Bidirectional Recurrent Neural Networks

What is a Bidirectional Recurrent Neural Network?

A Bidirectional Recurrent Neural Network (BRNN) is a type of Recurrent Neural Network (RNN) that can significantly increase the amount of input information available to the network. Traditional RNNs have a unidirectional flow of data from past to future, meaning that they process sequences in a forward direction, which limits their ability to understand context in situations where future input may affect the understanding of the past. BRNNs address this limitation by processing data in both forward and backward directions with two separate hidden layers, which are then fed forwards to the same output layer.

How Bidirectional Recurrent Neural Networks Work

BRNNs consist of two RNNs stacked on top of each other. The first RNN moves forward through time beginning from the start of the sequence, while the second RNN moves backward through time beginning from the end of the sequence. This architecture allows the networks to have both backward and forward information about the sequence at every time step.

The forward RNN processes the sequence from the start to the end, while the backward RNN processes the sequence from the end to the start. The outputs of both RNNs are typically combined at each time step, which provides a complete past and future context to the network. This combined output can then be passed on to other layers in the network or used to make predictions.

Applications of Bidirectional Recurrent Neural Networks

BRNNs are particularly useful in fields such as natural language processing (NLP) and speech recognition, where understanding the context is crucial. For instance, in language modeling and translation, the meaning of a word can depend heavily on the words that come both before and after it. BRNNs are also applied in handwriting recognition and bioinformatics, particularly in gene sequencing where the sequence context is important for accurate predictions.

Training Bidirectional Recurrent Neural Networks

Training a BRNN is similar to training a traditional RNN, but it involves updating two sets of weights simultaneously—one for the forward direction and one for the backward direction. The training process involves forward propagation, where input data is passed through both RNNs, and backward propagation, where gradients from the output layer are passed back through the network to update the weights. Both forward and backward passes are necessary to capture the dependencies in both directions.

One of the challenges with training BRNNs, similar to other RNNs, is the issue of vanishing and exploding gradients. However, this can be mitigated by using gated units such as Long Short-Term Memory (LSTM) units or Gated Recurrent Units (GRUs) in the architecture.

Advantages of Bidirectional Recurrent Neural Networks

The primary advantage of BRNNs is their ability to take into account both past and future data, which can lead to more accurate models for certain types of problems. This bidirectional context is particularly beneficial in tasks where the complete sequence is known and the context from both directions is necessary to understand the content.

Limitations of Bidirectional Recurrent Neural Networks

Despite their advantages, BRNNs also have some limitations. They require the complete sequence to be known before processing, which makes them unsuitable for real-time processing tasks where the entire sequence is not available upfront. Additionally, BRNNs can be more computationally intensive to train due to the doubled number of calculations for the forward and backward passes.


Bidirectional Recurrent Neural Networks are a powerful variation of RNNs that provide a more nuanced understanding of sequence data by incorporating both past and future context. They have proven to be particularly useful in complex sequence modeling tasks where context is key to making accurate predictions. However, they are not without their challenges and are best suited for applications where the entire sequence is available for processing.


1. Schuster, M., & Paliwal, K. K. (1997). Bidirectional recurrent neural networks. IEEE Transactions on Signal Processing, 45(11), 2673-2681.

2. Graves, A., & Schmidhuber, J. (2005). Framewise phoneme classification with bidirectional LSTM and other neural network architectures. Neural Networks, 18(5-6), 602-610.

3. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.

Please sign up or login with your details

Forgot password? Click here to reset