cross attention代码

### 关于Cross Attention的代码实现 在处理跨模态数据或多源信息融合的任务时，cross attention机制能够有效地捕捉不同序列间的关系。下面是一个基于TensorFlow框架下的cross attention模块的具体实现方法[^2]。 ```python import tensorflow as tf class CrossAttention(tf.keras.layers.Layer): def __init__(self, d_model, num_heads): super(CrossAttention, self).__init__() self.num_heads = num_heads self.d_model = d_model assert d_model % self.num_heads == 0 self.depth = d_model // self.num_heads self.wq = tf.keras.layers.Dense(d_model) self.wk = tf.keras.layers.Dense(d_model) self.wv = tf.keras.layers.Dense(d_model) self.dense = tf.keras.layers.Dense(d_model) def split_heads(self, x, batch_size): """分头操作""" x = tf.reshape(x, (batch_size, -1, self.num_heads, self.depth)) return tf.transpose(x, perm=[0, 2, 1, 3]) def call(self, v, k, q, mask=None): batch_size = tf.shape(q)[0] q = self.wq(q) k = self.wk(k) v = self.wv(v) q = self.split_heads(q, batch_size) k = self.split_heads(k, batch_size) v = self.split_heads(v, batch_size) scaled_attention, _ = self.scaled_dot_product_attention(q, k, v, mask=mask) scaled_attention = tf.transpose(scaled_attention, perm=[0, 2, 1, 3]) concat_attention = tf.reshape(scaled_attention,(batch_size, -1, self.d_model)) output = self.dense(concat_attention) return output @staticmethod def scaled_dot_product_attention(q, k, v, mask=None): matmul_qk = tf.matmul(q, k, transpose_b=True) dk = tf.cast(tf.shape(k)[-1], tf.float32) scaled_attention_logits = matmul_qk / tf.math.sqrt(dk) if mask is not None: scaled_attention_logits += (mask * -1e9) attention_weights = tf.nn.softmax(scaled_attention_logits, axis=-1) output = tf.matmul(attention_weights, v) return output, attention_weights ``` 上述代码定义了一个`CrossAttention`类来完成cross attention的功能。通过线性变换将查询(query)，键(key)以及值(value)映射到相同维度空间下，并采用多头机制提高模型表达能力。scaled dot-product attention部分实现了对齐权重计算过程中的缩放和平滑处理，使得梯度更加稳定。