新闻主题分类任务——torchtext 库进行文本分类
目录
- 简介
- 导入相关的torch工具包
- 访问原始数据集迭代器
- 使用原始训练数据集构建词汇表
- 生成数据批处理和迭代器
- 定义模型
- 定义函数来训练模型和评估结果
- 实例化并运行模型
- 使用测试数据集评估模型
- 测试随机新闻
- 完整代码
- 参考链接
简介
使用浅层网络构建新闻主题分类器。
以一段新闻报道中的文本描述内容为输入, 使用模型帮助我们判断它最有可能属于哪一种类型的新闻, 这是典型的文本分类问题, 我们这里假定每种类型是互斥的, 即文本描述有且只有一种类型。
导入相关的torch工具包
import time
import torch
import torch.nn as nn
from torchtext.datasets import AG_NEWS
from torchtext.data.utils import get_tokenizer
from torchtext.vocab import build_vocab_from_iterator
from torch.utils.data import DataLoader
from torch.utils.data.dataset import random_split
from torchtext.data.functional import to_map_style_dataset
from TextClassificationModule import TextClassificationModule
访问原始数据集迭代器
torchtext 库提供了一些原始数据集迭代器，它们产生原始文本字符串。例如，AG_NEWS数据集迭代器将原始数据生成为标签和文本的元组。
# 可用设备检测, 有GPU的话将优先使用GPU
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# 基本的英文分词器
tokenizer = get_tokenizer('basic_english')
# 训练数据加载器
train_iter = AG_NEWS(split="train")
test_iter = AG_NEWS(split="test")
对读取到的数据进行测试，该读取的数据是从网上自动下载到缓存，其中读取到的 train_iter 和 test_iter 为训练集和测试集，且均为迭代器类型。
print('test:')
train_data = iter(train_iter)
test_data = iter(test_iter)
print(next(train_data))
print(next(test_data))
运行结果
test:
(3, "Wall St. Bears Claw Back Into the Black (Reuters) Reuters - Short-sellers, Wall Street's dwindling\\band of ultra-cynics, are seeing green again.")
(3, "Fears for T N pension after talks Unions representing workers at Turner Newall say they are 'disappointed' after talks with stricken parent firm Federal Mogul.")
使用原始训练数据集构建词汇表
其中分词生成器中的 “_” 表示一个不用的变量即类别，text 表示新闻文本，如：
_ = 3
text = Wall St. Bears Claw Back Into the Black (Reuters) Reuters - Short-sellers, Wall Street's dwindling\\band of ultra-cynics, are seeing green again.
python 中 yield 的作用就是把一个函数变成一个 generator，带有 yield 的函数不再是一个普通函数，Python 解释器会将其视为一个 generator，调用 fab(5) 不会执行 fab 函数，而是返回一个 iterable 对象。
示例
def yield_test(n):
for i in range(n):
yield call(i)
print("i=",i)
#做一些其它的事情
print("do something.")
print("end.")
def call(i):
return i*2
#使用for循环
for i in yield_test(5):
print(i,",")
运行结果
0 ,
i= 0
2 ,
i= 1
4 ,
i= 2
6 ,
i= 3
8 ,
i= 4
do something.
end.
使用原始训练数据集构建词汇表
# 分词生成器
def yield_tokens(data_iter):
for _, text in data_iter:
yield tokenizer(text)
# 根据训练数据构建词汇表，torchtext.vocab
vocab = build_vocab_from_iterator(yield_tokens(train_iter), specials=["" ])
# 设置默认索引，当某个单词不在词汇表 vocab 时（OOV)，返回该单词索引
vocab.set_default_index(vocab["" ])
# 词汇表会将 token 映射到词汇表中的索引上
print(vocab(["here", "is", "an", "example"]))
# 构建数据加载器 dataloader
# text_pipeline 将一个文本字符串转换为整数 List, List 中每项对应词汇表 vocab 中的单词的索引号
text_pipeline = lambda x: vocab(tokenizer(x))
# label_pipeline 将 label 转换为整数
label_pipeline = lambda x: int(x) - 1
# pipeline example
print(text_pipeline("hello world! I'am happy"))
print(label_pipeline("10"))
运行结果
[475, 21, 30, 5297]
[12544, 50, 764, 282, 16, 1913, 2734]
9
生成数据批处理和迭代器
def collate_batch(batch):
label_list, text_list, offsets = [], [], [0]
for (_label, _text) in batch:
label_list.append(label_pipeline(_label))
processed_text = torch.tensor(text_pipeline(_text), dtype=torch.int64)
text_list.append(processed_text)
offsets.append(processed_text.size(0))
label_list = torch.tensor(label_list, dtype=torch.int64)
offsets = torch.tensor(offsets[:-1]).cumsum(dim=0)
text_list = torch.cat(text_list)
return label_list.to(device), text_list.to(device), offsets.to(device)
# 加载数据集合，转换为张量
dataloader = DataLoader(train_iter, batch_size=8, shuffle=False, collate_fn=collate_batch)
定义模型
该模型由 nn.EmbeddingBag 层和用于分类目的的线性层组成。nn.EmbeddingBag 使用默认模式“mean”计算嵌入“bag”的平均值。尽管此处的文本条目具有不同的长度，但 nn.EmbeddingBag 模块在此处不需要填充，因为文本长度保存在偏移量中。
nn.EmbeddingBag 可以提高性能和内存效率以处理一系列张量。
import torch.nn as nn
class TextClassificationModule(nn.Module):
def __init__(self, vocab_size, embed_dim, num_class):
"""
文本分类模型
description: 类的初始化函数
:param vocab_size: 整个语料包含的不同词汇总数
:param embed_dim: 指定词嵌入的维度
:param num_class: 文本分类的类别总数
"""
super(TextClassificationModule, self).__init__()
# 实例化embedding层, sparse=True代表每次对该层求解梯度时, 只更新部分权重
self.embedding = nn.EmbeddingBag(vocab_size, embed_dim, sparse=True)
# 实例化全连接层, 参数分别是embed_dim和num_class
self.fc = nn.Linear(embed_dim, num_class)
# 为各层初始化权重
self.init_weights()
def init_weights(self):
"""初始化权重函数"""
# 指定初始权重的取值范围数
initrange = 0.5
# 各层的权重参数都是初始化为均匀分布
self.embedding.weight.data.uniform_(-initrange, initrange)
self.fc.weight.data.uniform_(-initrange, initrange)
# 偏置初始化为0
self.fc.bias.data.zero_()
def forward(self, text, offsets):
"""
:param text: 文本数值映射后的结果
:return: 与类别数尺寸相同的张量, 用以判断文本类别
"""
embedded = self.embedding(text, offsets)
return self.fc(embedded)
定义函数来训练模型和评估结果
def train(dataloader):
model.train()
total_acc, total_count = 0, 0
log_interval = 500
start_time = time.time()
for idx, (label, text, offsets) in enumerate(dataloader):
optimizer.zero_grad()
predicted_label = model(text, offsets)
loss = criterion(predicted_label, label)
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), 0.1)
optimizer.step()
total_acc += (predicted_label.argmax(1) == label).sum().item()
total_count += label.size(0)
if idx % log_interval == 0 and idx > 0:
elapsed = time.time() - start_time
print('| epoch {:3d} | {:5d}/{:5d} batches '
'| accuracy {:8.3f}'.format(epoch, idx, len(dataloader),
total_acc / total_count))
total_acc, total_count = 0, 0
start_time = time.time()
def evaluate(dataloader):
model.eval()
total_acc, total_count = 0, 0
with torch.no_grad():
for idx, (label, text, offsets) in enumerate(dataloader):
predicted_label = model(text, offsets)
loss = criterion(predicted_label, label)
total_acc += (predicted_label.argmax(1) == label).sum().item()
total_count += label.size(0)
return total_acc / total_count
实例化并运行模型
# 加载数据集合，转换为张量
dataloader = DataLoader(train_iter, batch_size=8, shuffle=False, collate_fn=collate_batch)
# 一个嵌入维度为 64 的模型。词汇大小等于词汇实例的长度。类的数量等于标签的数量，
num_class = len(set([label for (label, text) in train_iter]))
vocab_size = len(vocab)
emsize = 64
model = TextClassificationModule(vocab_size, emsize, num_class).to(device)
# 训练轮数
EPOCHS = 10
# 学习率
LR = 5
# 训练数据规模
BATCH_SIZE = 64
# 交叉熵损失函数
criterion = torch.nn.CrossEntropyLoss()
# 优化器
optimizer = torch.optim.SGD(model.parameters(), lr=LR)
# 调整学习率机制
scheduler = torch.optim.lr_scheduler.StepLR(optimizer, 1, gamma=0.1)
total_accu = None
train_dataset = to_map_style_dataset(train_iter)
test_dataset = to_map_style_dataset(test_iter)
# 划分训练集中5%的数据最为验证集
num_train = int(len(train_dataset) * 0.95)
split_train_, split_valid_ = random_split(train_dataset, [num_train, len(train_dataset) - num_train])
train_dataloader = DataLoader(split_train_, batch_size=BATCH_SIZE, shuffle=True, collate_fn=collate_batch)
valid_dataloader = DataLoader(split_valid_, batch_size=BATCH_SIZE, shuffle=True, collate_fn=collate_batch)
test_dataloader = DataLoader(test_dataset, batch_size=BATCH_SIZE, shuffle=True, collate_fn=collate_batch)
for epoch in range(1, EPOCHS + 1):
epoch_start_time = time.time()
train(train_dataloader)
accu_val = evaluate(valid_dataloader)
if total_accu is not None and total_accu > accu_val:
scheduler.step()
else:
total_accu = accu_val
print('-' * 59)
print('| end of epoch {:3d} | time: {:5.2f}s | '
'valid accuracy {:8.3f} '.format(epoch,
time.time() - epoch_start_time,
accu_val))
print('-' * 59)
运行结果
| epoch 1 | 500/ 1782 batches | accuracy 0.689
| epoch 1 | 1000/ 1782 batches | accuracy 0.856
| epoch 1 | 1500/ 1782 batches | accuracy 0.873
-----------------------------------------------------------
| end of epoch 1 | time: 23.38s | valid accuracy 0.879
-----------------------------------------------------------
| epoch 2 | 500/ 1782 batches | accuracy 0.896
| epoch 2 | 1000/ 1782 batches | accuracy 0.904
| epoch 2 | 1500/ 1782 batches | accuracy 0.900
-----------------------------------------------------------
| end of epoch 2 | time: 32.21s | valid accuracy 0.891
-----------------------------------------------------------
| epoch 3 | 500/ 1782 batches | accuracy 0.915
| epoch 3 | 1000/ 1782 batches | accuracy 0.916
| epoch 3 | 1500/ 1782 batches | accuracy 0.915
-----------------------------------------------------------
| end of epoch 3 | time: 36.85s | valid accuracy 0.899
-----------------------------------------------------------
| epoch 4 | 500/ 1782 batches | accuracy 0.925
| epoch 4 | 1000/ 1782 batches | accuracy 0.925
| epoch 4 | 1500/ 1782 batches | accuracy 0.922
-----------------------------------------------------------
| end of epoch 4 | time: 20.15s | valid accuracy 0.897
-----------------------------------------------------------
| epoch 5 | 500/ 1782 batches | accuracy 0.937
| epoch 5 | 1000/ 1782 batches | accuracy 0.938
| epoch 5 | 1500/ 1782 batches | accuracy 0.936
-----------------------------------------------------------
| end of epoch 5 | time: 28.52s | valid accuracy 0.905
-----------------------------------------------------------
| epoch 6 | 500/ 1782 batches | accuracy 0.939
| epoch 6 | 1000/ 1782 batches | accuracy 0.938
| epoch 6 | 1500/ 1782 batches | accuracy 0.941
-----------------------------------------------------------
| end of epoch 6 | time: 33.47s | valid accuracy 0.905
-----------------------------------------------------------
| epoch 7 | 500/ 1782 batches | accuracy 0.940
| epoch 7 | 1000/ 1782 batches | accuracy 0.941
| epoch 7 | 1500/ 1782 batches | accuracy 0.939
-----------------------------------------------------------
| end of epoch 7 | time: 20.75s | valid accuracy 0.904
-----------------------------------------------------------
| epoch 8 | 500/ 1782 batches | accuracy 0.941
| epoch 8 | 1000/ 1782 batches | accuracy 0.941
| epoch 8 | 1500/ 1782 batches | accuracy 0.940
-----------------------------------------------------------
| end of epoch 8 | time: 27.11s | valid accuracy 0.906
-----------------------------------------------------------
| epoch 9 | 500/ 1782 batches | accuracy 0.942
| epoch 9 | 1000/ 1782 batches | accuracy 0.942
| epoch 9 | 1500/ 1782 batches | accuracy 0.942
-----------------------------------------------------------
| end of epoch 9 | time: 34.83s | valid accuracy 0.906
-----------------------------------------------------------
| epoch 10 | 500/ 1782 batches | accuracy 0.942
| epoch 10 | 1000/ 1782 batches | accuracy 0.942
| epoch 10 | 1500/ 1782 batches | accuracy 0.940
-----------------------------------------------------------
| end of epoch 10 | time: 22.78s | valid accuracy 0.906
-----------------------------------------------------------
使用测试数据集评估模型
print('Checking the results of test dataset.')
accu_test = evaluate(test_dataloader)
print('test accuracy {:8.3f}'.format(accu_test))
运行结果
Checking the results of test dataset.
test accuracy 0.906
测试随机新闻
# 测试随机新闻
# 使用迄今为止最好的模型并测试高尔夫新闻。
ag_news_label = {1: "World",
2: "Sports",
3: "Business",
4: "Sci/Tec"}
def predict(text, text_pipeline):
with torch.no_grad():
text = torch.tensor(text_pipeline(text))
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."
model = model.to("cpu")
print("This is a %s news" % ag_news_label[predict(ex_text_str, text_pipeline)])
运行结果
This is a Sports news
完整代码
import time
import torch
import torch.nn as nn
from torchtext.datasets import AG_NEWS
from torchtext.data.utils import get_tokenizer
from torchtext.vocab import build_vocab_from_iterator
from torch.utils.data import DataLoader
from torch.utils.data.dataset import random_split
from torchtext.data.functional import to_map_style_dataset
# 可用设备检测, 有GPU的话将优先使用GPU
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# 基本的英文分词器
tokenizer = get_tokenizer('basic_english')
# 训练数据加载器
train_iter = AG_NEWS(split="train")
test_iter = AG_NEWS(split="test")
# print('test:')
# train_data = iter(train_iter)
# test_data = iter(test_iter)
# print(next(train_data))
# print(next(test_data))
# 分词生成器
def yield_tokens(data_iter):
for _, text in data_iter:
yield tokenizer(text)
# 根据训练数据构建词汇表
vocab = build_vocab_from_iterator(yield_tokens(train_iter), specials=["" ])
# 设置默认索引，当某个单词不在词汇表 vocab 时（OOV)，返回该单词索引
vocab.set_default_index(vocab["" ])
# 词汇表会将 token 映射到词汇表中的索引上
# print(vocab(["here", "is", "an", "example"]))
# 构建数据加载器 dataloader
# text_pipeline 将一个文本字符串转换为整数 List, List 中每项对应词汇表 vocab 中的单词的索引号
text_pipeline = lambda x: vocab(tokenizer(x))
# label_pipeline 将 label 转换为整数
label_pipeline = lambda x: int(x) - 1
# pipeline example
# print(text_pipeline("hello world! I'am happy"))
# print(label_pipeline("10"))
# 模型
class TextClassificationModule(nn.Module):
def __init__(self, vocab_size, embed_dim, num_class):
"""
文本分类模型
description: 类的初始化函数
:param vocab_size: 整个语料包含的不同词汇总数
:param embed_dim: 指定词嵌入的维度
:param num_class: 文本分类的类别总数
"""
super(TextClassificationModule, self).__init__()
# 实例化embedding层, sparse=True代表每次对该层求解梯度时, 只更新部分权重
self.embedding = nn.EmbeddingBag(vocab_size, embed_dim, sparse=True)
# 实例化全连接层, 参数分别是embed_dim和num_class
self.fc = nn.Linear(embed_dim, num_class)
# 为各层初始化权重
self.init_weights()
def init_weights(self):
"""初始化权重函数"""
# 指定初始权重的取值范围数
initrange = 0.5
# 各层的权重参数都是初始化为均匀分布
self.embedding.weight.data.uniform_(-initrange, initrange)
self.fc.weight.data.uniform_(-initrange, initrange)
# 偏置初始化为0
self.fc.bias.data.zero_()
def forward(self, text, offsets):
"""
:param text: 文本数值映射后的结果
:return: 与类别数尺寸相同的张量, 用以判断文本类别
"""
embedded = self.embedding(text, offsets)
return self.fc(embedded)
def collate_batch(batch):
label_list, text_list, offsets = [], [], [0]
for (_label, _text) in batch:
label_list.append(label_pipeline(_label))
processed_text = torch.tensor(text_pipeline(_text), dtype=torch.int64)
text_list.append(processed_text)
offsets.append(processed_text.size(0))
label_list = torch.tensor(label_list, dtype=torch.int64)
offsets = torch.tensor(offsets[:-1]).cumsum(dim=0)
text_list = torch.cat(text_list)
return label_list.to(device), text_list.to(device), offsets.to(device)
def train(dataloader):
model.train()
total_acc, total_count = 0, 0
log_interval = 500
start_time = time.time()
for idx, (label, text, offsets) in enumerate(dataloader):
optimizer.zero_grad()
predicted_label = model(text, offsets)
loss = criterion(predicted_label, label)
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), 0.1)
optimizer.step()
total_acc += (predicted_label.argmax(1) == label).sum().item()
total_count += label.size(0)
if idx % log_interval == 0 and idx > 0:
elapsed = time.time() - start_time
print('| epoch {:3d} | {:5d}/{:5d} batches '
'| accuracy {:8.3f}'.format(epoch, idx, len(dataloader),
total_acc / total_count))
total_acc, total_count = 0, 0
start_time = time.time()
def evaluate(dataloader):
model.eval()
total_acc, total_count = 0, 0
with torch.no_grad():
for idx, (label, text, offsets) in enumerate(dataloader):
predicted_label = model(text, offsets)
loss = criterion(predicted_label, label)
total_acc += (predicted_label.argmax(1) == label).sum().item()
total_count += label.size(0)
return total_acc / total_count
# 加载数据集合，转换为张量
dataloader = DataLoader(train_iter, batch_size=8, shuffle=False, collate_fn=collate_batch)
# 一个嵌入维度为 64 的模型。词汇大小等于词汇实例的长度。类的数量等于标签的数量，
num_class = len(set([label for (label, text) in train_iter]))
vocab_size = len(vocab)
emsize = 64
model = TextClassificationModule(vocab_size, emsize, num_class).to(device)
# 训练轮数
EPOCHS = 10
# 学习率
LR = 5
# 训练数据规模
BATCH_SIZE = 64
# 交叉熵损失函数
criterion = torch.nn.CrossEntropyLoss()
# 优化器
optimizer = torch.optim.SGD(model.parameters(), lr=LR)
# 调整学习率机制
scheduler = torch.optim.lr_scheduler.StepLR(optimizer, 1, gamma=0.1)
total_accu = None
train_dataset = to_map_style_dataset(train_iter)
test_dataset = to_map_style_dataset(test_iter)
# 划分训练集中5%的数据最为验证集
num_train = int(len(train_dataset) * 0.95)
split_train_, split_valid_ = random_split(train_dataset, [num_train, len(train_dataset) - num_train])
train_dataloader = DataLoader(split_train_, batch_size=BATCH_SIZE, shuffle=True, collate_fn=collate_batch)
valid_dataloader = DataLoader(split_valid_, batch_size=BATCH_SIZE, shuffle=True, collate_fn=collate_batch)
test_dataloader = DataLoader(test_dataset, batch_size=BATCH_SIZE, shuffle=True, collate_fn=collate_batch)
for epoch in range(1, EPOCHS + 1):
epoch_start_time = time.time()
train(train_dataloader)
accu_val = evaluate(valid_dataloader)
if total_accu is not None and total_accu > accu_val:
scheduler.step()
else:
total_accu = accu_val
print('-' * 59)
print('| end of epoch {:3d} | time: {:5.2f}s | '
'valid accuracy {:8.3f} '.format(epoch,
time.time() - epoch_start_time,
accu_val))
print('-' * 59)
'''使用测试数据集评估模型'''
print('Checking the results of test dataset.')
accu_test = evaluate(test_dataloader)
print('test accuracy {:8.3f}'.format(accu_test))
# 测试随机新闻
# 使用迄今为止最好的模型并测试高尔夫新闻。
ag_news_label = {1: "World",
2: "Sports",
3: "Business",
4: "Sci/Tec"}
def predict(text, text_pipeline):
with torch.no_grad():
text = torch.tensor(text_pipeline(text))
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."
model = model.to("cpu")
print("This is a %s news" % ag_news_label[predict(ex_text_str, text_pipeline)])
运行结果
| epoch 1 | 500/ 1782 batches | accuracy 0.689
| epoch 1 | 1000/ 1782 batches | accuracy 0.856
| epoch 1 | 1500/ 1782 batches | accuracy 0.873
-----------------------------------------------------------
| end of epoch 1 | time: 23.38s | valid accuracy 0.879
-----------------------------------------------------------
| epoch 2 | 500/ 1782 batches | accuracy 0.896
| epoch 2 | 1000/ 1782 batches | accuracy 0.904
| epoch 2 | 1500/ 1782 batches | accuracy 0.900
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| end of epoch 2 | time: 32.21s | valid accuracy 0.891
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| epoch 3 | 500/ 1782 batches | accuracy 0.915
| epoch 3 | 1000/ 1782 batches | accuracy 0.916
| epoch 3 | 1500/ 1782 batches | accuracy 0.915
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| end of epoch 3 | time: 36.85s | valid accuracy 0.899
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| epoch 4 | 500/ 1782 batches | accuracy 0.925
| epoch 4 | 1000/ 1782 batches | accuracy 0.925
| epoch 4 | 1500/ 1782 batches | accuracy 0.922
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| end of epoch 4 | time: 20.15s | valid accuracy 0.897
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| epoch 5 | 500/ 1782 batches | accuracy 0.937
| epoch 5 | 1000/ 1782 batches | accuracy 0.938
| epoch 5 | 1500/ 1782 batches | accuracy 0.936
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| end of epoch 5 | time: 28.52s | valid accuracy 0.905
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| epoch 6 | 500/ 1782 batches | accuracy 0.939
| epoch 6 | 1000/ 1782 batches | accuracy 0.938
| epoch 6 | 1500/ 1782 batches | accuracy 0.941
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| end of epoch 6 | time: 33.47s | valid accuracy 0.905
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| epoch 7 | 500/ 1782 batches | accuracy 0.940
| epoch 7 | 1000/ 1782 batches | accuracy 0.941
| epoch 7 | 1500/ 1782 batches | accuracy 0.939
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| end of epoch 7 | time: 20.75s | valid accuracy 0.904
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| epoch 8 | 500/ 1782 batches | accuracy 0.941
| epoch 8 | 1000/ 1782 batches | accuracy 0.941
| epoch 8 | 1500/ 1782 batches | accuracy 0.940
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| end of epoch 8 | time: 27.11s | valid accuracy 0.906
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| epoch 9 | 500/ 1782 batches | accuracy 0.942
| epoch 9 | 1000/ 1782 batches | accuracy 0.942
| epoch 9 | 1500/ 1782 batches | accuracy 0.942
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| end of epoch 9 | time: 34.83s | valid accuracy 0.906
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| epoch 10 | 500/ 1782 batches | accuracy 0.942
| epoch 10 | 1000/ 1782 batches | accuracy 0.942
| epoch 10 | 1500/ 1782 batches | accuracy 0.940
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| end of epoch 10 | time: 22.78s | valid accuracy 0.906
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Checking the results of test dataset.
test accuracy 0.906
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Process finished with exit code 0
参考链接
https://pytorch.org/tutorials/beginner/text_sentiment_ngrams_tutorial.html