package org.nd4j.linalg.activations; import org.apache.commons.math3.util.Pair; import org.nd4j.linalg.activations.impl.*; import org.nd4j.linalg.api.ndarray.INDArray; import org.nd4j.shade.jackson.annotation.JsonAutoDetect; import org.nd4j.shade.jackson.annotation.JsonSubTypes; import org.nd4j.shade.jackson.annotation.JsonTypeInfo; import java.io.Serializable; /** * Interface for implementing custom activation functions */ @JsonTypeInfo(use = JsonTypeInfo.Id.NAME, include = JsonTypeInfo.As.WRAPPER_OBJECT) @JsonSubTypes(value = {@JsonSubTypes.Type(value = ActivationCube.class, name = "Cube"), @JsonSubTypes.Type(value = ActivationELU.class, name = "ELU"), @JsonSubTypes.Type(value = ActivationHardSigmoid.class, name = "HardSigmoid"), @JsonSubTypes.Type(value = ActivationHardTanH.class, name = "HardTanh"), @JsonSubTypes.Type(value = ActivationIdentity.class, name = "Identity"), @JsonSubTypes.Type(value = ActivationLReLU.class, name = "LReLU"), @JsonSubTypes.Type(value = ActivationRationalTanh.class, name = "RationalTanh"), @JsonSubTypes.Type(value = ActivationRectifiedTanh.class, name = "RectifiedTanh"), @JsonSubTypes.Type(value = ActivationReLU.class, name = "ReLU"), @JsonSubTypes.Type(value = ActivationRReLU.class, name = "RReLU"), @JsonSubTypes.Type(value = ActivationSigmoid.class, name = "Sigmoid"), @JsonSubTypes.Type(value = ActivationSoftmax.class, name = "Softmax"), @JsonSubTypes.Type(value = ActivationSoftPlus.class, name = "SoftPlus"), @JsonSubTypes.Type(value = ActivationSoftSign.class, name = "SoftSign"), @JsonSubTypes.Type(value = ActivationTanH.class, name = "TanH")}) @JsonAutoDetect(fieldVisibility = JsonAutoDetect.Visibility.ANY, getterVisibility = JsonAutoDetect.Visibility.NONE, setterVisibility = JsonAutoDetect.Visibility.NONE) public interface IActivation extends Serializable { /** * 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 * @param in * @param training * @return transformed activation */ INDArray getActivation(INDArray in, boolean training); /** * Backpropagate the errors through the activation function, given input z and epsilon dL/da.<br> * Returns 2 INDArrays:<br> * (a) The gradient dL/dz, calculated from dL/da, and<br> * (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. * * @param in Input, before applying the activation function (z, or 'preOut') * @param epsilon Gradient to be backpropagated: dL/da, where L is the loss function * @return dL/dz and dL/dw, for weights w (null if activatino function has no weights) */ Pair<INDArray, INDArray> backprop(INDArray in, INDArray epsilon); int numParams(int inputSize); void setParametersViewArray(INDArray viewArray, boolean initialize); INDArray getParametersViewArray(); void setGradientViewArray(INDArray viewArray); INDArray getGradientViewArray(); }