org.nd4j.autodiff.samediff.ops.SDLoss Maven / Gradle / Ivy
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* * terms of the Apache License, Version 2.0 which is available at
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* * information regarding copyright ownership.
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//================== GENERATED CODE - DO NOT MODIFY THIS FILE ==================
package org.nd4j.autodiff.samediff.ops;
import static org.nd4j.autodiff.samediff.ops.SDValidation.isSameType;
import java.lang.String;
import org.nd4j.autodiff.loss.LossReduce;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
public class SDLoss extends SDOps {
public SDLoss(SameDiff sameDiff) {
super(sameDiff);
}
/**
* Absolute difference loss: {@code sum_i abs( label[i] - predictions[i] )}
*
* @param label Label array (NUMERIC type)
* @param predictions Predictions array (NUMERIC type)
* @param weights Weights array. May be null. If null, a weight of 1.0 is used (NUMERIC type)
* @param lossReduce Reduction type for the loss. See LossReduce for more details. Default: LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT
* @return output loss variable (NUMERIC type)
*/
public SDVariable absoluteDifference(SDVariable label, SDVariable predictions, SDVariable weights,
LossReduce lossReduce) {
SDValidation.validateNumerical("absoluteDifference", "label", label);
SDValidation.validateNumerical("absoluteDifference", "predictions", predictions);
SDValidation.validateNumerical("absoluteDifference", "weights", weights);
SDVariable out = new org.nd4j.linalg.api.ops.impl.loss.AbsoluteDifferenceLoss(sd,label, predictions, weights, lossReduce).outputVariable();
out.markAsLoss();
return out;
}
/**
* Absolute difference loss: {@code sum_i abs( label[i] - predictions[i] )}
*
* @param name name May be null. Name for the output variable
* @param label Label array (NUMERIC type)
* @param predictions Predictions array (NUMERIC type)
* @param weights Weights array. May be null. If null, a weight of 1.0 is used (NUMERIC type)
* @param lossReduce Reduction type for the loss. See LossReduce for more details. Default: LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT
* @return output loss variable (NUMERIC type)
*/
public SDVariable absoluteDifference(String name, SDVariable label, SDVariable predictions,
SDVariable weights, LossReduce lossReduce) {
SDValidation.validateNumerical("absoluteDifference", "label", label);
SDValidation.validateNumerical("absoluteDifference", "predictions", predictions);
SDValidation.validateNumerical("absoluteDifference", "weights", weights);
SDVariable out = new org.nd4j.linalg.api.ops.impl.loss.AbsoluteDifferenceLoss(sd,label, predictions, weights, lossReduce).outputVariable();
out.markAsLoss();
return sd.updateVariableNameAndReference(out, name);
}
/**
* Absolute difference loss: {@code sum_i abs( label[i] - predictions[i] )}
*
* @param label Label array (NUMERIC type)
* @param predictions Predictions array (NUMERIC type)
* @param weights Weights array. May be null. If null, a weight of 1.0 is used (NUMERIC type)
* @return output loss variable (NUMERIC type)
*/
public SDVariable absoluteDifference(SDVariable label, SDVariable predictions,
SDVariable weights) {
SDValidation.validateNumerical("absoluteDifference", "label", label);
SDValidation.validateNumerical("absoluteDifference", "predictions", predictions);
SDValidation.validateNumerical("absoluteDifference", "weights", weights);
SDVariable out = new org.nd4j.linalg.api.ops.impl.loss.AbsoluteDifferenceLoss(sd,label, predictions, weights, org.nd4j.autodiff.loss.LossReduce.MEAN_BY_NONZERO_WEIGHT_COUNT).outputVariable();
out.markAsLoss();
return out;
}
/**
* Absolute difference loss: {@code sum_i abs( label[i] - predictions[i] )}
*
* @param name name May be null. Name for the output variable
* @param label Label array (NUMERIC type)
* @param predictions Predictions array (NUMERIC type)
* @param weights Weights array. May be null. If null, a weight of 1.0 is used (NUMERIC type)
* @return output loss variable (NUMERIC type)
*/
public SDVariable absoluteDifference(String name, SDVariable label, SDVariable predictions,
SDVariable weights) {
SDValidation.validateNumerical("absoluteDifference", "label", label);
SDValidation.validateNumerical("absoluteDifference", "predictions", predictions);
SDValidation.validateNumerical("absoluteDifference", "weights", weights);
SDVariable out = new org.nd4j.linalg.api.ops.impl.loss.AbsoluteDifferenceLoss(sd,label, predictions, weights, org.nd4j.autodiff.loss.LossReduce.MEAN_BY_NONZERO_WEIGHT_COUNT).outputVariable();
out.markAsLoss();
return sd.updateVariableNameAndReference(out, name);
}
/**
* Cosine distance loss: {@code 1 - cosineSimilarity(x,y)} or {@code 1 - sum_i label[i] * prediction[i]}, which is
* equivalent to cosine distance when both the predictions and labels are normalized.
* Note: This loss function assumes that both the predictions and labels are normalized to have unit l2 norm.
* If this is not the case, you should normalize them first by dividing by norm2(String, SDVariable, boolean, int...)
* along the cosine distance dimension (with keepDims=true).
*
* @param label Label array (NUMERIC type)
* @param predictions Predictions array (NUMERIC type)
* @param weights Weights array. May be null. If null, a weight of 1.0 is use (NUMERIC type)
* @param lossReduce Reduction type for the loss. See LossReduce for more details. Default: LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT
* @param dimension Dimension to perform the cosine distance over
* @return output Cosine distance loss (NUMERIC type)
*/
public SDVariable cosineDistance(SDVariable label, SDVariable predictions, SDVariable weights,
LossReduce lossReduce, int dimension) {
SDValidation.validateNumerical("cosineDistance", "label", label);
SDValidation.validateNumerical("cosineDistance", "predictions", predictions);
SDValidation.validateNumerical("cosineDistance", "weights", weights);
SDVariable out = new org.nd4j.linalg.api.ops.impl.loss.CosineDistanceLoss(sd,label, predictions, weights, lossReduce, dimension).outputVariable();
out.markAsLoss();
return out;
}
/**
* Cosine distance loss: {@code 1 - cosineSimilarity(x,y)} or {@code 1 - sum_i label[i] * prediction[i]}, which is
* equivalent to cosine distance when both the predictions and labels are normalized.
* Note: This loss function assumes that both the predictions and labels are normalized to have unit l2 norm.
* If this is not the case, you should normalize them first by dividing by norm2(String, SDVariable, boolean, int...)
* along the cosine distance dimension (with keepDims=true).
*
* @param name name May be null. Name for the output variable
* @param label Label array (NUMERIC type)
* @param predictions Predictions array (NUMERIC type)
* @param weights Weights array. May be null. If null, a weight of 1.0 is use (NUMERIC type)
* @param lossReduce Reduction type for the loss. See LossReduce for more details. Default: LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT
* @param dimension Dimension to perform the cosine distance over
* @return output Cosine distance loss (NUMERIC type)
*/
public SDVariable cosineDistance(String name, SDVariable label, SDVariable predictions,
SDVariable weights, LossReduce lossReduce, int dimension) {
SDValidation.validateNumerical("cosineDistance", "label", label);
SDValidation.validateNumerical("cosineDistance", "predictions", predictions);
SDValidation.validateNumerical("cosineDistance", "weights", weights);
SDVariable out = new org.nd4j.linalg.api.ops.impl.loss.CosineDistanceLoss(sd,label, predictions, weights, lossReduce, dimension).outputVariable();
out.markAsLoss();
return sd.updateVariableNameAndReference(out, name);
}
/**
* Cosine distance loss: {@code 1 - cosineSimilarity(x,y)} or {@code 1 - sum_i label[i] * prediction[i]}, which is
* equivalent to cosine distance when both the predictions and labels are normalized.
* Note: This loss function assumes that both the predictions and labels are normalized to have unit l2 norm.
* If this is not the case, you should normalize them first by dividing by norm2(String, SDVariable, boolean, int...)
* along the cosine distance dimension (with keepDims=true).
*
* @param label Label array (NUMERIC type)
* @param predictions Predictions array (NUMERIC type)
* @param weights Weights array. May be null. If null, a weight of 1.0 is use (NUMERIC type)
* @param dimension Dimension to perform the cosine distance over
* @return output Cosine distance loss (NUMERIC type)
*/
public SDVariable cosineDistance(SDVariable label, SDVariable predictions, SDVariable weights,
int dimension) {
SDValidation.validateNumerical("cosineDistance", "label", label);
SDValidation.validateNumerical("cosineDistance", "predictions", predictions);
SDValidation.validateNumerical("cosineDistance", "weights", weights);
SDVariable out = new org.nd4j.linalg.api.ops.impl.loss.CosineDistanceLoss(sd,label, predictions, weights, org.nd4j.autodiff.loss.LossReduce.MEAN_BY_NONZERO_WEIGHT_COUNT, dimension).outputVariable();
out.markAsLoss();
return out;
}
/**
* Cosine distance loss: {@code 1 - cosineSimilarity(x,y)} or {@code 1 - sum_i label[i] * prediction[i]}, which is
* equivalent to cosine distance when both the predictions and labels are normalized.
* Note: This loss function assumes that both the predictions and labels are normalized to have unit l2 norm.
* If this is not the case, you should normalize them first by dividing by norm2(String, SDVariable, boolean, int...)
* along the cosine distance dimension (with keepDims=true).
*
* @param name name May be null. Name for the output variable
* @param label Label array (NUMERIC type)
* @param predictions Predictions array (NUMERIC type)
* @param weights Weights array. May be null. If null, a weight of 1.0 is use (NUMERIC type)
* @param dimension Dimension to perform the cosine distance over
* @return output Cosine distance loss (NUMERIC type)
*/
public SDVariable cosineDistance(String name, SDVariable label, SDVariable predictions,
SDVariable weights, int dimension) {
SDValidation.validateNumerical("cosineDistance", "label", label);
SDValidation.validateNumerical("cosineDistance", "predictions", predictions);
SDValidation.validateNumerical("cosineDistance", "weights", weights);
SDVariable out = new org.nd4j.linalg.api.ops.impl.loss.CosineDistanceLoss(sd,label, predictions, weights, org.nd4j.autodiff.loss.LossReduce.MEAN_BY_NONZERO_WEIGHT_COUNT, dimension).outputVariable();
out.markAsLoss();
return sd.updateVariableNameAndReference(out, name);
}
/**
* CTC Loss: Connectionist Temporal Classification Loss. See:
* https://dl.acm.org/citation.cfm?id=1143891
*
* @param targetLabels Label array (NUMERIC type)
* @param logitInput Inputs (NUMERIC type)
* @param targetLabelLengths Length of the target label (NUMERIC type)
* @param logitInputLengths Length of the input (NUMERIC type)
* @return output Ctc loss (NUMERIC type)
*/
public SDVariable ctcLoss(SDVariable targetLabels, SDVariable logitInput,
SDVariable targetLabelLengths, SDVariable logitInputLengths) {
SDValidation.validateNumerical("ctcLoss", "targetLabels", targetLabels);
SDValidation.validateNumerical("ctcLoss", "logitInput", logitInput);
SDValidation.validateNumerical("ctcLoss", "targetLabelLengths", targetLabelLengths);
SDValidation.validateNumerical("ctcLoss", "logitInputLengths", logitInputLengths);
SDVariable out = new org.nd4j.linalg.api.ops.impl.loss.CtcLoss(sd,targetLabels, logitInput, targetLabelLengths, logitInputLengths).outputVariable();
out.markAsLoss();
return out;
}
/**
* CTC Loss: Connectionist Temporal Classification Loss. See:
* https://dl.acm.org/citation.cfm?id=1143891
*
* @param name name May be null. Name for the output variable
* @param targetLabels Label array (NUMERIC type)
* @param logitInput Inputs (NUMERIC type)
* @param targetLabelLengths Length of the target label (NUMERIC type)
* @param logitInputLengths Length of the input (NUMERIC type)
* @return output Ctc loss (NUMERIC type)
*/
public SDVariable ctcLoss(String name, SDVariable targetLabels, SDVariable logitInput,
SDVariable targetLabelLengths, SDVariable logitInputLengths) {
SDValidation.validateNumerical("ctcLoss", "targetLabels", targetLabels);
SDValidation.validateNumerical("ctcLoss", "logitInput", logitInput);
SDValidation.validateNumerical("ctcLoss", "targetLabelLengths", targetLabelLengths);
SDValidation.validateNumerical("ctcLoss", "logitInputLengths", logitInputLengths);
SDVariable out = new org.nd4j.linalg.api.ops.impl.loss.CtcLoss(sd,targetLabels, logitInput, targetLabelLengths, logitInputLengths).outputVariable();
out.markAsLoss();
return sd.updateVariableNameAndReference(out, name);
}
/**
* Hinge loss: a loss function used for training classifiers.
* Implements {@code L = max(0, 1 - t * predictions)} where t is the label values after internally converting to {-1,1}
* from the user specified {0,1}. Note that Labels should be provided with values {0,1}.
*
* @param label Label array. Each value should be 0.0 or 1.0 (internally -1 to 1 is used) (NUMERIC type)
* @param predictions Predictions array (NUMERIC type)
* @param weights Weights array. May be null. If null, a weight of 1.0 is used (NUMERIC type)
* @param lossReduce Reduction type for the loss. See LossReduce for more details. Default: LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT
* @return output Loss variable (NUMERIC type)
*/
public SDVariable hingeLoss(SDVariable label, SDVariable predictions, SDVariable weights,
LossReduce lossReduce) {
SDValidation.validateNumerical("hingeLoss", "label", label);
SDValidation.validateNumerical("hingeLoss", "predictions", predictions);
SDValidation.validateNumerical("hingeLoss", "weights", weights);
SDVariable out = new org.nd4j.linalg.api.ops.impl.loss.HingeLoss(sd,label, predictions, weights, lossReduce).outputVariable();
out.markAsLoss();
return out;
}
/**
* Hinge loss: a loss function used for training classifiers.
* Implements {@code L = max(0, 1 - t * predictions)} where t is the label values after internally converting to {-1,1}
* from the user specified {0,1}. Note that Labels should be provided with values {0,1}.
*
* @param name name May be null. Name for the output variable
* @param label Label array. Each value should be 0.0 or 1.0 (internally -1 to 1 is used) (NUMERIC type)
* @param predictions Predictions array (NUMERIC type)
* @param weights Weights array. May be null. If null, a weight of 1.0 is used (NUMERIC type)
* @param lossReduce Reduction type for the loss. See LossReduce for more details. Default: LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT
* @return output Loss variable (NUMERIC type)
*/
public SDVariable hingeLoss(String name, SDVariable label, SDVariable predictions,
SDVariable weights, LossReduce lossReduce) {
SDValidation.validateNumerical("hingeLoss", "label", label);
SDValidation.validateNumerical("hingeLoss", "predictions", predictions);
SDValidation.validateNumerical("hingeLoss", "weights", weights);
SDVariable out = new org.nd4j.linalg.api.ops.impl.loss.HingeLoss(sd,label, predictions, weights, lossReduce).outputVariable();
out.markAsLoss();
return sd.updateVariableNameAndReference(out, name);
}
/**
* Hinge loss: a loss function used for training classifiers.
* Implements {@code L = max(0, 1 - t * predictions)} where t is the label values after internally converting to {-1,1}
* from the user specified {0,1}. Note that Labels should be provided with values {0,1}.
*
* @param label Label array. Each value should be 0.0 or 1.0 (internally -1 to 1 is used) (NUMERIC type)
* @param predictions Predictions array (NUMERIC type)
* @param weights Weights array. May be null. If null, a weight of 1.0 is used (NUMERIC type)
* @return output Loss variable (NUMERIC type)
*/
public SDVariable hingeLoss(SDVariable label, SDVariable predictions, SDVariable weights) {
SDValidation.validateNumerical("hingeLoss", "label", label);
SDValidation.validateNumerical("hingeLoss", "predictions", predictions);
SDValidation.validateNumerical("hingeLoss", "weights", weights);
SDVariable out = new org.nd4j.linalg.api.ops.impl.loss.HingeLoss(sd,label, predictions, weights, org.nd4j.autodiff.loss.LossReduce.MEAN_BY_NONZERO_WEIGHT_COUNT).outputVariable();
out.markAsLoss();
return out;
}
/**
* Hinge loss: a loss function used for training classifiers.
* Implements {@code L = max(0, 1 - t * predictions)} where t is the label values after internally converting to {-1,1}
* from the user specified {0,1}. Note that Labels should be provided with values {0,1}.
*
* @param name name May be null. Name for the output variable
* @param label Label array. Each value should be 0.0 or 1.0 (internally -1 to 1 is used) (NUMERIC type)
* @param predictions Predictions array (NUMERIC type)
* @param weights Weights array. May be null. If null, a weight of 1.0 is used (NUMERIC type)
* @return output Loss variable (NUMERIC type)
*/
public SDVariable hingeLoss(String name, SDVariable label, SDVariable predictions,
SDVariable weights) {
SDValidation.validateNumerical("hingeLoss", "label", label);
SDValidation.validateNumerical("hingeLoss", "predictions", predictions);
SDValidation.validateNumerical("hingeLoss", "weights", weights);
SDVariable out = new org.nd4j.linalg.api.ops.impl.loss.HingeLoss(sd,label, predictions, weights, org.nd4j.autodiff.loss.LossReduce.MEAN_BY_NONZERO_WEIGHT_COUNT).outputVariable();
out.markAsLoss();
return sd.updateVariableNameAndReference(out, name);
}
/**
* Huber loss function, used for robust regression. It is similar both squared error loss and absolute difference loss,
* though is less sensitive to outliers than squared error.
* Huber loss implements:
*
* {@code L = 0.5 * (label[i] - predictions[i])^2 if abs(label[i] - predictions[i]) < delta}
* {@code L = delta * abs(label[i] - predictions[i]) - 0.5 * delta^2 otherwise}
*
*
* @param label Label array (NUMERIC type)
* @param predictions Predictions array (NUMERIC type)
* @param weights Weights array. May be null. If null, a weight of 1.0 is used (NUMERIC type)
* @param lossReduce Reduction type for the loss. See LossReduce for more details. Default: LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT
* @param delta Loss function delta value
* @return output Huber loss (NUMERIC type)
*/
public SDVariable huberLoss(SDVariable label, SDVariable predictions, SDVariable weights,
LossReduce lossReduce, double delta) {
SDValidation.validateNumerical("huberLoss", "label", label);
SDValidation.validateNumerical("huberLoss", "predictions", predictions);
SDValidation.validateNumerical("huberLoss", "weights", weights);
SDVariable out = new org.nd4j.linalg.api.ops.impl.loss.HuberLoss(sd,label, predictions, weights, lossReduce, delta).outputVariable();
out.markAsLoss();
return out;
}
/**
* Huber loss function, used for robust regression. It is similar both squared error loss and absolute difference loss,
* though is less sensitive to outliers than squared error.
* Huber loss implements:
*
* {@code L = 0.5 * (label[i] - predictions[i])^2 if abs(label[i] - predictions[i]) < delta}
* {@code L = delta * abs(label[i] - predictions[i]) - 0.5 * delta^2 otherwise}
*
*
* @param name name May be null. Name for the output variable
* @param label Label array (NUMERIC type)
* @param predictions Predictions array (NUMERIC type)
* @param weights Weights array. May be null. If null, a weight of 1.0 is used (NUMERIC type)
* @param lossReduce Reduction type for the loss. See LossReduce for more details. Default: LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT
* @param delta Loss function delta value
* @return output Huber loss (NUMERIC type)
*/
public SDVariable huberLoss(String name, SDVariable label, SDVariable predictions,
SDVariable weights, LossReduce lossReduce, double delta) {
SDValidation.validateNumerical("huberLoss", "label", label);
SDValidation.validateNumerical("huberLoss", "predictions", predictions);
SDValidation.validateNumerical("huberLoss", "weights", weights);
SDVariable out = new org.nd4j.linalg.api.ops.impl.loss.HuberLoss(sd,label, predictions, weights, lossReduce, delta).outputVariable();
out.markAsLoss();
return sd.updateVariableNameAndReference(out, name);
}
/**
* Huber loss function, used for robust regression. It is similar both squared error loss and absolute difference loss,
* though is less sensitive to outliers than squared error.
* Huber loss implements:
*
* {@code L = 0.5 * (label[i] - predictions[i])^2 if abs(label[i] - predictions[i]) < delta}
* {@code L = delta * abs(label[i] - predictions[i]) - 0.5 * delta^2 otherwise}
*
*
* @param label Label array (NUMERIC type)
* @param predictions Predictions array (NUMERIC type)
* @param weights Weights array. May be null. If null, a weight of 1.0 is used (NUMERIC type)
* @param delta Loss function delta value
* @return output Huber loss (NUMERIC type)
*/
public SDVariable huberLoss(SDVariable label, SDVariable predictions, SDVariable weights,
double delta) {
SDValidation.validateNumerical("huberLoss", "label", label);
SDValidation.validateNumerical("huberLoss", "predictions", predictions);
SDValidation.validateNumerical("huberLoss", "weights", weights);
SDVariable out = new org.nd4j.linalg.api.ops.impl.loss.HuberLoss(sd,label, predictions, weights, org.nd4j.autodiff.loss.LossReduce.MEAN_BY_NONZERO_WEIGHT_COUNT, delta).outputVariable();
out.markAsLoss();
return out;
}
/**
* Huber loss function, used for robust regression. It is similar both squared error loss and absolute difference loss,
* though is less sensitive to outliers than squared error.
* Huber loss implements:
*
* {@code L = 0.5 * (label[i] - predictions[i])^2 if abs(label[i] - predictions[i]) < delta}
* {@code L = delta * abs(label[i] - predictions[i]) - 0.5 * delta^2 otherwise}
*
*
* @param name name May be null. Name for the output variable
* @param label Label array (NUMERIC type)
* @param predictions Predictions array (NUMERIC type)
* @param weights Weights array. May be null. If null, a weight of 1.0 is used (NUMERIC type)
* @param delta Loss function delta value
* @return output Huber loss (NUMERIC type)
*/
public SDVariable huberLoss(String name, SDVariable label, SDVariable predictions,
SDVariable weights, double delta) {
SDValidation.validateNumerical("huberLoss", "label", label);
SDValidation.validateNumerical("huberLoss", "predictions", predictions);
SDValidation.validateNumerical("huberLoss", "weights", weights);
SDVariable out = new org.nd4j.linalg.api.ops.impl.loss.HuberLoss(sd,label, predictions, weights, org.nd4j.autodiff.loss.LossReduce.MEAN_BY_NONZERO_WEIGHT_COUNT, delta).outputVariable();
out.markAsLoss();
return sd.updateVariableNameAndReference(out, name);
}
/**
* L2 loss: 1/2 * sum(x^2)
*
* @param var Variable to calculate L2 loss of (NUMERIC type)
* @return output L2 loss (NUMERIC type)
*/
public SDVariable l2Loss(SDVariable var) {
SDValidation.validateNumerical("l2Loss", "var", var);
SDVariable out = new org.nd4j.linalg.api.ops.impl.loss.L2Loss(sd,var).outputVariable();
out.markAsLoss();
return out;
}
/**
* L2 loss: 1/2 * sum(x^2)
*
* @param name name May be null. Name for the output variable
* @param var Variable to calculate L2 loss of (NUMERIC type)
* @return output L2 loss (NUMERIC type)
*/
public SDVariable l2Loss(String name, SDVariable var) {
SDValidation.validateNumerical("l2Loss", "var", var);
SDVariable out = new org.nd4j.linalg.api.ops.impl.loss.L2Loss(sd,var).outputVariable();
out.markAsLoss();
return sd.updateVariableNameAndReference(out, name);
}
/**
* Log loss, i.e., binary cross entropy loss, usually used for binary multi-label classification. Implements:
* {@code -1/numExamples * sum_i (labels[i] * log(predictions[i] + epsilon) + (1-labels[i]) * log(1-predictions[i] + epsilon))}
*
* @param label Label array (NUMERIC type)
* @param predictions Predictions array (NUMERIC type)
* @param weights Weights array. May be null. If null, a weight of 1.0 is used (NUMERIC type)
* @param lossReduce Reduction type for the loss. See LossReduce for more details. Default: LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT
* @param epsilon epsilon
* @return output Log loss (NUMERIC type)
*/
public SDVariable logLoss(SDVariable label, SDVariable predictions, SDVariable weights,
LossReduce lossReduce, double epsilon) {
SDValidation.validateNumerical("logLoss", "label", label);
SDValidation.validateNumerical("logLoss", "predictions", predictions);
SDValidation.validateNumerical("logLoss", "weights", weights);
SDVariable out = new org.nd4j.linalg.api.ops.impl.loss.LogLoss(sd,label, predictions, weights, lossReduce, epsilon).outputVariable();
out.markAsLoss();
return out;
}
/**
* Log loss, i.e., binary cross entropy loss, usually used for binary multi-label classification. Implements:
* {@code -1/numExamples * sum_i (labels[i] * log(predictions[i] + epsilon) + (1-labels[i]) * log(1-predictions[i] + epsilon))}
*
* @param name name May be null. Name for the output variable
* @param label Label array (NUMERIC type)
* @param predictions Predictions array (NUMERIC type)
* @param weights Weights array. May be null. If null, a weight of 1.0 is used (NUMERIC type)
* @param lossReduce Reduction type for the loss. See LossReduce for more details. Default: LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT
* @param epsilon epsilon
* @return output Log loss (NUMERIC type)
*/
public SDVariable logLoss(String name, SDVariable label, SDVariable predictions,
SDVariable weights, LossReduce lossReduce, double epsilon) {
SDValidation.validateNumerical("logLoss", "label", label);
SDValidation.validateNumerical("logLoss", "predictions", predictions);
SDValidation.validateNumerical("logLoss", "weights", weights);
SDVariable out = new org.nd4j.linalg.api.ops.impl.loss.LogLoss(sd,label, predictions, weights, lossReduce, epsilon).outputVariable();
out.markAsLoss();
return sd.updateVariableNameAndReference(out, name);
}
/**
* Log loss, i.e., binary cross entropy loss, usually used for binary multi-label classification. Implements:
* {@code -1/numExamples * sum_i (labels[i] * log(predictions[i] + epsilon) + (1-labels[i]) * log(1-predictions[i] + epsilon))}
*
* @param label Label array (NUMERIC type)
* @param predictions Predictions array (NUMERIC type)
* @return output Log loss (NUMERIC type)
*/
public SDVariable logLoss(SDVariable label, SDVariable predictions) {
SDValidation.validateNumerical("logLoss", "label", label);
SDValidation.validateNumerical("logLoss", "predictions", predictions);
SDVariable out = new org.nd4j.linalg.api.ops.impl.loss.LogLoss(sd,label, predictions, null, org.nd4j.autodiff.loss.LossReduce.MEAN_BY_NONZERO_WEIGHT_COUNT, 0.0).outputVariable();
out.markAsLoss();
return out;
}
/**
* Log loss, i.e., binary cross entropy loss, usually used for binary multi-label classification. Implements:
* {@code -1/numExamples * sum_i (labels[i] * log(predictions[i] + epsilon) + (1-labels[i]) * log(1-predictions[i] + epsilon))}
*
* @param name name May be null. Name for the output variable
* @param label Label array (NUMERIC type)
* @param predictions Predictions array (NUMERIC type)
* @return output Log loss (NUMERIC type)
*/
public SDVariable logLoss(String name, SDVariable label, SDVariable predictions) {
SDValidation.validateNumerical("logLoss", "label", label);
SDValidation.validateNumerical("logLoss", "predictions", predictions);
SDVariable out = new org.nd4j.linalg.api.ops.impl.loss.LogLoss(sd,label, predictions, null, org.nd4j.autodiff.loss.LossReduce.MEAN_BY_NONZERO_WEIGHT_COUNT, 0.0).outputVariable();
out.markAsLoss();
return sd.updateVariableNameAndReference(out, name);
}
/**
* Log poisson loss: a loss function used for training classifiers.
* Implements {@code L = exp(c) - z * c} where c is log(predictions) and z is labels.
*
* @param label Label array. Each value should be 0.0 or 1.0 (NUMERIC type)
* @param predictions Predictions array (has to be log(x) of actual predictions) (NUMERIC type)
* @param weights Weights array. May be null. If null, a weight of 1.0 is used (NUMERIC type)
* @param lossReduce Reduction type for the loss. See LossReduce for more details. Default: LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT
* @param full Boolean flag. true for logPoissonFull, false for logPoisson
* @return output Loss variable (NUMERIC type)
*/
public SDVariable logPoisson(SDVariable label, SDVariable predictions, SDVariable weights,
LossReduce lossReduce, boolean full) {
SDValidation.validateNumerical("logPoisson", "label", label);
SDValidation.validateNumerical("logPoisson", "predictions", predictions);
SDValidation.validateNumerical("logPoisson", "weights", weights);
SDVariable out = new org.nd4j.linalg.api.ops.impl.loss.LogPoissonLoss(sd,label, predictions, weights, lossReduce, full).outputVariable();
out.markAsLoss();
return out;
}
/**
* Log poisson loss: a loss function used for training classifiers.
* Implements {@code L = exp(c) - z * c} where c is log(predictions) and z is labels.
*
* @param name name May be null. Name for the output variable
* @param label Label array. Each value should be 0.0 or 1.0 (NUMERIC type)
* @param predictions Predictions array (has to be log(x) of actual predictions) (NUMERIC type)
* @param weights Weights array. May be null. If null, a weight of 1.0 is used (NUMERIC type)
* @param lossReduce Reduction type for the loss. See LossReduce for more details. Default: LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT
* @param full Boolean flag. true for logPoissonFull, false for logPoisson
* @return output Loss variable (NUMERIC type)
*/
public SDVariable logPoisson(String name, SDVariable label, SDVariable predictions,
SDVariable weights, LossReduce lossReduce, boolean full) {
SDValidation.validateNumerical("logPoisson", "label", label);
SDValidation.validateNumerical("logPoisson", "predictions", predictions);
SDValidation.validateNumerical("logPoisson", "weights", weights);
SDVariable out = new org.nd4j.linalg.api.ops.impl.loss.LogPoissonLoss(sd,label, predictions, weights, lossReduce, full).outputVariable();
out.markAsLoss();
return sd.updateVariableNameAndReference(out, name);
}
/**
* Log poisson loss: a loss function used for training classifiers.
* Implements {@code L = exp(c) - z * c} where c is log(predictions) and z is labels.
*
* @param label Label array. Each value should be 0.0 or 1.0 (NUMERIC type)
* @param predictions Predictions array (has to be log(x) of actual predictions) (NUMERIC type)
* @param weights Weights array. May be null. If null, a weight of 1.0 is used (NUMERIC type)
* @param full Boolean flag. true for logPoissonFull, false for logPoisson
* @return output Loss variable (NUMERIC type)
*/
public SDVariable logPoisson(SDVariable label, SDVariable predictions, SDVariable weights,
boolean full) {
SDValidation.validateNumerical("logPoisson", "label", label);
SDValidation.validateNumerical("logPoisson", "predictions", predictions);
SDValidation.validateNumerical("logPoisson", "weights", weights);
SDVariable out = new org.nd4j.linalg.api.ops.impl.loss.LogPoissonLoss(sd,label, predictions, weights, org.nd4j.autodiff.loss.LossReduce.MEAN_BY_NONZERO_WEIGHT_COUNT, full).outputVariable();
out.markAsLoss();
return out;
}
/**
* Log poisson loss: a loss function used for training classifiers.
* Implements {@code L = exp(c) - z * c} where c is log(predictions) and z is labels.
*
* @param name name May be null. Name for the output variable
* @param label Label array. Each value should be 0.0 or 1.0 (NUMERIC type)
* @param predictions Predictions array (has to be log(x) of actual predictions) (NUMERIC type)
* @param weights Weights array. May be null. If null, a weight of 1.0 is used (NUMERIC type)
* @param full Boolean flag. true for logPoissonFull, false for logPoisson
* @return output Loss variable (NUMERIC type)
*/
public SDVariable logPoisson(String name, SDVariable label, SDVariable predictions,
SDVariable weights, boolean full) {
SDValidation.validateNumerical("logPoisson", "label", label);
SDValidation.validateNumerical("logPoisson", "predictions", predictions);
SDValidation.validateNumerical("logPoisson", "weights", weights);
SDVariable out = new org.nd4j.linalg.api.ops.impl.loss.LogPoissonLoss(sd,label, predictions, weights, org.nd4j.autodiff.loss.LossReduce.MEAN_BY_NONZERO_WEIGHT_COUNT, full).outputVariable();
out.markAsLoss();
return sd.updateVariableNameAndReference(out, name);
}
/**
* Mean pairwise squared error.
* MPWSE loss calculates the difference between pairs of consecutive elements in the predictions and labels arrays.
* For example, if predictions = [p0, p1, p2] and labels are [l0, l1, l2] then MPWSE is:
* {@code [((p0-p1) - (l0-l1))^2 + ((p0-p2) - (l0-l2))^2 + ((p1-p2) - (l1-l2))^2] / 3}
*
* @param label Label array (NUMERIC type)
* @param predictions Predictions array (NUMERIC type)
* @param weights Weights array. May be null. If null, a weight of 1.0 is used. Must be either null, scalar, or have shape [batchSize] (NUMERIC type)
* @param lossReduce Reduction type for the loss. See LossReduce for more details. Default: LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT
* @return output Loss variable, scalar output (NUMERIC type)
*/
public SDVariable meanPairwiseSquaredError(SDVariable label, SDVariable predictions,
SDVariable weights, LossReduce lossReduce) {
SDValidation.validateNumerical("meanPairwiseSquaredError", "label", label);
SDValidation.validateNumerical("meanPairwiseSquaredError", "predictions", predictions);
SDValidation.validateNumerical("meanPairwiseSquaredError", "weights", weights);
SDVariable out = new org.nd4j.linalg.api.ops.impl.loss.MeanPairwiseSquaredErrorLoss(sd,label, predictions, weights, lossReduce).outputVariable();
out.markAsLoss();
return out;
}
/**
* Mean pairwise squared error.
* MPWSE loss calculates the difference between pairs of consecutive elements in the predictions and labels arrays.
* For example, if predictions = [p0, p1, p2] and labels are [l0, l1, l2] then MPWSE is:
* {@code [((p0-p1) - (l0-l1))^2 + ((p0-p2) - (l0-l2))^2 + ((p1-p2) - (l1-l2))^2] / 3}
*
* @param name name May be null. Name for the output variable
* @param label Label array (NUMERIC type)
* @param predictions Predictions array (NUMERIC type)
* @param weights Weights array. May be null. If null, a weight of 1.0 is used. Must be either null, scalar, or have shape [batchSize] (NUMERIC type)
* @param lossReduce Reduction type for the loss. See LossReduce for more details. Default: LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT
* @return output Loss variable, scalar output (NUMERIC type)
*/
public SDVariable meanPairwiseSquaredError(String name, SDVariable label, SDVariable predictions,
SDVariable weights, LossReduce lossReduce) {
SDValidation.validateNumerical("meanPairwiseSquaredError", "label", label);
SDValidation.validateNumerical("meanPairwiseSquaredError", "predictions", predictions);
SDValidation.validateNumerical("meanPairwiseSquaredError", "weights", weights);
SDVariable out = new org.nd4j.linalg.api.ops.impl.loss.MeanPairwiseSquaredErrorLoss(sd,label, predictions, weights, lossReduce).outputVariable();
out.markAsLoss();
return sd.updateVariableNameAndReference(out, name);
}
/**
* Mean pairwise squared error.
* MPWSE loss calculates the difference between pairs of consecutive elements in the predictions and labels arrays.
* For example, if predictions = [p0, p1, p2] and labels are [l0, l1, l2] then MPWSE is:
* {@code [((p0-p1) - (l0-l1))^2 + ((p0-p2) - (l0-l2))^2 + ((p1-p2) - (l1-l2))^2] / 3}
*
* @param label Label array (NUMERIC type)
* @param predictions Predictions array (NUMERIC type)
* @param weights Weights array. May be null. If null, a weight of 1.0 is used. Must be either null, scalar, or have shape [batchSize] (NUMERIC type)
* @return output Loss variable, scalar output (NUMERIC type)
*/
public SDVariable meanPairwiseSquaredError(SDVariable label, SDVariable predictions,
SDVariable weights) {
SDValidation.validateNumerical("meanPairwiseSquaredError", "label", label);
SDValidation.validateNumerical("meanPairwiseSquaredError", "predictions", predictions);
SDValidation.validateNumerical("meanPairwiseSquaredError", "weights", weights);
SDVariable out = new org.nd4j.linalg.api.ops.impl.loss.MeanPairwiseSquaredErrorLoss(sd,label, predictions, weights, org.nd4j.autodiff.loss.LossReduce.MEAN_BY_NONZERO_WEIGHT_COUNT).outputVariable();
out.markAsLoss();
return out;
}
/**
* Mean pairwise squared error.
* MPWSE loss calculates the difference between pairs of consecutive elements in the predictions and labels arrays.
* For example, if predictions = [p0, p1, p2] and labels are [l0, l1, l2] then MPWSE is:
* {@code [((p0-p1) - (l0-l1))^2 + ((p0-p2) - (l0-l2))^2 + ((p1-p2) - (l1-l2))^2] / 3}
*
* @param name name May be null. Name for the output variable
* @param label Label array (NUMERIC type)
* @param predictions Predictions array (NUMERIC type)
* @param weights Weights array. May be null. If null, a weight of 1.0 is used. Must be either null, scalar, or have shape [batchSize] (NUMERIC type)
* @return output Loss variable, scalar output (NUMERIC type)
*/
public SDVariable meanPairwiseSquaredError(String name, SDVariable label, SDVariable predictions,
SDVariable weights) {
SDValidation.validateNumerical("meanPairwiseSquaredError", "label", label);
SDValidation.validateNumerical("meanPairwiseSquaredError", "predictions", predictions);
SDValidation.validateNumerical("meanPairwiseSquaredError", "weights", weights);
SDVariable out = new org.nd4j.linalg.api.ops.impl.loss.MeanPairwiseSquaredErrorLoss(sd,label, predictions, weights, org.nd4j.autodiff.loss.LossReduce.MEAN_BY_NONZERO_WEIGHT_COUNT).outputVariable();
out.markAsLoss();
return sd.updateVariableNameAndReference(out, name);
}
/**
* Mean squared error loss function. Implements {@code (label[i] - prediction[i])^2} - i.e., squared error on a per-element basis.
* When averaged (using LossReduce#MEAN_BY_WEIGHT or LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT (the default))
* this is the mean squared error loss function.
*
* @param label Label array (NUMERIC type)
* @param predictions Predictions array (NUMERIC type)
* @param weights Weights array. May be null. If null, a weight of 1.0 is used (NUMERIC type)
* @param lossReduce Reduction type for the loss. See LossReduce for more details. Default: LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT
* @return output Loss variable (NUMERIC type)
*/
public SDVariable meanSquaredError(SDVariable label, SDVariable predictions, SDVariable weights,
LossReduce lossReduce) {
SDValidation.validateNumerical("meanSquaredError", "label", label);
SDValidation.validateNumerical("meanSquaredError", "predictions", predictions);
SDValidation.validateNumerical("meanSquaredError", "weights", weights);
SDVariable out = new org.nd4j.linalg.api.ops.impl.loss.MeanSquaredErrorLoss(sd,label, predictions, weights, lossReduce).outputVariable();
out.markAsLoss();
return out;
}
/**
* Mean squared error loss function. Implements {@code (label[i] - prediction[i])^2} - i.e., squared error on a per-element basis.
* When averaged (using LossReduce#MEAN_BY_WEIGHT or LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT (the default))
* this is the mean squared error loss function.
*
* @param name name May be null. Name for the output variable
* @param label Label array (NUMERIC type)
* @param predictions Predictions array (NUMERIC type)
* @param weights Weights array. May be null. If null, a weight of 1.0 is used (NUMERIC type)
* @param lossReduce Reduction type for the loss. See LossReduce for more details. Default: LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT
* @return output Loss variable (NUMERIC type)
*/
public SDVariable meanSquaredError(String name, SDVariable label, SDVariable predictions,
SDVariable weights, LossReduce lossReduce) {
SDValidation.validateNumerical("meanSquaredError", "label", label);
SDValidation.validateNumerical("meanSquaredError", "predictions", predictions);
SDValidation.validateNumerical("meanSquaredError", "weights", weights);
SDVariable out = new org.nd4j.linalg.api.ops.impl.loss.MeanSquaredErrorLoss(sd,label, predictions, weights, lossReduce).outputVariable();
out.markAsLoss();
return sd.updateVariableNameAndReference(out, name);
}
/**
* Mean squared error loss function. Implements {@code (label[i] - prediction[i])^2} - i.e., squared error on a per-element basis.
* When averaged (using LossReduce#MEAN_BY_WEIGHT or LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT (the default))
* this is the mean squared error loss function.
*
* @param label Label array (NUMERIC type)
* @param predictions Predictions array (NUMERIC type)
* @param weights Weights array. May be null. If null, a weight of 1.0 is used (NUMERIC type)
* @return output Loss variable (NUMERIC type)
*/
public SDVariable meanSquaredError(SDVariable label, SDVariable predictions, SDVariable weights) {
SDValidation.validateNumerical("meanSquaredError", "label", label);
SDValidation.validateNumerical("meanSquaredError", "predictions", predictions);
SDValidation.validateNumerical("meanSquaredError", "weights", weights);
SDVariable out = new org.nd4j.linalg.api.ops.impl.loss.MeanSquaredErrorLoss(sd,label, predictions, weights, org.nd4j.autodiff.loss.LossReduce.MEAN_BY_NONZERO_WEIGHT_COUNT).outputVariable();
out.markAsLoss();
return out;
}
/**
* Mean squared error loss function. Implements {@code (label[i] - prediction[i])^2} - i.e., squared error on a per-element basis.
* When averaged (using LossReduce#MEAN_BY_WEIGHT or LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT (the default))
* this is the mean squared error loss function.
*
* @param name name May be null. Name for the output variable
* @param label Label array (NUMERIC type)
* @param predictions Predictions array (NUMERIC type)
* @param weights Weights array. May be null. If null, a weight of 1.0 is used (NUMERIC type)
* @return output Loss variable (NUMERIC type)
*/
public SDVariable meanSquaredError(String name, SDVariable label, SDVariable predictions,
SDVariable weights) {
SDValidation.validateNumerical("meanSquaredError", "label", label);
SDValidation.validateNumerical("meanSquaredError", "predictions", predictions);
SDValidation.validateNumerical("meanSquaredError", "weights", weights);
SDVariable out = new org.nd4j.linalg.api.ops.impl.loss.MeanSquaredErrorLoss(sd,label, predictions, weights, org.nd4j.autodiff.loss.LossReduce.MEAN_BY_NONZERO_WEIGHT_COUNT).outputVariable();
out.markAsLoss();
return sd.updateVariableNameAndReference(out, name);
}
/**
* Sigmoid cross entropy: applies the sigmoid activation function on the input logits (input "pre-sigmoid preductions")
* and implements the binary cross entropy loss function. This implementation is numerically more stable than using
* standard (but separate) sigmoid activation function and log loss (binary cross entropy) loss function.
* Implements:
* {@code -1/numExamples * sum_i (labels[i] * log(sigmoid(logits[i])) + (1-labels[i]) * log(1-sigmoid(logits[i])))}
* though this is done in a mathematically equivalent but more numerical stable form.
*
* When label smoothing is > 0, the following label smoothing is used:
*
* {@code numClasses = labels.size(1);
* label = (1.0 - labelSmoothing) * label + 0.5 * labelSmoothing}
*
*
* @param label Label array (NUMERIC type)
* @param predictionLogits Predictions array (NUMERIC type)
* @param weights Weights array. May be null. If null, a weight of 1.0 is used (NUMERIC type)
* @param lossReduce Reduction type for the loss. See LossReduce for more details. Default: LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT
* @param labelSmoothing Label smoothing value. Default value: 0
* @return output Loss variable (NUMERIC type)
*/
public SDVariable sigmoidCrossEntropy(SDVariable label, SDVariable predictionLogits,
SDVariable weights, LossReduce lossReduce, double labelSmoothing) {
SDValidation.validateNumerical("sigmoidCrossEntropy", "label", label);
SDValidation.validateNumerical("sigmoidCrossEntropy", "predictionLogits", predictionLogits);
SDValidation.validateNumerical("sigmoidCrossEntropy", "weights", weights);
SDVariable out = new org.nd4j.linalg.api.ops.impl.loss.SigmoidCrossEntropyLoss(sd,label, predictionLogits, weights, lossReduce, labelSmoothing).outputVariable();
out.markAsLoss();
return out;
}
/**
* Sigmoid cross entropy: applies the sigmoid activation function on the input logits (input "pre-sigmoid preductions")
* and implements the binary cross entropy loss function. This implementation is numerically more stable than using
* standard (but separate) sigmoid activation function and log loss (binary cross entropy) loss function.
* Implements:
* {@code -1/numExamples * sum_i (labels[i] * log(sigmoid(logits[i])) + (1-labels[i]) * log(1-sigmoid(logits[i])))}
* though this is done in a mathematically equivalent but more numerical stable form.
*
* When label smoothing is > 0, the following label smoothing is used:
*
* {@code numClasses = labels.size(1);
* label = (1.0 - labelSmoothing) * label + 0.5 * labelSmoothing}
*
*
* @param name name May be null. Name for the output variable
* @param label Label array (NUMERIC type)
* @param predictionLogits Predictions array (NUMERIC type)
* @param weights Weights array. May be null. If null, a weight of 1.0 is used (NUMERIC type)
* @param lossReduce Reduction type for the loss. See LossReduce for more details. Default: LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT
* @param labelSmoothing Label smoothing value. Default value: 0
* @return output Loss variable (NUMERIC type)
*/
public SDVariable sigmoidCrossEntropy(String name, SDVariable label, SDVariable predictionLogits,
SDVariable weights, LossReduce lossReduce, double labelSmoothing) {
SDValidation.validateNumerical("sigmoidCrossEntropy", "label", label);
SDValidation.validateNumerical("sigmoidCrossEntropy", "predictionLogits", predictionLogits);
SDValidation.validateNumerical("sigmoidCrossEntropy", "weights", weights);
SDVariable out = new org.nd4j.linalg.api.ops.impl.loss.SigmoidCrossEntropyLoss(sd,label, predictionLogits, weights, lossReduce, labelSmoothing).outputVariable();
out.markAsLoss();
return sd.updateVariableNameAndReference(out, name);
}
/**
* Sigmoid cross entropy: applies the sigmoid activation function on the input logits (input "pre-sigmoid preductions")
* and implements the binary cross entropy loss function. This implementation is numerically more stable than using
* standard (but separate) sigmoid activation function and log loss (binary cross entropy) loss function.
* Implements:
* {@code -1/numExamples * sum_i (labels[i] * log(sigmoid(logits[i])) + (1-labels[i]) * log(1-sigmoid(logits[i])))}
* though this is done in a mathematically equivalent but more numerical stable form.
*
* When label smoothing is > 0, the following label smoothing is used:
*
* {@code numClasses = labels.size(1);
* label = (1.0 - labelSmoothing) * label + 0.5 * labelSmoothing}
*
*
* @param label Label array (NUMERIC type)
* @param predictionLogits Predictions array (NUMERIC type)
* @param weights Weights array. May be null. If null, a weight of 1.0 is used (NUMERIC type)
* @return output Loss variable (NUMERIC type)
*/
public SDVariable sigmoidCrossEntropy(SDVariable label, SDVariable predictionLogits,
SDVariable weights) {
SDValidation.validateNumerical("sigmoidCrossEntropy", "label", label);
SDValidation.validateNumerical("sigmoidCrossEntropy", "predictionLogits", predictionLogits);
SDValidation.validateNumerical("sigmoidCrossEntropy", "weights", weights);
SDVariable out = new org.nd4j.linalg.api.ops.impl.loss.SigmoidCrossEntropyLoss(sd,label, predictionLogits, weights, org.nd4j.autodiff.loss.LossReduce.MEAN_BY_NONZERO_WEIGHT_COUNT, 0.0).outputVariable();
out.markAsLoss();
return out;
}
/**
* Sigmoid cross entropy: applies the sigmoid activation function on the input logits (input "pre-sigmoid preductions")
* and implements the binary cross entropy loss function. This implementation is numerically more stable than using
* standard (but separate) sigmoid activation function and log loss (binary cross entropy) loss function.
* Implements:
* {@code -1/numExamples * sum_i (labels[i] * log(sigmoid(logits[i])) + (1-labels[i]) * log(1-sigmoid(logits[i])))}
* though this is done in a mathematically equivalent but more numerical stable form.
*
* When label smoothing is > 0, the following label smoothing is used:
*
* {@code numClasses = labels.size(1);
* label = (1.0 - labelSmoothing) * label + 0.5 * labelSmoothing}
*
*
* @param name name May be null. Name for the output variable
* @param label Label array (NUMERIC type)
* @param predictionLogits Predictions array (NUMERIC type)
* @param weights Weights array. May be null. If null, a weight of 1.0 is used (NUMERIC type)
* @return output Loss variable (NUMERIC type)
*/
public SDVariable sigmoidCrossEntropy(String name, SDVariable label, SDVariable predictionLogits,
SDVariable weights) {
SDValidation.validateNumerical("sigmoidCrossEntropy", "label", label);
SDValidation.validateNumerical("sigmoidCrossEntropy", "predictionLogits", predictionLogits);
SDValidation.validateNumerical("sigmoidCrossEntropy", "weights", weights);
SDVariable out = new org.nd4j.linalg.api.ops.impl.loss.SigmoidCrossEntropyLoss(sd,label, predictionLogits, weights, org.nd4j.autodiff.loss.LossReduce.MEAN_BY_NONZERO_WEIGHT_COUNT, 0.0).outputVariable();
out.markAsLoss();
return sd.updateVariableNameAndReference(out, name);
}
/**
* Applies the softmax activation function to the input, then implement multi-class cross entropy:
* {@code -sum_classes label[i] * log(p[c])} where {@code p = softmax(logits)}
* If LossReduce#NONE is used, returned shape is [numExamples] out for [numExamples, numClasses] predicitons/labels;
* otherwise, the output is a scalar.
*
* When label smoothing is > 0, the following label smoothing is used:
*
* {@code numClasses = labels.size(1);
* oneHotLabel = (1.0 - labelSmoothing) * oneHotLabels + labelSmoothing/numClasses}
*
*
* @param oneHotLabels Label array. Should be one-hot per example and same shape as predictions (for example, [mb, nOut]) (NUMERIC type)
* @param logitPredictions Predictions array (pre-softmax) (NUMERIC type)
* @param weights Weights array. May be null. If null, a weight of 1.0 is used (NUMERIC type)
* @param lossReduce Reduction type for the loss. See LossReduce for more details. Default: LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT
* @param labelSmoothing Label smoothing value. Default value: 0
* @return output Loss variable (NUMERIC type)
*/
public SDVariable softmaxCrossEntropy(SDVariable oneHotLabels, SDVariable logitPredictions,
SDVariable weights, LossReduce lossReduce, double labelSmoothing) {
SDValidation.validateNumerical("softmaxCrossEntropy", "oneHotLabels", oneHotLabels);
SDValidation.validateNumerical("softmaxCrossEntropy", "logitPredictions", logitPredictions);
SDValidation.validateNumerical("softmaxCrossEntropy", "weights", weights);
SDVariable out = new org.nd4j.linalg.api.ops.impl.loss.SoftmaxCrossEntropyLoss(sd,oneHotLabels, logitPredictions, weights, lossReduce, labelSmoothing).outputVariable();
out.markAsLoss();
return out;
}
/**
* Applies the softmax activation function to the input, then implement multi-class cross entropy:
* {@code -sum_classes label[i] * log(p[c])} where {@code p = softmax(logits)}
* If LossReduce#NONE is used, returned shape is [numExamples] out for [numExamples, numClasses] predicitons/labels;
* otherwise, the output is a scalar.
*
* When label smoothing is > 0, the following label smoothing is used:
*
* {@code numClasses = labels.size(1);
* oneHotLabel = (1.0 - labelSmoothing) * oneHotLabels + labelSmoothing/numClasses}
*
*
* @param name name May be null. Name for the output variable
* @param oneHotLabels Label array. Should be one-hot per example and same shape as predictions (for example, [mb, nOut]) (NUMERIC type)
* @param logitPredictions Predictions array (pre-softmax) (NUMERIC type)
* @param weights Weights array. May be null. If null, a weight of 1.0 is used (NUMERIC type)
* @param lossReduce Reduction type for the loss. See LossReduce for more details. Default: LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT
* @param labelSmoothing Label smoothing value. Default value: 0
* @return output Loss variable (NUMERIC type)
*/
public SDVariable softmaxCrossEntropy(String name, SDVariable oneHotLabels,
SDVariable logitPredictions, SDVariable weights, LossReduce lossReduce,
double labelSmoothing) {
SDValidation.validateNumerical("softmaxCrossEntropy", "oneHotLabels", oneHotLabels);
SDValidation.validateNumerical("softmaxCrossEntropy", "logitPredictions", logitPredictions);
SDValidation.validateNumerical("softmaxCrossEntropy", "weights", weights);
SDVariable out = new org.nd4j.linalg.api.ops.impl.loss.SoftmaxCrossEntropyLoss(sd,oneHotLabels, logitPredictions, weights, lossReduce, labelSmoothing).outputVariable();
out.markAsLoss();
return sd.updateVariableNameAndReference(out, name);
}
/**
* Applies the softmax activation function to the input, then implement multi-class cross entropy:
* {@code -sum_classes label[i] * log(p[c])} where {@code p = softmax(logits)}
* If LossReduce#NONE is used, returned shape is [numExamples] out for [numExamples, numClasses] predicitons/labels;
* otherwise, the output is a scalar.
*
* When label smoothing is > 0, the following label smoothing is used:
*
* {@code numClasses = labels.size(1);
* oneHotLabel = (1.0 - labelSmoothing) * oneHotLabels + labelSmoothing/numClasses}
*
*
* @param oneHotLabels Label array. Should be one-hot per example and same shape as predictions (for example, [mb, nOut]) (NUMERIC type)
* @param logitPredictions Predictions array (pre-softmax) (NUMERIC type)
* @param weights Weights array. May be null. If null, a weight of 1.0 is used (NUMERIC type)
* @return output Loss variable (NUMERIC type)
*/
public SDVariable softmaxCrossEntropy(SDVariable oneHotLabels, SDVariable logitPredictions,
SDVariable weights) {
SDValidation.validateNumerical("softmaxCrossEntropy", "oneHotLabels", oneHotLabels);
SDValidation.validateNumerical("softmaxCrossEntropy", "logitPredictions", logitPredictions);
SDValidation.validateNumerical("softmaxCrossEntropy", "weights", weights);
SDVariable out = new org.nd4j.linalg.api.ops.impl.loss.SoftmaxCrossEntropyLoss(sd,oneHotLabels, logitPredictions, weights, org.nd4j.autodiff.loss.LossReduce.MEAN_BY_NONZERO_WEIGHT_COUNT, 0.0).outputVariable();
out.markAsLoss();
return out;
}
/**
* Applies the softmax activation function to the input, then implement multi-class cross entropy:
* {@code -sum_classes label[i] * log(p[c])} where {@code p = softmax(logits)}
* If LossReduce#NONE is used, returned shape is [numExamples] out for [numExamples, numClasses] predicitons/labels;
* otherwise, the output is a scalar.
*
* When label smoothing is > 0, the following label smoothing is used:
*
* {@code numClasses = labels.size(1);
* oneHotLabel = (1.0 - labelSmoothing) * oneHotLabels + labelSmoothing/numClasses}
*
*
* @param name name May be null. Name for the output variable
* @param oneHotLabels Label array. Should be one-hot per example and same shape as predictions (for example, [mb, nOut]) (NUMERIC type)
* @param logitPredictions Predictions array (pre-softmax) (NUMERIC type)
* @param weights Weights array. May be null. If null, a weight of 1.0 is used (NUMERIC type)
* @return output Loss variable (NUMERIC type)
*/
public SDVariable softmaxCrossEntropy(String name, SDVariable oneHotLabels,
SDVariable logitPredictions, SDVariable weights) {
SDValidation.validateNumerical("softmaxCrossEntropy", "oneHotLabels", oneHotLabels);
SDValidation.validateNumerical("softmaxCrossEntropy", "logitPredictions", logitPredictions);
SDValidation.validateNumerical("softmaxCrossEntropy", "weights", weights);
SDVariable out = new org.nd4j.linalg.api.ops.impl.loss.SoftmaxCrossEntropyLoss(sd,oneHotLabels, logitPredictions, weights, org.nd4j.autodiff.loss.LossReduce.MEAN_BY_NONZERO_WEIGHT_COUNT, 0.0).outputVariable();
out.markAsLoss();
return sd.updateVariableNameAndReference(out, name);
}
/**
* As per softmaxCrossEntropy(String, SDVariable, SDVariable, LossReduce) but the labels variable
* is represented as an integer array instead of the equivalent one-hot array.
* i.e., if logits are rank N, then labels have rank N-1
*
* @param logits Logits array ("pre-softmax activations") (NUMERIC type)
* @param labels Labels array. Must be an integer type. (INT type)
* @return output Softmax cross entropy (NUMERIC type)
*/
public SDVariable sparseSoftmaxCrossEntropy(SDVariable logits, SDVariable labels) {
SDValidation.validateNumerical("sparseSoftmaxCrossEntropy", "logits", logits);
SDValidation.validateInteger("sparseSoftmaxCrossEntropy", "labels", labels);
SDVariable out = new org.nd4j.linalg.api.ops.impl.loss.SparseSoftmaxCrossEntropyLossWithLogits(sd,logits, labels).outputVariable();
out.markAsLoss();
return out;
}
/**
* As per softmaxCrossEntropy(String, SDVariable, SDVariable, LossReduce) but the labels variable
* is represented as an integer array instead of the equivalent one-hot array.
* i.e., if logits are rank N, then labels have rank N-1
*
* @param name name May be null. Name for the output variable
* @param logits Logits array ("pre-softmax activations") (NUMERIC type)
* @param labels Labels array. Must be an integer type. (INT type)
* @return output Softmax cross entropy (NUMERIC type)
*/
public SDVariable sparseSoftmaxCrossEntropy(String name, SDVariable logits, SDVariable labels) {
SDValidation.validateNumerical("sparseSoftmaxCrossEntropy", "logits", logits);
SDValidation.validateInteger("sparseSoftmaxCrossEntropy", "labels", labels);
SDVariable out = new org.nd4j.linalg.api.ops.impl.loss.SparseSoftmaxCrossEntropyLossWithLogits(sd,logits, labels).outputVariable();
out.markAsLoss();
return sd.updateVariableNameAndReference(out, name);
}
}