public class LossSquaredHinge extends DifferentialFunction implements ILossFunction
dimensions, extraArgs, inPlace, sameDiff, scalarValue
Constructor and Description |
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LossSquaredHinge() |
Modifier and Type | Method and Description |
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INDArray |
computeGradient(INDArray labels,
INDArray preOutput,
IActivation activationFn,
INDArray mask)
Compute the gradient of the loss function with respect to the inputs: dL/dOutput
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org.nd4j.linalg.primitives.Pair<Double,INDArray> |
computeGradientAndScore(INDArray labels,
INDArray preOutput,
IActivation activationFn,
INDArray mask,
boolean average)
Compute both the score (loss function value) and gradient.
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double |
computeScore(INDArray labels,
INDArray preOutput,
IActivation activationFn,
INDArray mask,
boolean average)
Compute the score (loss function value) for the given inputs.
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INDArray |
computeScoreArray(INDArray labels,
INDArray preOutput,
IActivation activationFn,
INDArray mask)
Compute the score (loss function value) for each example individually.
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List<SDVariable> |
doDiff(List<SDVariable> f1)
The actual implementation for automatic differentiation.
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void |
initFromOnnx(OnnxProto3.NodeProto node,
SameDiff initWith,
Map<String,OnnxProto3.AttributeProto> attributesForNode,
OnnxProto3.GraphProto graph)
Iniitialize the function from the given
OnnxProto3.NodeProto |
void |
initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph)
Initialize the function from the given
NodeDef |
String |
name()
The opName of this function
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String |
onnxName()
The opName of this function in onnx
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String |
opName()
The name of the op
|
Op.Type |
opType()
The type of the op
|
SDVariable[] |
outputVariables()
Return the output variables for this differential function.
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SDVariable[] |
outputVariables(String baseName)
Return the output functions for this differential function.
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INDArray |
scoreArray(INDArray labels,
INDArray preOutput,
IActivation activationFn,
INDArray mask) |
String |
tensorflowName()
The opName of this function tensorflow
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String |
toString() |
arg, arg, argNames, args, asProperties, attributeAdaptersForFunction, calculateOutputShape, configFieldName, diff, dup, equals, f, getNumOutputs, getValue, hashCode, hasPlaceHolderInputs, isConfigProperties, larg, mappingsForFunction, onnxNames, opNum, outputVariable, outputVariablesNames, propertiesForFunction, rarg, resolvePropertiesFromSameDiffBeforeExecution, setInstanceId, setValueFor, tensorflowNames
public INDArray scoreArray(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask)
public double computeScore(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask, boolean average)
ILossFunction
computeScore
in interface ILossFunction
labels
- Label/expected preOutputpreOutput
- Output of the model (neural network)activationFn
- Activation function that should be applied to preOutputmask
- Mask array; may be nullaverage
- Whether the score should be averaged (divided by number of rows in labels/preOutput) or not @return Loss function valuepublic INDArray computeScoreArray(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask)
ILossFunction
computeScoreArray
in interface ILossFunction
labels
- Labels/expected outputpreOutput
- Output of the model (neural network)activationFn
- Activation function that should be applied to preOutputmask
- @return Loss function value for each example; column vectorpublic INDArray computeGradient(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask)
ILossFunction
computeGradient
in interface ILossFunction
labels
- Label/expected outputpreOutput
- Output of the model (neural network), before the activation function is appliedactivationFn
- Activation function that should be applied to preOutputmask
- Mask array; may be nullpublic org.nd4j.linalg.primitives.Pair<Double,INDArray> computeGradientAndScore(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask, boolean average)
ILossFunction
ILossFunction.computeScore(INDArray, INDArray, IActivation, INDArray, boolean)
and ILossFunction.computeGradient(INDArray, INDArray, IActivation, INDArray)
individuallycomputeGradientAndScore
in interface ILossFunction
labels
- Label/expected outputpreOutput
- Output of the model (neural network)activationFn
- Activation function that should be applied to preOutputmask
- Mask array; may be nullaverage
- Whether the score should be averaged (divided by number of rows in labels/output) or notpublic String name()
name
in interface ILossFunction
public SDVariable[] outputVariables()
DifferentialFunction
outputVariables
in class DifferentialFunction
public SDVariable[] outputVariables(String baseName)
DifferentialFunction
outputVariables
in class DifferentialFunction
public List<SDVariable> doDiff(List<SDVariable> f1)
DifferentialFunction
doDiff
in class DifferentialFunction
public String opName()
DifferentialFunction
opName
in class DifferentialFunction
public Op.Type opType()
DifferentialFunction
opType
in class DifferentialFunction
public void initFromTensorFlow(NodeDef nodeDef, SameDiff initWith, Map<String,AttrValue> attributesForNode, GraphDef graph)
DifferentialFunction
NodeDef
initFromTensorFlow
in class DifferentialFunction
public void initFromOnnx(OnnxProto3.NodeProto node, SameDiff initWith, Map<String,OnnxProto3.AttributeProto> attributesForNode, OnnxProto3.GraphProto graph)
DifferentialFunction
OnnxProto3.NodeProto
initFromOnnx
in class DifferentialFunction
public String onnxName()
DifferentialFunction
onnxName
in class DifferentialFunction
public String tensorflowName()
DifferentialFunction
tensorflowName
in class DifferentialFunction
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