def __init__(self):
super(Net, self).__init__()
self.conv1 = GraphConv(dataset.num_features, 32)
self.conv2 = GraphConv(32, 64)
self.conv3 = GraphConv(64, 64)
self.fc1 = torch.nn.Linear(64, 64)
self.fc2 = torch.nn.Linear(64, 32)
self.fc3 = torch.nn.Linear(32, dataset.num_classes)
def __init__(self):
super(Net, self).__init__()
# This is the update step
self.conv1 = GraphConv(dataset.num_features, 32)
self.conv2 = GraphConv(32, 64)
self.conv3 = GraphConv(64, 64)
# The transforms the update step into something usable
self.fc1 = torch.nn.Linear(64, 64)
self.fc2 = torch.nn.Linear(64, 32)
self.fc3 = torch.nn.Linear(32, dataset.num_classes)
def __init__(self):
super(Net, self).__init__()
M_in, M_out = dataset.num_features, 32
nn1 = Sequential(Linear(5, 128), ReLU(), Linear(128, M_in * M_out))
self.conv1 = NNConv(M_in, M_out, nn1)
M_in, M_out = M_out, 64
nn2 = Sequential(Linear(5, 128), ReLU(), Linear(128, M_in * M_out))
self.conv2 = NNConv(M_in, M_out, nn2)
M_in, M_out = M_out, 64
nn3 = Sequential(Linear(5, 128), ReLU(), Linear(128, M_in * M_out))
self.conv3 = NNConv(M_in, M_out, nn3)
self.conv6 = GraphConv(64 + num_i_3, 64)
self.conv7 = GraphConv(64, 64)
self.fc1 = torch.nn.Linear(2 * 64, 64)
self.fc2 = torch.nn.Linear(64, 32)
self.fc3 = torch.nn.Linear(32, 1)
def __init__(self,config,args):
super(Net, self).__init__()
self.conv1 = GraphConv(config.num_features, 64)
self.conv2 = GraphConv(64, 64)
self.conv3 = GraphConv(64, 64)
self.conv4 = GraphConv(64 + config.num_i_2, 64)
self.conv5 = GraphConv(64, 64)
self.conv6 = GraphConv(64 + config.num_i_3, 64)
self.conv7 = GraphConv(64, 64)
self.fc1 = torch.nn.Linear(3 * 64, 64)
self.fc2 = torch.nn.Linear(64, 32)
self.fc3 = torch.nn.Linear(32, 1)
if args.type=="TU":
self.fc3 = torch.nn.Linear(32, config.num_classes)
self.args=args
self.sigmoid = nn.Sigmoid()