参考链接： https://blog.goodaudience.com/introduction-to-1d-convolutional-neural-networks-in-keras-for-time-sequences-3a7ff801a2cf

何时使用 1D-CNN

A 1D CNN is very effective when you expect to derive interesting
features from shorter (fixed-length) segments of the overall data set
and where the location of the feature within the segment is not of
high relevance.

适用数据： 传感器时序数据

1D-CNN 与 2D-CNN 的区别

Keras 构造网络：

model_m = Sequential()
model_m.add(Reshape((TIME_PERIODS, num_sensors), input_shape=(input_shape,)))
model_m.add(Conv1D(100, 10, activation='relu', input_shape=(TIME_PERIODS, num_sensors)))
model_m.add(Conv1D(100, 10, activation='relu'))
model_m.add(MaxPooling1D(3))
model_m.add(Conv1D(160, 10, activation='relu'))
model_m.add(Conv1D(160, 10, activation='relu'))
model_m.add(GlobalAveragePooling1D())
model_m.add(Dropout(0.5))
model_m.add(Dense(num_classes, activation='softmax'))
print(model_m.summary())
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
reshape_45 (Reshape)         (None, 80, 3)             0         
_________________________________________________________________
conv1d_145 (Conv1D)          (None, 71, 100)           3100      
_________________________________________________________________
conv1d_146 (Conv1D)          (None, 62, 100)           100100    
_________________________________________________________________
max_pooling1d_39 (MaxPooling (None, 20, 100)           0         
_________________________________________________________________
conv1d_147 (Conv1D)          (None, 11, 160)           160160    
_________________________________________________________________
conv1d_148 (Conv1D)          (None, 2, 160)            256160    
_________________________________________________________________
global_average_pooling1d_29  (None, 160)               0         
_________________________________________________________________
dropout_29 (Dropout)         (None, 160)               0         
_________________________________________________________________
dense_29 (Dense)             (None, 6)                 966       
=================================================================
Total params: 520,486
Trainable params: 520,486
Non-trainable params: 0
_________________________________________________________________
输入数据：

每个样本为3轴数据，每轴包含4秒数据，采样频率为20Hz，即每轴有80个数值，共240个数值
Reshape层： 将一个样本的240个数值转为 80×3 矩阵
第一个Conv1D层：
卷积核长度为10，深度（通道数）为3，步长为1，卷完后数据由 80×3 变为 71×1
共100个卷积核，输出 71×100
参数个数：每个卷积核 10×3+1=31 个参数，100个卷积核共 3100 个参数
第二个Conv1D层：
卷积核长度为10，深度（通道数）为100，步长为1，卷完后数据由 71×100 变为 62×1
共100个卷积核，输出 62×100
参数个数：每个卷积核 10×100+1=101 个参数，100个卷积核共 10100 个参数
MaxPooling层：
池化核长度为3，步长为3， 池化后数据长度为 (62-3) / 3 + 1 = 20
[1,2,3] [4,5,6] [ ... ] [58,59,60] [61,62
   ↓       ↓                ↓
  max1    max2    ...      max20
数据深度（通道数）为100，输出 20×100
第三个Conv1D层：
卷积核长度为10，深度为100，步长为1，卷积核个数为160，输出为

(20-10+1)×160=11×160
参数个数：(10×100+1)×160=160160
第四个Conv1D层：
卷积核长度为10，深度为160，步长为1，卷积核个数为160，输出为

(11-10+1)×160=2×160
参数个数：(10×160+1)×160=256160
GlobalAveragePooling1D层：
每个通道取均值，共160个通道，输出 160 个数值
Dropout层
Dense层（输出层）：
输入为160个数值，输出为6个数值，激活函数为 softmax
参数个数：(160+1)×6=966
callbacks_list = [
    keras.callbacks.ModelCheckpoint(
        filepath='best_model.{epoch:02d}-{val_loss:.2f}.h5',
        monitor='val_loss', save_best_only=True),
    keras.callbacks.EarlyStopping(monitor='acc', patience=1)
model_m.compile(loss='categorical_crossentropy',
                optimizer='adam', metrics=['accuracy'])
BATCH_SIZE = 400
EPOCHS = 50
history = model_m.fit(x_train,
                      y_train,
                      batch_size=BATCH_SIZE,