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R Deep Learning Cookbook

You're reading from  R Deep Learning Cookbook

Product type Book
Published in Aug 2017
Publisher Packt
ISBN-13 9781787121089
Pages 288 pages
Edition 1st Edition
Languages
Authors (2):
PKS Prakash PKS Prakash
Profile icon PKS Prakash
Achyutuni Sri Krishna Rao Achyutuni Sri Krishna Rao
Profile icon Achyutuni Sri Krishna Rao
View More author details
Toc

Table of Contents (17) Chapters close

Title Page
Credits
About the Authors
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface
1. Getting Started 2. Deep Learning with R 3. Convolution Neural Network 4. Data Representation Using Autoencoders 5. Generative Models in Deep Learning 6. Recurrent Neural Networks 7. Reinforcement Learning 8. Application of Deep Learning in Text Mining 9. Application of Deep Learning to Signal processing 10. Transfer Learning

Using functions to flatten the densely connected layer


The CNN generally ends with a fully connected multilayered perceptron using softmax activation in the output layer. Here, each neuron in the previous convoluted-flattened layer is connected to every neuron in the next (fully connected) layer.

Note

The key purpose of the fully convoluted layer is to use the features generated in the convolution and pooling stage to classify the given input image into various outcome classes (here, 10L). It also helps in learning the non-linear combinations of these features to define the outcome classes.

In this chapter, we use two fully connected layers for optimization. This function is primarily used to define the mathematical formulas in the TensorFlow graph, which is later used in actual computation during optimization.

Getting ready

The (create_fc_layer) function takes four input parameters, which are as follows:

  • Input: This is similar to the input of the new convolution layer function
  • Num_inputs: This...
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