Operator Reference
create_dl_layer_elementwise (Operator)
create_dl_layer_elementwise — Create an elementwise layer.
Signature
create_dl_layer_elementwise( : : DLLayerInputs, LayerName, Operation, Coefficients, GenParamName, GenParamValue : DLLayerElementWise)
Description
The operator create_dl_layer_elementwise creates an element-wise
layer whose handle is returned in DLLayerElementWise.
An elementwise layer applies a certain operation to every data tensor of the input layers handles and to each element of the data tensor. As a consequence, all input data tensors should be of the same shape and the output tensor has the same shape as the first input tensor.
The parameter DLLayerInputs determines the feeding input layers.
This layer expects multiple layers as input. For Operation =
'division' exactly two input layers are expected.
The parameter LayerName sets an individual layer name.
Note that if creating a model using create_dl_model each layer of
the created network must have a unique name.
The parameter Operation specifies the operation that is applied.
Depending on Operation, the layer supports implicit broadcasting.
I.e., if one of the shape dimensions
(batch_size, depth, height, width)
of the second or any of the following input tensors is 1, the values are
implicitly multiplied along that dimension to match the shape of the first
input.
The supported values are:
-
'division': Element-wise division. Broadcasting is fully supported.
-
'maximum': Element-wise maximum. Broadcasting is fully supported.
-
'minimum': Element-wise minimum. Broadcasting is fully supported.
-
'product': Element-wise product. Broadcasting is supported, but all inputs following the second input must have the same shape as the second input.
-
'sum': Element-wise summation. Broadcasting is not supported.
The optional parameter Coefficients determines a weighting
coefficient for every input tensor.
The number of values in Coefficients must match the number of feeding
layers in DLLayerInputs.
Set Coefficients equal to [] if
no coefficients shall be used in the element-wise operation.
Restriction:
No coefficients can be set for
Operation = 'product'.
Example: for Operation = 'sum',
the -th element of the output data tensor is given by
where is the number of input data tensors.
The following generic parameters GenParamName and the corresponding
values GenParamValue are supported:
- 'div_eps':
-
Small scalar value that is added to the elements of the denominator to avoid a division by zero (for
Operation= 'division').Default: 1e-10
- 'is_inference_output':
-
Determines whether
apply_dl_modelwill include the output of this layer in the dictionaryDLResultBatcheven without specifying this layer inOutputs('true') or not ('false').Default: 'false'
Certain parameters of layers created using this operator
create_dl_layer_elementwise can be set and retrieved using
further operators.
The following tables give an overview, which parameters can be set
using set_dl_model_layer_param and which ones can be retrieved
using get_dl_model_layer_param or get_dl_layer_param.
Note, the operators set_dl_model_layer_param and
get_dl_model_layer_param require a model created by
create_dl_model.
| Layer Parameters | set |
get |
|---|---|---|
'coefficients' (Coefficients) |
x
|
|
'input_layer' (DLLayerInputs) |
x
|
|
'name' (LayerName) |
x |
x
|
'operation' (Operation) |
x
|
|
'output_layer' (DLLayerElementWise) |
x
|
|
| 'shape' | x
|
|
| 'type' | x
|
| Generic Layer Parameters | set |
get |
|---|---|---|
| 'div_eps' | x |
x
|
| 'is_inference_output' | x |
x
|
| 'num_trainable_params' | x
|
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
DLLayerInputs (input_control) dl_layer(-array) → (handle)
Feeding input layers.
LayerName (input_control) string → (string)
Name of the output layer.
Operation (input_control) string → (string)
Element-wise operations.
Default: 'sum'
List of values: 'division', 'maximum', 'minimum', 'product', 'sum'
Coefficients (input_control) number(-array) → (real)
Optional input tensor coefficients.
Default: []
GenParamName (input_control) attribute.name(-array) → (string)
Generic input parameter names.
Default: []
List of values: 'is_inference_output'
GenParamValue (input_control) attribute.value(-array) → (string / integer / real)
Generic input parameter values.
Default: []
Suggested values: 'true', 'false'
DLLayerElementWise (output_control) dl_layer → (handle)
Elementwise layer.
Module
Deep Learning Professional