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nnf_fractional_max_pool3d

Fractional_max_pool3d


Description

Applies 3D fractional max pooling over an input signal composed of several input planes.

Usage

nnf_fractional_max_pool3d(
  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 * k) or a tuple (kT, kH, kW)

output_size

the target output size of the form oT * oH * oW. Can be a tuple (oT, oH, oW) or a single number oH for a cubic output oH * 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

undocumented argument.

Details

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

The max-pooling operation is applied in kT * 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

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

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