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Erschienen in:
2021 | OriginalPaper | Buchkapitel
6. Estimators
verfasst von : Poornachandra Sarang
Erschienen in: Artificial Neural Networks with TensorFlow 2
Verlag: Apress
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Abstract
Any machine learning project consists of many stages that include training, evaluation, prediction, and finally exporting it for serving on a production server. You learned these stages in previous chapters where the classification and regression machine learning projects were discussed. To develop the best performing model, you played around with different ANN architectures. Basically, you experimented with several different prototypes to achieve the desired results. Prior to TF 2.0, this entire experimentation was not so easy as for every change that you make in the code, you were required to build a computational graph and run it in a session. The estimators that you are going to study in this chapter were designed to handle all this plumbing. The entire process of creating graphs and running them in sessions was a time-consuming job and posed lots of challenges in debugging the code.