GNN tensorflow 代码

图神经网络（GNN）在TensorFlow中通常用于处理和分析图结构数据，如社交网络、化学分子等。下面是一个简单的例子，展示如何使用TensorFlow的Eager Execution模式和Graph Neural Network Library (TF-GNN)构建一个基本的GNN模型：

# 首先安装必要的库
!pip install tensorflow-gnn

import tensorflow as tf
from tensorflow_gnn import graph_constants, modules, layers

# 假设我们有一个邻接矩阵和特征数据
adjacency = ... # 一个邻接矩阵，表示图的结构
node_features = ... # 一个二维数组，每个节点的特征向量

# 将数据转换为适合TF-GNN的格式
adjacency_tensor = tf.SparseTensor(adjacency['indices'], adjacency['values'], adjacency['dense_shape'])
node_sets = {'nodes': tf.RaggedTensor.from_row_lengths(node_features)}

# 定义GNN模型
class GNNModule(modules.GraphModule):
    def __init__(self, output_size):
        super(GNNModule, self).__init__()
        self.conv1 = layers.InteractionNetworkBlock(
            edge_model=layers.MessagePassingLayer(num_hidden=64),
            node_model=layers.MLP(output_size=output_size)
        )
        self.conv2 = layers.GraphNetworkBlock(
            edge_model=layers.MessagePassingLayer(num_hidden=64),
            node_model=layers.MLP(output_size=output_size)
        )

    def forward(self, inputs):
        node_features = inputs['nodes']
        updated_node_features = self.conv1([node_features, adjacency_tensor])
        updated_node_features = self.conv2([updated_node_features, adjacency_tensor])
        return {'nodes': updated_node_features}

# 创建并配置模型
model = GNNModule(output_size=32)
optimizer = tf.keras.optimizers.Adam(learning_rate=0.01)

# 假设我们有一个训练步骤
def train_step(inputs):
    with tf.GradientTape() as tape:
        predictions = model(inputs)
        loss = ... # 根据具体任务计算损失，例如交叉熵
    gradients = tape.gradient(loss, model.trainable_variables)
    optimizer.apply_gradients(zip(gradients, model.trainable_variables))
    return loss

# 执行训练循环
num_epochs = 10
for epoch in range(num_epochs):
    losses = []
    for batch_data in ...: # 你的数据迭代器
        losses.append(train_step(batch_data))
    print(f"Epoch {epoch+1}: Loss: {tf.reduce_mean(losses)}")