My datasets are not actual images, so using methods with ImageDataGenerator or pre-trained networks might not apply in this case.
Data Structure: Each "image" is a 2048-long vector that has float values between 0 and 1.
Each "image" was associated with a label (multi-label classifcation) and the goal is to perform classification via Keras 2D CNN's.
What are common techniques for finding which parts of the "images" contribute most to classification via convolutional neural nets?
I already implemented the CNNs in keras and have already successfully trained on my images.
*No my data is not time series; however, my model works with either the keras Conv1D and Conv2D layers.