ඔබත් මේ ගැන ලතැවෙන කෙනෙක්ද..? වෙහෙසෙන ඇස් රැකගන්න මේ දේවල්වලට පුළුවන්..
For pooling (in case of graph-amount outputs instead of node-amount outputs) it's best to specify an easy pooling matrix that collects functions from their respective graph circumstances, as illustrated under:
ලොව වේගවත්ම අධිවේගී දුම්රියේ මූලාකෘතිය එළිදකී...
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කටකතා ගොඩකට පස්සේ ඒ දෙන්නාගේ දික්කසාදය ගැන ඇසෙන අලුත් කතාව මෙන්න..
输出预测：最后，可以在最后一层的节点特征上应用全局池化操作（如平均池化或最大池化），然后将结果输入到一个全连接层进行分类或回归预测。
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我们遗漏了所分析的结点本身的信息，比如，结果矩阵的第一行中应该包含结点A的信息才对。
. ගුරුවරියක් කළ දේ ගැන ඇසෙන අමුතු කතාව මෙන්න..
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现在这个问题变成了我们要如何标准化加和得到的向量？或者说我们如何将特定结点邻居的信息传递给该结点？
我们应该使用平均值函数甚至是更好的加权平均值函数而非直接加和来处理邻居的特征向量。那为什么不能直接使用加和函数呢？原因就在于，当使用加和函数的时候，具有较大度值的结点会有很大的表示向量，而较低度的结点会有较小的聚合向量，这可能会导致梯度爆炸或梯度消失的问题（比如使用sigmoid函数时）。此外，神经网络对于输入数据的标度是非常敏感的，我们需要将这些向量进行标准化以消除潜在的问题。