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In the documentation it says if you run repeat(), the default behavior (if count is None or -1) is for the dataset be repeated indefinitely (https://www.tensorflow.org/api_docs/python/tf/data/Dataset#repeat). But what is indefinitely exactly?

I read somewhere that, for the code below, "defaule is repeated till number of time batch run", but I don't understand what that means or if it is even correct.

train_univariate.cache().shuffle(BUFFER_SIZE).batch(BATCH_SIZE).repeat()
Ana G
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  • Does this answer your question? [What does batch, repeat, and shuffle do with TensorFlow Dataset?](https://stackoverflow.com/questions/53514495/what-does-batch-repeat-and-shuffle-do-with-tensorflow-dataset) – Innat May 26 '23 at 19:42
  • Please provide enough code so others can better understand or reproduce the problem. – Community May 26 '23 at 23:58
  • Not really, my question is related to the absence of an argument in the method Dataset.repeat(), i.e. when count=None. I don't understand what, in practical terms, is indefinitely. – Ana G May 27 '23 at 00:56

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