Package ai.djl.nn.convolutional

package ai.djl.nn.convolutional

Contains classes that define convolutional operations extending Convolution and Deconvolution.

Related Packages

Package	Description
ai.djl.nn	Contains classes to construct neural networks.
ai.djl.nn.core	Contains classes that define simple neural network operations.
ai.djl.nn.norm	Contains classes that define normalizing neural network operations.
ai.djl.nn.pooling	Contains pooling neural network operations in Pool and helpers for it.
ai.djl.nn.recurrent	Contains classes for recurrent neural network operations.
ai.djl.nn.transformer	Contains blocks for transformer models.

Classes

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.
Conv1dTranspose	A Conv1dTranspose layer works similar to Deconvolution layer with the exception of the number of dimension it operates on being only one, which is LayoutType.WIDTH.
Conv1dTranspose.Builder	The Builder to construct a Conv1dTranspose 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.
Conv2dTranspose	The input to a Conv2dTranspose is an NDList with a single 4-D NDArray.
Conv2dTranspose.Builder	The Builder to construct a Conv2dTranspose 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.
Deconvolution	Transposed convolution, also named fractionally-strided convolution Dumoulin & Visin or deconvolution Long et al., 2015, serves this purpose.
Deconvolution.DeconvolutionBuilder<T extends Deconvolution.DeconvolutionBuilder>	A builder that can build any Deconvolution block.