Creating Custom Estimators in TensorFlow- Google Developers Blog

What this post added

This post introduces the concept and implementation of custom Estimators in TensorFlow, providing a structured way for developers to define and train their own machine learning models. It covers the essential components of an Estimator, including model_fn, params, and config, and demonstrates how to use them for both training and evaluation. The post highlights the benefits of using Estimators for model organization, reproducibility, and integration with TensorFlow's distributed training capabilities.