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Develop Custom Mini-Batch Datastore

A mini-batch datastore is an implementation of a datastore with support for reading data in batches. You can use a mini-batch datastore as a source of training, validation, test, and prediction data sets for deep learning applications that use Deep Learning Toolbox™.

To preprocess sequence, time series, or text data, build your own mini-batch datastore using the framework described here. For an example showing how to use a custom mini-batch datastore, see Train Network Using Custom Mini-Batch Datastore for Sequence Data.


Build your custom datastore interface using the custom datastore classes and objects. Then, use the custom datastore to bring your data into MATLAB®.

Designing your custom mini-batch datastore involves inheriting from the and classes, and implementing the required properties and methods. You optionally can add support for shuffling during training.

Processing Needs


Mini-batch datastore for training, validation, test, and prediction data sets in Deep Learning Toolbox and

See Implement MiniBatchable Datastore.

Mini-batch datastore with support for shuffling during training,, and

See Add Support for Shuffling.

Implement MiniBatchable Datastore

To implement a custom mini-batch datastore named MyDatastore, create a script MyDatastore.m. The script must be on the MATLAB path and should contain code that inherits from the appropriate class and defines the required methods. The code for creating a mini-batch datastore for training, validation, test, and prediction data sets in Deep Learning Toolbox must:

In addition to these steps, you can define any other properties or methods that you need to process and analyze your data.


If you are training a network and trainingOptions specifies 'Shuffle' as 'once' or 'every-epoch', then you must also inherit from the class. For more information, see Add Support for Shuffling.

This example shows how to create a custom mini-batch datastore for processing sequence data. Save the script in a file called MySequenceDatastore.m.



  1. Begin defining your class. Inherit from the base class and the class.


  2. Define properties.

    • Redefine the MiniBatchSize and NumObservations properties. You optionally can assign additional property attributes to either property. For more information, see Property Attributes (MATLAB).

    • You can also define properties unique to your custom mini-batch datastore.


  3. Define methods.

    • Implement the custom mini-batch datastore constructor.

    • Implement the hasdata method.

    • Implement the read method, which must return data as a table with the predictors in the first column and responses in the second column.

      For sequence data, the sequences must be matrices of size D-by-S, where D is the number of features and S is sequence length. The value of S can vary between mini-batches.

    • Implement the reset method.

    • Implement the progress method.

    • You can also define methods unique to your custom mini-batch datastore.


  4. End the classdef section.

classdef MySequenceDatastore < & ...
    properties(SetAccess = protected)

    properties(Access = private)
        % This property is inherited from Datastore

        function ds = MySequenceDatastore(folder)
            % Construct a MySequenceDatastore object

            % Create a file datastore. The readSequence function is
            % defined following the class definition.
            fds = fileDatastore(folder, ...
                'ReadFcn',@readSequence, ...
            ds.Datastore = fds;

            % Read labels from folder names
            numObservations = numel(fds.Files);
            for i = 1:numObservations
                file = fds.Files{i};
                filepath = fileparts(file);
                [~,label] = fileparts(filepath);
                labels{i,1} = label;
            ds.Labels = categorical(labels);
            ds.NumClasses = numel(unique(labels));
            % Determine sequence dimension. When you define the LSTM
            % network architecture, you can use this property to
            % specify the input size of the sequenceInputLayer.
            X = preview(fds);
            ds.SequenceDimension = size(X,1);
            % Initialize datastore properties.
            ds.MiniBatchSize = 128;
            ds.NumObservations = numObservations;
            ds.CurrentFileIndex = 1;

        function tf = hasdata(ds)
            % Return true if more data is available
            tf = ds.CurrentFileIndex + ds.MiniBatchSize - 1 ...
                <= ds.NumObservations;

        function [data,info] = read(ds)            
            % Read one mini-batch batch of data
            miniBatchSize = ds.MiniBatchSize;
            info = struct;
            for i = 1:miniBatchSize
                predictors{i,1} = read(ds.Datastore);
                responses(i,1) = ds.Labels(ds.CurrentFileIndex);
                ds.CurrentFileIndex = ds.CurrentFileIndex + 1;
            data = preprocessData(ds,predictors,responses);

        function data = preprocessData(ds,predictors,responses)
            % data = preprocessData(ds,predictors,responses) preprocesses
            % the data in predictors and responses and returns the table
            % data
            miniBatchSize = ds.MiniBatchSize;
            % Pad data to length of longest sequence.
            sequenceLengths = cellfun(@(X) size(X,2),predictors);
            maxSequenceLength = max(sequenceLengths);
            for i = 1:miniBatchSize
                X = predictors{i};
                % Pad sequence with zeros.
                if size(X,2) < maxSequenceLength
                    X(:,maxSequenceLength) = 0;
                predictors{i} = X;
            % Return data as a table.
            data = table(predictors,responses);

        function reset(ds)
            % Reset to the start of the data
            ds.CurrentFileIndex = 1;

    methods (Hidden = true)

        function frac = progress(ds)
            % Determine percentage of data read from datastore
            frac = (ds.CurrentFileIndex - 1) / ds.NumObservations;


end % end class definition
The implementation of the read method of your custom datastore uses a function called readSequence. You must create this function to read sequence data from a MAT-file.
function data = readSequence(filename)
% data = readSequence(filename) reads the sequence X from the MAT-file
% filename

S = load(filename);
data = S.X;

Add Support for Shuffling

To add support for shuffling, first follow the instructions in Implement MiniBatchable Datastore and then update your implementation code in MySequenceDatastore.m to:

This example code adds shuffling support to the MySequenceDatastore class. Vertical ellipses indicate where you should copy code from the MySequenceDatastore implementation.



  1. Update the class definition to also inherit from the class.


  2. Add the definition for shuffle to the existing methods section.

classdef MySequenceDatastore < & ...
              & ...
   % previously defined properties 


        % previously defined methods
        function dsNew = shuffle(ds)
            % dsNew = shuffle(ds) shuffles the files and the
            % corresponding labels in the datastore.
            % Create a copy of datastore
            dsNew = copy(ds);
            dsNew.Datastore = copy(ds.Datastore);
            fds = dsNew.Datastore;
            % Shuffle files and corresponding labels
            numObservations = dsNew.NumObservations;
            idx = randperm(numObservations);
            fds.Files = fds.Files(idx);
            dsNew.Labels = dsNew.Labels(idx);



Validate Custom Mini-Batch Datastore

If you have followed all the instructions presented here, then the implementation of your custom mini-batch datastore is complete. Before using this datastore, qualify it using the guidelines presented in Testing Guidelines for Custom Datastores (MATLAB).

See Also

Related Examples

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