或者,你可以自定义self.weight,通过以下代码: nn.Embedding.from_pretrained() 1. 随后,通过 out = embedding(input) 1. 它调用了forward方法,该方法调用了包下functional的embedding方法: def forward(self, input): return F.embedding( input, sel
示例2 defbuild_graph(self):"""Build the model graph."""opts=self._options# The training data. A text file.(words,counts,words_per_epoch,current_epoch,total_words_processed,examples,labels)=word2vec.skipgram(filename=opts.train_data,batch_size=opts.batch_size,window_size=opts.window_size,...