get_params_class_mlpT_get_params_class_mlpGetParamsClassMlpGetParamsClassMlpget_params_class_mlp (Operator)

Name

get_params_class_mlpT_get_params_class_mlpGetParamsClassMlpGetParamsClassMlpget_params_class_mlp — Return the parameters of a multilayer perceptron.

Signature

get_params_class_mlp( : : MLPHandle : NumInput, NumHidden, NumOutput, OutputFunction, Preprocessing, NumComponents)

Herror T_get_params_class_mlp(const Htuple MLPHandle, Htuple* NumInput, Htuple* NumHidden, Htuple* NumOutput, Htuple* OutputFunction, Htuple* Preprocessing, Htuple* NumComponents)

void GetParamsClassMlp(const HTuple& MLPHandle, HTuple* NumInput, HTuple* NumHidden, HTuple* NumOutput, HTuple* OutputFunction, HTuple* Preprocessing, HTuple* NumComponents)

Hlong HClassMlp::GetParamsClassMlp(Hlong* NumHidden, Hlong* NumOutput, HString* OutputFunction, HString* Preprocessing, Hlong* NumComponents) const

static void HOperatorSet.GetParamsClassMlp(HTuple MLPHandle, out HTuple numInput, out HTuple numHidden, out HTuple numOutput, out HTuple outputFunction, out HTuple preprocessing, out HTuple numComponents)

int HClassMlp.GetParamsClassMlp(out int numHidden, out int numOutput, out string outputFunction, out string preprocessing, out int numComponents)

def get_params_class_mlp(mlphandle: HHandle) -> Tuple[int, int, int, str, str, int]

Description

get_params_class_mlpget_params_class_mlpGetParamsClassMlpGetParamsClassMlpGetParamsClassMlpget_params_class_mlp returns the parameters of a multilayer perceptron (MLP) that were specified when the MLP was created with create_class_mlpcreate_class_mlpCreateClassMlpCreateClassMlpCreateClassMlpcreate_class_mlp. This is particularly useful if the MLP was read from a file with read_class_mlpread_class_mlpReadClassMlpReadClassMlpReadClassMlpread_class_mlp. The output of get_params_class_mlpget_params_class_mlpGetParamsClassMlpGetParamsClassMlpGetParamsClassMlpget_params_class_mlp can, for example, be used to check whether the feature vectors and, if necessary, the target data to be used with the MLP have the correct lengths. For a description of the parameters, see create_class_mlpcreate_class_mlpCreateClassMlpCreateClassMlpCreateClassMlpcreate_class_mlp.

Execution Information

Parameters

MLPHandleMLPHandleMLPHandleMLPHandleMLPHandlemlphandle (input_control)  class_mlp HClassMlp, HTupleHHandleHTupleHtuple (handle) (IntPtr) (HHandle) (handle)

MLP handle.

NumInputNumInputNumInputNumInputnumInputnum_input (output_control)  integer HTupleintHTupleHtuple (integer) (int / long) (Hlong) (Hlong)

Number of input variables (features) of the MLP.

NumHiddenNumHiddenNumHiddenNumHiddennumHiddennum_hidden (output_control)  integer HTupleintHTupleHtuple (integer) (int / long) (Hlong) (Hlong)

Number of hidden units of the MLP.

NumOutputNumOutputNumOutputNumOutputnumOutputnum_output (output_control)  integer HTupleintHTupleHtuple (integer) (int / long) (Hlong) (Hlong)

Number of output variables (classes) of the MLP.

OutputFunctionOutputFunctionOutputFunctionOutputFunctionoutputFunctionoutput_function (output_control)  string HTuplestrHTupleHtuple (string) (string) (HString) (char*)

Type of the activation function in the output layer of the MLP.

PreprocessingPreprocessingPreprocessingPreprocessingpreprocessingpreprocessing (output_control)  string HTuplestrHTupleHtuple (string) (string) (HString) (char*)

Type of preprocessing used to transform the feature vectors.

NumComponentsNumComponentsNumComponentsNumComponentsnumComponentsnum_components (output_control)  integer HTupleintHTupleHtuple (integer) (int / long) (Hlong) (Hlong)

Preprocessing parameter: Number of transformed features.

Result

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

Possible Predecessors

create_class_mlpcreate_class_mlpCreateClassMlpCreateClassMlpCreateClassMlpcreate_class_mlp, read_class_mlpread_class_mlpReadClassMlpReadClassMlpReadClassMlpread_class_mlp

Possible Successors

add_sample_class_mlpadd_sample_class_mlpAddSampleClassMlpAddSampleClassMlpAddSampleClassMlpadd_sample_class_mlp, train_class_mlptrain_class_mlpTrainClassMlpTrainClassMlpTrainClassMlptrain_class_mlp

See also

evaluate_class_mlpevaluate_class_mlpEvaluateClassMlpEvaluateClassMlpEvaluateClassMlpevaluate_class_mlp, classify_class_mlpclassify_class_mlpClassifyClassMlpClassifyClassMlpClassifyClassMlpclassify_class_mlp

Module

Foundation