class Net(torch.nn.Module):
def __init__(self, dataset):
super(Net, self).__init__()
self.conv1 = ARMAConv(dataset.num_features,
args.hidden,
args.num_stacks,
args.num_layers,
args.shared_weights,
dropout=args.skip_dropout)
self.conv2 = ARMAConv(args.hidden,
dataset.num_classes,
args.num_stacks,
args.num_layers,
args.shared_weights,
dropout=args.skip_dropout)
def reset_parameters(self):
self.conv1.reset_parameters()
self.conv2.reset_parameters()
def forward(self, data):
x, edge_index = data.x, data.edge_index
x = F.relu(self.conv1(x, edge_index))
x = F.dropout(x, p=args.dropout, training=self.training)
x = self.conv2(x, edge_index)
return F.log_softmax(x, dim=1)
class ARMA_Net(torch.nn.Module):
def __init__(self, features_num, num_class, hidden, num_stacks, num_layers,
shared_weights, dropout, skip_dropout):
super(ARMA_Net, self).__init__()
self.dropout = dropout
self.conv1 = ARMAConv(features_num,
hidden,
num_stacks,
num_layers,
shared_weights,
dropout=skip_dropout)
self.conv2 = ARMAConv(hidden,
num_class,
num_stacks,
num_layers,
shared_weights,
dropout=skip_dropout)
def reset_parameters(self):
self.conv1.reset_parameters()
self.conv2.reset_parameters()
def forward(self, data):
x, edge_index = data.x, data.edge_index
x = F.relu(self.conv1(x, edge_index))
x = F.dropout(x, p=self.dropout, training=self.training)
x = self.conv2(x, edge_index)
return F.log_softmax(x, dim=-1)
def __repr__(self):
return self.__class__.__name__