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Introduction
RecurrentNeuralNetworks (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 (UnidirectionalRNNs) 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.
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)
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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'])
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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))
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Difference Between Bidirectional RNN and Unidirectional RNN
In this section, we have listed some major differences between Bidirectional and Unidirectional RNN in tabular format.
Bidirectional RNN
Unidirectional RNN
Bidirectional RNNs can process sequences in both directions simultaneously.
Unidirectional RNNs can process sequences in only a single direction.
For each state, the output depends on both past and future outputs.
In Unidirectional RNNs, the current state output only depends on the past state.
Bidirectional RNNs are trained on both forward and backward sequences.
Unidirectional RNNs are only trained on a single sequence.
It requires more computational power.
It requires comparatively less computational power.
It requires comparatively more memory due to bidirectional processing.
It requires comparatively less memory due to processing in only one direction.
It has more training time.
It takes less time to train.
Bidirectional RNNs are used in applications such as NLP, Speech recognition, Handwriting Recognition, etc.
Unidirectional RNNs are used in Time series predictions, language translations, etc.
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.