evaluate_class_mlpT_evaluate_class_mlpEvaluateClassMlpEvaluateClassMlpevaluate_class_mlp (算子)

名称

evaluate_class_mlpT_evaluate_class_mlpEvaluateClassMlpEvaluateClassMlpevaluate_class_mlp — 通过多层感知机计算特征向量的评估值。

签名

描述

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.

执行信息

• 多线程类型：可重入（与非独占算子并行运行）。
• 多线程作用域：全局（可从任何线程调用）。
• 未采用并行化处理。

参数

结果

如果参数有效，算子 evaluate_class_mlpevaluate_class_mlpEvaluateClassMlpEvaluateClassMlpEvaluateClassMlpevaluate_class_mlp 返回值 2 ( H_MSG_TRUE )。如有必要，则抛出异常。

可能的前趋

train_class_mlptrain_class_mlpTrainClassMlpTrainClassMlpTrainClassMlptrain_class_mlp, read_class_mlpread_class_mlpReadClassMlpReadClassMlpReadClassMlpread_class_mlp

替代

classify_class_mlpclassify_class_mlpClassifyClassMlpClassifyClassMlpClassifyClassMlpclassify_class_mlp

另见

create_class_mlpcreate_class_mlpCreateClassMlpCreateClassMlpCreateClassMlpcreate_class_mlp

参考文献

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

模块

基础