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predictAndUpdateState

Predict responses using a trained recurrent neural network and update the network state

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Syntax

[updatedNet,Y] = predictAndUpdateState(recNet,sequences)
[updatedNet,Y] = predictAndUpdateState(recNet,X1,...,XN)
[updatedNet,Y] = predictAndUpdateState(recNet,mixed)
[updatedNet,Y1,...,YM] = predictAndUpdateState(___)
[___] = predictAndUpdateState(___,Name=Value)

Description

You can make predictions using a trained deep learning network on either a CPU or GPU. Using a GPU requires a Parallel Computing Toolbox™ license and a supported GPU device. For information about supported devices, see GPU Computing Requirements (Parallel Computing Toolbox). Specify the hardware requirements using the ExecutionEnvironment name-value argument.

example

[updatedNet,Y] = predictAndUpdateState(recNet,sequences) predicts responses for data in sequences using the trained recurrent neural network recNet and updates the network state.

This function supports recurrent neural networks only. The input recNet must have at least one recurrent layer such as an LSTM layer or a custom layer with state parameters.

[updatedNet,Y] = predictAndUpdateState(recNet,X1,...,XN) predicts the responses for the data in the numeric or cell arrays X1, …, XN for the multi-input network recNet. The input Xi corresponds to the network input recNet.InputNames(i).

[updatedNet,Y] = predictAndUpdateState(recNet,mixed) makes predictions using the multi-input network recNet with data of mixed data types.

[updatedNet,Y1,...,YM] = predictAndUpdateState(___) predicts responses for the M outputs of a multi-output network using any of the previous input arguments. The output Yj corresponds to the network output recNet.OutputNames(j). To return categorical outputs for the classification output layers, set the ReturnCategorical option to 1 (true).

[___] = predictAndUpdateState(___,Name=Value) makes predictions with additional options specified by one or more name-value arguments using any of the previous syntaxes. For example, MiniBatchSize=27 makes predictions using mini-batches of size 27.

Tip

When you make predictions with sequences of different lengths, the mini-batch size can impact the amount of padding added to the input data, which can result in different predicted values. Try using different values to see which works best with your network. To specify mini-batch size and padding options, use the MiniBatchSize and SequenceLength options, respectively.

Examples

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Open Live Script

Predict responses using a trained recurrent neural network and update the network state.

Load JapaneseVowelsNet, 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 JapaneseVowelsNet

View the network architecture.

net.Layers
ans = 
  5x1 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
     5   'classoutput'     Classification Output   crossentropyex with '1' and 8 other classes

Load the test data.

[XTest,TTest] = japaneseVowelsTestData;

Loop over the time steps in a sequence. Predict the scores of each time step and update the network state.

X = XTest{94};
numTimeSteps = size(X,2);
for i = 1:numTimeSteps
    v = X(:,i);
    [net,score] = predictAndUpdateState(net,v);
    scores(:,i) = score;
end

Plot the prediction scores. The plot shows how the prediction scores change between time steps.

classNames = string(net.Layers(end).Classes);
figure
lines = plot(scores');
xlim([1 numTimeSteps])
legend("Class " + classNames,Location="northwest")
xlabel("Time Step")
ylabel("Score")
title("Prediction Scores Over Time Steps")

Highlight the prediction scores over time steps for the correct class.

trueLabel = TTest(94)
trueLabel = categorical
     3 


lines(trueLabel).LineWidth = 3;

Figure contains an axes object. The axes object with title Prediction Scores Over Time Steps, xlabel Time Step, ylabel Score contains 9 objects of type line. These objects represent Class 1, Class 2, Class 3, Class 4, Class 5, Class 6, Class 7, Class 8, Class 9.

Display the final time step prediction in a bar chart.

figure
bar(score)
title("Final Prediction Scores")
xlabel("Class")
ylabel("Score")

Figure contains an axes object. The axes object with title Final Prediction Scores, xlabel Class, ylabel Score contains an object of type bar.

Input Arguments

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Trained recurrent neural network, specified as a SeriesNetwork or a DAGNetwork object. You can get a trained network by importing a pretrained network or by training your own network using the trainNetwork function.

recNet is a recurrent neural network. It must have at least one recurrent layer (for example, an LSTM network).

Sequence or time series data, specified as an N-by-1 cell array of numeric arrays, where N is the number of observations, a numeric array representing a single sequence, or a datastore.

For cell array or numeric array input, the dimensions of the numeric arrays containing the sequences depend on the type of data.

InputDescription
Vector sequencesc-by-s matrices, where c is the number of features of the sequences and s is the sequence length.
1-D image sequencesh-by-c-by-s arrays, where h and c correspond to the height and number of channels of the images, respectively, and s is the sequence length.
2-D image sequencesh-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.
3-D image sequencesh-by-w-by-d-by-c-by-s, where h, w, d, and c correspond to the height, width, depth, and number of channels of the 3-D images, respectively, and s is the sequence length.

For datastore input, the datastore must return data as a cell array of sequences or a table whose first column contains sequences. The dimensions of the sequence data must correspond to the table above.

Tip

To input complex-valued data into a neural network, the SplitComplexInputs option of the input layer must be 1.

Data Types: single | double | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64 | cell
Complex Number Support: Yes

Numeric or cell arrays for networks with multiple inputs.

For sequence predictor input, the input must be a numeric array representing a single sequence or a cell array of sequences, where the format of the predictors match the formats described in the sequences argument description. For image and feature predictor input, the input must be a numeric array and the format of the predictors must match the one of the following:

DataFormat
2-D images

h-by-w-by-c numeric array, where h, w, and c are the height, width, and number of channels of the images, respectively.

3-D imagesh-by-w-by-d-by-c numeric array, where h, w, d, and c are the height, width, depth, and number of channels of the images, respectively.
Feature data

c-by-1 column vectors, where c is the number of features.

For an example showing how to train a network with multiple inputs, see Train Network on Image and Feature Data.

Tip

To input complex-valued data into a network, the SplitComplexInputs option of the input layer must be 1.

Data Types: single | double | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64 | cell
Complex Number Support: Yes

Mixed data, specified as one of the following.

Data TypeDescriptionExample Usage
TransformedDatastoreDatastore that transforms batches of data read from an underlying datastore using a custom transformation function

  • Make predictions using networks with multiple inputs.

  • Transform outputs of datastores not supported by predictAndUpdateState so they have the required format.

  • Apply custom transformations to datastore output.

CombinedDatastoreDatastore that reads from two or more underlying datastores

  • Make predictions using networks with multiple inputs.

  • Combine predictors from different data sources.

Custom mini-batch datastoreCustom datastore that returns mini-batches of data

Make predictions using data in a format that other datastores do not support.

For details, see Develop Custom Mini-Batch Datastore.

You can use other built-in datastores for making predictions by using the transform and combine functions. These functions can convert the data read from datastores to the table or cell array format required by predictAndUpdateState. For more information, see Datastores for Deep Learning.

The datastore must return data in a table or a cell array. Custom mini-batch datastores must output tables. The format of the datastore output depends on the network architecture.

Datastore OutputExample Output

Cell array with numInputs columns, where numInputs is the number of network inputs.

The order of inputs is given by the InputNames property of the network.

data = read(ds)
data =

  4×3 cell array

    {12×50 double}    {28×1 double}
    {12×50 double}    {28×1 double}
    {12×50 double}    {28×1 double}
    {12×50 double}    {28×1 double}

For sequence predictor input, the input must be a numeric array representing a single sequence or a cell array of sequences, where the format of the predictors match the formats described in the sequences argument description. For image and feature predictor input, the input must be a numeric array and the format of the predictors must match the one of the following:

DataFormat
2-D images

h-by-w-by-c numeric array, where h, w, and c are the height, width, and number of channels of the images, respectively.

3-D imagesh-by-w-by-d-by-c numeric array, where h, w, d, and c are the height, width, depth, and number of channels of the images, respectively.
Feature data

c-by-1 column vectors, where c is the number of features.

For an example showing how to train a network with multiple inputs, see Train Network on Image and Feature Data.

Tip

To convert a numeric array to a datastore, use ArrayDatastore.

Name-Value Arguments

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.

Before R2021a, use commas to separate each name and value, and enclose Name in quotes.

Example: [updatedNet,Y] = predictAndUpdateState(recNet,C,MiniBatchSize=27) makes predictions using mini-batches of size 27.

Size of mini-batches to use for prediction, specified as a positive integer. Larger mini-batch sizes require more memory, but can lead to faster predictions.

When you make predictions with sequences of different lengths, the mini-batch size can impact the amount of padding added to the input data, which can result in different predicted values. Try using different values to see which works best with your network. To specify mini-batch size and padding options, use the MiniBatchSize and SequenceLength options, respectively.

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

Performance optimization, specified as one of the following:

  • "auto" — Automatically apply a number of optimizations suitable for the input network and hardware resources.

  • "none" — Disable all acceleration.

Using the Acceleration option "auto" can offer performance benefits, but at the expense of an increased initial run time. Subsequent calls with compatible parameters are faster. Use performance optimization when you plan to call the function multiple times using new input data.

Hardware resource, specified as one of the following:

  • "auto" — Use a GPU if one is available; otherwise, use the CPU.

  • "gpu" — Use the GPU. Using a GPU requires a Parallel Computing Toolbox license and a supported GPU device. For information about supported devices, see GPU Computing Requirements (Parallel Computing Toolbox). If Parallel Computing Toolbox or a suitable GPU is not available, then the software returns an error.

  • "cpu" — Use the CPU.

Option to return categorical labels, specified as 0 (false) or 1 (true).

If ReturnCategorical is 1 (true), then the function returns categorical labels for classification output layers. Otherwise, the function returns the prediction scores for classification output layers.

Option to pad, truncate, or split input sequences, specified as one of the following:

  • "longest" — Pad sequences in each mini-batch to have the same length as the longest sequence. This option does not discard any data, though padding can introduce noise to the neural network.

  • "shortest" — Truncate sequences in each mini-batch to have the same length as the shortest sequence. This option ensures that no padding is added, at the cost of discarding data.

  • Positive integer — For each mini-batch, pad the sequences to the length of the longest sequence in the mini-batch, and then split the sequences into smaller sequences of the specified length. If splitting occurs, then the software creates extra mini-batches. If the specified sequence length does not evenly divide the sequence lengths of the data, then the mini-batches containing the ends those sequences have length shorter than the specified sequence length. Use this option if the full sequences do not fit in memory. Alternatively, try reducing the number of sequences per mini-batch by setting the MiniBatchSize option to a lower value.

To learn more about the effect of padding, truncating, and splitting the input sequences, see Sequence Padding, Truncation, and Splitting.

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

Direction of padding or truncation, specified as one of the following:

  • "right" — Pad or truncate sequences on the right. The sequences start at the same time step and the software truncates or adds padding to the end of the sequences.

  • "left" — Pad or truncate sequences on the left. The software truncates or adds padding to the start of the sequences so that the sequences end at the same time step.

Because recurrent layers process sequence data one time step at a time, when the recurrent layer OutputMode property is 'last', any padding in the final time steps can negatively influence the layer output. To pad or truncate sequence data on the left, set the SequencePaddingDirection option to "left".

For sequence-to-sequence neural networks (when the OutputMode property is 'sequence' for each recurrent layer), any padding in the first time steps can negatively influence the predictions for the earlier time steps. To pad or truncate sequence data on the right, set the SequencePaddingDirection option to "right".

To learn more about the effect of padding, truncating, and splitting the input sequences, see Sequence Padding, Truncation, and Splitting.

Value by which to pad input sequences, specified as a scalar.

The option is valid only when SequenceLength is "longest" or a positive integer. Do not pad sequences with NaN, because doing so can propagate errors throughout the neural network.

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

Output Arguments

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Updated network. updatedNet is the same type of network as the input network.

Predicted scores or responses, returned as a numeric array, categorical array, or a cell array. The format of Y depends on the type of problem.

The following table describes the format for classification tasks.

TaskReturnCategoricalFormat
Sequence-to-label classification0 (false)N-by-K matrix, where N is the number of observations, and K is the number of classes
1 (true)N-by-1 categorical vector, where N is the number of observations
Sequence-to-sequence classification0 (false)

N-by-1 cell array of matrices, where N is the number of observations. The sequences are matrices with K rows, where K is the number of classes. Each sequence has the same number of time steps as the corresponding input sequence after the SequenceLength option is applied to each mini-batch independently.

1 (true)

N-by-1 cell array of categorical sequences, where N is the number of observations. The categorical sequences are categorical vectors with the same number of time steps as the corresponding input sequence after the SequenceLength option is applied to each mini-batch independently.

For sequence-to-sequence classification tasks with one observation, sequences can be a matrix. In this case, the format of Y depends on the ReturnCategorical option:

  • If ReturnCategoical is 0 (false), then Y is a K-by-S matrix scores, where K is the number of classes, and S is the total number of time steps in the corresponding input sequence.

  • If ReturnCategoical is 1 (true), then Y is a 1-by-S categorical vector, where S is the total number of time steps in the corresponding input sequence.

The following table describes the format for regression tasks.

TaskFormat
Sequence-to-one regressionN-by-R matrix, where N is the number of observations and R is the number of responses.
Sequence-to-sequence regression

N-by-1 cell array of numeric sequences, where N is the number of observations. The sequences are matrices with R rows, where R is the number of responses. Each sequence has the same number of time steps as the corresponding input sequence after applying the SequenceLength option to each mini-batch independently.

For sequence-to-sequence problems with one observation, sequences can be a matrix. In this case, Y is a matrix of responses.

Predicted scores or responses of networks with multiple outputs, returned as numeric arrays, categorical arrays, or cell arrays.

Each output Yj corresponds to the network output recNet.OutputNames(j) and has format as described in the Y output argument.

Algorithms

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Floating-Point Arithmetic

When you train a neural network using the trainNetwork function, or when you use prediction or validation functions with DAGNetwork and SeriesNetwork objects, the software performs these computations using single-precision, floating-point arithmetic. Functions for training, prediction, and validation include trainNetwork, predict, classify, and activations. The software uses single-precision arithmetic when you train neural networks using both CPUs and GPUs.

Reproducibility

To provide the best performance, deep learning using a GPU in MATLAB® is not guaranteed to be deterministic. Depending on your network architecture, under some conditions you might get different results when using a GPU to train two identical networks or make two predictions using the same network and data.

Alternatives

For recurrent neural networks with a single classification layer only, you can compute the predicted classes and scores and update the network state using the classifyAndUpdateState function.

To compute the activations of a network layer, use the activations function. The activations function does not update the network state.

To make predictions without updating the network state, use the classify function or the predict function.

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

Version History

Introduced in R2017b

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See Also

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