Is the number of feature maps equal to the number of kernels in the LeNet 5 architecture? In LeNet 5's first layer, the number of feature maps is equal to the number of kernels. However, the second convolutional layer has a depth different from the 3rd layer. Does the filter size dictate the number of feature maps?
The # of kernels will be the channel length, looking at the image you posted in your comment from post, I do not understand where you see the inconsistency.