evaluate_class_mlpT_evaluate_class_mlpEvaluateClassMlpEvaluateClassMlpevaluate_class_mlp (Operator)
Name
evaluate_class_mlpT_evaluate_class_mlpEvaluateClassMlpEvaluateClassMlpevaluate_class_mlp — Calculate the evaluation of a feature vector by a multilayer
perceptron.
Signature
def evaluate_class_mlp(mlphandle: HHandle, features: Sequence[float]) -> Sequence[float]
Description
evaluate_class_mlpevaluate_class_mlpEvaluateClassMlpEvaluateClassMlpEvaluateClassMlpevaluate_class_mlp computes the result ResultResultResultResultresultresult of
evaluating the feature vector FeaturesFeaturesFeaturesFeaturesfeaturesfeatures with the multilayer
perceptron (MLP) MLPHandleMLPHandleMLPHandleMLPHandleMLPHandlemlphandle. The formulas used for the
evaluation are described with create_class_mlpcreate_class_mlpCreateClassMlpCreateClassMlpCreateClassMlpcreate_class_mlp. Before
calling evaluate_class_mlpevaluate_class_mlpEvaluateClassMlpEvaluateClassMlpEvaluateClassMlpevaluate_class_mlp, the MLP must be trained with
train_class_mlptrain_class_mlpTrainClassMlpTrainClassMlpTrainClassMlptrain_class_mlp.
If the MLP is used for regression (function approximation), i.e., if
(OutputFunctionOutputFunctionOutputFunctionOutputFunctionoutputFunctionoutput_function = 'linear'"linear""linear""linear""linear""linear"), ResultResultResultResultresultresult is
the value of the function at the coordinate FeaturesFeaturesFeaturesFeaturesfeaturesfeatures. For
OutputFunctionOutputFunctionOutputFunctionOutputFunctionoutputFunctionoutput_function = 'logistic'"logistic""logistic""logistic""logistic""logistic" and
'softmax'"softmax""softmax""softmax""softmax""softmax", the values in ResultResultResultResultresultresult can be interpreted
as probabilities. Hence, for OutputFunctionOutputFunctionOutputFunctionOutputFunctionoutputFunctionoutput_function =
'logistic'"logistic""logistic""logistic""logistic""logistic" the elements of ResultResultResultResultresultresult 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 OutputFunctionOutputFunctionOutputFunctionOutputFunctionoutputFunctionoutput_function
= 'softmax'"softmax""softmax""softmax""softmax""softmax" usually the position of the maximum value of
ResultResultResultResultresultresult 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_mlpClassifyClassMlpClassifyClassMlpClassifyClassMlpclassify_class_mlp should be used instead of
evaluate_class_mlpevaluate_class_mlpEvaluateClassMlpEvaluateClassMlpEvaluateClassMlpevaluate_class_mlp because classify_class_mlpclassify_class_mlpClassifyClassMlpClassifyClassMlpClassifyClassMlpclassify_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
MLPHandleMLPHandleMLPHandleMLPHandleMLPHandlemlphandle (input_control) class_mlp → HClassMlp, HTupleHHandleHTupleHtuple (handle) (IntPtr) (HHandle) (handle)
MLP handle.
FeaturesFeaturesFeaturesFeaturesfeaturesfeatures (input_control) real-array → HTupleSequence[float]HTupleHtuple (real) (double) (double) (double)
Feature vector.
ResultResultResultResultresultresult (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_mlpEvaluateClassMlpEvaluateClassMlpEvaluateClassMlpevaluate_class_mlp
returns the value 2 (
H_MSG_TRUE)
. If necessary, an exception is
raised.
Possible Predecessors
train_class_mlptrain_class_mlpTrainClassMlpTrainClassMlpTrainClassMlptrain_class_mlp,
read_class_mlpread_class_mlpReadClassMlpReadClassMlpReadClassMlpread_class_mlp
Alternatives
classify_class_mlpclassify_class_mlpClassifyClassMlpClassifyClassMlpClassifyClassMlpclassify_class_mlp
See also
create_class_mlpcreate_class_mlpCreateClassMlpCreateClassMlpCreateClassMlpcreate_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