Turning a Keras model into an Estimator
Up to now, we have worked out our linear regression models using specific Estimators from the tf.estimator module. This has clear advantages because our model is mostly run automatically and we can easily deploy it in a scalable way on the cloud (such as Google Cloud Platform, offered by Google) and on different kinds of servers (CPU-, GPU-, and TPU-based). Anyway, by using Estimators, we may lack the flexibility in our model architecture as required by our data problem, which is instead offered by the Keras modular approach that we discussed in the previous chapter. In this recipe, we will remediate this by showing how we can transform Keras models into Estimators and thus take advantage of both the Estimators API and Keras versatility at the same time.
Getting ready
We will use the same Boston Housing dataset as in the previous recipe, while also making use of the make_input_fn function. As before, we need our core packages to be imported...
