Library/ManagedCuda/CudaDNN/LRNDescriptor.cs
/// <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>
This function performs the backward LRN layer computation.
This function performs the backward LRN layer computation .
public void cudnnLRNCrossChannelBackward(
                                      cudnnLRNMode lrnMode,
                                      ref float alpha,
                                      cudnnTensorDescriptor yDesc,
                                      CUdeviceptr y,
                                      cudnnTensorDescriptor dyDesc,
                                      CUdeviceptr dy,
                                      cudnnTensorDescriptor xDesc,
                                      CUdeviceptr x,
                                      ref float beta,
                                      cudnnTensorDescriptor dxDesc,
                                      CUdeviceptr dx)
        {
            res = CudaDNNNativeMethods.cudnnLRNCrossChannelBackward(_handle, _desc, lrnMode, ref alpha, yDesc, y, dyDesc, dy, xDesc, x, ref beta, dxDesc, dx);
            Debug.WriteLine(String.Format("{0:G}, {1}: {2}", DateTime.Now, "cudnnLRNCrossChannelBackward", res));
			if (res != cudnnStatus.Success) throw new CudaDNNException(res);
		}
public void cudnnLRNCrossChannelBackward ( cudnnLRNMode lrnMode , ref float alpha , cudnnTensorDescriptor yDesc , CUdeviceptr y , cudnnTensorDescriptor dyDesc , CUdeviceptr dy , cudnnTensorDescriptor xDesc , CUdeviceptr x , ref float beta , cudnnTensorDescriptor dxDesc , CUdeviceptr dx ) { res = CudaDNNNativeMethods . cudnnLRNCrossChannelBackward ( _handle , _desc , lrnMode , ref alpha , yDesc , y , dyDesc , dy , xDesc , x , ref beta , dxDesc , dx ) ; Debug . WriteLine ( String . Format ( " " , DateTime . Now , " " , res ) ) ; if ( res != cudnnStatus . Success ) throw new CudaDNNException ( res ) ; }
This function performs the backward LRN layer computation.
This function performs the backward LRN layer computation .
lrnMode
cudnnLRNMode
LRN layer mode of operation. Currently only
CUDNN_LRN_CROSS_CHANNEL_DIM1 is implemented. Normalization is
performed along the tensor's dimA[1].
LRN layer mode of operation . Currently only CUDNN_LRN_CROSS_CHANNEL_DIM1 is implemented . Normalization is performed along the tensor ' s dimA [ 1 ] .
float
ref
yDesc
cudnnTensorDescriptor
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).
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 ) .
y
CUdeviceptr
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).
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 ) .
dyDesc
cudnnTensorDescriptor
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).
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 ) .
dy
CUdeviceptr
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).
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 ) .
xDesc
cudnnTensorDescriptor
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.
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 .
x
CUdeviceptr
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.
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 .
dxDesc
cudnnTensorDescriptor
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).
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 ) .
dx
CUdeviceptr
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).
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 ) .
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.
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 .
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.
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 .