Exporting a model

This page describes the process of exporting models in the SavedModel format of TensorFlow 2. This format 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. You can find more information on exporting the TF1 Hub format models in TF1 Hub format export.


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, ...])

...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/bert/bert_models.py and nlp/bert/export_tfhub.py, note the split between core_model and pretrain_model) and the the latter approach for ResNet (see vision/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(MyMulModel, self).__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

The code at tensorflow/examples/saved_model/integration_tests/ contains larger examples, esp. the export_mnist.py and use_mnist.py pair.

Advice 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, but see tensorflow/examples/saved_model/integration_tests/export_mnist_cnn.py for some crude workarounds.)

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.

TensorFlow Model Garden

The TensorFlow Model Garden repo contains a lot of examples of creating reusable TF2 Saved Models to be uploaded on tfhub.dev.

Community Requests

The TensorFlow Hub team generates only a small fraction of the assets that are available on tfhub.dev. We rely primarily on researchers at Google and Deepmind, corporate and academic research institutions, and ML enthusiasts to produce models. As a result, we can't guarantee that we can fulfill community requests for specific assets, and we can't provide time estimates for new asset availability.

The Community Model Requests milestone below contains requests from the community for specific assets -- if you or someone you know is interested in producing the asset and sharing it on tfhub.dev, we welcome the submission!