LSTM-Attention
时间: 2023-10-31 19:44:04 浏览: 35
LSTM-Attention is a deep learning architecture that combines the power of Long Short-Term Memory (LSTM) networks and Attention mechanisms. LSTM networks are a type of recurrent neural networks (RNNs) that are designed to handle long-term dependencies in sequential data, making them ideal for natural language processing, speech recognition, and other time-series data.
Attention mechanisms, on the other hand, allow the model to focus on specific parts of the input sequence, enabling it to selectively attend to relevant information while ignoring the irrelevant parts. This helps improve the accuracy of the model while reducing the computational cost.
In the LSTM-Attention architecture, the LSTM network is augmented with an attention mechanism that learns to assign weights to each element of the input sequence based on its relevance to the current state of the LSTM. The attention mechanism uses these weights to compute a weighted sum of the input sequence, which is then fed into the LSTM for further processing.
This architecture has been successfully applied in various tasks such as machine translation, text classification, and sentiment analysis. It has shown to outperform traditional LSTM models in terms of accuracy and efficiency, making it a popular choice for many deep learning applications.