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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 | |