create_dl_layer_inputT_create_dl_layer_inputCreateDlLayerInputCreateDlLayerInputcreate_dl_layer_input (Operator)

Name

create_dl_layer_inputT_create_dl_layer_inputCreateDlLayerInputCreateDlLayerInputcreate_dl_layer_input — Create an input layer.

Signature

Description

The operator create_dl_layer_inputcreate_dl_layer_inputCreateDlLayerInputCreateDlLayerInputcreate_dl_layer_input creates an input layer with spatial dimensions given by ShapeShapeShapeshapeshape whose handle is returned in DLLayerInputDLLayerInputDLLayerInputDLLayerInputdllayer_input.

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

When the created model is applied using e.g., apply_dl_modelapply_dl_modelApplyDlModelApplyDlModelapply_dl_model or train_dl_model_batchtrain_dl_model_batchTrainDlModelBatchTrainDlModelBatchtrain_dl_model_batch, it must be possible to map an input with its corresponding input layer. Operators applying a model expect a feeding dictionary DLSample, see Deep Learning / Model. The mentioned mapping is done using dictionary entries, where the key matches the input layer name. Thus, for an input of this layer a sample dictionary will need an entry with the key LayerNameLayerNameLayerNamelayerNamelayer_name (except if the 'input_type'"input_type""input_type""input_type""input_type" is set to 'constant'"constant""constant""constant""constant", see below).

The parameter ShapeShapeShapeshapeshape defines the shape of the input values (the values given in the feeding dictionary DLSample) and must be a tuple of length three, containing width, height, and depth of the input. The tuple values must be given as integer values and have have different meaning depending on the input type:

• for an input image the layer ShapeShapeShapeshapeshape defines the image size. Images shall be given with type real (for information on image types see Image).

• for an input tuple its length will need to match the product of the individual values in ShapeShapeShapeshapeshape, i.e., width height depth.

  Tuple values are distributed along the column- (width), row- (height), and depth-axes in this order. Input tuple values can be given either as integer or real.

The batch size has to be set later with set_dl_model_paramset_dl_model_paramSetDlModelParamSetDlModelParamset_dl_model_param, once the model has been created by create_dl_modelcreate_dl_modelCreateDlModelCreateDlModelcreate_dl_model.

The following generic parameters GenParamNameGenParamNameGenParamNamegenParamNamegen_param_name and the corresponding values GenParamValueGenParamValueGenParamValuegenParamValuegen_param_value are supported:

'allow_smaller_tuple'"allow_smaller_tuple""allow_smaller_tuple""allow_smaller_tuple""allow_smaller_tuple":

For tuple inputs, setting 'allow_smaller_tuple'"allow_smaller_tuple""allow_smaller_tuple""allow_smaller_tuple""allow_smaller_tuple" to 'true'"true""true""true""true" allows to have an input tuple with less values than the total dimension given by ShapeShapeShapeshapeshape. E.g., this can be the case if an input corresponds to the number of objects within one image and the number of objects changes from image to image. If fewer than the maximum number of values given by the total dimension of ShapeShapeShapeshapeshape are present, the remaining values are set to zero.

ShapeShapeShapeshapeshape should be set such that it fits the maximum expected length. For the example above this would be the maximum number of objects within one image present in the whole dataset.

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

'const_val'"const_val""const_val""const_val""const_val":

Constant output value.

Restriction: Only an integer or float is settable. This value is only settable or gettable if 'input_type'"input_type""input_type""input_type""input_type" is set to 'constant'"constant""constant""constant""constant".

Default: 0.0.

'input_type'"input_type""input_type""input_type""input_type":

Defines the type of input that is expected. The following values are possible:

'default'"default""default""default""default":

The layer expects a number of input images corresponding to the batch size.

'region_to_bin'"region_to_bin""region_to_bin""region_to_bin""region_to_bin":

The layer expects a tuple of regions as input and internally converts it to a binary image where each region is encoded in one depth channel. Regions reaching out of the given dimensions are clipped to the width and height given by ShapeShapeShapeshapeshape. The maximum number of regions is defined by the depth of ShapeShapeShapeshapeshape. If fewer than the maximum number of regions are given, the output is filled up with empty (zero) images. For example, this can be the case if the regions are corresponding to objects within an image and the number of objects changes from image to image.

'constant'"constant""constant""constant""constant":

The layer does not expect any key value pair in the input dictionary. Instead all entries within the output of this layer are filled with the value given by 'const_val'"const_val""const_val""const_val""const_val".

Default: 'default'"default""default""default""default".

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

Determines whether apply_dl_modelapply_dl_modelApplyDlModelApplyDlModelapply_dl_model will include the output of this layer in the dictionary DLResultBatchDLResultBatchDLResultBatchDLResultBatchdlresult_batch even without specifying this layer in OutputsOutputsOutputsoutputsoutputs ('true'"true""true""true""true") or not ('false'"false""false""false""false").

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

Certain parameters of layers created using create_dl_layer_inputcreate_dl_layer_inputCreateDlLayerInputCreateDlLayerInputcreate_dl_layer_input 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_paramSetDlModelLayerParamSetDlModelLayerParamset_dl_model_layer_param and which ones can be retrieved using get_dl_model_layer_paramget_dl_model_layer_paramGetDlModelLayerParamGetDlModelLayerParamget_dl_model_layer_param or get_dl_layer_paramget_dl_layer_paramGetDlLayerParamGetDlLayerParamget_dl_layer_param. Note, the operators set_dl_model_layer_paramset_dl_model_layer_paramSetDlModelLayerParamSetDlModelLayerParamset_dl_model_layer_param and get_dl_model_layer_paramget_dl_model_layer_paramGetDlModelLayerParamGetDlModelLayerParamget_dl_model_layer_param require a model created by create_dl_modelcreate_dl_modelCreateDlModelCreateDlModelcreate_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.

This operator returns a handle. Note that the state of an instance of this handle type may be changed by specific operators even though the handle is used as an input parameter by those operators.

Parameters

Example (HDevelop)

* Create a model for summation.
create_dl_layer_input ('input_a', [2, 3, 4], [], [], DLLayerInputA)
create_dl_layer_input ('input_b', [2, 3, 4], [], [], DLLayerInputB)
create_dl_layer_elementwise ([DLLayerInputA, DLLayerInputB], 'sum', \
                              'sum', [], [], [], DLLayerElementWise)
create_dl_model (DLLayerElementWise, DLModel)
set_dl_model_param (DLModel, 'runtime', 'cpu')
*
* Add 'input_a' as an inference model output.
set_dl_model_layer_param (DLModel, 'input_a', 'is_inference_output', 'true')
*
* Feed input data as tuple (a) or image (b).
create_dict (Sample)
set_dict_tuple (Sample, 'input_a', [1:(2*3*4)])
gen_empty_obj (InputB)
for I := 1 to 4 by 1
    gen_image_const (Channel, 'real', 2, 3)
    get_region_points (Channel, Rows, Cols)
    set_grayval (Channel, Rows, Cols, gen_tuple_const(|Rows|, I))
    append_channel (InputB, Channel, InputB)
endfor
set_dict_object (InputB, Sample, 'input_b')
*
* Apply the model for summation and get results.
set_dl_model_param (DLModel, 'batch_size', 2)
apply_dl_model (DLModel, [Sample,Sample], [], Result)
get_dict_object (Sum, Result[0], 'sum')
get_dict_object (TupleInputA, Result[1], 'input_a')

Possible Successors

create_dl_layer_activationcreate_dl_layer_activationCreateDlLayerActivationCreateDlLayerActivationcreate_dl_layer_activation, create_dl_layer_batch_normalizationcreate_dl_layer_batch_normalizationCreateDlLayerBatchNormalizationCreateDlLayerBatchNormalizationcreate_dl_layer_batch_normalization, create_dl_layer_class_id_conversioncreate_dl_layer_class_id_conversionCreateDlLayerClassIdConversionCreateDlLayerClassIdConversioncreate_dl_layer_class_id_conversion, create_dl_layer_class_id_conversioncreate_dl_layer_class_id_conversionCreateDlLayerClassIdConversionCreateDlLayerClassIdConversioncreate_dl_layer_class_id_conversion, create_dl_layer_concatcreate_dl_layer_concatCreateDlLayerConcatCreateDlLayerConcatcreate_dl_layer_concat, create_dl_layer_convolutioncreate_dl_layer_convolutionCreateDlLayerConvolutionCreateDlLayerConvolutioncreate_dl_layer_convolution, create_dl_layer_densecreate_dl_layer_denseCreateDlLayerDenseCreateDlLayerDensecreate_dl_layer_dense, create_dl_layer_depth_maxcreate_dl_layer_depth_maxCreateDlLayerDepthMaxCreateDlLayerDepthMaxcreate_dl_layer_depth_max, create_dl_layer_dropoutcreate_dl_layer_dropoutCreateDlLayerDropoutCreateDlLayerDropoutcreate_dl_layer_dropout, create_dl_layer_elementwisecreate_dl_layer_elementwiseCreateDlLayerElementwiseCreateDlLayerElementwisecreate_dl_layer_elementwise, create_dl_layer_loss_cross_entropycreate_dl_layer_loss_cross_entropyCreateDlLayerLossCrossEntropyCreateDlLayerLossCrossEntropycreate_dl_layer_loss_cross_entropy, create_dl_layer_loss_ctccreate_dl_layer_loss_ctcCreateDlLayerLossCtcCreateDlLayerLossCtccreate_dl_layer_loss_ctc, create_dl_layer_loss_distancecreate_dl_layer_loss_distanceCreateDlLayerLossDistanceCreateDlLayerLossDistancecreate_dl_layer_loss_distance, create_dl_layer_loss_focalcreate_dl_layer_loss_focalCreateDlLayerLossFocalCreateDlLayerLossFocalcreate_dl_layer_loss_focal, create_dl_layer_loss_hubercreate_dl_layer_loss_huberCreateDlLayerLossHuberCreateDlLayerLossHubercreate_dl_layer_loss_huber, create_dl_layer_lrncreate_dl_layer_lrnCreateDlLayerLrnCreateDlLayerLrncreate_dl_layer_lrn, create_dl_layer_poolingcreate_dl_layer_poolingCreateDlLayerPoolingCreateDlLayerPoolingcreate_dl_layer_pooling, create_dl_layer_reducecreate_dl_layer_reduceCreateDlLayerReduceCreateDlLayerReducecreate_dl_layer_reduce, create_dl_layer_reshapecreate_dl_layer_reshapeCreateDlLayerReshapeCreateDlLayerReshapecreate_dl_layer_reshape, create_dl_layer_softmaxcreate_dl_layer_softmaxCreateDlLayerSoftmaxCreateDlLayerSoftmaxcreate_dl_layer_softmax, create_dl_layer_transposed_convolutioncreate_dl_layer_transposed_convolutionCreateDlLayerTransposedConvolutionCreateDlLayerTransposedConvolutioncreate_dl_layer_transposed_convolution, create_dl_layer_zoom_factorcreate_dl_layer_zoom_factorCreateDlLayerZoomFactorCreateDlLayerZoomFactorcreate_dl_layer_zoom_factor, create_dl_layer_zoom_sizecreate_dl_layer_zoom_sizeCreateDlLayerZoomSizeCreateDlLayerZoomSizecreate_dl_layer_zoom_size, create_dl_layer_zoom_to_layer_sizecreate_dl_layer_zoom_to_layer_sizeCreateDlLayerZoomToLayerSizeCreateDlLayerZoomToLayerSizecreate_dl_layer_zoom_to_layer_size

Module

Deep Learning Professional