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Recurrent Neural Networks with Python Quick Start Guide

You're reading from   Recurrent Neural Networks with Python Quick Start Guide Sequential learning and language modeling with TensorFlow

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Product type Paperback
Published in Nov 2018
Publisher Packt
ISBN-13 9781789132335
Length 122 pages
Edition 1st Edition
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Author (1):
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Simeon Kostadinov Simeon Kostadinov
Author Profile Icon Simeon Kostadinov
Simeon Kostadinov
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Toc

Coding the recurrent neural network

As mentioned before, the aim of our task is to build a recurrent neural network that predicts the parity of a bit sequence. We will approach this problem in a slightly different way. Since the parity of a sequence depends on the number of ones, we will sum up the elements of the sequence and find whether the result is even or not. If it is even, we will output 0, otherwise, 1

This section of the chapter includes code samples and goes through the following steps:

  • Generating data to train the model
  • Building the TensorFlow graph (using TensorFlow's built-in functions for recurrent neural networks)
  • Training the neural network with the generated data
  • Evaluating the model and determining its accuracy

Generating data

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You have been reading a chapter from
Recurrent Neural Networks with Python Quick Start Guide
Published in: Nov 2018
Publisher: Packt
ISBN-13: 9781789132335
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