predict - Compute deep learning network output for inference - MATLAB (original) (raw)

Compute deep learning network output for inference

Syntax

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.

[[Y1,...,YN](#mw%5Ffe039f0e-7fba-4848-9cfd-50a70b067e69)] = predict([net](#function%5Fdlnetwork%5Fsep%5Fpredict%5Fsep%5Fmw%5F4ac40767-f907-4a61-a521-58888cf294ae),[X1,...,XM](#mw%5F35ba553b-bd7e-4f4f-9130-ba785e9cfdb7)) returns the network outputs Y1, …, YN for inference given the input data X1, …, XM and the neural networknet.

example

[[Y1,...,YN](#mw%5Ffe039f0e-7fba-4848-9cfd-50a70b067e69),[state](#function%5Fdlnetwork%5Fsep%5Fpredict%5Fsep%5Fmw%5F4538a911-3a0a-4894-8a92-6f4bd2abbc9e)] = predict(___) also returns the updated network state.

___ = predict(___,[Name=Value](#namevaluepairarguments)) specifies additional options using one or more name-value arguments.

Examples

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Load a pretrained SqueezeNet neural network into the workspace.

[net,classNames] = imagePretrainedNetwork;

Read an image from a PNG file and classify it. To classify the image, first convert it to the data type single.

im = imread("peppers.png"); figure imshow(im)

Figure contains an axes object. The hidden axes object contains an object of type image.

X = single(im); scores = predict(net,X); [label,score] = scores2label(scores,classNames);

Display the image with the predicted label and corresponding score.

figure imshow(im) title(string(label) + " (Score: " + score + ")")

Figure contains an axes object. The hidden axes object with title bell pepper (Score: 0.89394) contains an object of type image.

Input Arguments

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Neural network, specified as one of these values:

To prune a deep neural network, you require the Deep Learning Toolbox™ Model Compression 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 Compression Library.

Input data, each specified as one of these values:

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 output data in the layout that the network expects. If your data is in a different layout to what the network expects, then indicate that your data has a different layout by using the InputDataFormats option or by specifying input data as a formatted dlarray object. It is usually easiest to adjust the InputDataFormats training option than to preprocess the input data.

For more information, see Deep Learning Data Formats.

To create a neural network that receives unformatted data, use an inputLayer object and do not specify a format. To input unformatted data into a network directly, do not specify the InputDataFormats argument. (since R2025a)

Before R2025a: For neural networks that do not have input layers, you must specify a format using the InputDataFormats argument or use formatted dlarray objects as input.

Name-Value Arguments

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Specify optional pairs of arguments asName1=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: Y = predict(net,X,InputDataFormats="CBT") makes predictions with sequence data that has format "CBT" (channel, batch, time).

Neural network outputs, specified as a string array or a cell array of character vectors of layer names or layer output paths. Specify the output using one of these forms:

If you do not specify the layers to extract outputs from, then, by default, the software uses the outputs specified by net.Outputs.

Since R2023b

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:

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.

To create a neural network that receives unformatted data, use an inputLayer object and do not specify a format. To input unformatted data into a network directly, do not specify the InputDataFormats argument. (since R2025a)

Before R2025a: For neural networks that do not have input layers, you must specify a format using the InputDataFormats argument or use formatted dlarray objects as input.

Data Types: char | string | cell

Since R2023b

Description of the output data dimensions, specified as one of these values:

A data format is a string of characters, where each character describes the type of the corresponding data dimension.

The characters are:

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

Performance optimization, specified as one of these values:

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:

For quantized networks, the "mex" option requires a CUDA® enabled NVIDIA® GPU with compute capability 6.1, 6.3, or higher.

Output Arguments

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Output data of network with multiple outputs, returned as a one of these values:

The data type matches the data type of the input data.

The order of the outputs Y1, …, YN match the order of the outputs specified by the Outputs argument.

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 labelsTTrain, then the order of the scores matches the order of the categories given by categories(TTrain).

Updated network state, returned as a table.

The network state is a table with three columns:

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

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 the layer.

Update the state of a dlnetwork using the State property.

Algorithms

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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.

Extended Capabilities

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Usage notes and limitations:

Usage notes and limitations:

The predict function supports GPU array input with these usage notes and limitations:

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

Version History

Introduced in R2019b

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If you specify unformatted data as input to the neural network and do not specify the InputDataFormats argument, then the function passes the unformatted data to the network directly.

To create a neural network that receives unformatted data, use an inputLayer object and do not specify a format.

Make predictions using numeric arrays and unformatted dlarray objects.

Specify the input and output data formats using the InputDataFormats and OutputDataFormats options, respectively.

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);