Tensorflow Best Practises: Named Inputs and Outputs

Original article was published by Theodoros Ntakouris on Deep Learning on Medium


Most machine learning pipelines read data from a structured source ( database, CSV files/ Pandas Dataframes , TF Records), perform feature selection, cleaning, (and possibly) preprocessing, passing a raw multidimensional array (tensor) to a model along with another tensor representing the correct prediction for each input sample.

Reorder or rename input features in production?Useless results or the client — side breaks in production

Absent Features? Missing Data? Bad output value interpretation? Mixing up integer indices by mistake? Useless Results or the client — side breaks in production

Want to know what feature columns were used for training in order to provide the same ones for inference?You can’t — Misinterpretation Errors

Want to know what value output values represent?You can’t — Misinterpretation Errors