3.4.4 · D1Convolutional Neural Networks
Foundations — Feature maps and receptive fields
0. The grid: what an image actually is
Before any symbol, we need the object everything sits on: a grid of numbers.

Look at the figure: the pixel at row 1, column 2 is just one number. We are going to slide a small stencil over exactly this kind of grid. Every symbol below is a count of boxes on this grid.
1. and — the size of the grid
- Picture: the two edges of the rectangle in the figure above — one arrow pointing down (length ), one pointing right (length ).
- Why the topic needs it: the output-size formula starts from these two numbers. You cannot count where a stencil fits until you know how big the board is.
2. The filter and — the sliding stencil

- Picture: the small yellow square in the figure, sitting on top of a block of the input. Under each yellow cell is one input box; we multiply pair-by-pair and add.
- Why the topic needs it: tells us how many boxes the stencil "eats" at each position. A bigger sees more at once but leaves fewer landing spots — this is exactly the trade-off inside the output formula.
3. Stride — how far the stencil jumps

- Picture: in the figure, the stencil's first position is at column 0, the next at column . The gap between the two yellow outlines is the stride.
- Why the topic needs it: stride controls two things the parent uses. First, how many outputs fit (bigger jumps → fewer outputs). Second, it enters the receptive-field product — each jump upstream stretches how far downstream neurons can "reach". More in 3.4.03-Padding-and-stride.
4. Padding — a border of fake boxes
- Picture: the pale outer frame in the figure of §2 — those cells were not in the original image; we added them so the stencil can sit centred on the very edge pixels.
- Why the topic needs it: padding adds to each dimension (one ring on each side), which is why the parent's formula begins with . Without it, edge pixels get visited fewer times and the grid shrinks every layer. See 3.4.03-Padding-and-stride.
5. The floor bracket — round down
- Picture: you can only land the stencil at whole box positions — half a jump is not a real landing. If the arithmetic gives landings, only are real, so we floor.
- Why the topic needs it: it appears around every output-size division. A stride that does not divide the board evenly leaves a partial step that simply does not count.
6. Channels — stacking many opinions
- Picture: imagine several transparent sheets, each the output of one stencil, layered into a loaf. Each sheet is one "opinion" (edges here, blobs there).
- Why the topic needs it: the parent describes a feature map as "one filter's opinion" — is the count of opinions, and it turns the 2D feature map into the 3D tensor CNNs actually pass along. See 3.4.05-CNN-architectures.
7. The layer index , and — the receptive field number

- Picture: the fan-out cone in the figure. One box high up (deep layer) traces down through the sheets, widening at each level, until it covers a big square of the original image — that square's side is .
- Why the topic needs it: this is the whole second half of the parent. Deeper neurons "see" wider context because their influence cone widens each layer.
8. The product and sum — accumulate over layers
- Picture: each upstream jump magnifies the step size in original-image space. Two jumps of stride 2 downstream means one downstream step slides original boxes — that multiplication is the .
- Why the topic needs it: the recursive receptive-field rule scales each layer's extra reach by this cumulative jump product: Every symbol here was defined above: (§7), (§2), and the product (§3, §8).
The prerequisite map
Read top to bottom: the grid and filter come first; stride and padding modify how the filter lands; floor and the output formula turn that into a feature map; channels stack many; strides and the product feed the receptive field; both meet at the topic.
Equipment checklist
Cover the right side and test yourself — you are ready for the parent note only if every reveal matches.
What do and count?
The number of rows and columns of boxes in the grid.
What is ?
The side length of the filter (stencil); a filter has .
What does stride measure?
How many boxes the stencil jumps between landings — a distance, not a size.
What does padding do to the grid size?
Adds one ring of boxes on every side, increasing each dimension by .
What does mean and why is it here?
Round down; you can only land the filter at whole positions, so partial steps don't count.
Write the output-side formula from memory.
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Why the "+1" in the output formula?
Fencepost counting — positions 0,1,2 are three positions, not two.
What is ?
The number of filters, hence the number of stacked feature maps (opinions).
What does measure, and what is ?
The side length of the original-image region influencing one layer- box; .
What does represent?
The cumulative downsampling — how many original boxes one downstream step spans.