/usr/bin/env python# coding:utf8import torchimport torch.nn as nnimport torch.nn.functional as Ffrom torch.autograd import Variabletorch.manual_seed(123456)class BLSTM(nn.Module): """ Implementation of BLSTM Concatenation for sentiment classification task """ def __init__(self, embeddi...
importtorch.nnasnnclassBiLSTM(nn.Module):def__init__(self,input_size,hidden_size,output_size):super(BiLSTM,self).__init__()self.lstm=nn.LSTM(input_size,hidden_size,bidirectional=True)self.fc=nn.Linear(hidden_size*2,output_size)# 由于是双向,所以hidden_size乘以2defforward(self,x):lstm_...
m = nn.Bilinear(20, 30, 40) input1 = torch.randn(128, 20) input2 = torch.randn(128, 30) output = m(input1, input2) print(output.size()) arr_output = output.data.cpu().numpy() weight = m.weight.data.cpu().numpy() bias = m.bias.data.cpu().numpy() x1 = input1.data....
fw_out = torch.index_select(rnn_outs, 2, Variable(torch.LongTensor([0])).cuda()) fw_out = fw_out.view(batch_size * max_len, -1) bw_out = torch.index_select(rnn_outs, 2, Variable(torch.LongTensor([1])).cuda()) bw_out = bw_out.view(batch_size * max_len, -1) batch_r...
import torch import torch.nn as nn import torch.optim as optim torch.manual_seed(1) # some helper functions def argmax(vec): # return the argmax as a python int #第1维度上最大值的下标 # input: tensor([[2,3,4]]) # output: 2 _, idx = torch.max(vec,1) return idx.item() ...
[max_len, batch_size, n_class] hidden_state = torch.randn(1*2,batch_size,n_hidden) # [num_layers(=1) * num_directions(=2), batch_size, n_hidden] cell_state = torch.randn(1*2,batch_size,n_hidden) # [num_layers(=1) * num_directions(=2), batch_size, n_hidden] outputs, ...
(input_label_tmp)returntorch.Tensor(input_data),torch.LongTensor(input_label)classBiLSTM(nn.Module):def__init__(self):super(BiLSTM,self).__init__()# LSTM层:input_size: 每个x的特征个数,hidden_size:隐藏层输出的维度, num_layer:lstm单元的个数self.lstm=nn.LSTM(input_size=vocab_size,...
编码成功后,我们的每个句子转换为了数字类型,除此之外,如果有需要我们还需要将其转换为one-Hot编码,不过在torch模型中不是很需要,所以这里我们就不转换了。 数据预处理后,我们就需要创建模型了,为了模型更加贴合我们的实验,我选择了双层双向LSTM。双向LSTM(Bidirectional LSTM)是一种常用的循环神经网络模型,它可以同时...
outs = torch.cat([fw_out, bw_out], dim=1) return outs def forward(self, sen_batch, sen_lengths, sen_mask_matrix): """ :param sen_batch: (batch, sen_length), tensor for sentence sequence :param sen_lengths: :param sen_mask_matrix: ...
torch.randn(len(tag2idx), len(tag2idx))给定句子 x，其标签序列为 y 的概率用下面的公式计算。公式中的 score 用下面的式子计算，其中 Emit 对应发射概率 (即 LSTM 输出的概率)，而 Trans 对应了转移概率 (即 CRF 转移矩阵对应的数值)BiLSTM+CRF 采用最大似然法训练，对应的损失函数如下：其中 score(x,...