nn_avg_pool2d | R Documentation |
In the simplest case, the output value of the layer with input size (N, C, H, W),
output (N, C, H_{out}, W_{out}) and kernel_size
(kH, kW)
can be precisely described as:
nn_avg_pool2d( kernel_size, stride = NULL, padding = 0, ceil_mode = FALSE, count_include_pad = TRUE, divisor_override = NULL )
kernel_size |
the size of the window |
stride |
the stride of the window. Default value is |
padding |
implicit zero padding to be added on both sides |
ceil_mode |
when TRUE, will use |
count_include_pad |
when TRUE, will include the zero-padding in the averaging calculation |
divisor_override |
if specified, it will be used as divisor, otherwise |
out(N_i, C_j, h, w) = \frac{1}{kH * kW} ∑_{m=0}^{kH-1} ∑_{n=0}^{kW-1} input(N_i, C_j, stride[0] \times h + m, stride[1] \times w + n)
If padding
is non-zero, then the input is implicitly zero-padded on both sides
for padding
number of points.
The parameters kernel_size
, stride
, padding
can either be:
a single int
– in which case the same value is used for the height and width dimension
a tuple
of two ints – in which case, the first int
is used for the height dimension,
and the second int
for the width dimension
Input: (N, C, H_{in}, W_{in})
Output: (N, C, H_{out}, W_{out}), where
H_{out} = ≤ft\lfloor\frac{H_{in} + 2 \times \mbox{padding}[0] - \mbox{kernel\_size}[0]}{\mbox{stride}[0]} + 1\right\rfloor
W_{out} = ≤ft\lfloor\frac{W_{in} + 2 \times \mbox{padding}[1] - \mbox{kernel\_size}[1]}{\mbox{stride}[1]} + 1\right\rfloor
if (torch_is_installed()) { # pool of square window of size=3, stride=2 m <- nn_avg_pool2d(3, stride = 2) # pool of non-square window m <- nn_avg_pool2d(c(3, 2), stride = c(2, 1)) input <- torch_randn(20, 16, 50, 32) output <- m(input) }
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