Does the input X to ReLU1_1 exclude the conv1_1 RGB contributions and only pass through the bias?

[learnpython1985]

Hi, thanks for this excellent visualization!

I have a question about the conv1_1 → ReLU1_1 path. The output of a
convolution layer should be the sum of the element-wise products across
all input channels (R, G, B) plus the bias term:

conv1_1 output = sum(kernel_R * input_R) 
               + sum(kernel_G * input_G) 
               + sum(kernel_B * input_B) 
               + bias

When I inspect the value X that feeds into ReLU1_1, it looks like the
RGB contributions from conv1_1 are NOT being included, and only the bias
value is being passed through.

Is my understanding correct? Is this intended behavior, or am I
misreading how the value is computed/displayed?

Thank you for your time!