TensorFlow’s 1.4 release brings many new features — one of our favorites is support for converting a Keras model to a TensorFlow Estimator via the model_to_estimator() method.
Why would you want to do this? By wrapping your Keras code in a Estimator, you can serve predictions using TensorFlow Serving or deploy your model on Cloud ML Engine, a managed service for training and serving your TensorFlow models at scale. Using a TensorFlow Estimator, you can also take advantage of distributed training on your own cluster.
In this post, we’ll update the code we wrote in the article building a text classification model with Keras. If you haven’t read that blog post, we used Stack Overflow data from BigQuery to train a model to predict the tag of a Stack Overflow question. To jump to the code, find the full Jupyter notebook for this blog post here.
By Sara Robinson and Josh Gordon, Developer Advocates
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