resetState

Reset state parameters of neural network

Syntax

netUpdated = resetState(net)

Description

netUpdated = resetState(net) resets the state parameters of a neural network. Use this function to reset the state of a recurrent neural network such as an LSTM network.

Examples

Reset Network State

Reset the network state between sequence predictions.

Load dlnetJapaneseVowels, a pretrained long short-term memory (LSTM) network trained on the Japanese Vowels data set as described in [1] and [2]. This network was trained on the sequences sorted by sequence length with a mini-batch size of 27.

load dlnetJapaneseVowels

View the network architecture.

net.Layers

ans = 
  4x1 Layer array with layers:

     1   'sequenceinput'   Sequence Input    Sequence input with 12 dimensions
     2   'lstm'            LSTM              LSTM with 100 hidden units
     3   'fc'              Fully Connected   9 fully connected layer
     4   'softmax'         Softmax           softmax

Load the test data.

load JapaneseVowelsTestData

Classify a sequence and update the network state.

X = XTest{94};
[scores,state] = predict(net,X,InputDataFormats="CT");
net.State = state;
label = scores2label(scores,classNames)

label = categorical
     3 

Classify another sequence using the updated network.

X = XTest{1};
scores = predict(net,X,InputDataFormats="CT");
label = scores2label(scores,classNames)

label = categorical
     7 

Compare the final prediction with the true label.

trueLabel = TTest(1)

trueLabel = categorical
     1 

The updated state of the network may have negatively influenced the classification. Reset the network state and predict on the sequence again.

net = resetState(net);
scores = predict(net,X,InputDataFormats="CT");
label = scores2label(scores,classNames)

label = categorical
     1 

Input Arguments

net — Neural network

dlnetwork object

Neural network, specified as a dlnetwork object.

The resetState function only has an effect if net has state parameters (for example, a network with at least one recurrent layer such as an LSTM layer). If the input network does not have state parameters, then the function has no effect and returns the input network.

Output Arguments

netUpdated — Updated network

dlnetwork object

Updated network, returned as a dlnetwork object.

The resetState function only has an effect if net has state parameters (for example, a network with at least one recurrent layer such as an LSTM layer). If the input network does not have state parameters, then the function has no effect and returns the input network.

References

[1] M. Kudo, J. Toyama, and M. Shimbo. "Multidimensional Curve Classification Using Passing-Through Regions." Pattern Recognition Letters. Vol. 20, No. 11–13, pages 1103–1111.

[2] UCI Machine Learning Repository: Japanese Vowels Dataset. https://archive.ics.uci.edu/ml/datasets/Japanese+Vowels

Extended Capabilities

C/C++ Code Generation

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

Usage notes and limitations:

• Code generation for Intel^® MKL-DNN target does not support the combination of 'SequenceLength','longest', 'SequencePaddingDirection','left', and 'SequencePaddingValue',0 name-value arguments.

GPU Code Generation

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

Usage notes and limitations:

• GPU code generation for the resetState function is only supported for recurrent neural networks targeting cuDNN and TensorRT libraries.

Version History

Introduced in R2017b

R2024a: DAGNetwork and SeriesNetwork objects are not recommended

Starting in R2024a, DAGNetwork and SeriesNetwork objects are not recommended, use dlnetwork objects instead. This recommendation means that DAGNetwork and SeriesNetwork input to the resetState function is also not recommended.

There are no plans to remove support for DAGNetwork and SeriesNetwork objects. However, dlnetwork objects have these advantages and are recommended instead:

• 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.

To convert a trained DAGNetwork or SeriesNetwork object to a dlnetwork object, use the dag2dlnetwork function.

The resetState function supports dlnetwork objects and has the same behavior.