Operator Reference

HALCON Operator Reference / Classification / Neural Nets

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evaluate_class_mlpT_evaluate_class_mlpEvaluateClassMlpEvaluateClassMlpevaluate_class_mlp (Operator)

evaluate_class_mlpT_evaluate_class_mlpEvaluateClassMlpEvaluateClassMlpevaluate_class_mlp — Calculate the evaluation of a feature vector by a multilayer perceptron.

Signature

evaluate_class_mlp( : : MLPHandle, Features : Result)

Herror T_evaluate_class_mlp(const Htuple MLPHandle, const Htuple Features, Htuple* Result)

void EvaluateClassMlp(const HTuple& MLPHandle, const HTuple& Features, HTuple* Result)

HTuple HClassMlp::EvaluateClassMlp(const HTuple& Features) const

static void HOperatorSet.EvaluateClassMlp(HTuple MLPHandle, HTuple features, out HTuple result)

HTuple HClassMlp.EvaluateClassMlp(HTuple features)

def evaluate_class_mlp(mlphandle: HHandle, features: Sequence[float]) -> Sequence[float]

Description

evaluate_class_mlpevaluate_class_mlpEvaluateClassMlpEvaluateClassMlpevaluate_class_mlp computes the result ResultResultResultresultresult of evaluating the feature vector FeaturesFeaturesFeaturesfeaturesfeatures with the multilayer perceptron (MLP) MLPHandleMLPHandleMLPHandleMLPHandlemlphandle. The formulas used for the evaluation are described with create_class_mlpcreate_class_mlpCreateClassMlpCreateClassMlpcreate_class_mlp. Before calling evaluate_class_mlpevaluate_class_mlpEvaluateClassMlpEvaluateClassMlpevaluate_class_mlp, the MLP must be trained with train_class_mlptrain_class_mlpTrainClassMlpTrainClassMlptrain_class_mlp.

If the MLP is used for regression (function approximation), i.e., if (OutputFunctionOutputFunctionOutputFunctionoutputFunctionoutput_function = 'linear'"linear""linear""linear""linear"), ResultResultResultresultresult is the value of the function at the coordinate FeaturesFeaturesFeaturesfeaturesfeatures. For OutputFunctionOutputFunctionOutputFunctionoutputFunctionoutput_function = 'logistic'"logistic""logistic""logistic""logistic" and 'softmax'"softmax""softmax""softmax""softmax", the values in ResultResultResultresultresult can be interpreted as probabilities. Hence, for OutputFunctionOutputFunctionOutputFunctionoutputFunctionoutput_function = 'logistic'"logistic""logistic""logistic""logistic" the elements of ResultResultResultresultresult represent the probabilities of the presence of the respective independent attributes. Typically, a threshold of 0.5 is used to decide whether the attribute is present or not. Depending on the application, other thresholds may be used as well. For OutputFunctionOutputFunctionOutputFunctionoutputFunctionoutput_function = 'softmax'"softmax""softmax""softmax""softmax" usually the position of the maximum value of ResultResultResultresultresult is interpreted as the class of the feature vector, and the corresponding value as the probability of the class. In this case, classify_class_mlpclassify_class_mlpClassifyClassMlpClassifyClassMlpclassify_class_mlp should be used instead of evaluate_class_mlpevaluate_class_mlpEvaluateClassMlpEvaluateClassMlpevaluate_class_mlp because classify_class_mlpclassify_class_mlpClassifyClassMlpClassifyClassMlpclassify_class_mlp directly returns the class and corresponding probability.

Execution Information

• Multithreading type: reentrant (runs in parallel with non-exclusive operators).
• Multithreading scope: global (may be called from any thread).
• Processed without parallelization.

Parameters

MLPHandleMLPHandleMLPHandleMLPHandlemlphandle (input_control)  class_mlp → HClassMlp, HTupleHHandleHTupleHtuple (handle) (IntPtr) (HHandle) (handle)

MLP handle.

FeaturesFeaturesFeaturesfeaturesfeatures (input_control)  real-array → HTupleSequence[float]HTupleHtuple (real) (double) (double) (double)

Feature vector.

ResultResultResultresultresult (output_control)  real-array → HTupleSequence[float]HTupleHtuple (real) (double) (double) (double)

Result of evaluating the feature vector with the MLP.

Result

If the parameters are valid, the operator evaluate_class_mlpevaluate_class_mlpEvaluateClassMlpEvaluateClassMlpevaluate_class_mlp returns the value 2 ( H_MSG_TRUE) . If necessary, an exception is raised.

Possible Predecessors

train_class_mlptrain_class_mlpTrainClassMlpTrainClassMlptrain_class_mlp, read_class_mlpread_class_mlpReadClassMlpReadClassMlpread_class_mlp

Alternatives

classify_class_mlpclassify_class_mlpClassifyClassMlpClassifyClassMlpclassify_class_mlp

See also

create_class_mlpcreate_class_mlpCreateClassMlpCreateClassMlpcreate_class_mlp

References

Christopher M. Bishop: “Neural Networks for Pattern Recognition”; Oxford University Press, Oxford; 1995.
Andrew Webb: “Statistical Pattern Recognition”; Arnold, London; 1999.

Module

Foundation

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