Attention is All You Need is a revolutionary research paper by Vaswani, Ashish & Shazeer, Noam & Parmar, Niki & Uszkoreit, Jakob & Jones, Llion & Gomez, Aidan & Kaiser, Lukasz & Polosukhin, Illia. Those who can focus on the notations on the paper will be able to understand:
- Positional encoding, its importance
- Positional encoding in transformers
- Code and visualize a positional encoding matrix in Python using NumPy
An attention and transformer model is a type of neural network architecture that exploits concepts of self-attention. It provides an efficient solution for learning large unlabeled protein sequences and can be used for accurate prediction of folded macromolecules. Transformers are usually pre-trained on a large set of data. They are available in a variety of popular deep-learning frameworks. However, they are computationally expensive and require considerable memory space. Researchers are working on reducing the time and memory requirements of transformers.
A typical structure for a transformer consists of a feedforward layer, a decoder layer, and a self-attention layer. The decoder layer processes input vectors, the feedforward layer processes the sequence, and the self-attention layer generates a new representation per word.
Attention and transformer models have successfully predicted many types of natural language processing applications. They are especially useful in tasks involving the identification of proteins. Proteins have complex 3D structures that can be represented as linear sequences. In addition to recognizing highly similar amino acid pairs, the model also recognizes specific functions.
The original transformer architecture consisted of six encoders and a decoder stack. Each layer in the model has multiple attention heads. These attention heads are responsible for encoding relevant relevance relations. For example, one attention head attends to a token only in the context of another token. Some heads use special tokens to attend to nothing.
Do transformers Use Attention?
If you are interested in using transformers in your application, you may want to know more about the concept behind them. Transformers are a type of deep neural network. They are designed to improve natural language processing tasks, such as detecting salient words or sentence parts. These models can also be used to create machine vision applications that use pixels to learn about images.
A basic model of a transformer consists of two parts: the encoder and the decoder. The encoder transforms the input sequence into a vector. It then passes the vector into the feedforward neural network, which processes the vector and generates outputs.
The decoder then adds these results to the output sequence, thereby generating a translated target sentence. This is followed by the softmax layer, which produces the final linear transformation.
In the original architecture, there were six encoders and six decoders. Each decoder was composed of a self-attention layer and a decoder attention layer. The embeddings from each of these layers were then multiplied by three weight matrices.
During the training phase, the network learns the projection vectors. This enables the model to determine the distance between different word sequences. After learning the projections, the model can be trained to generate a sequence of embeddings, which are then multiplied by the weight matrices.
Typically, the attention matrix contains very small values. In order to calculate the attention score, the model has to learn these values through training.
Attention Mechanism in the Transformers Model
The Transformers model is an example of a machine-learning architecture that leverages Attention to encode positions. This architecture is different from the traditional sequence-to-sequence models. It does not involve recurrent connections or convolutions. Instead, it consists of encoder and decoder stacks, each with a multihead attention layer.
Multi-head Attention provides the transformer with greater power to encoding nuances. For example, it allows the attention layer to weight states according to relevance. The layer can determine the distance between words by comparing the input sequence members.
Each token in the input sequence is analyzed by the attention mechanism. A “query”, “key”, and “value” vector is computed. These abstractions are used to calculate the attention weight.
The “query” is a linear projection of the matrix, and the “key” and “value” are produced by multiplying the matrix by a pair of weight matrices. The attention weight between token i and token j is a dot product of the query and key vectors.
The “magic number” is the square root of the key vector dimension. This value, called the softmax, is used to drown out irrelevant words. In addition, the value is also useful for training due to the computational matrix operations optimizations.
A related feat is splitting the vector representation into subspaces. This process is related to disentangled representation training.
The original transformer was made up of a decoder and an encoder stack. Each has a self-attention layer and a feedforward layer. The self-attention layer performs multi-head attention with a few special tokens.