Why some TensorFlow-Hub models are not fine tunable?

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I am just learning image classification with TensorFlow and found that there is a TensorFlow hub where we can use a lot of models for a lot of classification tasks. For example, I want to build food classification and develop the model so the model would cover foods in my country and have a higher accuracy on some specific foods. I try to use and tune this model: https://tfhub.dev/google/aiy/vision/classifier/food_V1/1, but why there is information that the model is not fine-tunable?

What makes a model can be fine-tuned and can't be fine-tuned?

Thank you.

tensorflow-hub tensorflow classification image-processing python
1 Answers

The publisher/creator of the model makes he decision on whether the model is fine-tunable or not. Making a model fine-tunable requires model creator to make sure that the TF computation graph supports fine-tuning. For example, if the model contains dropout or batchnorm, the computation graph for fine-tuning and for inference-only will be different. The publisher/creator of the model has to make sure that model is exported correctly to support both these cases. Sometimes publishers do not to go through these steps and mark the model as non fine-tunable. 

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