SAGEConv的hidden_feats指的是什么
时间: 2023-06-24 15:04:44 浏览: 48
SAGEConv是一种图神经网络中的卷积层,用于学习节点的表示。其中,hidden_feats指的是SAGEConv层输出的节点表示的维度大小,即每个节点在SAGEConv层输出的向量的维度大小。在SAGEConv中,hidden_feats的大小直接影响节点表示的表达能力和模型的复杂度。通常情况下,hidden_feats的大小需要根据具体的任务和数据集进行调整,以达到最佳的性能和效果。
相关问题
class GraphSAGE(nn.Module): def __init__(self, in_feats, hidden_feats, out_feats, num_layers, activation): super(GraphSAGE, self).__init__() self.num_layers = num_layers self.conv1 = SAGEConv(in_feats, hidden_feats, aggregator_type='mean') self.convs = nn.ModuleList() for i in range(num_layers - 2): self.convs.append(SAGEConv(hidden_feats, hidden_feats, aggregator_type='mean')) self.conv_last = SAGEConv(hidden_feats, out_feats, aggregator_type='mean') self.activation = activation def forward(self, blocks, x): h = x for i, block in enumerate(blocks): h_dst = h[:block.number_of_dst_nodes()] h = self.convs[i](block, (h, h_dst)) if i != self.num_layers - 2: h = self.activation(h) h = self.conv_last(blocks[-1], (h, h_dst)) return h改写一下,让它适用于异质图
class GraphSAGE(nn.Module):
def __init__(self, in_feats, hidden_feats, out_feats, num_layers, activation):
super(GraphSAGE, self).__init__()
self.num_layers = num_layers
self.conv1 = SAGEConv(in_feats, hidden_feats, aggregator_type='mean')
self.convs = nn.ModuleList()
for i in range(num_layers - 2):
self.convs.append(SAGEConv(hidden_feats, hidden_feats, aggregator_type='mean'))
self.conv_last = SAGEConv(hidden_feats, out_feats, aggregator_type='mean')
self.activation = activation
def forward(self, blocks, x_dict):
h = {k: v for k, v in x_dict.items()}
for i, block in enumerate(blocks):
edge_type = block.edata['type']
h_dst = h[str(edge_type)][block.dstdata[dgl.NID]]
h = self.convs[i](block, (h, h_dst))
if i != self.num_layers - 2:
h = self.activation(h)
h_dst = h[str(edge_type)][blocks[-1].dstdata[dgl.NID]]
h = self.conv_last(blocks[-1], (h, h_dst))
return h
计算机视觉领域 att_feats, avg_feats是什么意思,一般是什么size
Att_feats指的是Attention features,也就是注意力机制的特征向量,它是通过图像中的一些卷积特征来计算出来的,用于表示图像中不同位置的重要性。Avg_feats则是指Average features,即平均池化特征,它是对图像中不同位置的卷积特征进行平均池化得到的。一般来说,Att_feats的size相对较大,通常为[14, 14, 1024],而Avg_feats的size比较小,通常为[1, 1, 2048]。
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