Recurrent neural networks (RNN) Deep Learning

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# Recurrent neural networks (RNN) 
 Deep Learning - Part 2

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  <a href="/training-material/hall-of-fame/kxk302/" class="contributor-badge contributor-kxk302"><img src="https://avatars.githubusercontent.com/kxk302?s=36" alt="Kaivan Kamali avatar" width="36" class="avatar" />
    Kaivan Kamali</a>
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## Requirements

Before diving into this slide deck, we recommend you to have a look at:

- [Introduction to Galaxy Analyses](/training-material/topics/introduction)

- [Statistics and machine learning](/training-material/topics/statistics)
    - Introduction to deep learning: [<i class="fas fa-laptop" aria-hidden="true"></i><span class="visually-hidden">tutorial</span> hands-on](/training-material/topics/statistics/tutorials/intro_deep_learning/tutorial.html)
    - Deep Learning (Part 1) - Feedforward neural networks (FNN): [<i class="fab fa-slideshare" aria-hidden="true"></i><span class="visually-hidden">slides</span> slides](/training-material/topics/statistics/tutorials/FNN/slides.html) - [<i class="fas fa-laptop" aria-hidden="true"></i><span class="visually-hidden">tutorial</span> hands-on](/training-material/topics/statistics/tutorials/FNN/tutorial.html)

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### <i class="far fa-question-circle" aria-hidden="true"></i><span class="visually-hidden">question</span> Questions

- What is a recurrent neural network (RNN)?

- What are some applications of RNN?

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### <i class="fas fa-bullseye" aria-hidden="true"></i><span class="visually-hidden">objectives</span> Objectives

- Understand the difference between feedforward neural networks (FNN) and RNN

- Learn various RNN types and architectures

- Learn how to create a neural network using Galaxy’s deep learning tools

- Solve a sentiment analysis problem on IMDB movie review dataset using RNN in Galaxy

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# What is a recurrent neural network (RNN)?

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What is a recurrent neural network (RNN)?

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# Recurrent Neural Network (RNN)

- RNN models sequential data (temporal/ordinal)
- In RNN, training example is a sequence, which is presented to RNN one at a time 
	- E.g., sequence of English words is passed to RNN, one at a time 
	- And, RNN generates a sequence of Persian words, one at a time
- In RNN, output of network at time *t* is used as input at time *t+1*
- RNN applied to image description, machine translation, sentiment analysis, etc.

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# One-to-many RNN

![Neurons forming a one-to-many recurrent neural network](/training-material/topics/statistics/images/RNN_1_to_n.png)

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# Many-to-one RNN

![Neurons forming a many-to-one recurrent neural network](/training-material/topics/statistics/images/RNN_n_to_1.png)

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# Many-to-many RNN

![Neurons forming a many-to-many recurrent neural network](/training-material/topics/statistics/images/RNN_n_to_m.png)

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# RNN architectures

- Vanilla RNN
	- Suffers from *vanishing gradient* problem
- LSTM and GRU
	- Uses *gates* to avoid vanishing gradient problem

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# Sentiment analysis

- We perform sentiment analysis on IMDB movie reviews dataset
- Train RNN on training dataset (25000 positive/negative movie reviews)
- Test RNN on test set (25000 positive/negative movie reviews)
- Training and test sets have no overlap
- Since dealing with text data, good to review mechanisms for representing text data

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# Text preprocessing

- Tokenize a document, i.e., break it down into words
- Remove punctuations, URLs, and stop words (‘a’, ‘of’, ‘the’, etc.)
- Normalize the text, e.g., replace ‘brb’ with ‘Be right back’, etc
- Run the spell checker to fix typos
- Make all words lowercase

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# Text preprocessing

- Perform stemming/lemmatization
	- If we have words like ‘organizer’, ‘organize’, and ‘organized’
		- Want to reduce all of them to a single word
	- Stemming cuts end of these words for a single root
		- E.g., ‘organiz’.  May not be an actual word
	- Lemmatization reduces to a root that is actually a word
		- E.g., ‘organize’

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# Bag of words (BoW)

- If you don’t care about the order of the words in a document
- 2D array. Rows represent documents. Columns represent words in vocabulary
	- All unique words in all documents
- If a word not present in a document, we have a zero at row and column entry
- If a word is present in a document, we have a one at row and colum entry
	- Or, we could use the word count or frequency

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# Bag of words (BoW)

- Document 1: Magic passed the basketball to Kareem
- Document 2: Lebron stole the basketball from Curry

![Table showing a bag-of-words representation of sample documents](/training-material/topics/statistics/images/BoW.png)

- BoW is simple, but does not consider rarity of words across documents
	- Important for document classification

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# Term frequency inverse document frequency (TF-IDF)

- If you don’t care about the order of the words in a document
- Similar to BoW, we have an entry for each document-word pair
- Entry is product of
	- Term frequency, frequency of a word in a document, and
	- Inverse document frequency, total number of documents divided by number of documents that have word
		- Usually use logarithm of the IDF
- TF-IDF takes into account rarity of a word across documents

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# One-hot encoding (OHE)

- Technique to convert categorical variables such as words into a vector
- Suppose our vocabulary has 3 words: orange, apple, banana
- Each word is represented by a vector of size 3

![Mathematical vectors representing one-hot-encoding representation of words orange, apple, and banana](/training-material/topics/statistics/images/OHE.gif)

- OHE problems
	- For very large vocabulary sizes requires tremendous amount of storage
	- Also, no concept of word similarity

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# Word2Vec

- Each word represented as an *n* dimensional vector
	- *n* much smaller than vocabulary size
- Words that have similar meanings are close in vector space
- Words considered similar if they co-occur often in documents
- Two Word2Vec architectures
	- Continuous BoW
		- predicts probability of a word given the surrounding words
	- Continuous skip-gram
		- given a word predicts probability of the surrounding words

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# Sentiment analysis

- Sentiment classification of IMDB movie reviews with RNN
- Train RNN using IMDB movie reviews
- Goal is to learn a model such that given a review we predict whether review is positive/negative
- We evaluate the trained RNN on test dataset and plot confusion matrix

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# For references, please see tutorial's References section

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## Thank You!

This material is the result of a collaborative work. Thanks to the [Galaxy Training Network](https://training.galaxyproject.org) and all the contributors!

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		<td><abbr title="These people wrote the bulk of the tutorial, they may have done the analysis, built the workflow, and wrote the text themselves.">Author(s)</abbr></td>
		<td>
			
  <a href="/training-material/hall-of-fame/kxk302/" class="contributor-badge contributor-kxk302"><img src="https://avatars.githubusercontent.com/kxk302?s=36" alt="Kaivan Kamali avatar" width="36" class="avatar" />
    Kaivan Kamali</a>
  
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		<td><abbr title="These people reviewed this material for accuracy and correctness">Reviewers</abbr></td>
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  <a href="/training-material/hall-of-fame/shiltemann/" class="contributor-badge contributor-shiltemann">
    <img src="https://avatars.githubusercontent.com/shiltemann?s=36" alt="Saskia Hiltemann avatar" width="36" class="avatar" />
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  <a href="/training-material/hall-of-fame/kxk302/" class="contributor-badge contributor-kxk302">
    <img src="https://avatars.githubusercontent.com/kxk302?s=36" alt="Kaivan Kamali avatar" width="36" class="avatar" />
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Tutorial Content is licensed under <a rel="license" href="http://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License</a>.<br/>