minibatchpredict

Mini-batched neural network prediction

Since R2024a

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.

[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 networks with multiple inputs using the specified in-memory data.

[Y1,...,YM] = minibatchpredict(___,Name=Value) specifies additional options using one or more name-value arguments.

Examples

Make Predictions Using dlnetwork Object

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. 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

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 specify the execution environment, use the ExecutionEnvironment option.

[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

Input Arguments

net — Neural network

dlnetwork object

Neural network, specified as a dlnetwork object.

images — Image data

numeric array | dlarray object | datastore | minibatchqueue object

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

Tip

For sequences of images, for example video data, use the sequences input argument.

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

Tip

Neural networks expect input data with a specific layout. For example image classification networks typically expect an image to be represented as a h-by-w-by-c numeric array, 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 to what the network expects, then indicate that your data has a different layout by using the InputDataFormats option or by specifying input data as a formatted dlarray object. It is usually easiest to adjust the InputDataFormats option than to preprocess the input data.

For neural networks that do not have input layers, you must use the InputDataFormats option 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 layout of numeric arrays and unformatted dlarray objects depend on the type of image data and must be consistent with the InputDataFormats option.

Most networks expect image data in these layouts:

Data	Layout
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 option or use a formatted dlarray object. For more information, see Deep Learning Data Formats.

Datastore

Datastores read batches of images and targets. Datastores are best suited when you have data that does not fit in memory or when you want to apply augmentations or transformations to the data.

For image data, the minibatchpredict function supports these datastores:

Datastore	Description	Example Usage
ImageDatastore	Datastore of images saved on disk.	Make predictions with images saved on disk, where the images are the same size. When the images are different sizes, use an AugmentedImageDatastore object.
AugmentedImageDatastore	Datastore 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.
TransformedDatastore	Datastore 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.
CombinedDatastore	Datastore that reads from two or more underlying datastores.	Make predictions using networks with multiple inputs.
Custom mini-batch datastore	Custom 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

Use augmentedImageDatastore for efficient preprocessing of images for deep learning, including image resizing. Do not use the ReadFcn option of ImageDatastore objects.

ImageDatastore allows batch reading of JPG or PNG image files using prefetching. If you set the ReadFcn option to a custom function, then ImageDatastore does not prefetch and is usually significantly slower.

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 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 option instead. For minibatchqueue input, the PerprocessingEnvironment property must be "serial".

Note

This argument supports complex-valued predictors and targets.

sequences — Sequence or time series data

cell array of numeric arrays | cell array of dlarray objects | numeric array | dlarray object | datastore | minibatchqueue object

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

If you have sequences of the same length that fits in memory that does not require additional processing, then it is usually easiest to specify the input data as a numeric array. If you have sequences of different lengths that fit in memory that does not require additional processing, then it is usually easiest to specify the input data as a cell array of numeric arrays. If you want to train with sequences stored on disk, or want to apply additional processing such as custom transformations, then it is usually easiest to use datastores.

Tip

Neural networks expect input data with a specific layout. For example, vector-sequence classification networks typically expect a sequence to be represented as a t-by-c numeric array, 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 to what the network expects, then indicate that your data has a different layout by using the InputDataFormats option or by specifying input data as a formatted dlarray object. It is usually easiest to adjust the InputDataFormats option than to preprocess the input data.

For neural networks that do not have input layers, you must use the InputDataFormats option 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 size and shape 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 option.

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

Data	Layout
Vector sequences	s-by-c matrices, where s and c are the numbers of time steps and channels (features) of the sequences, respectively.
1-D image sequences	h-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 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.
3-D image sequences	h-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 option or use a formatted dlarray object. For more information, see Deep Learning Data Formats.

Datastore

Datastores read batches of sequences and targets. Datastores are best suited 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:

Datastore	Description	Example Usage
TransformedDatastore	Datastore 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.
CombinedDatastore	Datastore that reads from two or more underlying datastores.	Make predictions using network with multiple inputs
Custom mini-batch datastore	Custom 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 for prediction 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 option instead. For minibatchqueue input, the PerprocessingEnvironment property must be "serial".

Note

This argument supports complex-valued predictors and targets.

features — Feature or tabular data

numeric array | dlarray object | table | datastore | minibatchqueue object

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 it is usually easiest to specify the input data as a numeric array or table. If you want to train with feature or tabular data stored on disk, or want to apply additional processing such as custom transformations, then it is usually easiest to use datastores.

Tip

Neural networks expect input data with a specific layout. For example feature classification networks typically expect feature and tabular data to be represented as a 1-by-c vector, 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 to what the network expects, then indicate that your data has a different layout by using the InputDataFormats option or by specifying input data as a formatted dlarray object. It is usually easiest to adjust the InputDataFormats option than to preprocess the input data.

For neural networks that do not have input layers, you must use the InputDataFormats option 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 layout of numeric arrays and unformatted dlarray objects depend must be consistent with the InputDataFormats option. Most networks with feature input expect input data specified as a N-by-numFeatures array, where N 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. 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. Datastores are best suited 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 Type	Description	Example Usage
TransformedDatastore	Datastore that transforms batches of data read from an underlying datastore using a custom transformation function.	Make predictions using neural networks with multiple inputs. Transform datastores with outputs not supported by the trainnet function. Apply custom transformations to datastore output.
CombinedDatastore	Datastore that reads from two or more underlying datastores.	Make predictions using neural networks with multiple inputs.
Custom mini-batch datastore	Custom 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 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 option instead. For minibatchqueue input, the PerprocessingEnvironment property must be "serial".

Note

This argument supports complex-valued predictors and targets.

data — Generic data or combinations of data types

numeric array | dlarray object | datastore | minibatchqueue object

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 that does not require additional processing, then it is usually easiest to specify the input data as a numeric array. If you want to train with data stored on disk, or want to apply additional processing, then it is usually easiest to use datastores.

Tip

Neural networks expect input data with a specific layout. For example, vector-sequence classification networks typically expect a sequence to be represented as a t-by-c numeric array, 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 to what the network expects, then indicate that your data has a different layout by using the InputDataFormats option or by specifying input data as a formatted dlarray object. It is usually easiest to adjust the InputDataFormats option than to preprocess the input data.

For neural networks that do not have input layers, you must use the InputDataFormats option 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 option or use a formatted dlarray object. For more information, see Deep Learning Data Formats.

Datastores

Datastores read batches of data and targets. Datastores are best suited 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 Type	Description	Example Usage
TransformedDatastore	Datastore that transforms batches of data read from an underlying datastore using a custom transformation function.	Make predictions using neural networks with multiple inputs. Transform outputs of datastores not supported by minibatchpredict to the have the required format. Apply custom transformations to datastore output.
CombinedDatastore	Datastore that reads from two or more underlying datastores.	Make predictions using neural networks with multiple inputs.
Custom mini-batch datastore	Custom 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 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 option instead. For minibatchqueue input, the PerprocessingEnvironment property must be "serial".

Note

This argument supports complex-valued predictors.

X1,...,XN — In-memory data for multi-input network

numeric array | dlarray object | cell array

In-memory data for multi-input network, specified as numeric arrays, dlarray objects, or cell arrays.

For multi-input networks, if you have data that fits in memory that does not require additional processing, then it is usually easiest to specify the input data as in-memory arrays. If you want to make predictions with data stored on disk, or want to apply additional processing, then it is usually easiest to use datastores.

Tip

Neural networks expect input data with a specific layout. For example, vector-sequence classification networks typically expect a sequence to be represented as a t-by-c numeric array, 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 to what the network expects, then indicate that your data has a different layout by using the InputDataFormats option or by specifying input data as a formatted dlarray object. It is usually easiest to adjust the InputDataFormats option than to preprocess the input data.

For neural networks that do not have input layers, you must use the InputDataFormats option 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.

MiniBatchSize — Size of mini-batches

128 (default) | positive integer

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

Outputs — Layers to extract outputs from

string array | cell array of character vectors

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.

Acceleration — Performance optimization

"auto" (default) | "mex" | "none"

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 when using a GPU only. The input data or the network learnable parameters must be stored 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 new 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 single precision is supported. The input data or the network learnable parameters must have underlying 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 dlfeval.

• 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.

ExecutionEnvironment — Hardware resource

"auto" (default) | "gpu" | "cpu"

Hardware resource, specified as one of these values:

• "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.

SequenceLength — Option to pad or truncate sequences

"longest" (default) | "shortest"

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

• "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 no padding is added, at the cost of discarding data.

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

SequencePaddingDirection — Direction of padding or truncation

"right" (default) | "left"

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 and truncating sequences, see Sequence Padding and Truncation.

SequencePaddingValue — Value to pad sequences

0 (default) | scalar

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

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

InputDataFormats — Description of input data dimensions

"auto" (default) | string array | cell array of character vectors | character vector

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

If InputDataFormats 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" at most once. The software ignores singleton trailing "U" dimensions after the second dimension.

For a neural networks 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

OutputDataFormats — Description of output data dimensions

"auto" (default) | string array | cell array of character vectors | character vector

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 to 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 option.

• Data formats, specified as a string array, 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" at most once. The software ignores singleton trailing "U" dimensions after the second dimension.

For more information, see Deep Learning Data Formats.

Data Types: char | string | cell

UniformOutput — Flag to return padded data as uniform array

1 (true) (default) | 0 (false)

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

Output Arguments

Y1,...,YM — Neural network predictions

numeric array | dlarray object | cell array

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

Floating-Point Arithmetic

The minibatchpredict function casts integer numeric array and datastore inputs to single precision. For minibatchqueue input, the software uses the datatype specified by their OutputCast property.

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.

Extended Capabilities

GPU Arrays

Accelerate code by running on a graphics processing unit (GPU) using Parallel Computing Toolbox™.

This function fully supports GPU acceleration.

By default, the minibatchpredict function uses a GPU if one is available. You can specify the hardware that the minibatchpredict function uses by setting the ExecutionEnvironment name-value argument.

For more information, see Run MATLAB Functions on a GPU (Parallel Computing Toolbox).

Version History

Introduced in R2024a