nnf_fractional_max_pool2d: Fractional_max_pool2d

In torch: Tensors and Neural Networks with 'GPU' Acceleration

Fractional_max_pool2d

Description

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

Usage

nnf_fractional_max_pool2d(
  input,
  kernel_size,
  output_size = NULL,
  output_ratio = NULL,
  return_indices = FALSE,
  random_samples = NULL
)

Arguments

input	the input tensor
kernel_size	the size of the window to take a max over. Can be a single number k (for a square kernel of k * k) or a tuple ⁠(kH, kW)⁠
output_size	the target output size of the image of the form oH * oW. Can be a tuple ⁠(oH, oW)⁠ or a single number oH for a square image oH * 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.
random_samples	optional random samples.

Details

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

The max-pooling operation is applied in kH * 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.

torch documentation built on May 29, 2024, 9:54 a.m.