AveragePooling: Average Pooling Layer

In Microsoft/CNTK-R: R Interface to the Microsoft Cognitive Toolkit (CNTK)

Description

Like Convolution(), AveragePooling() processes items arranged on an N-dimensional grid, such as an image. Typically, each item is a vector. For each item, average-pooling computes the element-wise mean over a window (“receptive field”) of items surrounding the item’s position on the grid.

Usage

AveragePooling(filter_shape, strides = 1, pad = FALSE, name = "")

Arguments

filter_shape	int or list of int - shape (spatial extent) of the receptive field, not including the input feature-map depth. E.g. (3,3) for a 2D convolution. receptive field, not including the input feature-map depth. E.g. (3,3) for a 2D convolution.
strides	(int or tuple of ints, defaults to 1) – stride of the operation. Use a list of ints to specify a per-axis value. (int or tuple of ints, defaults to 1) – stride (increment when sliding over the input). Use a tuple to specify a per-axis value.
pad	(bool or list of bools) – if False, then the operation will be shifted over the “valid” area of input, that is, no value outside the area is used. If pad=True on the other hand, the operation will be applied to all input positions, and positions outside the valid region will be considered containing zero. Use a list to specify a per-axis value. (bool or tuple of bools, defaults to False) – if False, then the
name	string (optional) the name of the Function instance in the network

Details

The size (spatial extent) of the receptive field is given by filter <- shape. E.g. for 2D pooling, filter <- shape should be a tuple of two integers, such as (5,5).

Value

A function that accepts one argument and applies the average-pooling operation to it

Examples

(f <- AveragePooling(c(3, 3), strides = 2))
(h <- op_input_variable(c(shape = 4)))

Microsoft/CNTK-R documentation built on May 28, 2019, 1:52 p.m.