torch: nn_fractional_max_pool2d – R documentation

Pricing

Become an expert in R — Interactive courses, Cheat Sheets, certificates and more!

Get Started for Free

Documentation

torch

nn_fractional_max_pool2d

Applies a 2D fractional max pooling over an input signal composed of several input planes.

Description

Fractional MaxPooling is described in detail in the paper Fractional MaxPooling by Ben Graham

Usage

nn_fractional_max_pool2d(
  kernel_size,
  output_size = NULL,
  output_ratio = NULL,
  return_indices = FALSE
)

Arguments

`kernel_size`	the size of the window to take a max over. Can be a single number k (for a square kernel of k x k) or a tuple `(kh, kw)`
`output_size`	the target output size of the image of the form `oH x oW`. Can be a tuple `(oH, oW)` or a single number oH for a square image `oH x oH`
`output_ratio`	If one wants to have an output size as a ratio of the input size, this option can be given. This has to be a number or tuple in the range (0, 1)
`return_indices`	if `TRUE`, will return the indices along with the outputs. Useful to pass to `nn_max_unpool2d()`. Default: `FALSE`

Details

The max-pooling operation is applied in kH \times kW regions by a stochastic step size determined by the target output size. The number of output features is equal to the number of input planes.

Examples

if (torch_is_installed()) {

}

torch

Tensors and Neural Networks with 'GPU' Acceleration

v0.3.0

MIT + file LICENSE

Authors

Daniel Falbel [aut, cre, cph], Javier Luraschi [aut], Dmitriy Selivanov [ctb], Athos Damiani [ctb], Christophe Regouby [ctb], Krzysztof Joachimiak [ctb], RStudio [cph]

Initial release

nn_fractional_max_pool2d

Description

Usage

Arguments

Details

Examples

torch

We don't support your browser anymore