5 Best Ways to Export Your TensorFlow Model with Python

Rate this post

πŸ’‘ Problem Formulation: After developing a machine learning model with TensorFlow in Python, the next challenge is exporting the model for deployment. Users typically seek to save their trained model such that it can be reused without retraining, shared with others, or deployed in production. The desired output is a saved file or set of files that encapsulate the model’s trained weights and architecture.

Method 1: Using the SavedModel Format

The SavedModel format is TensorFlow’s recommended way of exporting models, as it is a standalone serialization format that is language-agnostic and recoverable. This means that you can save a model built in Python and load it in another language that TensorFlow supports.

Here’s an example:

import tensorflow as tf

model = tf.keras.models.Sequential([...])
model.save('path_to_saved_model', save_format='tf')

Output: A SavedModel directory with multiple files and subdirectories containing the model.

This code snippet creates a simple TensorFlow Keras model and saves it to the specified path using the SavedModel format. The directory will include the model architecture, weights, and no-graph features, which makes it perfect for cross-platform deployment.

Method 2: Saving to an HDF5 File

HDF5 is a data model, library, and file format for storing and managing data. It is useful for handling large, complex, heterogeneous data. TensorFlow supports saving models in HDF5 format which encapsulates model architecture and weights in a single file.

Here’s an example:

import tensorflow as tf

model = tf.keras.models.Sequential([...])
model.save('my_model.h5')

Output: A single my_model.h5 file containing the model’s architecture and weights.

In this example, we’ve built a model and utilized the save method to create an HDF5 file. This file format is especially useful for those familiar with its ecosystem but lacks some TensorFlow-specific features present in the SavedModel format.

Method 3: Exporting the Model Weights Only

Sometimes, you might need to export only the weights of your TensorFlow model to re-use them later with the same model architecture. TensorFlow can store these weights as a series of checkpoint files.

Here’s an example:

import tensorflow as tf

model = tf.keras.models.Sequential([...])
model.save_weights('path_to_weights.ckpt')

Output: Checkpoint files at the specified path containing the model’s weights.

This minimalistic approach enables you to export the weights as a checkpoint file, reducing the exported content size. However, it assumes that you have access to the model’s architecture to reconstruct it before restoring these weights.

Method 4: Converting to TensorFlow Lite

For deployment on mobile and edge devices, TensorFlow Lite provides tools that convert your TensorFlow models into an efficient format for on-device inference.

Here’s an example:

import tensorflow as tf

model = tf.keras.models.Sequential([...])

converter = tf.lite.TFLiteConverter.from_keras_model(model)
tflite_model = converter.convert()

with open('model.tflite', 'wb') as f:
    f.write(tflite_model)

Output: A model.tflite file that can run on mobile and embedded devices.

This code snippet demonstrates the conversion of a Keras model to the TensorFlow Lite format, creating a file optimized for mobile and embedded deployments.

Bonus One-Liner Method 5: Saving as a JSON

In cases where you want to share or inspect your model’s architecture (excluding weights), you can export it as a JSON file.

Here’s an example:

import tensorflow as tf

model = tf.keras.models.Sequential([...])
json_config = model.to_json()

with open('model_config.json', 'w') as json_file:
    json_file.write(json_config)

Output: A model_config.json file with a JSON representation of the model’s architecture.

Quick and easy, this snippet saves the architecture of your TensorFlow model as a JSON configuration, making it portable and understandable for humans and machines alike.

Summary/Discussion

  • Method 1: SavedModel Format. Language-agnostic. Best for cross-platform deployment. Bigger file size since it includes comprehensive data.
  • Method 2: HDF5 File. Single file convenience. Good integration into the broader HDF5 ecosystem. Not as fully-featured for TensorFlow specific applications as the SavedModel format.
  • Method 3: Weights Only. Lightweight solution. Requires model architecture for reloading. Best when only weights need persisting.
  • Method 4: TensorFlow Lite. Optimizes the model for mobile and embedded devices. Essential for on-device machine learning applications.
  • Method 5: JSON Configuration. Ideal for sharing model architecture. Does not include weights, so not directly usable for inference.