Labelled / unlabelled data split by batch_size=100 during evaluation

[mikowals]

During evaluation all the data has labels so the labelled and unlabelled split should reflect this I think. The current split by batch_size is incorrect but ultimately does not have an impact because both the labelled and unlabelled data get treated the same during evaluation.

Previously though, this was not true and this split would have coincided with the batch normalization code being discuss in #3 to use evaluation set mean and var for normalization of incorrectly split unlabelled data. All my results discussed in #3 do not rely on changing this but instead only changed the evaluation results with changes to the batch normalization paths.

It may be clearer to split the inputs at a different point in the graph outside the encoder so that labelled data gets passed to the encoder with resulting labelled output nodes and then the same thing is done with unlabelled data to create different unlabelled output nodes.

I can attempt this in a pull request if you like.

[rinuboney]

works for me.

[pribadihcr]

Hi @rinuboney , @mikowals,

does the batch size depend to the number of the labelled data?.
refer to this snippet codes:
labeled = lambda x: tf.slice(x, [0, 0], [batch_size, -1]) if x is not None else x
unlabeled = lambda x: tf.slice(x, [batch_size, 0], [-1, -1]) if x is not None else x
split_lu = lambda x: (labeled(x), unlabeled(x))

Thanks