# nn_fractional_max_pool2d: Applies a 2D fractional max pooling over an input signal... In torch: Tensors and Neural Networks with 'GPU' Acceleration

 nn_fractional_max_pool2d R Documentation

## 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()) {
# pool of square window of size=3, and target output size 13x12
m <- nn_fractional_max_pool2d(3, output_size = c(13, 12))
# pool of square window and target output size being half of input image size
m <- nn_fractional_max_pool2d(3, output_ratio = c(0.5, 0.5))
input <- torch_randn(20, 16, 50, 32)
output <- m(input)
}


torch documentation built on June 7, 2023, 6:19 p.m.