In this question, we will try and understand $11$ convolutional kernels and their functions:

Given that the training mini$-$batch size is $32$ images, each of dimension $128128$ pixels and $3$ channels $($Red$,$ Blue, Green$).$ Assume that we apply a single $11$ convolutional filter with stride $1.$

What will be the ratio between the size of the tensor before applying the filter and its size after the application of the filter?

What is the interpretation of such a filter? Let us say we are designing a CNN to detect a blue$-$colour Pepsi can. What would you expect the weights of the trained $11$ convolutional filter to be like, when each of the weights are mapped to the $0-$to$-256$ range? $($Assume the convention of Red $-$ Blue $-$ Green for writing the weights$).$

Let us say the prior layer of your network has size $3232256($i$.$e$.$ Height $$ Width $$ Depth, where depth denotes the number of convolutional filters used$).$ If we use a Kernel of size $33,$ with stride of $1$ and $256$ such filters $($i$.$e$.256$ activation maps$),$ then what will be the total number of parameters?

Marks: $12$