Is it normal to obtain different test results with different batch sizes with tensorflow
This normally means that you did not set the phase_train parameter back to false after testing.
I am using tensorflow for a classification problem.
I have some utility for saving and loading my network. When I restore the network, I can specify a different batch size than for training.
My problem is that I am getting different test results when I restore with a different batch size. However, there is no difference when using the same batch size.
EDIT: Please note that I am not using dropout.
The difference is between 0% and 1% (0.5% on average).
My network is a fully connected layer that predicts two different outputs. I did not have the issue when I only had one task to predict.
My loss op is a sum of both losses.
What could be the issue? Does it have to do with Tensorflow's parallelization strategy?