New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

The effect of tf.train.ExponentialMovingAverage #16

Open

BlueWinters opened this issue Jul 12, 2017 · 0 comments

BlueWinters commented Jul 12, 2017

I have a little question about the code:
At line 55:
ewma = tf.train.ExponentialMovingAverage(decay=0.99)

My question is:

What is the role of the function tf.train.ExponentialMovingAverage? In my option, it may help improve the accuracy of the final classification for mnist? I also read the official doc of tensorflow(https://www.tensorflow.org/api_docs/python/tf/train/ExponentialMovingAverage), but I don't really understand. I guess that the code would also run correctly if there is no tf.train.ExponentialMovingAverage.

I hope some help me to answer this question.
thanks a lot.

The text was updated successfully, but these errors were encountered:

Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment