| Interface | 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. |