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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 = 'DPCNN'
- 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.num_filters = 250 # 卷积核数量(channels数)
-
-
- '''Deep Pyramid Convolutional Neural Networks for Text Categorization'''
-
-
- 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.conv_region = nn.Conv2d(1, config.num_filters, (3, config.embed), stride=1)
- self.conv = nn.Conv2d(config.num_filters, config.num_filters, (3, 1), stride=1)
- self.max_pool = nn.MaxPool2d(kernel_size=(3, 1), stride=2)
- self.padding1 = nn.ZeroPad2d((0, 0, 1, 1)) # top bottom
- self.padding2 = nn.ZeroPad2d((0, 0, 0, 1)) # bottom
- self.relu = nn.ReLU()
- self.fc = nn.Linear(config.num_filters, config.num_classes)
-
- def forward(self, x):
- x = x[0]
- x = self.embedding(x)
- x = x.unsqueeze(1) # [batch_size, 250, seq_len, 1]
- x = self.conv_region(x) # [batch_size, 250, seq_len-3+1, 1]
-
- x = self.padding1(x) # [batch_size, 250, seq_len, 1]
- x = self.relu(x)
- x = self.conv(x) # [batch_size, 250, seq_len-3+1, 1]
- x = self.padding1(x) # [batch_size, 250, seq_len, 1]
- x = self.relu(x)
- x = self.conv(x) # [batch_size, 250, seq_len-3+1, 1]
- while x.size()[2] > 2:
- x = self._block(x)
- x = x.squeeze() # [batch_size, num_filters(250)]
- x = self.fc(x)
- return x
-
- def _block(self, x):
- x = self.padding2(x)
- px = self.max_pool(x)
-
- x = self.padding1(px)
- x = F.relu(x)
- x = self.conv(x)
-
- x = self.padding1(x)
- x = F.relu(x)
- x = self.conv(x)
-
- # Short Cut
- x = x + px
- return x
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