How to convert a TensorFlow model to ONNX format?

@emie 

To convert a TensorFlow model to the ONNX format, you can use the onnx-tf library. Follow these steps to perform the conversion:

1. Install the necessary packages: pip install tensorflow==
2. Convert the TensorFlow model to ONNX format using the onnx-tf converter. Create a Python script and import the necessary libraries: import tf2onnx import tensorflow as tf
3. Load the TensorFlow model: model = tf.keras.models.load_model('path/to/model.h5')
4. Convert the TensorFlow model to ONNX format: onnx_model, _ = tf2onnx.convert.from_keras(model)
5. Save the ONNX model to a file: with open('path/to/model.onnx', 'wb') as file: file.write(onnx_model.SerializeToString())

Make sure to replace <tensorflow_version> with the specific version of TensorFlow you have installed. Also, update the file paths to match your system.

Note: The conversion process does not always guarantee full compatibility between TensorFlow and ONNX, as they have different sets of features and operations. Therefore, it's possible that some TensorFlow operations may not be supported in ONNX.

@emie 

It's important to note that while the tf2onnx library is a common tool for converting TensorFlow models to ONNX format, there can be limitations and issues in the conversion process, especially for complex models with custom layers or operations that are not fully supported in ONNX.

Additionally, the compatibility between TensorFlow and ONNX can vary based on the version of TensorFlow and ONNX you are using, as well as the specific operations and features used in the model. It's always recommended to test the ONNX model thoroughly after conversion to ensure that it behaves as expected.

If you encounter any errors or issues during the conversion process, you may need to manually adjust the model or explore alternative methods for conversion. There are also other libraries and tools available for converting TensorFlow models to ONNX format that you can explore based on your specific requirements and constraints.