基于lstm与cnn的文本分类_lstm-cnn文本分类

作者：黑客灵魂 | 2024-08-22 09:27:05

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lstm-cnn文本分类

主要内容

本文主要任务是基于文本信息进行用户评价分类，分为两类（即正面情绪和负面情绪）数据样例如下：

项目目录与地址

本文使用的数据有

停顿词（hit_stopwords.txt）来源：

停顿词项目目录预览 - stopwords - GitCode

data目录下的所有数据来源：

项目首页 - chinese_text_cnn - GitCode

所有项目代码地址：

text_classificationWithLSTM: 基于lstm与cnn的文本分类 (gitee.com)

一：数据预处理data_set.py

首先对所获取的数据进行停顿词处理，利用hit_stopwords.txt来进行清洗掉停顿词，对于一些去掉停顿词只剩空格或者符号无效内容的进行删掉，最后生成训练模型所需要的train.txt和test.txt


import pandas as pd
import jieba
 
 
# 数据读取
def load_tsv(file_path):
    data = pd.read_csv(file_path, sep='\t')
    data_x = data.iloc[:, -1]
    data_y = data.iloc[:, 1]
    return data_x, data_y
 
 
with open('./hit_stopwords.txt', 'r', encoding='UTF8') as f:
    stop_words = [word.strip() for word in f.readlines()]
    print('Successfully')
 
 
def drop_stopword(datas):
    for data in datas:
        for word in data:
            if word in stop_words:
                data.remove(word)
    return datas
 
 
def save_data(datax, path):
    with open(path, 'w', encoding="UTF8") as f:
        for lines in datax:
            for i, line in enumerate(lines):
                f.write(str(line))
                # 如果不是最后一行，就添加一个逗号
                if i != len(lines) - 1:
                    f.write(',')
            f.write('\n')
 
 
if __name__ == '__main__':
    train_x, train_y = load_tsv("./data/train.tsv")
    test_x, test_y = load_tsv("./data/test.tsv")
    train_x = [list(jieba.cut(x)) for x in train_x]
    test_x = [list(jieba.cut(x)) for x in test_x]
    train_x = drop_stopword(train_x)
    test_x = drop_stopword(test_x)
    save_data(train_x, './train.txt')
    save_data(test_x, './test.txt')
    print('Successfully')

二：lstm模型训练


import pandas as pd
import torch
from torch import nn
import jieba
from gensim.models import Word2Vec
import numpy as np
from data_set import load_tsv
from torch.utils.data import DataLoader, TensorDataset
 
 
# 数据读取
def load_txt(path):
    with open(path, 'r', encoding='utf-8') as f:
        data = [[line.strip()] for line in f.readlines()]
        return data
 
train_x = load_txt('train.txt')
test_x = load_txt('test.txt')
train = train_x + test_x
X_all = [i for x in train for i in x]
 
_, train_y = load_tsv("./data/train.tsv")
_, test_y = load_tsv("./data/test.tsv")
# 训练Word2Vec模型
word2vec_model = Word2Vec(sentences=X_all, vector_size=100, window=5, min_count=1, workers=4)
 
# 将文本转换为Word2Vec向量表示
def text_to_vector(text):
    vector = [word2vec_model.wv[word] for word in text if word in word2vec_model.wv]  # 将每个词转换为 Word2Vec 向量
    return sum(vector) / len(vector) if vector else [0] * word2vec_model.vector_size  # 计算平均向量
 
X_train_w2v = [[text_to_vector(text)] for line in train_x for text in line]  # 训练集文本转换为 Word2Vec 向量
X_test_w2v = [[text_to_vector(text)] for line in test_x for text in line]
 
# 将词向量转换为PyTorch张量
X_train_array = np.array(X_train_w2v, dtype=np.float32)  # 将训练集词向量转换为 NumPy 数组
X_train_tensor = torch.Tensor(X_train_array)  # 将 NumPy 数组转换为 PyTorch 张量
X_test_array = np.array(X_test_w2v, dtype=np.float32)  # 将测试集词向量转换为 NumPy 数组
X_test_tensor = torch.Tensor(X_test_array)  # 将 NumPy 数组转换为 PyTorch 张量
 
# 使用DataLoader打包文件
train_dataset = TensorDataset(X_train_tensor, torch.LongTensor(train_y))  # 构建训练集数据集对象
train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)  # 构建训练集数据加载器
test_dataset = TensorDataset(X_test_tensor, torch.LongTensor(test_y))  # 构建测试集数据集对象
test_loader = DataLoader(test_dataset, batch_size=64, shuffle=True)  # 构建测试集数据加载器
 
 
# 定义LSTM模型
class LSTMModel(nn.Module):
    def __init__(self, input_size, hidden_size, output_size):
        super(LSTMModel, self).__init__()
        self.lstm = nn.LSTM(input_size, hidden_size, batch_first=True)
        self.fc = nn.Linear(hidden_size, output_size)
 
    def forward(self, x):
        lstm_out, _ = self.lstm(x)
        output = self.fc(lstm_out[:, -1, :])  # 取序列的最后一个输出
        return output
 
 
# 定义模型
input_size = word2vec_model.vector_size
hidden_size = 50  # 隐藏层大小
output_size = 2  # 输出的大小，根据你的任务而定
 
model = LSTMModel(input_size, hidden_size, output_size)
# 定义损失函数和优化器
criterion = nn.CrossEntropyLoss()  # 交叉熵损失函数
optimizer = torch.optim.Adam(model.parameters(), lr=0.0002)  # Adam 优化器
 
if __name__ == "__main__":
    # 训练模型
    num_epochs = 100  # 迭代次数
    log_interval = 100  # 每隔100个批次输出一次日志
    loss_min = 100
    for epoch in range(num_epochs):
        model.train()  # 设置模型为训练模式
        for batch_idx, (data, target) in enumerate(train_loader):
            outputs = model(data)  # 模型前向传播
            loss = criterion(outputs, target)  # 计算损失
 
            optimizer.zero_grad()  # 梯度清零
            loss.backward()  # 反向传播
            optimizer.step()  # 更新参数
 
            if batch_idx % log_interval == 0:
                print('Epoch [{}/{}], Batch [{}/{}], Loss: {:.4f}'.format(
                    epoch + 1, num_epochs, batch_idx, len(train_loader), loss.item()))
            # 保存最佳模型
            if loss.item() < loss_min:
                loss_min = loss.item()
                torch.save(model, 'lstm_model.pth')
 
    # 模型评估
    with torch.no_grad():
        model.eval()
        correct = 0
        total = 0
        for data, target in test_loader:
            outputs = model(data)
            _, predicted = torch.max(outputs.data, 1)
            total += target.size(0)
            correct += (predicted == target).sum().item()
 
        accuracy = correct / total
        print('Test Accuracy: {:.2%}'.format(accuracy))

运行完截图

三：cnn模型训练


import pandas as pd
import torch
from torch import nn
import jieba
from gensim.models import Word2Vec
import numpy as np
from data_set import load_tsv
from torch.utils.data import DataLoader, TensorDataset
 
 
# 数据读取
def load_txt(path):
    with open(path, 'r', encoding='utf-8') as f:
        data = [[line.strip()] for line in f.readlines()]
        return data
 
train_x = load_txt('train.txt')
test_x = load_txt('test.txt')
train = train_x + test_x
X_all = [i for x in train for i in x]
 
_, train_y = load_tsv("./data/train.tsv")
_, test_y = load_tsv("./data/test.tsv")
# 训练Word2Vec模型
word2vec_model = Word2Vec(sentences=X_all, vector_size=100, window=5, min_count=1, workers=4)
 
 
# 将文本转换为Word2Vec向量表示
def text_to_vector(text):
    vector = [word2vec_model.wv[word] for word in text if word in word2vec_model.wv]
    return sum(vector) / len(vector) if vector else [0] * word2vec_model.vector_size
 
 
X_train_w2v = [[text_to_vector(text)] for line in train_x for text in line]
X_test_w2v = [[text_to_vector(text)] for line in test_x for text in line]
 
# 将词向量转换为PyTorch张量
X_train_array = np.array(X_train_w2v, dtype=np.float32)
X_train_tensor = torch.Tensor(X_train_array)
X_test_array = np.array(X_test_w2v, dtype=np.float32)
X_test_tensor = torch.Tensor(X_test_array)
# 使用DataLoader打包文件
train_dataset = TensorDataset(X_train_tensor, torch.LongTensor(train_y))
train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)
test_dataset = TensorDataset(X_test_tensor, torch.LongTensor(test_y))
test_loader = DataLoader(test_dataset, batch_size=64, shuffle=True)
 
 
# 定义cnn模型
class CNNModel(nn.Module):
    def __init__(self, input_size, output_size):
        super(CNNModel, self).__init__()
        self.conv1 = nn.Conv1d(input_size, 32, kernel_size=3, padding=1)  # 第一个一维卷积层
        self.conv2 = nn.Conv1d(32, 64, kernel_size=3, padding=1)  # 第二个一维卷积层
        self.fc = nn.Linear(64, output_size)  # 全连接层
 
    def forward(self, x):
        x = x.permute(0, 2, 1)  # # Conv1d期望输入格式为(batch_size, channels, sequence_length)
        x = torch.relu(self.conv1(x))  # 第一个卷积层的激活函数
        x = torch.relu(self.conv2(x))  # 第二个卷积层的激活函数
        x = torch.max_pool1d(x, kernel_size=x.size(2))  # 全局最大池化
        x = x.squeeze(2)  # 移除最后一个维度
        x = self.fc(x)  # 全连接层
        return x
 
 
# 定义CNN模型、损失函数和优化器
input_size = word2vec_model.vector_size  # 输入大小为 Word2Vec 向量大小
output_size = 2  # 输出大小
cnn_model = CNNModel(input_size, output_size)  # 创建 CNN 模型对象
criterion = nn.CrossEntropyLoss()  # 交叉熵损失函数
optimizer = torch.optim.Adam(cnn_model.parameters(), lr=0.0002)  # Adam 优化器
 
if __name__ == "__main__":
    # 训练和评估
    num_epochs = 100  # 迭代次数
    log_interval = 100  # 日志打印间隔
    loss_min = 100  # 最小损失值
    for epoch in range(num_epochs):
        cnn_model.train()  # 设置模型为训练模式
        for batch_idx, (data, target) in enumerate(train_loader):
            outputs = cnn_model(data)  # 模型前向传播
            loss = criterion(outputs, target)  # 计算损失
 
            optimizer.zero_grad()  # 梯度清零
            loss.backward()  # 反向传播
            optimizer.step()  # 更新参数
 
            if batch_idx % log_interval == 0:
                print('Epoch [{}/{}], Batch [{}/{}], Loss: {:.4f}'.format(
                    epoch + 1, num_epochs, batch_idx, len(train_loader), loss.item()))
            if loss.item() < loss_min:
                loss_min = loss.item()
                torch.save(cnn_model, 'cnn_model.pth')
 
    # 评估
    with torch.no_grad():
        cnn_model.eval()
        correct = 0
        total = 0
        for data, target in test_loader:
            outputs = cnn_model(data)
            _, predicted = torch.max(outputs.data, 1)
            total += target.size(0)
            correct += (predicted == target).sum().item()
 
        accuracy = correct / total
        print('测试准确率（CNN模型）：{:.2%}'.format(accuracy))

运行完截图：

四：测试模型


import torch
import jieba
from gensim.models import Word2Vec
import numpy as np
from lstm import LSTMModel
from cnn import CNNModel
 
 
 
# 数据读取
def load_txt(path):
    with open(path, 'r', encoding='utf-8') as f:
        data = [[line.strip()] for line in f.readlines()]
        return data
 
 
# 去停用词
def drop_stopword(datas):
    # 用于预处理文本数据
    with open('./hit_stopwords.txt', 'r', encoding='UTF8') as f:
        stop_words = [word.strip() for word in f.readlines()]
    datas = [x for x in datas if x not in stop_words]
    return datas
 
 
def preprocess_text(text):
    text = list(jieba.cut(text))
    text = drop_stopword(text)
    return text
 
 
# 将文本转换为Word2Vec向量表示
def text_to_vector(text):
    train_x = load_txt('train.txt')
    test_x = load_txt('test.txt')
    train = train_x + test_x
    X_all = [i for x in train for i in x]
    # 训练Word2Vec模型
    word2vec_model = Word2Vec(sentences=X_all, vector_size=100, window=5, min_count=1, workers=4)
    vector = [word2vec_model.wv[word] for word in text if word in word2vec_model.wv]
    return sum(vector) / len(vector) if vector else [0] * word2vec_model.vector_size
 
 
if __name__ == '__main__':
    user_input = input("Select model:\n1.lstm_model.pth\n2.cnn_model.pth\n")
    if user_input=="1":
        modelName="lstm_model.pth"
    elif user_input=="2":
        modelName="cnn_model.pth"
    else:
        print("no model name is "+user_input)
        exit(0)
    # input_text = "这个车完全就是垃圾,又热又耗油"
    input_text = "回头率还可以，无框门，上档次"
    label = {1: "正面情绪", 0: "负面情绪"}
    model = torch.load(modelName)
    # 预处理输入数据
    input_data = preprocess_text(input_text)
    # 确保输入词向量与模型维度和数据类型相同
    input_data = [[text_to_vector(input_data)]]
    input_arry = np.array(input_data, dtype=np.float32)
    input_tensor = torch.Tensor(input_arry)
    # 将输入数据传入模型
    with torch.no_grad():
        output = model(input_tensor)
    predicted_class = label[torch.argmax(output).item()]
    print(f"predicted_text:{input_text}")
    print(f"模型预测的类别: {predicted_class}")

测试截图：

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