Recurrent Neural Networks in Python: What Are They and How Do They Work?


Chatbots, virtual assistants, smart translations. These systems, so common today, are a reality thanks to the advances of Artificial Intelligence and Machine Learning. 


Specifically, the development of these applications comes from the use of programming models whose central element is recurrent neural networks. 


Below we tell you what they mean and how to create them in Python.


What are recurrent neural networks?


Recurrent neural networks are artificial intelligence models used in Deep Learning, a category of machine learning that aims to replicate the way the human brain works and processes the information it receives. 


Deep Learning uses artificial neural networks organized in layers, for the processing of large volumes of data. This then lets these systems be capable of making very accurate predictions.


This model uses those neurons, connected to each other in a layer structure, similar to the brain's scheme. 


So, through these neural networks, a system is formed that lets computers generate automatic learning from the processing of enormous amounts of information.


But in deep learning there are different types of neural networks, each linked to specific tasks. 


Recurrent neural networks -recurrent neural networks (RNN)- specialize in the processing of text data and sequences or time series, with a structure that makes the generation of artificial memory possible.


This way, they can help make predictions based on historical data. An example of this is predicting the sales volume of a certain product, using the historical behavior of its commercialization as a reference.


Likewise, recurrent neural networks can remember text they've processed and associate it with the sentences or phrases they begin to analyze, which is why they're very valuable in predictive text applications, for example. 


How do they manage to do this? Now we'll tell you how they work.





How does a recurrent neural network work?


Neural networks are models created from mathematical operations arranged in a structure that, as we said, is based on the use of layers made up of neurons, also called neurons, units or units.


Each neuron executes an operation and connects with other units of the surrounding layers, the previous and the next one, through what is known as weights. The function of these weights is to control the data that is transmitted from one neuron to another.


A recurrent neural network works as follows: it not only transmits data forward but can do so backward. 


This way, as it advances, it obtains information from the neurons that preceded it, and, in addition, from itself in its previous step.


This means that recurrent neural networks let the software or application they're part of remember and forget the data it analyzes. 


That's why they're said to be networks that have memory, and it's for that reason that they can relate data received at distant times.


Thus, it's a model that retains information it processed at the start of the sequencing and links it with the new data it receives.  


Each recurrent neural network is then made up of two groups of parameters: one is activated when the information from the previous layer is received or enters, and the other executes it on the output data. 


All this cyclical operation demands the development of optimization methods to reduce errors and to make the network's work optimal.


Thanks to all these capabilities, recurrent neural networks are usually used in these Deep Learning models:Chatbots used on web pages to provide customer service.Virtual assistants, which understand orders to execute actions, usually through voice commands, Siri-type.Automated translations, whether of text or voice, in real time.Image recognition.Sales or price prediction based on historical commercial data.


We already reviewed the definition and operation of recurrent neural networks. Let's now see the types that exist and what characterizes each of them. 


Types of recurrent neural networks


There are different types of recurrent neural networks, based on the data input and output format applied. These are the types:


One-to-many or Vector to sequenceIn this type of network the input of one piece of data is allowed, while many come out, forming a sequence. 


It's the case of networks that have been instructed to describe images. They receive the image -it's the input data- and generate a text that describes the image, which would be the output data.


Many-to-one or Sequence to vector Here the network, contrary to the previous one, has a group of data or input sequence, and then produces just one output. 


It would then be the example of the image but in the reverse sense: the network receives the description of the image and, after processing this information, generates the image from that text. There are numerous applications that work this type of function.


Many-to-many or Sequence to sequenceAs input we have a set of data or a sequence, which then serves to also produce a group of data or output sequence. 


An example is the applications that, based on a text, create new content, generate summaries or convert the text into audio or vice versa. Automatic translators also use this type of network.


LSTM (Long-Short Term Memory)Although, as we said, recurrent neurons have memory, this is limited; therefore, it's complicated to transmit information if the sequences are very distant from each other. 


This has a solution: the gates, which are operations that let you retain the information that is relevant and suppress the one that isn't for the system's learning.


So, the LSTM is one of the recurrent neural networks that arises from these gates. 


In it, the memory cells execute certain operations related to the flow of data, which is what defines whether the information is remembered by the system or forgotten.


GRU (Gated Recurrent Units)They're similar to LSTMs since they perform operations with their gates to keep important information and eliminate irrelevant data. They differ from LSTMs because they have fewer gates and that's why they train in a more accelerated way.


Examples of neural networks in Python


Python is a very popular programming language for developing machine learning models, so handling Python is a highly valued capability today and will certainly continue to be so in the future.


Free to download, it's a very efficient language, easy to learn and that can run on different platforms. It serves for developing applications, software, data science and, as we already noted, Artificial Intelligence.


Now, speaking of neural networks, in Python there are many libraries with which you can develop programming models based on neural networks. Selecting the library to use depends on the application the networks will have.


Broadly speaking, two models are distinguished:


Simple network models (multi-perceptron): They're of architectures that can be considered simple. There, the executions of the open-source machine learning libraries Scikit-learn and H2O stand out.


Deep learning: They're more complex models, where recurrent networks are precisely inscribed. They require specialized frameworks, like Tensorflow-Keras or Pytorch. Frameworks are schemes that facilitate development processes.


In creating models based on neural networks with Scikit-learn, the classes sklearn.neural_network.MLPRegressor and sklearn.neural_network.MLPClassifier,  for regression and classification, respectively, are usually used.


The arguments used to control the behavior of this type of model are numerous, but most of them have default values that, generally, work well in almost all cases. 


Arguments are the values sent to a function at the moment of programming them. 


In the case of neural networks, they govern elements like: the number and size of the networks' hidden layers; the activation of those layers; the optimization algorithms to learn the weights that control the passage of information between layers; among many others.


The information is broad and the possibilities, enormous. Learning these tools undoubtedly represents a great opportunity.


If you're interested in going deeper into the use of Python to develop neural networks, we recommend our online Python course, which in just 13 weeks will give you the bases to handle one of the most relevant programming languages that exist. 

If you'd like to keep exploring this topic, you can also read the best AI tools for workplace productivity.

Recommended Coderhouse courses

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