predict

Compute deep learning network output for inference

Since R2019b

Syntax

Y = predict(net,X)

Y = predict(net,X1,...,XM)

[Y1,...,YN] = predict(___)

[Y1,...,YK] = predict(___,'Outputs',layerNames)

[___] = predict(___,'Acceleration',acceleration)

[___,state] = predict(___)

Description

Some deep learning layers behave differently during training and inference (prediction). For example, during training, dropout layers randomly set input elements to zero to help prevent overfitting, but during inference, dropout layers do not change the input.

To compute network outputs for inference, use the predict function. To compute network outputs for training, use the forward function. For prediction with SeriesNetwork and DAGNetwork objects, see predict.

Tip

For prediction with SeriesNetwork and DAGNetwork objects, see predict.

Y = predict(net,X) returns the network output Y during inference given the input data X and the network net with a single input and a single output.

Y = predict(net,X1,...,XM) returns the network output Y during inference given the M inputs X1, ...,XM and the network net that has M inputs and a single output.

[Y1,...,YN] = predict(___) returns the N outputs Y1, …, YN during inference for networks that have N outputs using any of the previous syntaxes.

[Y1,...,YK] = predict(___,'Outputs',layerNames) returns the outputs Y1, …, YK during inference for the specified layers using any of the previous syntaxes.

[___] = predict(___,'Acceleration',acceleration) also specifies performance optimization to use during inference, in addition to the input arguments in previous syntaxes.

[___,state] = predict(___) also returns the updated network state.

Examples

Make Predictions Using dlnetwork Object

This example shows how to make predictions using a dlnetwork object by splitting data into mini-batches.

For large data sets, or when predicting on hardware with limited memory, make predictions by splitting the data into mini-batches. When making predictions with SeriesNetwork or DAGNetwork objects, the predict function automatically splits the input data into mini-batches. For dlnetwork objects, you must split the data into mini-batches manually.

Load dlnetwork Object

Load a trained dlnetwork object and the corresponding classes.

s = load("digitsCustom.mat");
dlnet = s.dlnet;
classes = s.classes;

Load Data for Prediction

Load the digits data for prediction.

digitDatasetPath = fullfile(matlabroot,'toolbox','nnet','nndemos', ...
    'nndatasets','DigitDataset');
imds = imageDatastore(digitDatasetPath, ...
    'IncludeSubfolders',true);

Make Predictions

Loop over the mini-batches of the test data and make predictions using a custom prediction loop.

Use minibatchqueue to process and manage the mini-batches of images. Specify a mini-batch size of 128. Set the read size property of the image datastore to the mini-batch size.

For each mini-batch:

• Use the custom mini-batch preprocessing function preprocessMiniBatch (defined at the end of this example) to concatenate the data into a batch and normalize the images.

• Format the images with the dimensions 'SSCB' (spatial, spatial, channel, batch). By default, the minibatchqueue object converts the data to dlarray objects with underlying type single.

• Make predictions on a GPU if one is available. By default, the minibatchqueue object converts the output to a gpuArray if a GPU is available. Using a GPU requires Parallel Computing Toolbox™ and a supported GPU device. For information on supported devices, see GPU Computing Requirements (Parallel Computing Toolbox).

miniBatchSize = 128;
imds.ReadSize = miniBatchSize;

mbq = minibatchqueue(imds,...
    "MiniBatchSize",miniBatchSize,...
    "MiniBatchFcn", @preprocessMiniBatch,...
    "MiniBatchFormat","SSCB");

Loop over the minibatches of data and make predictions using the predict function. Use the onehotdecode function to determine the class labels. Store the predicted class labels.

numObservations = numel(imds.Files);
YPred = strings(1,numObservations);

predictions = [];

% Loop over mini-batches.
while hasdata(mbq)
    
    % Read mini-batch of data.
    dlX = next(mbq);
       
    % Make predictions using the predict function.
    dlYPred = predict(dlnet,dlX);
   
    % Determine corresponding classes.
    predBatch = onehotdecode(dlYPred,classes,1);
    predictions = [predictions predBatch];
  
end

Visualize some of the predictions.

idx = randperm(numObservations,9);

figure
for i = 1:9
    subplot(3,3,i)
    I = imread(imds.Files{idx(i)});    
    label = predictions(idx(i));
    imshow(I)
    title("Label: " + string(label))
  
end

Mini-Batch Preprocessing Function

The preprocessMiniBatch function preprocesses the data using the following steps:

1. Extract the data from the incoming cell array and concatenate into a numeric array. Concatenating over the fourth dimension adds a third dimension to each image, to be used as a singleton channel dimension.

2. Normalize the pixel values between 0 and 1.

function X = preprocessMiniBatch(data)    
    % Extract image data from cell and concatenate
    X = cat(4,data{:});
    
    % Normalize the images.
    X = X/255;
end

Input Arguments

net — Network for custom training loops or custom pruning loops

dlnetwork object | TaylorPrunableNetwork object

This argument can represent either of these:

• Network for custom training loops, specified as a dlnetwork object.

• Network for custom pruning loops, specified as a TaylorPrunableNetwork object.

To prune a deep neural network, you require the Deep Learning Toolbox™ Model Quantization Library support package. This support package is a free add-on that you can download using the Add-On Explorer. Alternatively, see Deep Learning Toolbox Model Quantization Library.

X — Input data

formatted dlarray

Input data, specified as a formatted dlarray. For more information about dlarray formats, see the fmt input argument of dlarray.

layerNames — Layers to extract outputs from

string array | cell array of character vectors

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

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

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

acceleration — Performance optimization

'auto' (default) | 'mex' | 'none'

Performance optimization, specified as the comma-separated pair consisting of 'Acceleration' and 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 when using a GPU only. The input data or the network learnable parameters must be stored as gpuArray objects. Using a GPU requires Parallel Computing Toolbox™ and a supported GPU device. For information on supported devices, see GPU Computing Requirements (Parallel Computing Toolbox). If Parallel Computing Toolbox or a suitable GPU is not available, then the software returns an error.

• 'none' — Disable all acceleration.

The default option is 'auto'. If 'auto' is specified, MATLAB^® will apply a number of compatible optimizations. If you use the 'auto' option, MATLAB does not ever generate a MEX function.

Using the 'Acceleration' options 'auto' and 'mex' can offer performance benefits, but 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 only available when you are using a GPU. You must have a C/C++ compiler installed and the GPU Coder™ Interface for Deep Learning support package. Install the support package using the Add-On Explorer in MATLAB. For setup instructions, see MEX Setup (GPU Coder). GPU Coder is not required.

The 'mex' option has the following limitations:

• The state output argument is not supported.

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

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

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

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

• You cannot use MATLAB Compiler™ to deploy 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.

Example: 'Acceleration','mex'

Output Arguments

Y — Output data

formatted dlarray

Output data, returned as a formatted dlarray. For more information about dlarray formats, see the fmt input argument of dlarray.

state — Updated network state

table

Updated network state, returned as a table.

The network state is a table with three columns:

• Layer – Layer name, specified as a string scalar.

• Parameter – State parameter name, specified as a string scalar.

• Value – Value of state parameter, specified as a dlarray object.

Layer states contain information calculated during the layer operation to be retained for use in subsequent forward passes of the layer. For example, the cell state and hidden state of LSTM layers, or running statistics in batch normalization layers.

For recurrent layers, such as LSTM layers, with the HasStateInputs property set to 1 (true), the state table does not contain entries for the states of that layer.

Update the state of a dlnetwork using the State property.

Algorithms

Reproducibility

To provide the best performance, deep learning using a GPU in MATLAB is not guaranteed to be deterministic. Depending on your network architecture, under some conditions you might get different results when using a GPU to train two identical networks or make two predictions using the same network and data.

Extended Capabilities

C/C++ Code Generation

Generate C and C++ code using MATLAB® Coder™.

Usage notes and limitations:

• C++ code generation supports the following syntaxes:

  • Y = predict(net,X)

  • Y = predict(net,X1,...,XM)

  • [Y1,...,YN] = predict(__)

  • [Y1,...,YK] = predict(__,'Outputs',layerNames)

• The input data X must not have variable size. The size must be fixed at code generation time.

• The dlarray input to the predict method must be a single datatype.

GPU Code Generation

Generate CUDA® code for NVIDIA® GPUs using GPU Coder™.

Usage notes and limitations:

• GPU code generation supports the following syntaxes:

  • Y = predict(net,X)

  • Y = predict(net,X1,...,XM)

  • [Y1,...,YN] = predict(__)

  • [Y1,...,YK] = predict(__,'Outputs',layerNames)

• The input data X must not have variable size. The size must be fixed at code generation time.

• Code generation for TensorRT library does not support marking an input layer as an output by using the [Y1,...,YK] = predict(__,'Outputs',layerNames) syntax.

• The dlarray input to the predict method must be a single datatype.

GPU Arrays

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

Usage notes and limitations:

• This function runs on the GPU if either or both of the following conditions are met:

  • Any of the values of the network learnable parameters inside net.Learnables.Value are dlarray objects with underlying data of type gpuArray

  • The input argument X is a dlarray with underlying data of type gpuArray

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

Version History

Introduced in R2019b

R2021a: predict returns state values as dlarray objects

For dlnetwork objects, the state output argument returned by the predict function is a table containing the state parameter names and values for each layer in the network.

Starting in R2021a, the state values are dlarray objects. This change enables better support when using AcceleratedFunction objects. To accelerate deep learning functions that have frequently changing input values, for example, an input containing the network state, the frequently changing values must be specified as dlarray objects.

In previous versions, the state values are numeric arrays.

In most cases, you will not need to update your code. If you have code that requires the state values to be numeric arrays, then to reproduce the previous behavior, extract the data from the state values manually using the extractdata function with the dlupdate function.

state = dlupdate(@extractdata,net.State);