def __init__(self, g, in_feats, n_hidden, n_classes, n_layers, activation,
dropout):
super(GCN, self).__init__()
self.g = g
self.layers = gluon.nn.Sequential()
# input layer
self.layers.add(GraphConv(in_feats, n_hidden, activation=activation))
# hidden layers
for i in range(n_layers - 1):
self.layers.add(
GraphConv(n_hidden, n_hidden, activation=activation))
# output layer
self.layers.add(GraphConv(n_hidden, n_classes))
self.dropout = gluon.nn.Dropout(rate=dropout)
def __init__(self,
embedding_weights,
output_classes_num,
dropout=0.0,
gcn_hidden_size=30,
dense_hidden_size=20,
trainable_embeddings=False,
num_layers=3,
**kwargs):
super(GCNNet, self).__init__(**kwargs)
with self.name_scope():
self.dropout = mx.gluon.nn.Dropout(dropout)
# Embedding shape for number of features in the
# first graph convolution.
emb_in_dim = embedding_weights.shape[1]
# Given the sparse documentation for
# DGL + MXNet we store the word embeddings
# directly as an mxnet.nd matrix
# rather than a trainable embedding layer
self.embeddings = embedding_weights
self.gcn_span_a = nn.Sequential()
self.gcn_span_b = nn.Sequential()
'''
self.gcn_path = nn.Sequential()
'''
self.gcn_main = nn.Sequential()
# Set up initial layer for
# each graph
self.gcn_span_a.add(
GraphConv(
emb_in_dim,
gcn_hidden_size,
# num_heads = 1,
activation=mx.nd.relu))
self.gcn_span_b.add(
GraphConv(
emb_in_dim,
gcn_hidden_size,
# num_heads = 1,
activation=mx.nd.relu))
'''
self.gcn_path.add(
GraphConv(
emb_in_dim,
gcn_hidden_size,
# num_heads = 1,
activation = mx.nd.relu
)
)
'''
self.gcn_main.add(
GraphConv(
emb_in_dim,
gcn_hidden_size,
# num_heads = 1,
activation=mx.nd.relu))
# Add additional convolution layers for each graph
for i in range(num_layers):
self.gcn_span_a.add(
GraphConv(
gcn_hidden_size,
gcn_hidden_size,
# num_heads = 1,
activation=mx.nd.relu))
self.gcn_span_b.add(
GraphConv(
gcn_hidden_size,
gcn_hidden_size,
# num_heads = 1,
activation=mx.nd.relu))
'''
self.gcn_path.add(
GraphConv(
gcn_hidden_size,
gcn_hidden_size,
# num_heads = 1,
activation = mx.nd.relu
)
)
'''
self.gcn_main.add(
GraphConv(
gcn_hidden_size,
gcn_hidden_size,
# num_heads = 1,
activation=mx.nd.relu))
# For condensing all the different GCN outputs
self.dense_collect = mx.gluon.nn.Dense(units=dense_hidden_size,
activation='relu')
# Final dense classification layer
self.dense_out = mx.gluon.nn.Dense(units=output_classes_num, )