keras实现multi-head self-attention代码

以下是使用 Keras 实现 Multi-Head Self-Attention 的代码示例：

import tensorflow as tf
from tensorflow.keras.layers import Layer, Dense, Dropout, LayerNormalization

class MultiHeadSelfAttention(Layer):
    def __init__(self, num_heads, head_size, dropout_rate=0.1, **kwargs):
        super(MultiHeadSelfAttention, self).__init__(**kwargs)
        self.num_heads = num_heads
        self.head_size = head_size
        self.dropout_rate = dropout_rate

        self.query_dense = Dense(num_heads * head_size, use_bias=False)
        self.key_dense = Dense(num_heads * head_size, use_bias=False)
        self.value_dense = Dense(num_heads * head_size, use_bias=False)
        self.dropout = Dropout(dropout_rate)
        self.output_dense = Dense(head_size, use_bias=False)
        self.layer_norm = LayerNormalization()

    def call(self, inputs):
        q = self.query_dense(inputs)
        k = self.key_dense(inputs)
        v = self.value_dense(inputs)

        q = tf.reshape(q, [-1, tf.shape(q)[1], self.num_heads, self.head_size])
        k = tf.reshape(k, [-1, tf.shape(k)[1], self.num_heads, self.head_size])
        v = tf.reshape(v, [-1, tf.shape(v)[1], self.num_heads, self.head_size])

        q = tf.transpose(q, [0, 2, 1, 3])
        k = tf.transpose(k, [0, 2, 1, 3])
        v = tf.transpose(v, [0, 2, 1, 3])

        attention_scores = tf.matmul(q, k, transpose_b=True)
        attention_scores = attention_scores / tf.math.sqrt(tf.cast(self.head_size, tf.float32))
        attention_probs = tf.nn.softmax(attention_scores, axis=-1)
        attention_probs = self.dropout(attention_probs)

        context = tf.matmul(attention_probs, v)
        context = tf.transpose(context, [0, 2, 1, 3])
        context = tf.reshape(context, [-1, tf.shape(context)[1], self.num_heads * self.head_size])

        output = self.output_dense(context)
        output = self.dropout(output)
        output = self.layer_norm(inputs + output)

        return output

这个类可以用于 Transformer 等模型中。