GAT tensorflow 2.0代码有图
时间: 2023-07-03 17:13:31 浏览: 83
以下是使用TensorFlow 2.0实现GAT(Graph Attention Network)的代码:
``` python
import tensorflow as tf
from tensorflow.keras import layers
class GAT(layers.Layer):
def __init__(self, units, num_heads, activation='relu'):
super(GAT, self).__init__()
self.units = units
self.num_heads = num_heads
self.activation = activation
self.W = []
self.attention = []
for i in range(self.num_heads):
self.W.append(layers.Dense(units))
self.attention.append(layers.Dense(1))
self.dropout = layers.Dropout(0.5)
self.add = layers.Add()
def call(self, inputs):
# inputs shape: (batch_size, num_nodes, input_dim)
h = inputs
outputs = []
for i in range(self.num_heads):
Wh = self.W[i](h)
a = self.attention[i](Wh)
e = tf.nn.leaky_relu(a)
alpha = tf.nn.softmax(e, axis=1)
alpha = self.dropout(alpha)
h_prime = tf.matmul(alpha, Wh, transpose_a=True)
outputs.append(h_prime)
if self.num_heads > 1:
h_prime = self.add(outputs)
else:
h_prime = outputs[0]
if self.activation is not None:
h_prime = tf.nn.relu(h_prime)
return h_prime
```
该代码定义了一个GAT层,包含`units`个神经元和`num_heads`个头。在每个头中,会通过全连接层`W`和注意力层`attention`得到一个注意力系数`alpha`,然后将`alpha`和输入`h`做矩阵乘法和加权求和,得到新的节点表示`h_prime`。最后,将所有头得到的`h_prime`加起来,得到最终的输出。在计算`alpha`时,使用了LeakyReLU激活函数和softmax归一化。为了防止过拟合,还使用了dropout。