Creating Custom Estimators in TensorFlow

Part 3 of a blog series by the TensorFlow team focuses on creating custom Estimators within TensorFlow, expanding on previous discussions about pre-made Estimators and feature columns. This installment demonstrates how to construct a custom Estimator that replicates the behavior of the DNNClassifier when applied to the Iris dataset, highlighting the flexibility that custom Estimators offer by allowing users to define unique model functions and custom metrics. The process involves writing a model function that defines the model's architecture, including input, hidden, and output layers, using TensorFlow's Layers API to build deep neural networks, and involves handling different operational modes—training, evaluation, and prediction—through a model function that returns a tf.estimator.EstimatorSpec. The article also illustrates how to leverage TensorBoard for visualizing metrics like accuracy and loss during both training and evaluation phases, emphasizing the importance of defining a global step for performance tracking. Overall, the guide provides a comprehensive overview of the steps needed to create custom Estimators, offering the flexibility to tailor models to specific needs beyond the capabilities of pre-made Estimators.

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