def __init__(self, hete, hidden_size, dropout):
super(HeteroNet, self).__init__()
conv1, conv2 = generate_convs(
hete, HeteroSAGEConv, hidden_size, task='link_pred'
)
self.conv1 = HeteroConv(conv1)
self.conv2 = HeteroConv(conv2)
self.loss_fn = torch.nn.BCEWithLogitsLoss()
self.dropout = dropout
def __init__(self, hete, hidden_size, dropout):
super(HeteroNet, self).__init__()
conv1, conv2 = generate_convs(hete, HeteroSAGEConv, hidden_size)
self.conv1 = HeteroConv(conv1)
self.conv2 = HeteroConv(conv2)
self.relus1 = nn.ModuleDict()
self.relus2 = nn.ModuleDict()
self.dropouts1 = nn.ModuleDict()
self.dropouts2 = nn.ModuleDict()
for node_type in hete.node_types:
self.relus1[node_type] = nn.LeakyReLU()
self.relus2[node_type] = nn.LeakyReLU()
self.dropouts1[node_type] = nn.Dropout(p=dropout)
self.dropouts2[node_type] = nn.Dropout(p=dropout)
def __init__(self, hete, hidden_size, dropout):
super(HeteroNet, self).__init__()
conv1, conv2 = generate_convs(hete,
HeteroSAGEConv,
hidden_size,
task='link_pred')
self.conv1 = HeteroConv(conv1)
self.conv2 = HeteroConv(conv2)
self.loss_fn = torch.nn.BCEWithLogitsLoss()
self.dropout1 = nn.ModuleDict()
self.relu1 = nn.ModuleDict()
self.dropout2 = nn.ModuleDict()
self.relu2 = nn.ModuleDict()
for node_type in hete.node_types:
self.dropout1[node_type] = nn.Dropout(p=dropout)
self.dropout2[node_type] = nn.Dropout(p=dropout)
self.relu1[node_type] = nn.LeakyReLU()
self.relu2[node_type] = nn.LeakyReLU()
def __init__(self, hete, hidden_size, dropout):
super(HeteroNet, self).__init__()
self.dropout = dropout
conv1, conv2 = generate_convs(hete, HeteroSAGEConv, hidden_size)
self.conv1 = HeteroConv(conv1)
self.conv2 = HeteroConv(conv2)