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However, I do not know how robust this workaround is.
I suggest exporting the model to the TensorFlow saved_model format first using model.export("path/to/saved_model_folder") and then using:
Is there any news of getting t2fonnx with keras models working on recent versions of Keras/Tensorflow? I've tried the workarounds suggested above and tf2onnx.convert.from_keras() gives me an error "Cannot convert a symbolic tf.Tensor (input_1:0) to a numpy array." and model.export() gives error "AttributeError: module 'keras._tf_keras.keras.backend' has no attribute 'set_learning_phase'."
The switch from Keras 2 to Keras 3 in Tensorflow 2.16 apparently breaks
tf2onnx
:This is probably the same issue people are seeing with
tf.lite.TFLiteConverter
since Keras 3:keras-team/keras#18430
Is there an alternative route like
tf2onnx.convert.from_function
we could use as a workaround?The text was updated successfully, but these errors were encountered: