exportNetworkToSimulink - Generate Simulink model that contains deep learning layer blocks that correspond to deep learning

  layer objects - MATLAB ([original](https://www.mathworks.com/help/deeplearning/ref/dlnetwork.exportnetworktosimulink.html)) ([raw](?raw))

Generate Simulink model that contains deep learning layer blocks that correspond to deep learning layer objects

Since R2024b

Syntax

Description

[mdlInfo](#mw%5Fc55872a5-322b-4e8e-be20-e59812c07497) = exportNetworkToSimulink([net](#mw%5F1b39a89e-4f15-44de-ad8b-5055dea6cbe1)) creates a Simulink® model for a trained dlnetwork object, net. The model uses a fixed-step solver and contains deep learning layer blocks that correspond to layers in the network. The function returns a structure that contains information about the generated model.

For a list of deep learning layer blocks, see List of Deep Learning Layer Blocks. If the network contains a layer that does not have a corresponding deep learning layer block, the function generates a placeholder subsystem that contains a Stop Simulation (Simulink) block. You can manually replace the Stop Simulation block with an implementation of the layer.

Input Arguments

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net — Trained deep learning network

dlnetwork object

Trained deep learning network to export as a Simulink model, specified as a dlnetwork object.

Data Types: dlnetwork

Name-Value Arguments

Specify optional pairs of arguments asName1=Value1,...,NameN=ValueN, where Name is the argument name and Value is the corresponding value. Name-value arguments must appear after other arguments, but the order of the pairs does not matter.

Example: exportNetworkToSimulink(myNetwork,ModelName="myExportedModel")

ModelName — Name of generated Simulink model

'my_model' (default) | character vector | string

Name of the generated Simulink model, specified as a character vector or string.

Data Types: char | string

ModelPath — Folder path for generated model

character vector | string

Folder path for saving the generated model, specified as a character vector or string. If you do not specify ModelPath, the function saves the model to the current working directory.

Data Types: char | string

InputDataType — Data type for generated inports

character vector | string

Data type for generated inports, specified as a character vector or string. The value must correspond to a data type that is supported as the input type of anInport block, such as 'uint8','single', or 'Inherit: auto'. Other blocks in the generated model inherit the input type from the Inport blocks.

If you do not specify InputDataType, the generated inports use the value 'Inherit: auto'.

Data Types: char | string

ExpandNetworkSubsystem — Flag to expand layer blocks to top level

false (default) | true

Flag to expand layer blocks to the top level of the generated model without wrapping the blocks in a subsystem, specified as a boolean.

Data Types: logical

OpenSystem — Flag to open generated model

false (default) | true

Flag to open the model after generating it, specified as a boolean.

Data Types: logical

SampleTime — Model sample time

-1 (default) | '_`Ts`_' | '[_`Ts`_ 0]'

Sample time to use for the generated model, specified as a character vector or string. To use an inherited sample time, use a character vector or string of the form'_`Ts`_' or'[_`Ts`_ 0]', where_`Ts`_ is the sample time. For more information, see Specify Sample Time (Simulink).

Data Types: char | string

Stateful — Flag to use stateful prediction

false (default) | true

Flag to use stateful prediction, specified as a boolean. If you specifyfalse, the generated model uses stateless prediction. If you do not specify Stateful and the input network contains a stateful layer, the generated model uses stateful prediction by default.

Data Types: logical

FrameBased — Flag to use frame-based processing

false (default) | true

Flag to use frame-based processing for sequence networks, specified as a boolean.

If you specify false, the generated model uses sample-based processing. The model assumes that the input data uses Simulink step time to represent the frame rate.

If you specify true, the generated model uses frame-based processing. The model sets the time (T) dimension of the inport to the value of the MinLength property of the sequenceInputLayer object in the sequence network.

For more information about sample- and frame-based processing, see Sample- and Frame-Based Concepts (DSP System Toolbox).

Data Types: logical

SaveNetworkInModelWorkspace — Flag to save input network in model workspace

false (default) | true

Flag to save the input network in the workspace of the generated model, specified as a boolean.

Data Types: logical

Output Arguments

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mdlInfo — Information about generated model

structure

Information about the generated model, returned as a structure. This table describes the fields of the structure.

Limitations

• The exportNetworkToSimulink function supports only networks that have one input and one output.
• Because deep learning layer blocks do not support the batch (B) dimension, the exportNetworkToSimulink function ignores batch dimensions in layer objects and produces models that assume only one observation.
• The function supports only a limited set of layer objects and does not support certain property values for certain layer objects. For a list of supported layer objects and unsupported property values, see List of Deep Learning Layer Blocks.
  • If the input network contains a layer object that does not have a corresponding layer block, the function generates a placeholder subsystem that contains a Stop Simulation (Simulink) block. You can manually replace the Stop Simulation block with an implementation of the layer. For more information, see Implement Unsupported Deep Learning Layer Blocks.
  • If the input network contains a layer object that has a corresponding layer block but the object uses a property value that the block does not support, the function either throws an error or substitutes a different value. For specific behaviors, seeList of Deep Learning Layer Blocks or each layer block reference page. To change the value of a layer object property to a supported value, use the Deep Network Designer app.

Tips

• Because the generated model references the input network, you can update the weights that the model uses by retraining the network without re-exporting to Simulink.
• To export a SeriesNetwork or DAGNetwork object to Simulink, use the dag2dlnetwork function to convert the object to a dlnetwork object and pass that converted object to the exportNetworkToSimulink function.
• For networks that contain ProjectedLayer objects, use the unpackProjectedLayers function.
• For networks that contain networkLayer objects, use the expandLayers function.

Version History

Introduced in R2024b