Package ai.djl.nn.convolutional

Contains classes that define convolutional operations extending Convolution.

See: Description

Class Summary

Class	Description
Conv1D	A Conv1D layer works similar to Convolution layer with the exception of the number of dimension it operates on being only one, which is LayoutType.WIDTH.
Conv1D.Builder	The Builder to construct a Conv1D type of Block.
Conv2D	Being the pioneer of convolution layers, Conv2D layer works on two dimensions of input, LayoutType.WIDTH and LayoutType.HEIGHT as usually a Conv2D layer is used to process data with two spatial dimensions, namely image.
Conv2D.Builder	The Builder to construct a Conv2D type of Block.
Conv3D	Conv3D layer behaves just as Convolution does, with the distinction being it operates of 3-dimensional data such as medical images or video data.
Conv3D.Builder	The Builder to construct a Conv3D type of Block.
Convolution	A convolution layer does a dot product calculation on each channel of \(k\)-channel input data by specified number of filters, each containing \(k\) kernels for calculating each channel in the input data and then summed per filter, hence the number of filters denote the number of output channels of a convolution layer.
Convolution.ConvolutionBuilder<T extends Convolution.ConvolutionBuilder>	A builder that can build any Convolution block.

Package ai.djl.nn.convolutional Description

Contains classes that define convolutional operations extending Convolution.