attention-lstm
时间: 2023-09-27 08:12:26 浏览: 52
Attention-LSTM is a type of neural network architecture that combines the strengths of Long Short-Term Memory (LSTM) networks with attention mechanisms. The attention mechanism allows the model to focus on specific parts of the input sequence that are most relevant to the current task at hand, while the LSTM network is able to capture long-term dependencies in the sequence data.
In an Attention-LSTM model, the attention mechanism is typically implemented using a separate neural network that takes the current state of the LSTM and the entire input sequence as input and outputs a set of attention weights. These weights are then used to compute a weighted sum of the input sequence, which is passed as input to the LSTM at each time step.
The use of attention in LSTM networks has been shown to improve the performance of various sequence-to-sequence tasks, such as machine translation and speech recognition. It allows the model to selectively attend to relevant parts of the input sequence and ignore irrelevant information, leading to more accurate predictions.