黑马NLP实战 --- 新闻分类模型训练

关于《黑马程序员》课程中NLP中 训练新闻分类模型

最近在学习NLP的相关知识，找了资料比较全的黑马程序员中讲解NLP的课程，可是其中有一部分实战 新闻主题分类实战项目中，我发现黑马程序员代码有大两的错误，多处代码逻辑错误：

1. 首先是数据集下载太慢，因为需要翻墙才能下载，所以大部分情况在加载数据集就会出现Timeout异常
2. 数据集的处理，在课程中并没有提到，加载本地的csv数据集文件出现的格式不对的情况
3. 其次，generator_banth()这个方法中返回的数据对象元组形式是不对的，新闻数据集的元组是3项（type, title ,content）分别是新闻的类型，新闻的标题和新闻的内内容，但是在课程却只有两项。

!!!需要注意的是 torchtext 的版本是0.4 ，可能是版本更新后，这个模块被移走了，如果不是0.4 可能会出现from torchtext.datasets.text_classification 这句话错误！！！

针对上述问题，我整理了一个完整可以正常运行的完整代码，希望给个小心心或者关注我一下呀~

先放代码

from torchtext.datasets.text_classification import *
from torchtext.datasets.text_classification import _csv_iterator, _create_data_from_iterator
import os
import time
from torch import optim
import torch
from torch.utils.data import DataLoader
from torch.utils.data.dataset import random_split
import torch.nn as nn
from torchtext.data.utils import ngrams_iterator
from torchtext.data.utils import get_tokenizer




N_GRAMS =2
if not os.path.isdir('./data'):
    os.mkdir('./data')

BATCH_SIZE = 16
device = torch.device('cuda'if torch.cuda.is_available() else 'cpu')

# 定义创建数据集
def _setup_data_set(dataset_tar='./data/ag_news_csv.tar.gz',
                    n_grams=N_GRAMS, vocab=None,
                    include_unk=False):
    extracted_files = extract_archive(dataset_tar)
    train_csv_path = ''
    test_csv_path = ''
    for file_name in extracted_files:
        if file_name.endswith('train.csv'):
            train_csv_path = file_name
        if file_name.endswith('test.csv'):
            test_csv_path=file_name
        
    if vocab is None:
        print("Building Vocab based on %s" % train_csv_path)
        # 创建词典
        vocab = build_vocab_from_iterator(_csv_iterator(train_csv_path, ngrams=n_grams))
    else:
        if not isinstance(vocab, Vocab):
            raise TypeError("Passed vocabulary is not of type Vocab")
    print('Vocab has %d entries' % len(vocab))
    print('Creating training data')
    train_data, train_labels = _create_data_from_iterator(
        vocab, _csv_iterator(test_csv_path, n_grams, yield_cls=True), include_unk)
    print('Creating testing data')
    test_data, test_labels = _create_data_from_iterator(
        vocab, _csv_iterator(test_csv_path, n_grams, yield_cls=True), include_unk)
    if len(train_labels ^ test_labels) > 0:
        raise ValueError("Training and test labels on't match")
    # 返回数据集实例
    return (TextClassificationDataset(vocab, train_data, train_labels),
            TextClassificationDataset(vocab, test_data, test_labels))


train_data_set, test_data_set = _setup_data_set()

# 定义模型
class TextSentiment(nn.Module):
    def __init__(self, vocab_size, embed_dim, num_class):
        super().__init__()
        self.embedding = nn.EmbeddingBag(vocab_size, embed_dim, sparse=True)
        self.fc = nn.Linear(embed_dim, num_class)
        self.init_weights()
        
    def init_weights(self):
        init_range = 0.5
        self.embedding.weight.data.uniform_(-init_range, init_range)
        self.fc.weight.data.uniform_(-init_range, init_range)
        self.fc.bias.data.zero_()
    
    def forward(self, text, offsets):
        embedded = self.embedding(text, offsets)
        return self.fc(embedded)


VOCAB_SIZE = len(train_data_set.get_vocab())
EMBED_DIM = 32
NUM_CLASS = len(train_data_set.get_labels())
# 实列化
model = TextSentiment(VOCAB_SIZE, EMBED_DIM, NUM_CLASS).to(device)

N_EPOCH = 5
min_valid_loss = float('inf')

criterion = torch.nn.CrossEntropyLoss().to(device)
optimizer = optim.SGD(model.parameters(), lr=4.0)
scheduler = optim.lr_scheduler.StepLR(optimizer, 1, gamma=0.9)

train_len = int(len(train_data_set) * 0.95)
sub_train_, sub_valid_ = random_split(train_data_set, [train_len, len(train_data_set) - train_len])


def generate_batch(batch):
    label = torch.tensor([entry[0] for entry in batch])
    text = [entry[1] for entry in batch]
    offsets =[0] + [len(entry) for entry in text]
    offsets = torch.tensor(offsets[:-1]).cumsum(dim=0)
    
    text = torch.cat(text)
    return text, offsets, label

def train_function(sub_train_):
    
    loss_ = 0
    acc_ = 0
    data = DataLoader(sub_train_, batch_size=BATCH_SIZE, shuffle=True, collate_fn=generate_batch)
    for i, (text, offsets, cls) in enumerate(data):
        optimizer.zero_grad()
        text, offsets, cls = text.to(device), offsets.to(device), cls.to(device)
        output = model(text, offsets)
        loss_ = criterion(output, cls)
        loss_ += loss_.item()
        loss_.backward()
        optimizer.step()
        acc_ += (output.argmax(1) == cls).sum().item()
    # 调整学习率
    scheduler.step()
    
    return loss_ / len(sub_train_), acc_ / len(sub_train_)

def test(data_):
    loss = 0
    acc = 0
    data = DataLoader(data_, batch_size=BATCH_SIZE, collate_fn=generate_batch)
    for text, offsets, cls in data:
        text, offsets, cls = text.to(device), offsets.to(device), cls.to(device)
        with torch.no_grad():
            output = model(text, offsets)
            loss = criterion(output, cls)
            loss += loss.item()
            acc += (output.argmax(1) == cls).sum().item()
        
    return loss / len(data_), acc / len(data_)


for epoch in range(N_EPOCH):
    start_time = time.time()
    train_loss, train_acc = train_function(sub_train_)
    valid_loss, valid_acc = test(sub_valid_)
    
    secs = int(time.time() - start_time)
    mins = secs / 60
    secs = secs % 60
    
    print('Epoch:%d' % (epoch + 1), "| time in %d minutes, %d seconds" % (mins, secs))
    print(f"\tLoss:{train_loss:.4f}(train)\t|\tAcc:{train_acc * 100:.1f}%(train)")
    print(f"\tLoss:{valid_loss:.6f}(valid)\t|\tAcc:{valid_acc * 100:.6f}%(valid)")
    
    
    
# 测试模型
ag_news_label = {
    1: "World",
    2: "Sports",
    3: "Business",
    4: "Sci/Tec"
}


def predict(text, model, vocab, ngrams):
    tokenizer = get_tokenizer("basic_english")
    with torch.no_grad():
        text = torch.tensor([vocab[token] for token in ngrams_iterator(tokenizer(text),ngrams)])
        output = model(text, torch.tensor([0]))
        return output.argmax(1).item() + 1

ex_text_str = "MEMPHIS, Tenn. – Four days ago, Jon Rahm was \
        enduring the season’s worst weather conditions on Sunday at The \
        Open on his way to a closing 75 at Royal Portrush, which \
        considering the wind and the rain was a respectable showing. \
        Thursday’s first round at the WGC-FedEx St. Jude Invitational \
        was another story. With temperatures in the mid-80s and hardly any \
        wind, the Spaniard was 13 strokes better in a flawless round. \
        Thanks to his best putting performance on the PGA Tour, Rahm \
        finished with an 8-under 62 for a three-stroke lead, which \
        was even more impressive considering he’d never played the \
        front nine at TPC Southwind."

vocab = train_data_set.get_vocab()
model = model.to("cpu")

print("This is a %s news" % ag_news_label[predict(ex_text_str, model, vocab, 2)])
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184

运行效果：

Building Vocab based on ./data/ag_news_csv/train.csv
120000lines [00:06, 17621.91lines/s]
Vocab has 1308844 entries
Creating training data
7600lines [00:00, 8405.65lines/s]
Creating testing data
7600lines [00:00, 9790.11lines/s]
Epoch:1 | time in 0 minutes, 0 seconds
        Loss:0.0003(train)      |       Acc:40.9%(train)
        Loss:0.0038(valid)      |       Acc:47.1%(valid)
Epoch:2 | time in 0 minutes, 0 seconds
        Loss:0.0002(train)      |       Acc:70.4%(train)
        Loss:0.0031(valid)      |       Acc:67.9%(valid)
Epoch:3 | time in 0 minutes, 0 seconds
        Loss:0.0004(train)      |       Acc:82.4%(train)
        Loss:0.0126(valid)      |       Acc:52.6%(valid)
Epoch:4 | time in 0 minutes, 0 seconds
        Loss:0.0001(train)      |       Acc:88.3%(train)
        Loss:0.0026(valid)      |       Acc:60.8%(valid)
Epoch:5 | time in 0 minutes, 0 seconds
        Loss:0.0000(train)      |       Acc:91.9%(train)
        Loss:0.0002(valid)      |       Acc:79.7%(valid)
Epoch:6 | time in 0 minutes, 0 seconds
        Loss:0.0000(train)      |       Acc:94.8%(train)
        Loss:0.0001(valid)      |       Acc:81.8%(valid)
Epoch:7 | time in 0 minutes, 0 seconds
        Loss:0.0000(train)      |       Acc:96.7%(train)
        Loss:0.0001(valid)      |       Acc:83.4%(valid)
Epoch:8 | time in 0 minutes, 0 seconds
        Loss:0.0000(train)      |       Acc:98.5%(train)
        Loss:0.0001(valid)      |       Acc:83.4%(valid)
Epoch:9 | time in 0 minutes, 0 seconds
        Loss:0.0000(train)      |       Acc:99.3%(train)
        Loss:0.0001(valid)      |       Acc:81.1%(valid)
Epoch:10 | time in 0 minutes, 0 seconds
        Loss:0.0000(train)      |       Acc:99.6%(train)
        Loss:0.0002(valid)      |       Acc:82.1%(valid)
Epoch:11 | time in 0 minutes, 0 seconds
        Loss:0.0000(train)      |       Acc:99.8%(train)
        Loss:0.0001(valid)      |       Acc:84.7%(valid)
Epoch:12 | time in 0 minutes, 0 seconds
        Loss:0.0000(train)      |       Acc:99.9%(train)
        Loss:0.0001(valid)      |       Acc:83.2%(valid)
Epoch:13 | time in 0 minutes, 0 seconds
        Loss:0.0000(train)      |       Acc:100.0%(train)
        Loss:0.0001(valid)      |       Acc:83.7%(valid)
Epoch:14 | time in 0 minutes, 0 seconds
        Loss:0.0000(train)      |       Acc:100.0%(train)
        Loss:0.0001(valid)      |       Acc:83.2%(valid)
Epoch:15 | time in 0 minutes, 0 seconds
        Loss:0.0000(train)      |       Acc:100.0%(train)
        Loss:0.0001(valid)      |       Acc:84.2%(valid)
Epoch:16 | time in 0 minutes, 0 seconds
        Loss:0.0000(train)      |       Acc:100.0%(train)
        Loss:0.0001(valid)      |       Acc:85.0%(valid)
Epoch:17 | time in 0 minutes, 0 seconds
        Loss:0.0000(train)      |       Acc:100.0%(train)
        Loss:0.0001(valid)      |       Acc:85.0%(valid)
Epoch:18 | time in 0 minutes, 0 seconds
        Loss:0.0000(train)      |       Acc:100.0%(train)
        Loss:0.0000(valid)      |       Acc:85.5%(valid)
Epoch:19 | time in 0 minutes, 0 seconds
        Loss:0.0000(train)      |       Acc:100.0%(train)
        Loss:0.0001(valid)      |       Acc:84.2%(valid)
Epoch:20 | time in 0 minutes, 0 seconds
        Loss:0.0000(train)      |       Acc:100.0%(train)
        Loss:0.0001(valid)      |       Acc:84.5%(valid)
This is a Sports news
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68