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Deep Learning with TensorFlow

You're reading from  Deep Learning with TensorFlow

Product type Book
Published in Apr 2017
Publisher Packt
ISBN-13 9781786469786
Pages 320 pages
Edition 1st Edition
Languages
Authors (3):
Giancarlo Zaccone Giancarlo Zaccone
Profile icon Giancarlo Zaccone
Md. Rezaul Karim Md. Rezaul Karim
Profile icon Md. Rezaul Karim
Ahmed Menshawy Ahmed Menshawy
Profile icon Ahmed Menshawy
View More author details

Table of Contents (11) Chapters

Preface Getting Started with Deep Learning First Look at TensorFlow Using TensorFlow on a Feed-Forward Neural Network TensorFlow on a Convolutional Neural Network Optimizing TensorFlow Autoencoders Recurrent Neural Networks GPU Computing Advanced TensorFlow Programming Advanced Multimedia Programming with TensorFlow 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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