Step 1: Open Simulink and Access Library Browser But first, save the Resnet50 model in your directory in MATLAB. We'll integrate this model with Simulink in 3 easy steps. In this section, we will see how the resnet50 model imported from TensorFlow can be integrated into Simulink. Simulink helps explore a wide design space by modeling the system under test and the physical plant where you can use one multi-domain environment to simulate how all parts of the system behave. Often, deep learning models are used as a component in bigger systems. You can also target Intel and ARM CPUs using MATLAB Coder and FPGAs and SoCs using Deep Learning HDL Toolbox. In this example, we targeted the cuDNN libraries. Figure 6 shows a screen capture of the tool in action.įigure 6: Generated report for code generation. The report also provides a handy interactive code traceability tool to map between MATLAB code and CUDA. GPU Coder creates a code generation report that provides an interface to examine the original MATLAB code and generated CUDA code. This value corresponds to the input layer size of the ResNet50 network.Ĭfg.DeepLearningConfig = coder.DeepLearningConfig('cudnn') Ĭodegen -config cfg resnet50_predict -args -report Run the codegen command and specify an input size of. Use the coder.DeepLearningConfig function to create a CuDNN deep learning configuration object and assign it to the DeepLearningConfig property of the GPU code configuration object. To generate CUDA code for the resnet50_predict.m entry-point function, create a GPU code configuration object for a MEX target and set the target language to C++. Mynet = coder.loadDeepLearningNetwork('resnet50.mat','net') Ĭall the entry point function and generate C++ code targeting cudnn libraries The function uses a persistent object mynet to load the series network object and reuses the persistent object for prediction on subsequent calls. The resnet50_predict.m entry-point function takes an image input and runs prediction on the image using the imported ResNet50 model. envCfg = coder.gpuEnvConfig('host') įigure 5: Verify the GPU environment to make sure all the essential libraries are available Step 2: Define Entry Point Function This performs a complete check of all third-party tools required for GPU code generation. In this example we'll generate CUDA code, using GPU Coder, targeting the cuDNN library in 3 easy steps. One of the most common paths our customers take after importing a model is generating code, targeting different hardware platforms. You can also click on the Analyze button in the app (Figure 4b) and investigate the activation sizes and see if the network has errors like incorrect tensor shapes, misplaced connections, etc.įigure 4a: ResNet50 architecture inside the Deep Network Designer appįigure 4b: Analyze the imported network for errors and visualize the key components in the architecture – the skipped connections in the case of resnet50. Check this video out to learn how to interactively modify a deep learning network for transfer learning. You can at this stage use this network for transfer learning workflows. The layer architecture contains skip-connections which is typical of ResNet architectures. Once imported into the app, the network looks like Figure 4a. To load up the app, type deepNetworkDesigner in the command line and load the network from workspace. To understand the network, we'll use Deep Network Designer app to visualize the network architecture. The rest of this blog post will focus on what you can do with TensorFlow Models after they are brought into MATLAB.įigure 1: Common workflows after importing TensorFlow model into MATLAB 1. Related Post: Importing Models from TensorFlow, PyTorch, and ONNX > To see a more-detailed post on how to bring in TensorFlow model into MATLAB, check out this related post on bringing in TensorFlow (and other) networks > (Note: you can also use importTensorFlowLayers to import layers from TensorFlow). To bring models trained in TensorFlow 2 into MATLAB, you can use the function importTensorFlowNetwork, which enables you to import the model and its weights into MATLAB. In this blog, we will explore the ways you can use the converter for TensorFlow models and do the following: In release R2021a, a converter for TensorFlow models was released as a support package supporting import of TensorFlow 2 models into Deep Learning Toolbox. The following is a post from Shounak Mitra, Product Manager for Deep Learning Toolbox, here to talk about practical ways to work with TensorFlow and MATLAB.
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