In the realm of sequence modeling, capturing long-range dependencies has posed challenges for traditional models like recurrent neural networks and long short-term memory. Enter the Transformer, a groundbreaking model architecture that relies entirely on an attention mechanism.
Transformer Architecture
The Transformer is composed of an encoder and a decoder, each consisting of six stacked layers. The encoder layer and decoder layer are structured as follows:
Encoder Layer
- Multi-head Self Attention Mechanism: Allows the model to focus on different parts of the input sequence simultaneously.
- Position-wise Fully Connected Feed-forward Network: Processes the information captured by the attention mechanism.
- Residual Connections: Ensures the flow of information through skip connections around each sub-layer.
Decoder Layer
- Masked Multi-head Self Attention: Ensures that predictions for a position depend only on known outputs before that position.
- Multi-head Self Attention Mechanism: Captures information from different parts of the input sequence.
- Position-wise Fully Connected Feed-forward Network: Processes the information captured by the attention mechanisms.
- Residual Connections: Maintains the flow of information through skip connections around each sub-layer.
Attention Function
The crux of the Transformer lies in its attention function, which maps a query and a set of key-value pairs to an output. This innovative approach allows the model to pay more attention to words crucial for understanding the entire text, revolutionizing how information is processed in deep learning.
Self Attention
A specific type of attention employed by the Transformer is the self attention mechanism. This mechanism considers different positions of words to calculate attention weights, enhancing the model’s ability to discern relationships within the input sequence.
Multi-head Attention
A key feature of the Transformer is the Multi-head Attention Mechanism. This enables the model to consider multiple perspectives simultaneously, allowing it to capture diverse information from the input data. This versatility is achieved by processing the input through multiple attention heads, enriching the model’s understanding of complex relationships.
Note: This blog post is inspired by the groundbreaking work presented in the Attention is All You Need paper by Vaswani et al. The transformative ideas discussed here have significantly influenced the field of deep learning.