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:
- 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.
- Command line: This only supports basic model conversion.
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 TensorFlow 2.x model using
tf.lite.TFLiteConverter
. A TensorFlow 2.x model is stored using the SavedModel format and is generated either using the high-leveltf.keras.*
APIs (a Keras model) or the low-leveltf.*
APIs (from which you generate concrete functions). As a result, you have the following three options (examples are in the next few sections):tf.lite.TFLiteConverter.from_saved_model()
(recommended): Converts a SavedModel.tf.lite.TFLiteConverter.from_keras_model()
: Converts a Keras model.tf.lite.TFLiteConverter.from_concrete_functions()
: Converts concrete functions.
Convert a TensorFlow 1.x model using
tf.compat.v1.lite.TFLiteConverter
(examples are on Github):tf.compat.v1.lite.TFLiteConverter.from_saved_model()
: Converts a SavedModel.tf.compat.v1.lite.TFLiteConverter.from_keras_model_file()
: Converts a Keras model.tf.compat.v1.lite.TFLiteConverter.from_session()
: Converts a GraphDef from a session.tf.compat.v1.lite.TFLiteConverter.from_frozen_graph()
: Converts a Frozen GraphDef from a file. If you have checkpoints, then first convert it to a Frozen GraphDef file and then use this API as shown here.
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:
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.
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. Theregister_custom_opdefs
method takes a list of a string containing an OpDef (s). Below is an example of aTFLiteAwesomeCustomOp
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).