感觉还是挺简单，这里只是方便之后回来瞅瞅

import torch
import torch.nn as nn
import torch.nn.functional as Fclass Attention(torch.nn.Module):"""Attention Net.Input embedding vectors (produced by KCNN) of a candidate news and all of user's clicked news,produce final user embedding vectors with respect to the candidate news."""def __init__(self, config):super(Attention, self).__init__()self.config = configself.dnn = nn.Sequential(   #仅仅使用了两个线性层nn.Linear(   # 3 * 2 * 300个 len(self.config.window_sizes) * 2 * self.config.num_filters,16), nn.Linear(16, 1))   #  def forward(self, candidate_news_vector, clicked_news_vector):"""Args:candidate_news_vector: batch_size, len(window_sizes) * num_filtersclicked_news_vector: batch_size, num_clicked_news_a_user, len(window_sizes) * num_filtersReturns:user_vector: batch_size, len(window_sizes) * num_filters"""# batch_size, num_clicked_news_a_userclicked_news_weights = F.softmax(self.dnn(torch.cat((candidate_news_vector.expand(self.config.num_clicked_news_a_user, -1, -1).transpose(0, 1), clicked_news_vector),dim=2)).squeeze(dim=2),dim=1)# batch_size, len(window_sizes) * num_filtersuser_vector = torch.bmm(clicked_news_weights.unsqueeze(dim=1),   #两个相乘得到最终的向量clicked_news_vector).squeeze(dim=1)return user_vector