# @Author : bamtercelboo
# @Datetime : 2018/07/19 22:35
# @File : model_BiGRU.py
# @Last Modify Time : 2018/07/19 22:35
# @Contact : bamtercelboo@{gmail.com, 163.com}
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
import numpy as np
import random
from DataUtils.Common import seed_num
torch.manual_seed(seed_num)
random.seed(seed_num)
"""
Neural Networks model : Bidirection GRU
"""
class BiGRU(nn.Module):
def __init__(self, args):
super(BiGRU, self).__init__()
self.args = args
self.hidden_dim = args.lstm_hidden_dim
self.num_layers = args.lstm_num_layers
V = args.embed_num
D = args.embed_dim
C = args.class_num
self.embed = nn.Embedding(V, D, padding_idx=args.paddingId)
# pretrained embedding
if args.word_Embedding:
self.embed.weight.data.copy_(args.pretrained_weight)
# gru
self.bigru = nn.GRU(D, self.hidden_dim, dropout=args.dropout, num_layers=self.num_layers, bidirectional=True)
# linear
self.hidden2label = nn.Linear(self.hidden_dim * 2, C)
# dropout
self.dropout = nn.Dropout(args.dropout)
def forward(self, input):
embed = self.embed(input)
embed = self.dropout(embed)
input = embed.view(len(input), embed.size(1), -1)
# gru
gru_out, _ = self.bigru(input)
gru_out = torch.transpose(gru_out, 0, 1)
gru_out = torch.transpose(gru_out, 1, 2)
# pooling
# gru_out = F.tanh(gru_out)
gru_out = F.max_pool1d(gru_out, gru_out.size(2)).squeeze(2)
gru_out = F.tanh(gru_out)
# linear
y = self.hidden2label(gru_out)
logit = y
return logit
