keras实现MHSA

MHSA (Multi-Head Self-Attention) 是一种在自然语言处理 (NLP) 任务中常用的注意力机制。它可以用于基于序列数据的模型，例如机器翻译、情感分析等。

在 Keras 中实现 MHSA 的方法如下：

1. 安装必要的库：

pip install tensorflow
pip install keras

2. 导入所需的库：

from keras.layers import Layer
from keras import backend as K

3. 创建 MHSA 层的类，并实现 `__init__` 和 `call` 方法：

class MultiHeadSelfAttention(Layer):
    def __init__(self, embed_dim, num_heads=8):
        super(MultiHeadSelfAttention, self).__init__()
        self.embed_dim = embed_dim
        self.num_heads = num_heads
        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 = Dense(embed_dim)
        self.key_dense = Dense(embed_dim)
        self.value_dense = Dense(embed_dim)
        self.combine_heads = Dense(embed_dim)

    def attention(self, query, key, value):
        score = K.batch_dot(query, key, axes=2)
        dim_key = K.shape(key)[-1]
        scaled_score = score / K.sqrt(K.cast(dim_key, dtype=K.floatx()))
        weights = K.softmax(scaled_score, axis=-1)
        output = K.batch_dot(weights, value, axes=2)
        return output, weights

    def separate_heads(self, x, batch_size):
        x = K.reshape(x, (batch_size, -1, self.num_heads, self.projection_dim))
        return K.permute_dimensions(x, (0, 2, 1, 3))

    def call(self, inputs):
        # x.shape = [batch_size, seq_len, embedding_dim]
        query, key, value = inputs[:3]
        batch_size = K.shape(query)[0]

        # linear layers
        query = self.query_dense(query)
        key = self.key_dense(key)
        value = self.value_d