Book Image

R Deep Learning Cookbook

By : PKS Prakash, Achyutuni Sri Krishna Rao
Book Image

R Deep Learning Cookbook

By: PKS Prakash, Achyutuni Sri Krishna Rao

Overview of this book

Deep Learning is the next big thing. It is a part of machine learning. It's favorable results in applications with huge and complex data is remarkable. Simultaneously, R programming language is very popular amongst the data miners and statisticians. This book will help you to get through the problems that you face during the execution of different tasks and Understand hacks in deep learning, neural networks, and advanced machine learning techniques. It will also take you through complex deep learning algorithms and various deep learning packages and libraries in R. It will be starting with different packages in Deep Learning to neural networks and structures. You will also encounter the applications in text mining and processing along with a comparison between CPU and GPU performance. By the end of the book, you will have a logical understanding of Deep learning and different deep learning packages to have the most appropriate solutions for your problems.

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Table of Contents (17 chapters)
Title Page
Title Page
Credits
Credits
About the Authors
About the Authors
About the Reviewer
About the Reviewer
www.PacktPub.com
www.PacktPub.com
Customer Feedback
Customer Feedback
Preface
Preface
Free Chapter
1
Getting Started
Getting Started
Introduction
Installing R with an IDE
Installing a Jupyter Notebook application
Starting with the basics of machine learning in R
Setting up deep learning tools/packages in R
Installing MXNet in R
Installing TensorFlow in R
Installing H2O in R
Installing all three packages at once using Docker
2
Deep Learning with R
Deep Learning with R
Starting with logistic regression
Introducing the dataset
Performing logistic regression using H2O
Performing logistic regression using TensorFlow
Visualizing TensorFlow graphs
Starting with multilayer perceptrons
Setting up a neural network using H2O
Tuning hyper-parameters using grid searches in H2O
Setting up a neural network using MXNet
Setting up a neural network using TensorFlow
3
Convolution Neural Network
Convolution Neural Network
Introduction
Downloading and configuring an image dataset
Learning the architecture of a CNN classifier
Using functions to initialize weights and biases
Using functions to create a new convolution layer
Using functions to create a new convolution layer
Using functions to flatten the densely connected layer
Defining placeholder variables
Creating the first convolution layer
Creating the second convolution layer
Flattening the second convolution layer
Creating the first fully connected layer
Applying dropout to the first fully connected layer
Creating the second fully connected layer with dropout
Applying softmax activation to obtain a predicted class
Defining the cost function used for optimization
Performing gradient descent cost optimization
Executing the graph in a TensorFlow session
Evaluating the performance on test data
4
Data Representation Using Autoencoders
Data Representation Using Autoencoders
Introduction
Setting up autoencoders
Data normalization
Setting up a regularized autoencoder
Fine-tuning the parameters of the autoencoder
Setting up stacked autoencoders
Setting up denoising autoencoders
Building and comparing stochastic encoders and decoders
Learning manifolds from autoencoders
Evaluating the sparse decomposition
5
Generative Models in Deep Learning
Generative Models in Deep Learning
Comparing principal component analysis with the Restricted Boltzmann machine
Setting up a Restricted Boltzmann machine for Bernoulli distribution input
Training a Restricted Boltzmann machine
Backward or reconstruction phase of RBM
Understanding the contrastive divergence of the reconstruction
Initializing and starting a new TensorFlow session
Evaluating the output from an RBM
Setting up a Restricted Boltzmann machine for Collaborative Filtering
Performing a full run of training an RBM
Setting up a Deep Belief Network
Implementing a feed-forward backpropagation Neural Network
Setting up a Deep Restricted Boltzmann Machine
6
Recurrent Neural Networks
Recurrent Neural Networks
Setting up a basic Recurrent Neural Network
Setting up a bidirectional RNN model
Setting up a deep RNN model
Setting up a Long short-term memory based sequence model
7
Reinforcement Learning
Reinforcement Learning
Introduction
Setting up a Markov Decision Process
Performing model-based learning
Performing model-free learning
8
Application of Deep Learning in Text Mining
Application of Deep Learning in Text Mining
Performing preprocessing of textual data and extraction of sentiments
Analyzing documents using tf-idf
Performing sentiment prediction using LSTM network
Application using text2vec examples
9
Application of Deep Learning to Signal processing
Application of Deep Learning to Signal processing
Introducing and preprocessing music MIDI files
Building an RBM model
Generating new music notes
10
Transfer Learning
Transfer Learning
Introduction
Illustrating the use of a pretrained model
Setting up the Transfer Learning model
Building an image classification model
Training a deep learning model on a GPU
Comparing performance using CPU and GPU

Performing gradient descent cost optimization

In this recipe, let's define an optimizer that can minimize the cost. Post optimization, check for CNN performance.

Getting ready

The optimizer definition will require the cost recipe to be defined as it goes as input to the optimizer.

How to do it...

  1. Run an Adam optimizer with the objective of minimizing the cost for a given learning_rate:
optimizer = tf$train$AdamOptimizer(learning_rate=1e-4)$minimize(cost)
  1. Extract the number of correct_predictions and calculate the mean percentage accuracy:
correct_prediction = tf$equal(y_pred_cls, y_true_cls)
accuracy = tf$reduce_mean(tf$cast(correct_prediction, tf$float32))
Previous Section
End of Section 18
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