TensorFlow SavedModel: Best Practices for Model Export

In the realm of machine learning, exporting a model in a robust format ensures that it can be reused with high fidelity across various environments and purposes. TensorFlow's SavedModel is a serialization format for TensorFlow models that allows you to save the entire model—architecture, weights, and training configuration—into a standalone piece that can be loaded and used in different languages including Python, C++, and Java.

What is a TensorFlow SavedModel?

The SavedModel format is TensorFlow's standard method for saving and serving models. It is versatile and supports a variety of functionalities including full architecture saving, all parameter weights, and flexibility in cross-language compatibility.

How to Export a Model as a SavedModel

The process of exporting a model involves a few key steps that ensure your model is both accessible and safely stored. Here is a simple guide to follow:

First, make sure you have the necessary libraries:

import tensorflow as tf

Train or load your model. Assume you have a model trained and ready:

# Assume model is a tf.Keras.Model instance
model = tf.keras.Sequential([
    tf.keras.layers.Dense(units=16, activation='relu', input_shape=(10,)),
    tf.keras.layers.Dense(units=1)
])

model.compile(optimizer='adam',
              loss='mean_squared_error',
              metrics=['accuracy'])

Export the model by defining the directory path:

# Set export directory
export_path = 'my_model_directory'

# Save the model using the TensorFlow SavedModel format
model.save(export_path)

Best Practices for Exporting SavedModels

While exporting models can seem straightforward, there are several best practices to enhance the export process:

• Version your models: To prevent overwriting previous models, consider versioning them. This can keep a history of model iterations.
• Include signaturing: If your model functions differently for different kinds of inputs and checks, use signature definitions to explicitly define the model’s inputs and outputs. Here is how you can set it:

# Save with signature
imported_model = tf.keras.models.load_model(export_path)

# Define signature
@tf.function(input_signature=[tf.TensorSpec([None, 10], dtype=tf.float32)])
def custom_call(inputs):
    return imported_model(inputs)

signatures = {
    'custom_call': custom_call,
}

model.save('another_path', signatures=signatures)

• Ensure repeatability: Save any custom objects such as layers or models so your model can be fully reloaded without errors.
• Review dependencies: Ensure your environment preserves all dependencies related to the version of the libraries you’re using.

Loading a SavedModel

Loading a SavedModel is almost as crucial as saving it. Getting a model back into a usable state can be done simply:

# Load the model
loaded_model = tf.keras.models.load_model(export_path)

# Use loaded model directly for predictions
predictions = loaded_model.predict(data)

Consider testing the loaded model to ensure it's operating as expected:

# Validate that prediction works
sample_data = tf.ones((1, 10))

try:
    test_prediction = loaded_model(sample_data)
    print("Model prediction successful.")
except Exception as e:
    print("Error during model prediction:", e)

Conclusion

Exporting models in TensorFlow using SavedModel is a straightforward yet powerful process that supports various deployment platforms and programming languages. By following best practices around exporting, including version control and signature definitions, you ensure your models are not only stored correctly but ready for production use and scalable deployment.