dmlc--dgl
VoVAllen
a00636a02b
* several nn example * appnp * fix lint * lint * add dgi * fix * fix * fix * fff * docs * 111 * fix * change init * change result * tiaocan+1 * fix * fix lint * fix * fix
40 行
1.2 KiB
Python
40 行
1.2 KiB
Python
"""GCN using DGL nn package
|
|
|
|
References:
|
|
- Semi-Supervised Classification with Graph Convolutional Networks
|
|
- Paper: https://arxiv.org/abs/1609.02907
|
|
- Code: https://github.com/tkipf/gcn
|
|
"""
|
|
import tensorflow as tf
|
|
from tensorflow.keras import layers
|
|
from dgl.nn.tensorflow import GraphConv
|
|
|
|
class GCN(tf.keras.Model):
|
|
def __init__(self,
|
|
g,
|
|
in_feats,
|
|
n_hidden,
|
|
n_classes,
|
|
n_layers,
|
|
activation,
|
|
dropout):
|
|
super(GCN, self).__init__()
|
|
self.g = g
|
|
self.layer_list = []
|
|
# input layer
|
|
self.layer_list.append(GraphConv(in_feats, n_hidden, activation=activation))
|
|
# hidden layers
|
|
for i in range(n_layers - 1):
|
|
self.layer_list.append(GraphConv(n_hidden, n_hidden, activation=activation))
|
|
# output layer
|
|
self.layer_list.append(GraphConv(n_hidden, n_classes))
|
|
self.dropout = layers.Dropout(dropout)
|
|
|
|
def call(self, features):
|
|
h = features
|
|
for i, layer in enumerate(self.layer_list):
|
|
if i != 0:
|
|
h = self.dropout(h)
|
|
h = layer(self.g, h)
|
|
return h
|