I am using a custom keras model in Databricks environment. For a custom keras model, model.save(model.h5)
does not work, because custom model is not serializable. Instead it is recommended to use model.save_weights(path)
as an alternate.
model.save_weights(pathDirectory)
works. This yields 3 files checkpoint
, .data-00000-of-00001
, .index
in the pathDirectory
For loading weights, Following mechanism is working fine.
model = Model()
model.load_weights(path)
But I want to train my model on pretrained weights I just saved. Like I saved model weights, and continue training on these saved weights afterwards.
So, when I load model weights and apply training loop, I get this error, TypeError: 'CheckpointLoadStatus' object is not callable
After much research, I have found a workaround,
we can also save model using model.save("model.hpy5")
and read it the saved model in databricks.
model.h5 not work for customized models, but it works for standard models.
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