def __init__(
self,
initial_size,
output_size,
middle_size_1,
middle_size_2,
num_nodes,
num_relations,
device1,
device2,
):
super().__init__()
self.device1 = device1
self.device2 = device2
self.emb = nn.parameter.Parameter(torch.ones(
(num_nodes, initial_size)),
requires_grad=True).to(device1)
glorot_orthogonal(self.emb, 1)
self.conv1 = gnn.RGCNConv(initial_size,
middle_size_1,
num_relations,
num_bases=12).to(device1)
self.conv2 = gnn.RGCNConv(middle_size_1,
middle_size_2,
num_relations,
num_bases=12).to(device1)
self.conv3 = gnn.RGCNConv(
middle_size_2,
output_size - middle_size_2 - middle_size_1 - initial_size,
num_relations,
num_bases=12,
).to(device1)
self.drop = nn.Dropout(0.2)
def __init__(self, initial_size, num_nodes, num_relations, *args,
**kwargs):
super().__init__()
self.emb = nn.parameter.Parameter(torch.ones(
(num_nodes, initial_size)),
requires_grad=True)
glorot_orthogonal(self.emb, 1)
self.drop = nn.Dropout(0.2)
def reset_parameters(self):
glorot_orthogonal(self.lin_rbf.weight, scale=2.0)
glorot_orthogonal(self.lin_sbf.weight, scale=2.0)
glorot_orthogonal(self.lin_kj.weight, scale=2.0)
self.lin_kj.bias.data.fill_(0)
glorot_orthogonal(self.lin_ji.weight, scale=2.0)
self.lin_ji.bias.data.fill_(0)
self.W.data.normal_(mean=0, std=2 / self.W.size(0))
for res_layer in self.layers_before_skip:
res_layer.reset_parameters()
glorot_orthogonal(self.lin.weight, scale=2.0)
self.lin.bias.data.fill_(0)
for res_layer in self.layers_after_skip:
res_layer.reset_parameters()
def reset_parameters(self):
glorot_orthogonal(self.lin_rbf.weight, scale=2.0)
glorot_orthogonal(self.lin_up.weight, scale=2.0)
for lin in self.lins:
glorot_orthogonal(lin.weight, scale=2.0)
lin.bias.data.fill_(0)
self.lin.weight.data.fill_(0)
def reset_parameters(self):
glorot_orthogonal(self.lin_up.weight, scale=2.0)
for lin in self.lins:
glorot_orthogonal(lin.weight, scale=2.0)
lin.bias.data.fill_(0)
if self.output_init == 'zeros':
self.lin.weight.data.fill_(0)
if self.output_init == 'GlorotOrthogonal':
glorot_orthogonal(self.lin.weight, scale=2.0)
def reset_parameters(self):
glorot_orthogonal(self.lin_rbf1.weight, scale=2.0)
glorot_orthogonal(self.lin_rbf2.weight, scale=2.0)
glorot_orthogonal(self.lin_sbf1.weight, scale=2.0)
glorot_orthogonal(self.lin_sbf2.weight, scale=2.0)
glorot_orthogonal(self.lin_kj.weight, scale=2.0)
self.lin_kj.bias.data.fill_(0)
glorot_orthogonal(self.lin_ji.weight, scale=2.0)
self.lin_ji.bias.data.fill_(0)
glorot_orthogonal(self.lin_down.weight, scale=2.0)
glorot_orthogonal(self.lin_up.weight, scale=2.0)
for res_layer in self.layers_before_skip:
res_layer.reset_parameters()
glorot_orthogonal(self.lin.weight, scale=2.0)
self.lin.bias.data.fill_(0)
for res_layer in self.layers_before_skip:
res_layer.reset_parameters()
def reset_parameters(self):
glorot_orthogonal(self.lin1.weight, scale=2.0)
self.lin1.bias.data.fill_(0)
glorot_orthogonal(self.lin2.weight, scale=2.0)
self.lin2.bias.data.fill_(0)
def reset_parameters(self):
self.emb.weight.data.uniform_(-sqrt(3), sqrt(3))
self.lin_rbf_0.reset_parameters()
self.lin.reset_parameters()
glorot_orthogonal(self.lin_rbf_1.weight, scale=2.0)
def __init__(self, input_size, num_relations):
super().__init__()
self.rel = nn.parameter.Parameter(torch.ones(
(num_relations, input_size)),
requires_grad=True)
glorot_orthogonal(self.rel, 1)
