org.nd4j.linalg.lossfunctions.LossUtil Maven / Gradle / Ivy
package org.nd4j.linalg.lossfunctions;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.ops.impl.transforms.SoftMax;
import org.nd4j.linalg.factory.Nd4j;
import java.util.Arrays;
/**
* Created by Alex on 14/09/2016.
*/
public class LossUtil {
/**
* @deprecated No longer used
*/
@Deprecated
public static INDArray dLdZsoftmaxPreOut(INDArray dlda, INDArray z) {
return dLdZsoftmax(dlda, Nd4j.getExecutioner().execAndReturn(new SoftMax(z.dup())));
}
/**
* Calculate dL/dz for softmax activation function, from dL/da and a, where
* a: output activations
* dL/da: derivative of loss function with respect to the output activations
* Note: This version does NOT modify either input arrays. This is less efficient. For a more efficient implementation
* that modifies the input arrays, use {@link #dLdZsoftmaxi(INDArray, INDArray)}.
*
* @param dlda derivative of loss function with respect to the output activations (shape [minibatchSize, nOut])
* @param a output activations array (shape [minibatchSize, nOut])
* @deprecated No longer used
*/
@Deprecated
public static INDArray dLdZsoftmax(INDArray dlda, INDArray a) {
INDArray x = a.mul(dlda).sum(1);
return a.mul(dlda.subColumnVector(x));
}
/**
* Calculate dL/dz for softmax activation function, from dL/da and a, where
* a: output activations
* dL/da: derivative of loss function with respect to the output activations
* Note: This version WILL modify both input arrays (for efficiency). If this is not acceptable, use
* {@link #dLdZsoftmax(INDArray, INDArray)}.
*
* @param dlda derivative of loss function with respect to the output activations (shape [minibatchSize, nOut])
* @param a output activations array (shape [minibatchSize, nOut])
* @deprecated No longer used
*/
@Deprecated
public static INDArray dLdZsoftmaxi(INDArray dlda, INDArray a) {
INDArray x = a.mul(dlda).sum(1);
return a.muli(dlda.subiColumnVector(x));
}
public static boolean isPerOutputMasking(INDArray to, INDArray mask) {
return !mask.isColumnVector() || Arrays.equals(to.shape(), mask.shape());
}
public static void applyMask(INDArray to, INDArray mask) {
//Two possibilities exist: it's *per example* masking, or it's *per output* masking
//These cases have different mask shapes. Per example: column vector. Per output: same shape as score array
if (mask.isColumnVector()) {
to.muliColumnVector(mask);
} else if (Arrays.equals(to.shape(), mask.shape())) {
to.muli(mask);
} else {
throw new IllegalStateException("Invalid mask array: per-example masking should be a column vector, "
+ "per output masking arrays should be the same shape as the labels array. Mask shape: "
+ Arrays.toString(mask.shape()) + ", output shape: " + Arrays.toString(to.shape()));
}
}
}