def __init__(self, in_feats, out_feats, normalize=False, aggr="mean"): super(GraphSAGELayer, self).__init__() self.in_feats = in_feats self.out_feats = out_feats self.normalize = normalize if aggr == "mean": self.aggr = MeanAggregator(in_feats, out_feats) elif aggr == "sum": self.aggr = SumAggregator(in_feats, out_feats) else: raise NotImplementedError
def __init__(self, num_features, num_classes, hidden_size, num_layers, sample_size, dropout): super(Graphsage, self).__init__() self.adjlist = {} self.num_features = num_features self.num_classes = num_classes self.hidden_size = hidden_size self.num_layers = num_layers self.sample_size = sample_size self.dropout = dropout shapes = [num_features] + hidden_size + [num_classes] print(shapes) self.convs = nn.ModuleList([ MeanAggregator(shapes[layer], shapes[layer + 1], cached=True) for layer in range(num_layers) ])
def __init__(self, in_feats, out_feats): super(GraphSAGELayer, self).__init__() self.in_feats = in_feats self.out_feats = out_feats self.aggr = MeanAggregator(in_feats, out_feats, cached=True)