create_dl_layer_concatT_create_dl_layer_concatCreateDlLayerConcatCreateDlLayerConcatcreate_dl_layer_concat (Operator)

Name

create_dl_layer_concatT_create_dl_layer_concatCreateDlLayerConcatCreateDlLayerConcatcreate_dl_layer_concat — Create a concatenation layer.

Signature

Description

The operator create_dl_layer_concatcreate_dl_layer_concatCreateDlLayerConcatCreateDlLayerConcatCreateDlLayerConcatcreate_dl_layer_concat creates a concatenation layer whose handle is returned in DLLayerConcatDLLayerConcatDLLayerConcatDLLayerConcatDLLayerConcatdllayer_concat.

The parameter DLLayerInputsDLLayerInputsDLLayerInputsDLLayerInputsDLLayerInputsdllayer_inputs determines the feeding input layers.

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 an unique name.

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

'batch'"batch""batch""batch""batch""batch":

Concatenation is applied along the batch-dimension.

Example: if you concatenate two inputs A and B of shape (b, d, h, w) = (2, 1, 1, 1), where A = [A0, A1] and B = [B0, B1], you obtain the output [A0, A1, B0, B1] with shape (4, 1, 1, 1).

'batch_interleaved'"batch_interleaved""batch_interleaved""batch_interleaved""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 axis, all inputs need to have exactly the same shape.

Note that when the input batch_size is 1, the axes 'batch'"batch""batch""batch""batch""batch" and 'batch_interleaved'"batch_interleaved""batch_interleaved""batch_interleaved""batch_interleaved""batch_interleaved" are identical.

Example: if you concatenate two inputs A and B of shape (b, d, h, w) = (2, 1, 1, 1), where A = [A0, A1] and B = [B0, B1], you obtain the output [A0, A1, B0, B1] with shape (4, 1, 1, 1).

'depth'"depth""depth""depth""depth""depth":

Concatenation is applied along the depth-dimension.

Example: if you concatenate two inputs A and B of shape (b, d, h, w) = (2, 1, 1, 1), where A = [A0, A1] and B = [B0, B1], you obtain the output [A0, A1, B0, B1] with shape (2, 2, 1, 1).

'height'"height""height""height""height""height":

Concatenation is applied along the height-dimension.

'width'"width""width""width""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 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_concatcreate_dl_layer_concatCreateDlLayerConcatCreateDlLayerConcatCreateDlLayerConcatcreate_dl_layer_concat 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