Convolution2D: Two Dimensional Convolutional Layer

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

Description

Layer factory function to create a 2D convolution layer with optional non-linearity. Same as Convolution() except that filter_shape is verified to be 2-dimensional. See Convolution() for extensive documentation.

Usage

Convolution2D(filter_shape, num_filters = NULL,
  activation = activation_identity, init = init_glorot_uniform(),
  pad = FALSE, strides = 1, bias = TRUE, init_bias = 0,
  reduction_rank = 1, 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.
num_filters	(int, defaults to None) – number of filters (output feature-map depth), or () to denote scalar output items (output shape will have no depth axis). integer of number of filters
activation	(Function) - optional activation Function optional function to apply at end
init	(scalar or matrix or initializer, defaults to init_glorot_uniform()) – initial value of weights W vector array or cntk$initializer, defaults to init_glorot_uniform()) – initial value of weights W
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.
strides	(int or tuple of ints, defaults to 1) – stride of the operation. Use a list of ints to specify a per-axis value. integer of stride for convolution
bias	(bool) – whether to include bias logical if the layer should have a bias term
init_bias	(scalar or matrix or initializer, defaults to 0) – initial value of weights b
reduction_rank	integer whether input items have or do not have a depth axis
name	string (optional) the name of the Function instance in the network

Value

A cntk$ops$functions$Function class for defining tensor operations in network architecture.

References

https://www.cntk.ai/pythondocs/cntk.layers.layers.html?highlight=convolution2d#cntk.layers.layers.Convolution2D)

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