建设游戏网站,安徽运营网络营销推荐咨询,移动应用开发行业发展趋势,网站建站发布平台文章目录 1 前言1.1 背景 2 数据集3 实现过程4 CNN网络实现5 模型训练部分6 模型评估7 预测结果8 最后 1 前言
#x1f525; 优质竞赛项目系列#xff0c;今天要分享的是
基于CNN实现谣言检测
该项目较为新颖#xff0c;适合作为竞赛课题方向#xff0c;学长非常推荐 优质竞赛项目系列今天要分享的是
基于CNN实现谣言检测
该项目较为新颖适合作为竞赛课题方向学长非常推荐 更多资料, 项目分享
https://gitee.com/dancheng-senior/postgraduate
1.1 背景
社交媒体的发展在加速信息传播的同时也带来了虚假谣言信息的泛滥往往会引发诸多不安定因素并对经济和社会产生巨大的影响。
2 数据集
本项目所使用的数据是从新浪微博不实信息举报平台抓取的中文谣言数据数据集中共包含1538条谣言和1849条非谣言。
如下图所示每条数据均为json格式其中text字段代表微博原文的文字内容。 每个文件夹里又有很多新闻文本。 每个文本又是json格式具体内容如下 3 实现过程
步骤入下
*1解压数据读取并解析数据生成all_data.txt *2生成数据字典即dict.txt *3生成数据列表并进行训练集与验证集的划分train_list.txt 、eval_list.txt *4定义训练数据集提供器train_reader和验证数据集提供器eval_reader
import zipfile
import os
import io
import random
import json
import matplotlib.pyplot as plt
import numpy as np
import paddle
import paddle.fluid as fluid
from paddle.fluid.dygraph.nn import Conv2D, Linear, Embedding
from paddle.fluid.dygraph.base import to_variable#解压原始数据集将Rumor_Dataset.zip解压至data目录下
src_path/home/aistudio/data/data36807/Rumor_Dataset.zip #这里填写自己项目所在的数据集路径
target_path/home/aistudio/data/Chinese_Rumor_Dataset-master
if(not os.path.isdir(target_path)):z zipfile.ZipFile(src_path, r)z.extractall(pathtarget_path)z.close()#分别为谣言数据、非谣言数据、全部数据的文件路径
rumor_class_dirs os.listdir(target_path非开源数据集) # 这里填写自己项目所在的数据集路径
non_rumor_class_dirs os.listdir(target_path非开源数据集)
original_microblog target_path非开源数据集
#谣言标签为0非谣言标签为1
rumor_label0
non_rumor_label1#分别统计谣言数据与非谣言数据的总数
rumor_num 0
non_rumor_num 0
all_rumor_list []
all_non_rumor_list []#解析谣言数据
for rumor_class_dir in rumor_class_dirs: if(rumor_class_dir ! .DS_Store):#遍历谣言数据并解析with open(original_microblog rumor_class_dir, r) as f:rumor_content f.read()rumor_dict json.loads(rumor_content)all_rumor_list.append(rumor_label\trumor_dict[text]\n)rumor_num 1
#解析非谣言数据
for non_rumor_class_dir in non_rumor_class_dirs: if(non_rumor_class_dir ! .DS_Store):with open(original_microblog non_rumor_class_dir, r) as f2:non_rumor_content f2.read()non_rumor_dict json.loads(non_rumor_content)all_non_rumor_list.append(non_rumor_label\tnon_rumor_dict[text]\n)non_rumor_num 1print(谣言数据总量为str(rumor_num))
print(非谣言数据总量为str(non_rumor_num))#全部数据进行乱序后写入all_data.txt
data_list_path/home/aistudio/data/
all_data_pathdata_list_path all_data.txt
all_data_list all_rumor_list all_non_rumor_listrandom.shuffle(all_data_list)#在生成all_data.txt之前首先将其清空
with open(all_data_path, w) as f:f.seek(0)f.truncate() with open(all_data_path, a) as f:for data in all_data_list:f.write(data)
print(all_data.txt已生成) 接下来就是生成数据字典。
# 生成数据字典 def create_dict(data_path, dict_path): with open(dict_path, ‘w’) as f: f.seek(0) f.truncate() dict_set set()# 读取全部数据with open(data_path, r, encodingutf-8) as f:lines f.readlines()# 把数据生成一个元组for line in lines:content line.split(\t)[-1].replace(\n, )for s in content:dict_set.add(s)# 把元组转换成字典一个字对应一个数字dict_list []i 0for s in dict_set:dict_list.append([s, i])i 1# 添加未知字符dict_txt dict(dict_list)end_dict {: i}dict_txt.update(end_dict)# 把这些字典保存到本地中with open(dict_path, w, encodingutf-8) as f:f.write(str(dict_txt))print(数据字典生成完成,\t,字典长度为,len(dict_list))我们可以查看一下dict_txt的内容 接下来就是数据列表的生成
# 创建序列化表示的数据,并按照一定比例划分训练数据与验证数据 def create_data_list(data_list_path): with open(os.path.join(data_list_path, dict.txt), r, encodingutf-8) as f_data:dict_txt eval(f_data.readlines()[0])with open(os.path.join(data_list_path, all_data.txt), r, encodingutf-8) as f_data:lines f_data.readlines()i 0with open(os.path.join(data_list_path, eval_list.txt), a, encodingutf-8) as f_eval,\open(os.path.join(data_list_path, train_list.txt), a, encodingutf-8) as f_train:for line in lines:title line.split(\t)[-1].replace(\n, )lab line.split(\t)[0]t_ids if i % 8 0:for s in title:temp str(dict_txt[s])t_ids t_ids temp ,t_ids t_ids[:-1] \t lab \nf_eval.write(t_ids)else:for s in title:temp str(dict_txt[s])t_ids t_ids temp ,t_ids t_ids[:-1] \t lab \nf_train.write(t_ids)i 1print(数据列表生成完成)定义数据读取器
def data_reader(file_path, phrase, shuffleFalse): all_data [] with io.open(file_path, “r”, encoding‘utf8’) as fin: for line in fin: cols line.strip().split(“\t”) if len(cols) ! 2: continue label int(cols[1]) wids cols[0].split(,)all_data.append((wids, label))if shuffle:if phrase train:random.shuffle(all_data)def reader():for doc, label in all_data:yield doc, labelreturn readerclass SentaProcessor(object):def __init__(self, data_dir,):self.data_dir data_dirdef get_train_data(self, data_dir, shuffle):return data_reader((self.data_dir train_list.txt), train, shuffle)def get_eval_data(self, data_dir, shuffle):return data_reader((self.data_dir eval_list.txt), eval, shuffle)def data_generator(self, batch_size, phasetrain, shuffleTrue):if phase train:return paddle.batch(self.get_train_data(self.data_dir, shuffle),batch_size,drop_lastTrue)elif phase eval:return paddle.batch(self.get_eval_data(self.data_dir, shuffle),batch_size,drop_lastTrue)else:raise ValueError(Unknown phase, which should be in [train, eval])总之在数据处理这一块需要我们注意的是一共生成以下的几个文件。 4 CNN网络实现
接下来就是构建以及配置卷积神经网络(Convolutional Neural Networks, CNN)开篇也说了其实这里有很多模型的选择之所以选择CNN是因为让我们熟悉CNN的相关实现。 输入词向量序列产生一个特征图feature map对特征图采用时间维度上的最大池化max pooling over time操作得到此卷积核对应的整句话的特征最后将所有卷积核得到的特征拼接起来即为文本的定长向量表示对于文本分类问题将其连接至softmax即构建出完整的模型。在实际应用中我们会使用多个卷积核来处理句子窗口大小相同的卷积核堆叠起来形成一个矩阵这样可以更高效的完成运算。另外我们也可使用窗口大小不同的卷积核来处理句子。具体的流程如下 首先我们构建单层CNN神经网络。
#单层class SimpleConvPool(fluid.dygraph.Layer):def __init__(self,num_channels, # 通道数num_filters, # 卷积核数量filter_size, # 卷积核大小batch_sizeNone): # 16super(SimpleConvPool, self).__init__()self.batch_size batch_sizeself._conv2d Conv2D(num_channels num_channels,num_filters num_filters,filter_size filter_size,acttanh)self._pool2d fluid.dygraph.Pool2D(pool_size (150 - filter_size[0]1,1),pool_type max,pool_stride1)def forward(self, inputs):# print(SimpleConvPool_inputs数据纬度,inputs.shape) # [16, 1, 148, 128]x self._conv2d(inputs)x self._pool2d(x)x fluid.layers.reshape(x, shape[self.batch_size, -1])return xclass CNN(fluid.dygraph.Layer):def __init__(self):super(CNN, self).__init__()self.dict_dim train_parameters[vocab_size]self.emb_dim 128 #emb纬度self.hid_dim [32] #卷积核数量self.fc_hid_dim 96 #fc参数纬度self.class_dim 2 #分类数self.channels 1 #输入通道数self.win_size [[3, 128]] # 卷积核尺寸self.batch_size train_parameters[batch_size] self.seq_len train_parameters[padding_size]self.embedding Embedding( size[self.dict_dim 1, self.emb_dim],dtypefloat32, is_sparseFalse)self._simple_conv_pool_1 SimpleConvPool(self.channels,self.hid_dim[0],self.win_size[0],batch_sizeself.batch_size)self._fc1 Linear(input_dim self.hid_dim[0],output_dim self.fc_hid_dim,acttanh)self._fc_prediction Linear(input_dim self.fc_hid_dim,output_dim self.class_dim,actsoftmax)def forward(self, inputs, labelNone):emb self.embedding(inputs) # [2400, 128]# print(CNN_emb,emb.shape) emb fluid.layers.reshape( # [16, 1, 150, 128]emb, shape[-1, self.channels , self.seq_len, self.emb_dim])# print(CNN_emb,emb.shape)conv_3 self._simple_conv_pool_1(emb)fc_1 self._fc1(conv_3)prediction self._fc_prediction(fc_1)if label is not None:acc fluid.layers.accuracy(prediction, labellabel)return prediction, accelse:return prediction
接下来就是参数的配置不过为了在模型训练过程中更直观的查看我们训练的准确率我们首先利用python的matplotlib.pyplt函数实现一个可视化图具体的实现如下
def draw_train_process(iters, train_loss, train_accs): title“training loss/training accs” plt.title(title, fontsize24) plt.xlabel(“iter”, fontsize14) plt.ylabel(“loss/acc”, fontsize14) plt.plot(iters, train_loss, color‘red’, label‘training loss’) plt.plot(iters, train_accs, color‘green’, label‘training accs’) plt.legend() plt.grid() plt.show()
5 模型训练部分
def train(): with fluid.dygraph.guard(place fluid.CUDAPlace(0)): # 因为要进行很大规模的训练因此我们用的是GPU如果没有安装GPU的可以使用下面一句把这句代码注释掉即可 # with fluid.dygraph.guard(place fluid.CPUPlace()): processor SentaProcessor( data_dirdata/)train_data_generator processor.data_generator(batch_sizetrain_parameters[batch_size],phasetrain,shuffleTrue)model CNN()sgd_optimizer fluid.optimizer.Adagrad(learning_ratetrain_parameters[adam],parameter_listmodel.parameters())steps 0Iters,total_loss, total_acc [], [], []for eop in range(train_parameters[epoch]):for batch_id, data in enumerate(train_data_generator()):steps 1#转换为 variable 类型doc to_variable(np.array([np.pad(x[0][0:train_parameters[padding_size]], #对句子进行padding全部填补为定长150(0, train_parameters[padding_size] - len(x[0][0:train_parameters[padding_size]])),constant,constant_values(train_parameters[vocab_size])) # 用 的id 进行填补for x in data]).astype(int64).reshape(-1))#转换为 variable 类型label to_variable(np.array([x[1] for x in data]).astype(int64).reshape(train_parameters[batch_size], 1))model.train() #使用训练模式prediction, acc model(doc, label)loss fluid.layers.cross_entropy(prediction, label)avg_loss fluid.layers.mean(loss)avg_loss.backward()sgd_optimizer.minimize(avg_loss)model.clear_gradients()if steps % train_parameters[skip_steps] 0:Iters.append(steps)total_loss.append(avg_loss.numpy()[0])total_acc.append(acc.numpy()[0])print(eop: %d, step: %d, ave loss: %f, ave acc: %f %(eop, steps,avg_loss.numpy(),acc.numpy()))if steps % train_parameters[save_steps] 0:save_path train_parameters[checkpoints]/save_dir_ str(steps)print(save model to: save_path)fluid.dygraph.save_dygraph(model.state_dict(),save_path)# breakdraw_train_process(Iters, total_loss, total_acc)训练的过程以及训练的结果如下 6 模型评估
def to_eval(): with fluid.dygraph.guard(place fluid.CUDAPlace(0)): processor SentaProcessor(data_dir“data/”) #写自己的路径 eval_data_generator processor.data_generator(batch_sizetrain_parameters[batch_size],phaseeval,shuffleFalse)model_eval CNN() #示例化模型model, _ fluid.load_dygraph(data//save_dir_180.pdparams) #写自己的路径model_eval.load_dict(model)model_eval.eval() # 切换为eval模式total_eval_cost, total_eval_acc [], []for eval_batch_id, eval_data in enumerate(eval_data_generator()):eval_np_doc np.array([np.pad(x[0][0:train_parameters[padding_size]],(0, train_parameters[padding_size] -len(x[0][0:train_parameters[padding_size]])),constant,constant_values(train_parameters[vocab_size]))for x in eval_data]).astype(int64).reshape(-1)eval_label to_variable(np.array([x[1] for x in eval_data]).astype(int64).reshape(train_parameters[batch_size], 1))eval_doc to_variable(eval_np_doc)eval_prediction, eval_acc model_eval(eval_doc, eval_label)loss fluid.layers.cross_entropy(eval_prediction, eval_label)avg_loss fluid.layers.mean(loss)total_eval_cost.append(avg_loss.numpy()[0])total_eval_acc.append(eval_acc.numpy()[0])print(Final validation result: ave loss: %f, ave acc: %f %(np.mean(total_eval_cost), np.mean(total_eval_acc) )) 评估准确率如下 7 预测结果
# 获取数据 def load_data(sentence): # 读取数据字典 with open(‘data/dict.txt’, ‘r’, encoding‘utf-8’) as f_data: dict_txt eval(f_data.readlines()[0]) dict_txt dict(dict_txt) # 把字符串数据转换成列表数据 keys dict_txt.keys() data [] for s in sentence: # 判断是否存在未知字符 if not s in keys: s ‘’ data.append(int(dict_txt[s])) return data
train_parameters[batch_size] 1
lab [ 谣言, 非谣言]with fluid.dygraph.guard(place fluid.CUDAPlace(0)):data load_data(兴仁县今天抢小孩没抢走把孩子母亲捅了一刀看见这车的注意了真事车牌号辽HFM055赶紧散播 都别带孩子出去瞎转悠了 尤其别让老人自己带孩子出去 太危险了 注意了辽HFM055北京现代朗动在各学校门口抢小孩110已经 证实全市通缉)data_np np.array(data)data_np np.array(np.pad(data_np,(0,150-len(data_np)),constant,constant_values train_parameters[vocab_size])).astype(int64).reshape(-1)infer_np_doc to_variable(data_np)model_infer CNN()model, _ fluid.load_dygraph(data/save_dir_900.pdparams)model_infer.load_dict(model)model_infer.eval()result model_infer(infer_np_doc)print(预测结果为, lab[np.argmax(result.numpy())])8 最后 更多资料, 项目分享
https://gitee.com/dancheng-senior/postgraduate
