Types of Recurrent Neural Networks (RNNs): Many-to-One, One-to-Many & Seq2Seq Explained

Types of Recurrent Neural Networks (RNNs)

There are four primary RNN architectures defined by how they map inputs to outputs: Many-to-One, One-to-Many, Many-to-Many, and One-to-One.

1. Many-to-One Architecture

As the name suggests, this architecture accepts a sequence of inputs (like sentences, characters, or time-series data) and produces a single, non-sequential output (such as an integer or scalar value).

• How it works: We feed multiple data points into the network over time, but the system processes them to generate just one final output.

• Example—Sentiment Analysis: We input a text string (a sequence of words). The model processes the text word-by-word to determine the overall sentiment (e.g., Positive/Negative).

• Example—Rating Prediction: Consider an app that analyzes movie reviews to predict star ratings. The input is the textual review, but the output is a single number (e.g., 1 to 5 stars).

2. One-to-Many Architecture

This is essentially the reverse of the Many-to-One architecture. Here, we provide non-sequential input (like a single image or number), and the network produces a sequence of outputs (such as words, sentences, or time-series data).

• How it works: We provide the input once at the start. The network generates an output at the first timestamp, which is often fed into the next timestamp to help generate the next part of the sequence. This cycle continues until the full output sequence is created.

• Example—Image Captioning: We feed a single image into the neural network, and it generates a textual description of the image's content. For instance, if you input a photo of a sport, the network might output the sentence: "A man is playing cricket."

• Example—Music Generation: By passing a single input (like a genre or seed note), the model can generate a continuous sequence of musical notes.

3. Many-to-Many Architecture (Seq2Seq)

In this architecture, both the input and the output are sequences. This is often referred to as Seq2Seq (Sequence-to-Sequence) learning. It is divided into two distinct sub-types based on how the input and output lengths relate to each other:

A. Same Length (Synced Many-to-Many)

Here, the length of the input sequence exactly matches the length of the output sequence. The network generates an output for every single input timestep.

• Example—Parts of Speech (POS) Tagging: In NLP, we might input a sentence like "She plays piano". The goal is to label each word with its grammatical part of speech (Noun, Verb, etc.). Since we need one label per word, the input size (3 words) equals the output size (3 labels).

• Example—Named Entity Recognition (NER): Similarly, for a sentence like "Lets meet at 7pm at the airport", the model assigns a tag to every word indicating if it is a specific entity (like a time or location) or just a regular word.

B. Variable Length (Asynchronous Many-to-Many)

In this variation, the input and output sequences can be of different lengths. This approach is often implemented using an Encoder-Decoder architecture.

• How it works: We feed the entire input sequence into the network first (the Encoder), which processes and "understands" the context. Only after the full input is read does the network begin generating the output sequence (the Decoder).

• Example—Machine Translation: Think of Google Translate. If we translate a sentence from English to Hindi, the word count often changes. Furthermore, we cannot translate word-for-word instantly; the model must read the whole English sentence to understand the grammar and context before generating the Hindi translation.

4. One-to-One Architecture

Technically speaking, this is not a true RNN.

A "One-to-One" architecture accepts non-sequential input and provides non-sequential output. There are no timestamps and no recurrence involved. This is simply a standard Feed-Forward Neural Network or a Convolutional Neural Network (CNN).

• Example: Image Classification (Input: One Image → Output: One Class Label like "Cat").

Summary Note:

Strictly speaking, Recurrent Neural Networks are defined by their ability to handle sequences. Therefore, the core RNN families are Many-to-One, One-to-Many, and the two forms of Many-to-Many.