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TensorFlow Lite converter

The TensorFlow Lite converter takes a TensorFlow model and generates a TensorFlow Lite model (an optimized FlatBuffer format identified by the .tflite file extension). You have the following two options for using the converter:

  1. Python API (recommended): This makes it easier to convert models as part of the model development pipeline, apply optimizations, add metadata and has many more features.
  2. Command line: This only supports basic model conversion.

TFLite converter workflow

Python API

Helper code: To identify the installed TensorFlow version, run print(tf.__version__) and to learn more about the TensorFlow Lite converter API, run print(help(tf.lite.TFLiteConverter)).

If you've installed TensorFlow 2.x, you have the following two options: (if you've installed TensorFlow 1.x, refer to Github)

Convert a SavedModel (recommended)

The following example shows how to convert a SavedModel into a TensorFlow Lite model.

import tensorflow as tf

# Convert the model
converter = tf.lite.TFLiteConverter.from_saved_model(saved_model_dir) # path to the SavedModel directory
tflite_model = converter.convert()

# Save the model.
with open('model.tflite', 'wb') as f:
  f.write(tflite_model)

Convert a Keras model

The following example shows how to convert a Keras model into a TensorFlow Lite model.

import tensorflow as tf

# Create a model using high-level tf.keras.* APIs
model = tf.keras.models.Sequential([
    tf.keras.layers.Dense(units=1, input_shape=[1]),
    tf.keras.layers.Dense(units=16, activation='relu'),
    tf.keras.layers.Dense(units=1)
])
model.compile(optimizer='sgd', loss='mean_squared_error') # compile the model
model.fit(x=[-1, 0, 1], y=[-3, -1, 1], epochs=5) # train the model
# (to generate a SavedModel) tf.saved_model.save(model, "saved_model_keras_dir")

# Convert the model.
converter = tf.lite.TFLiteConverter.from_keras_model(model)
tflite_model = converter.convert()

# Save the model.
with open('model.tflite', 'wb') as f:
  f.write(tflite_model)

Convert concrete functions

The following example shows how to convert concrete functions into a TensorFlow Lite model.

import tensorflow as tf

# Create a model using low-level tf.* APIs
class Squared(tf.Module):
  @tf.function
  def __call__(self, x):
    return tf.square(x)
model = Squared()
# (ro run your model) result = Squared(5.0) # This prints "25.0"
# (to generate a SavedModel) tf.saved_model.save(model, "saved_model_tf_dir")
concrete_func = model.__call__.get_concrete_function()

# Convert the model
converter = tf.lite.TFLiteConverter.from_concrete_functions([concrete_func])
tflite_model = converter.convert()

# Save the model.
with open('model.tflite', 'wb') as f:
  f.write(tflite_model)

Other features

  • Apply optimizations. A common optimization used is post training quantization, which can further reduce your model latency and size with minimal loss in accuracy.

  • Handle unsupported operations. You have the following options if your model has operators:

    1. Supported in TensorFlow but unsupported in TensorFlow Lite: If you have size constraints, you need to create the TensorFlow Lite operator, otherwise just use TensorFlow operators in your TensorFlow Lite model.

    2. Unsupported in TensorFlow: You need to create the TensorFlow operator and then create the TensorFlow Lite operator. If you were unsuccessful at creating the TensorFlow operator or don't wish to create one (not recommended, proceed with caution), you can still convert using the register_custom_opdefs method and then directly create the TensorFlow Lite operator. The register_custom_opdefs method takes a list of a string containing an OpDef (s). Below is an example of a TFLiteAwesomeCustomOp with 1 input, 1 output, and 2 attributes:

        import tensorflow as tf

  custom_opdef = """name: 'TFLiteAwesomeCustomOp' input_arg:
  { name: 'In' type: DT_FLOAT } output_arg: { name: 'Out' type: DT_FLOAT }
  attr : { name: 'a1' type: 'float'} attr : { name: 'a2' type: 'list(float)'}"""

  # Register custom opdefs before the invocation of converter API.
  tf.lite.python.convert.register_custom_opdefs([custom_opdef])

  converter = tf.lite.TFLiteConverter.from_saved_model(...)
  converter.allow_custom_ops = True

Command Line Tool

It is highly recommended that you use the Python API listed above instead, if possible.

If you've installed TensorFlow 2.x from pip, use the tflite_convert command as follows: (if you've installed TensorFlow 2.x from source then you can replace 'tflite_convert' with 'bazel run //tensorflow/lite/python:tflite_convert --' in the following sections, and if you've installed TensorFlow 1.x then refer to Github (reference, examples))

tflite_convert: To view all the available flags, use the following command:

$ tflite_convert --help

`--output_file`. Type: string. Full path of the output file.
`--saved_model_dir`. Type: string. Full path to the SavedModel directory.
`--keras_model_file`. Type: string. Full path to the Keras H5 model file.
`--enable_v1_converter`. Type: bool. (default False) Enables the converter and flags used in TF 1.x instead of TF 2.x.

You are required to provide the `--output_file` flag and either the `--saved_model_dir` or `--keras_model_file` flag.

Converting a SavedModel 

tflite_convert \
  --saved_model_dir=/tmp/mobilenet_saved_model \
  --output_file=/tmp/mobilenet.tflite

Converting a Keras H5 model 

tflite_convert \
  --keras_model_file=/tmp/mobilenet_keras_model.h5 \
  --output_file=/tmp/mobilenet.tflite

Next Steps

  • Add metadata, which makes it easier to create platform specific wrapper code when deploying models on devices.
  • Use the TensorFlow Lite interpreter to run inference on a client device (e.g. mobile, embedded).