Register TensorFlow Addons' objects in TensorFlow global dictionaries.
register_all(keras_objects = TRUE, custom_kernels = TRUE)
| keras_objects | boolean, `TRUE` by default. If `TRUE`, register all Keras objects with `tf$keras$utils$register_keras_serializable(package="Addons")` If set to FALSE, doesn't register any Keras objects of Addons in TensorFlow. |
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| custom_kernels | boolean, `TRUE` by default. If `TRUE`, loads all custom kernels of TensorFlow Addons with `tf.load_op_library("path/to/so/file.so")`. Loading the SO files register them automatically. If `FALSE` doesn't load and register the shared objects files. Not that it might be useful to turn it off if your installation of Addons doesn't work well with custom ops. |
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When loading a Keras model that has a TF Addons' function, it is needed for this function to be known by the Keras deserialization process. There are two ways to do this, either do ``` tf$keras$models$load_model( "my_model.tf", custom_objects=list("LAMB": tfaddons::optimizer_lamb) ) ``` or you can do: ```python register_all() tf$keras$models$load_model("my_model.tf") ``` If the model contains custom ops (compiled ops) of TensorFlow Addons, and the graph is loaded with `tf$saved_model$load`, then custom ops need to be registered before to avoid an error of the type: ``` tensorflow.python.framework.errors_impl.NotFoundError: Op type not registered '...' in binary running on ... Make sure the Op and Kernel are registered in the binary running in this process. ``` In this case, the only way to make sure that the ops are registered is to call this function: ``` register_all() tf$saved_model$load("my_model.tf") ``` Note that you can call this function multiple times in the same process, it only has an effect the first time. Afterward, it's just a no-op.