该实验项目目录如图：
1、模型
1.1. TextCNN
# 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.predict_path = dataset + '/data/predict.txt'
# self.class_list = [x.strip() for x in open(dataset + '/data/class.txt', encoding='utf-8').readlines()] # 类别名单
self.class_list = ['财经', '房产', '股票', '教育', '科技', '社会', '时政', '体育', '游戏','娱乐']
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
1.2.TextRCNN
# 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 = 'TextRCNN'
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 = 1.02 # 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, 若使用了预训练词向量，则维度统一
self.hidden_size = 256 # lstm隐藏层
self.num_layers = 1 # lstm层数
'''Recurrent Convolutional Neural Networks for Text 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.maxpool = nn.MaxPool1d(config.pad_size)
self.fc = nn.Linear(config.hidden_size * 2 + config.embed, config.num_classes)
def forward(self, x):
x, _ = x
embed = self.embedding(x) # [batch_size, seq_len, embeding]=[64, 32, 64]
out, _ = self.lstm(embed)
out = torch.cat((embed, out), 2)
out = F.relu(out)
out = out.permute(0, 2, 1)
out = self.maxpool(out).squeeze()
out = self.fc(out)
return out
1.3.TextRNN
# coding: UTF-8
服务器托管网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()]
self.class_list = ['体育', '军事', '娱乐', '政治', '教育', '灾难', '社会', '科技', '财经', '违法']
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, 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
2、训练
2.1. utils.py代码编写
# coding: UTF-8
import os
import torch
import numpy as np
import pickle as pkl
from tqdm import tqdm
import time
from datetime import timedelta
MAX_VOCAB_SIZE = 10000 # 词表长度限制
UNK, PAD = '', '' # 未知字，padding符号
def build_vocab(file_path, tokenizer, max_size, min_freq):
vocab_dic = {}
with open(file_path, 'r', encoding='UTF-8') as f:
for line in tqdm(f):
lin = line.strip()
if not lin:
continue
content = lin.split('t')[0]
for word in tokenizer(content):
vocab_dic[word] = vocab_dic.get(word, 0) + 1
vocab_list = sorted([_ for _ in vocab_dic.items() if _[1] >= min_freq], key=lambda x: x[1],
reverse=True)[:max_size]
vocab_dic = {word_count[0]: idx for idx, word_count in enumerate(vocab_list)}
vocab_dic.update({UNK: len(vocab_dic), PAD: len(vocab_dic) + 1})
return vocab_dic
def build_dataset(config, ues_word):
if ues_word:
tokenizer = lambda x: x.split(' ') # 以空格隔开，word-level
else:
tokenizer = lambda x: [y for y in x] # char-level
if os.path.exists(config.vocab_path):
vocab = pkl.load(open(config.vocab_path, 'rb'))
else:
vocab = build_vocab(config.train_path, tokenizer=tokenizer, max_size=MAX_VOCAB_SIZE, min_freq=1)
pkl.dump(vocab, open(config.vocab_path, 'wb'))
print(f"Vocab size: {len(vocab)}")
def load_dataset(path, pad_size=32):
contents = []
with open(path, 'r', encoding='UTF-8') as f:
for line in tqdm(f):
lin = line.strip()
if not lin:
continue
content, label = lin.split('t')
words_line = []
token = tokenizer(content)
seq_len = len(token)
if pad_size:
if len(token) = self.n_batches:
self.index = 0
raise StopIteration
else:
batches = self.batches[self.index * self.batch_size: (self.index + 1) * self.batch_size]
self.index += 1
batches = self._to_tensor(batches)
return batches
def __iter__(self):
return self
def __len__(self):
if self.residue:
return self.n_batches + 1
else:
return self.n_batches
def build_iterator(dataset, config, predict):
if predict==True:
config.batch_size = 1
iter = DatasetIterater(dataset, config.batch_size, config.device)
return iter
def get_time_dif(start_time):
"""获取已使用时间"""
end_time = time.time()
time_dif = end_time - start_time
return timedelta(seconds=int(round(time_dif)))
if __name__ == "__main__":
'''提取预训练词向量'''
# 下面的目录、文件名按需更改。
train_dir = "./THUCNews/data/train.txt"
vocab_dir = "./THUCNews/data/vocab.pkl"
pretrain_dir = "./THUCNews/data/sgns.sogou.char"
emb_dim = 300
filename_trimmed_dir = "./THUCNews/data/embedding_SougouNews"
if os.path.exists(vocab_dir):
word_to_id = pkl.load(open(vocab_dir, 'rb'))
else:
# tokenizer = lambda x: x.split(' ') # 以词为单位构建词表(数据集中词之间以空格隔开)
tokenizer = lambda x: [y for y in x] # 以字为单位构建词表
word_to_id = build_vocab(train_dir, tokenizer=tokenizer, max_size=MAX_VOCAB_SIZE, min_freq=1)
pkl.dump(word_to_id, open(vocab_dir, 'wb'))
embeddings = np.random.rand(len(word_to_id), emb_dim)
f = open(pretrain_dir, "r", encoding='UTF-8')
for i, line in enumerate(f.readlines()):
# if i == 0: # 若第一行是标题，则跳过
# continue
lin = line.strip().split(" ")
if lin[0] in word_to_id:
idx = word_to_id[lin[0]]
emb = [float(x) for x in lin[1:301]]
embeddings[idx] = np.asarray(emb, dtype='float32')
f.close()
np.savez_compressed(filename_trimmed_dir, embeddings=embeddings)
2.2. train_eval.py代码编写
# coding: UTF-8
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from sklearn import metrics
import time
from torch.utils.tensorboard import SummaryWriter
from utils import get_time_dif
# 权重初始化，默认xavier
def init_network(model, method='xavier', exclude='embedding', seed=123):
for name, w in model.named_parameters():
if exclude not in name:
if 'weight' in name:
if method == 'xavier':
nn.init.xavier_normal_(w)
elif method == 'kaiming':
nn.init.kaiming_normal_(w)
else:
nn.init.normal_(w)
elif 'bias' in name:
nn.init.constant_(w, 0)
else:
pass
def train(config, model, train_iter, dev_iter, test_iter):
start_time = time.time()
model.train()
optimizer = torch.optim.Adam(model.parameters(), lr=config.learning_rate)
# 学习率指数衰减，每次epoch：学习率 = gamma * 学习率
# scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer, gamma=0.9)
total_batch = 0 # 记录进行到多少batch
dev_best_loss = float('inf')
last_improve = 0 # 记录上次验证集loss下降的batch数
flag = False # 记录是否很久没有效果提升
writer = SummaryWriter(log_dir=config.log_path + '/' + time.strftime('%m-%d_%H.%M', time.localtime()))
for epoch in range(config.num_epochs):
print('Epoch [{}/{}]'.format(epoch + 1, config.num_epochs))
# scheduler.step() # 学习率衰减
for i, (trains, labels) in enumerate(train_iter):
outputs = model(trains)
model.zero_grad()
loss = F.cross_entropy(outputs, labels)
loss.backward()
optimizer.step()
if total_batch % 100 == 0:
# 每多少轮输出在训练集和验证集上的效果
true = labels.data.cpu()
predic = torch.max(outputs.data, 1)[1].cpu()
train_acc = metrics.accuracy_score(true, predic)
dev_acc, dev_loss = evaluate(config, model, dev_iter)
if dev_loss 6}, Train Loss: {1:>5.2}, Train Acc: {2:>6.2%}, Val Loss: {3:>5.2}, '
'Val Acc: {4:>6.2%}, Time: {5} {6}'
print(msg.format(total_batch, loss.item(), train_acc, dev_loss, dev_acc, time_dif, improve))
writer.add_scalar("loss/train", loss.item(), total_batch)
writer.add_scalar("loss/dev", dev_loss, total_batch)
writer.add_scalar("acc/train", train_acc, total_batch)
writer.add_scalar("acc/dev", dev_acc, total_batch)
model.train()
total_batch += 1
if total_batch - last_improve > config.require_improvement:
# 验证集loss超过1000batch没下降，结束训练
print("No optimization for a long time, auto-stopping...")
flag = True
break
if flag:
break
writer.close()
test(config, model, test_iter)
def test(config, model, test_iter):
# test
model.load_state_dict(torch.load(config.save_path))
model.eval()
start_time = time.time()
test_acc, test_loss, test_report, test_confusion = evaluate(config, model, test_iter, test=True)
msg = 'Test Loss: {0:>5.2}, Test Acc: {1:>6.2%}'
print(msg.format(test_loss, test_acc))
print("Precision, Recall and F1-Score...")
print(test_report)
print("Confusion Matrix...")
print(test_confusion)
time_dif = get_time_dif(start_time)
print("Time usage:", time_dif)
def evaluate(config, model, data_iter, test=False):
model.eval()
loss_total = 0
predict_all = np.array([], dtype=int)
labels_all = np.array([], dtype=int)
with torch.no_grad():
for texts, labels in data_iter:
outputs = model(texts)
loss = F.cross_entropy(outputs, labels)
loss_total += loss
labels = labels.data.cpu().numpy()
predic = torch.max(outputs.data, 1)[1].cpu().numpy()
labels_all = np.append(labels_all, labels)
predict_all = np.append(predict_all, predic)
acc = metrics.accuracy_score(labels_all, predict_all)
if test:
report = metrics.classification_report(labels_all, predict_all, target_names=config.class_list, digits=4)
confusion = metrics.confusion_matrix(labels_all, predict_all)
return acc, loss_total / len(data_iter), report, confusion
return acc, loss_total / len(data_iter)
2.3. text_mixture_predict.py代码编写
# coding:utf-8
import to服务器托管网rch
import numpy as np
import pickle as pkl
from importlib import import_module
from utils import build_iterator
import argparse
parser = argparse.ArgumentParser(description='Chinese Text Classification')
parser.add_argument('--model', type=str, required=True, help='choose a model: TextCNN, TextRNN, TextRCNN')
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()
MAX_VOCAB_SIZE = 10000 # 词表长度限制
tokenizer = lambda x: [y for y in x] # char-level
UNK, PAD = '', '' # 未知字，padding符号
def load_dataset(content, vocab, pad_size=32):
contents = []
for line in content:
lin = line.strip()
if not lin:
continue
# content, label = lin.split('t')
words_line = []
token = tokenizer(line)
seq_len = len(token)
if pad_size:
if len(token) 
2.4. utils_fasttext.py代码编写
# coding: UTF-8
import os
import torch
import numpy as np
import pickle as pkl
from tqdm import tqdm
import time
from datetime import timedelta
MAX_VOCAB_SIZE = 10000
UNK, PAD = '', ''
def build_vocab(file_path, tokenizer, max_size, min_freq):
vocab_dic = {}
with open(file_path, 'r', encoding='UTF-8') as f:
for line in tqdm(f):
lin = line.strip()
if not lin:
continue
content = lin.split('t')[0]
for word in tokenizer(content):
vocab_dic[word] = vocab_dic.get(word, 0) + 1
vocab_list = sorted([_ for _ in vocab_dic.items() if _[1] >= min_freq], key=lambda x: x[1],
reverse=True)[:max_size]
vocab_dic = {word_count[0]: idx for idx, word_count in enumerate(vocab_list)}
vocab_dic.update({UNK: len(vocab_dic), PAD: len(vocab_dic) + 1})
return vocab_dic
def build_dataset(config, ues_word):
if ues_word:
tokenizer = lambda x: x.split(' ') # 以空格隔开，word-level
else:
tokenizer = lambda x: [y for y in x] # char-level
if os.path.exists(config.vocab_path):
vocab = pkl.load(open(config.vocab_path, 'rb'))
else:
vocab = build_vocab(config.train_path, tokenizer=tokenizer, max_size=MAX_VOCAB_SIZE, min_freq=1)
pkl.dump(vocab, open(config.vocab_path, 'wb'))
print(f"Vocab size: {len(vocab)}")
def biGramHash(sequence, t, buckets):
t1 = sequence[t - 1] if t - 1 >= 0 else 0
return (t1 * 14918087) % buckets
def triGramHash(sequence, t, buckets):
t1 = sequence[t - 1] if t - 1 >= 0 else 0
t2 = sequence[t - 2] if t - 2 >= 0 else 0
return (t2 * 14918087 * 18408749 + t1 * 14918087) % buckets
def load_dataset(path, pad_size=32):
contents = []
with open(path, 'r', encoding='UTF-8') as f:
for line in tqdm(f):
lin = line.strip()
if not lin:
continue
content, label = lin.split('t')
words_line = []
token = tokenizer(content)
seq_len = len(token)
if pad_size:
if len(token) = self.n_batches:
self.index = 0
raise StopIteration
else:
batches = self.batches[self.index * self.batch_size: (self.index + 1) * self.batch_size]
self.index += 1
batches = self._to_tensor(batches)
return batches
def __iter__(self):
return self
def __len__(self):
if self.residue:
return self.n_batches + 1
else:
return self.n_batches
def build_iterator(dataset, config, predict):
if predict == True:
config.batch_size = 1
iter = DatasetIterater(dataset, config.batch_size, config.device)
return iter
def get_time_dif(start_time):
"""获取已使用时间"""
end_time = time.time()
time_dif = end_time - start_time
return timedelta(seconds=int(round(time_dif)))
if __name__ == "__main__":
'''提取预训练词向量'''
vocab_dir = "./THUCNews/data/vocab.pkl"
pretrain_dir = "./THUCNews/data/sgns.sogou.char"
emb_dim = 300
filename_trimmed_dir = "./THUCNews/data/vocab.embedding.sougou"
word_to_id = pkl.load(open(vocab_dir, 'rb'))
embeddings = np.random.rand(len(word_to_id), emb_dim)
f = open(pretrain_dir, "r", encoding='UTF-8')
for i, line in enumerate(f.readlines()):
# if i == 0: # 若第一行是标题，则跳过
# continue
lin = line.strip().split(" ")
if lin[0] in word_to_id:
idx = word_to_id[lin[0]]
emb = [float(x) for x in lin[1:301]]
embeddings[idx] = np.asarray(emb, dtype='float32')
f.close()
np.savez_compressed(filename_trimmed_dir, embeddings=embeddings)
2.5. run.py代码编写
# coding: UTF-8
import time
import torch
import numpy as np
from train_eval import train, init_network
from importlib import import_module
import argparse
# 当碰到argparse的参数时，运行方式需要在终端执行我们的程序
parser = argparse.ArgumentParser(description='Chinese Text Classification')
parser.add_argument('--model', type=str, help='choose a model: TextCNN, TextRNN, TextRCNN', default="TextRNN") #调用模型
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()
if __name__ == '__main__':
dataset = 'THUCNews' # 数据集
# 搜狗新闻:embedding_SougouNews.npz, 腾讯:embedding_Tencent.npz, 随机初始化:random
embedding = 'embedding_SougouNews.npz'
if args.embedding == 'random':
embedding = 'random'
model_name = args.model # 'TextRCNN' # TextCNN, TextRNN, FastText, TextRCNN, TextRNN_Att, DPCNN, Transformer
if model_name == 'FastText':
from utils_fasttext import build_dataset, build_iterator, get_time_dif
embedding = 'random'
else:
from utils import build_dataset, build_iterator, get_time_dif
x = import_module('models.' + model_name)
config = x.Config(dataset, embedding)
np.random.seed(1)
torch.manual_seed(1)
torch.cuda.manual_seed_all(1)
torch.backends.cudnn.deterministic = True # 保证每次结果一样
start_time = time.time()
print("Loading data...")
vocab, train_data, dev_data, test_data = build_dataset(config, args.word)
train_iter = build_iterator(train_data, config, False)
dev_iter = build_iterator(dev_data, config, False)
test_iter = build_iterator(test_data, config, False)
# predict_iter = build_iterator(predict_data, config)
time_dif = get_time_dif(start_time)
print("Time usage:", time_dif)
# train
config.n_vocab = len(vocab)
model = x.Model(config).to(config.device)
if model_name != 'Transformer':
init_network(model)
print(model.parameters)
train(config, model, train_iter, dev_iter, test_iter)
3、训练效果
3.1. TextCNN训练效果
|
TextCNN训练效果 |
||||
|
Test Time |
0:04:23 |
|||
|
Test Loaa: |
0.3 |
Test Acc |
90.58% |
|
|
类别 |
Precision |
recall |
F1-score |
support |
|
财经 |
0.9123 |
0.8950 |
0.9036 |
1000 |
|
房产 |
0.9043 |
0.9360 |
0.9199 |
1000 |
|
股票 |
0.8812 |
0.8230 |
0.8511 |
1000 |
|
教育 |
0.9540 |
0.9530 |
0.9535 |
1000 |
|
科技 |
0.8354 |
0.8880 |
0.9609 |
1000 |
|
社会 |
0.8743 |
0.9110 |
0.8923 |
1000 |
|
时政 |
0.8816 |
0.8860 |
0.8838 |
1000 |
|
体育 |
0.9682 |
0.9430 |
0.9554 |
1000 |
|
游戏 |
0.9249 |
0.9110 |
0.9179 |
1000 |
|
娱乐 |
0.9287 |
0.9120 |
0.9203 |
1000 |
|
Accuracy |
|
|
0.9058 |
1000 |
|
Macro avg |
0.9065 |
0.9058 |
0.9059 |
1000 |
|
Weighted avg |
0.9065 |
0.9058 |
0.9059 |
1000 |
表1 TextCNN网络训练结果
3.2.TextRNN训练效果
|
TextRNN训练效果 |
||||
|
Test Time |
0:05:07 |
|||
|
Test Loaa: |
0.29 |
Test Acc |
91.03% |
|
|
类别 |
Precision |
recall |
F1-score |
support |
|
财经 |
0.9195 |
0.8790 |
0.8988 |
1000 |
|
房产 |
0.9181 |
0.9190 |
0.9185 |
1000 |
|
股票 |
0.8591 |
0.8290 |
0.8438 |
1000 |
|
教育 |
0.9349 |
0.9480 |
0.9414 |
1000 |
|
科技 |
0.8642 |
0.8720 |
0.8681 |
1000 |
|
社会 |
0.9190 |
0.9080 |
0.9135 |
1000 |
|
时政 |
0.8578 |
0.8990 |
0.8779 |
1000 |
|
体育 |
0.9690 |
0.9690 |
0.9690 |
1000 |
|
游戏 |
0.9454 |
0.9350 |
0.9402 |
1000 |
|
娱乐 |
0.9175 |
0.9450 |
0.9310 |
1000 |
|
Accuracy |
|
|
0.9103 |
1000 |
|
Macro avg |
0.9104 |
0.9103 |
0.9102 |
1000 |
|
Weighted avg |
0.9104 |
0.9103 |
0.9102 |
1000 |
表2 TextRNN网络训练结果
TextRNN网络的训练效果最好，准确率达到了91.03%，明显高于TextCNN网络的效果。
3.3.TextRCNN训练效果
|
TextRCNN训练效果 |
||||
|
Test Time |
0:03:20 |
|||
|
Test Loaa: |
0.29 |
Test Acc |
90.96% |
|
|
类别 |
Precision |
recall |
F1-score |
support |
|
财经 |
0.9134 |
0.8970 |
0.9051 |
1000 |
|
房产 |
0.9051 |
0.9350 |
0.9198 |
1000 |
|
股票 |
0.8658 |
0.8320 |
0.8485 |
1000 |
|
教育 |
0.9295 |
0.9500 |
0.9397 |
1000 |
|
科技 |
0.8352 |
0.8770 |
0.8556 |
1000 |
|
社会 |
0.8993 |
0.9290 |
0.9139 |
1000 |
|
时政 |
0.8921 |
0.9680 |
0.8799 |
1000 |
|
体育 |
0.9851 |
0.9670 |
0.9743 |
1000 |
|
游戏 |
0.9551 |
0.9140 |
0.9341 |
1000 |
|
娱乐 |
0.9233 |
0.9270 |
0.9251 |
1000 |
|
Accuracy |
|
|
0.9096 |
1000 |
|
Macro avg |
0.9101 |
0.9096 |
0.9096 |
1000 |
|
Weighted avg |
0.9101 |
0.9096 |
0.9096 |
1000 |
表3 TextRCNN网络训练结果
TextRCNN网络的效果为90.96%，与TextCNN网络模型效果相近。
|
mode |
time |
Cpu |
|
TextCNN |
0:04:23 |
CORE i5 |
|
TextRNN |
0:05:07 |
|
|
TextRCNN |
0:03:20 |
表4 模型对比
最终发现TextRNN的训练结果最好，但所需的时间也是最久的。
4、操作异常问题与解决方案
（1）ModuleNotFoundError: No module named ‘tensorboard’
使用以下命令在命令行中安装Tensorboard: pip install tensorboard
（2）module ‘distutils’ has no attribute ‘version’
发生此错误是因为 setuptools 版本59.6.0中的更改以某种方式中断了对 version 属性的调用，而且，在最新版本中，version 属性似乎已经从 distutils 中删除。使用tensorboard包时，setuptools版本过高导致的问题。
卸载当前setuptools：
pip uninstall setuptools
使用pip，不能使用 conda uninstall setuptools ;切记不能使用conda的命令，原因是，conda在卸载的时候，会自动分析与其相关的库，然后全部删除。
pip install setuptools==58.0.4
5、总结
通过分别对模型TextCNN、TextRNN、TextRCNN和不同的硬件环境进行实验，分别对实验结果中的训练时间、准确率、召回率和F1值进行比较，进一步确定哪个模型在给定数据集和硬件环境下表现最佳。最终发现TextRNN的训练结果最好，但所需的时间也是最久的，而TextRCNN模型的训练结果与TextCNN几乎相同，但TextRCNN所需的时间最少。
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