Let’s imagine an input image 3x3x2, the last number is the number of channels. This simplified setup helps me understand the next step. Consider a conv layer with 1 kernel of size 2x2x2, say I have 2 “subkernels” of size 2×2. Now I scan each input channel at stride 1 to create 2 feature maps, each is of size 2×2. At the very end I perform element wise sum of these 2 feature maps to create a single 2×2 output.

Let’s imagine an input image 3x3x2, the last number is the number of channels. This simplified setup helps me understand the next step. Consider a conv layer with 1 kernel of size 2x2x2, say I have 2 “subkernels” of size 2×2. Now I scan each input channel at stride 1 to create 2 feature maps, each is of size 2×2. At the very end I perform element wise sum of these 2 feature maps to create a single 2×2 output.

Let’s imagine an input image 3x3x2, the last number is the number of channels. This simplified setup helps me understand the next step. Consider a conv layer with 1 kernel of size 2x2x2, say I have 2 “subkernels” of size 2×2. Now I scan each input channel at stride 1 to create 2 feature maps, each is of size 2×2. At the very end I perform element wise sum of these 2 feature maps to create a single 2×2 output.

Let’s imagine an input image 3x3x2, the last number is the number of channels. This simplified setup helps me understand the next step. Consider a conv layer with 1 kernel of size 2x2x2, say I have 2 “subkernels” of size 2×2. Now I scan each input channel at stride 1 to create 2 feature maps, each is of size 2×2. At the very end I perform element wise sum of these 2 feature maps to create a single 2×2 output.