CIS tensorflow runtime_error ai_generated partial

InvalidArgumentError: Assign requires shapes of both tensors to match. lhs shape= [100,256] rhs shape= [200,256]

ID: tensorflow/checkpoint-incompatible-shape

Also available as: JSON · Markdown · 中文
80%Fix Rate
88%Confidence
1Evidence
2023-08-22First Seen

Version Compatibility

VersionStatusIntroducedDeprecatedNotes
tensorflow 2.10 active — — —
tensorflow 2.11 active — — —
tensorflow 2.12 active — — —

Root Cause

Attempting to restore a checkpoint into a model whose layer shapes differ from the saved checkpoint due to model architecture changes.

generic

中文

尝试将检查点恢复到模型时，由于模型架构更改，层形状与保存的检查点不匹配。

Official Documentation

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

Workarounds

  1. 85% success Modify the model to match the checkpoint shapes. For example, if the checkpoint has a Dense layer with 256 units but your model has 200 units, change to 256: model.add(tf.keras.layers.Dense(256)). Then restore: model.load_weights('path/to/checkpoint'). 
    Modify the model to match the checkpoint shapes. For example, if the checkpoint has a Dense layer with 256 units but your model has 200 units, change to 256: model.add(tf.keras.layers.Dense(256)). Then restore: model.load_weights('path/to/checkpoint').
  2. 75% success Use load_weights with by_name=True and skip_mismatch=True to load only matching layers: model.load_weights('path/to/checkpoint', by_name=True, skip_mismatch=True) 
    Use load_weights with by_name=True and skip_mismatch=True to load only matching layers: model.load_weights('path/to/checkpoint', by_name=True, skip_mismatch=True)

中文步骤

  1. Modify the model to match the checkpoint shapes. For example, if the checkpoint has a Dense layer with 256 units but your model has 200 units, change to 256: model.add(tf.keras.layers.Dense(256)). Then restore: model.load_weights('path/to/checkpoint').
  2. Use load_weights with by_name=True and skip_mismatch=True to load only matching layers: model.load_weights('path/to/checkpoint', by_name=True, skip_mismatch=True)

Dead Ends

Common approaches that don't work:

  1. Deleting and recreating the checkpoint file 90% fail

    The checkpoint is valid; the problem is the model definition mismatch. Deleting the checkpoint loses training progress without addressing the root cause.

  2. Changing learning rate or optimizer 99% fail

    The error is about tensor shape mismatch during assignment, not optimization hyperparameters.