Requirements

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

• Introduction to Galaxy Analyses

• Statistics and machine learning
  • Introduction to deep learning: tutorial hands-on
  • Deep Learning (Part 1) - Feedforward neural networks (FNN): slides slides - tutorial hands-on

question Questions

• What is a recurrent neural network (RNN)?

• What are some applications of RNN?

objectives 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

What is a recurrent neural network (RNN)?

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.

One-to-many RNN

Many-to-one RNN

Many-to-many RNN

RNN architectures

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

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

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

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’

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

Bag of words (BoW)

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

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

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

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

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

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

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

For references, please see tutorial's References section

Thank You!

This material is the result of a collaborative work. Thanks to the Galaxy Training Network and all the contributors!

Author(s)	Kaivan Kamali
Reviewers

Tutorial Content is licensed under Creative Commons Attribution 4.0 International License.