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

Layer factory function to create an average-pooling layer.

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BatchNormalization(map_rank = NULL, init_scale = 1,
  normalization_time_constant = 5000, blend_time_constant = 0,
  epsilon = 1e-05, use_cntk_engine = FALSE, name = "")

`epsilon`	(float, default 0.00001) - added to avoid division by 0
`name`	string (optional) the name of the Function instance in the network
`init`	(scalar or matrix or initializer, defaults to init_glorot_uniform()) – initial value of weights W_scale

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.

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).

joeddav/CNTK-R documentation built on May 6, 2019, 7:28 a.m.