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Migrating from TF1 to TF2 with TensorFlow Hub

This page explains how to keep using TensorFlow Hub while migrating your TensorFlow code from TensorFlow 1 to TensorFlow 2. It complements TensorFlow's general migration guide.

For TF2, TF Hub has switched away from the custom hub.Module format and its hub.Module API to the native SavedModel format of TF2 and its associated API of hub.load() and hub.KerasLayer.

The hub.Module API remains available in the tensorflow_hub library for use in TF1 and in the TF1 compatibility mode of TF2. It can only load assets in the hub.Module format.

The new API of hub.load() (and hub.KerasLayer, which wraps it for Keras) works for TensorFlow 1.15 (in eager and graph mode) and in TensorFlow 2. This new API can load the new TF2 SavedModel assets, and, with the restrictions laid out below, for the older hub.Module assets.

In general, it is recommended to use new API wherever possible.

Summary of the new API

hub.load() is the new low-level function to load a SavedModel from TensorFlow Hub (or compatible services). It wraps TF2's tf.saved_model.load(); TensorFlow's SavedModel Guide describes what you can do with the result.

m = hub.load(handle)
outputs = m(inputs)

The hub.KerasLayer class calls hub.load() and adapts the result for use in Keras alongside other Keras layers. (It may even be a convenient wrapper for loaded SavedModels used in other ways.)

model = tf.keras.Sequential([

Hub's tutorials are being updated to the new APIs. See in particular

If the hub.Module you use has a newer version that comes in the TF2 SavedModel format, we recommend to switch the API and the module version at the same time.

Loading old hub.Modules

It can happen that a new TF2 SavedModel is not yet available for your use-case and you need to load an old hub.Module.

If you use Keras, please wait for tensorflow_hub release 0.7, which will support this in hub.KerasLayer soon.

As of this writing, only hub.load() supports loading of the hub.Modules of TF1 into a TF2 program. The code is similar to calling a serving signature.

Instead of

# DEPRECATED: TensorFlow 1
m = hub.Module(handle)
tensor_out = m(tensor_in)
with tf.train.SingularMonitoredSession() as sess:

you can write

# TensorFlow 2
m = hub.load(handle, tags=[])
tensors_out_dict = m.signatures["default"](tensor_in)
tensor_out = tensors_out_dict["default"]
print(tensor_out.numpy())  # If executing in eager mode.

by spelling out the default tag set, signature name and output tensor key of a hub.Module.

More generally, instead of

# DEPRECATED: TensorFlow 1
m = hub.Module(handle, tags={"foo", "bar"})
tensors_out_dict = m(dict(x1=..., x2=...), signature="sig", as_dict=True)

you can write

# TensorFlow 2
m = hub.load(handle, tags={"foo", "bar"})
tensors_out_dict = m.signatures["sig"](x1=..., x2=...)

In these examples m.signatures is a dict of TensorFlow concrete functions keyed by signature names. Calling such a function computes all its outputs, even if unused. (This is different from the lazy evaluation of TF1's graph mode.)

Retraining hub.Modules loaded via hub.load() is not supported: Trainable variables are imported as such, but update ops (for batch normalization etc.) and regularization losses are dropped. Be sure to not capture the trainable variables of m in a gradient tape or otherwise in an optimizer. Do not import tags={"train"}.