org.nd4j.linalg.activations

Interface IActivation

All Superinterfaces:

Serializable

All Known Implementing Classes:

ActivationCube, ActivationELU, ActivationGELU, ActivationHardSigmoid, ActivationHardTanH, ActivationIdentity, ActivationLReLU, ActivationMish, ActivationPReLU, ActivationRationalTanh, ActivationRectifiedTanh, ActivationReLU, ActivationReLU6, ActivationRReLU, ActivationSELU, ActivationSigmoid, ActivationSoftmax, ActivationSoftPlus, ActivationSoftSign, ActivationSwish, ActivationTanH, ActivationThresholdedReLU, BaseActivationFunction

public interface IActivation
extends Serializable

Method Summary

Modifier and Type	Method and Description
Pair<INDArray,INDArray>	backprop(INDArray in, INDArray epsilon) Backpropagate the errors through the activation function, given input z and epsilon dL/da. Returns 2 INDArrays: (a) The gradient dL/dz, calculated from dL/da, and (b) The parameter gradients dL/dW, where w is the weights in the activation function.
INDArray	getActivation(INDArray in, boolean training) Carry out activation function on the input array (usually known as 'preOut' or 'z') Implementations must overwrite "in", transform in place and return "in" Can support separate behaviour during test
int	numParams(int inputSize)

Method Detail

getActivation

INDArray getActivation(INDArray in,
                       boolean training)

Carry out activation function on the input array (usually known as 'preOut' or 'z') Implementations must overwrite "in", transform in place and return "in" Can support separate behaviour during test

Parameters:

in - input array.

training - true when training.

Returns:

transformed activation

backprop

Pair<INDArray,INDArray> backprop(INDArray in,
                                 INDArray epsilon)

Backpropagate the errors through the activation function, given input z and epsilon dL/da.
Returns 2 INDArrays:
(a) The gradient dL/dz, calculated from dL/da, and
(b) The parameter gradients dL/dW, where w is the weights in the activation function. For activation functions with no gradients, this will be null.

Parameters:

in - Input, before applying the activation function (z, or 'preOut')

epsilon - Gradient to be backpropagated: dL/da, where L is the loss function

Returns:

dL/dz and dL/dW, for weights w (null if activation function has no weights)

numParams

int numParams(int inputSize)