title: Keras | How to load multiple input (images, scalars) and multiple output (regression) data
body: <p>I want to know what is the best strategy if I have images (RGB) and scalars as inputs and multiple scalar outputs (regression). The images are stored inside one folder. The associated scalars are stored inside a csv file.</p>
  
  <p>I know how to load images and single scalars such that I feed this into a model. </p>
  
  <p>But I want to know a more flexible way to load the data. I want to have different model_types (e.g. input: image, output: one scalar; input: image + scalar, output: one scala; input: image, output: multiple scalars, ...).</p>
  
  <p>The files are all stored inside one single folder I also need to to split the dataset into a training and a validation data set.</p>
  
  <p>Is there a concept by which I can achive this (tutorial are welcome) such that I do not have to change the whole code if the model_type changes?  </p>