食品餐饮网站建设,iis服务器网站301重定向怎么做,网站变成了百度推广,室内设计师要学哪些文章目录一、LSTM简介二、LSTM中的核心结构三、如何解决RNN中的梯度消失/爆炸问题四、情感分类实战#xff08;google colab#xff09;一、LSTM简介
LSTM#xff08;long short-term memory#xff09;长短期记忆网络#xff0c;RNN的改进#xff0c;克服了RNN中“记忆…
文章目录一、LSTM简介二、LSTM中的核心结构三、如何解决RNN中的梯度消失/爆炸问题四、情感分类实战google colab一、LSTM简介
LSTMlong short-term memory长短期记忆网络RNN的改进克服了RNN中“记忆低下”的问题。通过“门”结构实现信息的添加和移除通过记忆元将序列处理过程中的相关信息一直传递下去经典结构如下
二、LSTM中的核心结构
记忆元memory cell-长期记忆 就像一个cell一样信息通过这条只有少量线性交互的线传递。传递过程中有3种“门”结构可以告诉它该学习或者保存哪些信息 三个门结构-短期记忆 遗忘门用来决定当前状态哪些信息被移除 输入门决定放入哪些信息到细胞状态 输出门决定哪些信息用于输出 细节注意
新的细胞状态只需要遗忘门和输入门就可以更新公式为Ctft∗Ct−1it∗Ct~C_tf_t*C_{t-1}i_t* \tilde{C_t}Ctft∗Ct−1it∗Ct~注意所有的∗*∗都表示Hadamard 乘积只有隐状态h_t会传递到输出层记忆元完全属于内部信息不可手动修改
三、如何解决RNN中的梯度消失/爆炸问题
解决是指很大程度上缓解不是让它彻底消失。先解释RNN为什么会有这些问题 ∂Lt∂U∑k0t∂Lt∂Ot∂Ot∂St(∏jk1t∂Sj∂Sj−1)∂Sk∂U∂Lt∂W∑k0t∂Lt∂Ot∂Ot∂St(∏jk1t∂Sj∂Sj−1)∂Sk∂W\begin{aligned} \frac{\partial L_t}{\partial U} \sum_{k0}^{t}\frac{\partial L_t}{\partial O_t}\frac{\partial O_t}{\partial S_t}(\prod_{jk1}^{t}\frac{\partial S_j}{\partial S_{j-1}})\frac{\partial S_k}{\partial U}\\\frac{\partial L_t}{\partial W} \sum_{k0}^{t}\frac{\partial L_t}{\partial O_t}\frac{\partial O_t}{\partial S_t}(\prod_{jk1}^{t}\frac{\partial S_j}{\partial S_{j-1}})\frac{\partial S_k}{\partial W} \end{aligned} ∂U∂Ltk0∑t∂Ot∂Lt∂St∂Ot(jk1∏t∂Sj−1∂Sj)∂U∂Sk∂W∂Ltk0∑t∂Ot∂Lt∂St∂Ot(jk1∏t∂Sj−1∂Sj)∂W∂Sk具体过程可以看这里
上面是训练过程任意时刻更新W、U需要用到的求偏导的结果。实际使用会加上激活函数通常为tanh、sigmoid等 tanh和其导数图像如下 sigmoid和其导数如下 这些激活函数的导数都比1要小又因为∏jk1t∂Sj∂Sj−1∏jk1ttanh′(Ws)\prod_{jk1}^{t}\frac{\partial S_j}{\partial S_{j-1}}\prod_{jk1}^{t}tanh(W_s)∏jk1t∂Sj−1∂Sj∏jk1ttanh′(Ws)所以当WsW_sWs过小过大就会分别造成梯度消失和爆炸的问题特别是过小。 LSTM如何缓解 由链式法则和三个门的公式可以得到 ∂Ct∂Ct−1∂Ct∂ft∂ft∂ht−1∂ht−1∂Ct−1∂Ct∂it∂it∂ht−1∂ht−1∂Ct−1∂Ct∂Ct~∂Ct~∂ht−1∂ht−1∂Ct−1∂Ct∂Ct−1Ct−1σ′(⋅)Wf∗ot−1tanh′(Ct−1)Ct~σ′(⋅)Wi∗ot−1tanh′(Ct−1)ittanh′(⋅)Wc∗ot−1tanh′(Ct−1)ft\begin{aligned} \frac{\partial C_t}{\partial C_{t-1}}\\\frac{\partial C_t}{\partial f_t}\frac{\partial f_t}{\partial h_{t-1}}\frac{\partial h_{t-1}}{\partial C_{t-1}}\frac{\partial C_t}{\partial i_t}\frac{\partial i_t}{\partial h_{t-1}}\frac{\partial h_{t-1}}{\partial C_{t-1}}\frac{\partial C_t}{\partial \tilde{C_t}}\frac{\partial \tilde{C_t}}{\partial h_{t-1}}\frac{\partial h_{t-1}}{\partial C_{t-1}}\frac{\partial C_t}{\partial C_{t-1}}\\ C_{t-1}\sigma (\cdot)W_f*o_{t-1}tanh(C_{t-1})\tilde{C_t}\sigma (\cdot)W_i*o_{t-1}tanh(C_{t-1})\\i_ttanh(\cdot)W_c*o_{t-1}tanh(C_{t-1})f_t \end{aligned}∂Ct−1∂Ct∂ft∂Ct∂ht−1∂ft∂Ct−1∂ht−1∂it∂Ct∂ht−1∂it∂Ct−1∂ht−1∂Ct~∂Ct∂ht−1∂Ct~∂Ct−1∂ht−1∂Ct−1∂CtCt−1σ′(⋅)Wf∗ot−1tanh′(Ct−1)Ct~σ′(⋅)Wi∗ot−1tanh′(Ct−1)ittanh′(⋅)Wc∗ot−1tanh′(Ct−1)ft
由相乘变成了相加不容易叠加sigmoid函数使单元间传递结果非常接近0或者1使模型变成非线性并且可以在学习过程中内部调整
四、情感分类实战google colab
环境和库
!pip install torch
!pip install torchtext
!python -m spacy download en# K80 gpu for 12 hours
import torch
from torch import nn, optim
from torchtext import data, datasetsprint(GPU:, torch.cuda.is_available())torch.manual_seed(123)加载数据集
TEXT data.Field(tokenizespacy)
LABEL data.LabelField(dtypetorch.float)
train_data, test_data datasets.IMDB.splits(TEXT, LABEL)print(train_data.examples[15].text)
print(train_data.examples[15].label)网络结构
class RNN(nn.Module):def __init__(self, vocab_size, embedding_dim, hidden_dim):super(RNN, self).__init__()# [0-10001] [100]self.embedding nn.Embedding(vocab_size, embedding_dim)# [100] [256]self.rnn nn.LSTM(embedding_dim, hidden_dim, num_layers2, bidirectionalTrue, dropout0.5)# [256*2] [1]self.fc nn.Linear(hidden_dim*2, 1)self.dropout nn.Dropout(0.5)def forward(self, x):x: [seq_len, b] vs [b, 3, 28, 28]# [seq, b, 1] [seq, b, 100]embedding self.dropout(self.embedding(x))# output: [seq, b, hid_dim*2]# hidden/h: [num_layers*2, b, hid_dim]# cell/c: [num_layers*2, b, hid_di]output, (hidden, cell) self.rnn(embedding)# [num_layers*2, b, hid_dim] 2 of [b, hid_dim] [b, hid_dim*2]hidden torch.cat([hidden[-2], hidden[-1]], dim1)# [b, hid_dim*2] [b, 1]hidden self.dropout(hidden)out self.fc(hidden)return outEmbedding
rnn RNN(len(TEXT.vocab), 100, 256)pretrained_embedding TEXT.vocab.vectors
print(pretrained_embedding:, pretrained_embedding.shape)
rnn.embedding.weight.data.copy_(pretrained_embedding)
print(embedding layer inited.)optimizer optim.Adam(rnn.parameters(), lr1e-3)
criteon nn.BCEWithLogitsLoss().to(device)
rnn.to(device) 训练并测试
import numpy as npdef binary_acc(preds, y):get accuracypreds torch.round(torch.sigmoid(preds))correct torch.eq(preds, y).float()acc correct.sum() / len(correct)return accdef train(rnn, iterator, optimizer, criteon):avg_acc []rnn.train()for i, batch in enumerate(iterator):# [seq, b] [b, 1] [b]pred rnn(batch.text).squeeze(1)# loss criteon(pred, batch.label)acc binary_acc(pred, batch.label).item()avg_acc.append(acc)optimizer.zero_grad()loss.backward()optimizer.step()if i%10 0:print(i, acc)avg_acc np.array(avg_acc).mean()print(avg acc:, avg_acc)def eval(rnn, iterator, criteon):avg_acc []rnn.eval()with torch.no_grad():for batch in iterator:# [b, 1] [b]pred rnn(batch.text).squeeze(1)#loss criteon(pred, batch.label)acc binary_acc(pred, batch.label).item()avg_acc.append(acc)avg_acc np.array(avg_acc).mean()print(test:, avg_acc)for epoch in range(10):eval(rnn, test_iterator, criteon)train(rnn, train_iterator, optimizer, criteon)最后得到的准确率结果如下 完整colab链接lstm 完整代码
# -*- coding: utf-8 -*-
lstmAutomatically generated by Colaboratory.Original file is located athttps://colab.research.google.com/drive/1GX0Rqur8T45MSYhLU9MYWAbycfLH4-Fu
!pip install torch
!pip install torchtext
!python -m spacy download en# K80 gpu for 12 hours
import torch
from torch import nn, optim
from torchtext import data, datasetsprint(GPU:, torch.cuda.is_available())torch.manual_seed(123)TEXT data.Field(tokenizespacy)
LABEL data.LabelField(dtypetorch.float)
train_data, test_data datasets.IMDB.splits(TEXT, LABEL)print(len of train data:, len(train_data))
print(len of test data:, len(test_data))print(train_data.examples[15].text)
print(train_data.examples[15].label)# word2vec, glove
TEXT.build_vocab(train_data, max_size10000, vectorsglove.6B.100d)
LABEL.build_vocab(train_data)batchsz 30
device torch.device(cuda)
train_iterator, test_iterator data.BucketIterator.splits((train_data, test_data),batch_size batchsz,devicedevice
)class RNN(nn.Module):def __init__(self, vocab_size, embedding_dim, hidden_dim):super(RNN, self).__init__()# [0-10001] [100]self.embedding nn.Embedding(vocab_size, embedding_dim)# [100] [256]self.rnn nn.LSTM(embedding_dim, hidden_dim, num_layers2, bidirectionalTrue, dropout0.5)# [256*2] [1]self.fc nn.Linear(hidden_dim*2, 1)self.dropout nn.Dropout(0.5)def forward(self, x):x: [seq_len, b] vs [b, 3, 28, 28]# [seq, b, 1] [seq, b, 100]embedding self.dropout(self.embedding(x))# output: [seq, b, hid_dim*2]# hidden/h: [num_layers*2, b, hid_dim]# cell/c: [num_layers*2, b, hid_di]output, (hidden, cell) self.rnn(embedding)# [num_layers*2, b, hid_dim] 2 of [b, hid_dim] [b, hid_dim*2]hidden torch.cat([hidden[-2], hidden[-1]], dim1)# [b, hid_dim*2] [b, 1]hidden self.dropout(hidden)out self.fc(hidden)return outrnn RNN(len(TEXT.vocab), 100, 256)pretrained_embedding TEXT.vocab.vectors
print(pretrained_embedding:, pretrained_embedding.shape)
rnn.embedding.weight.data.copy_(pretrained_embedding)
print(embedding layer inited.)optimizer optim.Adam(rnn.parameters(), lr1e-3)
criteon nn.BCEWithLogitsLoss().to(device)
rnn.to(device)import numpy as npdef binary_acc(preds, y):get accuracypreds torch.round(torch.sigmoid(preds))correct torch.eq(preds, y).float()acc correct.sum() / len(correct)return accdef train(rnn, iterator, optimizer, criteon):avg_acc []rnn.train()for i, batch in enumerate(iterator):# [seq, b] [b, 1] [b]pred rnn(batch.text).squeeze(1)# loss criteon(pred, batch.label)acc binary_acc(pred, batch.label).item()avg_acc.append(acc)optimizer.zero_grad()loss.backward()optimizer.step()if i%10 0:print(i, acc)avg_acc np.array(avg_acc).mean()print(avg acc:, avg_acc)def eval(rnn, iterator, criteon):avg_acc []rnn.eval()with torch.no_grad():for batch in iterator:# [b, 1] [b]pred rnn(batch.text).squeeze(1)#loss criteon(pred, batch.label)acc binary_acc(pred, batch.label).item()avg_acc.append(acc)avg_acc np.array(avg_acc).mean()print(test:, avg_acc)for epoch in range(10):eval(rnn, test_iterator, criteon)train(rnn, train_iterator, optimizer, criteon)
