Deep Dive into TFLite On-Device Training

Description of image

Introduction

TensorFlow has revolutionized the field of machine learning by providing powerful tools for developers. Now, with on-device machine learning, TensorFlow is pushing the boundaries even further. This exciting capability allows machine learning models to run directly on user devices, unlocking new possibilities for performance, privacy, and personalized experiences. Let's delve into the world of TensorFlow Lite on-device training!

What is TFLite?

TensorFlow Lite (TFLite) is an open-source framework developed by Google. Its primary function is to efficiently run machine learning models on mobile devices and edge devices. Edge devices encompass a wide range of gadgets with limited processing power, including smartphones, embedded systems, and even tiny microcontrollers.

TFLite achieves this by streamlining models created with TensorFlow. It converts them into a format that can execute efficiently on these resource-constrained devices. This conversion process often involves reducing the model's size and complexity while striving to maintain its accuracy.

Key Benefits of TFLite

How to Convert a Model to TFLite

python
import tensorflow as tf

# Load your TensorFlow model
model = tf.keras.models.load_model('your_model.h5')

# Convert to TensorFlow Lite format
converter = tf.lite.TFLiteConverter.from_keras_model(model)
tflite_model = converter.convert()

# Save the optimized model
with open('model.tflite', 'wb') as f:
    f.write(tflite_model)

TFLite On-Device Training

TensorFlow Lite (TFLite) takes machine learning (ML) a step further by enabling on-device training. This empowers you to refine pre-trained models directly on the devices they run on, using new, user-generated data. This opens doors for exciting applications that personalize experiences, adapt to specific environments, and prioritize user privacy.

TFLite On-Device Training Involves a Cyclical Interplay Between Four Key Functions:

Synergy for Continuous Learning

These functions work in a harmonious loop:

  1. Restore the model's knowledge from the previous training session.
  2. Train the model on a new batch of data to further refine its performance.
  3. Save the updated model state to preserve progress.
  4. Use the infer function to make predictions on new data, demonstrating the model's learning.

By effectively orchestrating these functions, you can create TFLite models that continuously learn and adapt on the device, unlocking the potential of on-device machine learning.

Core of On-Device Training

Capabilities and Considerations

Saving Tflite model with defined Signatures

Step 1: Defining the Base TFLite Model

The foundation of our TFLite model starts with a base class BaseTFLiteModel. This class provides essential methods and attributes that any TFLite-compatible model should implement. Here's a breakdown of its components:

python
class BaseTFLiteModel(tf.Module):
    """Base TFLite model class to inherit from.

    Usage: Inherit from this class and annotate with @tflite_model_class.

    Attributes to override:
    - X_SHAPE: Shape of the input to the model.
    - Y_SHAPE: Shape of the output from the model.
    - model: tf.keras.Model instance.

    Provides default implementations of train, infer, parameters, restore.
    These methods are not annotated with @tf.function; they are supposed to be
    converted by @tflite_model_class.
    """

    X_SHAPE: list[int]
    Y_SHAPE: list[int]
    model: tf.keras.Model

    def train(self, x, y):
        return self.model.train_step((x, y))

    def infer(self, x):
        return {"logits": self.model(x)}

    def parameters(self):
        return {
            f"a{index}": weight
            for index, weight in enumerate(self.model.weights)
        }

    def restore(self, **parameters):
        for index, weight in enumerate(self.model.weights):
            parameter = parameters[f"a{index}"]
            weight.assign(parameter)
        assert self.parameters is not None
        return self.parameters()

Step 2: Converting to TFLite Model Class

To make the model TFLite compatible, we use the tflite_model_class decorator. This decorator converts methods using @tf.function with appropriate input_signature based on X_SHAPE and Y_SHAPE.

python
def tflite_model_class(cls):
    """Convert cls that inherits from BaseTFLiteModel to a TFLite model class."""
    cls.x_spec = tf.TensorSpec([None] + cls.X_SHAPE, tf.float32)
    cls.y_spec = tf.TensorSpec([None] + cls.Y_SHAPE, tf.float32)

    cls.train = tf.function(cls.train, input_signature=[cls.x_spec, cls.y_spec])
    cls.infer = tf.function(cls.infer, input_signature=[cls.x_spec])
    cls.parameters = tf.function(cls.parameters, input_signature=[])
    cls.restore = tf.function(cls.restore)

    return cls

Step 3: Saving the TFLite Model

Once the TensorFlow model is annotated and prepared with @tflite_model_class, it can be saved as a TFLite model using tf.saved_model.save.

python
def save_model(model, saved_model_dir):
    """Saves a TensorFlow model to the specified directory."""
    signatures = {
        "train": model.train.get_concrete_function(),
        "infer": model.infer.get_concrete_function(),
        "parameters": model.parameters.get_concrete_function(),
        "restore": model.restore.get_concrete_function(),
    }
    tf.saved_model.save(model, saved_model_dir, signatures=signatures)

Step 4: Converting to TFLite Format

Finally, convert the saved TensorFlow model to a TFLite model format using tf.lite.TFLiteConverter.

python
def convert_saved_model(saved_model_dir):
    converter = tf.lite.TFLiteConverter.from_saved_model(saved_model_dir)
    tflite_model = converter.convert()
    return tflite_model

Conclusion

In this guide, we've covered the essential steps to convert a TensorFlow model into a TFLite model suitable for deployment on mobile and embedded platforms. By leveraging TensorFlow's capabilities, including tf.function for method conversion and tf.lite.TFLiteConverter for format conversion, you can seamlessly optimize and deploy machine learning models to edge devices.

By following these steps, you can effectively bridge the gap between training complex machine learning models in TensorFlow and deploying them in resource-constrained environments using TensorFlow Lite.