生成自注意力模型的tensorflow 代码
时间: 2024-05-05 08:19:46 浏览: 17
以下是使用TensorFlow 2.x生成自注意力模型的示例代码:
```python
import tensorflow as tf
class SelfAttention(tf.keras.layers.Layer):
def __init__(self, num_heads, head_size):
super(SelfAttention, self).__init__()
self.num_heads = num_heads
self.head_size = head_size
self.total_size = num_heads * head_size
self.query_dense = tf.keras.layers.Dense(units=self.total_size, name='query')
self.key_dense = tf.keras.layers.Dense(units=self.total_size, name='key')
self.value_dense = tf.keras.layers.Dense(units=self.total_size, name='value')
self.concat_projection = tf.keras.layers.Dense(units=self.total_size, name='output')
def split_heads(self, inputs, batch_size):
inputs = tf.reshape(inputs, shape=(batch_size, -1, self.num_heads, self.head_size))
return tf.transpose(inputs, 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.split_heads(query, batch_size)
key = self.split_heads(key, batch_size)
value = self.split_heads(value, batch_size)
scaled_attention = tf.matmul(query, key, transpose_b=True)
scaled_attention = scaled_attention / tf.math.sqrt(tf.cast(self.head_size, dtype=tf.float32))
attention_weights = tf.nn.softmax(scaled_attention, axis=-1)
output = tf.matmul(attention_weights, value)
output = tf.transpose(output, perm=[0, 2, 1, 3])
output = tf.reshape(output, shape=(batch_size, -1, self.total_size))
output = self.concat_projection(output)
return output
```
在这个自注意力层中,我们使用了一个完全连接的层来生成查询、键和值,然后将它们分成多个头部。接下来,我们计算了每个头部的注意力权重,并将它们加权求和得到输出向量。最后,我们将每个头部的输出向量连接到一起,并将其投影到我们期望的输出大小。