Types of Recurrent Neural Networks

An artificial neural network that employs sequential data or time series data is known as a recurrent neural network (RNN). These deep learning algorithms are included in well-known programs like Siri, voice search, and Google Translate. They are frequently employed for ordinal or temporal issues, such as language translation, natural language processing (NLP), speech recognition, and image captioning. Recurrent neural networks (RNNs) use testing data to learn, just like feed-forward and convolutional neural networks (CNNs) do. They stand out because of their "memory," which allows them to affect the current input and output by using data from previous inputs.


Recurrent neural networks' outputs depend on the previous parts in the sequence, unlike typical deep neural networks, which presume that inputs and outputs are independent of one another. Unidirectional recurrent neural networks are unable to account for future events in their forecasts, even though they would be useful in deciding the output of a particular sequence.


Feedforward Neural Networks Vs Recurrent Neural Networks


Let's use an expression that is frequently used to describe someone who is ill—"feeling under the weather"—to help us understand RNNs. The idiom must be expressed in that particular order for it to make sense. Recurrent networks must therefore take into account the order in which each word appears in the idiom. It's this knowledge they use to predict the following word in a series.


Recurrent networks are distinguished by the fact that each layer of the network uses the same parameters. Recurrent neural networks share the same weight parameter inside each layer of the network, contrary to feedforward networks, which have distinct weights across each node. However, to support reinforcement learning, these weights have to undergo modifications using the techniques of backpropagation and gradient descent.


Recurrent neural networks use the backpropagation through time (BPTT) algorithm, which differs slightly from conventional backpropagation because it is tailored to sequence data and find the gradients. In classical backpropagation, the model trains itself by computing errors from its output layer to its input layer. This is how BPTT works. These computations enable us to accurately alter 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, a process that distinguishes it from the conventional technique.


RNNs frequently experience the two issues of "exploding gradients" and "vanishing gradients" throughout this process. The gradient's size, or the slope of the loss function along the error curve, is what categorizes these problems. When the gradient is insufficient, it keeps becoming smaller, updating the weight parameters until they are negligible, or 0. The algorithm stops learning when something happens. When the gradient is too great, exploding gradients happen, which makes the model unstable. In this scenario, the model weights will eventually become too enormous and be represented as NaN. Reducing the number of hidden layers in the neural network and hence some of the complexity in the RNN model is one way to address these problems.


Recurrent Neural Network Types


Although we've depicted recurrent neural networks in this way in the aforementioned diagrams, they do not truly have the limitation that feedforward networks have, which maps one input to one output. Instead, multiple types of RNNs are employed for various use cases, such as music production, sentiment categorization, and machine translation, and their inputs and outputs can have varying lengths. The various RNN varieties are:



●One-to-one
●One-to-many
●Many-to-one
●Many-to-many

Alternative RNN Architectures


Bidirectional Recurrent Neural Networks


These are alternative RNN network architectures. Bidirectional RNNs include future data to increase the accuracy of predictions made by unidirectional RNNs, which can only use historical data. If we go back to the example of "feeling under the weather" from earlier in this article, the model would be able to predict "under" better if it knew that "weather" would be the final word in the sequence.


Long Short-Term Memory (LSTM)


Sepp Hochreiter and Jürgen Schmidhuber created this well-known RNN architecture as a remedy for the vanishing gradient issue. They attempt to address the issue of long-term dependency in their study. In other words, the RNN model might not correctly forecast the present state if the previous state that is influencing the current prediction is not recent. If we wished to foretell the italicized phrase, "Alice is allergic to nuts. She doesn't like peanut butter."


We can prepare for the possibility that the meal she cannot consume contains nuts by considering the context of a nut allergy. It would be challenging for the RNN to connect the information if that context had been introduced a few words earlier. In the deep layers of the neural network, LSTMs have "cells" that have three gates: an input gate, an output gate, and a forget gate. These gates regulate the information flow that is necessary for the network to predict the output. For instance, you may leave out gender pronouns like "she" from the cell state if they were used repeatedly in earlier phrases.


GRUs (Gated Recurrent Units)


This RNN version is comparable to LSTMs since it attempts to solve the short-term memory issue that plagues RNN models. It employs hidden states to govern information rather than "cell states," and includes two gates—a reset gate and an update gate—instead of all three. The reset and update gates regulate how much and which information to retain, much as the gates within LSTMs.

RNN-Based Training


Recurrent neural networks are trained using the following procedure


●Instructing RNN in a single-time step
●Then, using the set of current input and the prior state, calculate the present state of the system.
●For the following time step, the current time becomes time-1.
●Depending on the issue, one can travel back as many time steps and combine the data from all the prior states.
●The final current state is used to determine the output after all the time steps have been finished.
●The error is then generated once the output is compared to the goal output, which is the actual output.

Recurrent Neural Network Benefits


●An RNN retains every piece of knowledge throughout time. Having the ability to remember prior inputs alone makes it beneficial for time series prediction.
●Convolutional layers and recurrent neural networks are combined to increase the effective pixel neighborhood.

Recurrent Neural Network Drawbacks


●Problems with gradient disappearing and explosions.
●It is exceedingly tough to train an RNN.
●If tanh or relu are used as the activation function, it cannot process very long sequences.

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