怎样用python在LSTM中加入注意力机制
时间: 2024-04-29 17:25:16 浏览: 178
可以使用Keras库中的Attention层来在LSTM中加入注意力机制,具体实现可以参考以下代码:
```python
from keras.layers import Input, LSTM, Dense, Dropout, TimeDistributed, Bidirectional, Concatenate, Dot, Activation
from keras.layers import RepeatVector, Embedding, Flatten, Lambda, Permute, Multiply
from keras.models import Model
from keras.activations import softmax
import keras.backend as K
# 定义注意力机制的函数
def attention(a, b):
a_reshape = Permute((2, 1))(a)
score = Dot(axes=[2, 1])([b, a_reshape])
alignment = Activation('softmax')(score)
context = Dot(axes=[2, 1])([alignment, a])
return context
# 定义输入和输出的形状和维度
input_shape = (None,)
output_shape = (None,)
# 定义输入层和嵌入层
input_layer = Input(shape=input_shape)
embedding_layer = Embedding(input_dim=vocab_size, output_dim=embedding_dim)(input_layer)
# 定义双向LSTM层
lstm_layer = Bidirectional(LSTM(units=lstm_units, return_sequences=True))(embedding_layer)
# 定义注意力层
attention_layer = attention(lstm_layer, lstm_layer)
# 将LSTM层和注意力层连接起来
concat_layer = Concatenate(axis=2)([lstm_layer, attention_layer])
# 定义全连接层和输出层
dense_layer = TimeDistributed(Dense(units=dense_units, activation='relu'))(concat_layer)
output_layer = TimeDistributed(Dense(units=output_vocab_size, activation='softmax'))(dense_layer)
# 构建模型
model = Model(inputs=[input_layer], outputs=[output_layer])
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
```
注意,上述代码中的函数`attention`就是实现注意力机制的关键。在模型中,我们先将输入通过嵌入层映射成词向量,然后经过双向LSTM层,得到前向和后向的隐状态。接着,我们将这两个隐状态作为注意力机制的输入,计算得到注意力权重,然后根据这个权重计算出每个词对应的上下文向量。最后,我们将原始的LSTM输出和上下文向量拼接起来,再通过全连接层和输出层进行预测。