update

README.md

@@ -43,6 +43,7 @@ TextRNN|91.12%|BiLSTM
 TextRNN_Att|90.90%|BiLSTM+Attention
 TextRCNN|91.54%|BiLSTM+池化
 FastText|92.23%|bow+bigram+trigram， 效果出奇的好
+DPCNN|91.25%|深层金字塔CNN
 
 ## 使用说明
 ```
@@ -61,6 +62,9 @@ python run.py --model TextRCNN
 
 # FastText, embedding层是随机初始化的
 python run.py --model FastText --embedding random 
+
+# DPCNN
+python run.py --model DPCNN
 ```
 
 ### 参数

models/DPCNN.py

+# 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.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').readlines()]                                # 类别名单
+        self.vocab_path = dataset + '/data/vocab.pkl'                                # 词表
+        self.save_path = dataset + '/saved_dict/TextCNN.ckpt'                          # 模型训练结果
+        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, 1, seq_len, 1]
+        x = self.conv_region(x)  # [batch_size, 1, seq_len-3+1, 1]
+
+        x = self.padding1(x)  # [batch_size, 1, seq_len, 1]
+        x = self.relu(x)
+        x = self.conv(x)  # [batch_size, 1, seq_len-3+1, 1]
+        x = self.padding1(x)  # [batch_size, 1, seq_len, 1]
+        x = self.relu(x)
+        x = self.conv(x)  # [batch_size, 1, seq_len-3+1, 1]
+        while x.size()[2] > 2:
+            x = self._block(x)
+        x = x.squeeze()  # [batch_size, num_filters]
+        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

models/TextRNN.py

@@ -56,7 +56,7 @@ class Model(nn.Module):
         out = self.fc(out[:, -1, :])  # 句子最后时刻的 hidden state
         return out
 
-    '''变长RNN'''
+    '''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)

models/TextRNN_Att.py

@@ -49,6 +49,7 @@ class Model(nn.Module):
         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.Tensor(config.hidden_size * 2))
         self.tanh2 = nn.Tanh()
         self.fc1 = nn.Linear(config.hidden_size * 2, config.hidden_size2)
@@ -60,6 +61,7 @@ class Model(nn.Module):
         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]

run.py

@@ -7,7 +7,7 @@ from importlib import import_module
 import argparse
 
 parser = argparse.ArgumentParser(description='Chinese Text Classification')
-parser.add_argument('--model', type=str, required=True, help='choose a model: TextCNN, TextRNN, FastText, TextRCNN, TextRNN_Att')
+parser.add_argument('--model', type=str, required=True, help='choose a model: TextCNN, TextRNN, FastText, TextRCNN, TextRNN_Att, DPCNN')
 parser.add_argument('--embedding', default='pre_trained', type=str, help='random or pre_trained')
 parser.add_argument('--word', default=False, type=bool, help='True for word, False for char')
 args = parser.parse_args()
@@ -20,7 +20,7 @@ if __name__ == '__main__':
     embedding = 'embedding_SougouNews.npz'
     if args.embedding == 'random':
         embedding = 'random'
-    model_name = args.model  # 'TextRCNN'  # TextCNN, TextRNN, FastText, TextRCNN, TextRNN_Att
+    model_name = args.model  # 'TextRCNN'  # TextCNN, TextRNN, FastText, TextRCNN, TextRNN_Att, DPCNN
     if model_name == 'FastText':
         from utils_fasttext import build_dataset, build_iterator, get_time_dif
         embedding = 'random'