Exporting TensorFlow models to ML Kit

Exporting TensorFlow models to ML Kit

- 3 mins

ML Kit is the new tool from the Firebase family to bring TensorFlow into your mobile applications. With ML Kit, it is easier than even to load and run trained models on Android, if you want to get started using ML Kit, I recommend you the following codelab: Identify objects in images using custom machine learning models with ML Kit for Firebase.

In this article, you will learn a way to export your custom TensorFlow models to the tflite format to run on ML Kit.

The steps to export a model are generally these:

  1. Export the graph (pb file format)
  2. Export the variables values (in the form of checkpoints)
  3. Freeze the grapth with the variables values
  4. Convert the frozen graph to TF Lite.

You can read more about the process in the Developer Guide for TF Lite.

However, I find more convenient if I can do those steps directly in my Jupyter Notebooks in a more streamlined way.

To be able to export models directly from our code, we can use the toco_convert method to convert the TensorFlow session graph to a TF Lite model.

This example here shows how to use toco_convert:

What you are seeing, is a simple TensorFlow model that has a single float input and a single float output, and performs a +1 operation. It is essentially a ++ operator implemented in TensorFlow.

The interesting part is the call to toco_convert, which converts the model to a TF Lite model, then we call to the write method to store it.

What happens when we incorporate variables into the mix? that just calling to toco_convert would not work.

In this example, we do a similar operation, our input is a 1x1 Tensor and we multiply it by another 1x1 Tensor (a variable w initialised to 0, which later is changed to 2).

When we try to export this example, we will get lots of errors in the console, which will look like these:

Converting unsupported operation: Variable...

That’s essentially because we are trying to convert variables to TF Lite, rather than their “frozen” values to constants.

To fix that, we need to freeze our graph first:

freeze_session is a modified version of the tool freeze_graph from TensorFlow, and can be found here: https://stackoverflow.com/a/45466355/673294

What freeze_session is doing internally is calling to convert_variables_to_constants to store the current variables values into fixed ones, so the exported model contains those values.

For example, if you train a model, you want to export the current values for your neural network weights so you can use it in your application.

In this example, the variable w keeps the value of 2 after exporting to TF Lite format.

In short, using toco_convert and freeze_session together, simplify exporting TensorFlow models to ML Kit directly from your Python code.

Just keep in mind, that not all operations are supported by TOCO/TF Lite, so you may have problems exporting certain neural networks like RNNs with LSTM cells. In that case, we can only hope and wait for them to be supported.

Are you interested in incorporating deep neural networks to your products? Do you have a need for Machine Learning optimised for mobile? Let’s talk! I am looking for future freelancing opportunities in mobile and machine learning. Check for more info: http://beltran.work/with-me/

Miguel Beltran

Miguel Beltran

Freelance Software Developer (Android, iOS, Flutter)

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