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You can adjust setting to use GPUs and run experiments in parallel easily through the app. This doc example walks through setting up and running the experiment:Ĭd(setupExample('nnet/ExpMgrTransferLearningExample')) setupExpMgr('FlowerTransferLearningProject') Fig 4: Setting network parameters in Experiment Manager AppĪs you probably know, training networks can take some time! Here we are training 3 of them - so you want to consider your hardware and problem before hitting run. Next, we needed to train and validate all 3 networks and compare the results! The Experiment Manager app is super helpful to stay organized and automate this part. We wanted varying levels of complexity for our competition, so we decided on squeezenet, googlenet, and inceptionv3. We started by exploring pretrained models using the Deep Network Designer app which provides a sense of the overall network architecture to help us select before investigating the detail.įig 3: Pretrained models in Deep Network Designer
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CNNs are very common as they involve a series of operations, which we can generally understand: convolutions, mathematical operations, and aggregations.įig 2: Convolutional Neural Network (CNN) diagramĪs you may recall from previous posts, we have some great starting points in this field! We used transfer learning, where you update a pretrained network with your data. We'll discuss more as we get into the details!įor our first problem, we compared CNN models to classify types of flowers. We used doc examples for repeatability (plus, reasonably sized data sets for a livestream!) and used apps in MATLAB to explore, train, and compare the models quickly. The image below shows some common networks used for different data types. Based on the data sets, we considered two types of models: Convolutional (CNN) and Long Short-Term Memory (LSTM) networks. We created two problems for image classification and timeseries regression.
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How can this be managed efficiently and quickly? Using a low code tool in MATLAB, the Experiment Manager app! Approach It could be a) comparing different networks (problem 1) or b) finding the right parameters for a particular network (problem 2). For deep learning models, there are different ways to assess what is the “best” model.
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This blog post follows the fabulous modeling competition LIVE on YouTube, MATLAB's Best Model: Deep Learning Basics to guide you in how to choose the best model. You can follow her on social media:, ,, and. Wishes from us in your journey to learning MATLAB.This post is from Heather Gorr, MATLAB product marketing.
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