Package org.deeplearning4j.nn.layers.convolution

Interface Summary

Interface	Description
ConvolutionHelper	Helper for the convolution layer.

Class Summary

Class	Description
Cnn3DLossLayer	3D Convolutional Neural Network Loss Layer. Handles calculation of gradients etc for various objective functions. NOTE: Cnn3DLossLayer does not have any parameters.
CnnLossLayer	Convolutional Neural Network Loss Layer. Handles calculation of gradients etc for various objective functions. NOTE: CnnLossLayer does not have any parameters.
Convolution1DLayer	1D (temporal) convolutional layer.
Convolution3DLayer	3D convolution layer implementation.
ConvolutionLayer	Convolution layer
Cropping1DLayer	Zero cropping layer for 1D convolutional neural networks.
Cropping2DLayer	Zero cropping layer for convolutional neural networks.
Cropping3DLayer	Cropping layer for 3D convolutional neural networks.
Deconvolution2DLayer	2D deconvolution layer implementation.
Deconvolution3DLayer	3D deconvolution layer implementation.
DepthwiseConvolution2DLayer	2D depth-wise convolution layer configuration.
SeparableConvolution2DLayer	2D Separable convolution layer implementation Separable convolutions split a regular convolution operation into two simpler operations, which are usually computationally more efficient.
SpaceToBatch	Space to batch utility layer for convolutional input types.
SpaceToDepth	Space to channels utility layer for convolutional input types.
ZeroPadding1DLayer	Zero padding 1D layer for convolutional neural networks.
ZeroPadding3DLayer	Zero padding 3D layer for convolutional neural networks.
ZeroPaddingLayer	Zero padding layer for convolutional neural networks.