KHS tensorflow config_error ai_generated true

NotImplementedError: Saving the model to HDF5 format is not supported when the model has multiple outputs with different loss functions

ID: tensorflow/keras-model-save-h5-weights

Also available as: JSON · Markdown · 中文
95%Fix Rate
86%Confidence
1Evidence
2023-07-01First Seen

Version Compatibility

VersionStatusIntroducedDeprecatedNotes
tensorflow>=2.11.0 active — — —
keras>=2.11 active — — —
python>=3.8 active — — —

Root Cause

Keras HDF5 serialization does not support models with heterogeneous loss functions across multiple outputs; the format cannot correctly store the loss configuration.

generic

中文

Keras的HDF5序列化不支持在多个输出上使用不同损失函数的模型；该格式无法正确存储损失配置。

Official Documentation

https://www.tensorflow.org/guide/keras/serialization_and_saving

Workarounds

  1. 95% success Save the model using the SavedModel format instead: `model.save('my_model', save_format='tf')` (or `model.export('my_model')` in TF2.x). This format supports multiple outputs and heterogeneous losses. 
    Save the model using the SavedModel format instead: `model.save('my_model', save_format='tf')` (or `model.export('my_model')` in TF2.x). This format supports multiple outputs and heterogeneous losses.
  2. 85% success Save only the model weights using `model.save_weights('model_weights.h5')` and then recreate the model architecture with the same loss configuration in code, then load the weights. 
    Save only the model weights using `model.save_weights('model_weights.h5')` and then recreate the model architecture with the same loss configuration in code, then load the weights.

中文步骤

  1. Save the model using the SavedModel format instead: `model.save('my_model', save_format='tf')` (or `model.export('my_model')` in TF2.x). This format supports multiple outputs and heterogeneous losses.
  2. Save only the model weights using `model.save_weights('model_weights.h5')` and then recreate the model architecture with the same loss configuration in code, then load the weights.

Dead Ends

Common approaches that don't work:

  1. Manually editing the HDF5 file to add loss entries. 95% fail

    HDF5 files are binary and not meant for manual editing; the internal structure is complex and likely to break the model loading.

  2. Setting all loss functions to the same value temporarily to save, then restoring them. 70% fail

    Even if the save succeeds, the model's loss configuration is lost; loading the model will have incorrect loss functions.