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)