TensorFlow Lite Conversion is the process of converting TensorFlow models to TensorFlow Lite format, which is optimized for mobile and embedded devices. This guide will provide you with a step-by-step overview of how to convert your TensorFlow models.

Prerequisites

  • TensorFlow installed on your system
  • A TensorFlow model (Keras or TF1 model)
  • Python environment

Step-by-Step Guide

  1. Prepare Your TensorFlow Model: Ensure that your TensorFlow model is trained and saved in the desired format (Keras or TF1).

  2. Install TensorFlow Lite Tools: Make sure you have the TensorFlow Lite Converter installed. You can install it by running the following command:

    pip install tensorflow-lite

  3. Convert Your Model: Use the TensorFlow Lite Converter to convert your model to TensorFlow Lite format. Here's an example command:

    tensorflow.lite.tflite_convert --input_graph path/to/your/model.pb --input_tensor input_tensor_name --output_file path/to/output.tflite

  4. Optimize Your Model: After conversion, you can further optimize your model using the TensorFlow Lite Optimizer:

    tensorflow.lite.optimize --input_file path/to/output.tflite --output_file path/to/optimized_output.tflite --representative_dataset path/to/representative_dataset.json

  5. Test Your Model: Finally, test your TensorFlow Lite model on a target device to ensure it performs as expected.

Additional Resources

For more detailed information and additional resources, please visit the following links:

TensorFlow Lite