CNN Convolutional Layer Operations 2

Question 1

What happens to spatial dimensions when a 5×5 filter is applied with stride=2 and no padding to a 32×32 input?

Accepted Answer

14×14

Answer

Output size = floor((32 - 5) / 2) + 1 = floor(13.5) + 1 = 14, giving 14×14.

Question 2

In a convolutional layer, what does 'same' padding ensure?

Accepted Answer

Output spatial size equals input spatial size

Answer

'Same' padding adds zeros around the input so the output height and width match the input when stride=1.

Question 3

Which operation sums element-wise products between a filter and a receptive field in a CNN?

Accepted Answer

Cross-correlation

Answer

Cross-correlation (often loosely called convolution in deep learning) computes the sum of element-wise products without flipping the filter.

Question 4

A convolutional layer has 64 filters of size 3×3 applied to a 3-channel input. How many learnable parameters does it have (excluding bias)?

Accepted Answer

1728

Answer

Parameters = filters × kernel_h × kernel_w × input_channels = 64 × 3 × 3 × 3 = 1728.

Question 5

What is the primary purpose of using multiple filters in a single convolutional layer?

Accepted Answer

To detect multiple distinct features simultaneously

Answer

Each filter learns to detect a different feature (e.g., edge, texture), producing one feature map per filter.

CNN - Convolutional Neural Networks Practice Test