SavedModels from TF Hub in TensorFlow 2

The SavedModel format of TensorFlow 2 is the recommended way to share pre-trained models and model pieces on TensorFlow Hub. It replaces the older TF1 Hub format and comes with a new set of APIs.

This page explains how to reuse TF2 SavedModels in a TensorFlow 2 program with the low-level hub.load() API and its hub.KerasLayer wrapper. (Typically, hub.KerasLayer is combined with other tf.keras.layers to build a Keras model or the model_fn of a TF2 Estimator.) These APIs can also load the legacy models in TF1 Hub format, within limits, see the compatibility guide.

Users of TensorFlow 1 can update to TF 1.15 and then use the same APIs. Older versions of TF1 do not work.

Using SavedModels from TF Hub

Using a SavedModel in Keras

Keras is TensorFlow's high-level API for building deep learning models by composing Keras Layer objects. The tensorflow_hub library provides the class hub.KerasLayer that gets initialized with the URL (or filesystem path) of a SavedModel and then provides the computation from the SavedModel, including its pre-trained weights.

Here is an example of using a pre-trained text embedding:

import tensorflow as tf
import tensorflow_hub as hub

hub_url = "https://tfhub.dev/google/nnlm-en-dim128/2"
embed = hub.KerasLayer(hub_url)
embeddings = embed(["A long sentence.", "single-word", "http://example.com"])
print(embeddings.shape, embeddings.dtype)

From this, a text classifier can be built in the usual Keras way:

model = tf.keras.Sequential([
    embed,
    tf.keras.layers.Dense(16, activation="relu"),
    tf.keras.layers.Dense(1, activation="sigmoid"),
])

The Text classification colab is a complete example how to train and evaluate such a classifier.

The model weights in a hub.KerasLayer are set to non-trainable by default. See the section on fine-tuning below for how to change that. Weights are shared between all applications of the same layer object, as usual in Keras.

Using a SavedModel in an Estimator

Users of TensorFlow's Estimator API for distributed training can use SavedModels from TF Hub by writing their model_fn in terms of hub.KerasLayer among other tf.keras.layers.

Behind the scenes: SavedModel downloading and caching

Using a SavedModel from TensorFlow Hub (or other HTTPS servers that implement its hosting protocol) downloads and decompresses it to the local filesystem if not already present. The environment variable TFHUB_CACHE_DIR can be set to override the default temporary location for caching the downloaded and uncompressed SavedModels. For details, see Caching.

Using a SavedModel in low-level TensorFlow

Model Handles

SavedModels can be loaded from a specified handle, where the handle is a filesystem path, valid TFhub.dev model URL (e.g. "https://tfhub.dev/..."). Kaggle Models URLs mirror TFhub.dev handles in accordance with our Terms and the license associated with the model assets, e.g., "https://www.kaggle.com/...". Handles from Kaggle Models are equivalent to their corresponding TFhub.dev handle.

The function hub.load(handle) downloads and decompresses a SavedModel (unless handle is already a filesystem path) and then returns the result of loading it with TensorFlow's built-in function tf.saved_model.load(). Therefore, hub.load() can handle any valid SavedModel (unlike its predecessor hub.Module for TF1).

Advanced topic: what to expect from the SavedModel after loading

Depending on the contents of the SavedModel, the result of obj = hub.load(...) can be invoked in various ways (as explained in much greater detail in TensorFlow's SavedModel Guide:

The serving signatures of the SavedModel (if any) are represented as a dictionary of concrete functions and can be called like tensors_out = obj.signatures["serving_default"](**tensors_in), with dictionaries of tensors keyed by the respective input and output names and subject to the signature's shape and dtype constraints.
The @tf.function-decorated methods of the saved object (if any) are restored as tf.function objects that can be called by all combinations of Tensor and non-Tensor arguments for which the tf.function had been traced prior to saving. In particular, if there is an obj.__call__ method with suitable traces, obj itself can be called like a Python function. A simple example could look like output_tensor = obj(input_tensor, training=False).

This leaves enormous liberty in the interfaces that SavedModels can implement. The Reusable SavedModels interface for obj establishes conventions such that client code, including adapters like hub.KerasLayer, know how to use the SavedModel.

Some SavedModels may not follow that convention, especially whole models not meant to be reused in larger models, and just provide serving signatures.

The trainable variables in a SavedModel are reloaded as trainable, and tf.GradientTape will watch them by default. See the section on fine-tuning below for some caveats, and consider avoiding this for starters. Even if you want to fine-tune, you may want to see if obj.trainable_variables advises to re-train only a subset of the originally trainable variables.

Creating SavedModels for TF Hub

Overview

SavedModel is TensorFlow's standard serialization format for trained models or model pieces. It stores the model's trained weights together with the exact TensorFlow operations to perform its computation. It can be used independently from the code that created it. In particular, it can be reused across different high-level model-building APIs like Keras, because TensorFlow operations are their common basic language.

Saving from Keras

Starting with TensorFlow 2, tf.keras.Model.save() and tf.keras.models.save_model() default to the SavedModel format (not HDF5). The resulting SavedModels that can be used with hub.load(), hub.KerasLayer and similar adapters for other high-level APIs as they become available.

To share a complete Keras Model, just save it with include_optimizer=False.

To share a piece of a Keras Model, make the piece a Model in itself and then save that. You can either lay out the code like that from the start....

piece_to_share = tf.keras.Model(...)
full_model = tf.keras.Sequential([piece_to_share, ...])
full_model.fit(...)
piece_to_share.save(...)

...or cut out the piece to share after the fact (if it aligns with the layering of your full model):

full_model = tf.keras.Model(...)
sharing_input = full_model.get_layer(...).get_output_at(0)
sharing_output = full_model.get_layer(...).get_output_at(0)
piece_to_share = tf.keras.Model(sharing_input, sharing_output)
piece_to_share.save(..., include_optimizer=False)

TensorFlow Models on GitHub uses the former approach for BERT (see nlp/tools/export_tfhub_lib.py, note the split between core_model for export and the pretrainer for restoring the checkpoint) and the latter approach for ResNet (see legacy/image_classification/tfhub_export.py).

Saving from low-level TensorFlow

This requires good familiarity with TensorFlow's SavedModel Guide.

If you want to provide more than just a serving signature, you should implement the Reusable SavedModel interface. Conceptually, this looks like

class MyMulModel(tf.train.Checkpoint):
  def __init__(self, v_init):
    super().__init__()
    self.v = tf.Variable(v_init)
    self.variables = [self.v]
    self.trainable_variables = [self.v]
    self.regularization_losses = [
        tf.function(input_signature=[])(lambda: 0.001 * self.v**2),
    ]

  @tf.function(input_signature=[tf.TensorSpec(shape=None, dtype=tf.float32)])
  def __call__(self, inputs):
    return tf.multiply(inputs, self.v)

tf.saved_model.save(MyMulModel(2.0), "/tmp/my_mul")

layer = hub.KerasLayer("/tmp/my_mul")
print(layer([10., 20.]))  # [20., 40.]
layer.trainable = True
print(layer.trainable_weights)  # [2.]
print(layer.losses)  # 0.004

Fine-Tuning

Training the already-trained variables of an imported SavedModel together with those of the model around it is called fine-tuning the SavedModel. This can result in better quality, but often makes the training more demanding (may take more time, depend more on the optimizer and its hyperparameters, increase the risk of overfitting and require dataset augmentation, esp. for CNNs). We advise SavedModel consumers to look into fine-tuning only after having established a good training regime, and only if the SavedModel publisher recommends it.

Fine-tuning changes the "continuous" model parameters that are trained. It does not change hard-coded transformations, such as tokenizing text input and mapping tokens to their corresponding entries in an embedding matrix.

For SavedModel consumers

Creating a hub.KerasLayer like

layer = hub.KerasLayer(..., trainable=True)

enables fine-tuning of the SavedModel loaded by the layer. It adds the trainable weights and weight regularizers declared in the SavedModel to the Keras model, and runs the SavedModel's computation in training mode (think of dropout etc.).

The image classification colab contains an end-to-end example with optional fine-tuning.

Re-exporting the fine-tuning result

Advanced users may want to save the results of fine-tuning back into a SavedModel that can be used instead of the originally loaded one. This can be done with code like

loaded_obj = hub.load("https://tfhub.dev/...")
hub_layer = hub.KerasLayer(loaded_obj, trainable=True, ...)

model = keras.Sequential([..., hub_layer, ...])
model.compile(...)
model.fit(...)

export_module_dir = os.path.join(os.getcwd(), "finetuned_model_export")
tf.saved_model.save(loaded_obj, export_module_dir)

For SavedModel creators

When creating a SavedModel for sharing on TensorFlow Hub, think ahead if and how its consumers should fine-tune it, and provide guidance in the documentation.

Saving from a Keras Model should make all the mechanics of fine-tuning work (saving weight regularization losses, declaring trainable variables, tracing __call__ for both training=True and training=False, etc.)

Choose a model interface that plays well with gradient flow, e.g., output logits instead of softmax probabilities or top-k predictions.

If the model use dropout, batch normalization, or similar training techniques that involve hyperparameters, set them to values that make sense across many expected target problems and batch sizes. (As of this writing, saving from Keras does not make it easy to let consumers adjust them.)

Weight regularizers on individual layers are saved (with their regularization strength coefficients), but weight regularization from within the optimizer (like tf.keras.optimizers.Ftrl.l1_regularization_strength=...)) is lost. Advise consumers of your SavedModel accordingly.

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