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Table of contents
1.
Introduction
2.
What is Bidirectional RNN?
3.
Example of Bidirectional RNN
3.1.
Step1 - Loading an Preprocessing the Data
3.2.
Python
3.3.
Step2 - Model Development
3.4.
Python
3.5.
Step3 - Model Training
3.6.
Python
3.7.
Step4 - Evaluation
3.8.
Python
4.
Difference Between Bidirectional RNN and Unidirectional RNN
5.
Advantages and Disadvantages of Bidirectional RNNs
5.1.
Advantages
5.2.
Disadvantages
6.
Frequently Asked Questions
6.1.
What are some of the common applications of Bidirectional RNN?
6.2.
Can we use Bidirectional RNN for real-time operations?
6.3.
How does the sequence length impact the performance of Bidirectional RNN?
7.
Conclusion
Last Updated: Mar 27, 2024
Medium

# Understanding Bidirectional RNN

Aayush Sharma
0 upvote
Leveraging ChatGPT - GenAI as a Microsoft Data Expert
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Prerita Agarwal
Data Specialist @
23 Jul, 2024 @ 01:30 PM

## Introduction

Recurrent Neural Networks (RNNs) are a very powerful tool for processing sequential data. This data may include time series, natural language, and audio signals. However, traditional RNN algorithms (Unidirectional RNNs) are not capable of processing sequences in both directions (forward and backward). This is where Bidirectional RNN comes into play. These algorithms are capable of processing sequences in both directions at the cost of some extra computational powers.

In this blog, we will discuss Bidirectional RNN in detail with a suitable example. We will also look at the differences between the traditional Unidirectional RNN networks and the Bidirectional RNNs, along with their advantages and disadvantages. But first, let us learn a bit more about Bidirectional RNNs.

## What is Bidirectional RNN?

A Bidirectional RNN is a type of neural network that can process sequences in both forward and backward directions simultaneously. It is basically an extension of UNidirectional RNNs which can only process sequences in only one direction. In a Bidirectional RNN, the input data is passed through two separate RNNs: one for the forward direction and one for the backward direction. After processing, both these outputs are combined together to produce the final output.

Image Explanation: The above image shows the sequential diagram of a Bidirectional RNN. A Bidirectional RNN is a combination of 2 individual unidirectional RNNs. One of these Unidirectional RNNs processes the input sequence from left to right while the other one processes the sequence from right to left.

The goal of Bidirectional RNNs is to capture the contextual dependencies in the input data by bidirectional processing. This technique is very useful for various NLP tasks like Speech Recognition, Handwriting Recognition etc. There are various modes in Bidirectional RNNs to combine the output of both forward and backward processing. These modes are commonly called merge modes. Some of the most common merge modes include:

• Concatenation

• Sum

• Average

• Maximum

These modes are selected and used depending on the specific needs of the model. Most of the time the Concatenation method works fine but in some cases, other merge modes might be more suitable.

The main advantage of Bidirectional RNN over Unidirectional RNN is the ability to capture a more detailed context from the input sequence. But this comes at the cost of increased memory requirement and computational complexity. This makes the Bidirectional RNN a good choice for dynamic length input sequences.

In the next section, we will look at an example to understand the steps required in implementing a Bidirectional RNN.

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## Example of Bidirectional RNN

In this section, we will look at an example to understand Bidirectional RNN in more detail. For training a Bidirectional RNN, multiple processes are involved, like preprocessing, model development, training, etc. In this example, we will use the IMDb movie review sentiment classification dataset from Keras to train a Bidirectional RNN and analyze its accuracy.

### Step1 - Loading an Preprocessing the Data

The first step is to load and preprocess the data.

• Python

### Python

``````import warnings

warnings.filterwarnings('ignore')

from keras.datasets import imdb

from keras_preprocessing.sequence import pad_sequences

# loading the dataset

features = 2000

len = 50

(X_train, y_train),

(X_test, y_test) = imdb.load_data(num_words=features)

# pad sequences are used to fix the length of the testing sets

X_train = pad_sequences(X_train, maxlen=len)

X_test = pad_sequences(X_test, maxlen=len)``````

### Step2 - Model Development

The next step in the process is to develop the model.

• Python

### Python

``````# Importing the module from Keras

from keras.models import Sequential

from keras.layers import Embedding,Bidirectional, SimpleRNN, Dense

#  set the embedding size and number of hidden units in LSTM

embedding = 128

hidden = 100

# Creating the Object

model = Sequential()

model.add(Embedding(features, embedding, input_length=len))

model.add(Bidirectional(SimpleRNN(hidden)))

model.add(Dense(1, activation='sigmoid'))

model.compile('adam', 'binary_crossentropy', metrics=['accuracy'])``````

### Step3 - Model Training

Now that we have developed the model, we need to train it by setting the batch size and the number of epochs.

• Python

### Python

``````# set the batch size and number of epochs

batch_size = 32

epochs = 20

model.fit(X_train, y_train, batch_size=batch_size, epochs=epochs, validation_data=(X_test, y_test))``````

The training process will produce some metrics for each iteration of the training.

### Step4 - Evaluation

After the model is trained on the set number of epochs, we can calculate its final accuracy and analyze the model.

• Python

### Python

``````

loss, accuracy = model.evaluate(X_test, y_test)

print('The accuracy of the model is: ', accuracy)``````

## Difference Between Bidirectional RNN and Unidirectional RNN

In this section, we have listed some major differences between Bidirectional and Unidirectional RNN in tabular format.

## Advantages and Disadvantages of Bidirectional RNNs

Now that we know about Bidirectional RNNs, we will look at their major advantages and disadvantages.

### Advantages

Below are some of the key advantages of using Bidirectional RNNs.

• Increased Accuracy

• Easier handling of variable length sequences

• Bidirectional processing

• Better processing in Natural Language Processing (NLP)

• Better Information Capture

### Disadvantages

Below are some of the disadvantages of using Bidirectional RNNs.

• Increased Computational Complexity

• More Memory requirement

• Slower training time

• Not suitable for real-time operations

## Frequently Asked Questions

### What are some of the common applications of Bidirectional RNN?

Bidirectional RNNs are used extensively in NLP (Natural Language Processing), sentiment analysis, speech recognition, handwriting recognition, pattern finding, time analysis, and other related fields.

### Can we use Bidirectional RNN for real-time operations?

We can use Bidirectional RNNs for real-time operations. However, it is not advised to do so because Bidirectional RNNs have very high computational intensity. As an alternative, we can use Unidirectional RNNs for real-time processing.

### How does the sequence length impact the performance of Bidirectional RNN?

Bidirectional RNNs depend intensively on the length of the sequence. As the length of the sequence increases, the computational overhead also increases. Hence for longer length sequences, we can use simpler unidirectional RNNs.

## Conclusion

In this article, we discussed Bidirectional RNN. We discussed Bidirectional RNN in detail with a suitable example. We also discussed the major differences between Bidirectional RNN and Neural Networks. In the end, we concluded by discussing some advantages and disadvantages of Bidirectional RNN and some frequently asked questions.

So now that you know about Bidirectional RNN, you can refer to similar articles.

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