public static int cudnnBatchNormalizationForwardInference(
        cudnnHandle handle, 
        int mode, 
        Pointer alpha, /** alpha[0] = result blend factor */
        Pointer beta, /** beta[0] = dest layer blend factor */
        cudnnTensorDescriptor xDesc, 
        Pointer x, /** NxCxHxW */
        cudnnTensorDescriptor yDesc, 
        Pointer y, /** NxCxHxW */
        cudnnTensorDescriptor bnScaleBiasMeanVarDesc, 
        Pointer bnScale, 
        Pointer bnBias, 
        Pointer estimatedMean, 
        Pointer estimatedVariance, 
        double epsilon)
    {
        return checkResult(cudnnBatchNormalizationForwardInferenceNative(handle, mode, alpha, beta, xDesc, x, yDesc, y, bnScaleBiasMeanVarDesc, bnScale, bnBias, estimatedMean, estimatedVariance, epsilon));
    }
public static int cudnnBatchNormalizationForwardInference ( cudnnHandle handle , int mode , Pointer alpha , Pointer beta , cudnnTensorDescriptor xDesc , Pointer x , cudnnTensorDescriptor yDesc , Pointer y , cudnnTensorDescriptor bnScaleBiasMeanVarDesc , Pointer bnScale , Pointer bnBias , Pointer estimatedMean , Pointer estimatedVariance , double epsilon ) { return checkResult ( cudnnBatchNormalizationForwardInferenceNative ( handle , mode , alpha , beta , xDesc , x , yDesc , y , bnScaleBiasMeanVarDesc , bnScale , bnBias , estimatedMean , estimatedVariance , epsilon ) ) ; }
<pre>
Performs Batch Normalization during Inference:
y[i] = bnScale[k]*(x[i]-estimatedMean[k])/sqrt(epsilon+estimatedVariance[k]) + bnBias[k]
with bnScale, bnBias, runningMean, runningInvVariance tensors indexed
according to spatial or per-activation mode. Refer to cudnnBatchNormalizationForwardTraining
above for notes on function arguments.
</pre>