exportNetworkToSimulink

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

Since R2024b

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    Syntax

    mdlInfo = exportNetworkToSimulink(net)

    Description

    mdlInfo = exportNetworkToSimulink(net) creates a Simulink® model for a trained dlnetwork object, net. The model uses a fixed-step solver and contains deep learning layer blocks and subsystems 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 and subsystems, see List of Deep Learning Layer Blocks and Subsystems. If the network contains a layer that does not have a corresponding deep learning layer block or subsystem, then the function generates a placeholder subsystem that contains an Assertion (Simulink) block. You can manually replace the Assertion block with an implementation of the layer.

    Input Arguments

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

    Data Types: dlnetwork

    Name-Value Arguments

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    Specify optional pairs of arguments as Name1=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")

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

    Data Types: char | string

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

    Data Types: char | string

    Data type for generated inports, specified as a character vector, string array, or cell array of character vectors.

    For networks with multiple inputs, you can specify an array of data types (since R2026a). In this case, InputDataType(i) corresponds to the data type of the input given by net.InputNames(i). If you specify a single data type, then the software uses the specified data type for all inputs.

    The values must correspond to a data type that is supported as the input type of an Inport 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, then the generated inports use the value "Inherit: auto".

    Data Types: char | string | cell

    Flag to expand layer blocks and subsystems to the top level of the generated model without wrapping the blocks in a subsystem, specified as a numeric or logical 1 (true) or 0 (false).

    Data Types: single | double | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64 | logical

    Flag to open the model after generating it, specified as a numeric or logical 1 (true) or 0 (false).

    Data Types: single | double | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64 | logical

    Sample time to use for the generated model, specified as a string scalar or character vector. To use an inherited sample time, use a value 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

    Flag to use stateful prediction, specified as one of these values:

    • 1 (true) — Use stateful prediction.

    • 0 (false) — Use stateless prediction.

    If the input network contains a supported stateful layer, then the default value is true. Otherwise, the default value is false.

    Data Types: single | double | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64 | logical

    Flag to use frame-based processing for sequence networks, specified as a numeric or logical 1 (true) or 0 (false).

    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: single | double | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64 | logical

    Flag to save the input network in the workspace of the generated model, specified as a numeric or logical 1 (true) or 0 (false).

    Data Types: single | double | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64 | logical

    Flag to save the generated model in the folder path as an SLX (*.slx) file, specified as a numeric or logical 1 (true) or 0 (false).

    Specify where to save the model using the ModelPath name-value argument. By default, the exportNetworkToSimulink function saves the generated model in the current directory.

    Data Types: single | double | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64 | logical

    Output Arguments

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

    FieldDescription
    ModelNameName of the generated model, returned as a character vector.
    NetworkNameName of the network in the generated model workspace, returned as a character vector.
    ModelPathFull file path of the generated model, returned as a character vector.
    InputDataTypeInput data type of the generated model. The value matches the InputDataType name-value argument.
    BlockParametersParameters of blocks in the generated model, returned as a structure array. Each structure in the array contains a field for each parameter of a block.
    BlockConnectionsConnections between blocks in the generated model, returned as a table. The table contains the variables Source and Destination. Each row in the table represents the source and destination blocks of a connection in the generated model.
    NumInportsThe number of inports in the generated model, returned as a scalar.
    NumOutportsThe number of outports in the generated model, returned as a scalar.
    SampleTimeThe sample time of the generated model, returned as a character vector.
    StatefulType of prediction that the model uses, either stateless or stateful, returned as a boolean.
    FrameBasedType of processing that the model uses, either frame-based or sample-based, returned as a boolean.

    Limitations

    • Before R2026a: The exportNetworkToSimulink function supports only networks that have one input and one output (networks where the InputNames and OutputNames properties are scalar).

    • Because deep learning layer blocks and subsystems 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.

      • If the input network contains a layer object that does not have a corresponding layer block or subsystem, then the function generates a placeholder subsystem for you to replace with your own 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, then the function either generates a placeholder subsystem (since R2026a), substitutes a different value, or throws an error.

        Before R2026a: The function throws an error for some networks with unsupported property values.

      For more information on supported layer objects, supported property values, and specific behaviors, see List of Deep Learning Layer Blocks and Subsystems.

    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.

    • Before R2026a: For networks that contain ProjectedLayer objects, use the unpackProjectedLayers function.

    • Before R2026a: For networks that contain networkLayer objects, use the expandLayers function.

    Version History

    Introduced in R2024b

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    See Also

    Topics