IAS tensorflow config_error ai_generated true

InvalidArgumentError: Assign requires shapes of both tensors to match. lhs shape= [5,10] rhs shape= [10,10]

ID: tensorflow/invalid-argument-optimizer-slot-variable-mismatch

Also available as: JSON · Markdown · 中文
85%Fix Rate
88%Confidence
1Evidence
2024-01-20First Seen

Version Compatibility

VersionStatusIntroducedDeprecatedNotes
tensorflow 2.12.0 active — — —
tensorflow 2.13.0 active — — —

Root Cause

Optimizer slot variable shape mismatch, often due to loading a checkpoint from a model with different architecture or incompatible optimizer state.

generic

中文

优化器槽变量形状不匹配，通常由于从具有不同架构或不兼容优化器状态的模型加载检查点导致。

Official Documentation

https://www.tensorflow.org/tutorials/keras/save_and_load

Workarounds

  1. 85% success Load only the model weights, not optimizer state, by excluding optimizer variables when restoring: model.load_weights('checkpoint.ckpt', by_name=True, skip_mismatch=True) # Or use: model.load_weights('checkpoint.ckpt', skip_mismatch=True) 
    Load only the model weights, not optimizer state, by excluding optimizer variables when restoring:
model.load_weights('checkpoint.ckpt', by_name=True, skip_mismatch=True)
# Or use: model.load_weights('checkpoint.ckpt', skip_mismatch=True)
  2. 90% success Reinitialize the optimizer and train from scratch, or use a checkpoint that matches the current model architecture exactly. 
    Reinitialize the optimizer and train from scratch, or use a checkpoint that matches the current model architecture exactly.

中文步骤

  1. Load only the model weights, not optimizer state, by excluding optimizer variables when restoring:
model.load_weights('checkpoint.ckpt', by_name=True, skip_mismatch=True)
# Or use: model.load_weights('checkpoint.ckpt', skip_mismatch=True)
  2. Reinitialize the optimizer and train from scratch, or use a checkpoint that matches the current model architecture exactly.

Dead Ends

Common approaches that don't work:

  1. 80% fail

    Checkpoint files are binary; manual modification corrupts them.

  2. 95% fail

    This flag only controls device placement, not tensor shapes.