Apologies for any inaccuracies due to the infancy in this field.
I'm trying to learn on how to return a dataset with three classes to be clustered by TensorFlow.
At this stage, I've read a lot and experimented with several Colabs, but none of them points to the clustering technique when it comes to text (just classification).
The whole point is I don't have any clue of how the text could be classified, and I though the recursive process used by TensorFlow would have offered a better approach and results of what I normally get by using K-Means.
I drafted a Colab myself, but it's a real mess of examples now, so I better not to share at this stage.
In a nutshell, I tokenised the keywords, created a dictionary, converted my text into a dense matrix and then started using the models. However, when it comes to using the fit function, I end up getting a series of error with the most recent one being "ValueError: No gradients provided for any variable".
I would appreciate some support and hints also on materials to read if any. The release of TensorFlow 2 makes things even more complicated as there are even less examples and trying to adapt the ones for TF1 not always work.
In my version I'm trying to train the model to learn three different classes. My questions at this stage are:
How can I create a suitable sets of labels when the Dense output is greater than 1?
Hot encoding is not an option apparently as the label shape is not inferred from the size of the array as I initially thought, but from the values itself.
How can I predict a new unseen keyword/text?
Here, do I need to train all the time the model to "see" the new keywords. I would expect so, but how?
How can I print/export the output of my prediction?