在深度学习中，经常需要动态调整学习率，以达到更好地训练效果，本文纪录在pytorch中的实现方法，其优化器实例为SGD优化器，其他如Adam优化器同样适用。

一般来说，在以SGD优化器作为基本优化器，然后根据epoch实现学习率指数下降，代码如下:

step = [10,20,30,40]base_lr = 1e-4sgd_opt = torch.optim.SGD(model.parameters(), lr=base_lr, nesterov=True, momentum=0.9)def adjust_lr(epoch): lr = base_lr * (0.1 ** np.sum(epoch >= np.array(step))) for params_group in sgd_opt.param_groups: params_group['lr'] = lr return lr

只需要在每个train的epoch之前使用这个函数即可。

for epoch in range(60): model.train() adjust_lr(epoch) for ind, each in enumerate(train_loader): mat, label = each ...

补充知识：Pytorch框架下应用Bi-LSTM实现汽车评论文本关键词抽取

需要调用的模块及整体Bi-lstm流程

import torchimport pandas as pdimport numpy as npfrom tensorflow import kerasimport torch.nn as nnimport torch.nn.functional as Fimport torch.optim as optimfrom torch.utils.data import DataLoaderfrom torch.utils.data import TensorDatasetimport gensimfrom sklearn.model_selection import train_test_splitclass word_extract(nn.Module): def __init__(self,d_model,embedding_matrix): super(word_extract, self).__init__() self.d_model=d_model self.embedding=nn.Embedding(num_embeddings=len(embedding_matrix),embedding_dim=200) self.embedding.weight.data.copy_(embedding_matrix) self.embedding.weight.requires_grad=False self.lstm1=nn.LSTM(input_size=200,hidden_size=50,bidirectional=True) self.lstm2=nn.LSTM(input_size=2*self.lstm1.hidden_size,hidden_size=50,bidirectional=True) self.linear=nn.Linear(2*self.lstm2.hidden_size,4) def forward(self,x): w_x=self.embedding(x) first_x,(first_h_x,first_c_x)=self.lstm1(w_x) second_x,(second_h_x,second_c_x)=self.lstm2(first_x) output_x=self.linear(second_x) return output_x

将文本转换为数值形式

def trans_num(word2idx,text): text_list=[] for i in text: s=i.rstrip().replace('\r','').replace('\n','').split(' ') numtext=[word2idx[j] if j in word2idx.keys() else word2idx['_PAD'] for j in s ] text_list.append(numtext) return text_list

将Gensim里的词向量模型转为矩阵形式，后续导入到LSTM模型中

def establish_word2vec_matrix(model): #负责将数值索引转为要输入的数据 word2idx = {"_PAD": 0} # 初始化 `[word : token]` 字典，后期 tokenize 语料库就是用该词典。 num2idx = {0: "_PAD"} vocab_list = [(k, model.wv[k]) for k, v in model.wv.vocab.items()] # 存储所有 word2vec 中所有向量的数组，留意其中多一位，词向量全为 0， 用于 padding embeddings_matrix = np.zeros((len(model.wv.vocab.items()) + 1, model.vector_size)) for i in range(len(vocab_list)): word = vocab_list[i][0] word2idx[word] = i + 1 num2idx[i + 1] = word embeddings_matrix[i + 1] = vocab_list[i][1] embeddings_matrix = torch.Tensor(embeddings_matrix) return embeddings_matrix, word2idx, num2idx

训练过程

def train(model,epoch,learning_rate,batch_size,x, y, val_x, val_y): optimizor = optim.Adam(model.parameters(), lr=learning_rate) data = TensorDataset(x, y) data = DataLoader(data, batch_size=batch_size) for i in range(epoch): for j, (per_x, per_y) in enumerate(data): output_y = model(per_x) loss = F.cross_entropy(output_y.view(-1,output_y.size(2)), per_y.view(-1)) optimizor.zero_grad() loss.backward() optimizor.step() arg_y=output_y.argmax(dim=2) fit_correct=(arg_y==per_y).sum() fit_acc=fit_correct.item()/(per_y.size(0)*per_y.size(1)) print('##################################') print('第{}次迭代第{}批次的训练误差为{}'.format(i + 1, j + 1, loss), end=' ') print('第{}次迭代第{}批次的训练准确度为{}'.format(i + 1, j + 1, fit_acc)) val_output_y = model(val_x) val_loss = F.cross_entropy(val_output_y.view(-1,val_output_y.size(2)), val_y.view(-1)) arg_val_y=val_output_y.argmax(dim=2) val_correct=(arg_val_y==val_y).sum() val_acc=val_correct.item()/(val_y.size(0)*val_y.size(1)) print('第{}次迭代第{}批次的预测误差为{}'.format(i + 1, j + 1, val_loss), end=' ') print('第{}次迭代第{}批次的预测准确度为{}'.format(i + 1, j + 1, val_acc)) torch.save(model,'./extract_model.pkl')#保存模型

主函数部分

if __name__ =='__main__': #生成词向量矩阵 word2vec = gensim.models.Word2Vec.load('./word2vec_model') embedding_matrix,word2idx,num2idx=establish_word2vec_matrix(word2vec)#输入的是词向量模型 # train_data=pd.read_csv('./数据.csv') x=list(train_data['文本']) # 将文本从文字转化为数值，这部分trans_num函数你需要自己改动去适应你自己的数据集 x=trans_num(word2idx,x) #x需要先进行填充，也就是每个句子都是一样长度，不够长度的以0来填充，填充词单独分为一类 # #也就是说输入的x是固定长度的数值列表,例如[50,123,1850,21,199,0，0，...] #输入的y是[2,0,1,0,0,1,3,3,3,3,3,.....] #填充代码你自行编写，以下部分是针对我的数据集 x=keras.preprocessing.sequence.pad_sequences( x,maxlen=60,value=0,padding='post', ) y=list(train_data['BIO数值']) y_text=[] for i in y: s=i.rstrip().split(' ') numtext=[int(j) for j in s] y_text.append(numtext) y=y_text y=keras.preprocessing.sequence.pad_sequences( y,maxlen=60,value=3,padding='post', ) # 将数据进行划分 fit_x,val_x,fit_y,val_y=train_test_split(x,y,train_size=0.8,test_size=0.2) fit_x=torch.LongTensor(fit_x) fit_y=torch.LongTensor(fit_y) val_x=torch.LongTensor(val_x) val_y=torch.LongTensor(val_y) #开始应用 w_extract=word_extract(d_model=200,embedding_matrix=embedding_matrix) train(model=w_extract,epoch=5,learning_rate=0.001,batch_size=50, x=fit_x,y=fit_y,val_x=val_x,val_y=val_y)#可以自行改动参数,设置学习率，批次，和迭代次数 w_extract=torch.load('./extract_model.pkl')#加载保存好的模型 pred_val_y=w_extract(val_x).argmax(dim=2)

以上这篇在pytorch中动态调整优化器的学习率方式就是小编分享给大家的全部内容了，希望能给大家一个参考，也希望大家多多支持。