When using TensorFlow Lite Converter with TensorFlow version 2.16.1, you may encounter a crash or error. This is likely due to a compatibility issue between TensorFlow Lite Converter and Keras version 3.0, which is the default Keras version used in TensorFlow 2.16.1.
Cause
TensorFlow Lite Converter is designed to convert Keras models to TensorFlow Lite models. However, there is a known issue in TensorFlow Lite Converter 2.16.1 that causes it to crash when converting Keras models that use certain layers, such as tf.keras.layers.Embedding. This issue is caused by a change in the way Keras layers are serialized in Keras version 3.0.
Solution
To resolve this issue, you can use the following solution:
Install the tf_keras package using pip:
pip install tf_keras
Set the TF_USE_LEGACY_KERAS environment variable: Set the TF_USE_LEGACY_KERAS environment variable to 1 to force TensorFlow to use Keras version 2.x. To do this, add the following line to your code before importing TensorFlow:
import os
os.environ["TF_USE_LEGACY_KERAS"] = "1"
Example
Consider the following code snippet:
import tensorflow as tf
import os
os.environ["TF_USE_LEGACY_KERAS"] = "1"
# Create a Keras model
model = tf.keras.Sequential()
model.add(tf.keras.layers.Embedding(100, 128))
# Convert the model to TensorFlow Lite
converter = tf.lite.TFLiteConverter.from_keras_model(model)
tflite_model = converter.convert()
When you run this code with TensorFlow version 2.16.1 and Keras version 2.x, will be used.
Additional Notes
If you are using a custom Keras layer, you may need to modify the layer to make it compatible with TensorFlow Lite Converter 2.16.1.
If you are unable to install Keras version 2.x or set the TF_USE_LEGACY_KERAS environment variable, you can try using an earlier version of TensorFlow Lite Converter.
Conclusion
By following the solutions outlined in this blog post, you should be able to resolve the crash or error that you are encountering when using TensorFlow Lite Converter with TensorFlow version 2.16.1.
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