GCN Model

gcn_model.py

from dgl.nn.tensorflow import GraphConv

class GCN(tf.keras.Model):
    def __init__(self, g, feat_dim, hidden_dim, class_num):
        super(GCN, self).__init__()
        self.g = g
        self.h1 = GraphConv(feat_dim, hidden_dim, activation=tf.nn.relu, allow_zero_in_degree=True)
        self.dropout = tf.keras.layers.Dropout(0.5)
        self.h2 = GraphConv(hidden_dim, class_num), allow_zero_in_degree=True
    
    def call(self, features):
        h = features
        h = self.h1(self.g, h)
        h = self.dropout(h)
        h = self.h2(self.g, h)
        return h

g_model = GCN(g, 1433, 16, 7)
loss_fcn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
opt = tf.keras.optimizers.Adam(0.01)