Category

Is dnn dead

It was arround when DNN got my attention for the first time. At that time DNN was, in my opinion, far better in every aspect than any other free CMS available, being the weakest point the lack of free modules although there were a lot of awesome modules like forum or blog ones, but when you needed for example a photo gallery module, you would only find one free option instead of a bunch of them and the strongest points the architecture, the user experience, the easy to develop skins and so on. I built some other small websites but mainly those two were the big ones. In my opinion, they wanted to make money from it way too early. They somehow stopped developing modules or maybe the people who developed the first modules stopped working on it. In version 4 to 7 of the CMS you would find the exact same free modules, technology and a dropping community support. So in the need of upgrading a website using DNN from a very old version to a newer one, I find discouraging how little they improved the CMS in so many years. Dnn is very powerful but you need modules to leverage it, without modules you get only a fatter and slower software that need a more expensive hosting service. They offer huge variety of modules for their respective targets and they do their job excelent. You are commenting using your WordPress.
doc rivers gif
diana zubiri hot photos
cute big butt girls nude
amtuer gone wild
free xxx bondagegiant nipples pornamateur mature swingers party

A blog about software development and B.I. by Alberto Polo

I first bumped into DNN around while working at a local university in the Charlotte area. At that time, I had no idea what DNN or open source was or the impact it would play in the next decade and beyond of my life. The DNN platform and community have definitely impacted my life. Around 2 years ago I was contacted with the challenge of re-engaging with, empowering, and reinvigorating the DNN Community. This happened as the acquisition occurred. Of course, these were all things I wanted to see happen and to get to be a part of it was even better. And while there may have been some bumps in the road, we have come a long way since then. NET Foundation which ensures the code base will always remain open source and the community now drives the roadmap for the platform. And last but not least, the documentation center was turned over to the community and DNNDocs. We have indeed come a long way and made great progress since the acquisition.
hayley atwell mr skinfat teen booty fuckedugly dirty naked girls

Navigation menu

Ordinarily, you'd be at the right spot, but we've recently launched a brand new community website For the community, by the community. Take Me to the Community! In order to participate you must be a registered DNNizen. Download DNN Platform. DNN Modules. DNN Themes. Store Blog. Evoq Preferred. Evoq Content.

By using our site, you acknowledge that you have read and understand our Cookie Policy , Privacy Policy , and our Terms of Service. Data Science Stack Exchange is a question and answer site for Data science professionals, Machine Learning specialists, and those interested in learning more about the field. It only takes a minute to sign up. For example, a large gradient flowing through a ReLU neuron could cause the weights to update in such a way that the neuron will never activate on any datapoint again.

If this happens, then the gradient flowing through the unit will forever be zero from that point on. That is, the ReLU units can irreversibly die during training since they can get knocked off the data manifold.

With a proper setting of the learning rate this is less frequently an issue. A "dead" ReLU always outputs the same value zero as it happens, but that is not important for any input. Probably this is arrived at by learning a large negative bias term for its weights.

In turn, that means that it takes no role in discriminating between inputs. For classification, you could visualise this as a decision plane outside of all possible input data. Once a ReLU ends up in this state, it is unlikely to recover, because the function gradient at 0 is also 0, so gradient descent learning will not alter the weights.

The sigmoid and tanh neurons can suffer from similar problems as their values saturate, but there is always at least a small gradient allowing them to recover in the long term. The gradient is 0 and so the weight will not be updated, not even a tiny bit, so where is "learning" in this case? If all inputs put the ReLU on the flat side, there's no hope that the weights change at all and the neuron is dead. A large learning rate amplifies this problem. ReLU neurons output zero and have zero derivatives for all negative inputs.

So, if the weights in your network always lead to negative inputs into a ReLU neuron, that neuron is effectively not contributing to the network's training. Mathematically, the gradient contribution to the weight updates coming from that neuron is always zero see the Mathematical Appendix for some details. What are the chances that your weights will end up producing negative numbers for all inputs into a given neuron? It's hard to answer this in general, but one way in which this can happen is when you make too large of an update to the weights.

Therefore, if your inputs are on roughly the same scale, a large step in the direction of the gradient can leave you with weights that give similar inputs which can end up being negative.

In general, what happens depends on how information flows through the network. You can imagine that as training goes on, the values neurons produce can drift around and make it possible for the weights to kill all data flow through some of them. Sometimes, they may leave these unfavorable configurations due to weight updates earlier in the network, though! I explored this idea in a blog post about weight initialization -- which can also contribute to this problem -- and its relation to data flow.

I think my point here can be illustrated by a plot from that article:. The plot displays activations in a 5 layer Multi-Layer Perceptron with ReLU activations after one pass through the network with different initialization strategies. You can see that depending on the weight configuration, the outputs of your network can be choked off.

The first term on the right can be computed recursively. From this you can see that if the outputs are always negative, the weights leading into the neuron are not updated, and the neuron does not contribute to learning. The "Dying ReLU" refers to neuron which outputs 0 for your data in training set. This causes ReLU to output 0. As derivative of ReLU is 0 in this case, no weight updates are made and neuron is stuck at outputting 0. Sign up to join this community. The best answers are voted up and rise to the top.

Ask Question. Asked 5 years, 6 months ago. Active 2 years, 1 month ago. Viewed 90k times. Could you please provide an intuitive explanation in simpler terms. As this is the first result on google attempts, it would be great if this question was edited with a reference. I use this method only together with freezing weights at different depths as the training continues to higher epochs I'm not sure if this is what we call phase transition Can now use higher learning rates, yields better overall accuracy only tested at linear regression. It's really easy to implement.

Active Oldest Votes. Neil Slater Neil Slater 24k 3 3 gold badges 53 53 silver badges 82 82 bronze badges. Getting rid of bias is much the same as saying all the decision planes must pass through the origin - with a few exceptions this is a bad choice.

In fact getting rid of bias terms in a neural network or related models like linear regression or logistic regression will usually mean that your model will suffer from bias! I don't know if that helps with "dying ReLU problem" -it likely would not change gradient values numerically very much because gradient is either 1 or 0 for the ReLU, and it is when it is 1 that it could overshoot, a small starting bias would appear to make very little difference.

Mostly I think it is just a trick to add a small boost to initial learning - but that might help by getting a better start, and having generally lower gradients sooner. No gradient will flow to any weight associated with the "dead" neuron in a feed-forward network, because all paths to those weights are cut - there are no alternative paths for the gradient to flow to the subset of weights feeding that ReLU unit.

You might view a ReLU in e. However, I'd view that as another instance of "for any input". A large positive gradient, caused by a large error value, can in turn cause a single step of the bias term to be large enough that it "kills" the neuron, so that it reaches a state for weights and bias that future inputs to the ReLU function never rise above 0.

MohamedEzz MohamedEzz 1, 1 1 gold badge 9 9 silver badges 6 6 bronze badges. I think my point here can be illustrated by a plot from that article: The plot displays activations in a 5 layer Multi-Layer Perceptron with ReLU activations after one pass through the network with different initialization strategies.

Andre P Andre P 2 2 silver badges 3 3 bronze badges. How weights are getting negative if the inputs are normalized? This is especially bad if we update the bias to be a large negative value. Misairu Misairu 61 1 1 silver badge 1 1 bronze badge. Things to note: Dying ReLU doesn't mean that neuron's output will remain zero at the test time as well. Depending on distribution differences this may or may not be the case. Dying ReLU is not permanent dead. If you add new training data or use pre-trained model for new training, these neurons might kick back!

It may happen that it does output non-zero for some data but number of epochs are not enough to move weights significantly. Shital Shah Shital Shah 4 4 bronze badges. Sign up or log in Sign up using Google. Sign up using Facebook. Sign up using Email and Password. Post as a guest Name. Email Required, but never shown.

The Overflow Blog. The Overflow How to lead with clarity and empathy in the remote world. Podcast How do you make software reliable enough for space travel?

Featured on Meta. A big thank you, Tim Post. Question closed notifications experiment results and graduation. Linked Related 6.

Hot Network Questions. Question feed.



98 :: 99 :: 100 :: 101 :: 102 :: 103 :: 104
Comments
  • Samujin13 days agoIn my opinion, it is an interesting question, I will take part in discussion. I know, that together we can come to a right answer.DNN Corp. (DotNetNuke) Willingly I accept.
Comments
  • Kazrazragore15 days agoIt seems to me it is good idea. I agree with you.Recent Posts Absolutely with you it agree.
Comments
  • Nikorg27 days agoVery amusing question