tensorflow data_error ai_generated true

NotFoundError: Key optimizer/slot_variable not found in checkpoint

ID: tensorflow/checkpoint-optimizer-slot-variable-mismatch

Also available as: JSON · Markdown · 中文
88%Fix Rate
87%Confidence
1Evidence
2023-04-10First Seen

Version Compatibility

VersionStatusIntroducedDeprecatedNotes
tensorflow 2.6 active — — —
tensorflow 2.7 active — — —
tensorflow 2.8 active — — —

Root Cause

The checkpoint was saved with a different optimizer (e.g., Adam) but is being restored with a model that uses a different optimizer (e.g., SGD), so the slot variables (e.g., m, v) are missing.

generic

中文

检查点使用不同的优化器（例如 Adam）保存，但恢复时模型使用了不同的优化器（例如 SGD），因此插槽变量（如 m、v）缺失。

Official Documentation

https://www.tensorflow.org/guide/checkpoint#loading_checkpoints

Workarounds

  1. 90% success Restore only the model weights using tf.train.Checkpoint(model=model).restore(path) and skip optimizer slot variables by not including optimizer in the checkpoint object. Then reinitialize the optimizer. 
    Restore only the model weights using tf.train.Checkpoint(model=model).restore(path) and skip optimizer slot variables by not including optimizer in the checkpoint object. Then reinitialize the optimizer.
  2. 95% success Switch to the original optimizer that was used during checkpoint saving. For example, if the checkpoint was saved with Adam, use tf.keras.optimizers.Adam() during restoration. 
    Switch to the original optimizer that was used during checkpoint saving. For example, if the checkpoint was saved with Adam, use tf.keras.optimizers.Adam() during restoration.

中文步骤

  1. 使用 tf.train.Checkpoint(model=model).restore(path) 仅恢复模型权重，并跳过优化器插槽变量（不在检查点对象中包含优化器）。然后重新初始化优化器。
  2. 切换到保存检查点时使用的原始优化器。例如，如果检查点使用 Adam 保存，则在恢复时使用 tf.keras.optimizers.Adam()。

Dead Ends

Common approaches that don't work:

  1. 70% fail

    While it resolves the mismatch, it discards all previously trained weights.

  2. 95% fail

    Checkpoint files are protocol buffers; manual edits corrupt the file and cause DataLossError.