Keras to TensorFlow is an open-source command-line utility that converts a trained Keras model into a frozen, inference-ready TensorFlow model. With about 1,650 stars on GitHub, it became a popular bridge in the TensorFlow 1.x era for taking models from training in Keras to deployment as standalone computation graphs.
The tool freezes a model’s variables into constants and saves the inference graph and weights into a binary protobuf (.pb) file that can be loaded at serving time without the original training code. It supports models with multiple outputs and lets you rename output tensors with a configurable prefix, and it can also export checkpoint and metagraph files for use with TensorFlow’s Saver if you want to continue training later. The converter works from standard Keras saves โ either a single HDF5 file holding both architecture and weights, or separate weights and JSON architecture files. For many teams it was the missing step between a notebook-trained model and a production endpoint; the README notes it targets the TensorFlow 1.x workflow rather than TF 2.0.
Keras to TensorFlow is completely free and open source under the MIT license. There is no paid version or hosted service โ it is a single-purpose script that you run locally as part of your model deployment pipeline.
ML engineers maintaining TensorFlow 1.x-era deployments, and teams that need to ship a Keras-trained model as a frozen graph for serving or embedding in other systems. Modern TF2 users should note that the tool predates the current SavedModel workflow.