hub.load

Resolves a handle and loads the resulting module.

hub.load(
    handle, tags=None, options=None
)

Used in the notebooks

Used in the tutorials
BERT Experts from TF-Hub Image Super Resolution using ESRGAN MoveNet: Ultra fast and accurate pose detection model. MoViNet for streaming action recognition Frame interpolation using the FILM model

This is the preferred API to load a Hub module in low-level TensorFlow 2. Users of higher-level frameworks like Keras should use the framework's corresponding wrapper, like hub.KerasLayer.

This function is roughly equivalent to the TF2 function tf.saved_model.load() on the result of hub.resolve(handle). Calling this function requires TF 1.14 or newer. It can be called both in eager and graph mode.

Note: Using in a tf.compat.v1.Session with variables placed on parameter servers requires setting experimental.share_cluster_devices_in_session within the tf.compat.v1.ConfigProto. (It becomes non-experimental in TF2.2.)

This function can handle the deprecated TF1 Hub format to the extent that tf.saved_model.load() in TF2 does. In particular, the returned object has attributes

• .variables: a list of variables from the loaded object;
• .signatures: a dict of TF2 ConcreteFunctions, keyed by signature names, that take tensor kwargs and return a tensor dict. However, the information imported by hub.Module into the collections of a tf.Graph is lost (e.g., regularization losses and update ops).

Args
handle	(string) the Module handle to resolve; see hub.resolve().
tags	A set of strings specifying the graph variant to use, if loading from a v1 module.
options	Optional, tf.saved_model.LoadOptions object that specifies options for loading. This argument can only be used from TensorFlow 2.3 onwards.

Returns
A trackable object (see tf.saved_model.load() documentation for details).

Raises
NotImplementedError	If the code is running against incompatible (1.x) version of TF.