STT-tensorflow/tensorflow/lite/g3doc/convert/index.md

TensorFlow Lite converter

The TensorFlow Lite converter takes a TensorFlow model and generates a TensorFlow Lite FlatBuffer file (.tflite). The converter supports SavedModel directories, tf.keras models, and concrete functions.

Note: This page contains documentation on the converter API for TensorFlow 2.0. The API for TensorFlow 1.X is available here.

New in TF 2.2

TensorFlow Lite has switched to use a new converter backend by default - in the nightly builds and TF 2.2 stable. Why we did we switch?

• Enables conversion of new classes of models, including Mask R-CNN, Mobile BERT, and many more
• Adds support for functional control flow (enabled by default in TensorFlow 2.x)
• Tracks original TensorFlow node name and Python code, and exposes them during conversion if errors occur
• Leverages MLIR, Google's cutting edge compiler technology for ML, which makes it easier to extend to accommodate feature requests
• Adds basic support for models with input tensors containing unknown dimensions
• Supports all existing converter functionality

In case you encounter any issues:

• Please create a GitHub issue with the component label “TFLiteConverter.” Please include:
  • Command used to run the converter or code if you’re using the Python API
  • The output from the converter invocation
  • The input model to the converter
  • If the conversion is successful, but the generated model is wrong, state what is wrong:
    • Producing wrong results and / or decrease in accuracy
    • Producing correct results, but the model is slower than expected (model generated from old converter)
• If you are using the allow_custom_ops feature, please read the Python API and Command Line Tool documentation
• Switch to the old converter by setting --experimental_new_converter=false (from the tflite_convert command line tool) or converter.experimental_new_converter=False (from the Python API)

Device deployment

The TensorFlow Lite FlatBuffer file is then deployed to a client device (e.g. mobile, embedded) and run locally using the TensorFlow Lite interpreter. This conversion process is shown in the diagram below:

Converting models

The TensorFlow Lite converter should be used from the Python API. Using the Python API makes it easier to convert models as part of a model development pipeline and helps mitigate compatibility issues early on. Alternatively, the command line tool supports basic models.