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get_params_class_mlpGetParamsClassMlpGetParamsClassMlpget_params_class_mlpT_get_params_class_mlp🔗

Short description🔗

get_params_class_mlpGetParamsClassMlpGetParamsClassMlpget_params_class_mlpT_get_params_class_mlp — Return the parameters of a multilayer perceptron.

Signature🔗

get_params_class_mlp( class_mlp MLPHandle, out integer NumInput, out integer NumHidden, out integer NumOutput, out string OutputFunction, out string Preprocessing, out integer NumComponents )void GetParamsClassMlp( const HTuple& MLPHandle, HTuple* NumInput, HTuple* NumHidden, HTuple* NumOutput, HTuple* OutputFunction, HTuple* Preprocessing, HTuple* NumComponents )static void HOperatorSet.GetParamsClassMlp( HTuple MLPHandle, out HTuple numInput, out HTuple numHidden, out HTuple numOutput, out HTuple outputFunction, out HTuple preprocessing, out HTuple numComponents )def get_params_class_mlp( mlphandle: HHandle ) -> Tuple[int, int, int, str, str, int]

Herror T_get_params_class_mlp( 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

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

Description🔗

get_params_class_mlpGetParamsClassMlp returns the parameters of a multilayer perceptron (MLP) that were specified when the MLP was created with create_class_mlpCreateClassMlp. This is particularly useful if the MLP was read from a file with read_class_mlpReadClassMlp. The output of get_params_class_mlpGetParamsClassMlp 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_mlpCreateClassMlp.

Execution information🔗

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🔗

MLPHandleMLPHandlemlphandle (input_control) class_mlp → (handle)HTuple (HHandle)HClassMlp, HTuple (IntPtr)HHandleHtuple (handle)

MLP handle.

NumInputnumInputnum_input (output_control) integer → (integer)HTuple (Hlong)HTuple (int / long)intHtuple (Hlong)

Number of input variables (features) of the MLP.

NumHiddennumHiddennum_hidden (output_control) integer → (integer)HTuple (Hlong)HTuple (int / long)intHtuple (Hlong)

Number of hidden units of the MLP.

NumOutputnumOutputnum_output (output_control) integer → (integer)HTuple (Hlong)HTuple (int / long)intHtuple (Hlong)

Number of output variables (classes) of the MLP.

OutputFunctionoutputFunctionoutput_function (output_control) string → (string)HTuple (HString)HTuple (string)strHtuple (char*)

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

Preprocessingpreprocessingpreprocessing (output_control) string → (string)HTuple (HString)HTuple (string)strHtuple (char*)

Type of preprocessing used to transform the feature vectors.

NumComponentsnumComponentsnum_components (output_control) integer → (integer)HTuple (Hlong)HTuple (int / long)intHtuple (Hlong)

Preprocessing parameter: Number of transformed features.

Result🔗

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

Combinations with other operators🔗

Combinations

Possible predecessors

create_class_mlpCreateClassMlp, read_class_mlpReadClassMlp

Possible successors

add_sample_class_mlpAddSampleClassMlp, train_class_mlpTrainClassMlp

See also

evaluate_class_mlpEvaluateClassMlp, classify_class_mlpClassifyClassMlp

Module🔗

Foundation