create_dl_layer_concatCreateDlLayerConcatCreateDlLayerConcatcreate_dl_layer_concatT_create_dl_layer_concat🔗

Short description🔗

create_dl_layer_concatCreateDlLayerConcatCreateDlLayerConcatcreate_dl_layer_concatT_create_dl_layer_concat — Create a concatenation layer.

Signature🔗

create_dl_layer_concat( dl_layer DLLayerInputs, string LayerName, string Axis, attribute.name GenParamName, attribute.value GenParamValue, out dl_layer DLLayerConcat )

Description🔗

The operator create_dl_layer_concatCreateDlLayerConcat creates a concatenation layer whose handle is returned in DLLayerConcatDLLayerConcatdllayer_concat.

The parameter DLLayerInputsDLLayerInputsdllayer_inputs determines the feeding input layers. This layer expects at least one layer as input.

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

A concatenation layer concatenates the data tensors of the input layers in DLLayerInputsDLLayerInputsdllayer_inputs and returns a single data tensor DLLayerConcatDLLayerConcatdllayer_concat. The parameter Axisaxisaxis specifies along which dimension the inputs should be concatenated. The supported options for Axisaxisaxis are:

'batch'"batch": Concatenation is applied along the batch-dimension.

Example: if you concatenate two inputs A and B of shape (h, w, d, b) = (1, 1, 1, 2), where A = [A0, A1] and B = [B0, B1], you obtain the output [A0, A1, B0, B1] with shape (1, 1, 1, 4).
'batch_interleaved'"batch_interleaved": Concatenation is applied along the depth-dimension, but the output is reshaped as if the data was concatenated along the batch-dimension. For this dimension, all inputs need to have exactly the same shape.

Note that when the input batch_size is 11, the concatenation is identical for 'batch'"batch" and 'batch_interleaved'"batch_interleaved".

Example: if you concatenate two inputs A and B of shape (h, w, d, b) = (1, 1, 1, 2), where A = [A0, A1] and B = [B0, B1], you obtain the output [A0, B0, A1, B1] with shape (1, 1, 1, 4).
'depth'"depth": Concatenation is applied along the depth-dimension.

Example: if you concatenate two inputs A and B of shape (h, w, d, b) = (1, 1, 1, 2), where A = [A0, A1] and B = [B0, B1], you obtain the output [A0, A1, B0, B1] with shape (1, 1, 2, 2).
'height'"height": Concatenation is applied along the height-dimension.
'width'"width": Concatenation is applied along the width-dimension.

Note that all non-concatenated dimensions must be equal for all input data tensors.

The following generic parameters GenParamNamegenParamNamegen_param_name and the corresponding values GenParamValuegenParamValuegen_param_value are supported:

'is_inference_output'"is_inference_output": Determines whether apply_dl_modelApplyDlModel will include the output of this layer in the dictionary DLResultBatchDLResultBatchdlresult_batch even without specifying this layer in Outputsoutputsoutputs ('true'"true") or not ('false'"false").

Default: 'false'"false"

Certain parameters of layers created using this operator create_dl_layer_concatCreateDlLayerConcat can be set and retrieved using further operators. The following tables give an overview, which parameters can be set using set_dl_model_layer_paramSetDlModelLayerParam and which ones can be retrieved using get_dl_model_layer_paramGetDlModelLayerParam or get_dl_layer_paramGetDlLayerParam. Note, the operators set_dl_model_layer_paramSetDlModelLayerParam and get_dl_model_layer_paramGetDlModelLayerParam require a model created by create_dl_modelCreateDlModel.

Layer Parameters	`set`	`get`
'input_layer'"input_layer" (`DLLayerInputsDLLayerInputsdllayer_inputs`)		`x`
'name'"name" (`LayerNamelayerNamelayer_name`)	`x`	`x`
'output_layer'"output_layer" (`DLLayerConcatDLLayerConcatdllayer_concat`)		`x`
'shape'"shape"		`x`
'type'"type"		`x`

Generic Layer Parameters	`set`	`get`
'is_inference_output'"is_inference_output"	`x`	`x`
'num_trainable_params'"num_trainable_params"		`x`

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🔗

DLLayerInputsDLLayerInputsdllayer_inputs (input_control) dl_layer(-array) → (handle)HTuple (HHandle)HDlLayer, HTuple (IntPtr)MaybeSequence[HHandle]Htuple (handle)

Feeding input layers.

LayerNamelayerNamelayer_name (input_control) string → (string)HTuple (HString)HTuple (string)strHtuple (char*)

Name of the output layer.

Axisaxisaxis (input_control) string → (string)HTuple (HString)HTuple (string)strHtuple (char*)

Dimension along which the input layers are concatenated.

Default: 'depth'"depth"
List of values: 'batch', 'batch_interleaved', 'depth', 'height', 'width'

GenParamNamegenParamNamegen_param_name (input_control) attribute.name(-array) → (string)HTuple (HString)HTuple (string)MaybeSequence[str]Htuple (char*)

Generic input parameter names.

Default: [][]
List of values: 'is_inference_output'"is_inference_output"

GenParamValuegenParamValuegen_param_value (input_control) attribute.value(-array) → (string / integer / real)HTuple (HString / Hlong / double)HTuple (string / int / long / double)MaybeSequence[Union[int, float, str]]Htuple (char* / Hlong / double)

Generic input parameter values.

Default: [][]
Suggested values: 'true', 'false'"true", "false"

DLLayerConcatDLLayerConcatdllayer_concat (output_control) dl_layer → (handle)HTuple (HHandle)HDlLayer, HTuple (IntPtr)HHandleHtuple (handle)

Concatenation layer.

Module🔗

Deep Learning Professional