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w, i) = f(w, i) = a(w, i) The point here is that the layer is built without a normalization per layer. When your network is trained, the normalization is learned as well, and so you actually get a more balanced distribution of weights for each layer. ~~~ richardlblair Thanks for the clarification. So it would seem that if we normalize the weights then the number of weights increases compared to how many neurons are actually used in the final layer, and the distribution of the weights becomes much more skewed. Correct? ~~~ AnimalMuppet Yes, that is one way to think of it. If you normalized the weights as well as the inputs, you would get much more equal-weighted layers, but it’s not the only way. If you normalized the weights and the inputs, you might end up with weights that are 1, which would be bad. (Also, it is possible to train a network without normalization of the weights. I don’t know enough about it to give you any good advice about that. Once again, it is probably not the only way.) ~~~ richardlblair Thanks, that is very helpful. I am not very familiar with the field of neural networks but after reading this: [https://arxiv.org/pdf/1406.6103.pdf](https://arxiv.org/pdf/1406.6103.pdf) and searching a bit online I think I would like to know a bit more about where we’re going with this whole weight normalization thing. ~~~ AnimalMuppet You may be interested in the following… [http://www.cs.nyu.edu/~roweis/papers/curseofrobustness.pdf](http://www.cs.nyu.edu/~roweis/papers/curseofrobustness.pdf) (Note that this is for Deep Learning, but the basic principle is the same for the CNN models. CNNs learn a pyramid of feature maps, and each layer is connected to all the feature maps in the previous layer. When you train a CNN, each layer learns to project features to all the feature maps in the previous layer. This is called “information sharing”. The papers in the above cite-draw a picture of the problem: When the weights are initialized to have mean 0 and standard deviation 1, this is the norm that each weight should reach at the end of training.

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