set_dl_model_paramT_set_dl_model_paramSetDlModelParamSetDlModelParam (Operator)

Name

set_dl_model_paramT_set_dl_model_paramSetDlModelParamSetDlModelParam — Set the parameters of a deep learning model.

Signature

Description

set_dl_model_paramset_dl_model_paramSetDlModelParamSetDlModelParamSetDlModelParam sets the parameters and hyperparameters GenParamNameGenParamNameGenParamNameGenParamNamegenParamName of the deep learning model DLModelHandleDLModelHandleDLModelHandleDLModelHandleDLModelHandle to the values GenParamValueGenParamValueGenParamValueGenParamValuegenParamValue.

The values GenParamNameGenParamNameGenParamNameGenParamNamegenParamName can attain, depend on the model type: There are parameters which can be set for any deep learning model while others can only be set for specific model types. A description of the specific parameters is given in get_dl_model_paramget_dl_model_paramGetDlModelParamGetDlModelParamGetDlModelParam, with the only exception 'runtime_init'"runtime_init""runtime_init""runtime_init""runtime_init", as you can only set its value but not retrieve it. In get_dl_model_paramget_dl_model_paramGetDlModelParamGetDlModelParamGetDlModelParam we also give an overview, for which type of model and using which operator a parameter can be set.

In the following we list the parameters GenParamNameGenParamNameGenParamNameGenParamNamegenParamName you can set using this operator, set_dl_model_paramset_dl_model_paramSetDlModelParamSetDlModelParamSetDlModelParam, sorted according to the model type.

Any Model

• 'batch_size'"batch_size""batch_size""batch_size""batch_size"

• 'class_ids'"class_ids""class_ids""class_ids""class_ids"

• 'gpu'"gpu""gpu""gpu""gpu"

• 'learning_rate'"learning_rate""learning_rate""learning_rate""learning_rate"

• 'momentum'"momentum""momentum""momentum""momentum"

• 'runtime'"runtime""runtime""runtime""runtime"

• 'runtime_init'"runtime_init""runtime_init""runtime_init""runtime_init": If called with 'immediately'"immediately""immediately""immediately""immediately", the GPU memory is initialized and the corresponding handle created. Otherwise this is done on demand, which may result in significantly larger execution times for the first call of apply_dl_modelapply_dl_modelApplyDlModelApplyDlModelApplyDlModel or train_dl_model_batchtrain_dl_model_batchTrainDlModelBatchTrainDlModelBatchTrainDlModelBatch.

  If the network architecture is changed subsequently, the GPU memory is reinitialized. This can happen e.g., for changes of 'batch_size'"batch_size""batch_size""batch_size""batch_size" or 'image_dimensions'"image_dimensions""image_dimensions""image_dimensions""image_dimensions" with subsequent calls of set_dl_model_paramset_dl_model_paramSetDlModelParamSetDlModelParamSetDlModelParam.

  Note, this parameter has no effect if running on CPUs, thus if 'runtime'"runtime""runtime""runtime""runtime" is set to 'cpu'"cpu""cpu""cpu""cpu".

• 'weight_prior'"weight_prior""weight_prior""weight_prior""weight_prior"

Models of 'type'"type""type""type""type"='detection'"detection""detection""detection""detection"

• 'max_overlap'"max_overlap""max_overlap""max_overlap""max_overlap"

• 'max_overlap_class_agnostic'"max_overlap_class_agnostic""max_overlap_class_agnostic""max_overlap_class_agnostic""max_overlap_class_agnostic"

• 'max_num_detections'"max_num_detections""max_num_detections""max_num_detections""max_num_detections"

• 'min_confidence'"min_confidence""min_confidence""min_confidence""min_confidence"

Models of 'type'"type""type""type""type"='segmentation'"segmentation""segmentation""segmentation""segmentation"

• 'ignore_class_ids'"ignore_class_ids""ignore_class_ids""ignore_class_ids""ignore_class_ids"

• 'image_dimensions'"image_dimensions""image_dimensions""image_dimensions""image_dimensions"

• 'image_height'"image_height""image_height""image_height""image_height", 'image_width'"image_width""image_width""image_width""image_width"

• 'image_num_channels'"image_num_channels""image_num_channels""image_num_channels""image_num_channels"

• 'image_range_max'"image_range_max""image_range_max""image_range_max""image_range_max", 'image_range_min'"image_range_min""image_range_min""image_range_min""image_range_min"

Attention

To successfully set 'gpu'"gpu""gpu""gpu""gpu" parameters, cuDNN and cuBLAS are required, i.e., to set the parameter GenParamNameGenParamNameGenParamNameGenParamNamegenParamName 'runtime'"runtime""runtime""runtime""runtime" to 'gpu'"gpu""gpu""gpu""gpu". For further details, please refer to the “Installation Guide”, paragraph “Requirements for Deep Learning”.

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

Result

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

Possible Predecessors

read_dl_modelread_dl_modelReadDlModelReadDlModelReadDlModel, get_dl_model_paramget_dl_model_paramGetDlModelParamGetDlModelParamGetDlModelParam

Possible Successors

get_dl_model_paramget_dl_model_paramGetDlModelParamGetDlModelParamGetDlModelParam, apply_dl_modelapply_dl_modelApplyDlModelApplyDlModelApplyDlModel, train_dl_model_batchtrain_dl_model_batchTrainDlModelBatchTrainDlModelBatchTrainDlModelBatch

See also

get_dl_model_paramget_dl_model_paramGetDlModelParamGetDlModelParamGetDlModelParam

Module

Deep Learning Inference