To learn how to create the training data set for this model, see Export Untrained Layer Graph to TensorFlow. SoftmaxLayer] lgraph = layerGraph(layers) InputSize = 12 numHiddenUnits = 100 numClasses = 9 layers = [īilstmLayer(numHiddenUnits,OutputMode= "last") For an example, see Transfer Learning with Deep Network Designer. With DND, you can interactively prepare the network for training, train the network, export the retrained network, and then use it for the new task. For an example, see Train Deep Learning Network to Classify New Images. It’s easy to do model surgery (prepare a network to train on new data) with a few lines of MATLAB code by using built-in functions that replace, remove, or add layers at any part of the network architecture. In MATLAB, you can perform transfer learning programmatically or interactively by using the Deep Network Designer (DND) app. Using transfer learning is usually faster and easier than training a network from scratch. For example, you are doing object detection in MATLAB, and you find a TensorFlow model that can improve the detection accuracy, but you need to retrain the model with your data. Transfer learning is the process of taking a pretrained deep learning model and fine-tuning to fit the model to a new problem. Why choose when you don’t have to? Convert Model from TensorFlow to MATLABĪ common reason to import a pretrained TensorFlow model into MATLAB is to perform transfer learning. 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.
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