为什么会有多个输入层？GraphSAGE

Question

我刚接触CNN和tutorials of GraphSAGE。我运行了GraphSAGE Cora节点分类示例graphsage-cora-example.py。本文的任务是对cora数据集的节点标签进行分类。

当您运行此代码时，您将获得以下模型摘要：

Layer (type)                    Output Shape         Param # 

    Connected to                     
==================================================================================================
input_2 (InputLayer)            [(None, 20, 1433)]   0                                            
__________________________________________________________________________________________________
input_3 (InputLayer)            [(None, 200, 1433)]  0                                            
__________________________________________________________________________________________________
input_1 (InputLayer)            [(None, 1, 1433)]    0                                            
__________________________________________________________________________________________________
reshape (Reshape)               (None, 1, 20, 1433)  0           input_2[0][0]                    
__________________________________________________________________________________________________
reshape_1 (Reshape)             (None, 20, 10, 1433) 0           input_3[0][0]                    
__________________________________________________________________________________________________
dropout_1 (Dropout)             (None, 1, 1433)      0           input_1[0][0]                    
__________________________________________________________________________________________________
dropout (Dropout)               (None, 1, 20, 1433)  0           reshape[0][0]                    
__________________________________________________________________________________________________
dropout_3 (Dropout)             (None, 20, 1433)     0           input_2[0][0]                    
__________________________________________________________________________________________________
dropout_2 (Dropout)             (None, 20, 10, 1433) 0           reshape_1[0][0]                  
__________________________________________________________________________________________________
mean_aggregator (MeanAggregator multiple             28680       dropout_1[0][0]                  
                                                                 dropout[0][0]                    
                                                                 dropout_3[0][0]                  
                                                                 dropout_2[0][0]                  
__________________________________________________________________________________________________
reshape_2 (Reshape)             (None, 1, 20, 20)    0           mean_aggregator[1][0]            
__________________________________________________________________________________________________
dropout_5 (Dropout)             (None, 1, 20)        0           mean_aggregator[0][0]            
__________________________________________________________________________________________________
dropout_4 (Dropout)             (None, 1, 20, 20)    0           reshape_2[0][0]                  
__________________________________________________________________________________________________
mean_aggregator_1 (MeanAggregat (None, 1, 20)        420         dropout_5[0][0]                  
                                                                 dropout_4[0][0]                  
__________________________________________________________________________________________________
reshape_3 (Reshape)             (None, 20)           0           mean_aggregator_1[0][0]          
__________________________________________________________________________________________________
lambda (Lambda)                 (None, 20)           0           reshape_3[0][0]                  
__________________________________________________________________________________________________
dense (Dense)                   (None, 7)            147         lambda[0][0]                     
==================================================================================================
Total params: 29,247
Trainable params: 29,247
Non-trainable params: 0

为什么会有多个输入层？这些输出形状的数量表明了什么？我读过the original GraphSAGE paper，但我还不明白。有人能告诉我为什么它们是多个InputLayers吗?输出形状中的那些数字是什么？

Answer 1

Graphsage以节点方式工作。因此，您对模型的第一个输入将是来自Input_layer_1N，1,1433的单个节点。我猜，您一定是设置了一个名为num_samples的超参数或每层的样本数为20，10。因此，将节点提供给graphsage模型的生成器将获取进入的第一个节点的20个相邻节点。第二层将获取第一个节点的邻居的另外10个邻居。