def forward(self, input, hidden_state):
f_output, h_output = self.gru(input, hidden_state)
output = matrix_mul(f_output, self.sent_weight, self.sent_bias)
output = matrix_mul(output, self.context_weight).permute(1, 0)
output = F.softmax(output)
output = element_wise_mul(f_output, output.permute(1, 0)).squeeze(0)
output = self.fc(output)
return output, h_output
def forward(self, input, hidden_state):
output = self.lookup(input)
f_output, h_output = self.gru(output.float(), hidden_state) # feature output and hidden state output
output = matrix_mul(f_output, self.word_weight, self.word_bias)
output = matrix_mul(output, self.context_weight).permute(1,0)
output = F.softmax(output)
output = element_wise_mul(f_output,output.permute(1,0))
return output, h_output
def forward(self, input):
f_output = self.mlp_graph(input)
f_output = self.r(f_output)
output = matrix_mul(f_output, self.word_weight, self.word_bias)
#print(output.shape)
output = matrix_mul(output, self.context_weight).permute(1, 0)
output = F.softmax(output, dim=1)
output = element_wise_mul(f_output, output.permute(1, 0))
return f_output, output
def forward(self, input, hidden_state):
output = self.lookup(input)
f_output, h_output = self.gru(
output.float(),
hidden_state) # feature output and hidden state output
#this is equivalent to calculating K . as we are doing , K= W_K (x : all words to attend to)
output = matrix_mul(f_output, self.word_weight, self.word_bias)
#maybe this is the Q.KT line. we are imagining that the correct query vector, Q, will be learnt somehow by context weight here.
#that is the context weight will learn the query, which is the most informative word in the sentence.
output = matrix_mul(output, self.context_weight).permute(1, 0)
output = F.softmax(output)
output = self.element_wise_mul(f_output, output.permute(1, 0))
return output, h_output
def forward(self, input, hidden_state):
# Sen Encoder 放入gru feature output and hidden state output，
f_output, h_output = self.gru(input, hidden_state)
# 增加tone-layer MLP tanh() get hidden representation
output = torch.tanh(
matrix_mul(f_output, self.sent_weight, self.sent_bias))
# a word sen context vector
output = matrix_mul(output, self.context_weight).permute(1, 0)
# 归一化得到权重矩阵 importance weight ait through a softmax function
output = F.softmax(output, dim=1)
# 通过attention权重矩阵，将文本向量看作组成这些句子向量的加权求和。
output = element_wise_mul(f_output, output.permute(1, 0)).squeeze(0)
# output = self.fc(output) #删除fc针对文本分类输出的处理
return output, h_output
def forward(self, input, hidden_state):
# 从数据字典中获取词向量
output = self.lookup(input)
# Word Encoder 放入gru feature output and hidden state output，
f_output, h_output = self.gru(output.float(), hidden_state)
# 增加tone-layer MLP tanh() get hidden representation
output = torch.tanh(
matrix_mul(f_output, self.word_weight, self.word_bias))
# a word level context vector
output = matrix_mul(output, self.context_weight).permute(1, 0)
# 归一化得到权重矩阵 importance weight ait through a softmax function
output = F.softmax(output, dim=1)
# 通过attention权重矩阵，将句子向量看作组成这些句子的词向量的加权求和。
output = element_wise_mul(f_output, output.permute(1, 0))
return output, h_output
def forward(self, input, hidden_state):
f_output, h_output = self.gru(input, hidden_state)
output = matrix_mul(f_output, self.sent_weight, self.sent_bias)
output_2 = matrix_mul(output, self.context_weight).permute(1, 0)
if torch.isnan(output_2[0][0]):
print('nan detected')
return None
output = F.softmax(output_2, dim=-1)
output = element_wise_mul(f_output, output.permute(1, 0)).squeeze(0)
output = self.dropout(self.fc(output))
return output, h_output
def forward(self, input, hidden_state):
#print(input.type())
#print(hidden_state.type())
f_output, h_output = self.gru(input, hidden_state)
#print('------------------>')
#print(f_output.shape)
output = matrix_mul(f_output, self.sent_weight, self.sent_bias)
#print(output.shape)
#print()
output = matrix_mul(output, self.context_weight).permute(1, 0)
output = F.softmax(output)
output = element_wise_mul(f_output, output.permute(1, 0)).squeeze(0)
#output = self.fc(output)
return f_output, output, h_output
