调试经验——使用Matlab绘制指数函数图形(Graph of Exponential Function),程序员大本营,技术文章内容聚合第一站。
topk pooling的select 过程是对节点的原始特征或representations ,使用一个可训练的p向量节点投影为一个值,则n个节点会产生一个长度为n的向量,然后经过一个softmax function将这个向量转化为score 向量, 通过简单的topk function选择score向量中k个最大的scores,完成选择过程,此时在top k pooling之后,n个节点减少到k...
Word2vec本质是一个输入不确定的softmax回归。普通的softmax回归输入是确定的,我们需要求出参数矩阵,而word2vec中,输入的向量和参数矩阵都是不确定的,并且输入向量才是我们真正需要的,参数矩阵则是副产品。普通的softmax回归是一个凸优化问题,然后word2vec由于输入不确定不是一个凸优化问题,需要采取类似于坐标上升的...
where zu∈Rdzu∈Rd is the embedding of node uu , yu∈{0,1}|C|yu∈{0,1}|C| is a one-hot vector of label for node uu , CC is the label set of classification, W∈R|C|×dW∈R|C|×d is trainable parameters, σσ is the softmax function σ(Wzu)[i]=expwizu∑j∈Cexpwj...
Edge count function.我们定义了邻接矩阵上的边计数函数(edge count function):E(C)=∑i,j1{AC[i,j]≠0}(4)E(C)=∑i,j1{AC[i,j]≠0}(4)其中ACAC 是社区 CC 的邻接矩阵。Edge density function.边密度函数将真实边计数与给定社区 CkCk 中的最大可能边数进行比较:d(k)=E(Ck)|Ck|(|Ck|−1)...
普通的softmax回归是一个凸优化问题,然后word2vec由于输入不确定不是一个凸优化问题,需要采取类似于坐标上升的优化方法。 由于以下两个原因: 1.一般来说语料库单词都是很多的,计算会非常长庞大。 2.正负样本极度不均衡。 一般不直接优化图1.1的表达式,有一些优化,比如分层softmax和负采样,一般来说现在最常用的是...
class GCN(torch.nn.Module): def __init__(self, input_dim, hidden_dim, output_dim, num_layers, dropout, return_embeds=False): # TODO: Implement this function that initializes self.convs, # self.bns, and self.softmax. super(GCN, self).__init__() # A list of GCNConv layers self...
αt is a softmax function. The scores are multiplied by the hidden states to calculate the weighted combination, st (the attention-weighted final output). 2.4.3 Graph explainers Several post-hoc feature attribution graph explainers have been presented in the literature including excitation back...
retain_graph (bool, optional) – If False, the graph used to compute the grad will be freed. Note that in nearly all cases setting this option to True is not needed and often can be worked around in a much more efficient way.Defaults to the value of create_graph. ...
Optionally, apply a softmax function: In the case of a classification task, the output node features are subject to a softmax function to produce the class probabilities. (6)P=softmax(Y) Note that these steps can be repeated for multiple layers to learn increasingly complex representations ...