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- # coding: UTF-8
- import torch
- import torch.nn as nn
- import numpy as np
-
-
- class Config(object):
-
- """配置参数"""
- def __init__(self, dataset, embedding):
- self.model_name = 'TextRNN'
- 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
-
-
- '''Recurrent Neural Network for Text Classification with Multi-Task Learning'''
-
-
- 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.fc = nn.Linear(config.hidden_size * 2, config.num_classes)
-
- def forward(self, x):
- x, _ = x
- out = self.embedding(x) # [batch_size, seq_len, embeding]=[128, 32, 300]
- out, _ = self.lstm(out)
- out = self.fc(out[:, -1, :]) # 句子最后时刻的 hidden state
- return out
-
- '''变长RNN,效果差不多,甚至还低了点...'''
- # def forward(self, x):
- # x, seq_len = x
- # out = self.embedding(x)
- # _, idx_sort = torch.sort(seq_len, dim=0, descending=True) # 长度从长到短排序(index)
- # _, idx_unsort = torch.sort(idx_sort) # 排序后,原序列的 index
- # out = torch.index_select(out, 0, idx_sort)
- # seq_len = list(seq_len[idx_sort])
- # out = nn.utils.rnn.pack_padded_sequence(out, seq_len, batch_first=True)
- # # [batche_size, seq_len, num_directions * hidden_size]
- # out, (hn, _) = self.lstm(out)
- # out = torch.cat((hn[2], hn[3]), -1)
- # # out, _ = nn.utils.rnn.pad_packed_sequence(out, batch_first=True)
- # out = out.index_select(0, idx_unsort)
- # out = self.fc(out)
- # return out
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