Learn about the many Advantages of Recurrent Neural Networks (RNNs) used in artificial intelligence, types their, and their capacity to process sequential input.
What is Recurrent Neural Network?
In order to develop a machine learning (ML) model that can generate sequential predictions or conclusions based on sequential inputs, a deep neural network known as a recurrent neural network, or RNN, is trained on sequential or time series data.
Using historical daily flood, tide, and weather data, an RNN might be used to forecast flood levels every day. However, RNNs can also be used to handle temporal or ordinal problems, including picture captioning, audio recognition, sentiment analysis, natural language processing (NLP), and language translation.
How Recurrent neural networks work
Recurrent neural networks learn from training data, just like conventional neural networks like feedforward neural networks and convolutional neural networks (CNNs). Their “memory,” which allows them to use information from previous inputs to affect the present input and output, sets them apart.
Recurrent neural networks’ output is dependent on the previous parts in the sequence, whereas standard deep learning networks presume that inputs and outputs are independent of one another. The output of a particular sequence could also be inferred from future events, however unidirectional recurrent neural networks are unable to take these events into account when making predictions.
To help us understand RNNs, let’s utilize a phrase that is frequently used when someone is ill, such “feeling under the weather.” The idiom must be used in that particular order in order to make sense. Recurrent networks must therefore take into consideration each word’s location within the idiom in order to forecast the subsequent word in the sequence.
The order is important because each word in the phrase “feeling under the weather” is a part of a sequence. By keeping a concealed state at every time step, the RNN keeps track of the context. The hidden state is sent from one time step to the next, forming a feedback loop. Information about earlier inputs is stored in the hidden state, which functions as a memory. The RNN processes the hidden state from the previous time step in addition to the current input (a word in a phrase, for instance) at each time step. This makes it possible for the RNN to “remember” earlier data points and apply them to the current result.
The fact that recurrent networks share parameters at every network layer is another property that sets them apart. Recurrent neural networks have the same weight parameter inside each network layer, but feedforward networks have varying weights across each node. However, in order to support reinforcement learning, these weights are still modified using gradient descent and backpropagation.
To find the gradients, recurrent neural networks employ forward propagation and backpropagation through time (BPTT) methods. This method differs slightly from regular backpropagation because it is tailored to sequence data. The model trains itself by computing errors from its output layer to its input layer. The basic ideas of BPTT are identical to those of traditional backpropagation. These computations enable us to suitably modify and fit the model’s parameters. In contrast to feedforward networks, which do not share parameters between layers, BPTT adds errors at each time step, which is how it varies from the conventional method.
Common activation functions
In order to create nonlinearity and enable the network to recognize increasingly intricate patterns in the input, an activation function is a mathematical function that is applied to the output of each layer of neurons in the network. The RNN would only calculate linear transformations of the input in the absence of activation functions, which would prevent it from tackling nonlinear situations. In tasks like natural language processing (NLP), time-series analysis, and sequential data prediction, nonlinearity is very important for learning and modeling complicated patterns.
By maintaining values within a predetermined range (for instance, between 0 and 1 or -1 and 1), the activation function regulates the neuron’s output magnitude and helps keep values from increasing too much or too little between the forward and backward passes. Activation functions are applied to the hidden states of RNNs at each time step, regulating how the network changes its internal memory (hidden state) in response to previous hidden states and current input.
Typical activation functions consist of:
The Sigmoid Function is used to operate gates that determine how much information should be remembered or forgotten, or to interpret the output as probabilities. Nevertheless, the sigmoid function is less suitable for deeper networks due to its susceptibility to the vanishing gradient problem.
Because it produces values that are centered around zero, the Tanh (Hyperbolic Tangent) Function is frequently used to improve gradient flow and make learning long-term dependencies simpler.
Because the ReLU (Rectified Linear Unit) is unbounded, it may result in problems with expanding gradients. Nevertheless, some of these problems have been addressed by variations like Leaky ReLU and Parametric ReLU.
Advantages of recurrent neural network
The capacity of recurrent neural networks (RNNs) to process sequential data while maintaining recollection of prior inputs is the main reason for their many benefits. Because of this, they are especially well-suited for jobs where information order is important. Here are a few main advantages:
Handling Sequential Data
When processing data where order is important, such time series, audio, and spoken language, RNNs perform exceptionally well. Better predictions and insights can result from their comprehension of the context and connections between the pieces in a sequence.
Memory and Context
RNNs, in contrast to conventional neural networks, may store and use data from prior inputs because of their internal memory. They can comprehend the context of a sequence and make better decisions because to this memory.
Variable Input Length
RNNs are adaptable for jobs like natural language processing, where sentences might have variable lengths, because they can accommodate inputs of different lengths.
Shared Weights
Because RNNs share weights across time steps, the model is more efficient and has fewer parameters.
Wide Range of Applications
RNNs can be used for a variety of purposes, such as
- Machine translation, text production, sentiment analysis, and speech recognition are examples of tasks that fall within the category of natural language processing, or NLP.
- Time series analysis includes financial data analysis, weather forecasting, and stock price prediction.
- Understanding speech patterns, identifying musical genres, and evaluating video information are all examples of audio and video processing.
Types of Recurrent Neural Networks
Although recurrent neural networks have been shown in this manner in previous designs, feedforward networks are not limited to mapping inputs and outputs one-to-one. Rather, the length of their inputs and outputs can differ, and different RNN types are employed for various applications, including machine translation, sentiment analysis, and music production. Typical variations of recurrent neural network architectures are as follows:
- Standard RNNs
- Bidirectional recurrent neural networks (BRRNs)
- Long short-term memory (LSTM)
- Gated recurrent units (GNUs)
- Encoder-decoder RNN
Standard RNNs
Long-term dependencies are challenging for the simplest RNNs to train because of issues like vanishing gradients, which cause the output at each time step to depend on both the current in put as well as the prior time step’s hidden state. Simple tasks with short-term dependencies, like forecasting the next value in a basic time series or the next word in a sentence, are areas in which they thrive.
RNNs perform well in applications that need sequential real-time data processing, including processing sensor data to identify anomalies in brief intervals, where predictions must be produced instantly based on the most recent inputs and inputs are received one at a time.
Bidirectional recurrent neural networks (BRRNs)
Bidirectional RNNs, also known as BRNNs, pull in future data to increase the accuracy of their predictions, whereas unidirectional RNNs can only use past inputs to do so. Using “feeling under the weather” as an example, a BRNN-based model may more accurately predict that the second word in that phrase is “under” if it is aware that the final word in the sequence is “weather.”
Long short-term memory (LSTM)
Sepp Hochreiter and Juergen Schmidhuber developed the well-known LSTM RNN architecture as a remedy for the vanishing gradient issue. In this effort, the issue of long-term dependency was addressed. In other words, the RNN model may not be able to forecast the present state with any degree of accuracy if the previous state that is influencing the current prediction is not recent.
Let’s use the example of “Alice is allergic to nuts” to anticipate the italicized terms. That is unable to consume peanut butter. It can predict that the food that is ineligible contains nuts by knowing the background of a nut allergy. It would be challenging or impossible for the RNN to connect the data, though, if that context came a few words earlier.
To address this, LSTM networks use “cells” in the artificial neural network’s hidden layers, each of which has three gates: an input gate, an output gate, and a forget gate. These gates regulate the information flow required to forecast the network’s output. For instance, you might remove a gender pronoun from the cell state if it was used repeatedly in previous statements, like “she.”
Gated recurrent units (GNUs)
An LSTM and a GRU are comparable in that they both solve the short-term memory issue with RNN models. It features two gates a reset gate and an update gate instead of three, and it employs hidden states to control information rather than a “cell state.” The reset and update gates regulate which and how much information is kept, much as the gates in LSTMs.
GRUs are computationally more efficient and require fewer parameters than LSTMs due to their simpler architecture. Because of this, they can be taught more quickly and are frequently better suited for real-time or resource-constrained applications.
RNNs that encode and decode
Machine translation and other sequence-to-sequence tasks frequently employ them. The input sequence is transformed into a fixed-length vector (context) by the encoder, and the output sequence is produced by the decoder using that context. However, particularly for lengthy input sequences, the fixed-length context vector may act as a bottleneck.
Encoder-decoder RNN
Although RNNs are still useful, their use in artificial intelligence has decreased, particularly in favor of architectures like transformer models. Due to their capacity to manage temporal dependencies, RNNs have historically been widely used for sequential data processing.
