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

 nn_fractional_max_pool3d R Documentation

## Applies a 3D 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_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

``````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 June 7, 2023, 6:19 p.m.