minibatchpredict
Syntax
Description
[
specifies additional options using one or more name-value arguments. For example,
Y1,...,YM
] = minibatchpredict(___,Name=Value
)MiniBatchSize=32
makes predictions by looping over mini-batches of size
32.
Examples
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 = 10×1 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 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
Input Arguments
Neural network, specified as a dlnetwork
object.
Image data, specified as a numeric array, categorical 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. Neural networks typically have an input layer that specifies the expected layout of the data.
Most datastores and functions return data in the layout that the network expects. If your data
is in a different layout, then indicate the layout by using the InputDataFormats
name-value argument or by specifying the input
data as a formatted dlarray
object. Specifying the
InputDataFormats
name-value argument is usually easier
than adjusting the layout of the input data manually.
For neural networks that do not have input layers, you must use the
InputDataFormats
name-value argument or 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.
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 |
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 |
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 instead. For more information, see Deep Learning Data Formats.
Categorical Array (since R2025a)
For images of categorical values (such as labeled pixel maps) that fit in memory and does not require additional processing, you can specify the images as categorical arrays.
The software automatically converts categorical inputs to numeric values and
passes them to the neural network. To specify how the software converts categorical
inputs to numeric values, use the CategoricalInputEncoding
argument. The layout of categorical arrays
depend on the type of image data and must be consistent with the InputDataFormats
.
Most networks expect categorical image data passed to the
minibatchpredict
function in the layouts in this
table.
Data | Layout |
---|---|
2-D categorical images | h-by-w-by-1-by-N array, where h and w are the height and width of the images, respectively, and N is the number of images. After the software converts this data to
numeric arrays, data in this layout has the data format
|
3-D categorical images | h-by-w-by-d-by-1-by-N array, where h, w, and d are the height, width, and depth of the images, respectively, and N is the number of images. Data in this layout has the data format
|
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 instead. For more information, see Deep Learning Data Formats.
Datastore
Datastores read batches of images. 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:
Datastore | Description | Example 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 | 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. |
|
CombinedDatastore | Datastore that reads from two or more underlying datastores. | Make predictions using a network 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
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. When you do, the minibatchpredict
function ignores the
MiniBatchSize
property of the object and uses the MiniBatchSize
name-value 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, categorical array,
dlarray
object, cell array, 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 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 return data in the layout that the network expects. If your data
is in a different layout, then indicate the layout by using the InputDataFormats
name-value argument or by specifying the input
data as a formatted dlarray
object. Specifying the
InputDataFormats
name-value argument is usually easier
than adjusting the layout of the input data manually.
For neural networks that do not have input layers, you must use the
InputDataFormats
name-value argument or formatted
dlarray
objects.
For more information, see Deep Learning Data Formats.
Numeric Array, Categorical 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,
categorical array, or a dlarray
object, or a data set of sequences as
a cell array of numeric arrays, categorical arrays, or dlarray
objects.
For cell array input, the cell array must be an
N-by-1 cell array of numeric arrays, categorical arrays, or
dlarray
objects, where N is the number of
observations.
The software automatically converts categorical
inputs to numeric values and passes them to the neural network. To specify how the
software converts categorical inputs to numeric values, use the CategoricalInputEncoding
argument.
The size and shape of the numeric arrays, categorical arrays, or
dlarray
objects that represent sequences depend on the type of
sequence data and must be consistent with the InputDataFormats
argument.
Most networks with a sequence input layer expect sequence data passed to the
minibatchpredict
function in the layouts in this table.
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. |
Categorical vector sequences | s-by-1 categorical arrays, where s is the number of time steps of the sequences. |
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. |
Categorical 1-D image sequences | h-by-1-by-s categorical arrays, where h corresponds to the height of the images 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. |
Categorical 2-D image sequences | h-by-w-by-1-by-s arrays, where h and w correspond to the height and width 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. |
Categorical 3-D image sequences | h-by-w-by-d-by-1-by-s, where h, w, and d correspond to the height, width, and depth 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 instead. For more information, see Deep Learning Data Formats.
Datastore
Datastores read batches of sequences. 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:
Datastore | Description | Example Usage |
---|---|---|
TransformedDatastore | Datastore that transforms batches of data read from an underlying datastore using a custom transformation function. |
|
CombinedDatastore | Datastore that reads from two or more underlying datastores. | Make predictions using a 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 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. When you do, the minibatchpredict
function ignores the
MiniBatchSize
property of the object and uses the MiniBatchSize
name-value 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, categorical 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 return data in the layout that the network expects. If your data
is in a different layout, then indicate the layout by using the InputDataFormats
name-value argument or by specifying the input
data as a formatted dlarray
object. Specifying the
InputDataFormats
name-value argument is usually easier
than adjusting the layout of the input data manually.
For neural networks that do not have input layers, you must use the
InputDataFormats
name-value argument or 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.
Categorical Array (since R2025a)
For discrete features that fit in memory and does not require additional processing like custom transformations, you can specify the feature data as a categorical array.
The software automatically converts categorical inputs to numeric values and
passes them to the neural network. To specify how the software converts categorical
inputs to numeric values, use the CategoricalInputEncoding
argument. The layout of categorical arrays must
be consistent with the InputDataFormats
argument.
Most networks with categorical feature input expect input data specified as a
N-by-1 vector, where N is the number of
observations. After the software converts this data to numeric arrays, data in this
layout has the data format "BC"
(batch, channel). The size of the
"C"
(channel) dimension depends on the CategoricalInputEncoding
argument.
For data in a different layout, indicate that your data has a different layout by
using the InputDataFormats
training option or use a formatted
dlarray
object instead. For more information, see Deep Learning Data Formats.
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.
Datastore
Datastores read batches of feature data. 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 Type | Description | Example Usage |
---|---|---|
TransformedDatastore | Datastore that transforms batches of data read from an underlying datastore using a custom transformation function. |
|
CombinedDatastore | Datastore that reads from two or more underlying datastores. | Make predictions using a neural network 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 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. When you do, the minibatchpredict
function ignores the
MiniBatchSize
property of the object and uses the MiniBatchSize
name-value 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,
categorical 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 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 return data in the layout that the network expects. If your data
is in a different layout, then indicate the layout by using the InputDataFormats
name-value argument or by specifying the input
data as a formatted dlarray
object. Specifying the
InputDataFormats
name-value argument is usually easier
than adjusting the layout of the input data manually.
For neural networks that do not have input layers, you must use the
InputDataFormats
name-value argument or formatted
dlarray
objects.
For more information, see Deep Learning Data Formats.
Numeric Arrays, Categorical Arrays, or dlarray
Objects
For data that fits in memory and does not require additional processing like
custom transformations, you can specify data as a numeric array, categorical array, or
a dlarray
object.
For a neural network with an inputLayer
object, the expected
layout of input data is a given by the InputFormat
property of the
layer.
The software automatically converts categorical inputs to numeric values and
passes them to the neural network. To specify how the software converts categorical
inputs to numeric values, use the CategoricalInputEncoding
argument. The layout of categorical arrays must
be consistent with the InputDataFormats
argument.
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 instead. For more information, see Deep Learning Data Formats.
Datastores
Datastores read batches of data. 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 Type | Description | Example Usage |
---|---|---|
TransformedDatastore | Datastore that transforms batches of data read from an underlying datastore using a custom transformation function. |
|
CombinedDatastore | Datastore that reads from two or more underlying datastores. | Make predictions using a neural network 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 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. When you do, the minibatchpredict
function ignores the
MiniBatchSize
property of the object and uses the MiniBatchSize
name-value 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, categorical 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 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 return data in the layout that the network expects. If your data
is in a different layout, then indicate the layout by using the InputDataFormats
name-value argument or by specifying the input
data as a formatted dlarray
object. Specifying the
InputDataFormats
name-value argument is usually easier
than adjusting the layout of the input data manually.
For neural networks that do not have input layers, you must use the
InputDataFormats
name-value argument or 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 affect 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
name-value argument.
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, thenOutputs(i)
is the name of the layer.If
Outputs(i)
corresponds to a layer with multiple outputs, thenOutputs(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 asgpuArray
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 Set Up Compiler (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 typesingle
.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 thedlfeval
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. Ifnet
is a quantized network with theTargetLibrary
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, although 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.
Recurrent layers process sequence data one time step at a time, so 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
name-value 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
name-value argument 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
Since R2025a
Encoding of categorical inputs, specified as one of these values:
"integer"
— Convert categorical inputs to their integer value. In this case, the network must have one input channel for each of the categorical inputs."one-hot"
— Convert categorical inputs to one-hot encoded vectors. In this case, the network must havenumCategories
channels for each of the categorical inputs, wherenumCategories
is the number of categories of the corresponding categorical input.
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 that represents a batch of sequences where the first,
second, and third dimensions correspond to channels, observations, and time steps,
respectively. You can describe the data as having 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 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
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 theminibatchpredict
function uses the format specified byInputDataFormats
. If the output data has a different number of dimensions than the input data, then theminibatchpredict
function automatically permutes the dimensions of the output data so that they are consistent with the network input layers or theInputDataFormats
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 that represents a batch of sequences where the first,
second, and third dimensions correspond to channels, observations, and time steps,
respectively. You can describe the data as having 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
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)
.
For a classification neural network, the elements of the output correspond to the scores for
each class. The order of the scores matches the order of the categories in the training
data. For example, if you train the neural network using the categorical labels
TTrain
, then the order of the scores matches the order of the
categories given by categories(TTrain)
.
More About
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.
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.
Tips
To make predictions in parallel using multiple GPUs, create a parallel pool with one worker per GPU, divide up your data, and make the predictions in parallel. For an example showing how to make predictions using multiple GPUs, see Train Network Using Automatic Multi-GPU Support.
Extended Capabilities
The minibatchpredict function fully supports GPU acceleration.
By default, minibatchpredict
uses a GPU if one is available. If
net
is a quantized network with the TargetLibrary
property set to "none"
, minibatchpredict
uses the CPU
even if a GPU is available. You can specify the hardware that the
minibatchpredict
function uses by specifying the ExecutionEnvironment
name-value argument.
For more information, see Run MATLAB Functions on a GPU (Parallel Computing Toolbox).
Version History
Introduced in R2024aTo specify how to convert categorical inputs to numeric values for making predictions
with a neural network, use the CategoricalInputEncoding
argument.
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