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Deep Learning with TensorFlow

You're reading from   Deep Learning with TensorFlow Explore neural networks with Python

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Product type Paperback
Published in Apr 2017
Publisher Packt
ISBN-13 9781786469786
Length 320 pages
Edition 1st Edition
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Authors (4):
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Md. Rezaul Karim Md. Rezaul Karim
Author Profile Icon Md. Rezaul Karim
Md. Rezaul Karim
Ahmed Menshawy Ahmed Menshawy
Author Profile Icon Ahmed Menshawy
Ahmed Menshawy
Giancarlo Zaccone Giancarlo Zaccone
Author Profile Icon Giancarlo Zaccone
Giancarlo Zaccone
Fabrizio Milo Fabrizio Milo
Author Profile Icon Fabrizio Milo
Fabrizio Milo
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Toc

Table of Contents (11) Chapters Close

Preface 1. Getting Started with Deep Learning FREE CHAPTER 2. First Look at TensorFlow 3. Using TensorFlow on a Feed-Forward Neural Network 4. TensorFlow on a Convolutional Neural Network 5. Optimizing TensorFlow Autoencoders 6. Recurrent Neural Networks 7. GPU Computing 8. Advanced TensorFlow Programming 9. Advanced Multimedia Programming with TensorFlow 10. Reinforcement Learning

TensorFlow GPU management

In TensorFlow the supported devices are represented as strings. For example:

  • /cpu:0: The CPU of your machine
  • /gpu:0: The GPU of your machine, if you have one
  • /gpu:1: The second GPU of your machine, and so on

The execution flow gives priority when an operation is assigned to a GPU device.

Programming example

To use a GPU in your TensorFlow program, just type the following:

with tf.device("/gpu:0"):

Followed by the setup operations. This line of code will create a new context manager, telling TensorFlow to perform those actions on the GPU.

Let's consider the following example, in which we want to execute the following sum of two matrices, An + Bn.

Define the basic imports:

import numpy as np 
import tensorflow as tf
import...
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