Keras: apply multiple filters to each feature map in CNN I am new to Keras, and I want to do the following: take a 2D image, and apply four 2D convolution kernels to it, giving four 2D feature maps. I could accomplish this. But then I want to apply two  distinct 2D convolutions to each of those 4 maps, giving 8 feature maps. Is that possible?
Here's what I have so far:
import keras
from keras.layers import Conv2D

input_img = keras.Input(shape=(N_rows, N_cols, 1))
x = Conv2D(4, (3,3))(input_img)

But then I don't know how to apply 2 kernels to each of the 4 channels, so that I have eight 2D maps.
You may try Keras DepthwiseConv2D layer

Depthwise Separable convolutions consist of performing just the first step in a depthwise spatial convolution (which acts on each input channel separately). The depth_multiplier argument controls how many output channels are generated per input channel in the depthwise step.

It will convolute each Channel separately. As shown in this depiction.

$\hspace{6cm}$Image credit - Blog by Chi-Feng Wang
With depth_multiplier argument, you can add more Filetrs i.e. more copy of the &quot;triplet&quot; shown in the depiction.

depth_multiplier:
The number of depthwise convolution output channels for each input channel. The total number of depthwise convolution output channels will be equal to filters_in * depth_multiplier.