set_dl_model_layer_weightsT_set_dl_model_layer_weightsSetDlModelLayerWeightsSetDlModelLayerWeightsset_dl_model_layer_weights (Operator)

Name

set_dl_model_layer_weightsT_set_dl_model_layer_weightsSetDlModelLayerWeightsSetDlModelLayerWeightsset_dl_model_layer_weights — Set the weights (or values) of a Deep Learning model layer.

Signature

set_dl_model_layer_weights(Weights : : DLModelHandle, LayerName, WeightsType : )

Description

The operator set_dl_model_layer_weightsset_dl_model_layer_weightsSetDlModelLayerWeightsSetDlModelLayerWeightsSetDlModelLayerWeightsset_dl_model_layer_weights sets for the model DLModelHandleDLModelHandleDLModelHandleDLModelHandleDLModelHandledlmodel_handle the given WeightsWeightsWeightsWeightsweightsweights in the specified LayerNameLayerNameLayerNameLayerNamelayerNamelayer_name.

The parameter WeightsTypeWeightsTypeWeightsTypeWeightsTypeweightsTypeweights_type determines which type of layer values are set. Which values can be set, please refer to the get_dl_model_layer_weightsget_dl_model_layer_weightsGetDlModelLayerWeightsGetDlModelLayerWeightsGetDlModelLayerWeightsget_dl_model_layer_weights documentation.

Attention

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

WeightsWeightsWeightsWeightsweightsweights (input_object) (multichannel-)image(-array) → object (real)

Input weights.

DLModelHandleDLModelHandleDLModelHandleDLModelHandleDLModelHandledlmodel_handle (input_control) dl_model → (handle)

Handle of the deep learning model.

LayerNameLayerNameLayerNameLayerNamelayerNamelayer_name (input_control) string → (string)

Name of the layer, whose weights are to be set.

WeightsTypeWeightsTypeWeightsTypeWeightsTypeweightsTypeweights_type (input_control) string → (string)

Selected type of layer values to be set.

Default: 'weights' "weights" "weights" "weights" "weights" "weights"

List of values: 'batchnorm_mean'"batchnorm_mean""batchnorm_mean""batchnorm_mean""batchnorm_mean""batchnorm_mean", 'batchnorm_mean_avg'"batchnorm_mean_avg""batchnorm_mean_avg""batchnorm_mean_avg""batchnorm_mean_avg""batchnorm_mean_avg", 'batchnorm_variance'"batchnorm_variance""batchnorm_variance""batchnorm_variance""batchnorm_variance""batchnorm_variance", 'batchnorm_variance_avg'"batchnorm_variance_avg""batchnorm_variance_avg""batchnorm_variance_avg""batchnorm_variance_avg""batchnorm_variance_avg", 'bias'"bias""bias""bias""bias""bias", 'weights'"weights""weights""weights""weights""weights"

Example (HDevelop)

* Create weights for a convolution layer.
gen_image_const (Weights, 'real', 1, 1)
paint_region (Weights, Weights, Weights, 1, 'fill')
gen_empty_obj (WeightsArray)
for Index := 0 to 10 by 1
    concat_obj (WeightsArray, Weights, WeightsArray)
endfor
*
* Input image with rows consisting of 1s to 10s.
gen_image_const (Image, 'real', 10, 10)
for Index := 0 to 9 by 1
    gen_rectangle1 (Rectangle, Index, 0, Index, 9)
    paint_region (Rectangle, Image, Image, Index + 1, 'fill')
endfor
*
* Create a small model network.
create_dl_layer_input ('image', [10, 2, 1], [], [], ImageNode)
create_dl_layer_convolution (ImageNode, 'conv', 1, 1, 2, 11, 1, 'none', \
                             'none', [], [], ConvNode)
create_dl_layer_zoom_factor (ConvNode, 'zoom', 2, 2, 'bilinear', 'true', [],\
                             [], ZoomNode)
create_dl_model (ZoomNode, DLModelHandle)
set_dl_model_param (DLModelHandle, 'runtime', 'cpu')
*
* Set the weights to the convolution layer.
set_dl_model_layer_weights (WeightsArray, DLModelHandle, 'conv', 'weights')

Possible Predecessors

create_dl_modelcreate_dl_modelCreateDlModelCreateDlModelCreateDlModelcreate_dl_model, get_dl_model_layer_weightsget_dl_model_layer_weightsGetDlModelLayerWeightsGetDlModelLayerWeightsGetDlModelLayerWeightsget_dl_model_layer_weights

Possible Successors

get_dl_model_layer_weightsget_dl_model_layer_weightsGetDlModelLayerWeightsGetDlModelLayerWeightsGetDlModelLayerWeightsget_dl_model_layer_weights

Module

Foundation. This operator uses dynamic licensing (see the ``Installation Guide''). Which of the following modules is required depends on the specific usage of the operator:
Deep Learning Training

Operators