Learn how to convert your Keras model to TensorFlow protobuf in order to speed up the training process and take advantage of TensorFlow optimizers.
Check out our video for more information:
With the release of TensorFlow 2.0, there are significant changes in the TF API. As a result, many existing models need to be ported to the new API. In addition, the new TF 2.0 introduces a new file format for storing models – the TF PB file. This format has a number of advantages over the previous HDF5 format, including better support for language bindings (such as Java) and improved performance when loading models into memory.
In this tutorial, we will show you how to convert your Keras model to the new TF PB format. We will be using a simple CNN model trained on the MNIST dataset.
Why Convert Your Keras Model to TensorFlow PB?
There are a number of reasons you might want to convert your Keras model to TensorFlow PB. Maybe you want to deploy your model on a server that only supports TensorFlow, or you want to take advantage of some of the advanced features of the TensorFlow platform. Either way, converting your model is a pretty straightforward process. Let’s take a look at how to do it.
How to Convert Your Keras Model to TensorFlow PB
If you have a Keras model that you have trained and saved, you can load it using the TensorFlow function `load_model`. You can then use the TensorFlow function `save` to convert your model to a TensorFlow `.pb` file.
You can do this by opening a terminal and running the following command:
tensorflowjs_converter – input_format keras keras_model.h5 tensorflowjs_model/
This will create a directory called `tensorflowjs_model`, which will contain your converted model.
Advantages of Converting Your Keras Model to TensorFlow PB
There are a few advantages of converting your Keras model to TensorFlow PB:
-You can use the TensorFlow optimizers
-You can use the TensorFlow distributed training infrastructure
-You can use TensorFlow serving
Disadvantages of Converting Your Keras Model to TensorFlow PB
There are a few disadvantages to converting your Keras model to TensorFlow PB:
-You won’t be able to use any of the advanced features of the Keras API, such as layers that have yet to be implemented in TF, or the automatic differentiation features of the Sequential model.
-Your model will be much slower to train, as TF does not support async training on multiple GPUs.
-You will need to retrain your model from scratch, as the weights are not compatible between the two frameworks.
At the end of this guide, you will know how to convert your Keras model to a TensorFlow PB file. This will allow you to use your model with the TensorFlow framework.
Keyword: How to Convert Your Keras Model to TensorFlow PB