In convolutional layers, a value greater than 1 for which parameter will reduce the output's shape?

In convolutional layers, the stride parameter determines how much the filter moves across the input data. When the stride value is greater than 1, it means that the filter will skip some of the input data points, effectively reducing the frequency at which the filter is applied. This results in fewer overlapping operations, which leads to a smaller output shape because the filter does not cover every possible position in the input.

For instance, if a convolutional layer has an input size of 32x32 and a filter size of 3x3 with a stride of 2, the filter will move two pixels at a time. Consequently, the output size will be smaller compared to using a stride of 1, where the filter would cover every single position in the input. The calculation for the output size takes into account the input size, filter size, and stride, thus a stride greater than 1 compresses the output dimension.

Padding, on the other hand, involves adding extra pixels around the input data, which can maintain or increase the output size rather than reduce it. The number of filters influences the depth of the output but not its spatial dimensions. Activation functions transform the output values but don't change the output shape. Therefore, the stride is the