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minibatchpredict

Mini-batched neural network prediction

Since R2024a

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    Syntax

    [Y1,...,YM] = minibatchpredict(net,images)
    [Y1,...,YM] = minibatchpredict(net,sequences)
    [Y1,...,YM] = minibatchpredict(net,features)
    [Y1,...,YM] = minibatchpredict(net,data)
    [Y1,...,YM] = minibatchpredict(net,X1,...,XN)
    [Y1,...,YM] = minibatchpredict(___,Name=Value)

    Description

    [Y1,...,YM] = minibatchpredict(net,images) makes neural network predictions by looping over mini-batches of the specified images, where M is the number of network outputs.

    example

    [Y1,...,YM] = minibatchpredict(net,sequences) makes neural network predictions by looping over mini-batches of the specified sequences.

    [Y1,...,YM] = minibatchpredict(net,features) makes neural network predictions by looping over mini-batches of the specified feature or tabular data.

    [Y1,...,YM] = minibatchpredict(net,data) makes neural network predictions by looping over mini-batches of other layouts or combinations of data.

    [Y1,...,YM] = minibatchpredict(net,X1,...,XN) makes neural network predictions for a network with multiple inputs using the specified in-memory data.

    [Y1,...,YM] = minibatchpredict(___,Name=Value) specifies additional options using one or more name-value arguments. For example, MiniBatchSize=32 makes predictions by looping over mini-batches of size 32.

    Examples

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    Open Live Script

    This example shows how to make predictions using a dlnetwork object by looping over mini-batches.

    For large data sets, or when predicting on hardware with limited memory, make predictions by looping over mini-batches of the data using the minibatchpredict function.

    Load dlnetwork Object

    Load a trained dlnetwork object and the corresponding class names into the workspace. The neural network has one input and two outputs. It takes images of handwritten digits as input, and predicts the digit label and angle of rotation.

    load dlnetDigits

    Load Data for Prediction

    Load the digits test data for prediction.

    load DigitsDataTest

    View the class names.

    classNames
    classNames = 10x1 cell
    {'0'}
    {'1'}
    {'2'}
    {'3'}
    {'4'}
    {'5'}
    {'6'}
    {'7'}
    {'8'}
    {'9'}

    View some of the images and the corresponding labels and angles of rotation.

    numObservations = size(XTest,4);
numPlots = 9;
idx = randperm(numObservations,numPlots);

figure
for i = 1:numPlots
    nexttile(i)
    I = XTest(:,:,:,idx(i));
    label = labelsTest(idx(i));
    imshow(I)
    title("Label: " + string(label) + newline + "Angle: " + anglesTest(idx(i)))
end

    Figure contains 9 axes objects. Hidden axes object 1 with title Label: 8 Angle: 5 contains an object of type image. Hidden axes object 2 with title Label: 9 Angle: -45 contains an object of type image. Hidden axes object 3 with title Label: 1 Angle: -11 contains an object of type image. Hidden axes object 4 with title Label: 9 Angle: -40 contains an object of type image. Hidden axes object 5 with title Label: 6 Angle: -42 contains an object of type image. Hidden axes object 6 with title Label: 0 Angle: -18 contains an object of type image. Hidden axes object 7 with title Label: 2 Angle: -9 contains an object of type image. Hidden axes object 8 with title Label: 5 Angle: -17 contains an object of type image. Hidden axes object 9 with title Label: 9 Angle: -27 contains an object of type image.

    Make Predictions

    Make predictions using the minibatchpredict function, and convert the classification scores to labels using the scores2label function. By default, the minibatchpredict function uses a GPU if one is available. Using a GPU requires a Parallel Computing Toolbox™ license and a supported GPU device. For information on supported devices, see GPU Computing Requirements (Parallel Computing Toolbox). Otherwise, the function uses the CPU. To select the execution environment manually, use the ExecutionEnvironment argument of the minibatchpredict function.

    [scoresTest,Y2Test] = minibatchpredict(net,XTest);
Y1Test = scores2label(scoresTest,classNames);

    Visualize some of the predictions.

    idx = randperm(numObservations,numPlots);

figure
for i = 1:numPlots
    nexttile(i)
    I = XTest(:,:,:,idx(i));
    label = Y1Test(idx(i));
    imshow(I)
    title("Label: " + string(label) + newline + "Angle: " + Y2Test(idx(i)))
end

    Figure contains 9 axes objects. Hidden axes object 1 with title Label: 9 Angle: 20.3954 contains an object of type image. Hidden axes object 2 with title Label: 1 Angle: 3.7015 contains an object of type image. Hidden axes object 3 with title Label: 9 Angle: 23.5494 contains an object of type image. Hidden axes object 4 with title Label: 9 Angle: -36.4954 contains an object of type image. Hidden axes object 5 with title Label: 4 Angle: 16.428 contains an object of type image. Hidden axes object 6 with title Label: 7 Angle: 3.0644 contains an object of type image. Hidden axes object 7 with title Label: 1 Angle: 33.1356 contains an object of type image. Hidden axes object 8 with title Label: 4 Angle: 30.7531 contains an object of type image. Hidden axes object 9 with title Label: 9 Angle: 0.55887 contains an object of type image.

    Input Arguments

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    Neural network, specified as a dlnetwork object.

    Image data, specified as a numeric array, dlarray object, datastore, or minibatchqueue object.

    Tip

    For sequences of images, such as video data, use the sequences input argument, instead.

    If you have data that fits in memory that does not require additional processing, then specifying the input data as a numeric array is usually the easiest option. If you want to make predictions with image files stored on your system, or want to apply additional processing, then datastores are usually the easiest option.

    Tip

    Neural networks expect input data with a specific layout. For example, image classification networks typically expect image representations to be h-by-w-by-c numeric arrays, where h, w, and c are the height, width, and number of channels of the images, respectively. Most neural networks have an input layer that specifies the expected layout of the data.

    Most datastores and functions output data in the layout that the network expects. If your data is in a different layout than what the network expects, then indicate that your data has a different layout by using the InputDataFormats argument or by specifying input data as a formatted dlarray object. Adjusting the InputDataFormats argument is usually easier than preprocessing the input data.

    For neural networks that do not have input layers, you must use the InputDataFormats argument or use formatted dlarray objects.

    For more information, see Deep Learning Data Formats.

    Numeric Array or dlarray Object

    For data that fits in memory and does not require additional processing, you can specify a data set of images as a numeric array or a dlarray object.

    The layouts of numeric arrays and unformatted dlarray objects depend on the type of image data, and must be consistent with the InputDataFormats argument.

    Most networks expect image data in these layouts:

    DataLayout
    2-D images

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

    Data in this layout has the data format "SSCB" (spatial, spatial, channel, batch).

    3-D images

    h-by-w-by-d-by-c-by-N array, where h, w, d, and c are the height, width, depth, and number of channels of the images, respectively, and N is the number of images.

    Data in this layout has the data format "SSSCB" (spatial, spatial, spatial, channel, batch).

    For data in a different layout, indicate that your data has a different layout by using the InputDataFormats argument or use a formatted dlarray object. For more information, see Deep Learning Data Formats.

    Datastore

    Datastores read batches of images and targets. Use datastores when you have data that does not fit in memory, or when you want to apply transformations to the data.

    For image data, the minibatchpredict function supports these datastores:

    DatastoreDescriptionExample Usage
    ImageDatastore

    Datastore of images saved on disk.

    Make predictions with images saved on your system, where the images are the same size. When the images are different sizes, use an augmentedImageDatastore object.

    augmentedImageDatastoreDatastore that applies random affine geometric transformations, including resizing.

    Make predictions with images saved on disk, where the images are different sizes.

    When you make predictions using an augmented image datastore, do not apply additional augmentations such as rotation, reflection, shear, and translation.

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

    • Transform datastores with outputs not supported by the minibatchpredict function.

    • Apply custom transformations to datastore output.

    CombinedDatastoreDatastore that reads from two or more underlying datastores.

    Make predictions using a network with multiple inputs.

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

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

    For details, see Develop Custom Mini-Batch Datastore.

    Tip

    ImageDatastore objects allow batch reading of JPG or PNG image files using prefetching. For efficient preprocessing of images for deep learning, including image resizing, use an augmentedImageDatastore object. Do not use the ReadFcn property of ImageDatastore objects. If you set the ReadFcn property to a custom function, then the ImageDatastore object does not prefetch image files and is usually significantly slower.

    You can use other built-in datastores for testing deep learning neural networks by using the transform and combine functions. These functions can convert the data read from datastores to the layout required by the minibatchpredict function. The required layout of the datastore output depends on the neural network architecture. For more information, see Datastore Customization.

    minibatchqueue Object

    For greater control over how the software processes and transforms mini-batches, you can specify data as a minibatchqueue object.

    If you specify data as a minibatchqueue object, then the minibatchpredict function ignores the MiniBatchSize property of the object and uses the MiniBatchSize argument instead. For minibatchqueue input, the PreprocessingEnvironment property must be "serial".

    Note

    This argument supports complex-valued predictors.

    Sequence or time series data, specified as a numeric array, cell array of numeric arrays, dlarray object, cell array of dlarray objects, datastore, or minibatchqueue object.

    If you have sequences of the same length that fit in memory and do not require additional processing, then specifying the input data as a numeric array is usually the easiest option. If you have sequences of different lengths that fit in memory and do not require additional processing, then specifying the input data as a cell array of numeric arrays is usually the easiest option. If you want to train with sequences stored on your system, or want to apply additional processing such as custom transformations, then datastores are usually the easiest option.

    Tip

    Neural networks expect input data with a specific layout. For example, vector-sequence classification networks typically expect a vector-sequence representations to be t-by-c arrays, where t and c are the number of time steps and channels of sequences, respectively. Neural networks typically have an input layer that specifies the expected layout of the data.

    Most datastores and functions output data in the layout that the network expects. If your data is in a different layout than what the network expects, then indicate that your data has a different layout by using the InputDataFormats argument or by specifying input data as a formatted dlarray object. Adjusting the InputDataFormats argument is usually easier than preprocessing the input data.

    For neural networks that do not have input layers, you must use the InputDataFormats argument or use formatted dlarray objects.

    For more information, see Deep Learning Data Formats.

    Numeric Array, dlarray Object, or Cell Array

    For data that fits in memory and does not require additional processing like custom transformations, you can specify a single sequence as a numeric array or dlarray object or a data set of sequences as a cell array of numeric arrays, or dlarray objects.

    For cell array input, the cell array must be an N-by-1 cell array of numeric arrays or dlarray objects, where N is the number of observations. The sizes and shapes of the numeric arrays or dlarray objects that represent the sequences depend on the type of sequence data, and must be consistent with the InputDataFormats argument value.

    This table describes the expected layout of data for a neural network with a sequence input layer.

    DataLayout
    Vector sequencess-by-c matrices, where s and c are the numbers of time steps and channels (features) of the sequences, respectively.
    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 data in a different layout, indicate that your data has a different layout by using the InputDataFormats argument or use a formatted dlarray object. For more information, see Deep Learning Data Formats.

    Datastore

    Datastores read batches of sequences and targets. Use datastores when you have data that does not fit in memory, or when you want to apply transformations to the data.

    For sequence and time-series data, the minibatchpredict function supports these datastores:

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

    • Transform datastores with outputs not supported by the minibatchpredict function.

    • Apply custom transformations to datastore output.

    CombinedDatastoreDatastore that reads from two or more underlying datastores.

    Make predictions using a network with multiple inputs.

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

    Train neural network using data in a layout that other datastores do not support.

    For details, see Develop Custom Mini-Batch Datastore.

    You can use other built-in datastores by using the transform and combine functions. These functions can convert the data read from datastores to the layout required by the minibatchpredict function. For example, you can transform and combine data read from in-memory arrays and CSV files using ArrayDatastore and TabularTextDatastore objects, respectively. The required layout of the datastore output depends on the neural network architecture. For more information, see Datastore Customization.

    minibatchqueue Object

    For greater control over how the software processes and transforms mini-batches, you can specify data as a minibatchqueue object.

    If you specify data as a minibatchqueue object, then the minibatchpredict function ignores the MiniBatchSize property of the object and uses the MiniBatchSize argument instead. For minibatchqueue input, the PreprocessingEnvironment property must be "serial".

    Note

    This argument supports complex-valued predictors.

    Feature or tabular data, specified as a numeric array, datastore, table, or minibatchqueue object.

    If you have data that fits in memory that does not require additional processing, then specifying the input data as a numeric array or table is usually the easiest option. If you want to train with feature or tabular data stored on your system, or want to apply additional processing such as custom transformations, then datastores are usually the easiest option.

    Tip

    Neural networks expect input data with a specific layout. For example feature classification networks typically expect feature and tabular data representations to be 1-by-c vectors, where c is the number features of the data. Neural networks typically have an input layer that specifies the expected layout of the data.

    Most datastores and functions output data in the layout that the network expects. If your data is in a different layout than what the network expects, then indicate that your data has a different layout by using the InputDataFormats argument or by specifying input data as a formatted dlarray object. Adjusting the InputDataFormats argument is usually easier than preprocessing the input data.

    For neural networks that do not have input layers, you must use the InputDataFormats argument or use formatted dlarray objects.

    For more information, see Deep Learning Data Formats.

    Numeric Array or dlarray Objects

    For feature data that fits in memory and does not require additional processing like custom transformations, you can specify feature data as a numeric array or dlarray object.

    The layouts of numeric arrays and unformatted dlarray objects must be consistent with the InputDataFormats argument. Most networks with feature input expect input data specified as an numObservations-by-numFeatures array, where numObservations is the number of observations and numFeatures is the number of features of the input data.

    Table

    For feature data that fits in memory and does not require additional processing like custom transformations, you can specify feature data as a table.

    To specify feature data as a table, specify a table with numObservations rows and numFeatures+1 columns, where numObservations and numFeatures are the number of observations and channels of the input data, respectively. The minibatchpredict function uses the first numFeatures columns as the input features and uses the last column as the targets.

    Datastore

    Datastores read batches of feature data and targets. Use datastores when you have data that does not fit in memory, or when you want to apply transformations to the data.

    For feature and tabular data, the minibatchpredict function supports these datastores:

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

    • Make predictions using a neural network with multiple inputs.

    • Transform datastores with outputs not supported by the trainnet function.

    • Apply custom transformations to datastore output.

    CombinedDatastoreDatastore that reads from two or more underlying datastores.

    Make predictions using a neural network with multiple inputs.

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

    Make predictions using data in a layout 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 the minibatchpredict function. For more information, see Datastore Customization.

    minibatchqueue Object

    For greater control over how the software processes and transforms mini-batches, you can specify data as a minibatchqueue object.

    If you specify data as a minibatchqueue object, then the minibatchpredict function ignores the MiniBatchSize property of the object and uses the MiniBatchSize argument instead. For minibatchqueue input, the PreprocessingEnvironment property must be "serial".

    Note

    This argument supports complex-valued predictors.

    Generic data or combinations of data types, specified as a numeric array, dlarray object, datastore, or minibatchqueue object.

    If you have data that fits in memory and does not require additional processing, then specifying the input data as a numeric array is usually easiest option. If you want to train with data stored on your system, or you want to apply additional processing, then using datastores it is usually easiest option.

    Tip

    Neural networks expect input data with a specific layout. For example, vector-sequence classification networks typically expect a vector-sequence representations to be t-by-c arrays, where t and c are the number of time steps and channels of sequences, respectively. Neural networks typically have an input layer that specifies the expected layout of the data.

    Most datastores and functions output data in the layout that the network expects. If your data is in a different layout than what the network expects, then indicate that your data has a different layout by using the InputDataFormats argument or by specifying input data as a formatted dlarray object. Adjusting the InputDataFormats argument is usually easier than preprocessing the input data.

    For neural networks that do not have input layers, you must use the InputDataFormats argument or use formatted dlarray objects.

    For more information, see Deep Learning Data Formats.

    Numeric or dlarray Objects

    For data that fits in memory and does not require additional processing like custom transformations, you can specify feature data as a numeric array.

    For a neural network with an inputLayer object, the expected layout of input data is a given by the InputFormat property of the layer.

    For data in a different layout, indicate that your data has a different layout by using the InputDataFormats argument or use a formatted dlarray object. For more information, see Deep Learning Data Formats.

    Datastores

    Datastores read batches of data and targets. Use datastores when you have data that does not fit in memory, or when you want to apply transformations to the data.

    Generic data or combinations of data types, the minibatchpredict function supports these datastores:

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

    • Make predictions using a neural network with multiple inputs.

    • Transform outputs of datastores not supported by minibatchpredict to the have the required format.

    • Apply custom transformations to datastore output.

    CombinedDatastoreDatastore that reads from two or more underlying datastores.

    Make predictions using a neural network with multiple inputs.

    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 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 minibatchpredict. For more information, see Datastore Customization.

    minibatchqueue Object

    For greater control over how the software processes and transforms mini-batches, you can specify data as a minibatchqueue object.

    If you specify data as a minibatchqueue object, then the minibatchpredict function ignores the MiniBatchSize property of the object and uses the MiniBatchSize argument instead. For minibatchqueue input, the PreprocessingEnvironment property must be "serial".

    Note

    This argument supports complex-valued predictors.

    In-memory data for a neural network with multiple inputs, specified as numeric arrays, dlarray objects, or cell arrays.

    For a neural network with multiple inputs, if you have data that fits in memory and does not require additional processing, then specifying the input data as in-memory arrays is usually the easiest option. If you want to make predictions with data stored on your system, or you want to apply additional processing, then using datastores is usually the easiest option.

    Tip

    Neural networks expect input data with a specific layout. For example, vector-sequence classification networks typically expect a vector-sequence representations to be t-by-c arrays, where t and c are the number of time steps and channels of sequences, respectively. Neural networks typically have an input layer that specifies the expected layout of the data.

    Most datastores and functions output data in the layout that the network expects. If your data is in a different layout than what the network expects, then indicate that your data has a different layout by using the InputDataFormats argument or by specifying input data as a formatted dlarray object. Adjusting the InputDataFormats argument is usually easier than preprocessing the input data.

    For neural networks that do not have input layers, you must use the InputDataFormats argument or use formatted dlarray objects.

    For more information, see Deep Learning Data Formats.

    For each input X1,...,XN, where N is the number of inputs, specify the data as a numeric array, dlarray object, or cell array as described by the argument images, sequences, features, or data that matches the type of data. The input Xi corresponds to the network input net.InputNames(i).

    Note

    This argument supports complex-valued predictors.

    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.

    Example: minibatchpredict(net,images,MiniBatchSize=32) makes predictions by looping over images using mini-batches of size 32.

    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 padding options, use the SequenceLength arguments.

    Note

    If you specify the input data as a minibatchqueue object, then the minibatchpredict function uses the mini-batch size specified by this argument and not the MiniBatchSize property of the minibatchqueue object.

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

    Layers to extract outputs from, specified as a string array or a cell array of character vectors containing the layer names.

    • If Outputs(i) corresponds to a layer with a single output, then Outputs(i) is the name of the layer.

    • If Outputs(i) corresponds to a layer with multiple outputs, then Outputs(i) is the layer name followed by the / character and the name of the layer output: "layerName/outputName".

    The default value is net.OutputNames.

    Performance optimization, specified as one of these values:

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

    • "mex" — Compile and execute a MEX function. This option is available only when using a GPU. You must store the input data or the network learnable parameters as gpuArray objects. Using a GPU requires Parallel Computing Toolbox™ and a supported GPU device. For information on 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.

    • "none" — Disable all acceleration.

    When you use the "auto" or "mex" option, the software can offer performance benefits at the expense of an increased initial run time. Subsequent calls to the function are typically faster. Use performance optimization when you call the function multiple times using different input data.

    When Acceleration is "mex", the software generates and executes a MEX function based on the model and parameters you specify in the function call. A single model can have several associated MEX functions at one time. Clearing the model variable also clears any MEX functions associated with that model.

    When Acceleration is "auto", the software does not generate a MEX function.

    The "mex" option is available only when you use a GPU. You must have a C/C++ compiler installed and the GPU Coder™ Interface for Deep Learning support package. Install the support package using the Add-On Explorer in MATLAB®. For setup instructions, see MEX Setup (GPU Coder). GPU Coder is not required.

    The "mex" option has these limitations:

    • Only the single precision data type is supported. The input data or the network learnable parameters must have the underlying data type single.

    • Networks with inputs that are not connected to an input layer are not supported.

    • Traced dlarray objects are not supported. This means that the "mex" option is not supported inside a call to the dlfeval function.

    • Not all layers are supported. For a list of supported layers, see Supported Layers (GPU Coder).

    • MATLAB Compiler™ does not support deploying your network when using the "mex" option.

    For quantized networks, the "mex" option requires a CUDA® enabled NVIDIA® GPU with compute capability 6.1, 6.3, or higher.

    Hardware resource, specified as one of these values:

    • "auto" — Use a GPU if one is available. Otherwise, use the CPU. If net is a quantized network with the TargetLibrary property set to "none", use the CPU even if a GPU is available.

    • "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 pad or truncate the input sequences, specified as one of these options:

    • "longest" — Pad sequences 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 to have the same length as the shortest sequence. This option ensures that the function does not add padding, at the cost of discarding data.

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

    Direction of padding or truncation, specified as one of these options:

    • "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 each sequence.

    • "left" — Pad or truncate sequences on the left. The software truncates or adds padding to the start of each sequence 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 argument 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 effects of padding and truncating sequences, see Sequence Padding and Truncation.

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

    Do not pad sequences with NaN, because doing so can propagate errors through the neural network.

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

    Description of the input data dimensions, specified as a string array, character vector, or cell array of character vectors.

    If the InputDataFormats argument value is "auto", then the software uses the formats expected by the network input. Otherwise, the software uses the specified formats for the corresponding network input.

    A data format is a string of characters, where each character describes the type of the corresponding data dimension.

    The characters are:

    • "S" — Spatial

    • "C" — Channel

    • "B" — Batch

    • "T" — Time

    • "U" — Unspecified

    For example, consider an array containing a batch of sequences where the first, second, and third dimensions correspond to channels, observations, and time steps, respectively. You can specify that this array has the format "CBT" (channel, batch, time).

    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 a neural network with multiple inputs, net, specify an array of input data formats, where InputDataFormats(i) corresponds to the input net.InputNames(i).

    For more information, see Deep Learning Data Formats.

    Data Types: char | string | cell

    Description of the output data dimensions, specified as one of these values:

    • "auto" — If the output data has the same number of dimensions as the input data, then the minibatchpredict function uses the format specified by InputDataFormats. If the output data has a different number of dimensions than the input data, then the minibatchpredict function automatically permutes the dimensions of the output data so that they are consistent with the network input layers or the InputDataFormats value.

    • String, character vector, or cell array of character vectors — The minibatchpredict function uses the specified data formats.

    A data format is a string of characters, where each character describes the type of the corresponding data dimension.

    The characters are:

    • "S" — Spatial

    • "C" — Channel

    • "B" — Batch

    • "T" — Time

    • "U" — Unspecified

    For example, consider an array containing a batch of sequences where the first, second, and third dimensions correspond to channels, observations, and time steps, respectively. You can specify that this array has the format "CBT" (channel, batch, time).

    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.

    Data Types: char | string | cell

    Flag to return padded data as a uniform array, specified as a logical 1 (true) or 0 (false). When you set the value to 0, the software outputs a cell array of predictions.

    Output Arguments

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    Neural network predictions, returned as numeric arrays, dlarray objects, or cell arrays Y1,...,YM, where M is the number of network outputs.

    The predictions Yi correspond to the output Outputs(i).

    More About

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    Floating-Point Arithmetic

    The minibatchpredict function converts integer numeric array and datastore inputs to single precision floating point values. For minibatchqueue inputs, the software uses the data type specified by the OutputCast property of that input.

    When you use prediction or validation functions with a dlnetwork object with single-precision learnable and state parameters, the software performs the computations using single-precision, floating-point arithmetic.

    When you use prediction or validation functions with a dlnetwork object with double-precision learnable and state parameters:

    • If the input data is single precision, the software performs the computations using single-precision, floating-point arithmetic.

    • If the input data is double precision, the software performs the computations using double-precision, floating-point arithmetic.

    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. If you require determinism when performing deep learning operations using a GPU, use the deep.gpu.deterministicAlgorithms function (since R2024b).

    If you use the rng function to set the same random number generator and seed, then predictions made using the CPU are reproducible.

    Extended Capabilities

    Version History

    Introduced in R2024a

    See Also

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    Topics