keras实现multi-head self-attention代码
时间: 2023-06-21 17:22:53 浏览: 61
以下是使用 Keras 实现 Multi-Head Self-Attention 的代码示例:
```python
import tensorflow as tf
from tensorflow.keras import layers
class MultiHeadSelfAttention(layers.Layer):
def __init__(self, embed_dim, num_heads):
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} must 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):
batch_size = tf.shape(inputs)[0]
query = self.query_dense(inputs)
key = self.key_dense(inputs)
value = self.value_dense(inputs)
query = self.separate_heads(query, batch_size)
key = self.separate_heads(key, batch_size)
value = self.separate_heads(value, batch_size)
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
```
上面的代码实现了一个 Multi-Head Self-Attention 层,其中,`embed_dim` 代表输入向量的维度,`num_heads` 代表头数。在 `call` 方法中,我们首先通过 `query_dense`、`key_dense` 和 `value_dense` 层对输入进行线性变换,然后将其分成多个头,并进行 Attention 计算。最后,我们通过 `combine_heads` 层将多头的输出合并,得到最终的输出。