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Deep Learning in Simulink Using Deep Neural Networks Library

You can generate optimized code for prediction or detection of a variety of trained deep learning networks in your Simulink® models. You can use the Deep Neural Networks block library to make predictions or classifications from within the Simulink environment.

The Deep Neural Networks block library includes the:

  • Predict (Deep Learning Toolbox) block — To predict responses 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.

    For more information about working with the Predict block, see Lane and Vehicle Detection in Simulink Using Deep Learning (Deep Learning Toolbox).

  • Image Classifier (Deep Learning Toolbox) block — To classify data using a trained deep learning neural 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.

    For more information about working with the Image Classifier block, see Classify ECG Signals in Simulink Using Deep Learning (Deep Learning Toolbox).

The generated code implements the deep convolutional neural network (CNN) by using the architecture, the layers, and parameters that you specify in the input SeriesNetwork (Deep Learning Toolbox) or DAGNetwork (Deep Learning Toolbox) object.

You can configure the code generator to take advantage of the NVIDIA® CUDA® deep neural network library (cuDNN) and TensorRT™ high performance inference libraries for NVIDIA GPUs.

You can configure the code generator to take advantage of the Intel® Math Kernel Library for Deep Neural Networks (MKL-DNN) when targeting Intel processors or the ARM® Compute Library for computer vision and machine learning when targeting ARM processors.

Example: Classify Images by Using GoogLeNet

GoogLeNet has been trained on over a million images and can classify images into 1000 object categories (such as keyboard, coffee mug, pencil, and animals). The network has learned rich feature representations for a wide range of images. The network takes an image as input, and then outputs a label for the object in the image together with the probabilities for each of the object categories. This example show you how to perform simulation and generate CUDA code for the pretrained googlenet deep convolutional neural network and classify an image. The pretrained models are available as support packages from the Deep Learning Toolbox™.

  1. Load the pretrained GoogLeNet network. You can choose to load a different pretrained network for image classification. If you do not have the required support packages installed, install the software according to the instructions provided.

    net = googlenet;
    

  2. The object net contains the DAGNetwork object. Use the analyzeNetwork function to display an interactive visualization of the network architecture, to detect errors and issues in the network, and to display detailed information about the network layers. The layer information includes the sizes of layer activations and learnable parameters, the total number of learnable parameters, and the sizes of state parameters of recurrent layers.

    analyzeNetwork(net);
    

    Analyze network app showing network analysis of GoogLeNet.

  3. The image that you want to classify must have the same size as the input size of the network. For GoogLeNet, the size of the imageInputLayer is 224-by-224-by-3. The Classes property of the output classificationLayer contains the names of the classes learned by the network. View 10 random class names out of the total of 1000.

    classNames = net.Layers(end).Classes;
    numClasses = numel(classNames);
    disp(classNames(randperm(numClasses,10)))
    
        'speedboat'
        'window screen'
        'isopod'
        'wooden spoon'
        'lipstick'
        'drake'
        'hyena'
        'dumbbell'
        'strawberry'
        'custard apple'
    

Create GoogLeNet Model

  1. Create a new Simulink model and insert a Predict block from the Deep Neural Networks library.

  2. Add a Image From File (Computer Vision Toolbox) block from the Computer Vision Toolbox™ library and set the File name parameter to peppers.png. Add a Resize (Computer Vision Toolbox) block from the Computer Vision Toolbox library to the model. Set the Specify parameter of the Resize block to Number of output rows and columns and enter [224 224] as the value for Number of output rows and columns. This bock will resize the input image to that of the input layer of the network. Add a To Workspace (Simulink) to the model and change the variable name to yPred.

    Simulink model containing blocks for classifying images using GoogLeNet.

  3. Double-click on one of the Predict block. Select Network from MATLAB function for Network and googlenet for MATLAB function.

  4. Connect these blocks as shown in the diagram. Save the model as googlenetModel.

    Simulink model showing connection between the blocks.

Configure the Model for GPU Acceleration

Model configuration parameters determine the acceleration method used during simulation.

  1. Open the Configuration Parameters dialog box, Solver pane. To compile your model for acceleration and generate CUDA code, you must configure the model to use a fixed-step solver. The following table shows the solver configuration for this example.

    ParameterSettingEffect on Generated Code
    TypeFixed-stepMaintains a constant (fixed) step size, which is required for code generation
    Solverdiscrete (no continuous states)Applies a fixed-step integration technique for computing the state derivative of the model
    Fixed-step sizeautoSimulink chooses the step size

    Snapshot of the configuration parameters dialog showing solver options for simulation.

  2. Select the Simulation Target pane. Set the Language to C++.

  3. Select GPU acceleration. GPU Coder™ specific options are now visible in the Simulation Target > GPU Acceleration pane. For the purposes of this example, you can use the default values of these parameters.

    GPU Acceleration pane on the configuration parameters dialog of the model.

  4. On the Simulation Target pane. Set the Target Library in the Deep learning group to cuDNN. You can also select TensorRT.

    Snapshot of the configuration parameters dialog showing deep learning options for simulation acceleration.

  5. Click OK to save and close the Configuration Parameters dialog box.

  6. You can use set_param to configure the model parameter programmatically from the MATLAB command Window. For example,

    set_param('googlenetModel','GPUAcceleration','on');
    

Building the GPU Accelerated Model

  1. To build the GPU accelerated model and simulate it, you can start the model by selecting Run on the Simulation tab or by running the command:

    sim('googlenetModel');
    

    at the MATLAB prompt.

    The software first checks to see if CUDA/C++ code was previously compiled for your model. If code was created previously, the software runs the model. If code was not previously built, the software first generates and compiles the CUDA/C++ code, and then runs the model. The code generation tool places the generated code in a subfolder of the working folder called slprj/_slprj/googlenetModel.

  2. Display the top five predicted labels and their associated probabilities as a histogram. Because the network classifies images into so many object categories, and many categories are similar, it is common to consider the top-five accuracy when evaluating networks. The network classifies the image as a bell pepper with a high probability.

    im = imread('peppers.png');
    predict_scores = out.yPred.Data(:,:,1);
    [scores,indx] = sort(predict_scores,'descend');
    classNamesTop = classNames(indx(1:5))
    
    h = figure;
    h.Position(3) = 2*h.Position(3);
    ax1 = subplot(1,2,1);
    ax2 = subplot(1,2,2);
    
    image(ax1,im);
    barh(ax2,out.yout{1}.Values.Data(1,5:-1:1,1))
    xlabel(ax2,'Probability')
    yticklabels(ax2,classNamesTop(5:-1:1))
    ax2.YAxisLocation = 'right';
    sgtitle('Top 5 predictions using GoogLeNet')
    

Configure the Model for Code Generation

The model configuration parameters provide many options for the code generation and build process.

  1. Select the Code Generation pane. Set the System target file to grt.tlc. You can also use the Embedded Coder® target file ert.tlc.

  2. Set the Language to C++.

  3. Select Generate GPU code. GPU Coder specific options are now visible in the Code Generation > GPU Code pane.

  4. Select Generate code only.

  5. Select the Toolchain. For Linux® platforms select NVIDIA CUDA | gmake (64-bit Linux). For Windows® systems, select NVIDIA CUDA (w/Microsoft Visual C++ 20XX) | nmake (64-bit windows).

  6. On the Code Generation > Report pane, select Create code generation report and Open report automatically.

  7. On the Code Generation > Interface pane, set the Target Library in the Deep learning group to cuDNN. You can also select TensorRT.

  8. For the purposes of this example, you can use the default values of the GPU-specific parameters in Code Generation > GPU Code pane.

    GPU Code pane on the configuration parameters dialog of the model.

  9. Click OK to save and close the Configuration Parameters dialog box.

  10. You can also use set_param to configure the model parameter programmatically from the MATLAB command Window. For example,

    set_param('googlenetModel','GenerateGPUCode','CUDA');
    

Generate CUDA Code for the Model

  1. In the Simulink Editor, open the Simulink Coder app.

  2. Generate code.

    Messages appear in the Diagnostics Viewer. The code generator produces CUDA source and header files, and an HTML code generation report. The code generator places the files in a build folder, a subfolder named googlenetModel_grt_rtw under your current working folder.

  3. Example of the generated CUDA code.

     googlenetModel.cu

Limitations

  • Code generation for a deep learning network with custom layer is not supported in Simulink.

  • The Intel Math Kernel Library for Deep Neural Networks (MKL-DNN) requires C++11 standard. Setting the Target Library in the Deep learning group to MKL-DNN automatically generates C++11 code.

  • Use of MATLAB Function blocks in Stateflow® charts is not supported.

  • The code generator does not support all the data types from the MATLAB language. For supported data types, refer to the block documentation.

  • For GPU code generation, the Terminate function required parameter in the Code Generation > Interface pane must be enabled.

See Also

Functions

Related Topics