Completing Conv2D layer

[mathias-brandewinder]

CNTK.FSharp/Sequential.fs contains a module Conv2D, with basic blocks to build 2d CNN layers. What arguments or elements are missing to complete that layer? Both the CNTK Python API and Keras can provide hints as to what people would expect to find and that is missing.

[AviAvni]

python	f#	keras	comments
filter_shape	Conv2D.Kernel	kernel_size	optional non-linearity, e.g. activation=relu
num_filters	Conv2D.OutputFeatures	filters
activation	???	activation
init	Conv2D.Initializer	kernel_initializer
pad	???	padding	if False (default), then the filter will be shifted over the “valid” area of input, that is, no value outside the area is used. If pad is True on the other hand, the filter will be applied to all input positions, and values outside the valid region will be considered zero.
strides	???	strides	increment when sliding the filter over the input. E.g. (2,2) to reduce the dimensions by 2
bias	???	use_bias	if False, do not include a bias parameter
init_bias	???	bias_initializer	initializer for the bias
use_correlation	???	???	currently always True and cannot be changed. It indicates that Convolution() actually computes the cross-correlation rather than the true convolution
???	???	data_format	A string, one of channels_last (default) or channels_first. The ordering of the dimensions in the inputs. channels_last corresponds to inputs with shape (batch, height, width, channels) while channels_first corresponds to inputs with shape (batch, channels, height, width). It defaults to the image_data_format value found in your Keras config file at ~/.keras/keras.json. If you never set it, then it will be "channels_last".
???	???	dilation_rate	an integer or tuple/list of 2 integers, specifying the dilation rate to use for dilated convolution. Can be a single integer to specify the same value for all spatial dimensions. Currently, specifying any dilation_rate value != 1 is incompatible with specifying any stride value != 1.
???	???	kernel_regularizer	Regularizer function applied to the kernel weights matrix (see regularizer).
???	???	bias_regularizer	Regularizer function applied to the bias vector (see regularizer).
???	???	activity_regularizer	Regularizer function applied to the output of the layer (its "activation"). (see regularizer).
???	???	kernel_constraint	Constraint function applied to the kernel matrix (see constraints).
???	???	bias_constraint	Constraint function applied to the bias vector (see constraints).

[AviAvni]

python	f#	keras	comments
MaxPooling	Pool2D.PoolingType = PoolingType.Max	MaxPooling2D
AveragePooling	PoolingType.Average	AveragePooling2D
filter_shape	Pool2D.Window	pool_size
strides	Pool2D.Stride	strides
pad	???	padding
???	???	data_format

python	f#	keras	comments
GlobalMaxPooling	???	GlobalMaxPooling2D
GlobalAveragePooling	???	GlobalAveragePooling2D

[AviAvni]

@mathias-brandewinder this line https://github.com/mathias-brandewinder/CNTK.FSharp/blob/master/CNTK.FSharp/Sequential.fs#L128 is the strides parameter for conv layer and now it's not configurable do you want me to add this parameter to Conv2D?

[mathias-brandewinder]

That sounds like the right way to go! Thank you :)

[mathias-brandewinder]

Done, not 100% parity, but close enough: commit #17