I'm new to the usage of Tensorflow datasets and I try to understand the inner workings.
As far as i can tell tf.data.Dataset.save(dataset, path/name) creates a new folder named "name" at "path". The folder itself contains a "dataset_spec.pb" and a "snapshot.metadata", I assume containing metadata, as well as another folder with a random number as name, containing the data itself.
If I'd save another dataset with the same path/name I would expect it to replace all of the content, however it just replaces the metadata files and adds another "randomly named" folder.
How does tf.data.Dataset.load(path/name) handle the situation? Is it only capable to load the latest safe (given by the metadatafiles) or can I manipulate the choosen folder? If the latter would be True, I'd expect to do it by the "reader_func" load() takes as argument. If so, is there any decent documentation?
Thank you in advance for your help.
Stackoverflow asked me to describe what I tried to fix my problem. As this is a purely informative question and not related to a concrete coding problem, feel free to skip this part.
I saved multiple dummy datasets with different values using tf.data.Dataset.save(dataset,"same_name").
At first I expected it to overwrite the data, however I noticed the folder "same_name" got more and more entries
Afterwards I used tf.data.Dataset.load("same_name") half expecting to get random sets or a concatination of the different datasets. However I got only the latest addition.