classify_class_mlpT_classify_class_mlpClassifyClassMlpClassifyClassMlpclassify_class_mlp (算子)
名称
classify_class_mlpT_classify_class_mlpClassifyClassMlpClassifyClassMlpclassify_class_mlp — 通过多层感知机计算特征向量的类。
签名
def classify_class_mlp(mlphandle: HHandle, features: Sequence[float], num: Sequence[int]) -> Tuple[Sequence[int], Sequence[float]]
def classify_class_mlp_s(mlphandle: HHandle, features: Sequence[float], num: Sequence[int]) -> Tuple[int, float]
描述
classify_class_mlpclassify_class_mlpClassifyClassMlpClassifyClassMlpClassifyClassMlpclassify_class_mlp computes the best NumNumNumNumnumnum classes of
the feature vector FeaturesFeaturesFeaturesFeaturesfeaturesfeatures with the multilayer perceptron
(MLP) MLPHandleMLPHandleMLPHandleMLPHandleMLPHandlemlphandle and returns the classes in ClassClassClassClassclassValclass
and the corresponding confidences (probabilities) of the classes in
ConfidenceConfidenceConfidenceConfidenceconfidenceconfidence. Before calling classify_class_mlpclassify_class_mlpClassifyClassMlpClassifyClassMlpClassifyClassMlpclassify_class_mlp, the
MLP must be trained with train_class_mlptrain_class_mlpTrainClassMlpTrainClassMlpTrainClassMlptrain_class_mlp。
classify_class_mlpclassify_class_mlpClassifyClassMlpClassifyClassMlpClassifyClassMlpclassify_class_mlp can only be called if the MLP is used as
a classifier with OutputFunctionOutputFunctionOutputFunctionOutputFunctionoutputFunctionoutput_function = 'softmax'"softmax""softmax""softmax""softmax""softmax"
(see create_class_mlpcreate_class_mlpCreateClassMlpCreateClassMlpCreateClassMlpcreate_class_mlp). Otherwise, an error message is
returned. classify_class_mlpclassify_class_mlpClassifyClassMlpClassifyClassMlpClassifyClassMlpclassify_class_mlp corresponds to a call to
evaluate_class_mlpevaluate_class_mlpEvaluateClassMlpEvaluateClassMlpEvaluateClassMlpevaluate_class_mlp and an additional step that extracts the
best NumNumNumNumnumnum classes. As described with
evaluate_class_mlpevaluate_class_mlpEvaluateClassMlpEvaluateClassMlpEvaluateClassMlpevaluate_class_mlp, the output values of the MLP can be
interpreted as probabilities of the occurrence of the respective
classes. In most cases it should be sufficient
to use NumNumNumNumnumnum = 1 in order to decide whether the
probability of the best class is high enough. In some applications
it may be interesting to also take the second best class into
account (NumNumNumNumnumnum = 2), particularly if it can be
expected that the classes show a significant degree of overlap.
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
MLPHandleMLPHandleMLPHandleMLPHandleMLPHandlemlphandle (输入控制) class_mlp → HClassMlp, HTupleHHandleHTupleHtuple (handle) (IntPtr) (HHandle) (handle)
MLP 句柄。
FeaturesFeaturesFeaturesFeaturesfeaturesfeatures (输入控制) real-array → HTupleSequence[float]HTupleHtuple (real) (double) (double) (double)
Feature vector.
NumNumNumNumnumnum (输入控制) integer-array → HTupleSequence[int]HTupleHtuple (integer) (int / long) (Hlong) (Hlong)
Number of best classes to determine.
默认值：
1
建议值：
1, 2, 3, 4, 5
ClassClassClassClassclassValclass (输出控制) integer(-array) → HTupleSequence[int]HTupleHtuple (integer) (int / long) (Hlong) (Hlong)
Result of classifying the feature vector with
the MLP.
ConfidenceConfidenceConfidenceConfidenceconfidenceconfidence (输出控制) real(-array) → HTupleSequence[float]HTupleHtuple (real) (double) (double) (double)
Confidence(s) of the class(es) of the feature vector.
结果
如果参数有效，算子 classify_class_mlpclassify_class_mlpClassifyClassMlpClassifyClassMlpClassifyClassMlpclassify_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
apply_dl_classifierapply_dl_classifierApplyDlClassifierApplyDlClassifierApplyDlClassifierapply_dl_classifier,
evaluate_class_mlpevaluate_class_mlpEvaluateClassMlpEvaluateClassMlpEvaluateClassMlpevaluate_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