Why use sin and cos functions in transformer for positional encoding?

The Transformer model is a sequence-to-sequence model that uses a self-attention mechanism and is widely popular in the field of natural language processing. Positional encoding is an important component in the Transformer model. It can effectively introduce the order information in the sequence into the model, thereby better processing sequence data. With positional encoding, the model can model words based on their position in a sequence, providing contextual information about word order. This method avoids the vanishing or exploding gradient problem of traditional recurrent neural networks (RNN) when processing long sequences. Positional encoding is usually achieved by adding learnable vectors or fixed sine/cosine functions. In the Transformer model, the introduction of positional encoding enables the model to better understand the sequential relationship of sequence data, thereby improving its performance and expression capabilities.

In the Transformer model, position encoding is implemented through an independent position encoding matrix. Each row corresponds to a positional encoding vector, which is added to the input word embedding vector to add positional encoding information to each word in the input sequence. This method enables the model to capture the relative positional relationship of different words in the sequence, thereby better understanding the semantics of the input sequence.

The generation method of these positional encoding vectors uses the sin and cos functions. For each position i and each dimension j, the value in the position encoding matrix is ​​calculated by the following formula:

PE_{(pos,2i)}=sin(pos/10000^ {2i/d_{model}})

##PE_{(pos,2i 1)}=cos(pos/10000^{2i/d_{model}})

Among them, pos represents the current position, i represents the current dimension, and d_model represents the dimension of the model. As you can see, both the sin and cos functions use an exponential term. The base of this exponential term is 10000, and the power of the exponential is calculated based on the position and dimension.

So why use sin and cos functions as positional encoding? There are several reasons here:

1. Periodicity

The sin and cos functions are both periodic functions and can produce repeated periodicity model. In sequence data, position information is usually periodic. For example, in natural language processing, the position of a word in a sentence is usually periodic. Using the sin and cos functions can help the model capture this periodic information and thus better handle sequence data.

2. Coding differences between different positions

Using the sin and cos functions can produce coding differences between different positions, which is Because the sin and cos function values ​​at different positions are different. This difference can help the model better distinguish the differences between different positions and thus better handle the sequence data.

3. Interpretability

Another advantage of using sin and cos functions as positional encoding is that it is interpretable. Since these functions are classical functions in mathematics, their properties and characteristics are very clear, so their impact on the model can be better understood.

In general, using sin and cos functions as positional encoding is a very effective way to help the Transformer model better handle sequence data. At the same time, this method also has a certain interpretability and helps people better understand the operating mechanism of the model.

The above is the detailed content of Why use sin and cos functions in transformer for positional encoding?. For more information, please follow other related articles on the PHP Chinese website!