Exploring Neural Networks, p. 2

(see part 1 here)

Once I fixed all my bugs in Batch Normalisation implementation and fine-tuned all parameters, I started getting reasonable results. In particular, it turned out that I needed to significantly (more than 10 times) increase weight decay ratio constant. I also had to modify learning rate scheduling so that it decays much faster, this makes sense, because Batch Normalisation is supposed to speed up learning. Eventually, the network:

3 channels ->  64 3x3 convolutions -> 3x3 maxpool -> BN -> ReLU
           -> 128 3x3 convolutions -> 2x2 maxpool -> BN -> ReLU
           -> 1024 to 1024 product ->                BN -> ReLU
           -> 1024 to  512 product ->                BN -> Sigmoid
           ->  512 to   10 product
           ->  SoftMax

has achieved 79% success rate on the test set.

I was interested in the advantage of using BN. To investigate it, I created another network, which is an identical clone of the one described above, but no Batch Normalisations are performed at all. Comparing the results of these two networks should express the gain introduced by using BN.

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Exploring Neural Networks, p. 1

As a final assignment on the Neural Networks course I took part in (University of Wrocław, Institute of Computer Science, winter2015/2016), I am tasked with designing, implementing and training a neural net that would classify CIFAR-10 images with some reasonable success rate. I am also encouraged to experiment with the network by implementing some of the recent inventions that may, in one way or another, improve my network’s performance. I will be sharing my results and observations here, in this post, and in some that will follow soon within the next two weeks.

The source code I am using for my experiments is available at github. The sources come with a number of utilities that simplify running them on our lab’s computers, which may come in handy if you are a fellow student peeking at my progress, but if you are not, then you should ignore all files except the ones within ./project directory.

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