如何在keras中添加注意力机制？

Question

我目前正在使用从github
上的一次讨论中获得的这段代码，这是注意机制的代码：

_input = Input(shape=[max_length], dtype='int32')

# get the embedding layer
embedded = Embedding(
        input_dim=vocab_size,
        output_dim=embedding_size,
        input_length=max_length,
        trainable=False,
        mask_zero=False
    )(_input)

activations = LSTM(units, return_sequences=True)(embedded)

# compute importance for each step
attention = Dense(1, activation='tanh')(activations)
attention = Flatten()(attention)
attention = Activation('softmax')(attention)
attention = RepeatVector(units)(attention)
attention = Permute([2, 1])(attention)


sent_representation = merge([activations, attention], mode='mul')
sent_representation = Lambda(lambda xin: K.sum(xin, axis=-2), output_shape=(units,))(sent_representation)

probabilities = Dense(3, activation='softmax')(sent_representation)

这是正确的方法吗？我有点期待时间分布层的存在，因为关注机制分布在RNN的每个时间步中。我需要有人确认此实现（代码）是注意力机制的正确实现。谢谢。

Answer 1

如果您想在时间维度上关注，那么这段代码对我来说似乎是正确的：

activations = LSTM(units, return_sequences=True)(embedded)

# compute importance for each step
attention = Dense(1, activation='tanh')(activations)
attention = Flatten()(attention)
attention = Activation('softmax')(attention)
attention = RepeatVector(units)(attention)
attention = Permute([2, 1])(attention)

sent_representation = merge([activations, attention], mode='mul')

您已经计算出shape的注意力向量(batch_size, max_length)：

attention = Activation('softmax')(attention)

我以前从未看过这段代码，所以我不能说这段代码是否正确：

K.sum(xin, axis=-2)