EOS tensorflow data_error ai_generated true

OutOfRangeError: End of sequence

ID: tensorflow/out-of-range-end-of-sequence

Also available as: JSON · Markdown · 中文
90%Fix Rate
88%Confidence
1Evidence
2023-04-20First Seen

Version Compatibility

VersionStatusIntroducedDeprecatedNotes
tensorflow 2.9.0 active — — —
tensorflow 2.10.0 active — — —
tensorflow 2.11.0 active — — —

Root Cause

A tf.data iterator was exhausted before the training loop expected, often due to a mismatch between dataset size and number of steps.

generic

中文

tf.data 迭代器在训练循环预期结束前已耗尽，通常是由于数据集大小与步数不匹配。

Official Documentation

https://www.tensorflow.org/api_docs/python/tf/data/Dataset

Workarounds

  1. 95% success Ensure the dataset is repeated or the number of steps matches the dataset size. For example: `dataset = dataset.repeat()` and then `steps_per_epoch = len(dataset) // batch_size`. 
    Ensure the dataset is repeated or the number of steps matches the dataset size. For example: `dataset = dataset.repeat()` and then `steps_per_epoch = len(dataset) // batch_size`.

中文步骤

  1. Ensure the dataset is repeated or the number of steps matches the dataset size. For example: `dataset = dataset.repeat()` and then `steps_per_epoch = len(dataset) // batch_size`.

Dead Ends

Common approaches that don't work:

  1. 85% fail

    This does not fix the mismatch; it only changes the number of elements per step.

  2. 90% fail

    An infinite repeat (repeat()) can mask the error but leads to infinite loops if not handled.