Library/ManagedCuda/CudaDNN/CudaDNNNativeMethods.cs
/// <summary>
/// This function computes pooling of input values (i.e., the maximum or average of several
/// adjacent values) to produce an output with smaller height and/or width.
/// </summary>
/// <param name="handle">Handle to a previously created cuDNN context.</param>
/// <param name="poolingDesc">Handle to a previously initialized pooling descriptor.</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="xDesc">Handle to the previously initialized input tensor descriptor.</param>
/// <param name="x">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="yDesc">Handle to the previously initialized output tensor descriptor.</param>
/// <param name="y">Data pointer to GPU memory associated with the output tensor descriptor destDesc.</param>
This function computes pooling of input values  to produce an output with smaller height and/or width.
This function computes pooling of input values to produce an output with smaller height and / or width .
[DllImport(CUDNN_API_DLL_NAME)]
		public static extern cudnnStatus cudnnPoolingForward(  cudnnHandle handle,
														cudnnPoolingDescriptor poolingDesc,
														ref float alpha,
														cudnnTensorDescriptor xDesc,
														CUdeviceptr x,
														ref float beta,
														cudnnTensorDescriptor yDesc,
														CUdeviceptr y
													 );
[ DllImport ( CUDNN_API_DLL_NAME ) ] public static extern cudnnStatus cudnnPoolingForward ( cudnnHandle handle , cudnnPoolingDescriptor poolingDesc , ref float alpha , cudnnTensorDescriptor xDesc , CUdeviceptr x , ref float beta , cudnnTensorDescriptor yDesc , CUdeviceptr y ) ;
This function computes pooling of input values (i.e., the maximum or average of several
adjacent values) to produce an output with smaller height and/or width.
This function computes pooling of input values ( i . e . the maximum or average of several adjacent values ) to produce an output with smaller height and / or width .
handle
cudnnHandle
Handle to a previously created cuDNN context.
Handle to a previously created cuDNN context .
poolingDesc
cudnnPoolingDescriptor
Handle to a previously initialized pooling descriptor.
Handle to a previously initialized pooling descriptor .
float
ref
xDesc
cudnnTensorDescriptor
Handle to the previously initialized input tensor descriptor.
Handle to the previously initialized input tensor descriptor .
x
CUdeviceptr
Data pointer to GPU memory associated with the tensor descriptor srcDesc.
Data pointer to GPU memory associated with the tensor descriptor srcDesc .
yDesc
cudnnTensorDescriptor
Handle to the previously initialized output tensor descriptor.
Handle to the previously initialized output tensor descriptor .
y
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 .