可视化时间序列序列的 1D CNN 特征重要性

Question

我正在尝试从我的 1D CNN 中提取特征重要性。大多数在线文档都涉及 2D、3D、图像数据和分类问题。我有一个输出时间序列序列的多元时间序列。我尝试过 Shaply 和 keras_vis，但没有解决我的问题。一个问题是我的输入数据有 229 个特征，1DConv 的第一层映射了 64 个过滤器。从第一层提取权重，我可以确定哪个过滤器有助于该层的学习。但是，我无法将其转换为原始输出。

我的问题是双重的（如果我能同时做到）：

1. 鉴于第一层的权重，我如何可视化特定过滤器的显着性？
2. 如何提取重要特征；鉴于我的具体问题和拱。？

这是用于提取权重的摘要和代码....

Model: "model_3"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
CNN1-Input-Historical (InputLay [(None, 10, 229)]    0                                            
__________________________________________________________________________________________________
CNN1-Conv1D (Conv1D)            (None, 7, 64)        58688       CNN1-Input-Historical[0][0]      
__________________________________________________________________________________________________
conv1d_3 (Conv1D)               (None, 3, 128)       24704       CNN1-Conv1D[0][0]                
__________________________________________________________________________________________________
CNN1-MaxPooling (MaxPooling1D)  (None, 2, 128)       0           conv1d_3[0][0]                   
__________________________________________________________________________________________________
CNN1-Flatten (Flatten)          (None, 256)          0           CNN1-MaxPooling[0][0]            
__________________________________________________________________________________________________
dense_9 (Dense)                 (None, 50)           12850       CNN1-Flatten[0][0]               
__________________________________________________________________________________________________
dense_10 (Dense)                (None, 50)           2550        dense_9[0][0]                    
__________________________________________________________________________________________________
dropout_6 (Dropout)             (None, 50)           0           dense_10[0][0]                   
__________________________________________________________________________________________________
dense_11 (Dense)                (None, 100)          5100        dropout_6[0][0]                  
__________________________________________________________________________________________________
dropout_7 (Dropout)             (None, 100)          0           dense_11[0][0]                   
__________________________________________________________________________________________________
Output-1 (Dense)                (None, 3)            303         dropout_7[0][0]                  
__________________________________________________________________________________________________
Output-2 (Dense)                (None, 3)            303         dropout_7[0][0]                  
__________________________________________________________________________________________________
Output-3 (Dense)                (None, 3)            303         dropout_7[0][0]                  
==================================================================================================
Total params: 104,801
Trainable params: 104,801
Non-trainable params: 0

model.summary()
outputs = [layer.output for layer in model.layers[1:]] 
activation_model = Model(inputs=model.input, outputs=outputs)
activations = activation_model.predict(X.reshape(1,10,229))#3-d 10 timesteps and 229 featrues
first_layer_activation = activations[0]
second_layer_activation = activations[1]

第一层平均权重