You will see how straightforward it is to use TensorFlow with MATLAB and why I (and other engineers) like having the option to combine them for deep learning applications. Convert Model from MATLAB to TensorFlow.Convert Model from TensorFlow to MATLAB.
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Let’s get into more of the technical details on how to use TensorFlow with MATLAB in the following three sections. Now you have started to see the benefits of using TensorFlow with MATLAB.
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For more details on these examples, see Integrate TensorFlow Model into Simulink for Simulation and Code Generation and Climate Data Store Toolbox for MATLAB. TensorFlow users can also leverage MATLAB to generate, analyze, and visualize training data, post-process model output, and deploy trained neural networks to desktop, web apps, or embedded hardware.įor example, engineers have integrated TensorFlow models into Simulink (MATLAB simulation environment) to develop a battery state-of charge estimator for an electric vehicle and scientists have used MATLAB with TensorFlow to build a custom toolbox for reading climate data. Many MATLAB customers are interested in integrating TensorFlow models into their AI design, for creating customized tools, simulating complex systems, or optimizing data modeling. It can be combined with domain-specific toolboxes in areas such as computer vision, signal processing, and audio applications.įigure:Python and MATLAB are programming languages Python can leverage the TensorFlow library for deep learning workflows, while MATLAB provides the Deep Learning Toolbox.īoth TensorFlow and MATLAB are widely used for deep learning. Deep Learning Toolbox provides a framework for designing and implementing deep neural networks with algorithms, pretrained models, and apps. More specifically, MATLAB provides the Deep Learning Toolbox™ for deep learning workflows. Similarly to Python® libraries, MATLAB provides toolboxes for achieving different goals. MATLAB is also the foundation for Simulink®, a block diagram environment for simulating complex multi-domain systems. MATLAB includes a programming language, interactive apps, and tools for automatically generating embedded code.
MATLAB is a computing platform tailored for engineering and scientific applications like data analysis, signal and image processing, control systems, wireless communications, and robotics. Today’s post will show you how to use these features, and give you examples of when you might want to use them and how they connect the work of AI developers and engineers to enable domain-specific AI system design.
These interoperability features, offered by MATLAB, enable collaboration between colleagues, teams, and communities that work on different platforms. More specifically, I will show you how to convert pretrained TensorFlow models to MATLAB models, convert models from MATLAB to TensorFlow, and use MATLAB and TensorFlow together. In this blog post I will show you how to use TensorFlow™ with MATLAB® for deep learning applications. Posted by Sivylla Paraskevopoulou, Product Marketing Manager at MathWorks
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