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R Deep Learning Cookbook

You're reading from   R Deep Learning Cookbook Solve complex neural net problems with TensorFlow, H2O and MXNet

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Product type Paperback
Published in Aug 2017
Publisher Packt
ISBN-13 9781787121089
Length 288 pages
Edition 1st Edition
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Authors (2):
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Achyutuni Sri Krishna Rao Achyutuni Sri Krishna Rao
Author Profile Icon Achyutuni Sri Krishna Rao
Achyutuni Sri Krishna Rao
PKS Prakash PKS Prakash
Author Profile Icon PKS Prakash
PKS Prakash
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Toc

Table of Contents (11) Chapters Close

Preface 1. Getting Started FREE CHAPTER 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

Flattening the second convolution layer


In this recipe, let's flatten the second convolution layer that we created.

Getting ready

The following is the input to the function defined in the recipe Creating the second convolution layer, flatten_conv_layer:

  • Layer: This is the output of the second convolution layer, layer_conv2

How to do it...

  1. Run the flatten_conv_layer function with the preceding input parameter:
flatten_lay <- flatten_conv_layer(layer_conv2)
  1. Extract the flattened layer:
layer_flat <- flatten_lay$layer_flat
  1. Extract the number of (input) features generated for each image:
num_features <- flatten_lay$num_features

How it works...

Prior to connecting the output of the (second) convolution layer with a fully connected network, in step 1, we reshape the four-dimensional convolution layer into a two-dimensional tensor. The first dimension (?) represents any number of input images (as rows) and the second dimension represents the flattened vector of features generated for each image of...

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