SamePad()

Passed as an option to convolutional layers (and friends), this causes the padding to be chosen such that the input and output sizes agree (on the first N dimensions, the kernel or window) when stride==1. When stride≠1, the output size equals ceil(input_size/stride).

See also Conv, MaxPool.

Examples

			julia> xs = rand(Float32, 100, 100, 3, 50);  # a batch of images

julia> layer = Conv((2,2), 3 => 7, pad=SamePad())
Conv((2, 2), 3 => 7, pad=(1, 0, 1, 0))  # 91 parameters

julia> layer(xs) |> size  # notice how the dimensions stay the same with this padding
(100, 100, 7, 50)

julia> layer2 = Conv((2,2), 3 => 7)
Conv((2, 2), 3 => 7)  # 91 parameters

julia> layer2(xs) |> size  # the output dimension changes as the padding was not "same"
(99, 99, 7, 50)

julia> layer3 = Conv((5, 5), 3 => 7, stride=2, pad=SamePad())
Conv((5, 5), 3 => 7, pad=2, stride=2)  # 532 parameters

julia> layer3(xs) |> size  # output size = `ceil(input_size/stride)` = 50
(50, 50, 7, 50)