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Fixed a bug in the cuDNN binding that on rare occasions caused NaN outputs from

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batch normalization.  The running mean and variance need to be initialized when
calling cuDNN, even if the averaging factor is 1.  I don't think this was the
case prior to cuDNN v5, but it certainly is the case now.  This patch fixes
this.
This commit is contained in:
Davis King 2016-08-09 16:33:44 -04:00
parent 98da589f9d
commit ceb6a119d5
1 changed files with 16 additions and 0 deletions
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16
dlib/dnn/cudnn_dlibapi.cpp
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@ -445,6 +445,14 @@ namespace dlib
invstds.copy_size(means);
running_means.copy_size(means);
running_variances.copy_size(means);
// cuDNN requires that running_means and running_variances be initialized to
// some valid float values even if the averaging factor would have ignored
// them.
if (averaging_factor == 1)
{
running_means = 0;
running_variances = 1;
}
CHECK_CUDNN(cudnnBatchNormalizationForwardTraining(
context(),
@ -627,6 +635,14 @@ namespace dlib
invstds.copy_size(means);
running_means.copy_size(means);
running_variances.copy_size(means);
// cuDNN requires that running_means and running_variances be initialized to
// some valid float values even if the averaging factor would have ignored
// them.
if (averaging_factor == 1)
{
running_means = 0;
running_variances = 1;
}
CHECK_CUDNN(cudnnBatchNormalizationForwardTraining(
context(),