//计算Weights和Bias的梯度
private void ComputeGradient(TensorOld error)
{
var inputData = ForwardInput.GetRawValues();
var errorData = error.GetRawValues();
var features = WeightsGradient.shape[0];
Parallel.For(0, features, i =>
{
Parallel.For(0, UnitCount, j =>
{
var weightSum = 0d;
var biasSum = 0d;
for (int k = 0; k < sampleStartIndex.Length; k++)
{
weightSum += inputData[sampleStartIndex[k] + i] * errorData[errorStartIndex[k] + j];
biasSum += errorData[errorStartIndex[k] + j];
}
WeightsGradient[i, j] = weightSum;
BiasGradient.GetRawValues()[j] = biasSum;
});
});
}
public Task <HttpResponseMessage> ForwardAsync(LoggingContext loggingContext, string forwardTarget)
{
if (string.IsNullOrWhiteSpace(forwardTarget))
{
throw new ArgumentNullException(nameof(forwardTarget), nameof(forwardTarget) + " should not be null or whitespace");
}
string href = PlatformResource?.ForwardLink?.Href;
if (string.IsNullOrWhiteSpace(href))
{
throw new CapabilityNotAvailableException("Link to forward AudioVideoInvitation is not available.");
}
Uri forwardLink = UriHelper.CreateAbsoluteUri(BaseUri, href);
var input = new ForwardInput()
{
To = forwardTarget
};
return(PostRelatedPlatformResourceAsync(forwardLink, input, new ResourceJsonMediaTypeFormatter(), loggingContext));
}
