tensorflow/lite/g3doc/models/convert/convert_models.md
This page describes how to convert a TensorFlow model
to a TensorFlow Lite model (an optimized
FlatBuffer format identified by the
.tflite file extension) using the TensorFlow Lite converter.
Note: This guide assumes you've both
installed TensorFlow 2.x
and trained models in TensorFlow 2.x.
If your model is trained in TensorFlow 1.x, considering
migrating to TensorFlow 2.x.
To identify the installed TensorFlow version, run
print(tf.__version__).
The diagram below illustrations the high-level workflow for converting your model:
Figure 1. Converter workflow.
You can convert your model using one of the following options:
Note: In case you encounter any issues during model conversion, create a GitHub issue.
Helper code: To learn more about the TensorFlow Lite converter
API, run print(help(tf.lite.TFLiteConverter)).
Convert a TensorFlow model using
tf.lite.TFLiteConverter.
A TensorFlow model is stored using the SavedModel format and is
generated either using the high-level tf.keras.* APIs (a Keras model) or
the low-level tf.* 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.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)
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)
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(input_signature=[tf.TensorSpec(shape=[None], dtype=tf.float32)])
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],
model)
tflite_model = converter.convert()
# Save the model.
with open('model.tflite', 'wb') as f:
f.write(tflite_model)
Apply optimizations. A common optimization used is post training quantization, which can further reduce your model latency and size with minimal loss in accuracy.
Add metadata, which makes it easier to create platform specific wrapper code when deploying models on devices.
The following are common conversion errors and their solutions:
Error: Some ops are not supported by the native TFLite runtime, you can enable TF kernels fallback using TF Select. See instructions: https://www.tensorflow.org/lite/guide/ops_select. TF Select ops: ..., .., ...
Solution: The error occurs as your model has TF ops that don't have a corresponding TFLite implementation. You can resolve this by using the TF op in the TFLite model (recommended). If you want to generate a model with TFLite ops only, you can either add a request for the missing TFLite op in Github issue #21526 (leave a comment if your request hasn’t already been mentioned) or create the TFLite op yourself.
Error: .. is neither a custom op nor a flex op
Solution: If this TF op is:
Supported in TF: The error occurs because the TF op is missing from the allowlist (an exhaustive list of TF ops supported by TFLite). You can resolve this as follows:
Unsupported in TF: The error occurs because TFLite is unaware of the custom TF operator defined by you. You can resolve this as follows:
Note: 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. 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.
If you have the
TensorFlow 2.x source
downloaded and want to run the converter from that source without building and
installing the package,
you can replace 'tflite_convert' with
'bazel run tensorflow/lite/python:tflite_convert --' in the command.
tflite_convert \
--saved_model_dir=/tmp/mobilenet_saved_model \
--output_file=/tmp/mobilenet.tflite
tflite_convert \
--keras_model_file=/tmp/mobilenet_keras_model.h5 \
--output_file=/tmp/mobilenet.tflite
Use the TensorFlow Lite interpreter to run inference on a client device (e.g. mobile, embedded).