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I'm trying to create a deep learning network to classify news article based on the text and associated image. The idea comes from a novel use of GANs to classify based on generated data.

My approach was to use Tensorflow to generate word embeddings in the article, and then tranform the images into records - https://github.com/openai/improved-gan/blob/master/imagenet/convert_imagenet_to_records.py. This second component would also contain the label.

  • Is it wise to combine both modes into one neural net, or classify separately?

I'm also trying to work out how to concatenate the two tensors in Tensorflow. Can anyone give a steer.

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    $\begingroup$ You can try DS.SE if you do not get an answer here $\endgroup$ – DuttaA Aug 15 '18 at 12:26

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