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Hands-On Convolutional Neural Networks with TensorFlow

You're reading from   Hands-On Convolutional Neural Networks with TensorFlow Solve computer vision problems with modeling in TensorFlow and Python

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Product type Paperback
Published in Aug 2018
Publisher Packt
ISBN-13 9781789130331
Length 272 pages
Edition 1st Edition
Languages
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Authors (5):
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 Araujo Araujo
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Araujo
 Zafar Zafar
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Zafar
 Tzanidou Tzanidou
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Tzanidou
 Burton Burton
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Burton
 Patel Patel
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Patel
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Toc

Table of Contents (12) Chapters Close

Preface 1. Setup and Introduction to TensorFlow FREE CHAPTER 2. Deep Learning and Convolutional Neural Networks 3. Image Classification in TensorFlow 4. Object Detection and Segmentation 5. VGG, Inception Modules, Residuals, and MobileNets 6. Autoencoders, Variational Autoencoders, and Generative Adversarial Networks 7. Transfer Learning 8. Machine Learning Best Practices and Troubleshooting 9. Training at Scale 10. References 11. Other Books You May Enjoy

Image classification with TensorFlow

In this section, we will show you how to implement a relatively simple CNN architecture. We will also look at how to train it to classify the CIFAR-10 dataset.

Start by importing all the necessary libraries:

import fire 
import numpy as np 
import os 
import tensorflow as tf 
from tf.keras.datasets import cifar10 

We will define a Python class that will implement the training process. The class name is Train, and it implements two methods: build_graph and train. The train function is fired when the main program is executed:

class Train:  

   __x_ = []
__y_ = []
__logits = []
__loss = []
__train_step = []
__merged_summary_op = []
__saver = []
__session = []
__writer = []
__is_training = []
__loss_val = []
__train_summary = []
__val_summary = []

def __init__(self):
pass
...
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