Package deepboof.backward

package deepboof.backward

Interfaces

Class	Description
DActivationReLU<T extends deepboof.Tensor<T>>	Interface of ActivationReLU which adds functionality for the backwards step.
DActivationSigmoid<T extends deepboof.Tensor<T>>	Interface of ActivationSigmoid which adds functionality for the backwards step.
DActivationTanH<T extends deepboof.Tensor<T>>	Interface of ActivationTanH which adds functionality for the backwards step.
DBatchNorm<T extends deepboof.Tensor<T>>	Implementation of batch normalization for training networks.
DFunctionBatchNorm<T extends deepboof.Tensor<T>>	Implementation of Batch Normalization for training networks.
DFunctionDropOut<T extends deepboof.Tensor<T>>	Drop out is a technique introduced by [1] for regularizing a network and helps prevents over fitting.
DFunctionLinear<T extends deepboof.Tensor<T>>	Interface of FunctionLinear which adds functionality for the backwards step.
DSpatialBatchNorm<T extends deepboof.Tensor<T>>	Interface of Spatial Batch Normalization for training networks.
DSpatialConvolve2D<T extends deepboof.Tensor<T>>	Implementation of Spatial Convolve 2D for training networks.
DSpatialMaxPooling<T extends deepboof.Tensor<T>>	Interface of SpatialMaxPooling which adds functionality for the backwards step.
DSpatialPadding2D<T extends deepboof.Tensor<T>>	Interface for computing the gradient of a padded spatial tensor.
DSpatialPadding2D_F64
NumericalGradient<T extends deepboof.Tensor<T>>	Given a Function implementations of this interface will compute the gradient of its inputs and parameters.