Converting Tensorflow model to Onnx format – Human emotions detection

Tensorflow is a popular open-source machine learning library developed by Google, which is widely used for building and training deep learning models. Onnx (Open Neural Network Exchange) is an open standard format for representing machine learning models, which allows for interoperability between different machine learning frameworks. In this article, we will discuss the process of converting a Tensorflow model to the Onnx format for human emotions detection.

Why convert to Onnx format?

Converting a Tensorflow model to the Onnx format has several advantages. The Onnx format allows for interoperability between different machine learning frameworks, meaning that you can use the same model across different platforms and libraries. This can be particularly useful if you want to deploy your model on a different platform or integrate it with other machine learning frameworks.

Steps to convert to Onnx format

The first step in converting a Tensorflow model to the Onnx format is to install the tf2onnx package, which is a tool for converting Tensorflow models to the Onnx format. This can be done using pip:

pip install tf2onnx

Once you have installed the tf2onnx package, you can use the tf2onnx.convert function to convert your Tensorflow model to the Onnx format. This function takes in the path to the Tensorflow model as well as any conversion options:

import tf2onnx.convert
tf2onnx.convert.from_tensorflow((model_path, output_path)

After running the conversion function, you will have a new file in the Onnx format that represents your Tensorflow model, which you can then use for human emotions detection or any other application.

Conclusion

Converting a Tensorflow model to the Onnx format is a simple process that can provide a number of benefits, including interoperability between different machine learning frameworks and platforms. If you are working on a project that involves human emotions detection or any other machine learning task, consider converting your Tensorflow model to the Onnx format for greater flexibility and ease of use.