def __init__(self, graphs, coos):
super(NetTGCNBasic, self).__init__()
f1, g1, k1, h1 = 1, 32, 25, 12
self.conv1 = ChebTimeConv(f1, g1, K=k1, H=h1)
n1 = graphs[0].shape[0]
self.fc1 = torch.nn.Linear(n1 * g1, 10)
self.coos = coos
def __init__(self, mat_size):
super(NetTGCNBasic, self).__init__()
f1, g1, k1, h1 = 1, 64, 25, 15
self.conv1 = ChebTimeConv(f1, g1, K=k1, H=h1)
n2 = mat_size
c = 6
self.fc1 = torch.nn.Linear(int(n2 * g1), c)
self.coos = None
self.perm = None
def __init__(self, graphs, coos):
super(NetTGCN, self).__init__()
f1, g1, k1, h1 = 1, 32, 25, 30
#f1, g1, k1 = 1, 32, 25
self.conv1 = ChebTimeConv(f1, g1, K=k1, H=h1)
f2, g2, k2 = 32, 64, 25
self.conv2 = ChebConv(f2, g2, K=k2)
n2 = graphs[2].shape[0]
#self.fc1 = torch.nn.Linear(n1 * g1, 10)
d = 512
self.fc1 = torch.nn.Linear(int(n2 * g2), d)
# self.drop = nn.Dropout(0)
c = 10
self.fc2 = torch.nn.Linear(d, c)
self.coos = coos
def __init__(self, graphs, coos):
super(NetTGCN, self).__init__()
f1, g1, k1, h1 = 1, 32, 25, 15
self.conv1 = ChebTimeConv(f1, g1, K=k1, H=h1)
#self.drop1 = nn.Dropout(0.1)
g2, k2 = 64, 25
self.conv2 = ChebConv(g1, g2, K=k2)
n2 = graphs[0].shape[0]
c = 512
self.fc1 = torch.nn.Linear(int(n2 * g2), c)
#self.dense1_bn = nn.BatchNorm1d(d)
#self.drop2 = nn.Dropout(0.5)
d = 6
self.fc2 = torch.nn.Linear(c, d)
self.coos = coos