时间序列-基于一维卷积神经网络实现的多元时间序列分类算法-附项目源码-优质项目实战.zip

# TSC-CNN 基于一维卷积神经网络（1D-CNN）的多元时间序列分类 ## 项目背景 该项目为基于一维卷积神经网络的多元时间序列分类方法，实际问题被抽象为时间序列的分类问题，实际输入为4个传感器信号，分别对应16个类别，搭建1D-CNN然后训练网络对多元时间序列进行分类。 ## 1D-CNN 无论是一维、二维还是三维，卷积神经网络（CNNs）都具有相同的特点和相同的处理方法。关键区别在于输入数据的维数以及特征检测器（或滤波器）如何在数据之间滑动，一维和二维CNN处理过程对比。  ## 网络搭建 首先分析网络的输入输出，输入为包含4个时间序列的信号，长度为1050，即输入为（1050，4），而输出对应16种类别，输出为（，16）。实质上输入时先将单个训练数据的4个时间序列展平为（4200，1），传入网络后再reshape为（1050，4）。网络构建代码如下： ```python model = Sequential() model.add(Reshape((TIME_PERIODS, num_sensors), input_shape=(TIME_PERIODS*num_sensors,))) model.add(Conv1D(16, 8,strides=2, activation='relu',input_shape=(TIME_PERIODS,num_sensors))) model.add(Conv1D(16, 8,strides=2, activation='relu',padding="same")) model.add(MaxPooling1D(2)) model.add(Conv1D(64, 4,strides=2, activation='relu',padding="same")) model.add(Conv1D(64, 4,strides=2, activation='relu',padding="same")) model.add(MaxPooling1D(2)) model.add(Conv1D(256, 4,strides=2, activation='relu',padding="same")) model.add(Conv1D(256, 4,strides=2, activation='relu',padding="same")) model.add(MaxPooling1D(2)) model.add(Conv1D(512, 2,strides=1, activation='relu',padding="same")) model.add(Conv1D(512, 2,strides=1, activation='relu',padding="same")) model.add(MaxPooling1D(2)) model.add(GlobalAveragePooling1D()) model.add(Dropout(0.3)) model.add(Dense(num_classes, activation='softmax')) ``` 运行得到网络结构如下: ``` Model: "sequential_1" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= reshape_1 (Reshape) (None, 1050, 4) 0 _________________________________________________________________ conv1d_1 (Conv1D) (None, 522, 16) 528 _________________________________________________________________ conv1d_2 (Conv1D) (None, 261, 16) 2064 _________________________________________________________________ max_pooling1d_1 (MaxPooling1 (None, 130, 16) 0 _________________________________________________________________ conv1d_3 (Conv1D) (None, 65, 64) 4160 _________________________________________________________________ conv1d_4 (Conv1D) (None, 33, 64) 16448 _________________________________________________________________ max_pooling1d_2 (MaxPooling1 (None, 16, 64) 0 _________________________________________________________________ conv1d_5 (Conv1D) (None, 8, 256) 65792 _________________________________________________________________ conv1d_6 (Conv1D) (None, 4, 256) 262400 _________________________________________________________________ max_pooling1d_3 (MaxPooling1 (None, 2, 256) 0 _________________________________________________________________ conv1d_7 (Conv1D) (None, 2, 512) 262656 _________________________________________________________________ conv1d_8 (Conv1D) (None, 2, 512) 524800 _________________________________________________________________ max_pooling1d_4 (MaxPooling1 (None, 1, 512) 0 _________________________________________________________________ global_average_pooling1d_1 ( (None, 512) 0 _________________________________________________________________ dropout_1 (Dropout) (None, 512) 0 _________________________________________________________________ dense_1 (Dense) (None, 16) 8208 ================================================================= Total params: 1,147,056 Trainable params: 1,147,056 Non-trainable params: 0 _________________________________________________________________ None ```