如何用tensorflow定义一个Multi-head Self-attention模块,然后调用
时间: 2024-03-15 14:45:44 浏览: 36
以下是一个完整的 TensorFlow 2.x 实现示例,包括定义 Multi-head Self-attention 模块和调用它进行样例数据的前向计算。
```
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("embedding dimension must be divisible by number of 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
# 构造一个 Multi-head Self-attention 模块对象
attention_layer = MultiHeadSelfAttention(embed_dim=256, num_heads=8)
# 构造一个样例输入
inputs = tf.random.normal(shape=(32, 50, 256))
# 前向计算
outputs = attention_layer(inputs)
print(outputs.shape)
```
在这个示例中,我们首先定义了 Multi-head Self-attention 模块。然后,我们构造了一个该模块的对象 attention_layer,并将输入样例 inputs 传递给它进行前向计算。最后,我们打印输出张量的形状,以检查前向计算结果是否正确。
输出结果应该类似于:
```
(32, 50, 256)
```
其中,第一个维度表示输入样例的批次大小,第二个维度表示输入序列的长度,第三个维度表示每个输入向量的维度。
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