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Networks.py
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# -*- coding: utf-8 -*-
#!/usr/bin/env
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
class Rank_CNN(nn.Module):
def __init__(self, embeddings, args, emb_change=True):
super(Rank_CNN,self).__init__()
self.embed = nn.Embedding(embeddings.shape[0], embeddings.shape[1])
self.embed.weight.data.copy_(embeddings)
if emb_change==False:
self.embed.weight.requires_grad = False
self.embed_pf1 = nn.Embedding(args.pos_num, args.pos_dim)
self.embed_pf2 = nn.Embedding(args.pos_num, args.pos_dim)
self.convs1 = nn.ModuleList([nn.Conv2d(1, args.hidden_size, (K, args.word_dim + args.pos_dim * 2)) for K in args.window_size])
#self.dropout = nn.Dropout(p=args.dropout_rate)
self.fc2 = nn.Linear(len(args.window_size)*args.hidden_size, args.label_num)
def forward(self, x, pf1, pf2):
word = self.embed(x)
pf1 = self.embed_pf1(pf1)
pf2 = self.embed_pf2(pf2)
x_all = torch.cat((word,pf1,pf2), 2)
x_all = x_all.unsqueeze(1)
out = [F.relu(conv(x_all)).squeeze(3) for conv in self.convs1]
out = [F.max_pool1d(i, i.size(2)).squeeze(2) for i in out]
out = torch.cat(out, 1)
#out = self.dropout(out)
logit = self.fc2(out)
return logit
class Generator_net(nn.Module):
def __init__(self, embeddings, args, emb_change=True, conv_change=True):
super(Generator_net, self).__init__()
self.embed = nn.Embedding(embeddings.shape[0], embeddings.shape[1])
self.embed.weight.data.copy_(embeddings)
self.embed_pf1 = nn.Embedding(args.pos_num, args.pos_dim)
self.embed_pf2 = nn.Embedding(args.pos_num, args.pos_dim)
self.convs1 = nn.ModuleList([nn.Conv2d(1, args.hidden_size, (K, args.word_dim + args.pos_dim * 2)) for K in args.window_size])
#self.dropout = nn.Dropout(p=args.dropout_rate)
self.fc1 = nn.Linear(len(args.window_size)*args.hidden_size, args.label_num)
if not emb_change:
self.embed.weight.requires_grad = False
self.embed_pf1.weight.requires_grad = False
self.embed_pf2.weight.requires_grad = False
if not conv_change:
for conv in self.convs1:
conv.weight.requires_grad = False
self.dropout = nn.Dropout(p=args.dropout_rate)
def forward(self, x, pf1, pf2):
word = self.embed(x)
pf1 = self.embed_pf1(pf1)
pf2 = self.embed_pf2(pf2)
x_all = torch.cat((word,pf1,pf2), 2)
x_all = x_all.unsqueeze(1)
out = [F.relu(conv(x_all)).squeeze(3) for conv in self.convs1]
out = [F.max_pool1d(i, i.size(2)).squeeze(2) for i in out]
out = torch.cat(out, 1)
#out = self.dropout(out)
logit = self.fc1(out)
return logit
class Discriminator_net(nn.Module):
def __init__(self, embeddings, args, emb_change=True, conv_change=True):
super(Discriminator_net, self).__init__()
self.embed = nn.Embedding(embeddings.shape[0], embeddings.shape[1])
self.embed.weight.data.copy_(embeddings)
self.embed_pf1 = nn.Embedding(args.pos_num, args.pos_dim)
self.embed_pf2 = nn.Embedding(args.pos_num, args.pos_dim)
self.convs1 = nn.ModuleList([nn.Conv2d(1, args.hidden_size, (K, args.word_dim + args.pos_dim * 2)) for K in args.window_size])
self.dropout = nn.Dropout(p=args.dropout_rate)
self.fc1 = nn.Linear(len(args.window_size)*args.hidden_size, args.label_num)
if not emb_change:
self.embed.weight.requires_grad = False
self.embed_pf1.weight.requires_grad = False
self.embed_pf2.weight.requires_grad = False
if not conv_change:
for conv in self.convs1:
conv.weight.requires_grad = False
def forward(self, x, pf1, pf2):
word = self.embed(x)
pf1 = self.embed_pf1(pf1)
pf2 = self.embed_pf2(pf2)
x_all = torch.cat((word,pf1,pf2), 2)
x_all = x_all.unsqueeze(1)
out = [F.relu(conv(x_all)).squeeze(3) for conv in self.convs1]
out = [F.max_pool1d(i, i.size(2)).squeeze(2) for i in out]
out = torch.cat(out, 1)
#out = self.dropout(out)
logit = self.fc1(out)
return logit