Library/ManagedCuda/CudaDNN/CudaDNNNativeMethods.cs
/// <summary>
/// This function computes the convolution gradient with respect to the bias, which is the
/// sum of every element belonging to the same feature map across all of the images of the
/// input tensor. Therefore, the number of elements produced is equal to the number of
/// features maps of the input tensor.
/// </summary>
/// <param name="handle">Handle to a previously created cuDNN context.</param>
/// <param name="alpha">Pointer to scaling factors (in host memory) used to blend the computation
/// result with prior value in the output layer as follows: dstValue =
/// alpha[0]*result + beta[0]*priorDstValue. Please refer to this section for
/// additional details.</param>
/// <param name="dyDesc">Handle to the previously initialized input tensor descriptor.</param>
/// <param name="dy">Data pointer to GPU memory associated with the tensor descriptor srcDesc.</param>
/// <param name="beta">Pointer to scaling factors (in host memory) used to blend the computation
/// result with prior value in the output layer as follows: dstValue =
/// alpha[0]*result + beta[0]*priorDstValue. Please refer to this section for
/// additional details.</param>
/// <param name="dbDesc">Handle to the previously initialized output tensor descriptor.</param>
/// <param name="db">Data pointer to GPU memory associated with the output tensor descriptor destDesc.</param>
This function computes the convolution gradient with respect to the bias, which is the
sum of every element belonging to the same feature map across all of the images of the
input tensor. Therefore, the number of elements produced is equal to the number of
features maps of the input tensor.
This function computes the convolution gradient with respect to the bias which is the sum of every element belonging to the same feature map across all of the images of the input tensor . Therefore the number of elements produced is equal to the number of features maps of the input tensor .
[DllImport(CUDNN_API_DLL_NAME)]
		public static extern cudnnStatus cudnnConvolutionBackwardBias(cudnnHandle handle,
																	  ref float alpha,
																	  cudnnTensorDescriptor dyDesc,
																	  CUdeviceptr dy,
																	  ref float beta,
																	  cudnnTensorDescriptor dbDesc,
																	  CUdeviceptr db
															  );
[ DllImport ( CUDNN_API_DLL_NAME ) ] public static extern cudnnStatus cudnnConvolutionBackwardBias ( cudnnHandle handle , ref float alpha , cudnnTensorDescriptor dyDesc , CUdeviceptr dy , ref float beta , cudnnTensorDescriptor dbDesc , CUdeviceptr db ) ;
This function computes the convolution gradient with respect to the bias, which is the
sum of every element belonging to the same feature map across all of the images of the
input tensor.
This function computes the convolution gradient with respect to the bias which is the sum of every element belonging to the same feature map across all of the images of the input tensor .
handle
cudnnHandle
Handle to a previously created cuDNN context.
Handle to a previously created cuDNN context .
float
ref
dyDesc
cudnnTensorDescriptor
Handle to the previously initialized input tensor descriptor.
Handle to the previously initialized input tensor descriptor .
dy
CUdeviceptr
Data pointer to GPU memory associated with the tensor descriptor srcDesc.
Data pointer to GPU memory associated with the tensor descriptor srcDesc .
dbDesc
cudnnTensorDescriptor
Handle to the previously initialized output tensor descriptor.
Handle to the previously initialized output tensor descriptor .
db
CUdeviceptr
Data pointer to GPU memory associated with the output tensor descriptor destDesc.
Data pointer to GPU memory associated with the output tensor descriptor destDesc .
alpha
Pointer to scaling factors (in host memory) used to blend the computation
result with prior value in the output layer as follows: dstValue
alpha[0]*result + beta[0]*priorDstValue. Please refer to this section for
additional details.
Pointer to scaling factors ( in host memory ) used to blend the computation result with prior value in the output layer as follows : dstValue alpha [ 0 ] * result + beta [ 0 ] * priorDstValue . Please refer to this section for additional details .
beta
Pointer to scaling factors (in host memory) used to blend the computation
result with prior value in the output layer as follows: dstValue
alpha[0]*result + beta[0]*priorDstValue. Please refer to this section for
additional details.
Pointer to scaling factors ( in host memory ) used to blend the computation result with prior value in the output layer as follows : dstValue alpha [ 0 ] * result + beta [ 0 ] * priorDstValue . Please refer to this section for additional details .