#Whats new matlab 2019a code
Now it’s much easier on the developer to specify input requirements so they can spend more time writing the code for their actual algorithm, not fussing about argument checking and validation. You could always do this input validation, but it required if statements and type checking, more customized error handling (just more coding, not hard but tedious). You’d like to give some restrictions on what can be passed to the function (for example, you expect an image (matrix) but someone passes a table). You often write functions in deep learning, especially when applications get more complicated and you need to better organize your code. More info on App Designer: Function input argument validation Using logical, numeric, string, or cell arraysįor example, the below app was created with R2019b. Sort table UI components interactively when.It continues to improve each release and now you can make your app look better than ever in R2019b (lots of cosmetic-related functionality allow for customizing the look of your apps):Ĭolumns, or cells in a table UI component It works well for sharing DL applications with others, even creating web apps using your model. More info on tile layout: Lots of App Designer featuresĪpp Designer is a user-friendly app authoring environment.
You can control spacing between axes, the resize behavior, etc and it’s easy to use different sized axes in same figure and arrange for publications, like the example below. The syntax is nicer, but it's also more powerful than subplot. Now you can use tiledlayout + nexttile instead of subplot when creating multiple plots in the same figure. You also need to share your results, publish, and often customize visualizations. Visualizations are really important in DL- you’re always plotting results, comparing classes, layer activations, etc. More info on Python Functions: tiledLayout is the new subplot So, if you ever have conflicts or crashes when calling TensorFlow, PyTorch, etc from MATLAB in the same process, you can use a separate process by setting this in the pyenv function: This release we include an option to run Python functions in a separate process to avoid library conflicts. This has been around for several years now and we’re continuously improving it. You have the model sharing options via ONNX, TF importers, etc., but you can also call Python libraries directly from MATLAB and vice versa. You’re often using multiple deep learning models, trying different models and examples from the community, and sometimes you need to use MATLAB and Python together. More info on live editor tasks: Execute Python functions out of process And communication is such an important thing in DL.
Live Editor/ Live Scripts are very useful in Deep Learning in general, since they are so great for communicating – capturing code, output, documentation, equations, images, etc. There are Live Editor Tasks for common data cleaning needs like: missing data, outliers, smoothing, detrending, change point detection, and joining tables. For example, in the Smooth Data task (pictured below) you can choose different parameters like the smoothing method, immediately visualize the results, and keep doing that until you find a method appropriate for your data. These are intended to help with tasks that are very iterative and visual. These are like mini-apps that you can embed in a Live Script and are very useful for preprocessing data, which is super important in DL.