一种特殊类型的循环神经网络, 即长短期记忆网络, 也就是 Long Short-Term , LSTM, 它被设计用来解决传统 RNN 在处理长序列数据的时候所面临的梯度消失以及梯度爆炸问题, 进而能够有效地捕捉长期依赖关系。
LSTM中心在于其内部记忆单元即Cell, 还有控制信息流动的门结构, 这结构涵盖输入门也就是Input Gate, 以及被称作Gate的遗忘门, 还有同样被叫做Gate的输出门。
1. 遗忘门, 其作用是决定要从细胞状态里丢弃哪些信息, 它借助一个函数输出处于0到1之间的值, 其中1意味着完全保留, 0则意味着完全遗忘。
2. 关于输入门, 它有着这样的作用, 那就是决定哪些新的信息能够被存储到细胞状态里, 同样是借助一个函数来对要更新的信息进行控制, 之后再经由一个tanh函数去生成新的候选值。
3. 细胞状态, 乃是 LSTM 用以保存长期信息之处所。其借助遗忘门以及输入门, 进而对细胞状态予以更新。
4. 输出门：决定细胞状态的哪些部分将输出。
借着这些门结构一块儿协同发挥作用, LSTM 能够产生选择性得记住或者遗忘往昔信息，进而达成对于长序列数据的有效处置。
二、LSTM 实践
1. 数据准备: 首先, 要去收集那适合 LSTM 处理的时间序列数据, 接着对其加以整理, 之后还要开展预处理事宜, 像是归一化处理, 还有缺失值处理等。
2. 模型建造有如下流程: 运用深度学习框架, 像 、等, 去搭建 LSTM 模型。明确层数, 确定神经元数量, 选定激活函数等超参数。
3. 分别为, 把已准备好的数据, 划分成训练集, 以及验证集, 还有测试集。运用训练集数据针对模型施行训练, 基于反向传播算法持续地去调整模型的参数，以此来让损失函数达到最小化损失状态。
4. 进行超参数的调整, 要借助试验多种不同的学习率, 以及正则化参数等方式, 进而寻觅到最优的超参数组合, 以此来提升模型的性能。
5. 模型评估, 要运用验证集与测试集来评鉴训练好的模型, 常见的评估指标涵盖均方误差, 也就是MSE, 还有平均绝对误差, 即MAE等。
6. 进行模型部署, 把训练好的模型放置到实际应用里, 开展实时预测, 或者实施批处理预测。
以下是一个示例代码, 该示例代码是使用某一个库来实现简单的LSTM时间序列预测的, 代码如下:
numpy as np as tf from .keras. from .keras. LSTM, Dense # 生成示例时间序列数据 def _data(=1000, =1): x = np.(0, 2*np.pi, ) np.(y, (, ))WWw.wWW.BloG.4-P.cN/Article/details/4gdgbs.sHtML
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