Tuesday, September 11, 2018

Week 5/6 - Convolutional neural network (CNN) with CIFAR-10 dataset

Here I am in Week 5&6 of my mentor’s plan to practice deep-learning and start solving real problems. This time, my homework was about designing another CNN like the previous week but with the CIFAR-10 dataset.

At first, I was intimidated and thought that I really sucked and that I cannot really move on by myself. But Which I think was close to being true. :D But after several hours of looking closely at the dataset and reading the Keras documentation, I started to find results! Here they are.

You can find the whole python notebook on Github here.

UPDATE: I reran the code but for 100 epochs and could reach a 82.68% accuracy. The notebook is found here.

The accuracy I could get this time compared to previous MNIST homework reminded me of the difference between my 9X% results at school compared to the embarrassing university’s results :D

Anyways. I could get an accuracy of 74.53%. This already required a lot of time to train on my humble computer with no GPU (I think about 2 hours). That’s why I only have one model in my notebook this time. That’s because it took a lot of time to test a single model, so I decided to try something crazy; waiting for only one epoch to finish and looking at the resulting accuracy. If it was not so promising, I stop the process and try with a different design and so on. At the end, I decided to let the current one proceed till the end and see the result.

After reaching this number, I thought it is time to look online at how people solve such a problem. And then I found that link: https://github.com/BIGBALLON/cifar-10-cnn#accuracy-of-all-my-implementations

That person could also reach an accuracy of 76.27% at first with -I think- a known network that noobs like me don’t know yet. Then he had to go for more complicated and more famous networks to get much better results. Of course, the training time with a GPU was so scary; going for a day or even two.

What’s next

The conclusion of this week actually proves how good the plan I am following is. Because now I can see how complicated and time consuming it is to solve such a problem with small images and only 10 classes. That’s why next week of the plan is “transfer learning”. So I have to use an existing network (VGG16) and adapt it to my dataset to have better results.

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