create_dl_layer_lrnT_create_dl_layer_lrnCreateDlLayerLrnCreateDlLayerLrncreate_dl_layer_lrn (Operator)

Name

create_dl_layer_lrnT_create_dl_layer_lrnCreateDlLayerLrnCreateDlLayerLrncreate_dl_layer_lrn — Create a LRN layer.

Signature

Description

The operator create_dl_layer_lrncreate_dl_layer_lrnCreateDlLayerLrnCreateDlLayerLrnCreateDlLayerLrncreate_dl_layer_lrn creates a local response normalization layer which performs normalization over a local window and whose handle is returned in DLLayerLRNDLLayerLRNDLLayerLRNDLLayerLRNDLLayerLRNdllayer_lrn. Currently, for NormRegionNormRegionNormRegionNormRegionnormRegionnorm_region only 'across_channels'"across_channels""across_channels""across_channels""across_channels""across_channels" can be set, which results in a normalization across the channel dimension. More detailed, a value located in a channel with index is normalized with a scale factor depending on a local window,

where is the size of the local window given by LocalSizeLocalSizeLocalSizeLocalSizelocalSizelocal_size, is the total number of channels, AlphaAlphaAlphaAlphaalphaalpha is the scaling parameter (used as a normalization constant), BetaBetaBetaBetabetabeta is the exponent used as a contrast constant, and KKKKkk is a constant summand, which is used to avoid any singularities.

The parameter DLLayerInputDLLayerInputDLLayerInputDLLayerInputDLLayerInputdllayer_input determines the feeding input layer and expects the layer handle as value.

The parameter LayerNameLayerNameLayerNameLayerNamelayerNamelayer_name sets an individual layer name. Note that if creating a model using create_dl_modelcreate_dl_modelCreateDlModelCreateDlModelCreateDlModelcreate_dl_model each layer of the created network must have a unique name.

The following generic parameters GenParamNameGenParamNameGenParamNameGenParamNamegenParamNamegen_param_name and the corresponding values GenParamValueGenParamValueGenParamValueGenParamValuegenParamValuegen_param_value are supported:

'is_inference_output'"is_inference_output""is_inference_output""is_inference_output""is_inference_output""is_inference_output":

Determines whether apply_dl_modelapply_dl_modelApplyDlModelApplyDlModelApplyDlModelapply_dl_model will include the output of this layer in the dictionary DLResultBatchDLResultBatchDLResultBatchDLResultBatchDLResultBatchdlresult_batch even without specifying this layer in OutputsOutputsOutputsOutputsoutputsoutputs ('true'"true""true""true""true""true") or not ('false'"false""false""false""false""false").

Default: 'false'"false""false""false""false""false"

'num_trainable_params'"num_trainable_params""num_trainable_params""num_trainable_params""num_trainable_params""num_trainable_params":

Number of trainable parameters (weights and biases) of the layer.

Certain parameters of layers created using this operator create_dl_layer_lrncreate_dl_layer_lrnCreateDlLayerLrnCreateDlLayerLrnCreateDlLayerLrncreate_dl_layer_lrn can be set and retrieved using further operators. The following tables give an overview, which parameters can be set using set_dl_model_layer_paramset_dl_model_layer_paramSetDlModelLayerParamSetDlModelLayerParamSetDlModelLayerParamset_dl_model_layer_param and which ones can be retrieved using get_dl_model_layer_paramget_dl_model_layer_paramGetDlModelLayerParamGetDlModelLayerParamGetDlModelLayerParamget_dl_model_layer_param or get_dl_layer_paramget_dl_layer_paramGetDlLayerParamGetDlLayerParamGetDlLayerParamget_dl_layer_param. Note, the operators set_dl_model_layer_paramset_dl_model_layer_paramSetDlModelLayerParamSetDlModelLayerParamSetDlModelLayerParamset_dl_model_layer_param and get_dl_model_layer_paramget_dl_model_layer_paramGetDlModelLayerParamGetDlModelLayerParamGetDlModelLayerParamget_dl_model_layer_param require a model created by create_dl_modelcreate_dl_modelCreateDlModelCreateDlModelCreateDlModelcreate_dl_model.

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

Module

Deep Learning Training