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.