Stateful Predict
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.
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
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 | s-by-c matrices, where s is the sequence length, and c is the number of features of the sequences. |
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 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
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 adlnetwork
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
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 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: Data format that the network expects. For more information, see Deep Learning Data Formats. |
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 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 R2021aR2024a: 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 supportsdlnetwork
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 thanLayerGraph
andtrainNetwork
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
or
DAGNetwork
object, follow these steps to use a dlnetwork
object instead:
Convert the
SeriesNetwork
orDAGNetwork
object to adlnetwork
using thedag2dlnetwork
function.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.
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.
See Also
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