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TensorFlow Lite converter
The TensorFlow Lite converter is used to convert TensorFlow models into an optimized FlatBuffer format, so that they can be used by the TensorFlow Lite interpreter.
Note: This page contains documentation on the converter API for TensorFlow 1.x. The API for TensorFlow 2.0 is available here.
FlatBuffers
FlatBuffer is an efficient open-source cross-platform serialization library. It is similar to protocol buffers, with the distinction that FlatBuffers do not need a parsing/unpacking step to a secondary representation before data can be accessed, avoiding per-object memory allocation. The code footprint of FlatBuffers is an order of magnitude smaller than protocol buffers.
From model training to device deployment
The TensorFlow Lite converter generates a TensorFlow Lite
FlatBuffer file (.tflite) from a
TensorFlow model.
The converter supports the following input formats:
- SavedModels
- Frozen
GraphDef: Models generated by freeze_graph.py. tf.kerasHDF5 models.- Any model taken from a
tf.Session(Python API only).
The TensorFlow Lite FlatBuffer file is then deployed to a client device, and
the TensorFlow Lite interpreter uses the compressed model for on-device
inference. This conversion process is shown in the diagram below:
Options
The TensorFlow Lite Converter can be used from either of these two options:
- Python (Preferred): Using the Python API makes it easier to convert models as part of a model development pipeline, and helps mitigate compatibility issues early on.
- Command line