensor descriptor objects for the input and output tensors.</param>
 /// <param name="y">Output tensor data pointer in device memory.</param>
 public void cudnnLRNCrossChannelForward(
     cudnnLRNMode lrnMode,
     float alpha,
     cudnnTensorDescriptor xDesc,
     CUdeviceptr x,
     float beta,
     cudnnTensorDescriptor yDesc,
     CUdeviceptr y)
 {
     res = CudaDNNNativeMethods.cudnnLRNCrossChannelForward(_handle, _desc, lrnMode, ref alpha, xDesc, x, ref beta, yDesc, y);
     Debug.Write("");            //Line(String.Format("{0:G}, {1}: {2}", DateTime.Now, "cudnnLRNCrossChannelForward", res));
     if (res != cudnnStatus.Success)
     {
         throw new CudaDNNException(res);
     }
 }