|View source on GitHub|
Experimental: Create a ModuleSpec out of a SavedModel from TF1.
hub.create_module_spec_from_saved_model( saved_model_path, drop_collections=None )
DEPRECATION NOTE: This belongs to the hub.Module API and TF1 Hub format. For TF2, TensorFlow Hub ships plain SavedModels, removing the need for conversions like this.
Define a ModuleSpec from a SavedModel. Note that this is not guaranteed to work in all cases and it assumes the SavedModel has followed some conventions:
- The serialized SaverDef can be ignored and instead can be reconstructed.
- The init op and main op can be ignored and instead the module can be
initialized by using the conventions followed by
Note that the set of features supported can increase over time and have side effects that were not previously visible. The pattern followed to avoid surprises is forcing users to declare which features to ignore (even if they are not supported).
Note that this function creates a ModuleSpec that when exported exports a Module (based on a modified copy of the original SavedModel) and not a SavedModel.
||Directory with the SavedModel to use.|
||Additionally list of collection to drop.|