四、word2vec + siameseLSTM改進（1）
阿新 • • 發佈：2020-12-22
一、檢視GPU
>>> import torch >>> torch.cuda.device_count() 1 >>> torch.cuda.get_device_name(0) 'GeForce GTX 1060' >>> torch.cuda.is_available() True
二、CPU轉移到GPU步驟
""" 注意，轉移到GPU步驟： （1）設定種子：torch.cuda.manual_seed(123) （2）device = torch.device('cuda')定義裝置 （3）損失函式轉移：criterion = nn.BCEWithLogitsLoss().to(self.device) （4）網路模型轉移：myLSTM = SiameseLSTM(128).to(self.device) （5）資料轉移：a, b, label= a.cuda(), b.cuda(), label.cuda()"""
三、專案目錄
四、程式碼步驟
1、資料初始化
import numpy as np import pandas as pd import os class DataInitial(object): def __init__(self): pass def all_data(self): df1 = pd.read_csv("data/POI/negtive.csv") df2 = pd.read_csv("data/POI/positive.csv") df = pd.concat([df1,df2],ignore_index=True) df.to_csv("data/POI/all.csv",index=False,sep=',') def split(self): df = pd.read_csv('data/POI/all.csv') df = df.sample(frac=1.0) cut_idx = int(round(0.2 * df.shape[0])) df_test, df_train = df.iloc[:cut_idx], df.iloc[cut_idx:] df_test.to_csv("data/POI/test.csv",index=False,sep=',') df_train.to_csv("data/POI/train.csv", index=False, sep=',') def train_data(self): train_texta = pd.read_csv("data/POI/train.csv")["address_1"] train_textb = pd.read_csv("data/POI/train.csv")["address_2"] train_label = pd.read_csv("data/POI/train.csv")["tag"] return train_texta,train_textb,train_label def test_data(self): test_texta = pd.read_csv("data/POI/test.csv")["address_1"] test_textb = pd.read_csv("data/POI/test.csv")["address_2"] test_label = pd.read_csv("data/POI/test.csv")["tag"] return test_texta,test_textb,test_label
2、DataSet
import torch.utils.data as data import torch class DatasetIterater(data.Dataset): def __init__(self, texta, textb, label): self.texta = texta self.textb = textb self.label = label def __getitem__(self, item): texta = self.texta[item] textb = self.textb[item] label = self.label[item] return texta, textb, label def __len__(self): return len(self.texta)
3、詞嵌入
import jieba from gensim.models import Word2Vec import torch import gensim import numpy as np model = gensim.models.KeyedVectors.load_word2vec_format('model/word2vec.bin', binary=True) class WordEmbedding(object): def __init__(self): pass def sentenceTupleToEmbedding(self, data1, data2): aCutListMaxLen = max([len(list(jieba.cut(sentence_a))) for sentence_a in data1]) bCutListMaxLen = max([len(list(jieba.cut(sentence_a))) for sentence_a in data2]) maxLen = max(aCutListMaxLen,bCutListMaxLen) seq_len = maxLen a = self.sqence_vec(data1, seq_len) #batch_size, sqence, embedding b = self.sqence_vec(data2, seq_len) return torch.FloatTensor(a), torch.FloatTensor(b) def sqence_vec(self, data, seq_len): data_a_vec = [] for sequence_a in data: sequence_vec = [] # sequence * 128 for word_a in jieba.cut(sequence_a): if word_a in model: sequence_vec.append(model[word_a]) sequence_vec = np.array(sequence_vec) add = np.zeros((seq_len - sequence_vec.shape[0], 128)) sequenceVec = np.vstack((sequence_vec, add)) data_a_vec.append(sequenceVec) a_vec = np.array(data_a_vec) return a_vec
4、孿生LSTM
import torch from torch import nn class SiameseLSTM(nn.Module): def __init__(self, input_size): super(SiameseLSTM, self).__init__() self.lstm = nn.LSTM(input_size=input_size, hidden_size=10, num_layers=4, batch_first=True) self.fc = nn.Linear(10, 1) def forward(self, data1, data2): out1, (h1, c1) = self.lstm(data1) out2, (h2, c2) = self.lstm(data2) pre1 = out1[:, -1, :] pre2 = out2[:, -1, :] dis = torch.abs(pre1 - pre2) out = self.fc(dis) return out
5、主程式
import torch from torch import nn from torch.utils.data import DataLoader import pandas as pd from datasetIterater import DatasetIterater import jieba from wordEmbedding import WordEmbedding from siameseLSTM import SiameseLSTM import numpy as np from dataInitial import DataInitial word = WordEmbedding() """ 注意，轉移到GPU步驟： （1）設定種子：torch.cuda.manual_seed(123) （2）device = torch.device('cuda')定義裝置 （3）損失函式轉移：criterion = nn.BCEWithLogitsLoss().to(self.device) （4）網路模型轉移：myLSTM = SiameseLSTM(128).to(self.device) （5）資料轉移：a, b, label= a.cuda(), b.cuda(), label.cuda() """ class MainProcess(object): def __init__(self): self.learning_rate = 0.001 torch.cuda.manual_seed(123) self.device = torch.device('cuda') self.train_texta, self.train_textb, self.train_label = DataInitial().train_data() self.test_texta, self.test_textb, self.test_label = DataInitial().test_data() self.train_data = DatasetIterater(self.train_texta,self.train_textb,self.train_label) self.test_data = DatasetIterater(self.test_texta, self.test_textb, self.test_label) self.train_iter = DataLoader(dataset=self.train_data, batch_size=32, shuffle=True) self.test_iter = DataLoader(dataset=self.test_data, batch_size=128, shuffle=True) self.myLSTM = SiameseLSTM(128).to(self.device) self.criterion = nn.BCEWithLogitsLoss().to(self.device) self.optimizer = torch.optim.Adam(self.myLSTM.parameters(), lr=self.learning_rate) def binary_acc(self, preds, y): preds = torch.round(torch.sigmoid(preds)) correct = torch.eq(preds, y).float() acc = correct.sum() / len(correct) return acc def train(self, mynet, train_iter, optimizer, criterion, epoch): avg_acc = [] avg_loss = [] mynet.train() for batch_id, (data1, data2, label) in enumerate(train_iter): try: a, b = word.sentenceTupleToEmbedding(data1, data2) except Exception as e: continue a, b, label= a.cuda(non_blocking=True), b.cuda(non_blocking=True), label.cuda(non_blocking=True) distence = mynet(a, b) loss = criterion(distence, label.float().unsqueeze(-1)) acc = self.binary_acc(distence, label.float().unsqueeze(-1)).item() avg_acc.append(acc) optimizer.zero_grad() loss.backward() optimizer.step() if batch_id % 100==0: print("輪數：", epoch, "batch: ",batch_id,"訓練損失：", loss.item()) avg_loss.append(loss.item()) avg_acc = np.array(avg_acc).mean() avg_loss = np.array(avg_loss).mean() print('train acc:', avg_acc) print("train loss", avg_loss) def eval(self, mynet, test_iter, criteon, epoch): mynet.eval() avg_acc = [] avg_loss = [] with torch.no_grad(): for batch_id, (data1, data2, label) in enumerate(test_iter): try: a, b = word.sentenceTupleToEmbedding(data1, data2) except Exception as e: continue a, b, label= a.cuda(non_blocking=True), b.cuda(non_blocking=True), label.cuda(non_blocking=True) distence = mynet(a, b) loss = criteon(distence, label.float().unsqueeze(-1)) acc = self.binary_acc(distence, label.float().unsqueeze(-1)).item() avg_acc.append(acc) avg_loss.append(loss.item()) if batch_id>50: break avg_acc = np.array(avg_acc).mean() avg_loss = np.array(avg_loss).mean() print('>>test acc:', avg_acc) print(">>test loss:", avg_loss) return (avg_acc, avg_loss) def main(self): min_loss = 100000 for epoch in range(50): self.train(self.myLSTM, self.train_iter, self.optimizer, self.criterion, epoch) eval_acc, eval_loss = self.eval(self.myLSTM, self.test_iter, self.criterion, epoch) if eval_loss < min_loss: min_loss = eval_loss print("save model") torch.save(self.myLSTM.state_dict(), 'model.pth') if __name__ == '__main__': MainProcess().main()