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高性能网站建设 下载,软件开发工程师待遇怎么样,北海 网站建设,专业建站开发文章目录一、RNN简介二、RNN关键结构三、RNN的训练方式四、时间序列预测五、梯度弥散和梯度爆炸问题一、RNN简介 RNN#xff08;Recurrent Neural Network#xff09;中文循环神经网络#xff0c;用于处理序列数据。它与传统人工神经网络和卷积神经网络的输入和输出相互独立… 文章目录一、RNN简介二、RNN关键结构三、RNN的训练方式四、时间序列预测五、梯度弥散和梯度爆炸问题一、RNN简介 RNNRecurrent Neural Network中文循环神经网络用于处理序列数据。它与传统人工神经网络和卷积神经网络的输入和输出相互独立不同依赖它独特的神经结构循环核获得“记忆能力” 注意与递归神经网络(Recursive Neural Network)RNN区分同时循环神经网络为短期记忆与Long Short-Term Memory networksLSTM的长期记忆不同 二、RNN关键结构 各参数含义 xtx_txt​序列t的输入层的值sts_tst​序列t的隐藏层的值 oto_tot​序列t的输出层的值UUU输入层到隐藏层的权重矩阵 VVV隐藏层到输出层的权重矩阵WWW隐藏层上一次的值作为这一次输入的权重 注意事项 同不同序列t时的WVU相同即RNN的Weight sharing结构图中每一步都会有输出但实际中很可能只需最后一步的输出为了降低网络复杂度sts_tst​只包含前面若干隐藏层的状态 三、RNN的训练方式 本质还是梯度下降的反向传播由前向传播得到的预测值与真实值构建损失函数更新W、U、V求解最小值 Stf(U⋅XtW⋅St−1b)Otg(V⋅St)Lt12(Yt−Ot)2S_tf(U\cdot XtW\cdot S{t-1}b) \O_t g(V\cdot S_t) \ L_t\frac{1}{2}(Y_t-O_t)^2St​f(U⋅Xt​W⋅St−1​b)Ot​g(V⋅St​)Lt​21​(Yt​−Ot​)2 如果对t3t_3t3​的U、V、W求偏导如下 ∂L3∂V∂L3∂O3∂O3∂V∂L3∂U∂L3∂O3∂O3∂S3∂S3∂U∂L3∂O3∂O3∂S3∂S3∂S2∂S2∂U∂L3∂O3∂O3∂S3∂S3∂S2∂S2∂S1∂S1∂U∂L3∂W∂L3∂O3∂O3∂S3∂S3∂W∂L3∂O3∂O3∂S3∂S3∂S2∂S2∂W∂L3∂O3∂O3∂S3∂S3∂S2∂S2∂S1∂S1∂W因为有O3VS3b2S3UX3WS2b1S2UX2WS1b1S1UX1WS0b1\begin{aligned} \frac{\partial L_3}{\partial V}\frac{\partial L_3}{\partial O_3}\frac{\partial O_3}{\partial V} \ \frac{\partial L_3}{\partial U}\frac{\partial L_3}{\partial O_3}\frac{\partial O_3}{\partial S_3} \frac{\partial S_3}{\partial U}\frac{\partial L_3}{\partial O_3}\frac{\partial O_3}{\partial S_3} \frac{\partial S_3}{\partial S_2}\frac{\partial S_2}{\partial U}\frac{\partial L_3}{\partial O_3}\frac{\partial O_3}{\partial S_3} \frac{\partial S_3}{\partial S_2}\frac{\partial S_2}{\partial S_1}\frac{\partial S_1}{\partial U} \\frac{\partial L_3}{\partial W}\frac{\partial L_3}{\partial O_3}\frac{\partial O_3}{\partial S_3} \frac{\partial S_3}{\partial W}\frac{\partial L_3}{\partial O_3}\frac{\partial O_3}{\partial S_3} \frac{\partial S_3}{\partial S_2}\frac{\partial S_2}{\partial W}\frac{\partial L_3}{\partial O_3}\frac{\partial O_3}{\partial S_3} \frac{\partial S_3}{\partial S_2}\frac{\partial S_2}{\partial S_1}\frac{\partial S_1}{\partial W} \ 因为有\O_3 VS_3 b_2\S_3 UX_3WS_2b_1\S_2 UX_2WS_1b_1\S_1UX_1WS_0b_1 \end{aligned}​∂V∂L3​​∂O3​∂L3​​∂V∂O3​​∂U∂L3​​∂O3​∂L3​​∂S3​∂O3​​∂U∂S3​​∂O3​∂L3​​∂S3​∂O3​​∂S2​∂S3​​∂U∂S2​​∂O3​∂L3​​∂S3​∂O3​​∂S2​∂S3​​∂S1​∂S2​​∂U∂S1​​∂W∂L3​​∂O3​∂L3​​∂S3​∂O3​​∂W∂S3​​∂O3​∂L3​​∂S3​∂O3​​∂S2​∂S3​​∂W∂S2​​∂O3​∂L3​​∂S3​∂O3​​∂S2​∂S3​​∂S1​∂S2​​∂W∂S1​​因为有O3​VS3​b2​S3​UX3​WS2​b1​S2​UX2​WS1​b1​S1​UX1​WS0​b1​​ 可以看到U和W对于序列产生了依赖并且可以得到 ∂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 Lt}{\partial U} \sum{k0}^{t}\frac{\partial L_t}{\partial O_t}\frac{\partial O_t}{\partial St}(\prod{jk1}^{t}\frac{\partial Sj}{\partial S{j-1}})\frac{\partial S_k}{\partial U}\\frac{\partial Lt}{\partial W} \sum{k0}^{t}\frac{\partial L_t}{\partial O_t}\frac{\partial O_t}{\partial St}(\prod{jk1}^{t}\frac{\partial Sj}{\partial S{j-1}})\frac{\partial S_k}{\partial W} \end{aligned} ​∂U∂Lt​​k0∑t​∂Ot​∂Lt​​∂St​∂Ot​​(jk1∏t​∂Sj−1​∂Sj​​)∂U∂Sk​​∂W∂Lt​​k0∑t​∂Ot​∂Lt​​∂St​∂Ot​​(jk1∏t​∂Sj−1​∂Sj​​)∂W∂Sk​​​ 最后将结果放入激活函数即可 四、时间序列预测 预测一个正弦函数的走势 第一部分构建样本数据 start np.random.randint(3, size1)[0] time_steps np.linspace(start, start 10, num_time_steps) data np.sin(time_steps) data data.reshape(num_time_steps, 1) x torch.tensor(data[:-1]).float().view(1, num_time_steps - 1, 1) y torch.tensor(data[1:]).float().view(1, num_time_steps - 1, 1)第二部分构建循环神经网络结构 class Net(nn.Module):def init(self, ):super(Net, self).init()self.rnn nn.RNN(input_sizeinput_size,hidden_sizehidden_size,num_layers1,batchfirstTrue,)for p in self.rnn.parameters():nn.init.normal(p, mean0.0, std0.001)self.linear nn.Linear(hidden_size, output_size)def forward(self, x, hidden_prev):out, hidden_prev self.rnn(x, hidden_prev)# [b, seq, h]out out.view(-1, hidden_size)out self.linear(out) # [seq,h] [seq,1]out out.unsqueeze(dim0)# [1,seq,1]return out, hidden_prev第三部分迭代训练并计算loss model Net() criterion nn.MSELoss() optimizer optim.Adam(model.parameters(), lr)hidden_prev torch.zeros(1, 1, hidden_size)for iter in range(6000):start np.random.randint(10, size1)[0]time_steps np.linspace(start, start 10, num_time_steps)data np.sin(time_steps)data data.reshape(num_time_steps, 1)x torch.tensor(data[:-1]).float().view(1, num_time_steps - 1, 1)y torch.tensor(data[1:]).float().view(1, num_time_steps - 1, 1)output, hidden_prev model(x, hidden_prev)hidden_prev hidden_prev.detach() #不会具有梯度loss criterion(output, y)model.zero_grad()loss.backward()optimizer.step()if iter % 100 0:print(Iteration: {} loss {}.format(iter, loss.item()))第四部分绘制预测值并比较 predictions [] input x[:, 0, :] for _ in range(x.shape[1]):input input.view(1, 1, 1)(pred, hidden_prev) model(input, hidden_prev)input predpredictions.append(pred.detach().numpy().ravel()[0])x x.data.numpy().ravel() y y.data.numpy() plt.scatter(time_steps[:-1], x, s90) plt.plot(time_steps[:-1], x)plt.scatter(time_steps[1:], predictions) plt.show()迭代200次的图像 迭代6000次的图像 完整代码 import numpy as np import torch import torch.nn as nn import torch.optim as optim from matplotlib import pyplot as plt num_time_steps 50 input_size 1 hidden_size 16 output_size 1 lr0.01class Net(nn.Module):def init(self, ):super(Net, self).init()self.rnn nn.RNN(input_sizeinput_size,hidden_sizehidden_size,num_layers1,batchfirstTrue,)for p in self.rnn.parameters():nn.init.normal(p, mean0.0, std0.001)self.linear nn.Linear(hidden_size, output_size)def forward(self, x, hidden_prev):out, hidden_prev self.rnn(x, hidden_prev)# [b, seq, h]out out.view(-1, hidden_size)out self.linear(out) # [seq,h] [seq,1]out out.unsqueeze(dim0)# [1,seq,1]return out, hidden_prevmodel Net() criterion nn.MSELoss() optimizer optim.Adam(model.parameters(), lr)hidden_prev torch.zeros(1, 1, hidden_size)for iter in range(200):start np.random.randint(10, size1)[0]time_steps np.linspace(start, start 10, num_time_steps)data np.sin(time_steps)data data.reshape(num_time_steps, 1)x torch.tensor(data[:-1]).float().view(1, num_time_steps - 1, 1)y torch.tensor(data[1:]).float().view(1, num_time_steps - 1, 1)output, hidden_prev model(x, hidden_prev)hidden_prev hidden_prev.detach() #不会具有梯度loss criterion(output, y)model.zero_grad()loss.backward()optimizer.step()if iter % 100 0:print(Iteration: {} loss {}.format(iter, loss.item()))start np.random.randint(3, size1)[0] time_steps np.linspace(start, start 10, num_time_steps) data np.sin(time_steps) data data.reshape(num_time_steps, 1) x torch.tensor(data[:-1]).float().view(1, num_time_steps - 1, 1) y torch.tensor(data[1:]).float().view(1, num_time_steps - 1, 1)predictions [] input x[:, 0, :] for _ in range(x.shape[1]):input input.view(1, 1, 1)(pred, hidden_prev) model(input, hidden_prev)input predpredictions.append(pred.detach().numpy().ravel()[0])x x.data.numpy().ravel() y y.data.numpy() plt.scatter(time_steps[:-1], x, s90) plt.plot(time_steps[:-1], x)plt.scatter(time_steps[1:], predictions) plt.show()五、梯度弥散和梯度爆炸问题 梯度弥散(消失由于导数的链式法则连续多层小于1的梯度相乘会使梯度越来越小最终导致某层梯度为0。梯度被近距离梯度主导导致模型难以学到远距离的依赖关系梯度爆炸初始化权值过大梯度更新量是会成指数级增长的前面层会比后面层变化的更快就会导致权值越来越大 上面两个问题都是RNN训练时的难题解决它们需要不断的实操经验和更加升入的理解