2024年1月12日发(作者：)

keras实现external attention代码

External attention，也被称为 pointer-generator network，是一个常用于序列生成任务的注意力机制。这个机制可以同时学习从输入序列中获取信息和选择信息的能力。

下面是一个简单的 Keras 实现示例：

python复制代码

from import Model

from import Input, Embedding, LSTM, Dense, Lambda

import d as K

def scaled_dot_product_attention(q, k, v, mask=None):

"""Calculate the attention weights.

q, k, v must have matching leading dimensions.

k, v must have matching penultimate dimension, i.e.: seq_len_k =

seq_len_v.

The mask has different shapes depending on its type(padding or look

ahead)

but it must be broadcastable for addition.

Args:

q: query shape == (..., seq_len_q, depth)

k: key shape == (..., seq_len_k, depth)

v: value shape == (..., seq_len_v, depth_v)

mask: Float tensor with shape broadcastable

to (..., seq_len_q, seq_len_k). Defaults to None.

Returns:

output, attention_weights

"""

matmul_qk = _dot(q, k, axes=2) # (..., seq_len_q, seq_len_k)

# scale matmul_qk

dk = ((k)[2], ())

scaled_attention_logits = matmul_qk / (dk)

# add the mask to the scaled tensor.

if mask is not None:

scaled_attention_logits += (mask * -1e9) # broadcast addition

# softmax is normalized on the last axis (seq_len_k)

attention_weights = x(scaled_attention_logits, axis=-1) #

(..., seq_len_q, seq_len_k)

output = _dot(attention_weights, v) # (..., seq_len_q,

depth_v)

return output, attention_weights

def attention_layer(units):

def layer(inputs):

query, key, value = inputs

output, attention_weights = scaled_dot_product_attention(query,

key, value)

return output, attention_weights

return layer

这个代码定义了一个注意力层，该层使用 scaled dot-product attention 机制。这个机制首先计算 query 和 key 的点积，然后对结果进行缩放并应用 softmax 函数，最后用得到的权重和 value 进行加权求和。这里的 query、key 和 value 都来自输入的张量。然后返回输出张量和注意力权重。你可以在模型中使用这个自定义的注意力层。