What is the Tensorflow Estimator API?Estimators represent a complete model but also look intuitive enough to less user. The Estimator API provides methods to train the model, to judge the model’s accuracy, and to generate predictions. TensorFlow provides a programming stack consisting of multiple API layers like in the below image: There are two types of estimators; you can either choose the pre-made Estimators, alternatively, you can write your own custom Estimators. Estimators-based models can be run on local hosts as well as on distributed multi-server environment without changing your model. In addition, you can run Estimators-based models on CPUs, GPUs, or TPUs without having to record your model.
Estimators Encapsulate Four Main Features:
- Training- they will train a model on a given input for a fixed number of steps
- Evaluation- they will evaluate the model based on a test set.
- Prediction- estimators will run inference using the trained model.
- Export your model for serving.
Estimators Come with Numerous Benefits:
- Estimators simplify sharing implementations between model developers.
- You can develop a great model with high-level intuitive code, as they usually are easier to use if you need to create models compared to the low-level TensorFlow APIs.
- Estimators are themselves built on tf.keras.layers, that makes customization a lot easier.
- Estimators will make your life easier by building the graph for you.
- Estimators provide a safely distributed training loop that controls how and when to:
- build the graph
- initialize variables
- load data
- handle exceptions
- create checkpoint files and recover from failures
- save summaries for TensorBoard
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