多头注意力机制的意义

多头注意力机制是一种注意力机制的变体，它可以帮助模型更好地捕捉输入序列中的相关信息。它通过将输入向量分成多个头部并在每个头部上执行注意力操作来实现这一点。每个头部都可以关注不同的位置，从而使模型能够同时从不同的角度理解输入序列。这种方法在自然语言处理和计算机视觉等领域中被广泛使用，因为它可以提高模型的性能并提高其泛化能力。

以下是一个多头注意力机制的示例代码：

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers

# 定义一个多头注意力层
class MultiHeadAttention(layers.Layer):
    def __init__(self, embed_dim, num_heads):
        super(MultiHeadAttention, self).__init__()
        self.num_heads = num_heads
        self.embed_dim = embed_dim
        if embed_dim % num_heads != 0:
            raise ValueError(
                f"embedding dimension = {embed_dim} should be divisible by number of heads = {num_heads}"
            )
        self.projection_dim = embed_dim // num_heads
        self.query_dense = layers.Dense(embed_dim)
        self.key_dense = layers.Dense(embed_dim)
        self.value_dense = layers.Dense(embed_dim)
        self.combine_heads = layers.Dense(embed_dim)

    def attention(self, query, key, value):
        score = tf.matmul(query, key, transpose_b=True)
        dim_key = tf.cast(tf.shape(key)[-1], tf.float32)
        scaled_score = score / tf.math.sqrt(dim_key)
        weights = tf.nn.softmax(scaled_score, axis=-1)
        output = tf.matmul(weights, value)
        return output, weights

    def separate_heads(self, x, batch_size):
        x = tf.reshape(x, (batch_size, -1, self.num_heads, self.projection_dim))
        return tf.transpose(x, perm=[0, 2, 1, 3])

    def call(self, inputs):
        # 获取输入
        query, key, value, mask = inputs["query"], inputs["key"], inputs["value"], inputs["mask"]
        batch_size = tf.shape(query)[0]

        # 将输入通过全连接层进行变换
        query = self.query_dense(query)
        key = self.key_dense(key)
        value = self.value_dense(value)

        # 将输入分成多个头部
        query = self.separate_heads(query, batch_size)
        key = self.separate_heads(key, batch_size)
        value = self.separate_heads(value, batch_size)

        # 计算注意力权重并将多个头部的结果合并
        attention, weights = self.attention(query, key, value)
        attention = tf.transpose(attention, perm=[0, 2, 1, 3])
        concat_attention = tf.reshape(attention, (batch_size, -1, self.embed_dim))
        output = self.combine_heads(concat_attention)

        return output