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Python Deep Learning Cookbook

You're reading from   Python Deep Learning Cookbook Over 75 practical recipes on neural network modeling, reinforcement learning, and transfer learning using Python

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Product type Paperback
Published in Oct 2017
Publisher Packt
ISBN-13 9781787125193
Length 330 pages
Edition 1st Edition
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Author (1):
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Indra den Bakker Indra den Bakker
Author Profile Icon Indra den Bakker
Indra den Bakker
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Toc

Table of Contents (15) Chapters Close

Preface 1. Programming Environments, GPU Computing, Cloud Solutions, and Deep Learning Frameworks 2. Feed-Forward Neural Networks FREE CHAPTER 3. Convolutional Neural Networks 4. Recurrent Neural Networks 5. Reinforcement Learning 6. Generative Adversarial Networks 7. Computer Vision 8. Natural Language Processing 9. Speech Recognition and Video Analysis 10. Time Series and Structured Data 11. Game Playing Agents and Robotics 12. Hyperparameter Selection, Tuning, and Neural Network Learning 13. Network Internals 14. Pretrained Models

Visualizing training with TensorBoard

In the previous chapter, we demonstrated how to set up TensorBoard with Keras. However, as already mentioned, TensorBoard can also be used with TensorFlow (among others). In this recipe, we will show you how to use TensorBoard with TensorFlow when classifying Fashion-MNIST.

How to do it..

  1. Let's start by importing TensorFlow and a tool to load mnist datasets, as follows:
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
  1. Next, we specify the Fashion MNIST dataset and load it:
mnist = input_data.read_data_sets('Data/fashion', one_hot=True)
  1. Let's create the placeholders for the input data:
n_classes = 10
input_size = 784

x = tf.placeholder...
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