ConvolutionLayer

represents a trainable convolutional net layer having n output channels and using kernels of size s to compute the convolution.

represents a layer performing one-dimensional convolutions with kernels of size s.

represents a layer performing two-dimensional convolution with kernels of size h×w.

includes options for padding and other parameters.

Details and Options

ConvolutionLayer[n,…] represents a layer that, applied to an input tensor with m input channels and one or more spatial dimensions, effectively performs n×m distinct convolutions across the spatial dimensions to produce an output tensor with n channels. The channel dimension is taken to be the first dimension of the input and output tensors.
In ConvolutionLayer[n,s], the dimensionality of the convolution layer will be inferred when the layer is connected in a NetChain, NetGraph, etc.
The following optional parameters can be included:

"Biases"	Automatic	initial vector of kernel biases
"Dilation"	1	dilation factor
"Dimensionality"	Automatic	number of spatial dimensions of the convolution
"PaddingSize"	0	amount of zero padding to apply to the input
"Stride"	1	convolution step size to use
"Weights"	Automatic	initial tensor of kernel weights

The settings for "PaddingSize", "Dilation" and "Stride" can be of the following forms:
n use the value n for all dimensions

{…,n_i,…} use the value n_i for the i dimension
With Automatic settings, weights and biases are added automatically when NetInitialize or NetTrain is used.
The setting "Biases"->None specifies that no biases should be used.
If weights and biases have been added, ConvolutionLayer[…][input] explicitly computes the output from applying the layer.
ConvolutionLayer[…][{input₁,input₂,…}] explicitly computes outputs for each of the input_i.
NetExtract can be used to extract weights and biases from a ConvolutionLayer object.
ConvolutionLayer is typically used inside NetChain, NetGraph, etc.
ConvolutionLayer exposes the following ports for use in NetGraph etc.:
"Input" a matrix or rank-3 tensor

"Output" a matrix or rank-3 tensor
When it cannot be inferred from other layers in a larger net, the option "Input"->{d₁,…,d_n} can be used to fix the input dimensions of ConvolutionLayer.
Given an input tensor of dimensions d₁×…×d_i×…, the output tensor will be of dimensions ×…××…, where the channel dimension =n and the sizes d_i are transformed according to , where is the padding size, is the kernel size, is the stride size, and is the dilation factor for each dimension.