Package org.nd4j.linalg.activations.impl
Class ActivationMish
- java.lang.Object
-
- org.nd4j.linalg.activations.BaseActivationFunction
-
- org.nd4j.linalg.activations.impl.ActivationMish
-
- All Implemented Interfaces:
Serializable
,IActivation
public class ActivationMish extends BaseActivationFunction
https://arxiv.org/ftp/arxiv/papers/1908/1908.08681.pdf- See Also:
- Serialized Form
-
-
Constructor Summary
Constructors Constructor Description ActivationMish()
-
Method Summary
All Methods Instance Methods Concrete 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 testString
toString()
-
Methods inherited from class org.nd4j.linalg.activations.BaseActivationFunction
assertShape, numParams
-
-
-
-
Method Detail
-
getActivation
public INDArray getActivation(INDArray in, boolean training)
Description copied from interface:IActivation
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
public Pair<INDArray,INDArray> backprop(INDArray in, INDArray epsilon)
Description copied from interface:IActivation
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)
-
-