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Computer Vision Projects with OpenCV and Python 3

You're reading from   Computer Vision Projects with OpenCV and Python 3 Six end-to-end projects built using machine learning with OpenCV, Python, and TensorFlow

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Product type Paperback
Published in Dec 2018
Publisher Packt
ISBN-13 9781789954555
Length 182 pages
Edition 1st Edition
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Author (1):
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Matthew Rever Matthew Rever
Author Profile Icon Matthew Rever
Matthew Rever
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Table of Contents (9) Chapters Close

Preface 1. Setting Up an Anaconda Environment FREE CHAPTER 2. Image Captioning with TensorFlow 3. Reading License Plates with OpenCV 4. Human Pose Estimation with TensorFlow 5. Handwritten Digit Recognition with scikit-learn and TensorFlow 6. Facial Feature Tracking and Classification with dlib 7. Deep Learning Image Classification with TensorFlow 8. Other Books You May Enjoy

Speeding up computation with your GPU

In this section, we'll talk briefly about speeding up computations with your GPU. The good news is that TensorFlow is actually very smart about using the GPU, so if you have everything set up, then it's pretty simple.

Let's see what things look like if we have the GPU properly set up. First, import TensorFlow as follows:

import tensorflow

Next, we print tensorflow.Session(). This just gives us information about our CPU and GPU (if it is properly set up):

print(tensorflow.Session())

The output is as follows:

As we can see from the output, we're using a laptop with a GeForce GTX 970M, which is CUDA-compatible. This is needed in order to run TensorFlow with the GPU. If everything is set up properly, you will see a message very similar to the preceding output for your GPU, your card model, and details about it such as its...

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