I’m taking a kaggle competition with tabular, texts and images as input data. I’ve tried to use keras but got bad results so I shifted to fast.ai(using part of the data) and it got much better.
But the problem is that fast.ai don’t have a high-level wrapper for data with multiple kinds of inputs. It’s possible to pre-process the image data using some existing model and then save the features and treat them as numerical data. However I would like to first use ~1 dense layer for each kind of data before concatenating them (as the same kind of data are more relevant to each other and different kinds of data are suitable for different dropout rate etc.), so what should I do?