You're reading from Deep Learning with TensorFlow 2 and Keras - Second Edition

Product typeBook

Published inDec 2019

Reading LevelBeginner

PublisherPackt

ISBN-139781838823412

Edition2nd Edition

Languages

Python

Tools

TensorFlow

Concepts

Deep Learning

Authors (3):

Antonio Gulli

Amita Kapoor

Sujit Pal

View More author details

TensorFlow Estimators

TensorFlow provides Estimators as higher-level APIs, to provide scalable and production-oriented solutions. They take care of all behind-the-scene activities such as creating computational graphs, initializing the variables, training the model, saving checkpoints, and logging TensorBoard files. TensorFlow provides two types of Estimators:

Canned Estimators: These are premade Estimators available in the TensorFlow estimator module. These are models in a box; you just pass them the input features and they are ready to use. Some examples are Linear Classifier, Linear Regressor, DNN Classifier, and so on.
Custom Estimators: Users can also create their own estimators from the models they build in TensorFlow Keras. These are user-defined Estimators.

Before being able to use TensorFlow Estimator let us understand two important components of the Estimator pipeline:

Feature columns

The feature_column module of TensorFlow 2.0 acts as a bridge between...

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Deep Learning with TensorFlow 2 and Keras - Second Edition

Published in: Dec 2019Publisher: PacktISBN-13: 9781838823412

Authors (3)

Antonio Gulli

Antonio Gulli has a passion for establishing and managing global technological talent for innovation and execution. His core expertise is in cloud computing, deep learning, and search engines. Currently, Antonio works for Google in the Cloud Office of the CTO in Zurich, working on Search, Cloud Infra, Sovereignty, and Conversational AI.
Read more about Antonio Gulli

Amita Kapoor

Amita Kapoor is an accomplished AI consultant and educator, with over 25 years of experience. She has received international recognition for her work, including the DAAD fellowship and the Intel Developer Mesh AI Innovator Award. She is a highly respected scholar in her field, with over 100 research papers and several best-selling books on deep learning and AI. After teaching for 25 years at the University of Delhi, Amita took early retirement and turned her focus to democratizing AI education. She currently serves as a member of the Board of Directors for the non-profit Neuromatch Academy, fostering greater accessibility to knowledge and resources in the field. Following her retirement, Amita also founded NePeur, a company that provides data analytics and AI consultancy services. In addition, she shares her expertise with a global audience by teaching online classes on data science and AI at the University of Oxford.
Read more about Amita Kapoor

Sujit Pal

Sujit Pal is a Technology Research Director at Elsevier Labs, an advanced technology group within the Reed-Elsevier Group of companies. His interests include semantic search, natural language processing, machine learning, and deep learning. At Elsevier, he has worked on several initiatives involving search quality measurement and improvement, image classification and duplicate detection, and annotation and ontology development for medical and scientific corpora.
Read more about Sujit Pal

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You're reading from Deep Learning with TensorFlow 2 and Keras - Second Edition

TensorFlow Estimators

Feature columns

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