# nn_fractional_max_pool3d: Applies a 3D fractional max pooling over an input signal... In torch: Tensors and Neural Networks with 'GPU' Acceleration

## Description

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

## Usage

 ```1 2 3 4 5 6``` ```nn_fractional_max_pool3d( 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 x k) or a tuple `(kt x kh x kw)` `output_size` the target output size of the image of the form `oT x oH x oW`. Can be a tuple `(oT, oH, oW)` or a single number oH for a square image `oH x 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_unpool3d()`. Default: `FALSE`

## Details

The max-pooling operation is applied in kTxkHxkW 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

 ```1 2 3 4 5 6 7 8 9``` ```if (torch_is_installed()) { # pool of cubic window of size=3, and target output size 13x12x11 m = nn_fractional_max_pool3d(3, output_size=c(13, 12, 11)) # pool of cubic window and target output size being half of input size m = nn_fractional_max_pool3d(3, output_ratio=c(0.5, 0.5, 0.5)) input = torch_randn(20, 16, 50, 32, 16) output = m(input) } ```

torch documentation built on Oct. 7, 2021, 9:22 a.m.