本文主要是介绍python pytorch- TextCNN TextRNN FastText Transfermer （中英文）文本情感分类实战（附数据集，代码皆可运行），希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！
python pytorch- TextCNN TextRNN FastText Transfermer 文本情感分类实战（附数据集，代码皆可运行）
注：本次实验，主要注重代码实现这些模型，博主的数据集质量较差，模型评估效果并不是十分理想，后续同学们可以自行使用自己的数据集去运行这些模型，训练自己的优质模型。数据集我会上传到我得资源当中，大家可以自行下载。
最近博主做了基于深度学习的文本情感分类的实验，在这个实验中，我们用到了四个比较热门的深度学习文本分类模型TextCNN TextRNN FastText Transfermer 。使用的是pytorch框架实现的。
在这篇博文中，博主不会介绍这些模型的数学原理，主要还是讲可运行的代码放在这篇博文里。
这篇博客分为如下几个部分
1.数据集介绍
2.数据集预处理思路简要介绍和实现代码
3.TextCNN 文本分类实战
4.TextRNN 文本分类实战
5.FastText 文本分类实战
6.Transfermer 文本分类实战
1.数据集介绍
如下是我们的数据集：
数据集由六个txt文件组成，如下图：
分别是三个训练集文件和三个测试集文件，三个训练集文件分别对应消极、积极、中性的三种文本数据。同理三个测试机文件也分别对应消极、积极、中性的三种文本数据。
其中每一个数据集都是一个一个的句子组成，每个句子占一行。
如下：
只有stopwrods.txt比较特别：
其实由一个个的停用词组成，每一行为一个停用词。
建议大家可以下载我得数据集，也可以在私聊我，我可以将数据集发给你们。
2.数据集预处理思路简要介绍和实现代码
（1）将句子通过jieba库进行分词操作。
（2）另外在数据处理过程中我们使用了停用词库。
（3）由于文本分词之后长度不一，但是使用的四个模型都要求长度统一的文本，所以我们对分词之后的单词列表进行调整，为了尽量不是数据丢失，我们将一个句子单词数量设定为20，对于单词小于20的句子进行补足，补‘#’单词，对于单词数量大于20的句子，我们进行裁剪，一般裁剪前20个单词。
（4）考到模型的特性，一般情况下，如果单词小于20，我们是在末尾进行‘#’补足,但是对于TextRNN，考虑到其对信息的记忆，在开头进行‘#’补足，这样，可以更多关注后续的信息。
（5）对于停用词集，我们根据数据集，也自己添加了一部分停用词。
这里我们附上我们的数据预处理代码：
import osimport jieba
import restring = "This is a string with 12345 numbers"path=r"D:\work\10-5\use_data"def get_stop_words():file_object = open(r'D:\work\10-5\use_data\stopwords.txt',encoding='utf-8')stop_words = []for line in file_object.readlines():line = line[:-1]line = line.strip()stop_words.append(line)return stop_words
stop_words=get_stop_words()
stop_words.append('%')
stop_words.append('\n')
#print(stop_words)
def get_data():setences=[]label=[]setences_test=[]label_test=[]for file in os.listdir(path):print(file)if file.startswith('s')==False and 'train' in file:fp=open(path+'//'+file,encoding='utf8')for line in fp.readlines():if file.startswith('zp'):label.append(0)if file.startswith('zs'):label.append(1)if file.startswith('zn'):label.append(2)line = re.sub(r'\d+', '', line)words=jieba.lcut(line, cut_all=False)words_s=[ i for i in words if i not in stop_words]if len(words_s)<=20:for i in range(20-len(words_s)):words_s=['#']+words_selse:words_s=words_s[0:20]# print(words_s)words_s=" ".join(words_s)# print(words_s)setences.append(words_s)fp.close()if file.startswith('s')==False and 'test' in file:fp=open(path+'//'+file,encoding='utf8')for line in fp.readlines():if file.startswith('zp'):label_test.append(0)if file.startswith('zs'):label_test.append(1)if file.startswith('zn'):label_test.append(2)line = re.sub(r'\d+', '', line)words=jieba.lcut(line, cut_all=False)words_s=[ i for i in words if i not in stop_words]# print(words_s)if len(words_s)<20:for i in range(20-len(words_s)):words_s=['#']+words_selse:words_s=words_s[0:20]words_s=" ".join(words_s)print(words_s)setences_test.append(words_s)fp.close()return setences,label,setences_test,label_test3.TextCNN 文本分类实战
在这个算法中，对每个单词赋予一个随机的词向量，让后堆叠成图像那样的二维矩阵，之后使用卷积神经网络的方式，对其进行卷积操作。模型如下图：
模型我们就不介绍了，这里我们直接附上实现代码，并且该代码还涉及模型评估的代码：
#coding=gbk
from cgi import test
import torch
import numpy as np
import torch.nn as nn
import torch.optim as optim
import torch.utils.data as Data
import torch.nn.functional as F
from data_process import get_data
dtype = torch.FloatTensor
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# 3 words sentences (=sequence_length is 3)
import matplotlib.pyplot as pltsentences,labels,setences_test,label_test=get_data()
print(sentences,labels)
#sentences = ["i love you", "he loves me", "she likes baseball", "i hate you", "sorry for that", "this is awful"]
#labels = [1, 1, 1, 0, 0, 0] # 1 is good, 0 is not good.embedding_size = 100
num_classes = len(set(labels))
batch_size = 10
classnum=3
sequence_length = 20
word_list = " ".join(sentences).split()
word_list2 = " ".join(setences_test).split()
vocab = list(set(word_list+word_list2))word2idx = {w:i for i,w in enumerate(vocab)}vocab_size = len(vocab)def make_data(sentences, labels):inputs = []for sen in sentences:l=[word2idx[n] for n in sen.split()]if len(l)<sequence_length:length=len(l)for i in range(sequence_length-length):l.append(0)inputs.append(l)else:inputs.append(l[0:sequence_length])targets = []for out in labels:targets.append(out)return inputs, targetsinput_batch, target_batch = make_data(sentences, labels)print(input_batch, target_batch)
print("fdsfafas")
input_batch= torch.LongTensor(input_batch)
target_batch= torch.LongTensor(target_batch)print("*"*100)
print(input_batch.size(),target_batch.size())dataset = Data.TensorDataset(input_batch,target_batch)
loader = Data.DataLoader(dataset, batch_size, True)
epoch=100
class TextCNN(nn.Module):def __init__(self):super(TextCNN, self).__init__()self.W = nn.Embedding(vocab_size, embedding_size)output_channel = 3self.conv = nn.Sequential(nn.Conv2d(1, output_channel, kernel_size=(4,embedding_size)), # inpu_channel, output_channel, 卷积核高和宽 n-gram 和 embedding_sizenn.ReLU(),nn.MaxPool2d((2,1)))self.fc = nn.Linear(24,num_classes)def forward(self, X):'''X: [batch_size, sequence_length]'''batch_size = X.shape[0]embedding_X = self.W(X) # [batch_size, sequence_length, embedding_size]embedding_X = embedding_X.unsqueeze(1) # add channel(=1) [batch, channel(=1), sequence_length, embedding_size]conved = self.conv(embedding_X) # [batch_size, output_channel,1,1]flatten = conved.view(batch_size, -1)# [batch_size, output_channel*1*1]output = self.fc(flatten)return outputmodel = TextCNN().to(device)
criterion = nn.CrossEntropyLoss().to(device)
optimizer = optim.Adam(model.parameters(), lr=1e-3)
loss_list=[]
# Training
for epoch in range(epoch):for batch_x, batch_y in loader:batch_x, batch_y = batch_x.to(device), batch_y.to(device)pred = model(batch_x)loss = criterion(pred, batch_y)loss_list.append(loss)if (epoch + 1) % 5 == 0:print('Epoch:', '%04d' % (epoch + 1), 'loss =', '{:.6f}'.format(loss))optimizer.zero_grad()loss.backward()optimizer.step()#testinput_batch, target_batch = make_data(setences_test, label_test)print(input_batch, target_batch)
print("fdsfafas")
input_batch= torch.LongTensor(input_batch)
target_batch= torch.LongTensor(target_batch)print("*"*100)
print(input_batch.size(),target_batch.size())dataset = Data.TensorDataset(input_batch,target_batch)
loader = Data.DataLoader(dataset, batch_size, True)
test_loss = 0
correct = 0
total = 0
target_num = torch.zeros((1,classnum))
predict_num = torch.zeros((1,classnum))
acc_num = torch.zeros((1,classnum))
for batch_x, batch_y in loader:batch_x, batch_y = batch_x.to(device), batch_y.to(device)pred = model(batch_x)loss = criterion(pred, batch_y)print('Epoch:', '%04d' % (epoch + 1), 'loss =', '{:.6f}'.format(loss))print(pred.argmax(1))print(batch_y)test_loss += loss_, predicted = torch.max(pred.data, 1)total += batch_y.size(0)correct += predicted.eq(batch_y.data).cpu().sum()pre_mask = torch.zeros(pred.size()).scatter_(1, predicted.cpu().view(-1, 1), 1.)predict_num += pre_mask.sum(0)tar_mask = torch.zeros(pred.size()).scatter_(1, batch_y.data.cpu().view(-1, 1), 1.)target_num += tar_mask.sum(0)acc_mask = pre_mask*tar_maskacc_num += acc_mask.sum(0)recall = acc_num/target_num
precision = acc_num/predict_num
F1 = 2*recall*precision/(recall+precision)
accuracy = acc_num.sum(1)/target_num.sum(1)
recall = (recall.numpy()[0]*100).round(3)
precision = (precision.numpy()[0]*100).round(3)
F1 = (F1.numpy()[0]*100).round(3)
accuracy = (accuracy.numpy()[0]*100).round(3)
# 打印格式方便复制
print('recall'," ".join('%s' % id for id in recall))
print('precision'," ".join('%s' % id for id in precision))
print('F1'," ".join('%s' % id for id in F1))
print('accuracy',accuracy)
plt.plot(loss_list,label='TextCNN')
plt.legend()
plt.title('loss-epoch')
plt.show()
模型跑出的结果如下：
3.TextRNN 文本分类实战
在这个算法中，进行Word Embedding后，输入到双向LSTM中，然后对最后一位的输出输入到全连接层中，在对其进行softmax分类即可，模型如下图：
网络结构图如下：
代码如下
#coding=gbk
from cgi import test
import torch
import numpy as np
import torch.nn as nn
import torch.optim as optim
import torch.utils.data as Data
import torch.nn.functional as F
from data_process import get_data
dtype = torch.FloatTensor
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# 3 words sentences (=sequence_length is 3)import matplotlib.pyplot as pltdef make_data(sentences):input_data = []input_label = []for sen in sentences:words = sen.split()input_data_tmp = [word2id[i] for i in words[:-1]]input_label_tmp = word2id[words[-1]]input_data.append(np.eye(vocab_size)[input_data_tmp])input_label.append(input_label_tmp)return input_data, input_labelclass TextRNN(nn.Module):def __init__(self):super(TextRNN, self).__init__()# 每个词向量的维度是词表长度，隐藏层输出特征大小是n_hiddenself.rnn = nn.RNN(input_size=vocab_size, hidden_size=n_hidden)self.fc = nn.Linear(n_hidden, vocab_size)def forward(self, h0, X):# X: [batch_size, n_step, vocab_size]the_input = X.transpose(0, 1) # RNN需要的数据得一二维度转置一下# RNN的输入是X和# RNN层会返回所有x1,x2对应的输出为out,我们只取最后一个输出# hidden是最后一个词计算得到的隐藏状态（符号RNN的图）# print("fds",the_input.size(),h0.size())out, hidden = self.rnn(the_input, h0)out = out[-1]res = self.fc(out)return resif __name__ == '__main__':# 准备一些简单的数据sentences,labels,setences_test,label_test=get_data()print(sentences,labels)#sentences = ["i love you", "he loves me", "she likes baseball", "i hate you", "sorry for that", "this is awful"]#labels = [1, 1, 1, 0, 0, 0] # 1 is good, 0 is not good.for i in range(len(sentences)):sentences[i]=sentences[i]+' '+str(labels[i])for i in range(len(setences_test)):setences_test[i]=setences_test[i]+' '+str(label_test[i])embedding_size = 100sequence_length = 20num_classes = len(set(labels))batch_size = 10word_list = " ".join(sentences).split()word_list2 = " ".join(setences_test).split()word_list=['0','1','2']+word_listvocab = list(set(word_list+word_list2))epoch=100n_step = 20 # n_step是输入的话的x部分的长度，因为我们的话只有三个单词所以就是2n_hidden = 100 # 隐藏输出特征的大小word2id = {w: i for i, w in enumerate(vocab)}id2word = {i: w for i, w in enumerate(vocab)}vocab_size = len(vocab)# 构造dataset, dataloaderinput_data, input_label = make_data(sentences)# print( input_data, input_label)#for i in input_data[0:10]:# print(i)# print(i[0])# print(len(i))input_data = torch.Tensor(input_data)input_label= torch.LongTensor(input_label)dataset = Data.TensorDataset(input_data, input_label)# 此时得到的输入数据是index形式的，不是向量形式dataloader = Data.DataLoader(dataset, batch_size, True)model = TextRNN()criterion = nn.CrossEntropyLoss()optimizer = optim.Adam(model.parameters(), lr=0.001)loss_list=[]# 训练部分for i in range(epoch):for x, y in dataloader:h0 = torch.zeros(1, x.shape[0], n_hidden)pred = model(h0, x)loss = criterion(pred, y)loss_list.append(loss)if (i + 1) % 5 == 0:print("epoch: ", '%04d' % (epoch + 1), 'cost =', '{:.6f}'.format(loss))optimizer.zero_grad()loss.backward()optimizer.step()# input = [sen.split()[:2] for sen in sentences]
# # Predict
# hidden = torch.zeros(1, len(input), n_hidden)
# predict = model(hidden, input_data).data.max(1, keepdim=True)[1]
# print([sen.split()[:2] for sen in sentences], '->', [id2word[n.item()] for n in predict.squeeze()])
#testinput_data, input_label = make_data(setences_test)# print( input_data, input_label)#for i in input_data[0:10]:# print(i)# print(i[0])# print(len(i))input_data = torch.Tensor(input_data)
input_label= torch.LongTensor(input_label)dataset = Data.TensorDataset(input_data, input_label)# 此时得到的输入数据是index形式的，不是向量形式
dataloader = Data.DataLoader(dataset, batch_size, True)
print("fdsfafas")classnum=3
test_loss = 0
correct = 0
total = 0
target_num =[0,0,0]
predict_num = [0,0,0]
acc_num =[0,0,0]for x, y in dataloader:h0 = torch.zeros(1, x.shape[0], n_hidden)pred = model(h0, x)loss = criterion(pred, y)if (epoch + 1) % 5 == 0:print("epoch: ", '%04d' % (epoch + 1), 'cost =', '{:.6f}'.format(loss))for i in y:target_num[int(id2word[int(i)])]+=1test_loss += lossp=0for i in pred:print(i.argmax())index=int(i.argmax())if id2word[index] in ['0','1','2']:predict_num[int(id2word[index])]+=1print(id2word[index],id2word[p])if index==int(y[p]):p=p+1acc_num[int(id2word[index])]+=1print(y)recall = [acc_num[i]/target_num[i] for i in range(3)]
precision = [acc_num[i]/predict_num[i] for i in range(3)]
F1 = [2*recall[i]*precision[i]/(recall[i]+precision[i]) for i in range(3)]
accuracy = sum(acc_num)/sum(target_num) # 打印格式方便复制
print('recall'," ".join('%s' % id for id in recall))
print('precision'," ".join('%s' % id for id in precision))
print('F1'," ".join('%s' % id for id in F1))
print('accuracy',accuracy)
plt.plot(loss_list,label='TextRNN')
plt.legend()
plt.title('loss-epoch')
plt.show()
评估结果：
5.FastText 文本分类实战
FastText使用x1,x2…xn表示一个ngram向量，其使用多个向量来表示一个词，然后再使用全部的ngram去预测指定的类别。
网络结构如下：
实现代码如下：
#coding=gbkimport torch
import torch.nn as nn
import torch.optim as optim
import pandas as pd
import numpy as np
from data_process import get_data
import matplotlib.pyplot as plt
loss_list=[]
class FastText(nn.Module):def __init__(self, vocab, w2v_dim, classes, hidden_size):super(FastText, self).__init__()#创建embeddingself.embed = nn.Embedding(len(vocab), w2v_dim) #embedding初始化，需要两个参数，词典大小、词向量维度大小self.embed.weight.requires_grad = True #需要计算梯度，即embedding层需要被训练self.fc = nn.Sequential( #序列函数nn.Linear(w2v_dim, hidden_size), #这里的意思是先经过一个线性转换层nn.BatchNorm1d(hidden_size), #再进入一个BatchNorm1dnn.ReLU(inplace=True), #再经过Relu激活函数nn.Linear(hidden_size, classes)#最后再经过一个线性变换)def forward(self, x): x = self.embed(x.type(dtype=torch.LongTensor)) #先将词id转换为对应的词向量out = self.fc(torch.mean(x, dim=1)) #这使用torch.mean()将向量进行平均return out
def train_model(net, epoch, lr, data, label): #训练模型print("begin training")net.train() # 将模型设置为训练模式，很重要！optimizer = optim.Adam(net.parameters(), lr=lr) #设置优化函数Loss = nn.CrossEntropyLoss() #设置损失函数for i in range(epoch): # 循环optimizer.zero_grad() # 清除所有优化的梯度output = net(data) # 传入数据，前向传播，得到预测结果loss = Loss(output, label) #计算预测值和真实值之间的差异，得到lossloss_list.append(loss)loss.backward() #loss反向传播optimizer.step() #优化器优化参数# 打印状态信息print("train epoch=" + str(i) + ",loss=" + str(loss.item()))print('Finished Training')predict_list=[]
def model_test(net, test_data, test_label):net.eval() # 将模型设置为验证模式correct = 0total = 0with torch.no_grad():outputs = net(test_data)# torch.max()[0]表示最大值的值，troch.max()[1]表示回最大值的每个索引_, predicted = torch.max(outputs.data, 1) # 每个output是一行n列的数据，取一行中最大的值total += test_label.size(0)print(test_label)print(predicted)predict_list.append(predicted)# correct += (predicted == test_label).sum().item()correct += (predicted == test_label).sum().item()print('Accuracy: %d %%' % (100 * correct / total))if __name__ == "__main__":#这里没有写具体数据的处理方法，毕竟大家所做的任务不一样sentences,labels,setences_test,label_test=get_data()print(sentences,labels)#sentences = ["i love you", "he loves me", "she likes baseball", "i hate you", "sorry for that", "this is awful"]#labels = [1, 1, 1, 0, 0, 0] # 1 is good, 0 is not good.#for i in range(len(sentences)):# sentences[i]=sentences[i]+' '+str(labels[i])#for i in range(len(setences_test)):# setences_test[i]=setences_test[i]+' '+str(label_test[i])word_list = " ".join(sentences).split()word_list2 = " ".join(setences_test).split()word_list=['0','1','2']+word_listvocab = list(set(word_list+word_list2))vocab_size=len(vocab)batch_size = 64epoch = 1000 # 迭代次数w2v_dim = 300 # 词向量维度lr = 0.001hidden_size = 128classes = len(set(labels))word2id = {w: i for i, w in enumerate(vocab)}id2word = {i: w for i, w in enumerate(vocab)}sequence_length=20def make_data(sentences, labels):inputs = []for sen in sentences:l=[word2id[n] for n in sen.split()]if len(l)<sequence_length:length=len(l)for i in range(sequence_length-length):l.append(0)inputs.append(l)else:inputs.append(l[0:sequence_length])targets = []print("labels",labels)for out in labels:targets.append(out)return inputs, targetsinput_data, input_label = make_data(sentences,labels)# print( input_data, input_label)#for i in input_data[0:10]:# print(i)# print(i[0])# print(len(i))input_data = torch.Tensor(input_data)input_label= torch.LongTensor(input_label)# 定义模型net = FastText(vocab=vocab, w2v_dim=w2v_dim, classes=classes, hidden_size=hidden_size)# 训练print("开始训练模型")train_model(net, epoch, lr, input_data, input_label)# 保存模型print("开始测试模型")input_data, input_label= make_data(setences_test,label_test)# print( input_data, input_label)#for i in input_data[0:10]:# print(i)# print(i[0])# print(len(i))input_data = torch.Tensor(input_data)input_label= torch.LongTensor(input_label)model_test(net, input_data, input_label)
test_loss = 0
correct = 0
total = 0
target_num =[0,0,0]
predict_num = [0,0,0]
p=0
acc_num =[0,0,0]for i in label_test:target_num[i]+=1for i in predict_list[0]:print(i.argmax())index=int(i)if index in [0,1,2]:predict_num[index]+=1print(id2word[index],id2word[p])if index==label_test[p]:acc_num[index]+=1p=p+1recall = [acc_num[i]/target_num[i] for i in range(3)]
precision = [acc_num[i]/predict_num[i] for i in range(3)]
F1 = [2*recall[i]*precision[i]/(recall[i]+precision[i]) for i in range(3)]
accuracy = sum(acc_num)/sum(target_num) plt.plot(loss_list,label='FastText')
plt.legend()
plt.title('loss-epoch')
plt.show()# 打印格式方便复制
print('recall'," ".join('%s' % id for id in recall))
print('precision'," ".join('%s' % id for id in precision))
print('F1'," ".join('%s' % id for id in F1))
print('accuracy',accuracy)
评估结果：
6.Transfermer 文本分类实战
对于Transfermer算法，我们将其decoder的输入用特殊字符#代替，这样其原本的翻译模型也可以修改为分类模型了。
其网络结构图如下：
Transfermer的代码比较多：
#coding=gbkfrom cgi import test
import torch
import numpy as np
import torch.nn as nn
import torch.optim as optim
import torch.utils.data as Data
import torch.nn.functional as F
import matplotlib.pyplot as pltfrom data_process import get_data
dtype = torch.FloatTensor
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# 3 words sentences (=sequence_length is 3)
# S: Symbol that shows starting of decoding input
# E: Symbol that shows starting of decoding output
# P: Symbol that will fill in blank sequence if current batch data size is short than time steps
sentencesz,labels,setences_test,label_test=get_data()
sentences_t=[]
for i in range(len(sentencesz)):a=[]sentencesz[i]=' '.join(sentencesz[i].split())a.append(sentencesz[i])a.append('#')a.append(str(labels[i]))sentences_t.append(a)print(sentences_t)sentences=sentences_tword_list = " ".join(sentencesz).split()
word_list2 = " ".join(setences_test).split()
word_list=word_list
vocab = list(set(word_list+word_list2))# Padding Should be Zero
#src_vocab = {'P' : 0, 'ich' : 1, 'mochte' : 2, 'ein' : 3, 'bier' : 4, 'cola' : 5}src_vocab = {w: i for i, w in enumerate(vocab)}src_vocab_size = len(src_vocab)tgt_vocab = {'0' : 0, '1' : 1, '2' : 2, '#' : 3}setences_test_z=[]
for i in range(len(setences_test)):a=[]setences_test[i]=' '.join(setences_test[i].split())a.append(setences_test[i])a.append('#')a.append(str(label_test[i]))setences_test_z.append(a)idx2word = {i: w for i, w in enumerate(tgt_vocab)}tgt_vocab_size = len(tgt_vocab)src_len = 20 # enc_input max sequence length
tgt_len = 1 # dec_input(=dec_output) max sequence lengthdef make_data(sentences):enc_inputs, dec_inputs, dec_outputs = [], [], []for i in range(len(sentences)):enc_input = [[src_vocab[n] for n in sentences[i][0].split()]] # [[1, 2, 3, 4, 0], [1, 2, 3, 5, 0]]dec_input = [[tgt_vocab[n] for n in sentences[i][1].split()]] # [[6, 1, 2, 3, 4, 8], [6, 1, 2, 3, 5, 8]]dec_output = [[tgt_vocab[n] for n in sentences[i][2].split()]] # [[1, 2, 3, 4, 8, 7], [1, 2, 3, 5, 8, 7]]enc_inputs.extend(enc_input)dec_inputs.extend(dec_input)dec_outputs.extend(dec_output)return torch.LongTensor(enc_inputs), torch.LongTensor(dec_inputs), torch.LongTensor(dec_outputs)enc_inputs, dec_inputs, dec_outputs = make_data(sentences)
enc_inputs_test, dec_inputs_test, dec_outputs_test = make_data(setences_test_z)print("enc_inputs",enc_inputs)
print("dec_inputs",dec_inputs,)
print("dec_outputs",dec_outputs)class MyDataSet(Data.Dataset):def __init__(self, enc_inputs, dec_inputs, dec_outputs):super(MyDataSet, self).__init__()self.enc_inputs = enc_inputsself.dec_inputs = dec_inputsself.dec_outputs = dec_outputsdef __len__(self):return self.enc_inputs.shape[0]def __getitem__(self, idx):return self.enc_inputs[idx], self.dec_inputs[idx], self.dec_outputs[idx]loader = Data.DataLoader(MyDataSet(enc_inputs, dec_inputs, dec_outputs), 40, True)
loader_test = Data.DataLoader(MyDataSet(enc_inputs_test, dec_inputs_test, dec_outputs_test), 1, True)# Transformer Parameters
d_model =500 # Embedding Size
d_ff = 1000 # FeedForward dimension
d_k = d_v = 64 # dimension of K(=Q), V
n_layers = 6 # number of Encoder of Decoder Layer
n_heads = 8 # number of heads in Multi-Head Attention#for enc_inputs, dec_inputs, dec_outputs in loader:
# print(enc_inputs, dec_inputs, dec_outputs)def get_sinusoid_encoding_table(n_position, d_model):def cal_angle(position, hid_idx):return position / np.power(10000, 2 * (hid_idx // 2) / d_model)def get_posi_angle_vec(position):return [cal_angle(position, hid_j) for hid_j in range(d_model)]sinusoid_table = np.array([get_posi_angle_vec(pos_i) for pos_i in range(n_position)])sinusoid_table[:, 0::2] = np.sin(sinusoid_table[:, 0::2]) # dim 2isinusoid_table[:, 1::2] = np.cos(sinusoid_table[:, 1::2]) # dim 2i+1return torch.FloatTensor(sinusoid_table)
def get_attn_pad_mask(seq_q, seq_k):'''seq_q: [batch_size, seq_len]seq_k: [batch_size, seq_len]seq_len could be src_len or it could be tgt_lenseq_len in seq_q and seq_len in seq_k maybe not equal'''batch_size, len_q = seq_q.size()batch_size, len_k = seq_k.size()# eq(zero) is PAD tokenpad_attn_mask = seq_k.data.eq(0).unsqueeze(1) # [batch_size, 1, len_k], False is maskedreturn pad_attn_mask.expand(batch_size, len_q, len_k) # [batch_size, len_q, len_k]
def get_attn_subsequence_mask(seq):'''seq: [batch_size, tgt_len]'''attn_shape = [seq.size(0), seq.size(1), seq.size(1)]subsequence_mask = np.triu(np.ones(attn_shape), k=1) # Upper triangular matrixsubsequence_mask = torch.from_numpy(subsequence_mask).byte()return subsequence_mask
class ScaledDotProductAttention(nn.Module):def __init__(self):super(ScaledDotProductAttention, self).__init__()def forward(self, Q, K, V, attn_mask):'''Q: [batch_size, n_heads, len_q, d_k]K: [batch_size, n_heads, len_k, d_k]V: [batch_size, n_heads, len_v(=len_k), d_v]attn_mask: [batch_size, n_heads, seq_len, seq_len]'''scores = torch.matmul(Q, K.transpose(-1, -2)) / np.sqrt(d_k) # scores : [batch_size, n_heads, len_q, len_k]scores.masked_fill_(attn_mask, -1e9) # Fills elements of self tensor with value where mask is True.attn = nn.Softmax(dim=-1)(scores)context = torch.matmul(attn, V) # [batch_size, n_heads, len_q, d_v]return context, attn
class MultiHeadAttention(nn.Module):def __init__(self):super(MultiHeadAttention, self).__init__()self.W_Q = nn.Linear(d_model, d_k * n_heads, bias=False)self.W_K = nn.Linear(d_model, d_k * n_heads, bias=False)self.W_V = nn.Linear(d_model, d_v * n_heads, bias=False)self.fc = nn.Linear(n_heads * d_v, d_model, bias=False)def forward(self, input_Q, input_K, input_V, attn_mask):'''input_Q: [batch_size, len_q, d_model]input_K: [batch_size, len_k, d_model]input_V: [batch_size, len_v(=len_k), d_model]attn_mask: [batch_size, seq_len, seq_len]'''residual, batch_size = input_Q, input_Q.size(0)# (B, S, D) -proj-> (B, S, D_new) -split-> (B, S, H, W) -trans-> (B, H, S, W)Q = self.W_Q(input_Q).view(batch_size, -1, n_heads, d_k).transpose(1,2) # Q: [batch_size, n_heads, len_q, d_k]K = self.W_K(input_K).view(batch_size, -1, n_heads, d_k).transpose(1,2) # K: [batch_size, n_heads, len_k, d_k]V = self.W_V(input_V).view(batch_size, -1, n_heads, d_v).transpose(1,2) # V: [batch_size, n_heads, len_v(=len_k), d_v]attn_mask = attn_mask.unsqueeze(1).repeat(1, n_heads, 1, 1) # attn_mask : [batch_size, n_heads, seq_len, seq_len]# context: [batch_size, n_heads, len_q, d_v], attn: [batch_size, n_heads, len_q, len_k]context, attn = ScaledDotProductAttention()(Q, K, V, attn_mask)context = context.transpose(1, 2).reshape(batch_size, -1, n_heads * d_v) # context: [batch_size, len_q, n_heads * d_v]output = self.fc(context) # [batch_size, len_q, d_model]return nn.LayerNorm(d_model)(output + residual), attn
class PoswiseFeedForwardNet(nn.Module):def __init__(self):super(PoswiseFeedForwardNet, self).__init__()self.fc = nn.Sequential(nn.Linear(d_model, d_ff, bias=False),nn.ReLU(),nn.Linear(d_ff, d_model, bias=False))def forward(self, inputs):'''inputs: [batch_size, seq_len, d_model]'''residual = inputsoutput = self.fc(inputs)return nn.LayerNorm(d_model)(output + residual) # [batch_size, seq_len, d_model]
class EncoderLayer(nn.Module):def __init__(self):super(EncoderLayer, self).__init__()self.enc_self_attn = MultiHeadAttention()self.pos_ffn = PoswiseFeedForwardNet()def forward(self, enc_inputs, enc_self_attn_mask):'''enc_inputs: [batch_size, src_len, d_model]enc_self_attn_mask: [batch_size, src_len, src_len]'''# enc_outputs: [batch_size, src_len, d_model], attn: [batch_size, n_heads, src_len, src_len]enc_outputs, attn = self.enc_self_attn(enc_inputs, enc_inputs, enc_inputs, enc_self_attn_mask) # enc_inputs to same Q,K,Venc_outputs = self.pos_ffn(enc_outputs) # enc_outputs: [batch_size, src_len, d_model]return enc_outputs, attn
class Encoder(nn.Module):def __init__(self):super(Encoder, self).__init__()self.src_emb = nn.Embedding(src_vocab_size, d_model)self.pos_emb = nn.Embedding.from_pretrained(get_sinusoid_encoding_table(src_vocab_size, d_model),freeze=True)self.layers = nn.ModuleList([EncoderLayer() for _ in range(n_layers)])def forward(self, enc_inputs):'''enc_inputs: [batch_size, src_len]'''word_emb = self.src_emb(enc_inputs) # [batch_size, src_len, d_model]pos_emb = self.pos_emb(enc_inputs) # [batch_size, src_len, d_model]enc_outputs = word_emb + pos_embenc_self_attn_mask = get_attn_pad_mask(enc_inputs, enc_inputs) # [batch_size, src_len, src_len]enc_self_attns = []for layer in self.layers:# enc_outputs: [batch_size, src_len, d_model], enc_self_attn: [batch_size, n_heads, src_len, src_len]enc_outputs, enc_self_attn = layer(enc_outputs, enc_self_attn_mask)enc_self_attns.append(enc_self_attn)return enc_outputs, enc_self_attns
class DecoderLayer(nn.Module):def __init__(self):super(DecoderLayer, self).__init__()self.dec_self_attn = MultiHeadAttention()self.dec_enc_attn = MultiHeadAttention()self.pos_ffn = PoswiseFeedForwardNet()def forward(self, dec_inputs, enc_outputs, dec_self_attn_mask, dec_enc_attn_mask):'''dec_inputs: [batch_size, tgt_len, d_model]enc_outputs: [batch_size, src_len, d_model]dec_self_attn_mask: [batch_size, tgt_len, tgt_len]dec_enc_attn_mask: [batch_size, tgt_len, src_len]'''# dec_outputs: [batch_size, tgt_len, d_model], dec_self_attn: [batch_size, n_heads, tgt_len, tgt_len]dec_outputs, dec_self_attn = self.dec_self_attn(dec_inputs, dec_inputs, dec_inputs, dec_self_attn_mask)# dec_outputs: [batch_size, tgt_len, d_model], dec_enc_attn: [batch_size, h_heads, tgt_len, src_len]dec_outputs, dec_enc_attn = self.dec_enc_attn(dec_outputs, enc_outputs, enc_outputs, dec_enc_attn_mask)dec_outputs = self.pos_ffn(dec_outputs) # [batch_size, tgt_len, d_model]return dec_outputs, dec_self_attn, dec_enc_attn
class Decoder(nn.Module):def __init__(self):super(Decoder, self).__init__()self.tgt_emb = nn.Embedding(tgt_vocab_size, d_model)self.pos_emb = nn.Embedding.from_pretrained(get_sinusoid_encoding_table(tgt_vocab_size, d_model),freeze=True)self.layers = nn.ModuleList([DecoderLayer() for _ in range(n_layers)])def forward(self, dec_inputs, enc_inputs, enc_outputs):'''dec_inputs: [batch_size, tgt_len]enc_intpus: [batch_size, src_len]enc_outputs: [batsh_size, src_len, d_model]'''word_emb = self.tgt_emb(dec_inputs) # [batch_size, tgt_len, d_model]pos_emb = self.pos_emb(dec_inputs) # [batch_size, tgt_len, d_model]dec_outputs = word_emb + pos_embdec_self_attn_pad_mask = get_attn_pad_mask(dec_inputs, dec_inputs) # [batch_size, tgt_len, tgt_len]dec_self_attn_subsequent_mask = get_attn_subsequence_mask(dec_inputs) # [batch_size, tgt_len]dec_self_attn_mask = torch.gt((dec_self_attn_pad_mask + dec_self_attn_subsequent_mask), 0) # [batch_size, tgt_len, tgt_len]dec_enc_attn_mask = get_attn_pad_mask(dec_inputs, enc_inputs) # [batc_size, tgt_len, src_len]dec_self_attns, dec_enc_attns = [], []for layer in self.layers:# dec_outputs: [batch_size, tgt_len, d_model], dec_self_attn: [batch_size, n_heads, tgt_len, tgt_len], dec_enc_attn: [batch_size, h_heads, tgt_len, src_len]dec_outputs, dec_self_attn, dec_enc_attn = layer(dec_outputs, enc_outputs, dec_self_attn_mask, dec_enc_attn_mask)dec_self_attns.append(dec_self_attn)dec_enc_attns.append(dec_enc_attn)return dec_outputs, dec_self_attns, dec_enc_attns
class Transformer(nn.Module):def __init__(self):super(Transformer, self).__init__()self.encoder = Encoder()self.decoder = Decoder()self.projection = nn.Linear(d_model, tgt_vocab_size, bias=False)def forward(self, enc_inputs, dec_inputs):'''enc_inputs: [batch_size, src_len]dec_inputs: [batch_size, tgt_len]'''# tensor to store decoder outputs# outputs = torch.zeros(batch_size, tgt_len, tgt_vocab_size).to(self.device)# enc_outputs: [batch_size, src_len, d_model], enc_self_attns: [n_layers, batch_size, n_heads, src_len, src_len]enc_outputs, enc_self_attns = self.encoder(enc_inputs)# dec_outpus: [batch_size, tgt_len, d_model], dec_self_attns: [n_layers, batch_size, n_heads, tgt_len, tgt_len], dec_enc_attn: [n_layers, batch_size, tgt_len, src_len]dec_outputs, dec_self_attns, dec_enc_attns = self.decoder(dec_inputs, enc_inputs, enc_outputs)dec_logits = self.projection(dec_outputs) # dec_logits: [batch_size, tgt_len, tgt_vocab_size]return dec_logits.view(-1, dec_logits.size(-1)), enc_self_attns, dec_self_attns, dec_enc_attns
model = Transformer()
criterion = nn.CrossEntropyLoss().to(device)
optimizer = optim.Adam(model.parameters(), lr=0.000008)
loss_list=[]
for epoch in range(35):co=0to=0for enc_inputs, dec_inputs, dec_outputs in loader:'''enc_inputs: [batch_size, src_len]dec_inputs: [batch_size, tgt_len]dec_outputs: [batch_size, tgt_len]'''# enc_inputs, dec_inputs, dec_outputs = enc_inputs.to(device), dec_inputs.to(device), dec_outputs.to(device)# outputs: [batch_size * tgt_len, tgt_vocab_size]outputs, enc_self_attns, dec_self_attns, dec_enc_attns = model(enc_inputs, dec_inputs)#print(outputs.argmax(1))#print(dec_outputs)index=outputs.argmax(1)print(index)#print(dec_outputs)for i in range(len(dec_outputs)):if index[i]==dec_outputs[i]:co+=1to=to+len(index)loss = criterion(outputs, dec_outputs.view(-1))loss_list.append(loss)# print(outputs,dec_outputs)# print('Epoch:', '%04d' % (epoch + 1), 'loss =', '{:.6f}'.format(loss))optimizer.zero_grad()loss.backward()optimizer.step()print("epoch is: ",epoch)print("accurac is: ",co/to)
enc_inputs, dec_inputs, _ = next(iter(loader))print("test")correct=0
total=0
for enc_inputs, dec_inputs, dec_outputs in loader_test:outputs, enc_self_attns, dec_self_attns, dec_enc_attns = model(enc_inputs, dec_inputs)#print(outputs.argmax(1))#print(dec_outputs)index=outputs.argmax(1)print(index)#print(dec_outputs)for i in range(len(dec_outputs)):if index[i]==dec_outputs[i]:correct+=1total=total+len(index)print( "accuracy:",correct/total)plt.plot(loss_list,label='transfermer')
plt.legend()
plt.title('loss-epoch')
plt.show()
评估结果如下：
好的，这次实验，博主认为可以对于大家在学习这几个模型有着一些帮助。
这篇关于python pytorch- TextCNN TextRNN FastText Transfermer （中英文）文本情感分类实战（附数据集，代码皆可运行）的文章就介绍到这儿，希望我们推荐的文章对编程师们有所帮助！
