用keras实现注意力机制

以下是使用Keras实现注意力机制的步骤：

1. 定义输入层和LSTM层

from keras.layers import Input, LSTM

input_seq = Input(shape=(None, input_dim))
lstm_layer = LSTM(hidden_dim, return_sequences=True)(input_seq)

2. 定义注意力层

from keras.layers import Dense, TimeDistributed, Activation, RepeatVector, Lambda
from keras import backend as K

def attention_layer(inputs):
    hidden_states, last_hidden_state = inputs
    hidden_size = int(hidden_states.shape[2])
    last_hidden_state = K.expand_dims(last_hidden_state, axis=1)
    last_hidden_state = K.repeat_elements(last_hidden_state, hidden_states.shape[1], axis=1)
    score = TimeDistributed(Dense(hidden_size, activation='tanh'))(hidden_states)
    score = TimeDistributed(Dense(1, activation='linear'))(score)
    attention_weights = Activation('softmax')(score)
    context_vector = Lambda(lambda x: K.sum(x[0]*x[1], axis=1))([hidden_states, attention_weights])
    return context_vector

attention = attention_layer([lstm_layer, lstm_layer[:, -1, :]])

3. 定义输出层

output = Dense(output_dim, activation='softmax')(attention)

4. 定义模型

from keras.models import Model

model = Model(inputs=input_seq, outputs=output)

这样，我们就定义了一个带有注意力机制的LSTM模型。在训练过程中，模型将会学习到如何注意到输入序列中的重要部分，以便更好地预测输出序列。