IActivation (nd4j-api 1.0.0-M2.1 API)

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

All Methods Instance Methods Abstract Methods
Modifier and Type	Method	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)
```