Predict

Predict responses using a trained deep learning neural network

Since R2020b

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  • Predict block

Libraries:
Deep Learning Toolbox / Deep Neural Networks

Description

The Predict block predicts responses for the data at the input by using the trained network specified through the block parameter. This block allows loading of a pretrained network into the Simulink® model from a MAT file or from a MATLAB® function.

Note

Use the Predict block to make predictions in Simulink. To make predictions programmatically using MATLAB code, use the minibatchpredict or predict function.

Examples

Lane and Vehicle Detection in Simulink Using Deep Learning

Lane and Vehicle Detection in Simulink Using Deep Learning

Use deep convolutional neural networks inside a Simulink® model to perform lane and vehicle detection. This example takes the frames from a traffic video as an input, outputs two lane boundaries that correspond to the left and right lanes of the ego vehicle, and detects vehicles in the frame.

Ports

Input

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The input ports of the Predict block takes the names of the input layers of the loaded network. For example, if you specify imagePretrainedNetwork for MATLAB function, then the input port of the Predict block has the label data. Based on the network loaded, the input to the predict block can be image, sequence, or time series data.

The layout of the input depend on the type of data.

DataLayout of Predictors
2-D imagesA 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.
Vector sequencess-by-c matrices, where s is the sequence length, and c is the number of features of the sequences.
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.
FeaturesA N-by-numFeatures numeric array, where N is the number of observations, and numFeatures is the number of features of the input data.

If the array contains NaNs, then they are propagated through the network.

Output

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The outputs port of the Predict block takes the names of the output layers of the network loaded. For example, if you specify imagePretrainedNetwork for MATLAB function, then the output port of the Predict block is labeled prob_flatten. Based on the network loaded, the output of the Predict block can represent predicted scores or responses.

The predicted scores or responses is returned as a K-by-N array, where K is the number of classes, and N is the number of observations.

If you enable Activations for a network layer, the Predict block creates a new output port with the name of the selected network layer. This port outputs the activations from the selected network layer.

The activations from the network layer is returned as a numeric array. The format of output depends on the type of input data and the type of layer output.

For 2-D image output, activations is an h-by-w-by-c-by-n array, where h, w, and c are the height, width, and number of channels for the output of the chosen layer, respectively, and n is the number of images.

For a single time-step containing vector data, activations is a c-by-n matrix, where c is the number of features in the sequence and n is the number of sequences.

For a multi time-step containing vector data, activations is a c-by-n-by-s matrix, where c is the number of features in the sequence, n is the number of sequences and s is the sequence length.

For a single time-step containing 2-D image data, activations is a h-by-w-by-c-by-n array, where n is the number of sequences, h, w, and c are the height, width, and the number of channels of the images, respectively.

Parameters

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Specify the source for the trained network. Select one of the following:

  • Network from MAT file— Import a trained network from a MAT file containing a dlnetwork object.

  • Network from MATLAB function— Import a pretrained network from a MATLAB function. For example, to use a pretrained GoogLeNet, create a function pretrainedGoogLeNet in a MATLAB M-file, and then import this function. 

    function net = pretrainedGoogLeNet
  net = imagePretrainedNetwork("googlenet");
end

Programmatic Use

Block Parameter: Network
Type: character vector, string
Values: 'Network from MAT file' | 'Network from MATLAB function'
Default: 'Network from MAT file'

This parameter specifies the name of the MAT file that contains the trained deep learning network to load. If the file is not on the MATLAB path, use the Browse button to locate the file.

Dependencies

To enable this parameter, set the Network parameter to Network from MAT file.

Programmatic Use

Block Parameter: NetworkFilePath
Type: character vector, string
Values: MAT file path or name
Default: 'untitled.mat'

This parameter specifies the name of the MATLAB function for the pretrained deep learning network. For example, to use a pretrained GoogLeNet, create a function pretrainedGoogLeNet in a MATLAB M-file, and then import this function. 

function net = pretrainedGoogLeNet
  net = imagePretrainedNetwork("googlenet");
end

Dependencies

To enable this parameter, set the Network parameter to Network from MATLAB function.

Programmatic Use

Block Parameter: NetworkFunction
Type: character vector, string
Values: MATLAB function name
Default: 'squeezenet'

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.

Programmatic Use

Block Parameter: MiniBatchSize
Type: character vector, string
Values: positive integer
Default: '128'

Enable output ports that return predicted scores or responses.

Programmatic Use

Block Parameter: Predictions
Type: character vector, string
Values: 'off' | 'on'
Default: 'on'

This parameter specifies the input data format expected by the trained dlnetwork.

Data format, specified as a string scalar or a character vector. Each character in the string must be one of the following dimension labels:

  • "S" — Spatial

  • "C" — Channel

  • "B" — Batch

  • "T" — Time

  • "U" — Unspecified

For example, for an array containing a batch of sequences where the first, second, and third dimension correspond to channels, observations, and time steps, respectively, you can specify that it has the format "CBT".

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.

By default, the parameter uses the data format that the network expects.

Dependencies

To enable this parameter, set the Network parameter to Network from MAT file to import a trained dlnetwork object from a MAT file.

Programmatic Use

Block Parameter: InputDataFormats
Type: character vector, string
Values: For a network with one or more inputs, specify text in the form of: "{'inputlayerName1', 'SSC'; 'inputlayerName2', 'SSCB'; ...}". For a network with no input layer and multiple input ports, specify text in the form of: "{'inputportName1/inport1, 'SSC'; 'inputportName2/inport2, 'SSCB'; ...}".
Default: Data format that the network expects. For more information, see Deep Learning Data Formats.

Use the Activations list to select the layer to extract features from. The selected layers appear as an output port of the Predict block.

Programmatic Use

Block Parameter: Activations
Type: character vector, string
Values: character vector in the form of '{'layerName1',layerName2',...}'
Default: ''

Tips

Extended Capabilities

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Version History

Introduced in R2020b

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See Also

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