lstm attention
时间: 2023-09-26 08:08:33 浏览: 44
LSTM attention is a neural network architecture that combines Long Short-Term Memory (LSTM) and attention mechanisms. The LSTM is a type of recurrent neural network (RNN) that can handle long-term dependencies and has been widely used in natural language processing (NLP) tasks. The attention mechanism, on the other hand, allows the network to focus on specific parts of the input sequence that are more relevant to the task at hand.
In the LSTM attention architecture, the LSTM is used to encode the input sequence into a fixed-length vector representation. Then, the attention mechanism is applied to the encoded sequence to give weights to each element in the sequence based on its relevance to the output. These weights are used to compute a weighted sum of the encoded sequence, which is then passed through a fully connected layer to produce the final output.
LSTM attention has been shown to be effective in various NLP tasks, such as machine translation, text classification, and sentiment analysis. By using the attention mechanism, the network can selectively attend to important parts of the input sequence, which can improve its performance on complex tasks.