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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 = 'TextCNN'
- 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 = 20 # 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
- self.filter_sizes = (2, 3, 4) # 卷积核尺寸
- self.num_filters = 256 # 卷积核数量(channels数)
-
-
- '''Convolutional Neural Networks for Sentence 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.convs = nn.ModuleList(
- [nn.Conv2d(1, config.num_filters, (k, config.embed)) for k in config.filter_sizes])
- self.dropout = nn.Dropout(config.dropout)
- self.fc = nn.Linear(config.num_filters * len(config.filter_sizes), config.num_classes)
-
- def conv_and_pool(self, x, conv):
- x = F.relu(conv(x)).squeeze(3)
- x = F.max_pool1d(x, x.size(2)).squeeze(2)
- return x
-
- def forward(self, x):
- out = self.embedding(x[0])
- out = out.unsqueeze(1)
- out = torch.cat([self.conv_and_pool(out, conv) for conv in self.convs], 1)
- out = self.dropout(out)
- out = self.fc(out)
- return out
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