keras实现multi-head self-attention代码
时间: 2023-06-21 19:11:08 浏览: 182
以下是使用Keras实现multi-head self-attention的代码:
```python
import tensorflow as tf
from tensorflow.keras.layers import Layer, Dense, Dropout, LayerNormalization
class MultiHeadAttention(Layer):
def __init__(self, num_heads, embedding_dim, dropout_rate=0.1):
super(MultiHeadAttention, self).__init__()
self.num_heads = num_heads
self.embedding_dim = embedding_dim
self.dropout_rate = dropout_rate
assert embedding_dim % self.num_heads == 0
self.depth = embedding_dim // self.num_heads
self.query_dense = Dense(embedding_dim)
self.key_dense = Dense(embedding_dim)
self.value_dense = Dense(embedding_dim)
self.dropout = Dropout(dropout_rate)
self.output_dense = Dense(embedding_dim)
def split_heads(self, x, batch_size):
x = tf.reshape(x, (batch_size, -1, self.num_heads, self.depth))
return tf.transpose(x, perm=[0, 2, 1, 3])
def call(self, inputs):
query, key, value, mask = inputs['query'], inputs['key'], inputs['value'], inputs['mask']
batch_size = tf.shape(query)[0]
query = self.query_dense(query)
key = self.key_dense(key)
value = self.value_dense(value)
query = self.split_heads(query, batch_size)
key = self.split_heads(key, batch_size)
value = self.split_heads(value, batch_size)
scaled_attention_logits = tf.matmul(query, key, transpose_b=True)
scaled_attention_logits /= tf.math.sqrt(tf.cast(self.depth, tf.float32))
if mask is not None:
mask = tf.cast(mask, tf.float32)
mask = tf.expand_dims(mask, axis=1)
scaled_attention_logits += (mask * -1e9)
attention_weights = tf.nn.softmax(scaled_attention_logits, axis=-1)
attention_weights = self.dropout(attention_weights)
output = tf.matmul(attention_weights, value)
output = tf.transpose(output, perm=[0, 2, 1, 3])
concat_output = tf.reshape(output, (batch_size, -1, self.embedding_dim))
output = self.output_dense(concat_output)
return output
```
在上面的代码中,我们定义了一个名为MultiHeadAttention的自定义Keras层,该层实现了multi-head self-attention。这个层接收四个输入:query,key,value和mask。query,key和value都是张量,而mask是可选的,用于掩盖不相关的序列元素。
在call方法中,我们首先将query、key和value分别传递到三个密集层中。然后,我们将每个张量分成num_heads个头,通过对头进行矩阵乘法,计算每个头的注意力权重。注意力权重被应用于value,然后我们将每个头的输出拼接在一起,并通过一个密集层进行传递。最后,我们返回输出张量。
我们还实现了一个split_heads方法,该方法将张量沿着深度维度分成num_heads个头,并将其转置以便于计算注意力权重。
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