dmlc--dgl
Hongzhi (Steve), Chen
704bcaf6dd
Co-authored-by: Ubuntu <ubuntu@ip-172-31-28-63.ap-northeast-1.compute.internal>
41 行
1.1 KiB
Python
41 行
1.1 KiB
Python
"""GCN using DGL nn package
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References:
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- Semi-Supervised Classification with Graph Convolutional Networks
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- Paper: https://arxiv.org/abs/1609.02907
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- Code: https://github.com/tkipf/gcn
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"""
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import tensorflow as tf
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from dgl.nn.tensorflow import GraphConv
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from tensorflow.keras import layers
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class GCN(tf.keras.Model):
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def __init__(
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self, g, in_feats, n_hidden, n_classes, n_layers, activation, dropout
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):
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super(GCN, self).__init__()
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self.g = g
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self.layer_list = []
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# input layer
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self.layer_list.append(
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GraphConv(in_feats, n_hidden, activation=activation)
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)
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# hidden layers
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for i in range(n_layers - 1):
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self.layer_list.append(
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GraphConv(n_hidden, n_hidden, activation=activation)
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)
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# output layer
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self.layer_list.append(GraphConv(n_hidden, n_classes))
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self.dropout = layers.Dropout(dropout)
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def call(self, features):
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h = features
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for i, layer in enumerate(self.layer_list):
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if i != 0:
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h = self.dropout(h)
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h = layer(self.g, h)
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return h
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