If you’re using TensorFlow to develop machine learning models, you may be wondering how to convert them to the TFLite format for on-device inference. TFLite is a great choice for mobile and embedded devices, as it’s much smaller and faster than full-fledged TensorFlow. In this blog post, we’ll show you how to convert your TensorFlow models to TFLite.
For more information check out our video:
In this tutorial, we’ll show you how to convert a TensorFlow model into a TFLite model. We’ll use a simple example: converting a model that predicts whether an image is a cat or not.
First, we’ll need to install TensorFlow. We recommend using virtualenv for this (you can install it with pip install virtualenv).
Next, we need to download the TensorFlow Lite Conversion tool. We’ll be using the command-line interface, so you can either clone the GitHub repository or download the tool as a ZIP file.
Once you have the conversion tool, you’re ready to start converting your models!
What is TensorFlow?
TensorFlow is a free and open-source software library for data analysis and machine learning. Originally developed by Google Brain team members Geoffrey Hinton, Iaonnis Nikou, and Andrew Ng, TensorFlow was designed to run on multiple CPUs or GPUs and even mobile computing platforms like smartphones. TensorFlow allows you to build custom algorithms to optimize and improve your machine learning models, and it has been used by major companies such as Google, Facebook, IBM, and Intel.
What is TFLite?
TFLite is TensorFlow’s lightweight solution for mobile and embedded devices. It enables on-device machine learning inference with low latency and a small binary size.
Why Convert TensorFlow Models to TFLite?
There are a number of reasons why you might want to convert a TensorFlow model to TFLite. Perhaps you want to deploy your model on a mobile device or edge device, and TFLite is more efficient for those scenarios. Or maybe you simply want to shrink the size of your model so that it’s easier to work with.
Either way, converting a TensorFlow model to TFLite is relatively straightforward. In this article, we’ll show you how to do it.
How to Convert TensorFlow Models to TFLite?
TF2 has easier conversions to TFLite than TF1. There are two options:
1. You can use the `tflite_convert` command-line tool that is in the [tensorflow/lite/python/tools](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/lite/python/tools) directory. You can install it using pip:
pip install tflite_convert
2. Or you can use the [TensorFlow Lite Converter Python API](https://www.tensorflow.org/lite/api_docs/python/#tflite_convert).
Advantages of Converting TensorFlow Models to TFLite
There are several advantages to converting TensorFlow models to TFLite:
-TFLite models are much smaller in size, making them easier to deploy on mobile devices.
-TFLite models can be executed faster on mobile devices, due to the smaller model size and optimized kernels.
-TFLite supports a limited subset of TensorFlow ops, which helps to improve model inference performance.
Disadvantages of Converting TensorFlow Models to TFLite
There are a few disadvantages to converting TensorFlow models to TFLite:
-TFLite models are typically much smaller than their TensorFlow counterparts, so they may not be able to take advantage of all the features and information in your original model.
-TFLite models can only be used on devices that support the TFLite format, so you may not be able to use your converted model on all devices.
– Converting a TensorFlow model to TFLite can be a time-consuming process, so you may want to consider whether it is worth the effort.
We have seen how to convert a TensorFlow model to TFLite. We have also seen how to run inference on a TFLite model and how to get accurate results.
If you want to learn more about converting TensorFlow models to TFLite, we suggest checking out the following resources:
-The official TensorFlow documentation on [converting TensorFlow models to TFLite](https://www.tensorflow.org/lite/guide/get_started#3_convert_a_tensorflow_model_to_tflite)
-A guide to [deploying TensorFlow Lite models on Android](https://www.novatec-gmbh.de/en/blog/a-guide-to-deploying-tensorflow-lite-models-on-android/)
-A tutorial on [building and deploying iOS apps with TFLite models](https://www.raywenderlich.com/4161005-machine-learning-with-coreml-and turicreate)
There are several ways to convert a TensorFlow model to TensorFlow Lite:
-The easiest way is to use the TensorFlow Lite Converter.
-You can also convert your models using the command line or by using the Python API.
-The Developer Preview of TensorFlow Lite also includes a tool for converting Keras models.
Keyword: How to Convert TensorFlow Models to TFLite