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You're reading from  Deep Learning with Keras

Product typeBook
Published inApr 2017
Reading LevelIntermediate
PublisherPackt
ISBN-139781787128422
Edition1st Edition
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Authors (2):
Antonio Gulli
Antonio Gulli
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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

Sujit Pal
Sujit Pal
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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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Configuring Keras


Keras has a very minimalist configuration file. Let's load it with a vi session. The parameters are very simple:

Parameters

Values

image_dim_ordering

Can be either tf for the TensorFlow image ordering or th for Theano image ordering

epsilon

The epsilon value used during computation

floatx

Can be either float32 or float64

backend

Can be either tensorflow or theano

The image_dim_ordering of th value gives you a somewhat non-intuitive dimension ordering for images (depth, width, and height), instead of (width, height, and depth), for tf. The following are the default parameters in my machine:

Note

If you install a GPU-enabled TensorFlow version, then Keras will automatically use your configured GPU when TensorFlow is selected as the backend.

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Deep Learning with Keras
Published in: Apr 2017Publisher: PacktISBN-13: 9781787128422

Authors (2)

author image
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

author image
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