# KMaxPool¶

## Description¶

Info

Parenr class: Module

Derived classes: -

K-Max Pooling is a pooling operation, which is a generalization of max-pooling by the time parameter used in the Max-TDNN model, which differs from the local max-pooling used in convolutional networks for object recognition (LeCun et al., 1998).

K-Max Pooling selects a subsequence out of the k maximum values of the sequence. This module implements a special case when the relative positions of the selected elements are not saved: the sorted values are returned to the output.

## Initializing¶

def __init__(self, topk, axis, name=None):


Parameters

Parameters Allowed types Description Default
topk int Number of maximum values -
axis int Axis along which the operation is calculated -
name str Layer name None

Explanations

-

## Examples¶

Necessary imports.

import numpy as np
from PuzzleLib.Backend import gpuarray
from PuzzleLib.Modules import KMaxPool


Info

gpuarray is required to properly place the tensor in the GPU

np.random.seed(123)
data = gpuarray.to_gpu(np.random.randint(0, 9, (1, 1, 10)).astype(np.float32))
print(data)

[[[2. 2. 6. 1. 3. 6. 1. 0. 1. 0.]]]

topk, axis = 2, 2
kmaxpool = KMaxPool(topk=3, axis=2)
print(kmaxpool(data))

[[[3. 6. 6.]]]