Stateful Predict - Predict responses using a trained recurrent neural network - Simulink (original) (raw)

Predict responses using a trained recurrent neural network

Since R2021a

Description

The Stateful Predict block predicts responses for the data at the input by using the trained recurrent neural network specified through the block parameter. This block allows loading of a pretrained network into the Simulink® model from a MAT-file or from a MATLAB® function. This block updates the state of the network with every prediction.

To reset the state of recurrent neural network to its initial state, place theStateful Predict block inside a Resettable Subsystem (Simulink) block and use the Reset control signal as trigger.

Examples

Limitations

• CPU acceleration using the Intel® MKL-DNN library and GPU acceleration using the NVIDIA® CuDNN or TensorRT libraries are not supported for Stateful Predict blocks that use a dlnetwork object.

Ports

Input

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input — Sequence or time series data

numeric array

The input ports of the Stateful Predict block takes the names of the input layers of the network loaded. Based on the network loaded, the input to the predict block can be sequence or time series data.

The dimensions of the numeric arrays containing the sequences depend on the type of data.

Output

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output — Predicted scores or responses

numeric array

The outputs port of the Stateful Predict block takes the names of the output layers of the network loaded. Based on the network loaded, the output of the Stateful Predict block can represent predicted scores or responses.

For sequence-to-label classification, the output is a_K_-by-N matrix, where K is the number of classes, and N is the number of observations.

For sequence-to-sequence classification problems, the output is a_K_-by-S matrix of scores, where_K_ is the number of classes, and S is the total number of time steps in the corresponding input sequence.

Parameters

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Network — Source for trained recurrent neural network

Network from MAT-file (default) | Network from MATLAB function

Specify the source for the trained recurrent neural network. The trained network must have at least one recurrent layer (for example, an LSTM network). Select one of the following:

• Network from MAT-file— Import a trained recurrent neural network from a MAT-file containing a dlnetwork object.
• Network from MATLAB function— Import a pretrained recurrent neural network from a MATLAB function.

Programmatic Use

File path — MAT-file containing trained recurrent neural network

untitled.mat (default) | MAT-file name

This parameter specifies the name of the MAT-file that contains the trained recurrent neural network to load. If the file is not on the MATLAB path, use the Browse button to locate the file.

Dependencies

To enable this parameter, set the Network parameter to Network from MAT-file.

Programmatic Use

MATLAB function — MATLAB function name

untitled (default) | MATLAB function name

This parameter specifies the name of the MATLAB function for the pretrained recurrent neural network.

Dependencies

To enable this parameter, set the Network parameter to Network from MATLAB function.

Programmatic Use

Sample time — Output sample period and optional time offset

-1 (default) | scalar | vector

The Sample time parameter specifies when the block computes a new output value during simulation. For details, see Specify Sample Time (Simulink).

Specify the Sample time parameter as a scalar when you do not want the output to have a time offset. To add a time offset to the output, specify the Sample time parameter as a 1-by-2 vector where the first element is the sampling period and the second element is the offset.

By default, the Sample time parameter value is -1 to inherit the value.

Programmatic Use

Input data formats — Input data format of dlnetwork

character vector | string

This parameter specifies the input data format expected by the trained dlnetwork.

Data format, specified as a string scalar or a character vector. Each character in the string must be one of these dimension labels:

• "S" — Spatial
• "C" — Channel
• "B" — Batch
• "T" — Time
• "U" — Unspecified

For example, for an array containing a batch of sequences where the first, second, and third dimension correspond to channels, observations, and time steps, respectively, you can specify that it has the format "CBT".

You can specify multiple dimensions labeled "S" or "U". You can use the labels "C", "B", and"T" once each, at most. The software ignores singleton trailing"U" dimensions after the second dimension.

For more information, see Deep Learning Data Formats.

By default, the parameter uses the data format that the network expects.

Dependencies

To enable this parameter, set the Network parameter toNetwork from MAT-file to import a trained dlnetwork object from a MAT-file.

Programmatic Use

Extended Capabilities

C/C++ Code Generation

Generate C and C++ code using Simulink® Coder™.

Usage notes and limitations:

• To generate generic C code that does not depend on third-party libraries, in theConfiguration Parameters > Code Generation general category, set the Language parameter toC.
• To generate C++ code, in the Configuration Parameters >Code Generation general category, set theLanguage parameter to C++. To specify the target library for code generation, in the Code Generation > Interface category, set theTarget Library parameter. Setting this parameter toNone generates generic C++ code that does not depend on third-party libraries.
• For ERT-based targets, the Support: variable-size signals parameter in the Code Generation>Interface pane must be enabled.
• For a list of networks and layers supported for code generation, see Networks and Layers Supported for Code Generation (MATLAB Coder).

GPU Code Generation

Generate CUDA® code for NVIDIA® GPUs using GPU Coder™.

Usage notes and limitations:

• The Language parameter in the Configuration Parameters > Code Generation general category must be set to C++.
• GPU code generation supports this block only when targeting the cuDNN library.

Version History

Introduced in R2021a

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R2024a: SeriesNetwork and DAGNetwork are not recommended

Starting in R2024a, the SeriesNetwork and DAGNetwork objects are not recommended. This recommendation means that SeriesNetwork and DAGNetwork inputs to the Stateful Predict block are not recommended. Use the dlnetwork objects instead.dlnetwork objects have these advantages:

• dlnetwork objects are a unified data type that supports network building, prediction, built-in training, visualization, compression, verification, and custom training loops.
• dlnetwork objects support a wider range of network architectures that you can create or import from external platforms.
• The trainnet function supports dlnetwork objects, which enables you to easily specify loss functions. You can select from built-in loss functions or specify a custom loss function.
• Training and prediction with dlnetwork objects is typically faster than LayerGraph and trainNetwork workflows.

Simulink block models with dlnetwork objects behave differently. The predicted scores are returned as a _K_-by-N matrix, where K is the number of classes, and N is the number of observations. If you have an existing Simulink block model with a SeriesNetwork orDAGNetwork object, follow these steps to use a dlnetwork object instead:

1. Convert the SeriesNetwork or DAGNetwork object to a dlnetwork using the dag2dlnetwork function.
2. If the input to your block is a vector sequence, transpose the matrix using a transpose block to a size s_-by-c, where_s is the sequence length, and c is the number of features of the sequences.
3. Transpose the predicted scores using a transpose block to an_N_-by-K array, where N is the number of observations, and K is the number of classes.