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Ning
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How to improve the training accuracy in example of Train Convolutional Neural Network for Regression?

Asked by Ning
on 10 Jun 2019
Latest activity Answered by Shounak Mitra on 10 Jun 2019
The example of 'Train Convolutional Neural Network for Regression' shows how to predict the angles of rotation of handwritten digits using convolutional neural networks. However, the accuracy of the CNN network is not good enought. Any ideas to improve the network accuracy, like adjusting learnable parameters or net structures?
Thanks.

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Answer by Shounak Mitra on 10 Jun 2019

Great question!
You're right, sometimes the routine augmentations do not add additional value. In such cases, often I rely upon an optimizer to find optimal hyperparameters like learning rate, mini-batch size,momentum etc. See the documentation https://www.mathworks.com/help/deeplearning/examples/deep-learning-using-bayesian-optimization.html.
With regards to your question on finding the best net structures, it is a area of research and often words like AutoML are used for such workflows. What I mentioned earlier, regarding bayesian optimization, is also a part of AutoML. The downside of trying to use an automated technique to find the best network structure is that it is computationally very very expensive. Having said that, we've an example in the documentation that shows how you can do a parameter sweep on the depth of a network --> https://www.mathworks.com/help/deeplearning/examples/use-parfeval-to-train-multiple-deep-learning-networks.html

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