写一个基于tensorflow的Self-Attention层的代码

以下是基于 TensorFlow 的 Self-Attention 层的代码：

import tensorflow as tf

class SelfAttention(tf.keras.layers.Layer):
    def __init__(self, units):
        super(SelfAttention, self).__init__()
        self.W1 = tf.keras.layers.Dense(units)
        self.W2 = tf.keras.layers.Dense(units)
        self.V = tf.keras.layers.Dense(1)

    def call(self, inputs):
        # inputs shape: (batch_size, seq_len, embedding_dim)
        score = tf.nn.tanh(self.W1(inputs) + self.W2(inputs))
        attention_weights = tf.nn.softmax(self.V(score), axis=1)
        context_vector = attention_weights * inputs
        context_vector = tf.reduce_sum(context_vector, axis=1)
        return context_vector

希望这个代码能够帮助你！