Stateful Predict

Predict responses using a trained recurrent neural network

Since R2021a

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Libraries:
Deep Learning Toolbox / Deep Neural Networks

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 the Stateful Predict block inside a Resettable Subsystem (Simulink) block and use the Reset control signal as trigger.

Examples

Predict and Update Network State in Simulink

Predict responses for a trained recurrent neural network in Simulink® by using the Stateful Predict block. This example uses a pretrained long short-term memory (LSTM) network.

Open Script

Ports

Input

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.

Input	Description
Vector sequences	c-by-s matrices, where c is the number of features of the sequences and s is the sequence length.
2-D image sequences	h-by-w-by-c-by-s arrays, where h, w, and c correspond to the height, width, and number of channels of the images, respectively, and s is the sequence length.

Output

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 N-by-K matrix, where N is the number of observations, and K is the number of classes.

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

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 SeriesNetwork, DAGNetwork, or dlnetwork object.

• Network from MATLAB function— Import a pretrained recurrent neural network from a MATLAB function.

Programmatic Use

Block Parameter: Network

Type: character vector, string

Values: 'Network from MAT-file' | 'Network from MATLAB function'

Default: 'Network from MAT-file'

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

Block Parameter: NetworkFilePath

Type: character vector, string

Values: MAT-file path or name

Default: 'untitled.mat'

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

Block Parameter: NetworkFunction

Type: character vector, string

Values: MATLAB function name

Default: 'untitled'

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

Block Parameter: SampleTime

Type: character vector

Values: scalar | vector

Default: '-1'

Input data formats — Input data format of dlnetwork

'' (default) | character vector | string

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

A data format is a string of characters, where each character describes the type of the corresponding dimension of the data. 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". For more information, see Deep Learning Data Formats.

Dependencies

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

Programmatic Use

Block Parameter: InputDataFormats

Type: character vector, string

Values: For a network with one or more inputs, use character vector in the form of: {'inputlayerName1', 'SSC'; 'inputlayerName2', 'SSCB'; ...}'. For a network with no input layer and multiple input ports, use character vector in the form of: '{'inputportName1/inport1, 'SSC'; 'inputportName2/inport2, 'SSCB'; ...}'.

Default: ''

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 the Configuration Parameters > Code Generation general category, set the Language parameter to C.

• To generate C++ code, in the Configuration Parameters > Code Generation general category, set the Language parameter to C++. To specify the target library for code generation, in the Code Generation > Interface category, set the Target Library parameter. Setting this parameter to None 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.

• Code generation for the Stateful Predict block does not support dlnetwork.

• 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.

• Code generation for the Stateful Predict block does not support dlnetwork.

Version History

Introduced in R2021a