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- # coding: UTF-8
- import torch
- import torch.nn as nn
- import torch.nn.functional as F
- import numpy as np
-
-
- class Config(object):
-
- """配置参数"""
- def __init__(self, dataset, embedding):
- self.model_name = 'TextRNN_Att'
- self.train_path = dataset + '/data/train.txt' # 训练集
- self.dev_path = dataset + '/data/dev.txt' # 验证集
- self.test_path = dataset + '/data/test.txt' # 测试集
- self.class_list = [x.strip() for x in open(
- dataset + '/data/class.txt', encoding='utf-8').readlines()] # 类别名单
- self.vocab_path = dataset + '/data/vocab.pkl' # 词表
- self.save_path = dataset + '/saved_dict/' + self.model_name + '.ckpt' # 模型训练结果
- self.log_path = dataset + '/log/' + self.model_name
- self.embedding_pretrained = torch.tensor(
- np.load(dataset + '/data/' + embedding)["embeddings"].astype('float32'))\
- if embedding != 'random' else None # 预训练词向量
- self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') # 设备
-
- self.dropout = 0.5 # 随机失活
- self.require_improvement = 1000 # 若超过1000batch效果还没提升,则提前结束训练
- self.num_classes = len(self.class_list) # 类别数
- self.n_vocab = 0 # 词表大小,在运行时赋值
- self.num_epochs = 10 # epoch数
- self.batch_size = 128 # mini-batch大小
- self.pad_size = 32 # 每句话处理成的长度(短填长切)
- self.learning_rate = 1e-3 # 学习率
- self.embed = self.embedding_pretrained.size(1)\
- if self.embedding_pretrained is not None else 300 # 字向量维度, 若使用了预训练词向量,则维度统一
- self.hidden_size = 128 # lstm隐藏层
- self.num_layers = 2 # lstm层数
- self.hidden_size2 = 64
-
-
- '''Attention-Based Bidirectional Long Short-Term Memory Networks for Relation Classification'''
-
-
- class Model(nn.Module):
- def __init__(self, config):
- super(Model, self).__init__()
- if config.embedding_pretrained is not None:
- self.embedding = nn.Embedding.from_pretrained(config.embedding_pretrained, freeze=False)
- else:
- self.embedding = nn.Embedding(config.n_vocab, config.embed, padding_idx=config.n_vocab - 1)
- self.lstm = nn.LSTM(config.embed, config.hidden_size, config.num_layers,
- bidirectional=True, batch_first=True, dropout=config.dropout)
- self.tanh1 = nn.Tanh()
- # self.u = nn.Parameter(torch.Tensor(config.hidden_size * 2, config.hidden_size * 2))
- self.w = nn.Parameter(torch.zeros(config.hidden_size * 2))
- self.tanh2 = nn.Tanh()
- self.fc1 = nn.Linear(config.hidden_size * 2, config.hidden_size2)
- self.fc = nn.Linear(config.hidden_size2, config.num_classes)
-
- def forward(self, x):
- x, _ = x
- emb = self.embedding(x) # [batch_size, seq_len, embeding]=[128, 32, 300]
- H, _ = self.lstm(emb) # [batch_size, seq_len, hidden_size * num_direction]=[128, 32, 256]
-
- M = self.tanh1(H) # [128, 32, 256]
- # M = torch.tanh(torch.matmul(H, self.u))
- alpha = F.softmax(torch.matmul(M, self.w), dim=1).unsqueeze(-1) # [128, 32, 1]
- out = H * alpha # [128, 32, 256]
- out = torch.sum(out, 1) # [128, 256]
- out = F.relu(out)
- out = self.fc1(out)
- out = self.fc(out) # [128, 64]
- return out
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