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predict

Predict responses 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.

Use this function to predict responses using a trained SeriesNetwork or DAGNetwork object. For information on predicting responses using a dlnetwork object, see predict.

example

Y = predict(net,images) predicts the responses of the specified images using the trained network net.

example

Y = predict(net,sequences) predicts the responses of the specified sequences using the trained network net.

Y = predict(net,features) predicts the responses of the specified feature data using the trained network net.

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

Y = predict(net,mixed) predicts the responses using the trained network net with multiple inputs of mixed data types.

[Y1,...,YM] = predict(___) predicts responses for the M outputs of a multi-output network using any of the previous input arguments. The output Yj corresponds to the network output net.OutputNames(j). To return categorical outputs for the classification output layers, set the ReturnCategorical option to 1 (true).

___ = predict(___,Name=Value) predicts the responses with additional options specified by one or more name-value arguments.

Tip

  • Use the predict function to predict responses using a regression network or to classify data using a multi-output network. To classify data using a single-output classification network, use the classify function.

  • 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.

  • For predicting responses using dlnetwork objects, see predict.

Examples

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Load the pretrained network digitsRegressionNet. This network is a regression convolutional neural network that predicts the angle of rotation of handwritten digits.

load digitsRegressionNet

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

layers = net.Layers
layers = 
  18x1 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   'avgpool2d_1'        2-D Average Pooling   2x2 average 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   'avgpool2d_2'        2-D Average Pooling   2x2 average 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   'conv_4'             2-D Convolution       32 3x3x32 convolutions with stride [1  1] and padding 'same'
    14   'batchnorm_4'        Batch Normalization   Batch normalization with 32 channels
    15   'relu_4'             ReLU                  ReLU
    16   'dropout'            Dropout               20% dropout
    17   'fc'                 Fully Connected       1 fully connected layer
    18   'regressionoutput'   Regression Output     mean-squared-error with response 'Response'

Load the test images.

XTest = digitTest4DArrayData;

Predict the responses of the input data using the predict function.

YTest = predict(net,XTest);

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

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

sz = size(XTest,1);
offset = sz/2;

figure
tiledlayout("flow")

for i = 1:numPlots
    nexttile
    imshow(XTest(:,:,:,idx(i)))
    title("Observation " + idx(i))

    hold on
    plot(offset*[1-tand(YTest(idx(i))) 1+tand(YTest(idx(i)))],[sz 0],"r--")
    hold off
end

Figure contains 9 axes objects. Axes object 1 with title Observation 4074 contains 2 objects of type image, line. Axes object 2 with title Observation 4529 contains 2 objects of type image, line. Axes object 3 with title Observation 635 contains 2 objects of type image, line. Axes object 4 with title Observation 4565 contains 2 objects of type image, line. Axes object 5 with title Observation 3160 contains 2 objects of type image, line. Axes object 6 with title Observation 488 contains 2 objects of type image, line. Axes object 7 with title Observation 1391 contains 2 objects of type image, line. Axes object 8 with title Observation 2731 contains 2 objects of type image, line. Axes object 9 with title Observation 4780 contains 2 objects of type image, line.

Load the pretrained network freqNet. This network is an LSTM regression neural network that predicts the frequency of waveforms.

load freqNet

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

net.Layers
ans = 
  4x1 Layer array with layers:

     1   'sequenceinput'      Sequence Input      Sequence input with 3 dimensions
     2   'lstm'               LSTM                LSTM with 100 hidden units
     3   'fc'                 Fully Connected     1 fully connected layer
     4   'regressionoutput'   Regression Output   mean-squared-error with response 'Response'

Load the test sequences.

load WaveformData
X = data;

Predict the responses of the input data using the predict function. Because the network was trained using sequences truncated to the shortest sequence length of each mini-batch, also truncate the test sequences by setting the SequenceLength option to "shortest".

Y = predict(net,X,SequenceLength="shortest");

Visualize the first few predictions in a plot.

figure
tiledlayout(2,2)
for i = 1:4
    nexttile
    stackedplot(X{i}',DisplayLabels="Channel " + (1:3))

    xlabel("Time Step")
    title("Predicted Frequency: " + string(Y(i)))
end

Figure contains objects of type stackedplot. The chart of type stackedplot has title Predicted Frequency: 5.0212. The chart of type stackedplot has title Predicted Frequency: 2.7818. The chart of type stackedplot has title Predicted Frequency: 4.4988. The chart of type stackedplot has title Predicted Frequency: 4.4981.

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.

For information on predicting responses using dlnetwork objects, see predict.

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 predict.

  • 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 predict 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 predict.

  • 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 predict 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 predict. 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 predict.

  • 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 predict 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 predict. 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 predict 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 predict. 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 return categorical labels, specified as 0 (false) or 1 (true).

If ReturnCategorical is 1 (true), then the function returns categorical labels for classification output layers. Otherwise, the function returns the prediction scores for classification output layers.

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 responses, returned as a numeric array, a categorical array, or a cell array. The format of Y depends on the type of problem.

The following table describes the format for regression problems.

TaskFormat
2-D image regression
  • N-by-R matrix, where N is the number of images and R is the number of responses

  • 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 image regression
  • N-by-R matrix, where N is the number of images and R is the number of responses

  • h-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

Sequence-to-one regressionN-by-R matrix, where N is the number of sequences and R is the number of responses
Sequence-to-sequence regression

N-by-1 cell array of numeric sequences, where N is the number of sequences. The sequences are matrices with R rows, where R is the number of responses. 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 regression tasks with one observation, sequences can be a matrix. In this case, Y is a matrix of responses.

Feature regression

N-by-R matrix, where N is the number of observations and R is the number of responses

For sequence-to-sequence regression problems with one observation, sequences can be a matrix. In this case, Y is a matrix of responses.

If ReturnCategorical is 0 (false) and the output layer of the network is a classification layer, then Y is the predicted classification scores. This table describes the format of the scores for classification tasks.

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.

If ReturnCategorical is 1 (true), and the output layer of the network is a classification layer, then Y is a categorical vector or a cell array of categorical vectors. This table describes the format of the labels 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 of networks with multiple outputs, returned as numeric arrays, categorical arrays, or cell arrays.

Each output Yj corresponds to the network output net.OutputNames(j) and has format as described in the Y output argument.

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

For networks with a single classification layer only, you can compute the predicted classes and the predicted scores from a trained network using the classify 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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