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classify

Classify data using trained deep learning neural network

Description

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

For networks with multiple outputs, use the predict function instead and set the ReturnCategorical option to true.

example

Y = classify(net,images) predicts the class labels of the specified images using the trained network net.

example

Y = classify(net,sequences) predicts the class labels of the specified sequences using the trained network net.

example

Y = classify(net,features) predicts the class labels of the specified feature data using the trained network net.

Y = classify(net,X1,...,XN) predicts the class labels for the data in the numeric arrays or cell arrays X1, …, XN for the multi-input network net. The input Xi corresponds to the network input net.InputNames(i).

example

Y = classify(net,mixed) predicts the class labels using the trained network net with multiple inputs of mixed data types.

[Y,scores] = classify(___) also returns the classification scores corresponding to the class labels using any of the previous input arguments.

example

___ = classify(___,Name=Value) predicts class labels with additional options specified by one or more name-value arguments.

Tip

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

Examples

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Load the pretrained network digitsNet. This network is a classification convolutional neural network that classifies handwritten digits.

load digitsNet

View the network layers. The output layer of the network is a classification layer.

layers = net.Layers
layers = 
  15x1 Layer array with layers:

     1   'imageinput'    Image Input             28x28x1 images with 'zerocenter' normalization
     2   'conv_1'        2-D Convolution         8 3x3x1 convolutions with stride [1  1] and padding 'same'
     3   'batchnorm_1'   Batch Normalization     Batch normalization with 8 channels
     4   'relu_1'        ReLU                    ReLU
     5   'maxpool_1'     2-D Max Pooling         2x2 max pooling with stride [2  2] and padding [0  0  0  0]
     6   'conv_2'        2-D Convolution         16 3x3x8 convolutions with stride [1  1] and padding 'same'
     7   'batchnorm_2'   Batch Normalization     Batch normalization with 16 channels
     8   'relu_2'        ReLU                    ReLU
     9   'maxpool_2'     2-D Max Pooling         2x2 max pooling with stride [2  2] and padding [0  0  0  0]
    10   'conv_3'        2-D Convolution         32 3x3x16 convolutions with stride [1  1] and padding 'same'
    11   'batchnorm_3'   Batch Normalization     Batch normalization with 32 channels
    12   'relu_3'        ReLU                    ReLU
    13   'fc'            Fully Connected         10 fully connected layer
    14   'softmax'       Softmax                 softmax
    15   'classoutput'   Classification Output   crossentropyex with '0' and 9 other classes

Load the test images.

digitDatasetPath = fullfile(toolboxdir("nnet"),"nndemos","nndatasets","DigitDataset");
imdsTest = imageDatastore(digitDatasetPath,IncludeSubfolders=true);

Classify the images using the classify function.

YTest = classify(net,imdsTest);

View some of the test images at random with their predictions.

numImages = 9;
idx = randperm(numel(imdsTest.Files),numImages);

figure
tiledlayout("flow")
for i = 1:numImages
    nexttile
    imshow(imdsTest.Files{idx(i)});
    title("Predicted Label: " + string(YTest(idx(i))))
end

Figure contains 9 axes objects. Axes object 1 with title Predicted Label: 8 contains an object of type image. Axes object 2 with title Predicted Label: 9 contains an object of type image. Axes object 3 with title Predicted Label: 1 contains an object of type image. Axes object 4 with title Predicted Label: 9 contains an object of type image. Axes object 5 with title Predicted Label: 6 contains an object of type image. Axes object 6 with title Predicted Label: 0 contains an object of type image. Axes object 7 with title Predicted Label: 2 contains an object of type image. Axes object 8 with title Predicted Label: 5 contains an object of type image. Axes object 9 with title Predicted Label: 9 contains an object of type image.

Load the pretrained network JapaneseVowelsNet. This network is a pretrained LSTM network trained on the Japanese Vowels data set as described in [1] and [2]. It was trained on the sequences sorted by sequence length with a mini-batch size of 27.

load JapaneseVowelsNet

View the network architecture.

net.Layers
ans = 
  5x1 Layer array with layers:

     1   'sequenceinput'   Sequence Input          Sequence input with 12 dimensions
     2   'lstm'            LSTM                    LSTM with 100 hidden units
     3   'fc'              Fully Connected         9 fully connected layer
     4   'softmax'         Softmax                 softmax
     5   'classoutput'     Classification Output   crossentropyex with '1' and 8 other classes

Load the test data.

[XTest,TTest] = japaneseVowelsTestData;

Classify the test data.

YTest = classify(net,XTest);

View the predictions in a confusion chart.

figure
confusionchart(TTest,YTest)

Figure contains an object of type ConfusionMatrixChart.

Calculate the classification accuracy of the predictions.

accuracy = mean(YTest == TTest)
accuracy = 0.8595

Load the pretrained network TransmissionCasingNet. This network classifies the gear tooth condition of a transmission system given a mixture of numeric sensor readings, statistics, and categorical inputs.

load TransmissionCasingNet

View the network architecture.

net.Layers
ans = 
  7x1 Layer array with layers:

     1   'input'         Feature Input           22 features with 'zscore' normalization
     2   'fc_1'          Fully Connected         50 fully connected layer
     3   'batchnorm'     Batch Normalization     Batch normalization with 50 channels
     4   'relu'          ReLU                    ReLU
     5   'fc_2'          Fully Connected         2 fully connected layer
     6   'softmax'       Softmax                 softmax
     7   'classoutput'   Classification Output   crossentropyex with classes 'No Tooth Fault' and 'Tooth Fault'

Read the transmission casing data from the CSV file "transmissionCasingData.csv".

filename = "transmissionCasingData.csv";
tbl = readtable(filename,TextType="string");

Convert the labels for prediction to categorical using the convertvars function.

labelName = "GearToothCondition";
tbl = convertvars(tbl,labelName,"categorical");

To make predictions using categorical features, you must first convert the categorical features to numeric. First, convert the categorical predictors to categorical using the convertvars function by specifying a string array containing the names of all the categorical input variables. This data set has two categorical features named "SensorCondition" and "ShaftCondition".

categoricalInputNames = ["SensorCondition" "ShaftCondition"];
tbl = convertvars(tbl,categoricalInputNames,"categorical");

Loop over the categorical input variables. For each variable:

  • Convert the categorical values to one-hot encoded vectors using the onehotencode function.

  • Add the one-hot vectors to the table using the addvars function. Specify insertion of the vectors after the column containing the corresponding categorical data.

  • Remove the corresponding column containing the categorical data.

for i = 1:numel(categoricalInputNames)
    name = categoricalInputNames(i);
    oh = onehotencode(tbl(:,name));
    tbl = addvars(tbl,oh,After=name);
    tbl(:,name) = [];
end

Split the vectors into separate columns using the splitvars function.

tbl = splitvars(tbl);

View the first few rows of the table.

head(tbl)
    SigMean     SigMedian    SigRMS    SigVar     SigPeak    SigPeak2Peak    SigSkewness    SigKurtosis    SigCrestFactor    SigMAD     SigRangeCumSum    SigCorrDimension    SigApproxEntropy    SigLyapExponent    PeakFreq    HighFreqPower    EnvPower    PeakSpecKurtosis    No Sensor Drift    Sensor Drift    No Shaft Wear    Shaft Wear    GearToothCondition
    ________    _________    ______    _______    _______    ____________    ___________    ___________    ______________    _______    ______________    ________________    ________________    _______________    ________    _____________    ________    ________________    _______________    ____________    _____________    __________    __________________

    -0.94876     -0.9722     1.3726    0.98387    0.81571       3.6314        -0.041525       2.2666           2.0514         0.8081        28562              1.1429             0.031581            79.931            0          6.75e-06       3.23e-07         162.13                0                1                1              0           No Tooth Fault  
    -0.97537    -0.98958     1.3937    0.99105    0.81571       3.6314        -0.023777       2.2598           2.0203        0.81017        29418              1.1362             0.037835            70.325            0          5.08e-08       9.16e-08         226.12                0                1                1              0           No Tooth Fault  
      1.0502      1.0267     1.4449    0.98491     2.8157       3.6314         -0.04162       2.2658           1.9487        0.80853        31710              1.1479             0.031565            125.19            0          6.74e-06       2.85e-07         162.13                0                1                0              1           No Tooth Fault  
      1.0227      1.0045     1.4288    0.99553     2.8157       3.6314        -0.016356       2.2483           1.9707        0.81324        30984              1.1472             0.032088             112.5            0          4.99e-06        2.4e-07         162.13                0                1                0              1           No Tooth Fault  
      1.0123      1.0024     1.4202    0.99233     2.8157       3.6314        -0.014701       2.2542           1.9826        0.81156        30661              1.1469              0.03287            108.86            0          3.62e-06       2.28e-07         230.39                0                1                0              1           No Tooth Fault  
      1.0275      1.0102     1.4338     1.0001     2.8157       3.6314         -0.02659       2.2439           1.9638        0.81589        31102              1.0985             0.033427            64.576            0          2.55e-06       1.65e-07         230.39                0                1                0              1           No Tooth Fault  
      1.0464      1.0275     1.4477     1.0011     2.8157       3.6314        -0.042849       2.2455           1.9449        0.81595        31665              1.1417             0.034159            98.838            0          1.73e-06       1.55e-07         230.39                0                1                0              1           No Tooth Fault  
      1.0459      1.0257     1.4402    0.98047     2.8157       3.6314        -0.035405       2.2757            1.955        0.80583        31554              1.1345               0.0353            44.223            0          1.11e-06       1.39e-07         230.39                0                1                0              1           No Tooth Fault  

Extract the test labels from the table.

TTest = tbl{:,labelName};

Predict the labels of the test data using the trained network and calculate the accuracy. Specify the same mini-batch size used for training.

YTest = classify(net,tbl(:,1:end-1));

Visualize the predictions in a confusion matrix.

figure
confusionchart(TTest,YTest)

Figure contains an object of type ConfusionMatrixChart.

Calculate the classification accuracy. The accuracy is the proportion of the labels that the network predicts correctly.

accuracy = mean(YTest == TTest)
accuracy = 0.9952

Input Arguments

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Trained network, specified as a SeriesNetwork or a DAGNetwork object. You can get a trained network by importing a pretrained network (for example, by using the googlenet function) or by training your own network using trainNetwork.

Image data, specified as one of the following.

Data TypeDescriptionExample Usage
DatastoreImageDatastoreDatastore 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.

AugmentedImageDatastoreDatastore that applies random affine geometric transformations, including resizing, rotation, reflection, shear, and translation

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

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

  • Transform datastores with outputs not supported by classify.

  • Apply custom transformations to datastore output.

CombinedDatastoreDatastore that reads from two or more underlying datastores

  • Make predictions using networks with multiple inputs.

  • Combine predictors from different data sources.

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

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

For details, see Develop Custom Mini-Batch Datastore.

Numeric arrayImages specified as a numeric arrayMake predictions using data that fits in memory and does not require additional processing like resizing.
TableImages specified as a tableMake predictions using data stored in a table.

When you use a datastore with networks with multiple inputs, the datastore must be a TransformedDatastore or CombinedDatastore object.

Tip

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

Datastore

Datastores read mini-batches of images and responses. Use datastores when you have data that does not fit in memory or when you want to resize the input data.

These datastores are directly compatible with classify for image data.:

Note that ImageDatastore objects allow for batch reading of JPG or PNG image files using prefetching. If you use a custom function for reading the images, then ImageDatastore objects do not prefetch.

Tip

Use augmentedImageDatastore for efficient preprocessing of images for deep learning, including image resizing.

Do not use the readFcn option of the imageDatastore function for preprocessing or resizing, as this option 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 format required by classify.

The required format of the datastore output depends on the network architecture.

Network ArchitectureDatastore OutputExample Output
Single input

Table or cell array, where the first column specifies the predictors.

Table elements must be scalars, row vectors, or 1-by-1 cell arrays containing a numeric array.

Custom datastores must output tables.

data = read(ds)
data =

  4×1 table

        Predictors    
    __________________

    {224×224×3 double}
    {224×224×3 double}
    {224×224×3 double}
    {224×224×3 double}
data = read(ds)
data =

  4×1 cell array

    {224×224×3 double}
    {224×224×3 double}
    {224×224×3 double}
    {224×224×3 double}
Multiple input

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

The first numInputs columns specify the predictors for each input.

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

data = read(ds)
data =

  4×2 cell array

    {224×224×3 double}    {128×128×3 double}
    {224×224×3 double}    {128×128×3 double}
    {224×224×3 double}    {128×128×3 double}
    {224×224×3 double}    {128×128×3 double}

The format of the predictors depends on the type of data.

DataFormat
2-D images

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

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

For more information, see Datastores for Deep Learning.

Numeric Array

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

The size and shape of the numeric array depends on the type of image data.

DataFormat
2-D images

h-by-w-by-c-by-N numeric 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

3-D imagesh-by-w-by-d-by-c-by-N numeric 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

Table

As an alternative to datastores or numeric arrays, you can also specify images in a table.

When you specify images in a table, each row in the table corresponds to an observation.

For image input, the predictors must be in the first column of the table, specified as one of the following:

  • Absolute or relative file path to an image, specified as a character vector

  • 1-by-1 cell array containing a h-by-w-by-c numeric array representing a 2-D image, where h, w, and c correspond to the height, width, and number of channels of the image, respectively

Tip

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

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

Sequence or time series data, specified as one of the following.

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

  • Transform datastores with outputs not supported by classify.

  • Apply custom transformations to datastore output.

CombinedDatastoreDatastore that reads from two or more underlying datastores

  • Make predictions using networks with multiple inputs.

  • Combine predictors from different data sources.

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

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

For details, see Develop Custom Mini-Batch Datastore.

Numeric or cell arrayA single sequence specified as a numeric array or a data set of sequences specified as cell array of numeric arraysMake predictions using data that fits in memory and does not require additional processing like custom transformations.

Datastore

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

These datastores are directly compatible with classify for sequence data:

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 classify. For example, you can transform and combine data read from in-memory arrays and CSV files using an ArrayDatastore and an TabularTextDatastore object, respectively.

The datastore must return data in a table or cell array. Custom mini-batch datastores must output tables.

Datastore OutputExample Output
Table
data = read(ds)
data =

  4×2 table

        Predictors    
    __________________

    {12×50 double}
    {12×50 double}
    {12×50 double}
    {12×50 double}
Cell array
data = read(ds)
data =

  4×2 cell array

    {12×50 double}
    {12×50 double}
    {12×50 double}
    {12×50 double}

The format of the predictors depends on the type of data.

DataFormat of Predictors
Vector sequence

c-by-s matrix, where c is the number of features of the sequence and s is the sequence length

1-D image sequence

h-by-c-by-s array, where h and c correspond to the height and number of channels of the image, respectively, and s is the sequence length.

Each sequence in the mini-batch must have the same sequence length.

2-D image sequence

h-by-w-by-c-by-s array, where h, w, and c correspond to the height, width, and number of channels of the image, respectively, and s is the sequence length.

Each sequence in the mini-batch must have the same sequence length.

3-D image sequence

h-by-w-by-d-by-c-by-s array, where h, w, d, and c correspond to the height, width, depth, and number of channels of the image, respectively, and s is the sequence length.

Each sequence in the mini-batch must have the same sequence length.

For predictors returned in tables, the elements must contain a numeric scalar, a numeric row vector, or a 1-by-1 cell array containing a numeric array.

For more information, see Datastores for Deep Learning.

Numeric 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 a data set of sequences as a cell array of numeric arrays.

For cell array input, the cell array must be an N-by-1 cell array of numeric arrays, where N is the number of observations. The size and shape of the numeric array representing a sequence depends on the type of sequence data.

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

Tip

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

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

Feature data, specified as one of the following.

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

  • Transform datastores with outputs not supported by classify.

  • Apply custom transformations to datastore output.

CombinedDatastoreDatastore that reads from two or more underlying datastores

  • Make predictions using networks with multiple inputs.

  • Combine predictors from different data sources.

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

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

For details, see Develop Custom Mini-Batch Datastore.

TableFeature data specified as a tableMake predictions using data stored in a table.
Numeric arrayFeature data specified as numeric arrayMake predictions using data that fits in memory and does not require additional processing like custom transformations.

Datastore

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

These datastores are directly compatible with classify for feature data:

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 classify. For more information, see Datastores for Deep Learning.

For networks with multiple inputs, the datastore must be a TransformedDatastore or CombinedDatastore object.

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

Network ArchitectureDatastore OutputExample Output
Single input layer

Table or cell array with at least one column, where the first column specifies the predictors.

Table elements must be scalars, row vectors, or 1-by-1 cell arrays containing a numeric array.

Custom mini-batch datastores must output tables.

Table for network with one input:

data = read(ds)
data =

  4×2 table

        Predictors    
    __________________

    {24×1 double}
    {24×1 double}
    {24×1 double}
    {24×1 double}

Cell array for network with one input:

data = read(ds)
data =

  4×1 cell array

    {24×1 double}
    {24×1 double}
    {24×1 double}
    {24×1 double}

Multiple input layers

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

The first numInputs columns specify the predictors for each input.

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

Cell array for network with two inputs:

data = read(ds)
data =

  4×3 cell array

    {24×1 double}    {28×1 double}
    {24×1 double}    {28×1 double}
    {24×1 double}    {28×1 double}
    {24×1 double}    {28×1 double}

The predictors must be c-by-1 column vectors, where c is the number of features.

For more information, see Datastores for Deep Learning.

Table

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

Each row in the table corresponds to an observation. The arrangement of predictors in the table columns depends on the type of task.

TaskPredictors
Feature classification

Features specified in one or more columns as scalars.

Numeric Array

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.

The numeric array must be an N-by-numFeatures numeric array, where N is the number of observations and numFeatures is the number of features of the input data.

Tip

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

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

Numeric or cell arrays for networks with multiple inputs.

For image, sequence, and feature predictor input, the format of the predictors must match the formats described in the images, sequences, or features argument descriptions, respectively.

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

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

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

Mixed data, specified as one of the following.

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

  • Make predictions using networks with multiple inputs.

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

  • Apply custom transformations to datastore output.

CombinedDatastoreDatastore that reads from two or more underlying datastores

  • Make predictions using networks with multiple inputs.

  • Combine predictors from different data sources.

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

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

For details, see Develop Custom Mini-Batch Datastore.

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

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

Datastore OutputExample Output

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

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

data = read(ds)
data =

  4×3 cell array

    {24×1 double}    {28×1 double}
    {24×1 double}    {28×1 double}
    {24×1 double}    {28×1 double}
    {24×1 double}    {28×1 double}

For image, sequence, and feature predictor input, the format of the predictors must match the formats described in the images, sequences, or features argument descriptions, respectively.

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

Tip

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

Name-Value Arguments

Specify optional pairs of arguments as Name1=Value1,...,NameN=ValueN, where Name is the argument name and Value is the corresponding value. Name-value arguments must appear after other arguments, but the order of the pairs does not matter.

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

Example: MiniBatchSize=256 specifies the mini-batch size as 256.

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

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

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

Performance optimization, specified as one of the following:

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

  • "mex" — Compile and execute a MEX function. This option is available only when you use a GPU. 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.

If Acceleration is "auto", then MATLAB® applies a number of compatible optimizations and does not generate a MEX function.

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

The "mex" option generates and executes a MEX function based on the network and parameters used in the function call. You can have several MEX functions associated with a single network at one time. Clearing the network variable also clears any MEX functions associated with that network.

The "mex" option is available when you use a single GPU.

To use the "mex" option, you must have a C/C++ compiler installed and the GPU Coder™ Interface for Deep Learning Libraries 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 supports networks that contain the layers listed on the Supported Layers (GPU Coder) page, except for the sequenceInputLayer and featureInputLayer objects.

MATLAB Compiler™ does not support deploying networks when you use the "mex" option.

Hardware resource, specified as one of the following:

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

  • "gpu" — Use the GPU. Using a GPU requires 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.

  • "cpu" — Use the CPU.

  • "multi-gpu" — Use multiple GPUs on one machine, using a local parallel pool based on your default cluster profile. If there is no current parallel pool, the software starts a parallel pool with pool size equal to the number of available GPUs.

  • "parallel" — Use a local or remote parallel pool based on your default cluster profile. If there is no current parallel pool, the software starts one using the default cluster profile. If the pool has access to GPUs, then only workers with a unique GPU perform computation. If the pool does not have GPUs, then computation takes place on all available CPU workers instead.

For more information on when to use the different execution environments, see Scale Up Deep Learning in Parallel, on GPUs, and in the Cloud.

The "gpu", "multi-gpu", and "parallel" options require Parallel Computing Toolbox. To use a GPU for deep learning, you must also have a supported GPU device. For information on supported devices, see GPU Computing Requirements (Parallel Computing Toolbox). If you choose one of these options and Parallel Computing Toolbox or a suitable GPU is not available, then the software returns an error.

To make predictions in parallel with networks with recurrent layers (by setting ExecutionEnvironment to either "multi-gpu" or "parallel"), the SequenceLength option must be "shortest" or "longest".

Networks with custom layers that contain State parameters do not support making predictions in parallel.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Output Arguments

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Predicted class labels, returned as a categorical vector or a cell array of categorical vectors. The format of Y depends on the type of task.

The following table describes the format for classification tasks.

TaskFormat
Image or feature classificationN-by-1 categorical vector of labels, where N is the number of observations
Sequence-to-label classification
Sequence-to-sequence classification

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

For sequence-to-sequence classification tasks with one observation, sequences can be a matrix. In this case, Y is a categorical sequence of labels.

Predicted scores or responses, returned as a matrix or a cell array of matrices. The format of scores depends on the type of task.

The following table describes the format of scores.

TaskFormat
Image classificationN-by-K matrix, where N is the number of observations and K is the number of classes
Sequence-to-label classification
Feature classification
Sequence-to-sequence classification

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

For sequence-to-sequence classification tasks with one observation, sequences can be a matrix. In this case, scores is a matrix of predicted class scores.

For an example exploring classification scores, see Classify Webcam Images Using Deep Learning.

Algorithms

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

Alternatives

To classify data using a network with multiple output layers, use the predict function and set the ReturnCategorical option to 1 (true).

To compute the predicted classification scores, you can also use the predict function.

To compute the activations from a network layer, use the activations function.

For recurrent networks such as LSTM networks, you can make predictions and update the network state using classifyAndUpdateState and predictAndUpdateState.

References

[1] Kudo, Mineichi, Jun Toyama, and Masaru Shimbo. “Multidimensional Curve Classification Using Passing-through Regions.” Pattern Recognition Letters 20, no. 11–13 (November 1999): 1103–11. https://doi.org/10.1016/S0167-8655(99)00077-X.

[2] UCI Machine Learning Repository: Japanese Vowels Dataset. https://archive.ics.uci.edu/ml/datasets/Japanese+Vowels.

Extended Capabilities

Version History

Introduced in R2016a

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