tensorflow的图卷积神经网络的代码Python

以下是一个简单的图卷积神经网络的 TensorFlow 2.0 实现：

import tensorflow as tf
from tensorflow.keras.layers import Input, Dense, Dropout
from tensorflow.keras.models import Model
from tensorflow.keras.optimizers import Adam

class GraphConvolution(tf.keras.layers.Layer):
    def __init__(self, output_dim):
        super(GraphConvolution, self).__init__()
        self.output_dim = output_dim
    
    def build(self, input_shape):
        self.kernel = self.add_weight(name='kernel',
                                      shape=(input_shape[-1], self.output_dim),
                                      initializer='glorot_uniform',
                                      trainable=True)
    
    def call(self, inputs):
        features, adj = inputs
        output = tf.matmul(adj, features)
        output = tf.matmul(output, self.kernel)
        return output

class GCNModel(tf.keras.models.Model):
    def __init__(self, input_dim, hidden_dim, output_dim, dropout_rate):
        super(GCNModel, self).__init__()
        self.input_layer = Input(shape=(input_dim,))
        self.dropout_layer = Dropout(rate=dropout_rate)(self.input_layer)
        self.hidden_layer = GraphConvolution(hidden_dim)([self.dropout_layer, adj])
        self.hidden_layer = tf.nn.relu(self.hidden_layer)
        self.dropout_layer = Dropout(rate=dropout_rate)(self.hidden_layer)
        self.output_layer = GraphConvolution(output_dim)([self.dropout_layer, adj])
        self.output_layer = tf.nn.softmax(self.output_layer, axis=-1)
        self.model = Model(inputs=self.input_layer, outputs=self.output_layer)
    
    def call(self, inputs):
        return self.model(inputs)

# 定义超参数
input_dim = adj.shape[0]
hidden_dim = 16
output_dim = 7
dropout_rate = 0.5
learning_rate = 0.01
epochs = 200

# 定义模型、优化器和损失函数
model = GCNModel(input_dim, hidden_dim, output_dim, dropout_rate)
optimizer = Adam(lr=learning_rate)
loss_fn = tf.keras.losses.SparseCategoricalCrossentropy()

# 训练模型
for epoch in range(epochs):
    with tf.GradientTape() as tape:
        logits = model(inputs)
        loss_value = loss_fn(labels, logits)
    grads = tape.gradient(loss_value, model.trainable_variables)
    optimizer.apply_gradients(zip(grads, model.trainable_variables))
    
    if epoch % 10 == 0:
        print(f"Epoch {epoch}: Loss = {loss_value.numpy()}")

在这个实现中，`GraphConvolution` 是一个自定义层，用于实现图卷积操作。`GCNModel` 是一个自定义模型，由两个 `GraphConvolution` 层和一个 Dropout 层组成。在训练过程中，我们使用 Adam 优化器和交叉熵损失函数。