Hierarchical Attention Networks for Document Classification
一、模型
二、代碼
import torch.nn.functional as F
from torch import nn
class SelfAttention(nn.Module):
def __init__(self, input_size, hidden_size):
super(SelfAttention, self).__init__()
self.W = nn.Linear(input_size, hidden_size, True)
self.u = nn.Linear(hidden_size, 1)
def forward(self, x):
u = torch.tanh(self.W(x))
a = F.softmax(self.u(u), dim=1)
x = a.mul(x).sum(1)
return x
class HAN(nn.Module):
def __init__(self):
super(HAN1, self).__init__()
num_embeddings = 5844 + 1
num_classes = 10
num_sentences = 30
num_words = 60
embedding_dim = 200 # 200
hidden_size_gru = 50 # 50
hidden_size_att = 100 # 100
self.num_words = num_words
self.embed = nn.Embedding(num_embeddings, embedding_dim, 0)
self.gru1 = nn.GRU(embedding_dim, hidden_size_gru, bidirectional=True, batch_first=True)
self.att1 = SelfAttention(hidden_size_gru * 2, hidden_size_att)
self.gru2 = nn.GRU(hidden_size_att, hidden_size_gru, bidirectional=True, batch_first=True)
self.att2 = SelfAttention(hidden_size_gru * 2, hidden_size_att)
# 這裏fc的參數很少，不需要dropout
self.fc = nn.Linear(hidden_size_att, num_classes, True)
def forward(self, x):
# 64 512 200
x = x.view(x.size(0) * self.num_words, -1).contiguous()
x = self.embed(x)
x, _ = self.gru1(x)
x = self.att1(x)
x = x.view(x.size(0) // self.num_words, self.num_words, -1).contiguous()
x, _ = self.gru2(x)
x = self.att2(x)
x = self.fc(x)
x = F.log_softmax(x, dim=1) # softmax
return x
